Big data. Small data. Any data.

In the last couple months I've spent a lot of time with senior level marketers on three different continents. Some of them are quite successful, but sadly many of them were not. In the latter group I discovered two common themes:

Silly not in their eyes, silly in my eyes. And silly simply because as soon as the strategy/success metric being obsessed about was mentioned, it was clear they would fail.

A silly metric, I better define it :), is one that distracts you from focusing on business investments that lead to bottom-line impact. They get you fired. Sometimes sooner. Other times a little later than sooner.

I'll expand my purview a little bit in this post, from just looking at silly metrics to also looking at silly data myths. My hope is to use these eight examples to illustrate to you how, if you are spending 30% of your time with data, you can use crazy cool data strategies/metrics to ensure many, many promotions.

Eight data myths that marketing people believe that get them fired:

Excited? I am, let's go!

1. Real-time data is life changing.

A lot of people get fired for this.

Sadly not right away, because it takes time to realize how spectacular of a waste of money getting to real-time data was.

It seems absolutely stupid to say, "No, I don't want real-time data." It's like saying no to Chocolate/Jesus. You just don't say no!

But I want you to.

I want you to say: "I don't want real-time data, I want right-time data. Let's understand the speed of decision making in our company. If we make real-time decisions, let's get real-time data. If we make decisions over two days, let's go with that data cycle. If it take ten days to make a decision to change bids on our PPC campaigns, let's go with that data cycle."

Right-time.

Here's why… Real-time data is very expensive. It is expensive from a systems/platforms/data processing/data reporting perspective. You end up paying substantial amounts of money to your analytics/big data vendor – for questionable value. It is also very expensive from a decision-making perspective because if you have real-time data you'll darn well make sure that it is being shoved down every single person's throat. That approach will ensure that, even in the best case scenario of the proverbial pigs flying, they'll obsess about tactical things. All the time. The normal case, after a few days of "OMG I can't believe I have data! It is real-time! Call my mom!!!" their eyes will glaze over and they will ignore all the numbers flying by because they won't know what to do with it.

So shoot for right-time data.

That is a cheaper systems/platform/data strategy. (And remember even the most idiotic system in the world now gives you data that is a couple hours old with zero extra investment from you. So when you say real time you are really saying "Nope, two hours is not enough for me!").

That is also a way to get people to sync the data analysis (not data puking, sorry I meant data reporting) with the speed at which the company actually makes decisions (data > analyst > manager > director > VP > question back to manager > yells at the analyst > back to director> VP = 6 days).

As a result of the above two outcomes, you'll get promoted.

PS: The phrase "real-time data analysis" is an oxymoron.

PPS: I've mentioned one exception in the past. Real-time data is super valuable if zero human beings are involved from data collection to action being taken. Say for example, artificially intelligent platforms that do automated trading on Wall Street.

2. All you need to do is fix the bounce rate.

No. You'll get fired. In three months.

I love bounce rate. It is a really good metric. But it is not a key performance indicator.

The difference between a KPI and a metric is that the former has a direct line of sight to your bottom-line, while the latter is helpful in diagnosing tactical challenges.

Bounce rate is really useful for finding things you suck at. Bad ad creatives. Horrible landing pages. Poorly targeted ads. Missing calls to action. Et. Al. You measure bounce rate and you can find those things, then figure out if the problem is at the source (ads) or destination (your site). Finally, like magnificent little troopers that you are, you'll fix the problems.

Along the way you also learn how not to stink. Bounce rate goes from 70% to a manageable 30%. Takes three months.

Now what?

Stop obsessing about bounce rate. By lowering your bounce rate all you managed to accomplish is get your ads created and targeted properly and optimize the landing pages. You got the person to the site, they did not puke and leave right away. Great. Time to focus on rest of the process.

From the time people land on your site it might take another 12 – 25 pages for them to buy or submit a lead. Focus on all that stuff. The tough stuff. Then you'll make money.

Fixing the bounce rate is mandatory, without that you are coming to play the super bowl naked – you are not going to win.

But just fixing that won't ensure you win. Focus on the actual game. Focus on incredible behavior metrics like Pages/Visit, focus on the Visitor Flow report, obsess about Checkout Abandonment Rate, make love to Average Order Size.

All that will get you promoted. Because you are going to focus, not on metrics, but on key performance indicators that have a direct line to the bottom-line of your company.

3. Number of Likes represents social awesomeness.

Ohh … this one will get you fired even faster. :) Because it does not take a very long time for your Senior Management to figure out how lame the Likes metric is and that it drives 1. Zero value on Facebook and 2. Zero squared economic value or cost savings to the business.

There are many spectacular reasons for why Like (and +1s, Followers) is a horrible metric. Here's one: We are looking at two consumer product brands, the tiny company Innocent Drinks and the Goliath called Tide Detergent.

Checkout the number of Likes each has.

Innocent has 0.3 million and Tide has 3.7 million. Small difference.

Now if Tide was lame they would use the number of Likes they have and boast about that to their CMO. Bad idea.

Tide is not lame. They also look at the number right next to the Likes number. For Tide that number reads 15k. For Innocent that number reads 58k!

Even with 10x the number of Likes on Facebook the giant called Tide has 4x fewer people talking about their brand when compared to the David called Innocent.

That's primarily because Tide does not truly understand how to do Conversation Marketing. Their relentless day after day mix of "here are pictures of our products repeated every day, here are the people we sponsor and their ads" is insufficient to create brand engagement.

And if Tide were lame and used Likes as victory, they would not know this.

On the other hand, Innocent clearly understands Conversational Marketing (they are a small company, not yet corporatized I suspect, and likely deal with a smaller combination of lawyers/PR types/Social Media Gurus/Agencies – a sad but likely bane on Tide's existence). They manage to get hundreds upon hundreds of comments (Conversation Rate) and thousands upon thousands of comment Likes (Applause Rate). I'm not using hundreds and thousands as metaphors:

[Update: As no less than three comments mention below, Innocent is 90% owned by Coca Cola. Fooled me! Please read the above section in that context. But I have to admit this only makes me even more impressed with Innocent. In a massively large company they've carved out an identity uniquely their own. They refuse to be corrupted by Coca Cola's own Facebook strategy of constant self-pimping and product ads masquerading as "updates." As a result pound for pound Innocent's fan engagement on its page is multiple time better than Coca Cola's - even if the latter has many more likes. So Innocent, I'm sorry of thinking if you were a David but I'm mighty proud that you can remain that inside a Corporate Machine. There is hope yet for all other brands at P&G, Unilever, Nestle, Kraft, Pepsico, J&J, etc. etc.]

Tide is not lame. They are not going to obsess about the number of Likes. They will measure the four sexy magnificent best social media metrics and use them to drive a magnificent conversation marketing strategy on Facebook (and YouTube and Google+ and Twitter and Pintrest and …).

Neither should you Ms./Mr. Marketer. Because Likes (and +1s, Followers) measure a fleeting "hello." It is what you do after that first hello that creates business value. Focus on that, and you'll get big 5. Buying targeted impressions = business impact.

PS: Bonus: Facebook Advertising / Marketing: Best Metrics, ROI, Business Value

4. # 1 Search Results Ranking = SEO Success.

Not going to happen.

So what is it? When you search for a brand or a category term, most companies, small or big, want to show up number one in the search results.

Much money is spent on all kinds of demands to SEOs, big or small, which results in all kinds of shenanigans to try and get to #1. Sadly, all that yields very little.

The main reason, as all decent SEOs will tell you, is that search results are no longer standardized. Rather they are personalized. I might even say, hyper-personalized. Regardless of if you are logged in or not.

When I search for "avinash" on Google I might rank #1 in the search results because I'm logged into my Google account, the engine has my search history, my computer IP address, it also has searches by others in my vicinity, local stories right now, and so many other signals.

But when you search for "avinash" your first search result might be a unicorn. Because the search engine has determined that the perfect search result for you for the keyword avinash is a unicorn.

[Do the search wherever you are, do you see me or a unicorn? Share via comments!]

You should give up obsessing about only the number one keyword ranking for just this reason. But of course there are many others. Universal search for example means that personalized results will not only look for information from web pages, they also look for YouTube/Vimeo videos, social listings, images of course, and so on and so forth. Then let's not forget that, proportionally, there are very few head searches and your long tail searches will be huge. Oh, and remember that searchers rarely type just a word or two, and instead use long phrases.

There are a ton more reasons why obsessing about the rank of a handful of words on the search engine results page (SERP) is a very poor decision.

So check your keyword ranking if it pleases you. If your boss insists that your brand must rank #1 for x, y, z keywords, before you show him/her progress log, into Bing on his/her computer, search for x, y, z and click on your brand (even if it is on page 2), and chances are when he/she searches for x, y, z they might see your brand ranking #1. :)

But don't make it your KPI.

There are many other better, no-way-I'll-get-fired SEO metrics.

For purely SEO, you can use Crawl Rate/Depth, Inbound Links (just good ones) and growth (or lack thereof) in your target key phrases as decent starting points. You can graduate to looking at search traffic by site content or types of content (it's a great signal your SEO is working). Measuring Visits and Conversions in aggregate first and then segmented by keywords (or even key word clusters) will get you on the path to showing real impact. That gives you short-term acquisition quality, you can then move to long-term quality by focusing on metrics like lifetime value.

Don't start with LTV, it is hard. Use the step-by-step process above. And you'll get promoted.

5. REDUCE MY CPC! REDUCE MY CPC NOW!!

I. Hate. This. One.

H. A. T. E.

Deep breath.

It upsets me greatly when I see companies create entire Pay Per Click / Search Engine Marketing strategies based on this single metric.

I've heard this more times than I care to remember: "Our CPC for our Bing/Yandex campaigns was $2.25. This quarter the goal of our PPC campaign is to get that to $2."

Arrrrhhhhh!

This is the image that comes to my mind.

Image Credit Frits Ahlefeldt

Let me use an example here.

Let's say you have a stock portfolio with someone like E-Trade. As a prudent investor you'll buy low and sell high. (Easier said than done!) When you trade stocks you'll incur a per-trade fee from E-Trade, usually $10.

Would you measure the success of your trades based on cost per trade? Would your overall trading strategy be to reduce that price to $9 next month?

Of course not, that would be insane.

You will measure the impact on the bottom-line as a result of the trades. How much profit did you generate?

Likewise for the results of your Bing/Yandex campaigns.

You should not care if the CPC is $2 or $25. If someone is on Bing and raising their hand that they want to buy a vacuum cleaner, then you should be willing to pay to show up to sell a vacuum cleaner because that is what your company does.

You should judge the success of that showing up by measuring whether you made money! Did you earn any profit?

As you buy paid search tools, hire smart people/agencies, you should measure if you are able to make more and more profit. You should measure whether, over a long period of time, you are able to increase the lifetime value of PPC acquired customers.

CPC is a profoundly silly indication of your search strategy success. It causes companies to make silly decisions, which leads to poor results (both for the digital strategy and your career).

Friends don't let friends use CPC as a KPI. Unless said friends want to get their friend fired.

6. Page views. Give me more page views, more and more and more!

Content consumption is a horrible metric. It incentivizes sub-optimal behavior in your employees/agencies.

If you are a news site, you can get millions of page views writing stories like (not kidding, all real headlines) "10 Reasons Apple's Innovation Machine is Dead" or "The Kardashians Are On Vacation In Greece" or "Guy Performs 'Do My Thing' In Sailor Moon Outfits Made Of Construction Paper." And it will probably get you transient traffic.

Then that traffic goes away. So your next story is "22 Longer Sex Condom Ads From Around The World."

That works for 40 minutes. Then what?

And what about business impact from all these one-night stands ? Unless the spammy display ads on your site are getting you $14,000 CPM, your company won't be able to afford your salary for too long.

If you are in the content-only business (say my beloved New York Times) a better metric to focus on is Visitor Loyalty (and if like me you are a paying subscriber: Customer Loyalty). Rather than focusing on a transient metric, you focus on your site's ability to deliver such value to the visitor that they come back again and again – and pay you!

If you are in the lead generation business and do the "OMG, let's publish an infographic on dancing monkey tricks which will get us a billion page views, even though we have nothing to do with dancing or monkeys or tricks" thing, measure success on the number of leads received and not how "viral" the infographic went and how many reshares it got on Twitter.

If you are in the ecommerce business, the only reason to care about pageviews (in a pages/visit context) is to obsess about understanding how to create the most optimal shopping and purchase experience on your site (and the fewest pageviews to happen in the checkout!).

Don't obsess about page views. Spend a short amount of time thinking what causes your salary to get paid, the bottom-line. Then measure the metric closest to that. Hopefully some ideas above will help get you promoted.

7. Impressions. Go, get me some impressions, stat!

Display advertising is an integral part of any digital marketer's repertoire. As it should be.

But I'm heart broken when the success of their Facebook/AOL/Doubleclick/Video ads is purely based on impressions.

If you are forced to watch, say a TV or Digital Video ad, perhaps we can summarize that you actually saw it. You can imagine how tenuous the connection to impact is for "impressions" of banner ads (all shapes, sizes, levels of intrusiveness). Did they really see them?

My hypothesis is that TV/Radio/Magazines have created this bad habit. We can measure so little, almost next to nothing, that we've brought our immensely shaky GRP metric from TV to digital. Here it's called impressions.

Don't buy impressions.

I know you are holding a gun to my head so I'll say this instead: Buy engagement. Of course define what it means first.

I'll be a smidgen more delighted if you evolve to measuring clicks on those ads. It is a signal that someone saw something and clicked on it.

If you are willing to go to clicks, do one better and measure Visits. At least they showed up on your mobile/desktop site.

Now if you are a newbie, measure bounce rate. If you have a tiny amount of experience, measure Visit Duration. If you are a pro, measure Revenue. If you are an Analysis Ninja, measure Profit.

Impressions suck. Profit rocks.

Perhaps you disagree. That is ok. We are both reasonable people. You can buy impressions if you can prove via a simple controlled experiment that when we show impressions we get more engagement/sales and when we don't show impressions we do not get more engagement/sales. And I'll be extra sweet to you, the display/video impressions don't even have to be last click! Actually you don't even have to have a click!!

If the simple A/B (test/control) experiment demonstrates that delivering display banner ad impressions to the test group delivers increased revenue, buy impressions to your heart's content. I'll only recommend that you repeat the experiment once a quarter. See, I can be reasonable.

But if you won't do the experiment and you use the number of impressions as a measure of success, don't get too comfortable in your chair. Bye, bye, coming soon.

8. Demographics and psychographics. That is all I need! Don't care for intent!

This is not a metric, this is more of a "what data you'll use to target your advertising" issue.

It’s another legacy from the old advertising channels like TV, magazines and radio. Our primary method of buying advertising and marketing is: "I would like to reach 90-year-old grandmas who love knitting; what TV channel should I advertise on?" Or they might say: "I would like to reach 18-to-24-year-olds with college education who supported Barack Obama for president." An example of demographic and psychographic segments.

We would have no idea if Grandma actually wanted what we sold or if the young person was remotely interested in our service/product. But we had no intent. We just had results of questions asked during dinner by phone surveys and content habits. We took that on very thin ice data, we bought advertising. That was our lot in life.

[Did you know 50% of TV viewership is on networks that each have <1% share? Per industry.bnet.com. I dare you to imagine how difficult it is to measure who they are, and how to target them to pimp your shampoo, car, cement.]

We do the same on the web. We go to Google+ or Facebook or AOL and say "Hey, you have a billion people, give me their age and education and let me send a deluge of display ads to them or push my promoted posts!"

The click-through rates prove the poorness of that strategy. Just look at your own reports.

Intent beats demographics and psychographics. Always.

So if you have advertising money to spend, first spend it all on advertising that provides you intent data. (Don't worry, you'll have plenty left over to gamble on demographic/psychographic data. I promise.)

Two examples.

Search has a ton of strong intent. It does not matter if you are a grandma or an 18-year-old. If you are on Baidu and you search for the HTC One, you are expressing strong intent. Second, content consumption has built-in intent. If I'm reading lots of articles about how to get pregnant, you could show me an ad related to that (even if I'm a man, I'm actively expressing interest in getting pregnant!).

The first intent is strong, the second one is weaker. (If you use remarketing you can make that second intent even stronger.) But in both cases the most important thing is not how old I am or my gender or my education or the number of legs I have.

There is a lot of intent data on the web. That is our key strength. Don't waste your marketing and advertising on mental models for targeting, delivery and engagement that were created in the age of Mad Men. Leverage the mental models of our day and age and you'll be immensely successful.

I wish you all the very best!

Ok, as always, it is your turn now.

There is perhaps no limit to the number of silly mistakes Marketers (or pall bearers or butchers or …) can make. Do you have favorite mistakes that are not listed above? Which personal "mistake" taught you the most valuable lesson of your career? Do you agree with the eight above? Which one do you most disagree with? Any thoughts on the cultural challenges that cause us to make these mistakes?

Your insights, advice, helpful tips and critique are most welcome.

Thank you.

Eight Silly Data Myths Marketing People Believe That Get Them Fired. is a post from: Occam's Razor by Avinash Kaushik

Yet this structure rarely exists in companies.

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. Sometimes, we escape the clutches of this sub optimal existence and do pick good metrics or engage in simple A/B testing.

But it is not routine.

So, how do we fix this problem?

This thought was in my mind as I was reading Lean Analytics a new book by my friend Alistair Croll and his collaborator Benjamin Yoskovitz.

The book introduces a wonderful process called the Lean Analytics Cycle, which aims to help you create a sustainable way to pick metrics that matter by tying them to fundamental business problems, creating hypotheses you can test and driving change in the business from the learnings you identify.

I've asked Alistair to share his thoughts on this process with us. In this post, we’ll look at each of the four steps in the Lean Analytics Cycle in more detail. Then, for fun, we’ll look at three real-world case studies where companies put the steps to work so we can see the cycle in action.

Let's listen in as Alistair discusses the lean analytics model…

______________________________________________________

The Lean Analytics Cycle is a simple, four-step process that shows you how to improve a part of your business.

First, you figure out what you want to improve; then you create an experiment; then you run the experiment; then you measure the results and decide what to do.

The cycle combines concepts from the world of Lean Startup — which is all about continuous, iterative improvement — with analytics fundamentals. It helps you to amplify what’s proven to work, throw away what isn’t, and tweak the goal-posts when data indicates that they may be in the wrong place.

Here's a pictorial representation of the complete lean analytics cycle:

[Note: Due to the limited screen real estate, I've redone all the images you see in this post. They preserve almost all original intent, but if you read the book, or see the cycle elsewhere, please don't be surprised to see a slightly different version. -Avinash]

While the process above might seem complex, we can simplify it to four key steps that any business of any size can apply to their analytics practice.

Step 1: Figure out what to improve.

One thing the cycle can't do is help you understand your own business. That part is your job. You need to know what the most important aspect of your business is, and how you need to change it.

• Perhaps it's an increase in your conversion rate;
• Or a higher number of visitors who sign up;
• Or a greater number of people who share content with one another;
• Or a lower monthly churn rate for users of your application;
• Maybe it's even something as simple as getting more people into your restaurant.

The point is, it's a critical metric for your business. You might need help from the business owner to figure out what the metric is. That's a good thing! It means you're relevant to the business, and if your trip around the cycle is successful, you'll have helped the organization get closer to its goals.

Another way to find the metric you want to change is to look at your business model. If you were running a lemonade stand, your business model would be a spreadsheet that showed the price of lemons and sugar; the number of people who passed by your stand; how many of them stopped to buy a drink; and what you could charge. Right there you have four things that are critical to the business and one of them is ripe for improvement. This is the one metric that matters to your business right now. You're choosing only one metric because you want to optimize it.

That metric is tied to a KPI. If it's the number of people buying, the metric is conversion rate. If it's the number of invites sent, it's virality. If it's the number of paying users who quit, it's churn.

The business model also tells you what the metric should be. You might, for example, need to sell each glass of lemonade for $5 to break even. That's your goalpost. It's the target for your KPI.

So grab a piece of paper and write down three key business metrics you'd like to change. For each of them, write down the KPI you're measuring, and what that KPI should be for you to consider your efforts a success. Do it now; we'll wait.

Step 2. Form a hypothesis.

This is where you get creative. Experiments come in all shapes and sizes:

• A marketing campaign
• The redesign of an application
• A change in the way you price things
• Building shipping costs into a purchase
• Changing how you appeal to people
• The use of a different platform
• Changing the wording on buttons
• Testing out a new feature

Whatever the case, this is where you need inspiration. You can find that inspiration in one of two ways.

If you have no data, then you can try almost anything.

• Try to understand your market. Run a survey, or look at what else they do, or examine customer feedback, or simply pick up the phone.
• Steal from your competitors. If someone is doing something well, then imitate them. It's the sincerest form of flattery. Don't be different for the sake of being different.
• Use best practices. Read up on ways that companies are growing their business, from growth hacking to content marketing, and use that as inspiration.

If you have data, then figure out what’s different about the people who are doing what you want. Let’s say, for example, that you’re trying to lower the churn rate on an application. Some of your users each month don’t quit. What do they have in common? What makes your most loyal customers different from the rest? Did they all come from the same place? Are they all buying the same things?

Either way, the hypothesis comes from getting inside the head of your audience, asking them questions, or understanding what makes them tick.

The word hypothesis means a lot of different things, but in this context I like this definition from Wikipedia the best: People refer to a trial solution to a problem as a hypothesis, often called an "educated guess”, because it provides a suggested solution based on the evidence.

We’re making an educated guess about what could improve the KPI based on what we learned in step 1.

Step 3. Create the experiment.

Once you have a hypothesis, you need to answer three questions to turn it into an experiment.

On the surface, these three questions—who, what, and why—don’t seem hard to answer. But they are. That’s because they require you to have a deep understanding of your customers. In the Lean Startup world, this is called customer development. The experiment will almost always look like this:

Find out if WHO will do WHAT because WHY enough to improve KPI by the Target you've defined.

This gets to the deliberate nature of the actions we want to take. You start with a great hypothesis, and you’ll get a great experiment. This also keeps you honest, because everyone recognizes the point of the activity beforehand.

In our case studies, below, you'll see that the KPIs were things like Property Bookings, Number of Engaged Users, and Daily App Use. If you have access to existing data, take some time to document what the current performance looks like. Of course, it’s possible that you don’t have access to the data (as in the Airbnb case study below.) That’s OK. We have a path for that as well.

Once you have your experiment, set up your analytics to measure the KPI against its current baseline and the goal you’ve set. Then run your experiment.

Step 4. Measure and decide what to do.

At this point, you’ll know whether your experiment was a success. This leaves us with several options:

The underlying beauty here is that we’re being smart, fast, and iterative. We’re making a deliberate plan, measuring its results, and circling ever closer to our goal. Identify, hypothesize, test, react. Repeat.

Let’s look at some case studies that will really help to drive the Lean Analytics Cycle home. We don’t know everything about the metrics these companies dealt with — they’re private companies, after all — and in some cases we’ve estimated numbers to make the explanations more clear. But even with these changes, the examples will help make all of this a bit more real.

Case Study 1: Airbnb

Airbnb is a hugely popular marketplace for rental-by-owner properties. They’ve found dozens of creative ways to grow, but they’re always judicious and data-driven.

Step 1: Figure out what metric to improve

The metric they wanted to improve was the number of nights that a property was rented. Notice that this is more central to their business than simply measuring revenue: Airbnb does well if its homeowners do well, and for it to succeed, it needs listed properties to be rented often so the homeowners will stick around.

The company knew that to succeed, they needed a significant improvement in rental rates per property.

Step 2: Form a hypothesis

We don’t know how Airbnb came up with its hypothesis. But we know it had access to property listings that rented well.

However they got there, they formed a hypothesis: Properties with better pictures rent more often.

Step 3: Create the experiment

Armed with the hypothesis, it was time to create the experiment. As is often the case, having a clear hypothesis makes devising the experiment fairly easy. Their who, what, and why are as follows:

So for them, the experiment was

Find out if travelers will book more properties because of professionally photographed listings enough to improve the property bookings by X%.

Notice that in this case, Airbnb didn’t really need any current data. It might just as easily have been a random comment over lunch that led to the hypothesis. But even if the hypothesis isn’t founded in hard data, the experiment design must be.

To run this experiment, Airbnb created what Lean Startup calls a curated minimum viable product. This is like the Wizard of Oz: most of the hard work is done behind the curtains, but the end user thinks they’re seeing a final product.

Airbnb wasn’t sure whether or not the experiment would work, so the team didn’t want to hire a staff of photographers or invest in a new part of the application. But at the same time, they had to have a real test of an actual feature.

Before we go further, there’s an important lesson to take away here. You can do this at Nordstroms, or Expedia, or Unilever. You don’t need to build a magnificent shining castle. You don’t need a beautiful beast to go out and test. You can start small, lean, and mean — with just the customer-facing pieces you want to test — and go validate (or disprove!) your hypothesis.

Airbnb’s experiment consisted of something that looked like a real feature, but under the covers was really just humans and contracted photographers. During the experiment they took pictures of properties, and then measured the KPI, comparing properties that had been photographed to those that hadn’t.

Step 4. Measure performance.

In this case Airbnb measured the bookings from the few properties that had professional photos and compared the rate of bookings with properties that only had photos taken by property owners. The result? The properties with professional photography had 2-3 times the number of bookings!

[Remember that the raw number is not the only important part, we would also measure statistical significance. Airbnb had enough data points to be confident in their results. -Avinash]

By 2011, the company had 20 full-time photographers on staff.

The graph is impressive, right? There were many other things going right with Airbnb's business and business model. But the lean process was a key contributor to improving the bookings rate. Clearly, the experiment was a success. They celebrated a bit, then went on to fix the next biggest problem in the business.

Case Study 2: Circle of Friends

Circle of Friends was a social community built atop Facebook that launched in 2007. It essentially allowed you to create a group of friends who could interact and share content, much as people do today with Google+, before such features were part of Facebook.

As a social network based on user-generated content, Circle of Friends only grew when its users were engaged. The company’s creators wanted users not only to create groups, but also to send messages within those groups, invite others, and interact with Facebook elements such as news feeds.

Step 1: Figure out what metric to improve

The founders had several measures of “engagement”, from whether people attached a picture to a post, to whether they clicked on Facebook notifications, to the length of posts they wrote. Any of these actions — and several others — constituted “engagement.” All of this rolled up into a simple KPI: number of engaged users.

While Circle of Friends’ launch was hugely successful in terms of raw attention — they had 10 million users! — engagement simply wasn’t happening. None of the metrics they used to measure the health of their communities was where they wanted it to be.

The rather nebulous “level of engagement” was a compound metric, which can be dangerous to rely on. But one of the clear metrics they tracked was “number of circles with activity”, meaning that there had been some form of interaction within a group in the past few days.

• One Metric That Matters: User engagement
• KPI: Number of active users; number of circles with activity
• Target: (not publicly known)
• Current level: Less than 20% of circles had any activity after creation

The challenge, here, was how to kickstart engagement. That’s not an easy thing to do. Fortunately, Circle of Friends had plenty of user data to mine.

Step 2: Form a hypothesis

Circle of Friends had huge volumes of information on its users and how they used the product. They looked at two groups of users: those that were engaged, and those that weren’t. Then they looked at what those users had in common. In other words, they defined what engagement meant (i.e. “someone who has returned in the last week”) and segmented users into two groups. Then they looked at other things those people had in common.

What they found changed their business. It turned out that the engaged users were much more likely to be mothers. Looking at moms who use the application:

• Messages to one another were, on average, 50% longer
• 115% more likely to attach a picture to a post they wrote
• 110% more likely to engage in a threaded (i.e. deep) conversation
• Those who were friends of the circle owner were 50% more likely to engage
• 75% more likely to click on Facebook notifications
• 180% more likely to click on Facebook news feed items

Before we look at what they decided to do, stop for a minute and realize that simply having data isn’t that useful. Circle of Friends had all of this information. But asking the right questions of your data is a superpower. In this case, they asked, “what do engaged users have in common?” and it absolutely changed the destiny of their company.

From their data, they formed a hypothesis: If we focus only on moms we’ll have the engagement we need.

Step 3: Create the experiment

Their who, what, and why were as follows:

In the end, the founders made a huge bet — the kind of bet that only startups with nothing to lose can make. They decided to completely rebrand the company as Circle of Moms, and focus everything on attracting and engaging mothers.

So their hypothesis was:

Find out if moms will join, relate and engage in groups because of a community targeted specifically at them enough to improve the number of active circles and engaged users by X%.

They were betting that if they stopped trying to please everyone, and instead focused specifically on mothers, they’d have a higher percentage of engagement, which would in turn lead to the company’s overall success.

Step 4. Measure performance

It took a few months to carry out the experiment, since it involved re-launching the entire company. The initial result was a huge drop in users, of course, since most of the communities weren't made up of mothers.

They lost millions of users.

They feared this would happen, but they knew that if they didn't get engagement where it needed to be they might as well close up shop.

If they'd been focusing on the wrong KPI, such as number of subscribers, they'd have quickly concluded that the experiment was a disaster. But because they'd been deliberate about their hypothesis, they knew that the most important metric was engagement. Sure enough, the users that remained were engaged. Overall engagement and active circles climbed significantly, to the point where the business model was healthy.

Of course, having achieved the engagement they sought, the next experiment was about whether they could grow a more narrowly focused community to a decent size. By late 2009, that experiment was a success, too; they'd climbed back up to 4.5 million users, with strong engagement.

Case Study 3: High Score House

High Score House is a tool that lets parents manage household chores and rewards for their kids. The founders envisioned a world where parents and children sat down each day to identify chores, keep score of what the kids have done, and drool over rewards they might claim.

Step 1: Figure out what metric to improve

In the early stages of launch, the most important metric was the number of families who fired up the app at least once a day. Those who did were considered "active", while those who didn't were assumed to have abandoned the application. Unfortunately, while High Score House was getting installations and great press reviews, users weren't active.

No active users, no business.

• One Metric That Matters: "Active families"
• KPI: Percent of families who've used the app in the last 24 hours (AKA "percent daily use")
• Target: Over 60%
• Current level: Below 20%

Step 2: Form a hypothesis

The founders went around the Lean Analytics Cycle several times:

• They tried changing the look and feel of the application.
• They tried sending people messages urging them to come back to the application.
• They tried adding new features.

None of it worked; fewer than 20 percent of families who were part of the early release used the app each day.

Each time, their hypothesis went something like, if we send a reminder email to parents each day, the percent daily use will exceed 60%.

Step 3: Create the experiment

The High Score House team went dutifully through the motions of the Lean Analytics Cycle. For the notification hypothesis, the who, what, why looked like this:

In other words, their experiment (for the notification model) was:

Find out if parents will use the app daily because we remind them to do so enough get the percent daily use to 60%.

They ran the experiment.

Step 4. Measure performance

The experiment disproved the hypothesis. Email reminders didn’t measurably improve percent daily use. Like many of the other changes the team had tried, they couldn’t move the needle.

At this point, Kyle, the founder of the company, decided it was time to ditch the data and go qualitative. He picked up the phone and started talking to users. And this simple, messy, unquantified, anecdotal process yielded an amazing insight:

Many families would set aside a specific day of the week to plan their chores and count their rewards.

Kyle was floored. Families were getting value from the product. They were just using it in a different way from what the designers intended. Kyle now had new information which he could use. But it wasn’t for another experiment. This time, Kyle was going to move the goalposts/target.

Changing your target should never happen lightly. It’s only OK to change the goal when the customers have given you new, validated learning from which to make the change. In this case, since your goal is tied back to your business model (remember the $5 glass of lemonade?) changing the goal means changing your business model

In this case, HSH was going to have to change the way their application was designed, making it more suitable for week-by-week views and a different usage pattern. But by moving the goalposts, the team had a new KPI: Percent of families who've used the app in the last 7 days (AKA "percent weekly use"). And with this new definition, over 80% of the early adopters were using the application regularly.

We’ve seen three case studies. The first one, about Airbnb, shows a straightforward example of a hypothesis and an experiment without data. The second, about Circle of Friends, shows an example of an experiment based on data. And the third one, about High Score House, shows how to adjust the goalposts when they aren’t set properly in the first place. All three are examples of the Lean Analytics Cycle at work.

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Amazing, right? Life's pretty cool when you have a process!

For me the lean analytics cycle is, at it's very core, about driving change quickly, regardless of how much you know about your users or how much data you have. It discourages waiting for perfect data or perfect understanding of every variable or perfect understanding of what the business is trying to solve for (because in all three cases you will never get perfection!).

While it was developed with startups in mind, you can see the attraction of speed, learning and constant improvements for businesses of any size.

It is important to note that the cycle, and the Lean model as a whole, isn’t advocating chaos. They might deal with uncertainty, but they're not random.

There is great deliberation in step one to identify the KPI that will be a guiding light for us (embrace "One Metric That Matters"). There is a lot of deliberation in step two on ensuring that we have an optimal hypothesis to work from. Then figuring out how we are going to experiment with deep clarity from defining the who, what, why. Finally, understanding whether we actually succeeded by measuring performance. Then, then … you learn. You internalize. You win.

And how can you be immensely incredible at this? Go get Alistair and Ben's book Lean Analytics .

As always, it is your turn now.

Do you follow a structured process inside your company to ensure data-driven decisions are being made constantly? If you were to apply the Lean Analytics Cycle, what challenges might cause problems inside your company? As you look at the cycle, is there anything that might be missing, or tweaks you would make to improve it?

Please share your thoughts, critique, ideas, and musings via comments.

Thank you.

The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act is a post from: Occam's Razor by Avinash Kaushik

Today's post is about a new problem I'm starting to notice, which only exists because our tools have become so much cooler and handed us so much power: constant mismatching of hit- and session-level metrics and dimensions.

This particular problem exists primarily because Analytics allows us to create custom reports. In some scenarios you also bump into it when you do advanced segmentation and filtering.

The danger is that most of the time we don't even realize we are making a mistake because after we create our report we see numbers and they look like real numbers and it looks like something is happening. But it is all fake datagasams.

Let's define the problem, let's understand the optimal mental model, and let's look at some examples to firm up our learning.

I promise you, next time you log into SiteCatalyst or dive into Discover or surf with WebTrends or play naughty with Google Analytics, you are going to be a lot more cautious. :)

Hits? Sessions? What are we taking about?

To understand this problem we are going to have to get a little bit technical. It is very important context.

[It will be critical that you are familiar with what a metric is and what a dimension is. In the small chance you need a refresher: What is a dimension? + What is a metric?]

Say you type in a URL and visit a website. The very first page (usually the home page) starts to load, which triggers the web analytics javascript code on the page…

The javascript code sends data into the Cloud to your analytics tool. This data gets recorded as a "hit."

Any individual interaction that you have with the site is called a hit. Most typically a hit is a page view. But it can also be an event or a custom variable (if the scope is set to "hit level").

As you browse through the site, you keep sending hits back to the analytics tool. "The visitor just requested a product page." "The visitor added something to cart." "The visitor started a video." "The visitor started the checkout process." Etc. Etc.

Hits here and hits there and hits everywhere as you engage with the site (mobile, desktop, mobile app).

For each hit, a bunch of data is also being collected (ex: you saw page x for y minutes).

A collection of hits from one visit to the site (entry and exit, or entry and leaving tab/browser open for 29 mins of inactivity) is called a session.

That's the purple box…

Here's a helpful picture that you can use to remember the difference between hits and sessions. Hits are the collection of small things. The session is collecting those hits into one cohesive experience.

Custom variables has an asterisk next to it because there are three types of custom variable: visitor-, visit- and hit-level. Only the last one, obviously, qualifies as a hit.

Now that we understand hits and sessions, let's dig deeper into the core reason for this post.

Hits are from Venus and Sessions are from Mars.

The visitor, let's call her Kim, comes to the site. Kim leaves (like all parents, after making an expensive purchase for her daughter!).

The analytics tool collects loads of data about her visit related to the hits, the individual interactions. Examples of these hit-level metrics are things like Time on Page, Bounce Rate, Page Value, Page Abandonment Rate, and others.

These metrics only correctly measure, and can only accurately be used to report, what is happening at a hit level – i.e., the small individual interactions.

Then there are metrics that measure what happened at the overall session level: Time on Site, Goal Value, Conversion Rate, and many others.

They are meant to only identify performance of the purpose box.

Punch-line: You can only use hit-level metrics to measure hit-level dimensions, and you can only use session-level metrics to measure session-level dimensions.

[Once again, definitions: What is a dimension? + What is a metric?]

For example, it is wrong to have Search Keyword as your dimension and measure Page Value for that. See the problem? Session-level dimension (you are looking at what started the whole session) being measured by a hit-level dimension (what happened with one blue, red, green bar above).

What's the fix? Use Goal Value.

Another example, it is imprecise to look at Pages (a hit) and measure Conversion Rate (a dimension-level metric) for it. One blue, red, green bar can only be measured for a metric that is at its optimal altitude and not a metric that is for the whole visit (what about the other hits?).

What's the fix? Use Page Value.

When it comes to custom reporting, we mess up this calibration all the time. We measure hit/session dimensions using session/hit metrics! Sadly, this is a silent death because you don't even realize you are messing up.

Of course that is not going to happen any more. You've read this post.

Examples: Calibrating Hit- & Session-Level Metrics Optimally.

Once I started to look at this more carefully I realized that many web analytics tools, even in the standard reports they provide, mess up. So look at your standard reports very carefully when you log into your analytics tools. Otherwise big data is going to bring tiny insights.

Here's a standard report from Google Analytics.

We are looking at a session-level dimension, Source/Medium (where do visitors come from?).

This report is calibrated properly because Visits, Pages/Visit, Avg. Visit Duration, % New Visits are all session level metrics.

If you are not sure of why, scroll back up just a little and ask yourself: Are these metrics measuring the purpose box or the blue, red, green bars? Clearly, they are measuring the purple box. You can't have Pages/Visit for an individual hit, right?

Go look at the Landing Pages report. What do you see? Do you agree that the standard report is showing all hit-level metrics for the hit-level dimension (page)? You'll see that it is not that straight-forward.

What about the Geo Location report or the mobile devices report or your adwords campaign reports?

It stretches your brain to think this through.

Now, let's look at a custom report, which is typically where we all make this mistake.

In this report I'm looking at the Time on Page, Avg. Time on Page (I was not sure which of the two to use) and Page Value.

Lesson one is that Google Analytics continues to include astoundingly value-deficient metrics like Time on Page. This metric, it seems, is the summation of all time spent by everyone from a source. I'm struggling to imagine a single scenario, no matter how remote, where anyone could find this of value. I don't understand why these value-deficient metrics continue to live year after year! Rant over.

Let's consider the other two…

Average Time on Page is, by its definition, a hit-level metric. It measures what happens on one page. Looking at it at a session level is silly. After all, what does that 04:15 for Google actually mean? And what page's average is it?

Ditto for Page Value. That metric (and it is awesome by the way) is supposed to tell you how much economic value was generated by each page on your site. It can only be looked at for a hit-level dimension (Page or URL).

The silent death part is that as you stare at the table above it looks like something is happening. All the numbers are different. You might say t.co (twitter essentially) is fantastic at 10:09. But that is just a garbage number. Or you might look at $2.11 and celebrate Google (and you should, but) that would be unwise because you are looking at the wrong metric.

But unless you are careful and know to match hit-level metrics with hit-level dimensions, you would likely make big mistakes in any recommendations you make on this data.

So what is the optimal way to measure Sources of traffic? Use session level metrics…

Unique Visitors? Yep. Avg. Time on Screen? Only the lord knows what that is because even the Google Analytics Help Center fails to find this metric. But the metric is there when you are trying to pick time-based metrics. I know, I know, I should just let the GA team make our life miserable and move on. Okay, moving on … Average Visit Duration (also known as Avg Time on Site in other tools) is a perfect match here. And my absolute BFF Per Visit Goal Value is a perfect match because it is a session-level metric (it measures what happens over the entire purple box).

[I love PVGV because it measures not only e-commerce value - the macro conversion - but also goal values - the micro conversions! It forces each site owner to solve for 100% of the traffic rather than just the 2% that convert. I know of no better way to win on the web than to solve for 100% of your visitors.]

So you are all set now?

Match hit-level metrics with hit-level dimensions. Match session-level metrics with session-level dimensions. Life will be rosy.

Not yet all set?

Ok, let me share a couple more examples…

Here's a report by one of my Web Analytics Master Certification course students. They'd submitted this early in the course.

They are reporting on a hit-level dimension, Page.

You can see that they did ok with the first two metrics, Entrances and Bounce Rate are both really great hit-level metrics.

But then the rest of the stack contains session-level metrics. Per Visit Goal Value can't be measured for a hit (page) because it is only computed for the entire session. Ditto for Transactions. And you paid a cost (campaign cost) to get Kim to come to your site, but that has nothing to do with the page (it has to do with the visit, or to put it another way, the session). Same thing for Cost Per Acquisition – it's not a hit-level metric.

This was early in the course so we were able to correct the learning, but at this time this wonderful student had analyzed this data, later even applied advanced segments to it and recommended a bunch of actions. Only the actions were based on wrong data.

So be very, very careful.

One last quick example to really, really nail this down.

In this case, we are looking at hit-level dimension again, Page.

With the first metric, Visits, you would not be doing something totally imprecise, but it is super clean to use Unique Pageviews if you want to know uniquely how many visits was a page present in. If you want to go deeper, how many times a page is seen during one visit, use Pageviews. That would show how many times Page X was viewed and how many times it was unique in a visit. Nice!

The second and third metrics above are tricky. You feel like a page should be delivering Goals and Conversion Rates. Nyet!

An individual page does not deliver Goals and Conversions. Each page just moves the person to the next one in the session. It is just one hit amongst many that happen during a session. All the hits banded together to deliver the completion and conversion rate.

And see what I mean by the silent death. It looks like you are looking at some numbers and they are all different so you can read something into it. Nope.

So what's a better replacement here? Page Value . Because in computing that hit-level metric all the credit from goal completions and conversions is taken from each session and "distributed" to the hits present in that session. That helps you understand how each hit did (across multiple converting sessions!).

A little complicated. But not really. Right?

Exceptions to the Rule.

Bounce Rate is a great exception to the rule.

In this post we have used Bounce Rate to measure the performance of a hit, pageview.

But you can also measure Bounce Rate for a dimension, sources of traffic (keywords, referring URLs, campaigns, etc).

That's because in sessions that bounce, there is only one hit, the first pageview. Then nothing happens. Since the session = the hit, bounce rate can be used to measure performance of either session-level dimensions or hit-level dimensions.

It makes total sense. But I wanted to point out that sometimes there are gray areas. Good news, they are rare. :)

Closing Thoughts.

I've created this little picture for you as a quick something you can reference.

On the very far left of the top half are examples of dimensions. Campaigns, geographic locations, sites sending traffic, etc.

Then we see examples of metrics we have access to for measuring the overall experience Kim had on our site and the result of that experience. They all measure her session, they are session-level metrics.

Finally at the bottom we have examples of hit-level dimensions and metrics. Small interactions that happen during the course of a visit to your site/mobile app, and the metrics used to measure their performance.

If you calibrate your hit-level dimensions and only use hit-level metrics, you'll find accurate tactical insights about improving individual pieces of Kim's experience.

If you calibrate your session-level dimensions properly and only use session-level metrics, you'll find accurate strategic insights about improving big things (your overall acquisition strategy, your product mix, your strategy on macro and micro outcomes, etc.).

Remember: Friends don't let session-level dimensions drive with hit-level metrics!

Good luck!

[Bonus: In case you use Google Analytics, you'll find this page to be of value: Dimensions & Metrics Reference Guide]

As always, it is your turn now.

Does the guidance above make sense to you? In your company do you pay careful attention to calibrating metrics at the right hit/session level? Do you know of other standard reports in your analytics tool where this mistake is made? Are there other metrics you feel also fall into the gray area? Got a big "d'oh" moment you are comfortable sharing? Confused about whether a particular metric fits in the hit-level or dimension-level category?

Please share your feedback via comments.

Thank you.

Excellent Analytics Tip #23: Align Hits, Sessions, Metrics, Dimensions! is a post from: Occam's Razor by Avinash Kaushik

This blog is centered around creating incredible digital experiences powered by qualitative and quantitative data insights. Every post is about unleashing the power of digital analytics (the potent combination of data, systems, software and people). But we've never stopped to consider this question:

What is the return on investment (ROI) of digital analytics? What is the incremental revenue impact on the company's bottom-line for the investment in data, systems and people?

Isn't it amazing? We've not pointed the sexy arrow of accountability on ourselves!

Let's fix that in this post. Let's calculate the ROI of digital analytics. Let's show, with real numbers (!) and a mathematical formula (oh, my!), that we are worth it!

We shall do that in in two parts.

In part one, my good friend Jesse Nichols will present his wonderful formula for computing ROA (return on analytics).

In part two, we are going to build on the formula and create a model (ok, spreadsheet :)) that you can use to compute ROA for your own company. We'll have a lot of detail in the model. It contains a sample computation you can use to build your own. It also contains multiple tabs full of specific computations of revenue incrementality delivered for various analytical efforts (Paid Search, Email Marketing, Attribution Analysis, and more). It also has one tab so full of awesomeness, you are going to have to download it to bathe in its glory.

Bottom-line: The model will give you the context you need to shine the bright sunshine of Madam Accountability on your own analytics practice.

Ready? (It is okay if you are scared. :)).

Here's part one, let me hand you over to Jesse…

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Hello my dear, dear friends fighting the good fight of analytics. I felt compelled to write about this topic because I, too, am an analyst to the core. However, I've long felt the unsettling sensation of having a tremendous impact on the business, but still having to fight for attention and resources, and always wondered why that is.

After being an actual analyst for a while, I'm now managing the Google Analytics Certified Partner program, and I now realize that it wasn't just me, our entire INDUSTRY is affected by this issue.

Why is it that we analysts feel like we have some amazing untapped ability that could revolutionize any business we touch, and yet we have to fight to be included in strategic conversations where we could do the most good, and we have to fight not to be ignored when we have something important to say?

Why is it that most analytics departments are constantly under-funded and under-staffed compared to the budget-hogs in Marketing or the herds of tech workhorses in IT?

I would venture to say it’s because we’ve made an awfully poor case for the value of what we do. Businesses, by and large, don’t understand the ROI of analytics… the Return on Analytics, if you will.

Everyone else seems to get an ROI calculation, but not us. Marketing dollars (hopefully!) get measured by their Return on Ad Spend. Product improvements are quantified in incremental sales. Even internal tools are evaluated by work hours saved. Yet the analytics team rarely has its costs measured in terms of impact on The Company.

‘But we measure (and hopefully, improve!) the ROI of other things’, you say. 'The impact of analytics is the impact we have on other teams.'

Exactly! And therein lies the problem! The absolute best case scenario is that we spend all our time making everyone else look better, only to let them take all the credit.

Do we really think that if our executives believed that every dollar invested (properly) in analytics would result in ten dollars back for the company, that we would still face the massive hurdles that so many of us deal with daily? Heck no!

However, it’s true, analytics is worth at least 10x what you invest into it. These successes are ours to claim, and it’s high time we started claiming them.

So, what is ROI?

The ROI calculation is cliché and overused because it’s so simple, even a child could do it:

With that context in mind, here’s an equation I drew up to quantify your impact as the “Return on Analytics”:

Don’t worry, its bark is worse than its bite. All it’s asking you to do is put the ROI calculation in terms of how analytics works. The formula accommodates for the critical need to compute incremental impact from deployment of an analytics practice.

You see, the challenge with analytics is that you can’t just say “how much did you make in return?” because you were (likely) already going to make something in return. So we have to figure out the impact – the incremental return that you wouldn’t have had otherwise.

So what I’ve done here is:

The end result:

‘But it’s so much more complicated than this!’, you argue. Yes. Yes it is. But so is any computation of ROI, if you really want to be honest. What this does is to package your impact into a (relatively) easy to understand way.

Let’s take an example.

Say you’re a mid-sized company who sells hubcaps. Your digital marketing team has a monthly budget of $30k, and the company sees $120k in monthly sales from it. 400% ROI, not bad.

You then hire on an analysis ninja and pay them $5k per month to “fix” your analytics (a bargain, if you ask me), and after 6 months of data-driven improvements to campaigns & landing pages, all of a sudden the same marketing costs are bringing in $180k in sales per month, a success rate which continues on for 12 months (until a new line of hubcaps come out).

To summarize: Six months of effort, twelve months of success (/gain).

Clearly some of the credit for this goes to your marketing team. But before you jump to “my marketing is now making a 600% ROI, that’s fantastic!” and then promptly give the Marketing team more money, it is important to realize that none of this would have happened were it not for the analyst who took the holistic approach to identify the optimization opportunities.

So, let's go back to our formula (above). Punch in some of the relevant numbers and you'll see this:

Holy guacamole! You’ve hit a gold mine! Your six month analytics driven improvement delivered twelve months of astounding results. If every dollar you’ve invested in this team paid off even half as much, then your company would be the #1 hubcap dealer in the world in no time!

This is the potential power of calculating your ROA. Attributing success where it’s due so that you can fuel the true driver of growth.

Once you’ve taken a hard look at what your investment in analytics (everything from tools to people to professional services) has produced in terms of real business results, ask where you need to invest more in order to get to a positive ROA … and not just a positive one, but the one we all imagine ourselves to be capable of.

______________________________________________________

Simple and amazing, right?

Here's the really key part… Businesses often don't understand the ROI of analytics. In fact it is not uncommon that they often don't even understand what analytics is! Here's the hidden awesomeness of computing ROA: If we can prove that there is ROI, they don't need to understand what we do as Analysts! Just like other professions (say, Accounting – what is it that they really do? :)), the analytics practice, and Analysts, will earn the right to be left alone to add value because in a very compelling way Businesses and leaders, through ROA, will know that we are adding value!!

Yes. I hear you (and Jesse acknowledged this as well).

This part of the blog post is to deliver the specificity that you've come to expect on Occam's Razor. Practical examples and specific guidance that will give you a leg up if you are convinced that bringing accountability to your analytics effort is a good thing.

The guidance is going to come to you via a customizable model in a handy dandy spreadsheet. So to speed up your ROA computation, download: Return on Analytics Calculation Model.

The model has a summary tab, a tab full of awesome specific guidance on how to compute incrementality with pitfalls and caveats, and finally a whole bunch of tabs with sample computation of incrementality across various analytical efforts.

Let's walk through the model in detail.

Tab one contains the model for an actual client from whom you can find inspiration. The first thing you'll notice is that the formula has already been created for you. No need to touch this.

The second key element is the annual revenue for your company prior to the implementation of analytics (or a major expansion of your analytics practice). We are trying to establish a baseline. Type it in.

The third element is to calculate the total cost of ownership. Your cost! Ok, ok, you plus the hardware, software, army of consultants and BFFs. :)

Here's what you'll see for that element when you open the model…

The numbers are realistic but by no means reflect what they might look like in your company. I've typed in as many things as I could think about connected to having a web analytics practice (i.e. Total Cost of Ownership).

So say you have Adobe's SiteCatalyst. You have a fixed fee you have to pay. You have a variable cost. You have a hourly support contract. You have an external agency helping you with implementation and online support. You have other software deployed, like tag management (all in vogue now and you know what, it costs money!) and specialized PPC tools and email or other software.

It also includes the important bits, ones we often overlook when creating an analytics strategy: The cost of people inside your company whose primary analytics job is implementation/IT (tagging, retagging, etc.), people whose primary job (greater than 70%) it to provide data but without insights or recommendations ("reporting squirrels," a necessary expense in any large company) and finally people whose primary job (greater than 70%) is to do analysis (and hence not data puke but provide insights and recommendations only).

And the $50,000 for IT resource and $25 for an analysis resource is just a joke I desperately hope is not true in your company (big or small, remember the 10/90 rule for incredible digital analytics success).

Not every single one of these rows will apply to your business. Say you use Yahoo! Web Analytics, the first two rows disappear, the third might not apply, but the rest might. Say you are a medium-sized company using WebTrends or Omniture, the first two rows might not be 100k/50k rather be 1,000k/350k. If you are a larger-sized company, well, you know the drill. If you are a large company you might have an army of consultants, if you are a small business that might be the free time you are getting from your cousin Ali.

So adapt the model, type in your actual costs. Calculate your digital analytics total cost of ownership. It will be revealing. I promise.

Then comes the magical part. What does your company get for all this investment?

The structure is simple, you identify the change you drove and then identify bottom-line impact of the aforementioned change after implementation of your data-influenced recommendation.

Here's what the various bits of impact look like the ROA computation model you've downloaded…

There are literally n number of things you could be driving inside your company.

In the model there are three clusters: 1. Media Optimizations 2. Content / Website Optimizations 3. Product / Company Optimization.

In each case, as you'll note above, there are examples of the type of activity that data might have informed and an example of the incremental impact.

By the way, incremental means incremental. The analytics team found an insight via their data analysis (at this moment you'll really, really regret if the primary function of your analytics practice is to data puke), that insight bundled with a specific recommendation for action was communicated effectively to the senior management, they in turn ensured it was implemented, and revenue went up.

At this point let me say something immensely important. We (Analysts) are NOT trying to claim credit for the entire uplift. We found the insight in the data and recommended an action, but many people are involved from that point on. Your marketing team went and got it implemented. Your copywriter created new copy. Your designer created new graphics. And so on and so forth. We are not trying to say here that we were singularly responsible for the incremental revenue.

We are just trying to say that that incremental revenue came from an insight produced by data analysis. So we are trying to give credit to the data. We are NOT trying to steal credit or undermine the team effort it takes to get things done in every company.

I sincerely hope that this section of the model serves as an inspiration of sorts for the vast net that data can cast in terms of driving change.

You'll see reduction of checkout abandonment rates from quantitative analysis, you'll see impact from improving task completion rate from qualitative analysis (which might drive offline conversions), you'll see impact from technical improvements, you'll see impact on the company's long-term value by improving brand perception or social media presence.

Let your mind roam wild. Look in every nook and cranny. And if your analytics practice is not focused on everything listed in this section (why not?), there is a lot of upside for you!

At least at the moment, not all the rows will apply to your business. That is ok. Fill out the ones that do. Improve over time.

Right now you are surely wondering: "Wait, what about that incremental bit? You ran over that pretty fast. That is hard stuff! "

: )

No. Did not forget that!

First, identifying incrementality is an incredibly difficult challenge. While getting perfect answers is nothing short of a life time effort, getting a good enough answer does not have to be very difficult.

So why not start there?

In the model you'll be delighted to discover a number of examples of how to compute incrementality. For example here's a screenshot of identifying incremental impact from your email marketing program.

The first thing you'll notice is that you can do this exercise in layers.

You can start with something simple. Let's say the analytics team does analysis of current email marketing metrics and identifies improvements to how your company structures the emails that go out. The recommendations are implemented and that drives an additional 100k clicks from the email campaigns. Assuming that nothing else was changed, it is now easy to measure the incremental impact of these changes.

Or maybe nothing was changed in the campaigns, but conversion rate was improved from 2% to 5% by changing the checkout conversion process for email campaigns. Well, it is easy to calculate that impact.

Or maybe you have an advanced analytics team with lots of senior management support and are able to improve the email copy and calls to action, the checkout process and do much better cross-sells and upsells and improve average order value. Well, that third cluster shows you how your computation might look.

Is it a perfect approach? Almost. Does it get you going in the right direction? Emphatically, yes!

As Voltaire put it: "Le mieux est l'ennemi du bien." (The best is the enemy of the good.)

There are other examples in the spreadsheet that should serve as guidance/inspiration for approaches you can take when you compute incrementality of the impact you deliver via your analytics practice.

Here's the section on computing value delivered by your investment in software to do multi-channel attribution modeling and the person you hired specifically to do that work…

From an impact computation perspective you can see how brutally simple the process is. Either you delivered revenue increase, or you did not.

Multi-channel attribution modeling is not easy. It has an astounding track record of failure. Identifying which model to use to attribute credit for a single conversion across multiple media channels is immensely difficult. Yet calculating whether it improved the bottom-line, whether it delivered positive ROA, is simple. You fill out the blue cells. You look at the row called Incremental Revenue. If there is something there, your digital analytics investment is worth it. If you have nothing there … well, you know … let's figure out how to say data is always worth investing in. :)

There are a few more examples I wanted to insert to really make this concrete. We cover how to compute incrementality from improving conversions, but also how to do that for the micro conversions and capture the impact of the long term impact on the business by tracking micro conversions.

Here's an excerpted version of that section…

Excited? I hope so. I was giddy as a teenage school girl just creating these for the model!

There is also a tab to help you identify the incrementality from landing page optimization, and from improvements you make to the cart and checkout process. (You know my obsession with both, see best digital marketing experiences post.)

And we can't do anything related to data driven improvements without helping you compute the incrementality from insights we identify for our Paid Search campaigns.

I'll let you be delighted about both those tabs when you look at the model, and not spoil your surprise by posting images here.

The model contains one last present for you. Checkout the tab titled General Impact Analysis.

If you are new to the field you are perhaps wondering what kinds of actions you could be taking for each focus area (PPC, Email, Display, etc.). You'll find that in this tab. Column B provides description and examples of the types of outcomes you might drive in each initiative, Column D sheds light on the implementation difficulty of various types of analyses, Column E helps you understand the difficulty you'll face when computing incremental return and finally a reality check under the column titled validity of incremental return .

You are now all set to go!

Here's the link again: Download: Return on Analytics Calculation Model.

Closing Thought #1: "I ain't got no incrementality!!"

It is entirely possible that at the end of looking at all the tabs in the spreadsheet you have nothing to type into the ROA computation model proposed by Jesse. A likely reason for that is that you were unable to identify any action taken as a result of your analytics practice.

There might be a simple causal factor for that. Your analytics practice is focused on DC and DR. And it turns out that you need to obsessively focus on DA for your analytics practice to have an impact on the company's bottom-line.

DC, DR & DA are three key components of any analytics practice. Data capture, data reporting, and data analysis.

I discussed this framework extensively in a recent blog post: Web Analytics Consulting: A Simple Framework For Smarter Decisions.

As you'll note in the DC, DR, CA framework post, most analytics efforts (especially web analytics), consulting or in-house, are focused on collecting ever more data and in figuring out how to puke an ever-increasing amount of it in the form of standard reports via as much automation as possible. Sadly this rarely leads to the recipients gleaning any insights. Which in turn ensures that the organization is data-rich, but action-poor. Which, heartbreakingly, does result in zero actual impact on the company's bottom-line.

Hence your inability to type anything into the column titled Incremental Revenue/Impact.

So if you don't have anything to type into the various tabs in the spreadsheet I encourage you to read the DC, DR, DA post for specific guidance on what is contained in each area and how to ensure you have a better balance (egregiously focused on DA) for your analytics practice.

More investment in analytics (and your salary) will come from an ability to clearly demonstrate impact on the bottom line; otherwise, we will remain third-class citizens of the business world. The model outlined in the spreadsheet could possibly be a diagnostic tool in helping identify problems with your analytics practice (big data or small data) and figure out how to create a practice that is focused on ensuring incremental impact.

Closing Thought #2: Inspiration wrapped inside an exhortation!

You'll fail to attract investment in analytics inside your company (and a higher salary for yourself) if you are unable to show an impact on the company's bottom-line. You'll fail to show an impact on the company's bottom-line if you don't recommend actions your executives should take. You'll fail to recommend actions without an obsession on analysis of data. And yes, you'll fail to analyze data without collecting it.

If your analytics practice is not producing any actionable insights (hence no ROA) then it might be because the analytics practice is not focused on what's important to the business (advice: Biggest Mistake Web Analysts Make… And How To Avoid It!), or focused on reporting and not analysis (advice: Difference Between Reporting And Analysis), or perhaps needs a crash course in how to do better analysis (advice: Beginner's Guide To Web Data Analysis), or perhaps just needs to extract more value from the tool you have (advice: Google Analytics Tips: 10 Data Analysis Strategies That Pay Off Big! ). Identify and fix the problem. Promise me you are not going to settle for a lower salary and a boring job!

I wish you all the very best.

Before we go, my deepest thanks to Jesse Nichols for contributing to this post and inspiring a discussion that has been a long time coming.

As always, it is your turn now.

Does your company compute the incremental impact of its big data, digital analytics efforts? Is there a part of your effort that you are able to identify incremental impact for most easily? What are the biggest challenges you've faced to justify return on analytics? The model is centered on ecommerce/digital type businesses, what unique challenges do you face as a non-ecommerce/non-primarily-digital business? Do you have suggestions for improvements to Jesse's ROA formula? What are some salient hidden dangers we might be overlooking?

Please share via comments.

PS: An Ask from You: I feel that the model could use more tabs of incremental computation guidance. Can you help me create more tabs for various online or offline marketing initiatives powered by analytics? If yes, could you please create additions and email them to me? I'll be immensely grateful, and I'll add it as a tab to the model in this post (and of course credit it to you in the model, with a link to your blog / twitter profile / company). Please consider helping the community.

Thank you.

Excellent Analytics Tip #22: Calculate Return On Analytics Investment! is a post from: Occam's Razor by Avinash Kaushik

Yet, it would be fair to say we are also drowning in awful digital experiences – or, at the very minimum, experiences that seem to be stuck in 1991.

As a Digital Marketing Evangelist you can imagine how much that pains me.

When I work with companies, I do my very best to bring my deep and undying passion for creativity and digital awesomeness to them. One manifestation of that is the stories I tell by comparing and contrasting the client's digital existence with others I consider best of breed.

In this blog post I want to try and do something similar by sharing some of my favorite digital experiences with you. There are 7 in total.

Each example is truly amazing and for each I'll share my perspective on why. In each case there are also tips that highlight things that overtly or covertly make the company delightful.

What can you expect?

Inspiring landing pages, cool calls to action, delightful cart and checkout experiences, website copy delicious enough to eat, copy that convinces people to buy by respecting their intelligence, ecommerce reimagined, higher conversions via greater transparency, and examples of how to truly live your brand's values online through an experience that leaves your customers happy and willing to pay more for your products!

Here are the companies and stories covered in this post:

My goal with this post is that by the time you are finished reading, you'll have identified 7 specific things to work on in your digital experience (though there are 18 lessons listed here). And if at least some of you cringe when you contrast your website with the examples below, that's a plus.

Ready to see some pretty sexy-functional-cool stuff? Let's go …

Songza: Smart Homepages That Anticipate and Deliver Delight

I. Love. Songza.

I really do. It is such a cool service. The music collection is wonderful. I love the social nature of sharing playlists. The suggestions engine they have. And, I cannot stress how much I love this, the nag-free experience they deliver.

There is a lot any business — B2C, B2B, A2Z — can learn from Songza.

I want to highlight their home page … it is simple, beautiful and incredibly smart!

I'm visiting it on Saturday evening local time. The page knows that! Then it auto-magically creates five options for me of the music I could possibly want. Party options, relaxing options, entertaining cool friends or eating dinner. I press a button and – boom! – Music! Tied to one of 5 things almost all of us are likely to want to do at the moment we visit Songza.

It is not that hard to do this. They use geo/ip to work out where I am. They check if the service is available (they are not in all countries). They work out my local time. Then they match it back to their rules engine that serves me the best options. Try Songza at different times of the day. You'll be amazed at how clever they are with the music options they serve when you visit them at 0600 hrs (music for singing in the shower!) or 1345 hrs or 2200 hrs.

Songza demonstrates great use of technology, but not in an in your face look at how smart we are way. They deliver delight. I smile every time I see the home page they create for me.

Of course there is still the search box on top of the page. For the small % of times they might get day/time wrong, that text is a drop box so you can change it to what you want. At the bottom of the page they explain in three short sentences what they do. (Their wonderful slogan: Playlists by Music Experts. 100% Free. No Audio Ads.) Can you explain your value proposition in nine words? Then, they show external endorsements.

So why is it that YouTube's home page is still such a cluttered mess?

Why is it that when I visit www.3m.com or www.gillette.com they ask me to pick my country? (OMG! In 2013!)

Why is it that when I just visited news.yahoo.com, the ad says "New Rule in Texas!", and the page is exactly the same for me in California or someone in London? Why is it that www.orbitz.com uses none of my location signals to customize content on their home page to appeal to me? Why not, at least, show me "Current Deals from San Jose" (my closest airport)?

Sad, right?

There is one other thing that immensely delights me about Songza.

I was listening to my "Write Great Blog Posts" music. I noticed that I was not logged in. So no social features/recommendations available.

I prepare for the inevitable pause in music and click on Log in …

Astonishingly, there is no pause in music!

The page reloads with the log in screen and I'm able to type my username / password to now access more content / features. With, did I say this already, no interruption in my music!

I know it is such a small thing. But it matters so much from a consumer experience because it delivers a tiny jolt of delight and it makes your customers smile.

Starting with Amazon.com and right down to the smallest site in the world, when I click to log in I know everything I'm looking at, work I've already done, is going to go away. I'm taken to a new screen. After I type my info I know that they'll redirect me to the home page, regardless of where I was.

Why? Why not Songza your experience?

John Lewis: Thoughtful Cart and Checkout Experiences for Nonline Outcomes

If you've ever asked me the fastest way to improve your bottom-line of your digital experiences, it is likely that my answer was: "Reduce the cart and checkout abandonment rates."

It takes a lot to get someone to your website. It takes even more to get them to come back the 5 times it takes your average customer to hit the Add To Cart button. But if they do, take their money!

Sadly cart abandonment rates routinely run north of 65%. Abandonment rates during checkout are often even higher.

That is so sad. If someone has taken out their wallet to give you money, why not take it?

Part of the problem is that most cart experiences stink. They are full of nonsenseware – cross-sells and up-sells, all kinds of seals and logos, irrelevant other products, promotions, mobile app pimping and so much more.

Here's an awesome cart/checkout experience, from John Lewis, a UK brand I love …

The first thing you'll notice is that the cart page is uncluttered. The products you are purchasing dominate the page, right under the main site header. All other recommendations, logos, etc. are below the fold.

The cart itself has 7 different, subtle but clever, things (red numbers above).

1. Big font delivery text. No doubt about what I need to do to hit the goal. 2. The button, in a different color, is easy to find (no hunting expedition!), and does not say Start or Checkout or other boring stuff. It says Continue securely . How soothing. :) 3. Shows me again if the product is in stock. 4. In case I was thinking of abandoning, there is a nice heart logo and Add to Wish List , what a cool way to still keep the customer. 5, 6, 7. Another confidence builder: Communicates that they ship everywhere, and whether the particular product is available for each option. I was also impressed at their clever way to drive you to the store by having "Click & Collect" right there. A really nice way to do a multi-channel strategy.

Neither Macy's, Best Buy, nor PetSmart does anything close to this clever. Not just the online + offline part (though they all want multi-channel desperately), but the whole cart experience.

I also like the checkout experience….

8. The phone number is there (again in case you were thinking of abandoning). 9. This may be me but I love the truck, and subsequent art on the site. 10. They have a Help in the top nav (and tell you it will pop up :). 11. Lastly, they build confidence by showing a date and time (and in a subtle way, they push you to the store where you will surely buy other stuff!).

A really wonderful experience. Oh, and through the checkout process they refrain from nonsenseware. Just a simple, uncluttered nag-free, ads-free focused-on-checkout experience.

I can't resist, I have one more example for you. One of my favorite checkout experiences around, and it is from Ringadoc …

I like that they reiterate their value proposition again. They also make it clear they only serve California, rather than telling you after you click the Submit button!. (They say this all over their site but do so here again in case you missed it.) I really like the big boxes, big text. The entire "checkout" page is what you see above (I've reduced the size). There is nothing else on that page. Talk about nonsenseware-free!

Bonus: Ringadoc also has a great home page, in case you are looking for inspiration. They sell a very complicated product, and they do it very smartly.

Innocent: Passionate Copy and Heart-warming Visual Design

Television has its fair share of horrible commercials. But a vast majority of TV commercials, or magazine ads or even billboards, are clever/beautiful/joy-evoking. They go to great pains to create a connection with a product's brand by living their brand values in the ad (with imagery, with words, with music, with celebrities). The product/brand managers at 7 for all mankind clothes, Bertolli pasta sauce, Pepsi, Dodge Dart, etc. work very hard on every single frame or picture or second of music.

But when it comes to the web, they all seem to leave their creativity, passion, and brilliant minds at home. Try any of the sites mentioned above. They have boring images (or garish ones that say "LOOK AT ME!!!!!"). They have copy that a dead cat might have written.

Why?

Why leave your passion behind when it comes to the web?

Another example is Innocent (another UK brand!). Here's an example of what I mean by not leaving your passion, love, smarts behind when you crate your digital experience. How awesome is their page for roasted aubergine moussaka veg pot?

Then the Veg Pot itself (big image!), and underneath it the ingredients and nutritional info. Then the product description, more on this in a second. And finally (did you see the bee?) the reviews.

One nice clean and tight package that delivers everything you want as a customer, and nothing else – and all delivered with a unique brand experience.

My favorite part is the product description; take 30 seconds and read it:

Isn't it amazing? (If you are the aubergine type.) That is mouth-watering yummy text. It is copy by someone who loves their product and can't wait to share it with you. That is selling with passion!

I love the 3 bullet points. They could have ten, but they went with the 3 that communicate the product's value proposition with the biggest impact.

Read their invitation to get you to write a review. How nice.

Then some reviews (they float by). This one with only four circles, but somehow makes the whole thing more credible.

And this is not a one-off deal. Each product description is written with the same unique blend of love, passion, smarts and good copy.

Here's the one for bombay curry:

Mmm, hmm, good! :)

The potential impact of the copy we use on our websites is often deeply under-appreciated. Innocent has a great digital brand experience overall. But what stands out for me is the copy. It demonstrates love for their product, it sells with passion, it communicates the brand's core values.

There are a million sites you could contrast with Innocent. Let me use Ragu, the famous maker or pasta and pasta sauces.

This is their page for Old World Style Marinara sauce….

A small part of me died.

The complete copy on this page, completely bereft of any passion or love or soul, is the list of ingredients. IN ALL CAPS!

Nothing about what makes this product unique. Nothing about the brand's value or promise. Nothing about … anything. Not even nutrition facts or pictures of yummy food made with the sauce. Just the ingredients.

And this approach pervades the Ragu website. For example, check out their Meet Our Farmers page. Maybe you expect this page to showcase how cool Ragu is because it buys from small family farms or does organic farming or does not use chemicals. Or maybe you expect to see videos of farmers picking their produce or interviews to underline that Ragu is not a nameless, soulless corporate giant. Or maybe information about how Ragu farmers support local communities. Or … well, there are a million things I would do if I had a "Meet Our Farmers" page.

But if you were expecting any of those things, you'll be just as heart broken as I was. That page features 3 rolling images (which scroll by before you can finish reading or seeing the photos). Underneath are two, mostly likely stock, photos of Farmers "Chuck" and "Frank" bearing copy that is almost certainly lawyer-approved – it is bereft of any passion. Plus: No videos. No sweeping photos. No environment commitment. Nothing.

And I'm sure that like every big company, Ragu and its parent company spent millions of dollars on this website. I have zero doubt that the brand manager for Ragu and SVP of the foods division are amazingly smart people full of passion. Yet.

The web can sell product. The web can infect your potential customers with the passion you feel for your job. The web can, in a very real way, create experiences that live your brand's values. The web can really do … so much. But you have to try.

If you were looking for inspiration, look no further than Innocent. Oh, and if you want a taste of how much more awesome than above they are … just click on links titled bored?, beat january, and blog in the site header.

AAA Life Insurance : Truly Customer Centric "Convince Me To Buy" Experience

At a former employer we used to obsess about CMTB. Where's the CMTB? Wait that's not CMTB, that's just treating customers dumb? What subtle CMTB clues can we put on the page?

CMTB stood for convince me to buy. It was our short hand for persuasive content that always took the customer perspective, treated the customer with respect, and created a simple experience requiring the fewest back button presses / having to go back to Google.

This is a great story about CMTB.

I want to buy term life insurance. I only have the vaguest idea of what it is, but I know I want it. So I Google term life insurance online quote and spent most of my time with two companies. MetLife and AAA . Each website has a form you can fill.

The yellow box below is the MetLife form. It asks some basic information that is easy to type in, Female, Excellent Health, DoB, Coverage. I got tripped up on Term Length. I was not sure what the implication of my choice might be. I go back and complete the Life Insurance Calculator , but it does not cover this. Anyway. I choose 30 and hit Go.

The AAA form asks for a bit more info (height and weight) and, smartly, captures my email address. But there is nothing that trips me up. That makes me happy. I click Next.

MetLife presents me with this rather ok'ish page. The nice doggie says $377/month and asks me to Continue. There are three reiterating bullet points.

But there is very little CMTB.

And $377 seems like an insane amount of money to pay each month.

My only choice though seems to be to Modify Quote link (it looks a bit weird, but you can see it above if you try for a couple of seconds).

So I abandon.

AAA on the other hand takes me to a page that …. well … seems to be designed for humans. There is a happy family (awww). There is a bold claim: Term Life for Less. (Not: "Affordable coverage." Subtle difference in persuasiveness.)

Scroll back up. Look at the MetLife page (and that is the whole page up there). Then scroll back down and look at the AAA page. What do you think?

The AAA page also includes a handy dandy "Estimate Your Needs" calculator in case that is of value. Calculate Now! :)

But to me the pièce de résistance is the box you see in the middle. Rather than one number ($377!) I get an extremely simple-to-understand graph that is immediately soothing because it illustrates that I have a choice.

First thing to notice is that the graph tops out at $275 (I'm already saving $102 over MetLife, hurray!).

Let's look at the other delightful things about this awesome example of a CMTB page, first let's zoom into it …

1. I really like the clean title. Your Term Life Quote. MetLife has something inside the blue box, much less clearly stated. 2. There is a crystal clear path back to where I came from in case I made a mistake. 3. To ensure I made no mistakes my choices are clearly stated again. How nice is this? 4. AAA gives me lots of choice. Remember they never asked me to pick my term. They realized very cleverly that a lay person would not know what it is, and, more importantly, a lay person would not realize the implications of that choice (on rates). Now I can see what the implication of choosing 30-year term is: biggest bill. But … 5. This is a stroke of genius. Next to the text "Adjust Coverage Amount" they have a red bar I can move to figure out the if I can come up with a "happy number" for myself! Sweet!!

So what happens?

I don't abandon.

I move the bar! I try various positions, for example I adjust the coverage to $300,000 and realize that it would be extremely easy for me pay $48 per month….

Assuaged, I try other positions and end up with a 20 year term for $90/month that gives me pretty good coverage.

Then I click Next and start my application.

There are 2 major differences, IMHO, between the approaches of the companies. Actually, 3 differences:

1. AAA's visual experience is substantially better than MetLife. It is modern, easy on the eyes, and full of persuasive elements. 2. The team at AAA likely is not just a "web team," they understand what trips people up and they work to eliminate those trip wires. For example, they choose to present term options when they will make sense to prospective customers. 3. They assume intelligence at the other end (in their prospective customers). Rather than thinking "we can funnel the most if we just spit out one number," their approach is "let's create an extremely simple visual summary of choices, and put our customer in charge of figuring out what works for them."

Every large successful old school company has a tendency to try to do business online just as it has always been done offline. "Let's just get people on the phone and our sales reps will convert!" "When people walk into the office, they are very simple-minded and don't know what they want, let's do that on the web."

AAA shows that a old school company can learn new tricks, that it can assume a little bit of intelligence on the other side and deliver an experience that can convert at a higher rate, even with some data (ooohhhhh scary data!), by trusting their customers to have a big say in how much they want to pay and what tradeoffs they want to make.

I love AAA. And I do have a 30-year Term Life Insurance policy from them.

CMTB rocks!

Shopbop: (Luxury) Ecommerce and Branding Done Right!

I don't often get an opportunity to do this, but when I do I love shopping at Nordstrom. The stores are almost always beautifully done, the displays are lovingly put together, and – my favorite – the staff are informed and kind. It is on those rare occasions that I truly understand what "retail therapy" means.

Neiman Marcus is another example of a store where shopping is often fun. It is often clear that a lot of thought was put into every little thing, including the underside of tables, to encourage you to stay, explore and buy.

It is the look, the feel, the little touches that make you happily part with your cash.

Recently, I wanted to buy a dress for myself. I went through the shopping experience for a few different sites, picking the same brand BCBGMAXAZRIA.

So what do the NM and Nordstorm experiences look like?

One word: Generic.

See what I mean? This blog runs WordPress. I can buy a theme for $20 that would give me exactly the same layout, look and feel as the above two "luxury" "top-tier" retailers. How lame is that? $20!!

When I visit Macy's, I do have lower expectations. I don't mind that the BCBG page there looks generic, has utterly clinical product descriptions, and contains irrelevant stuff and text ads on the page. It is ok that when I'm shopping for a $368 dress they show "Ads by Google" to Lean Cuisine (PS: I'm not fat), NextTag, UGG, and Nordstrom. (Though the tradeoff between making two cents from me via the ad click rather than $368 from the dress might be questionable.)

But with Neiman Marcus, with Nordstrom, I don't expect that. I expect that they'll have a beautiful brand experience, use fast-loading technology to do clever things, allow me to explore products, make smart recommendations. And so much more. I expect all that because they expect me to pay their prices!

After spending millions upon millions of dollars in their offline retail experience, it is incredible that their choice of online experience is to barely beat one that can be had with a 20-dollar WordPress theme.

At least the Neiman Marcus page is not 30% empty white nothingness like Nordstrom's is.

So, is it possible to replicate the incredible in-store experience on the web? In fact, can you one-up the store and do things online that you can't even do offline?

Yes!

Meet the incredible luxurious, beautifully dressed-to-impress experience of Shopbop … The first thing you might notice (please do visit the site) is how focused (and beautiful, did I already say beautiful?) the experience is. Everything seems to be placed in exactly the right place with a great attention to detail. Unlike almost every other site on planet Earth, the product takes center stage on Shopbop. You see the dress, and that is all you see, until you are ready to see the other stuff.

The price is easy to see, colors easy to find and change, micro conversions cleverly tucked under the Add To Cart button.

The bottom part of the page is taken up by well thought out recommendations to accessorize the dress. [Many, many retailers get greedy here and throw up completely random stuff, or nothing at all. Both are such big mistakes. Remember, you are trying to create a unique brand experience.]

Let me drill down into some of the more subtle features that make Shopbop's experience so much better than its peer set:

1. I love clean headers. Hovering over them brings up focused small sub menus (unlike sub menus with 900 items each for NM, NO). 2. I love this. When you pick your size the Size & Fit box on the right automatically opens! It shows the normal distribution of XS, S, L etc, but it also shows, how cool is this, the Model's size (S in this case), her height, bust, waist and hip size! You can't try the dress on, but this data helps you understand if it will look on you just like it does on the model. (Not in my case.) 3. The description is not clinical, it paints a picture.

4. In case you are not ready to buy (don't abandon!) you can add it to a wish list, add it to your designers, email it to your mom (and beg her to buy it for you), etc. Micro-conversions FTW! 5. I lied earlier. This is my favorite bit. If you click to play the video it does not open a new pop-up, it does not open a new frame, it does not do things you expect on all other sites. The model you see above just starts moving! She shashays right where her image is, in the white space. Nothing else changes on the screen. It's like magic. Try it on the site.

6. When you click on the model, the zoom is delightful. It takes over the entire right side and shows a detailed view just as long as the model …

Both Nordstorm and Neiman Marcus have zooms, try them on their sites and you'll see why Shopbop's is so much better.

7. Hyper-relevant accessories to make the dress, and you, pop. The experience with the accessories is the same, big, bold, beautiful with just the information you need (right on the dress page) and nothing that you don't need…

On and did you see a subtle example of CMTB? When I picked my shoe size, I see "Only 1 left." Nice. I instantly head to the checkout. :)

Shopbop is not as big as Nordstorm or Neiman Marcus or Debenhams or other top-tier stores. It does not have the complexity or the number of products that they do. It does not have nearly as many layers of management as they all do.

But I humbly believe that none of those are reasons to have generic web experiences intended to sell non-generic products. If we don't put in the effort to create beautiful, smart, friendly, brand-enhancing, thoughtful experiences, how can we ever expect the web to deliver the kind of feeling someone gets when they walk into our stores? How can we expect to make lots of happy customers and lots of money as customers and their shopping preferences move online?

You want a premium price? How about a genuinely premium shopping experience.

Bonus: If you want another example of a great top-tier store that does digital really well, check out Barneys New York.

Security Choice: The Power of Conversion Through Transparency

This deep into the post I'm confident that you are on to my bias for white space (I love good white space, Shopbop, and don't love the wasted white space, Nordstorm) and uncluttered experiences (a common theme thus far). I have to admit, I do like sexy and nothing says sexy like uncluttered experiences where the content has lots of room to breathe.

That said, one of my most-tweeted quotes is: "Never sacrifice functionality at the alter of sexiness."

I firmly believe that.

This example will prove that I'm very comfortable with the cluttered and a little garish, as long as the experience is deeply functional.

The story is that like every other human being on the planet I worry about the safety of mi casa. At least in our neck of the woods the brand that many people know is ADT. So like every other human on the planet I go to my favorite search engine and type in "ADT Security Plans."

I was looking for two things. 1. What is the cost of, and options for, an ADT security system? 2. What will be the monthly fees?

I get a bunch of paid and organic search results.

Here's the first one…

Protectyourhome.com gives me some hints when it comes to the answer to the first question. I can see some equipment, though it is unclickable and the page does not say anything about the equipment you get for $49 (and note the weasel word: "Starting at").

I like the bonus. But there is no indication as to what the monthly cost will be, and there is no online quote option. Being wary of the phone hard sell, I just click the back button. It is easier.

I click on the next link, it takes me to Homesecurityteam.com. I scroll down the page and see the package, offer for free consultation and the happy family:

But there is no direct indication of what the monthly price will be. I'm still quite skeptical about these affiliates because I feel that they are just trying to pawn me off to ADT and collect their bounty. So I click on the back button.

(In writing this post I now see that the top nav has a link called Pricing . I'm not sure if that is for the security system or the monthly plans. I should have clicked on that to check.)

The third link is to the ADT website. Now they can acquire me (I'm pre-convinced!) by creating a great experience, reassuring me with clear pricing, and they won't even have to pay a bounty to their aggregators!

Here's the landing page…

It looks pretty. But a quick glance will show that they are offering me the worst possible deal. Not only are they not giving me the $100 Visa card like others, ADT is not telling me what the monthly price will be or what my options are for monthly security monitoring!

There is a "Low Monthly Fees" bullet, but there is little detail (weasel word alert: "about $1 a day").

And it is as if html has not been invented. My only option is to have ADT "Call Me."

I realize that ADT has always done phone sales and is comfortable with that. But in the age of digital (and at 0200 hrs when I'm searching for security), why not deliver a digital experience that expresses your value proposition in such a clear and compelling way that it can convert better than your call center?

But that is not the worst part. (Though it is all pretty awful.)

The worst part is the asterisk. Do you see it next to the $49? And five more times on the page?

I was really curious what the terms were to get $49 installation. What was included?

If you scroll and look carefully you'll find an * and a link toTerms and Conditions . If you click on it (get ready to weep) you'll see the page you see on the right.

K. M. N.

The finest American lawyers hugged the finest Direct Marketing experts and produced this horrendous baby.

It is impossible to understand. It is more complex than the International Space Program. And, worst of all, in the end all you can sadly think is: "ADT is one big scam."

Not the impression you want to have about a security company.

I can customize and buy a car online. Why can't ADT figure out how to create a digital experience that is transparent? One where I can go pick my components, choose between five plans, and send them an installation order?

IMHO there are 2, amongst others we can't guess, contributing factors. 1. ADT assumes there is zero intelligence on the other side (in its prospective customers). 2. ADT believes that its operators are best equipped to oversell me.

In the long run, both create unhappy customers.

But my experience was not entirely futile.

The last link I clicked on took me to this fantastic page from Security Choice … Take your time … Can you figure out how much the security service costs? :)

Nine dollars! The page answers the one question you want answered to get ADT into the consideration funnel. How hard was that?

It's got all the other stuff, too. But by naming the price, your mind does not go instantly on guard, like it did with all the other folks. You think, "Nine bucks, I can do that, thank you. Can you tell me more?" And you … click on the $9, and you get to a sweet comparison table of all the plans. It also happens to include textual explanations for those amongst us who want to know more (and of course delightfully will help the company's SEO strategy).

But there is more to the Security Choice landing page than just passing the all-important transparency test. There are 11 other things you should consider for your landing page optimization strategy:

1. Without a doubt my favorite element was the transparency / answering the first question I had quickly. 2. Nice CMTB "Act now and rest easy tonight!" 3. They want you to call them. Is that clear on this page? :) And when you call them the first thing they (or DirecTV or Crutchfield or any DR company) will ask for is the Promo Code. Clearly marked here. 4. Did you see, did you see? They can tell where I'm visiting them from! (3M, Gillette – boo!) Another nice CMTB.

5. This was close to my favorite: today's date and exactly how long they are open today! They know my location, my time zone, they tie it to their working hours and then create a sense for urgency for me. Nice. 6. Free system valued at $850. Need they say more? Another wonderful example of CMTB. 7. You had me at hello, but sure, I'll take a $100 card, too. 8. Telling me information I don't know ("save 20% on your homeowners insurance"), information that will push me to buy. 9. Everyone on the Internet loves speed. Does your page communicate what aspect of speed you deliver on?

10. If you are ready, a clear call to action stands out. Notice it does not say Call Us, it says Call Me. Make the conversion as easy and as painless as possible. 11. If all of the above did not already make the sale, there is additional information.

Of the tabs in 11 they should probably highlight the last one, "How Safe is Your Neighborhood." I type in 94043 and I get: 233 burglaries, 1,820 property crimes, 6 murders and more! Scary stuff, but ties clearly to what they are selling. The data is for 2008, but works like a charm in driving conversions.

ADT's business is undeniably complex, and it is difficult for someone from the outside, me, to understand it all. But if I might be so bold I would recommend that they fill out that form on Security Choice's website. The call is free and someone at Security Choice could share digital best practices with ADT. :)

Ok, question for you: Are your landing pages this good? Do they understand the singular thing the Visitor is looking for and deliver that answer in size 68 font this clearly? And do you have enough wood behind that arrow? All 11 of these incredibly persuasive elements?

No? Why not? Don't you love happy customers and big revenues?

Method: Amazing, Immersive, Love-Evoking Brand Experiences.

I've saved one of my absolute favorite sites for my last example. It is one that pulls together lots of little things that I've mentioned in this post, and does a few things above and beyond.

Rather than one page or one thing, I want to highlight the entire Method website (even though sometimes in Chrome it acts weird).

It is an amazing, immersive brand experience. Without trying very hard, you get a really great sense of the brand (and all its quirkiness). The site's visual design is beautifully consistent with that of the product, and consistent with the look and feel of the products when you bump into them at Target or other retail stores in the real world.

Amazing right?

Not just sexy, functional too.

The top nav is super clean, just three things: Shop, Clean Happy, Methodology. The color palette ensures that you see them easily.

Then if you want, a subtle second nav with product segments.

One big promo at the bottom of the page. And one giant rotating "hero" in the middle of the page that grabs your attention – as much with the beautifully photographed products as the delightful copy!

If you want to contrast how absolutely amazing Method's home page is, try comparing it with another company – (one that also spends millions upon millions on TV and other offline advertising — Lysol. Go ahead, click on that link. You'll see what I mean. Amazing, right?

With Method, beauty is not skin deep. It is very hard to make a dish washing liquid page one that people will remember visiting.

Checkout the product page for the clementine dish washing soap. Giant image (oh, I love this so much!). Beautiful copy, excerpt: "wash more dishes than you can shake a spatula at. when the plates pile up, there’s no greater relief (short of having someone else tackle them for you) than knowing you have an ample reserve of natural, ultra grease-fighting dish soap."

Clear call to action. Product reviews (integrated with Facebook). Sharing options. Two tabs with more details "what's in it" and "scents". Just reading the what's in it will improve your confidence in the brand 100x.

Another contrasting moment, with another much bigger, much richer company. Palmolive. (Small warning: The page automatically plays music!). If you choose the Original dishwashing liquid you are taken to this page. Compare it to Method's. Again, amazing difference.

If you click on any Buy Now link on any Palmolive product on the site you are unceremoniously dumped on this page.

It is a website called "econsumeraffairs.com", not what you might expect: Colgate or Palmolive. The site has no Palmolive branding on it. It doesn't remember the product you want to buy (OMG!). You are supposed to be so desperate to buy Palmolive that you'll survive the shock of ending up on this page and then answer seven (7!) questions

Yes. I was speechless.

(Oh, and I died on the first Want to buy a Colgate-Palmolive product? question. It says Select Product Type. Your options: Household surface care, Fabric care, Oral care, Personal care. What category does dish washing liquid fall into?)

What another example? Check out Finish, and any product (say, Finish Gel). Incredible.

Let's get back to the happy stuff.

As in the case of Innocent earlier, I'm really delighted with the love and attention the team at Method pays to the words they use. For example, click out the site footer…

Does that not make you smile? You can say "Like us on Facebook", sounds a bit desperate. Why not say "like to be our friend?" Much better, right? Or say "lights, camera, videos" for YouTube. Why not say "greenskeeping + sustainability" instead of "Environmental responsibility" like every other site?

Some sites just try to be cute, or come across as trying too hard. But as you see the footer above, or when you experience the Method site, you don't get that sense of neediness.

I'm sure the thing you clicked on first in the footer was the humanifesto. Here is that awesome beast of a thing:

Every Brand Marketer says that their brand stands for something. Every Brand Marketer has written down the brand persona. Every Brand Marketer knows the brand attributes.

Yet you would not know that were you to visit their site. Say Bounty or Iams or Downy or Gain or Vicks.

Why don't they have a Humanifesto? Does the internet stink at branding?

Today, you as a Brand Marketer are slowly losing control of your God-given right to deliver the first impression of your brand in a controlled 30-second TV ad or via a glossy print buy in Martha Stewart Living . If people discover you digitally, what is your plan to make them fall in love with your brand?

And if they do visit your digital existence, and they are impressed enough to sign up for a longer term relationship with you (OMG, true gift from god! Not just a unmemorable one-night stand via tv, but the start of an actual direct customer connection – and many future dates!), what does that experience look like.

Does it look like this?

First, I bow to the coolness of that graphic. These people really want my email address! They made me smile.

Next, notice the overall branding – consistent, lovely color palette, totally on brand.

Then, checkout the CMTB. Just two boxes and Submit (clear call to action). In bold people against dirty, me! The notice the cute "psst … read our privacy policy." How often do you draw attention to your privacy policy as a way of converting your website visitors?

Oh, and above is the whole page.

To get some perspective, let's try to do it for a different company. Let's pick Febreze (again, a brand with millions upon millions in actual spend on Marketing and Branding).

If you click on their Get Newsletter link, I get shuttled to a website called Home Made Simple and this profoundly generic page (compare to above) that is asking me to Complete Your Sign Up :

Where's Febreze?

I doubt the fact that they are asking me to fill out 14 fields will escape your attention. 14. And they are asking for my date of birth! And asking me to make up a security question and remember the security answer.

For. A. Newsletter!!

How is it possible that P&G, a company with unlimited money and unlimited creativity – and, I know for a fact, some of the smartest Brand Marketers on the planet – can create something so gosh darn …. cheap, onerous and uncreative?

Digital provides every Brand Marketer an almost infinite capability to express creativity. I love that the ones at Method do such an incredible job of expressing that creativity. End result? I pay more money for soap that I suspect is no better than any other, but boy do I feel better using it.

That, ladies and gentlemen, is marketing that works! You exchange creativity, passion and love for your products for my money.

Closing Thoughts / 7 Experiences, 18 Lessons.

Above and beyond all else, it is my hope that this post provides specific guidance on what incredible web design, branding and digital marketing experiences are.

I hope that the actual examples provide concrete evidence of the awesomeness that is all around us.

I hope they inspire you to take a long hard look at your own digital existence and want to move from good to magnificent. And do remember that you have to take none of this at face value. You have free tools like Google Analytics Content Experiments (now with the power of multi-armed bandits!), and eminently affordable ones like Optimizely and Visual Website Optimizer to run experiments that will prove I'm right. Sorry, I meant so say tools that will prove to you what works best for your business. The missing ingredient is inspiration (in spades above) and your courage (I know you have tons of it).

To accelerate your experimentation I've summarized a cluster of short lessons from experiences covered in this post. They are collected in this pdf file, please click the link to download: 18 Lessons from 7 Incredible Digital Experiences.

I hope you find these lessons to be of value.

Now go, carpe diem!

As always, it is your turn now.

Which of the 7 was your favorite example? Which one was a surprise? Do you have a digital experience that you absolutely positively love like crazy? Is there a digital experience you find utterly frustrating? What was your key learning from this blog post (from our 18 lessons)? Is there something specific you are going to change about your own digital experience?

Please share your lessons, frustrations, examples, stories, inspiration via comments below.

Thank you.

7 Incredible Web Design, Branding, Digital Marketing Experiences is a post from: Occam's Razor by Avinash Kaushik

But it turns out Google Analytics, just like SiteCatalyst, WebTrends, and other web analytics tools, already has plenty of pretty valuable deeply insightful stuff in it. Yet so few people have mastered what's already there. Sometimes I wonder if we should actually be all that excited about the insanely cool stuff if the sanely cool stuff remains unmastered.

As we hopefully look forward to an exciting year, let's take a moment to address the latter challenge. Allow me to help you with your resolution of mastering the sanely cool stuff!

One way to do it is for me to just tell you what my top ten Google Analytics reports are that you could familiarize yourself with. The problem is that you'll know where to go, but not what to look for when you get there.

Each selection by me of a top ten (standard!) report in Google Analytics below includes a small brain dump of quick insights, Google Analytics tips if you will, I seek when I'm looking at that report. The stories and examples will hopefully help you intelligently approach your own data in these reports and quickly find insights you can action / share with your management team.

[Sidebar for people who want to be BIG winners]

Before you log into Google Analytics it is really really helpful to get context about the company/client's business.

I realize that you are pressed for time and you might not want to do it. But in case you want to win big rather than just win, I encourage to read the six tips outlined in this post: The Biggest Mistake Web Analysts Make… And How To Avoid It!

I guarantee that if you invest this time, you'll find 5x better insights when you log into Google Analytics or Adobe SiteCatalyst. If you don't invest this upfront, fun, time you'll hurt my feelings but I'll understand, you don't want to win big. :)

[/Sidebar for people who want to be BIG winners]

Below are the top ten standard reports in Google Analytics that you should know well, especially if you are only a part-time user of Google Analytics.

If you are an Analyst, of any tool, check out the Bonus tips included to kick your efforts up a notch or two.

Everything here's simple. You don't have to be a particularly deep expert to find value in this training.

1. Sources Overview report.

Start with the pie. It helps you understand how reliant the company/client is on Search (too much is actually not good). What other sources are big for them? If you don't see other sources (campaigns – email, social, display) are not tagged. A very bad thing.

Like everything in life, you want a balanced portfolio (left).

Then go to Traffic Source > Sources > Campaigns to get a feel for how many display, social, email, other campaigns the company might be running. What's their performance? Very good context.

Search is always big for everyone. So you want to drill down into the Traffic Source > Sources > Search > Overview to understand the macro balance between Organic and Paid (this, by default, will only show AdWords though it can show Bing, Yandex etc).

It is hard to get overall search keyword performance in GA, so grab this quick custom report All Search Performance and apply the standard advanced segments to it (Non-paid Search Traffic, Paid Search Traffic). Tons and tons of insights here. Better organic keywords, performance for same words between organic and paid, goal value comparisons, so much more. Go crazy.

While you look at three reports, you quickly end up with a robust understanding of *all* the things the company is doing and a detailed understanding of paid and organic search performance.

Bonus: Download the All Traffic Source End to End report for best, in depth, analysis. [Make sure you are logged into GA, then click on the link, save the report to your account.]

2. Landing Pages report.

Zero companies will win without great landing pages. You stink there, bye, bye large amounts of money. Great landing pages equals more customers enticed to engage plus higher conversions plus higher (AdWords) quality score.

Start by looking at the top 20 landing pages. Content > Site Content > Landing Pages.

Identify ones with high bounce rates. What is wrong with them? Visit them. Missing calls to action broken links, not enough content, content unrelated to the ads, something else? Low hanging fruit. Fix it.

Learn to apply the top traffic segments (see #1 above) to this report. Find high bounce rates for one segment (Paid Search) and look at other segments (Display) where pages have low bounce rates. Learn from the winners, apply to the losers.

Bonus: Smart people look at the Page Value delivered by each landing page and not just bounce rates. Sadly it not easy to find. No worries, I've got your back. Download this custom report: Landing Pages Analysis .

For each page now you know how often it is a landing page (Entrances/Pageviews), how much it stinks (Bounce Rate), how much money it is making you (Page Value). Ignore your home page or any cart or checkout pages that might show up. Look at all others.

Why do some pages only make 97 cents and others make you almost four dollars? Prioritize using a mix of bounce and page value, analyze details using referring keywords and referring urls (drilldowns are already built into above custom report!).

3. Goals Report.

Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions. Macro + Micro Conversions.

Got it? Macro + Micro Conversions!

The difference between companies that win and the companies that will lose is simply this: Economic Value.

So look at the standard goals report. Conversions > Goals > Overview. This report shows all the goals converting, in addition to the ecommerce order now conversions.

Are there at least six micro conversions identified? Yes? Good. Does each goal have values identified? Yes? Magnificent. The company you are analyzing is ready to rock the web!

If the answer to either question is no, at best the company will scratch out a living on the web. More likely their competitors are going to slap them around.

What are the high micro conversions you need to start focusing on (G6, G7, G2, G1 above)? Do you understand how elements of your paid, owned, earned inbound marketing efforts drive each of these? How do these goals tile to your macro conversion, G3? Does the CEO understand the complete value of digital ($233,810 above)?

Bonus: Ecommerce is sexy, so don't forget to look at that. Specifically focus on what products are being sold. Go to Conversions > Ecommerce > Product Performance. (For this to work the ecommerce tag has to be implemented right. If it is not you have bigger problems.)

What are the top selling products, what's the average quantity? How about when you apply segments for your top traffic sources? What is Search really good at selling? What about Social? What about Display? What about in Florida vs. New York? Understand, have a smarter CEO conversation.

4. Conversion Funnels Report.

Fastest. Way. To. Make. Money.

The conversion path is three or four pages. What's your abandonment rate? Why is it a criminal 65%? Is there a better way to make money than to take it from people who have started the checkout process and want to give you money?

This post is about standard GA reports, but the standard cart/checkout funnel visualization in GA is value deficient. So as your standard report use Paditrack. For the same number of button presses you'll get 25x more value than Google Analytics.

Where do most people drop off? How can you have a minimum number of text fields? Is it possible to not have garish banner ads in the checkout process? When do people enter coupons? Is the error checking when the person submits the page or is it (awesomer) in-line when the person moves from one field to the next?

Bonus: Apply top traffic sources segments to the above report. Or just apply the top paid search referring keyword to the funnel report…

Do you see differences in abandonment rates? Why? What is causing a particular keyword, email campaign, display ad, offer, to convert higher or lower? What lessons can be applied to all other visitors? Go fix!

5. MCF Assisted Conversions Report.

Multi-channel attribution was the flavor of the month for every month in 2012. It will be the same in 2013. And just as in 2012 magic pills will be scarce, FUD will be plentiful, and vendors will promise the moon. You, I guarantee it, will be just as confused. :)

But get to know the assisted conversions report. It is fairly straightforward.

If *all* your campaigns *always* include campaign tracking parameters, this report is really good at answering this critical question: Is channel x more likely to be at the end of the conversion process or drive traffic that might convert later via a different channel? It is extremely valuable to know the answer.

Conversions > Multi-Channel Funnels > Assisted Conversions.

In the above case I was astonished that while our email was primarily a direct response "here's a coupon to convert" marketing, it actually drove more conversions via other channels (!).

Impact? 1. We were not giving email enough credit. 2. Were we sending emails to people we had seen recently on our site? 3. If email assists, can we understand its order in the conversion process and which channel it most assists? (Yes. Go to Top Conversion Paths reportand search for Email.)

Even if you never get into the mess of attribution modeling and all that other craziness, you are much smarter by just analyzing the data, and implications, from at this report.

Bonus: You will want to know what to do about attribution modeling craziness. :) Read answers to questions one, two and three here: Attribution Modeling, Org Culture, Deeper Analysis. After that if you can't resist the itch, go play with the, now free to everyone, Attribution Modeling Tool in GA. Read the three answers first, please.

6. Mobile Devices Report.

Mobile is all the rage. You can't walk into any about digital or not about digital at all meeting without a solid grasp of where the company is when it comes to mobile.

This is a standard report in GA, but I've pressed a few buttons to make it smarter. You'll find the report in Audience > Mobile > Devices. On top of the graph click on Select A Metric and choose Goal Conversion Rate. Now you know the Visits and the Conversions. Smart.

Then on top of the table click on the Pivot icon (see mouse below). Then from Pivot By choose Source.

First, you quickly learn what the main big mobile consumption platforms are. Second, equally quickly, you know the main sources of traffic via mobile are. [If you remember from our first report above, direct was #3 in overall and social was #4, but on mobile direct is #1 and social is #3. Did you realize your acquisition was distinct on mobile? Does your mobile marketing reflect that?]

As you look at the "scorecard" (just under the graph) you can look at the little numbers in gray and understand overall mobile performance compared to site performance. Very handy.

Bonus: Download a super awesome all-encompassing mobile custom report: Complete Mobile Performance Report. It has unique built in drill-downs, customized metrics that give you the ability to deeply analyze mobile data by devices, search behavior and content content consumption (click on each tab). You will never need another standard mobile report!

7. In-Page Analytics Report.

Traffic Sources > Content > In-Page Analytics.

There is no simpler way to understand how consumers are behaving on a company's website then to just look at their clicks. In-Page does that really well. Just look at the link, look at the corresponding number.

On the home page it is so easy now to see which product categories people really care about (Calico Critters! Put them on sale! Buy all the keywords! Run email campaigns! :). You can also easily see that zero people have clicked on the ScooterX Skateboard (time to remove it), at least some care about Mini-Motos but what people really care about is the Marble Run (pimp away!).

I hear you. Clicks are ok but you only care about money. No worries. Change the metric on top of the page to Goal Values and bam! What you now see is the distribution of which link is making you how much money. Sweetness.

This report is your easiest way into Web Analytics.

Bonus: Open your top landing pages in this report and then apply the Advanced Segment (button on top of the report) for your big traffic sources to see how differently your visitors click. Then at least for your top most landing pages, consider creating a custom one for each of the main traffic source.

Bonus 2: GA now allows for enhanced link attribution in this report. That is very cool because if you have a link in the header, a link in the side bar and a link in the main body all pointing to the same product page, Analytics will show you exactly how many people click on each of those links. You can then eliminate the big promo in the side bar because you now have data which shows that zero people click on it (because it looks like a banner ad!).

8. Location Report.

People have weird conceptions of where their traffic comes from. Sure they can sprout the number of tweets or top search keywords, but rarely do they have a robust understanding of the geographical distribution of their audience.

Illuminate yourself by going to Audience > Demographics > Location. Then on top of the graph change the metric from Visits to Goal Conversion Rate.

The default view (Visits) will always underline your bias. For me it is always USA #1 (hurray!). But USA is only 40% of my traffic. And when I look at Conversion Rates there are a whole bunch of countries that are way better than USA (#47!). There are 14 countries with Conversion Rates 2x of USA (OMG!).

That changes things, right? Changes campaign targeting, changes content development, changes social strategies, changes product mix, changes keywords for search engine optimization.

You can run this type of analysis at a State and a City level as well, the results are always eye opening / preconceived notions busting.

Bonus: Every GA report shows clicks you actually get, there is only one that shows you clicks you could possibly have gotten. Traffic Sources > Search Engine Optimization > Geographical Summary.

It shows, by country, where you currently show up on Google properties (Impressions) and the number of clicks you get. It took me 110k impressions to get 10k clicks in the UK and 60k impressions to get 10k clicks in Germany. Time to dial up SEO awesomeness in the UK!

9. Site Search Terms Report.

Another hugely underutilized resource is the intent your visitors are actually expressing on your site by typing into your site search engine (best way to stink is not to have one).

Content > Site Search > Search Terms. Admire the default view for a second, but quickly switch to Goal Set 1 (or Ecommerce if you are one of the aiming to hit a low bar with no Goals defined). You'll get this view…

Do you know what are the top things people are looking for that they can't find on first glance? Above. Do you know how many of those top expressed wishes then lead to a zero (!) percent conversion rate? Above. Do you know how much money you make off each search term/expressed intent? Above.

Now would you not want all the top things people look for to have a $2.39 per search goal value rather than 0.12 or 0.63? Of course. You have work to do.

Bonus: This might be stretching it a bit but 100% of your internal site search terms should probably be on your SEO keyword list and likely a part of your Paid Search campaigns. If people are coming to your site and looking for stuff (and you have it) then there is no better signal to grow your keyword list.

In my case that is 20,217 keywords I can quickly add to my Bing/Baidu/Yandex search campaigns and start measuring performance. My additions will be geo targeted by which keywords on my site were searched for from each country!

10. E2E Paid Search Report.

I tried really hard to keep this to just standard reports, but I had to squeeze in one "standard" custom report. It comes from my recent post Google Analytics Custom Reports: Paid Search Campaigns Analysis .

The report shows the end to end view of your search campaign performance.

Any Analyst worth their salt will spent a lot of time trying to understand what is happening on the site in conjunction with trying to understand what happening inside AdWords! This report does that very effectively. Above it merges data from AdWords with your site performance data (how cute is it that you can see cost per click and revenue per click right next to each other!).

Additionally it has pre-built drilldowns (below) that allow you to navigate this performance in context of your AdWords account structure.

Identify which campaigns are actually delivering value. Identify if you can optimize your AdGroups to deliver higher performance (impressions, clicks). Identify what your Match Type decisions are doing to your performance (Broad, Phrase, Exact, what's up?).

There is a lot more you can do in terms of AdWords Analytics, most of your starting points sit in the above report. Hence it is my standard AdWords report, even if it is a custom report. Download: E2E Paid Search Report.

That's it. Ten standard reports that high insights in plain sight. And a bonus five custom reports to allow you to truly bring out your inner Analysis Ninja!

If you are able to master the standard set, you'll be above average when it comes to understanding site performance. Better still, you'll be able to identify a robust set of actions that will please the toughest CEO and over a period of time earn you a glory and a higher salary.

Now that's something worthwhile to shoot for in 2013!

As always, it is your turn now.

Are these standard reports a part of your current Analysis Ninja arsenal? Do you have a favorite standard report that is not listed above? If yes, what is amazing about it? If you use these reports already, are these the types of insights you seek? Are there other hidden insights gems that I might have overlooked above? Got a omg this is my secret weapon custom report you want to share with us?

Please share your insights, questions, favorite reports, and feedback via comments below.

Thanks, and good luck!

Google Analytics Tips: 10 Data Analysis Strategies That Pay Off Big! is a post from: Occam's Razor by Avinash Kaushik