It’s a short book of only 146 pages (including the index), but it covers the topic beautifully. It even includes quizzes and exercises for the dedicated learner. I especially appreciate its thoughtful focus on the essence of science and scientific method, never venturing into territory that non-scientists would find esoteric or intimidating. If you’re like me, you probably assumed that there were many good books of this type available, but this is surprisingly not the case. Given the importance of science and the fact that everyone should understand what it is and how it is essentially performed, this is a tragic void. Thankfully, Carey must have recognized this two decades ago when he wrote the first edition and has continued to serve the ongoing need by updating it every few years with current examples.

Carey breaks the content into six chapters:

1. Science (This chapter defines science, describes the methods that are common across all branches of science, and argues for its importance.)
2. Observation (This chapter describe the process of effective observation.)
3. Explanation (This chapter focuses on the goal of explaining “why things happen as they do in the natural world,” including the special role of hypotheses and theories.)
4. Experimentation (This chapter describes the role of experimentation, various types of experiments, and the ways experiments should be designed and conducted to produce reliable findings.)
5. Establishing Causal Links (This chapter extends the topic of experimentation by addressing the special techniques, including statistics, that must be used to establish causation.)
6. Fallacies in the Name of Science (This chapter draws a clear distinction between science and pseudo-science, including basic tests for distinguishing science from its imitation.)

Unless you’re already trained in the ways of science, you’ll find this book enlightening and enjoyable. It’s quite possible that you’ve already published a research paper in your field of study but somehow never learned what this little book teaches. I’ve read many research papers, especially in my field of information visualization, which had the appearance of science, with technical jargon and lots of statistics (often misapplied), but were in fact pseudo-science because the researchers and their professors did not understand the basic methods of science. So many time-consuming but ultimately worthless projects might have been salvaged had the researchers read this simple little book.

Take care,

P.S. When I wrote this blog post, I’d forgotten how horribly expensive this books is. It lists for almost $100. Even discounted, it will still cost you nearly $80. This is unconscionable. I doubt that it was the author’s decision to price it out of reach. I suspect that this is an example of Wadsworth Publishing’s shortsightedness. They see it as a textbook that only students will purchase – students who will have no choice in the matter. In fact, this book would have a broad audience if it were reasonably priced; so much so that the publisher and author would earn a great deal more money. What a shame! Until this changes, try to find yourself a used copy.

People often ask “What’s the difference between the standard, free version of Google Analytics and what you get with Google Analytics Premium?” Well, we decided to put together a handy comparison one-sheeter to help clarify the differences. Simply enter your email below to get the download link.

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Would you like to learn more about Google Analytics Premium and discuss options for your business?

Please contact me within the next business day to discuss Google Analytics Premium!

The main differences between the standard and premium versions of Google Analytics deal with the volume of and sources of data. Here’s some elaboration on the areas in the comparison chart above.

Data Collection

Both versions of Google Analytics enable you to collect data from websites, mobile apps for iOS and Android, and from custom data sources via the Measurement Protocol. GAP has much higher limits on the amount of data that can be collected, with a tiered set of limits starting at 1 billion per month. It also has additional slots for custom data (custom dimensions & metrics) and more flexibility in organizing your data through higher limits on the number of properties and views you can have within an account. (Sorry I can’t be more specific on the exact limits on properties & views, but they are not public numbers.)

GAP also has Roll-up Properties, which allow you to combine data from multiple properties together (great for large organizations with collections of websites to avoid multiple sets of GA code to enable rolling up sites together).

Importing Data

GA has a variety of integrations with Google advertising platforms—you’re probably already familiar with the AdWords integration. This is a two-way integration which both imports data from AdWords to Analytics (bringing in cost and impression data), as well as allows the export of remarketing lists based on Analytics data to AdWords. GAP includes these features for AdWords as well as DoubleClick platform products DoubleClick Campaign Manager (formerly DoubleClick for Advertisers), DoubleClick Bid Manager, and DoubleClick for Publishers.

In addition to those direct integrations, GA allows importing data from custom data sources, including advertising cost data, product or content data, and more. GAP enhances this with query-time import, meaning we can see imported data with historic data, even if we’ve changed or updated the import after the data was originally collected.

Reporting

If you were hoping that GAP has some super-secret set of reports you’ve never seen in the standard version of Google Analytics, I’m sorry to disappoint. The table of contents of reports in GAP is pretty much the same (although with expanded opportunities, given the additional data collection and import capabilities described above).

One additional enhancement is in the area of attribution modeling. The standard attribution modeling tool can give some formula-based models for attributing conversions to channels (like first-touch, last-touch, time decay, etc.). But which of those is the “right” model? GAP includes a data-driven attribution model, which actually uses an algorithm on your data to understand where different channels make the most impact.

There are also enhanced custom funnel reporting options for GAP clients, enabling better flow reporting for on-site actions across users and sessions.

Sampling

Just like GAP allows you to collect more data, it also deals with large volumes of data better in reporting. As a reminder, when you request an ad-hoc report (a report that Google Analytics hasn’t already pre-calculated, such as a custom report or a segment applied to a standard report), if the data exceeds a certain number of sessions, Google Analytics employs sampling to achieve answers quickly.

GAP raises the limits for sampling to be much (much!) higher, so you’re much less likely to run into sampling as you explore your data. Sampling is moved to a View level, which means you can help reduce sampling by filtering Views down to smaller sets. If you are still hitting sampling, GAP also has a number of additional tools to prevent or eliminate sampling, like Custom Tables (to pre-aggregate commonly-used custom reports or other sampled data) and the ability to generate completely unsampled reports.

APIs

Google Analytics has a variety of APIs for reporting and configuration. Again, the differences here are mainly in retrieving large volumes of data—such as unsampled reports.

GAP also allows granular, session-level data to be exported from Google Analytics into BigQuery, Google’s big data storage and querying tool. This allows more complex analysis or exporting data into a data warehouse.

Service

Last but certainly not least, when you purchase GAP through a reseller like LunaMetrics, there are services built in. At a minimum you get a contractual service-level guarantee about the availability of Google Analytics and a support analyst to call or email with questions and problems.

As part of receiving the subscription to Google Analytics Premium from LunaMetrics, we include hundreds of hours of help to do project work for you. We do that for the first year that we work with you and then again for every year thereafter. We’ll typically bundle upfront strategy and guidance, full service implementations, followed by expert analysis. Plus, we’ll include onsite training services customized to meet your needs to give you the total package to set you up for success with Google Analytics.

As added perks, Google Analytics Premium clients often get to participate in events with Google, such as the annual Google Analytics Summit, as well as early access to beta programs and product feedback.

In Summary

The main differences between the standard and premium versions of Google Analytics deal with handling larger volumes of data, in collection, integrations with DoubleClick, unsampled reporting, and export to BigQuery. Download the chart at the top of this post for a handy reference guide!

The post Comparing Google Analytics Premium and Google Analytics appeared first on LunaMetrics.

Just follow the links to download/obtain them. All are free to use.

Tools are listed in no specific order though #10 is my current favourite…

1. Tag Assistant (Chrome extension)

Allows you to verify you have installed tracking tags correctly on your pages (not just GA). Helps you troubleshoot the Google Analytics Tracking Code (GATC), Google Tag Manager (GTM), and Adwords Conversion Tracking.

2. Google Analytics Debug (Chrome extension)

Inserts analytics_debug.js (or ga_debug.js) for you when you load any page containing a GATC in the Chrome browser. You can then view very useful information in the browser console. Includes error messages and warnings about your tracking code implementation and a detailed breakdown of each tracking beacon/variable sent to Google Analytics.

3. Page Analytics (Chrome extension)

Overlays GA report data directly over a page loaded in your browser. Includes date range comparison and segmentation tools.

4. Table Booster

This is the only tool listed that is not really about data quality, but visualisation instead. That said, good visualisation allows you to spot data quality issues – hence my inclusion. Table Booster turbo charges the stale data grid of Google Analytics with some very useful visualisation aides. Click image to enlarge.

5. Regular Expression Helpers

Regular expressions (regex) are used to match patterns within text. In Google Analytics, regular expressions are used for filtering—for both filtering within a report (table filter) and for creating separate report sets (View filters); For defining advanced segments; For configuring goal conversions and funnel steps. In other words, regular expressions are important!

Going beyond the basics things can rapidly appear complex to the uninitiated – because regular expression resemble algebra. Therefore, before attempting these try my jumpstart regular expression tutorial, then practice with these interactive sites:

• regexr.com (my preference – I use this a lot!)
• regexpal.com

6. Annotations Manager (Firefox addon)

This Greasemonkey script allows you to copy, delete, and export your chart annotations. Very useful when you are running multiple profile views for a property. Developed by Vincent Giersch.

7. Confidence Checker (bookmarklet – all browsers)

This clever bookmarklet takes a statistical approach for comparing two e-commerce conversion rates and your overall goal conversion rate. The bookmarklet performs a z-test to show the confidence interval of two selected dimensions. This shows if the differences you observe are statistically significant. Developed by Michael Wittaker. Click image to enlarge.

8. Web Developer Toolkit (Firefox and Chrome)

This tool adds a menu bar to your browser with a whole range of useful features for anyone who has an interest in creating web pages. It has an excellent browser error console and DOM inspector as well as quick lookup tools for cookies, source code, and so forth.

See also Firebug.

9. Campaign URL Builder (Chrome Extension)

Google already has a useful URL web page for building campaigns, but its a little cumbersome to use. This browser extension adds some very nice user friendly features such as: Loading the current web address automatically; Create and manage pre-configured sets of tags i.e. a template campaign url; Choose a template campaign as your default – great for fast editing of multiple landing page urls.

10. Tag Manager Injector (Chrome Extension)

If you are using Google Tag Manager (if not, why not?), this is a very powerful tool. It allows you to inject your own GTM container snippet onto a page. Why do this? So you can build your own complete test environment within GTM and then “deploy” it onto a site via your browser. That is, its a GTM install specific to you. Very powerful for creating/testing a setup before a real deployment.

Thanks to my friend and colleague David Vallejo for letting me know about this one.

More Apps…

Google Analytics maintains an App Gallery of third-party-developed add-ons that extend Google Analytics functionality. If you have a recommended tool, please add via a comment. Note I am looking for recommendations from real users – not promo material from vendors…

© Brian Clifton for Successful Analytics - the new Google Analytics book, 2015. | Permalink | 3 comments | Add to del.icio.us
Post tags: add-ons, addons, extensions, regex, regular expressions, tool, tools

Data Import is a feature that helps you combine data inside of Google Analytics with data you that you have outside of Google Analytics. Ideally, you’ll be able to use more personalized information inside of Google Analytics to help find meaningful and actionable insights. Not sure yet? Here’s an example:

By default, Google Analytics can tell us certain information about the pages people view on our site:

• page path (including query parameters)
• subdirectory levels
• title
• hostname

But what if we have additional information about our pages we want to see in Google Analytics? For example, a content site may have a lot of interesting meta information about its pages:

• author
• content type
• content tone
• publish date
• publish hour
• publish day
• publish week
• publish month
• publish year

• section
• sub-section
• headline
• sub-headline
• article category
• number of images
• number of videos
• character count
• word count

Data Import lets us add all the additional information we have about our pages, so we can segment the data in many interesting ways. Wouldn’t you like to know if pages with more images lead to a higher conversion rate on your site? Perhaps which categories or authors drive the most traffic?

More ideas and examples: 17 Ways to Revolutionize Your Reports with Data Import 

Enough theory, just show me how to do it!

Below are the step-by-step instructions for data import.

1. Create custom dimensions

Because you’re adding data into Google Analytics, it needs somewhere to live. Primarily, this will be stored as a custom dimension (although you could also use custom metrics).

So before we start uploading data into GA, we need to create the custom dimensions first.

If you’ve never created a custom dimension before, follow our instructions for creating and editing custom dimensions and metrics.

2. Create the Data Set

A Data Set is a container that holds the data you upload to GA. It also defines the schema for the data import. The schema boils down to two important things:

• key
• location for uploaded data

The key is a dimension on which you will join the data that already exists in GA with the data that you’re uploading. For example, you might use the Page dimension (which already exists in GA) to join additional information you have about the page (author, word count, etc.).

To create a Data Set, navigate to the Admin area of GA. In the middle column (Web Property settings), look down toward the bottom and click on the Data Import menu. From the Data Import screen, click on the button to create a new Data Set.

Next, choose the Data Set type. There are 8 options:

• Refund Data – help center article
• User Data – help center article
• Campaaign Data – help center article
• Geography Data – help center article
• Content Data – help center article
• Product Data – help center article
• Custom Data – help center article
• Cost Data – help center article

Choose the appropriate type according to the data you’re uploading, then click to the next step. For help in understanding the different options for the Data Set types, explore the linked help center articles from Google.

Next, give the Data Set a name and choose which Views this additional data will be available.

Next, you need to define the Data Set schema. This is just the key (the data that exists in GA and in the uploaded data) and the dimensions where the uploaded data will be stored. For the key, you’ll select from a list of dimensions in the dropdown. These can be standard dimensions in GA (like Page) and/or custom dimensions. The dimensions listed will be different depending on the Data Set type you chose.

Using our example from above, our final schema may look like the following:

You’ll have an option to control how the uploaded data gets joined with the existing data in the Overwrite hit data / Import Behavior section. For example, you can specify whether or not the data that you’re uploading should overwrite data values that are collected as part of the hit. In other words, if you’re passing in the author name from your Google Analytics tracking on your site, do you want to use the value that is on the hit, or overwrite it with your uploaded value?

The options for Cost Data import are slightly different. You can choose whether duplicate data is added (summed) to previously uploaded data, or if it should overwrite previously uploaded data.

Once you’ve chosen these options, click Save. Now you’ll see two buttons – Get schema and Get Custom Data Source ID (for API users).

Click on the Get schema button.

This gives you the CSV header to use as the first line in your uploaded CSV file. Alternatively, you can also download an Excel template that you can fill with your data.

3. Create the CSV

Using either the CSV header or the downloaded Excel template, you can now build the file that you will upload. Using our example (and the Excel template option), you would have a file that looks like below:

If you’re using an Excel file, save it as a CSV after you’ve added your data.

4. Upload the Data

Once you have your CSV file with your additional data, there are two options for uploading it. You can use the Google Analytics Management API to upload data, or you can upload it manually. For our example, let’s keep it simple and manually upload our file.

If you have data that changes frequently, like new product information or new articles published, you may want to consider a more technical solution to automatically update each night.

First, from the Data Import screen in the Admin, click the Manage uploads link next to the Data Set into which you want to upload data.

Then, click on the Upload file button, choose the CSV file you created earlier, and upload it.

It’s that simple!

5. View data in reports

Uploaded data only gets added to your data during the processing stage. This means it will not be applied retroactively (Google would have to reprocess all your historic data, and that’s not going to happen). It will only be applied to hits as they’re being received. Also, it may take up to 24 hours before the data shows up in your reports.

Once you’ve uploaded your data and it has started processing, you’ll be able to see the uploaded data in your reports in a number of ways. Since your uploaded data is being stored in custom dimensions, you’ll be able to view it in all the same ways (secondary dimensions, custom reports, custom dashboards). For more information on how to access these values, check out How to Report Custom Dimensions in Google Analytics.

Things to keep in mind

There are several other nuances and FAQs of Data Import that are common. Here are the top ones:

• You need edit permissions at Web Property level to configure Data Import  and Custom Dimensions.
• Data Import does not apply to historic data. There is an option for Google Analytics Premium, called Query Time import, which  does join your uploaded data to historical data (with some limitations).
• A good use case for data import is when you don’t want the data out in the open (viewable in the source code or by looking at the collect request). For example, you may want to see gross margin in GA for you analysis. This isn’t something you’d want to send along with the pageview hit, which is easy to see.

Additional resources

About Data Import (Google Analytics Help Center)

About Custom Data (Google Analytics Help Center)

Importing data into Google Analytics – Video (Google Analytics Academy)

Data Import Developer Documentation

The post Data Import Step-by-Step Guide for Google Analytics appeared first on LunaMetrics.

There are a few topics that deserve discussion before pursuing a GTM SDK mobile app project. Being aware of these topics, can help smooth some of the possible problems or complications that can arise from an implementation.

It’s More Complicated

One key thing to understand at the beginning is that GTM SDK implementation in apps, by default, is more intensive than a GA implementation.

The idea is that you’re adding an entire additional layer of event communication so every tap, swipe, and interaction is an event on the datalayer for GTM with the app. That way moving forward, we can modify our tracking however we like, without having to do an additional app update through the Apple or Google stores. You don’t HAVE to track everything on the app, but the less you implement initially, the less you can react to in the future.

It should be understood that the initial implementation will take a little more developer time than just a normal Android or iOS SDK implementation for GA, but it comes with additional benefits if you do it right.

IDFA Isn’t Automatic

Second, it’s important to understand how IDFA (identifier for advertisers) works with both Apple and Google stores. If you care about the referral information on your Apps, simply using the GTM SDK isn’t enough. You still need to properly set up your account, and use the correct tagging of all your marketing initiatives driving users to the appropriate stores. If your marketing links aren’t tagged, and you don’t set things up correctly, you won’t get that referral information.

It’s not hard, but if you can’t get your marketing department to use the proper tags, you simply won’t have the source information.

Debugging is a Pain

A third, and big one, is that debugging the app with GTM SDK is a pain.

There is no great way to debug the app at present, and in my opinion, the best that you can do while developing it is to have a network sniffer of some sort and watch the hits leave your app from your development environment. Once the app is built, the only real, and effective, way to debug is to have a “script” of user actions, and then test by following said script, and waiting for the data.

Real Time reports inside of Google Analytics don’t work well with apps because of the way the data is sent. Apps use a dispatch process and only sends the data at intervals to Google (covered below.)

Be prepared to debug things differently than you do with your web implementations.

Dispatching Works Differently

Another misunderstanding centers around when the data does get dispatched to Google Analytics.

The app will store the data and send it out in bursts (to save battery, etc.), but if the user turns the app off or minimizes it, the data may sit there. If the user doesn’t use the app again prior to midnight on the following day, the data will be rejected. This means that it’s important to understand for mobile app tracking that it can be a bit less accurate than web tracking which can send every hit as it happens.

If you are looking for 100% accuracy on app data tracking, you’re barking up the wrong tree. Getting that 100% accuracy would come at a huge cost on the user’s battery, and would make your mobile app tracker a real resource hog. Dispatch helps make the GTM SDK and Google Analytics friendly to apps and their users. It doesn’t mean the data won’t be accurate for what is tracked, and should be considered in the aggregate.

This seems to be a complicated matter to explain to the higher-ups, who insist on 100% accuracy at the hit level. They just need to understand going in that they’re not going to get that from anyone and the priority should be creating an app that works well, and that users will keep on their phone.

Measurement Strategy is of Paramount Importance

Lastly, and most importantly, you need to understand that you will require a strong measurement strategy and implementation guide, before any development is done.

When you distribute your app it needs the default container from Google Tag Manager, already contained within it as a binary. This way there are tags for the user’s first use of the app, and you can track them immediately. If there is nothing in the GTM binary, then the user’s first visit will essentially be lost, and have no tracking.

Eventually the app will update the container, and will do so on a regular basis, but when the user first loads the app, it won’t have had time to do this important step. So the best process is to write up a very specific measurement strategy, with very strict naming conventions, and then build the tags and GTM setup in the container, delivering the binary container file to the developers before the app is actually built.

This means that if the developers don’t follow the strict naming guidelines, then some tags and tracking won’t work, and while you can change things later on in GTM, the initial visits of users for the app could have their data affected.

Be very specific about what you want to track, and how you want every event and data value to be named.

Conclusion

As I said initially, there are tons of benefits to using the GTM SDK in your mobile app, but you need to be aware of these common pitfalls, in order to head off problems before they occur.

1) Have a very in-depth measurement strategy and implementation plan prior to development.

2) Understand that the development of the GTM SDK in mobile might take a little longer than the GA SDK depending on your plan.

3) Prepare for the debugging process to be a little awkward.

4) Educate your marketers to properly tag all their campaigns so you get your correct referral information.

5) Be comfortable with not necessarily getting every single piece of data because of the dispatch process and the realities of mobile devices.

As long as you understand all that, and prepare for it, you’ll be ready for the additional power and insight that a proper implementation using the GTM SDK will bring. It’s totally worth it.

Get tracking!

The post 5 Considerations for Implementing Google Tag Manager on Mobile Apps appeared first on LunaMetrics.

I have not personally devoted a lot of time with the Excel 2016 for Windows preview. It looks to me a lot like Excel 2013, and what I did confirmed that it behaved very much like Excel 2013, both in Excel itself and in VBA. I spent more time with Excel 2016 for Mac, which also looks a lot like Excel 2013 for Windows, but the back end (VBA) has a lot of differences and a lot of shortcomings which Microsoft is hustling to address. I’ve had to figure out lots of workarounds to make my programs work the way I want them to, and in fact, some of my programs don’t really work yet.

For Excel 2016, Microsoft has introduced several new chart types, described in Introducing new and modern chart types now available in Office 2016 Preview. Several of them are also included in Peltier Tech Charts for Excel, including waterfalls, histograms, Paretos, and box plots. Should I be worried that I’ll lose business to Microsoft? Well, maybe not too worried. I will keep these charts in my product, because people may prefer my chart styles and defaults, and they may have written their own VBA to use my add-in to create charts. And of course, Peltier Tech Charts for Excel has lots more than these few charts.

I did “get to” foray into Office 365, because that was the only way to get either the Mac or Windows preview of Office 2016. That was a little frustrating, because I felt like I no longer had control over my account (seems I have half a dozen accounts with Microsoft, and I never guessed the right credentials whenever I had to log in), and updates could be an adventure. Recently, though, things seem to work more smoothly.

Here are a handful of links to articles on Microsoft’s Office Blog that talk about Office 2106, including topics related to business analytics and some new charts introduced in Excel 2016.

• What is new in business analytics – Excel 2016
• Helping business analysts take full advantage of Excel 2016
• 11 blogs on Power Map
• New charts: Treemap and Sunburst
• New charts: Visualize statistics with Histogram, Pareto, Box and Whisker
• New charts: Introducing Waterfall chart
• More details on forecasting models in Power View

Here are a few articles from users outside of Microsoft

• Excel 2016: Ten Heartwarming Improvements in which Rob Collie describes improvements to Power Pivot and other data features
• My three favorite things in Excel 2016 where my new colleague Gašper Kamenšek describes four new things in Excel 2016, that’s right, four, three of his favorites and one that everyone else seems excited by
• Chris Webb’s What’s New In The Excel 2016 Preview For BI?
• Excel 2016 for Mac review: Spreadsheet app can do the job—as long as you don’t rely on macros at Macworld

Microsoft is implementing a new development cycle. They’re going to move away from the 3- to 4-year new version cycle, and roll out more substantial updates monthly or so. This will help eliminate bugs and add features more rapidly. It also means they will make Office on all platforms increasingly compatible.

Microsoft has added a new communication feature. If you have a great idea for a new feature, you can suggest it at the Excel Idea Box at UserVoice.com. Other users can review, comment on, and vote on your ideas. I’ve posted Give us a proper NULL() worksheet function; please go and vote for it. I’ve also supported Bring VBA into the modern world, Link the min and max values of a chart axis to cell value, Make it easier to find external links, and Improve the Concatenate Function, among others, and they could also use your votes.

The post Microsoft Releases Excel 2016 for Windows appeared first on Peltier Tech Blog.

Each of the reports below could not have previously been produced in the interface. If any are appealing to you, give them a shot.

Network Performance

AdWords Report Editor makes it easy to report on and visualize your Search network and Display network campaigns. Just pull the “campaign type” dimension into the report, with whatever metrics you’d like to view to get a better understanding of how search and content channels perform in your account.

Match Type Segmentation

AdWords Report Editor makes match type analysis a *snap*. These pie charts are great to help understand how your keywords are working by displaying the percentage of a performance metric by match type. Here we’ve selected conversions, and, at a high level, it appears that the work we’ve done to build out a complete keyword list has paid off with nearly half of total conversions coming from the exact match type.

Device Segmentation

AdWords Report Editor now also gives us the ability to quickly analyze device data. Throw the “device” dimension into your favorite reports to give it a whirl. This really helps us and our clients get a grasp on just how important the mobile audience is in this scenario. Maybe this will finally help you believe the hype surrounding mobile.

Location Segmentation

Location analysis is easy and looks great using AdWords Report Editor. Charts like this help our international clients understand whether to make greater investment domestically or overseas. Additionally, it helps identify areas where their services just aren’t producing the right results.

Time Segmentation

Adding visuals to any time-based reports immediately makes them more impactful. It’s nice to actually “see” how traffic typically flows to your website throughout an average day or week. From here, it’s just a few clicks to making the right optimization by either reducing our bids during the low converting periods or removing our ads from the auction entirely.  Use “hour of day” or “day of week” as your primary dimension to make the most of this report.

Search Term Report

For a quick view of traffic quality try putting together a quick search term bar chart. Maybe you see a lot of traffic from a term you wouldn’t expect or, better yet, a lot of conversions from a term you wouldn’t expect.  This is a really great way to discover new ideas that can be turned into immediate results.

AdWords Report Editor Wishlist

What are some features that should be incorporated in the long-run? I’m glad you asked! I’d personally like to see the following:

• Labels as a dimension
• Comparison reporting
• Export to Google Drive
• More GDN targeting options

As you can see, this new tool does a fantastic job of creating visuals for you and your clients. The reporting interface is really quite intuitive, so generating reports similar to those above shouldn’t be any problem for you.

Give each of these reports a shot.  Is there a great reporting function that I didn’t mention?  Let us know how you’re using the AdWords Report Editor

The post Use 6 Great Reports in AdWords Report Editor appeared first on LunaMetrics.

As in, the former is in the business of providing data, the latter in the business of understanding the performance implied by the data. That understanding leads to insights about why the performance occurred, which leads to so what we should do.

Do you see how far away a Reporting Squirrel's job is from that of an Analysis Ninja?

For one, I hope you see the massive investment in self-development of business skills required to have the foundation required to get to the why and, even more, the so what.

Pause. Reflect on the implication of that why and so what on your current skills/career.

I'm sure you came up with a set of actions you can take to evolve from a squirrel to a ninja, or, if you are already a ninja, how to become even more awesome at ninja'ness.

One of the actions that both clusters will come up with is the ability to communicate the insights you discover. Even if you have really amazing why and so what, I've observed many Analysts die at the last mile: Presenting their whys and the so whats, in the form of stories.

In fact 86.4% of all Analyst careers fail due to a lack of this critical last mile skill!

Ok, ok. I kid. I kid.

It is really 88%. : )

Tom Fishburne's wonderful cartoon is here for another purpose.

We send out our multi-tab spreadsheets, our best Google Analytics custom reports, our great dashboards full of data , and more to the tactical layer of data clients. The Directors, the Marketers, the Optimization employees and our resident social media gurus. The valiant hope is that they will do something.

But, as Tom illustrates above, some of our most important meetings, where data is the hero, are where you have to stand up and deliver your amazing discoveries as an Analyst using Keynote or PowerPoint. These meetings are important because you can't data puke (PowerPoint is not a great medium for that, not that you are not going try! :)), you have to present to perhaps the most important people at your company or client and they likely can do something about your career should you stink. The focus, hence, is on telling stories.

In this post, I want to address this specific use case. You are standing in front of a group of people, and your goal is to communicate your incredible brilliance (specifically the why behind the data, and the so whats ). We are going to discuss a cluster of strategies you can use to ensure that you present your message with radical simplicity and with an incredible focus. You goal is going to be to present your why and so what so quickly that the attention moves away from the data and on to a discussion. Yes. You are going to make the data a sideshow – in the meeting where the data is supposed to be the hero. Because, data is not the hero, the actions you'll take are the hero.

Before we go on…

Challenging Conventional Wisdom.

Let me make a career-limiting move and disagree with two things that Major Big Gurus have made into conventional wisdom. I'm sure there will be blow-back. But. These are absolutely corrosive points of view.

PowerPoint does not suck, you do.

Don't limit the number of slides, limit the number of ideas!

Don't listen to Major Big Gurus and blame PowerPoint or try to limit the wrong thing. Glory will be yours.

Ten rules to improve storytelling with data.

With those critical challenges to conventional wisdom out of the way, let's look at ten examples of data/insights presented via Keynote or PowerPoint and learn how to improve your effectiveness significantly. Our BFFs will drastic simplicity, incredible focus. We will apply liberal doses of our good taste, and common sense. The goal will be to get to the point faster, and move the conversation away from the data to what should be done.

Here are the ten lessons, in case you want to jump ahead (but why!):

The examples used below come from public presentations by various tool providers, industry analyst outputs, and other such sources. In almost all cases, except were unavoidable, they have been stripped of source identifiers and anonymized. Because it is not important where they come from, just pop open your personal slides folder and you likely don't have to go very far to find very similar examples. :)

It is important to share that my goal is not for you to create outputs that look exactly like the after versions below. My goal is that you'll learn a set of filters you'll use as you think about the best ways to create your stories, however you choose to tell them with whatever visual output you most love.

Ready? Let's go have an immersive learning experience and have a ton of fun! Ten lessons in simplicity and focus that help us become better storytellers…

#1. Don't suck twice, stop creating handouts.

The single biggest mistake we make when we present data: We create handouts! The text/data rich horrid creations whose primary purpose in life it to try and work when emailed out.

When you do this, you've decided to suck twice. First, when you present it (people can read faster than you can talk, you are going to read these slides word for word driving the audience to wish for a painless immediate death). Second, when you email out your 58 slide data stuffed monstrosity, no one will read it.

Don't create handouts. Instantly you suck less.

Just think about it. This slide is on the screen…. What thoughts are going through your head?

Perhaps, the sadness at the 20 mins it will require you to understand this. Perhaps, marveling at the creator's extraordinary ability to not leave any area unstuffed. Perhaps, the fact that the proverbial joke starts with the punch line. Perhaps, wondering if everything is already on the slide, why are you listening to someone reading it – why did they simply not email it to you?

Don't create handouts. Retain the respect of your audience.

Handouts can take all shapes, here's one used on TV while an Insights Analyst, not kidding, was talking in a small box on the screen…

Do you need the first two lines? The TV person was talking them any way. Do you need all the super cutified ball-piller things? Do you need the pimpy thing at the bottom (which was also scrolling by the screen).

Handouts. Don't do them. You mom will love you more.

#2. Take everything away except the absolute essential.

Actually, there are two lessons in this story. One, getting to the absolute essential. Two, developing a life-saving obsession with outcomes. (You'll see this obsession of mine threaded throughout this post. It is absolutely critical for Analysis Ninjas.)

When you are presenting, to an audience of 3 or 3,000, your goal should be to get the data out of the way as fast as you can, so that you can move to the so what conversation. If your discussions are constantly getting stymied in data quality issues, arguments about formulas, tool questions, and the this and that of data, you are doing it wrong. All that needs to happen prior to to you standing in front of the group.

Here is a fantastic example of good intentions going awry…

Take 30 seconds, ponder what's going on. You are a Ninja, it will likely take you less. I'm sure you'll come to two conclusions. First, someone worked really hard on this and created a really nice model for a smarter decision to be made for 2014. Second, you'll wonder if it was necessary to open the kimono this wide and expose yourself quite this openly.

The above slide, remember, good work, great intent, will simply lead to a discussion of the data. The puke of rows and sub-rows, are trying way too hard to earn credibility. Someone is going to pick something small and distract the whole freaking discussion down some freaking rabbit-hole.

There is one other problem. It is hidden. Scroll back up a bit, look at the image, did the Analyst make clear what they wanted to business leader/HiPPO to do? How could they, the Ninja in you has already noticed that there is no impact on the business above.

It is important to re-state that a lot of work was done on the slide above, but a wonderfully qualified person. That is what makes the output heartbreaking.

Ok. Let's fix it.

The easiest way to start is to take away elements that are cluttering the story.

In this case, you simply get rid of the sub-rows with the tiny fonts. They were not adding much value to the overall story (and screaming: I AM CREDIBLE, TRUST ME, LOOK AT ALL THIS DETAILED DATA!!!).

Here's the resulting output..

Simpler, right? Scroll back up, compare the two versions. Just, less stuff.

Don't underestimate the value of this strategy in your storytelling. Just. Less. Stuff.

Let's apply that principle again. You'll notice above that there are some numbers that are repeated multiple times. Why do we need them cluttering our story? Why not get rid of them? Oh, and impression share really adding value?

Here's an even less stuff view…

OMG! Less stuff rules! :)

You can present this slide, and you'll look like a champ. It tells the story you originally wanted to, in a simple and focused manner.

But, now that all the data clutter is gone, you'll start to see the problems you should have been worried about in the first place.

For example, it is a mistake of the most gigantic proportions to end any analysis on Investment Required. Rookie mistake. No boss/client wants the story to end on money going out. They want to know how much money is coming into the business!

You'll want to do that. It will make you realize you have no idea what the value of a Raw Contact it. You go and figure it out (yes, it is your job to compute economic value ).

It will take you a while, but you are going to die of happiness when you do finish the task.

When you have it, you'll move investment to the very top (it is what causes the rest to happen after all), and add two more rows at the bottom…

How cool is this? Attention is now shifted to what the company is actually going to get out of that investment (Economic Value minus Investment).

As the room now considers if we should go for stable spend or clicks or impressions or more clicks, they can see how much more comes to to bottom-line from each decision. A smart discussion ensues.

There is one more thing left.

You are the smartest person in the room when it comes to data. You've done lots of deep dive analysis (hence v1.0 of your data-rich slide!). You understand a good amount of how this area of marketing works, you understand the company's goals.

Why not use all that knowledge to make a recommendation in terms of what the company should choose?

Tighten the slide up just a bit, and put a ring on it…

Now, your boss, directors, VPs, everyone has a simple view of the data (even with all the numbers!), and they know exactly what you want them to do.

They might not agree with it. But, that's beside the point. You would have successfully moved the discussion away from the data and on to dealing with the so what. They'll argue about why not go for stable impression share or clicks, or, since they are so smart, why not go for total opportunity (how dare you not shoot for six mil!). All, very good discussion about the business, and not the data.

#winning

I hope you see that while we are fixing the slide, we really did so much more than that. We ended up in a place with a lot more business analysis, and you had to work harder to develop a point of view on what to do. We added to your analytical skills a demand for business savvy. That's what makes an Analysis Ninja (and simpler slides).

#3. Avoid complex visualizations – they get in the way!

The general belief that we need to visualize data is well founded. Lots of numbers look burdensome, and can often be boring (especially if your story has lots of these slides). You don't have to go very far, just scroll up a bit in this post. :)

But, often, despite our good intentions, we end up in a much worse situation when we deploy an extra-layer of effort to create something visual.

Here's a fantastic example of that…

How hard did you have to work to figure out what was going on here? Here are some reasons why it was way harder than necessary.

Add this to your rule-book, don't ever do percentages of percentages. It is very hard for people to get a real concrete sense for what's going on. In this case, 49% is 75% more of 28% is hard to get.

I'm sure you are wondering why the red parenthesis was used, it seems to imply that 49% is less than 28% (or all of the right is less than the left).

Surely you are curious why the lovely hipster gentleman in a t-shirt was necessary to communicate with B2B bosses.

At this point your head also hurts as you tried to commit to memory all the numbers so that you can compute the difference between each bubble.

Net, net. A delightful mess. The visualization actually gets in the way (besides the extra money you spent doing the work of making the bubbles and finding the optimal hipster).

This is a lot less pretty, but a lot more effective…

The use of colors helps drag your eye to the column that's actually important, the red is effective at giving the core message (I should have made the 21 green, dang!), and the percentage points are simpler to internalize.

Of course, to send your core message, you might decide you don't need the numbers for each year, you can further simplify…

No hipster, red parenthesis, or bubbles, but makes your point for you.

At this point, you have your choice. You can keep using the percentage point change, or you can switch to percentages. Either one works really well, because that column of numbers is all by itself…

Compare the more time-consuming, bubble-hipster original with the table. I've heard some say that the bubble-hipster is cooler. If that is you, I want you to focus on which one is easier to understand.

Your primary objective is to make sure performance is understood quickly, and you can move to the so what faster.

We are surrounded by such bubble-hipster efforts. Time-consuming anti-understanding look-at-me-I'm-so-cool visualizations that get in the way.

Take a deep breath, find a teddy bear to cuddle for emotional solace as I unleash the next example on you.

Teddy ready?

Take a look at this incredible craziness…

Dear lords of Saturn and Jupiter, what the heck is this!!! Old timey gas station pumps? A new race of aliens?

The visualization is getting in the way. While all slides in this post are shrunk to fit the width of this blog, even in it's full version, projected on a giant screen, it is hard to understand what's being communicated. And, remember, the thing you are looking at above took a lot of time to do, and it does have some good insights. Sadly, the visualization above is like cutting your legs off to run faster.

There are 50 ways to do this right/differently. I'm sure you can think of 40 of them. Let me share two.

For my first attempt, I tried to separate the two distinct things that were being communicated using the three different numbers above. When I presented the data, I showed the data in the blue (current ratings) and then click my mouse and the second set of data in the orange comes into view…

By not showing all the data at the same time, I control the pace of the story, and also spring a surprise on the audience. It turns out the biggest numbers on the right, do not indicate the best.

And, just look at Fox! Foxy!!

When you do this, you'll have to figure out what to do the sort on. I choose YOY Change for my sort (worst to best) as I thought that was the more important thing to drive the conversation.

You can sort the above by Reach. Quite ok. But, in a very subtle way, you'll drive a different conversation.

There is no wrong answer, your local knowledge will help you pick the right thing to focus on. It is important to pick though, don't be random about it.

And, just to prove to you that a good old table sometimes can be better than a ugly over-worked visualization, here's a table version…

The Conditional Formatting (Excel: Home > Conditional Formatting > Color Scales), is there to just draw your eye as an audience to where I want it to be to tell my story quickly and efficiently.

Better than the thing we started with, right?

Data visualization is always a good thing to consider. But. Don't let what you create cross your own boundaries of good-taste and common-sense. Don't let the visualization get in the way of the story you are trying to tell.

#4. Make performance comparisons easier!

I'm sure you are catching on by now that the slides and names have been changed to protect the innocent. And, the normal crud that pollutes a slide (logos, ugly template design elements, confidentiality declarations, company fonts) have also been removed for the same reason (and because I did not want you to weep when you see how much more hideous these slides would be with all that added in).

Ok. Take a look at this slide. Don't scroll. Just take a look at it, do you understand what it is saying? Write it down in just a few words on a post-it.

The design and layout are quite simple, definite kudos to the Analyst for that. Despite that, I bet it was still harder than necessary for you to figure out what is going on.

The reason is quite simple. We are trying two sets of comparisons. First, between RB and a collection of it's peers. Second, between 2012 and 2013. That's hard enough. But, the way the design is accomplished, you have to make too many hops to make the comparison.

For example, to understand Aquantive you have to leap over Flurry and Yahoo! to look at all the data. Maybe this is less difficult because of the huge bars, but it is difficult to do for Flurry and Yahoo!'s performance.

It is not all that hard to fix though. Just put the relevant set's of data together…

Easier to compare, right? I use animations to control the story, so things appear on screen as I'm talking about them (rather than puking out the whole slide at a time and allowing people to roam free and stop listening to me). Hence, the first view will look like above. It allows me to just focus on one thing: Aquantive.

You'll notice that the scale is gone. It is by design. It was hard for me to let go of it as some part of me as an Analyst always wants that there. But, look at the slide above. Does it really matter if the scale is there or not? Especially, if I know that the other bars are coming. Check out the whole slide, does it matter if the scale is there or not?

It is completely irrelevant. You are just comparing performance, and the numbers would only clutter the slide. When I present it, I'll say something like "Our peak investment, in Aquantive in 2013, was 700k." That gives the audience an anchor. Rest is irrelevant.

(Now, I know this is going to be hard for you to do. You can have the scale there if it will make you feel better. It really is ok. You don't have to go that far to de-clutter slides, if you'll be uncomfortable. But, do try it sometimes.)

Bottom-line, but moving things around you've made the data much easier to understand and allowed three companies to be looked at independently, even as they give context to each other.

Remember the common theme in this post? An obsession with outcomes.

What's the so what of the above collection of data?

In the original presentation it moved on to a whole bunch more points, and then in the end the Marketer made a random set of recommendations that could not be tied to the above (or really much of the) data provided.

As a great Analysis Ninja, you want to add one more thing to the slide.

You are comparing 2012 and 2013, add a row of data at the top that shows your computation of the size of the opportunity for 2014.

Boom!

Based on just the data you originally had, the conversation could have gone in five different directions. But, but adding that row on the top you've brought extreme focus to the conversation around the table.

Past performance is ok, but if you just had that you all might just talk about Aquantive and how much you out-stripped peer-set in 2013. Now, you are all going to discuss what your strategy should be with Flurry as you both stink-compared to your peer-set, perhaps not having taken mobile seriously, and it is where the biggest opportunity is. By more than 2X!

That is the power of obsessing about outcomes – the last mile.

This is optional. Sometimes you might not have the expertise to bring this to the table. Let me assure you that that is A-OK (though, over time I encourage you to build these business skills). But, complete all of the last mile.

Add a slide that comes after the one above, this one tells the company what four things they need to do in order to take full advantage of the 90 million opportunity size.

Each recommendation you make is sized by how much of the opportunity it can deliver. At the very minimum you are guaranteed a sign-off from your boss for Thing One and Think Two. They are too big to ignore.

We end up with two slides instead of one. You put the recommendations of what to do, close to the data (rather than dump them all at the end). You present a complete story, you drive a very focused and productive discussion where the only thing data did is got out of the way quickly.

You might not believe me, but these strategies also cut the meeting time by 50% easily. Now, that's priceless.

#5. Convert words into pictures, and expose complexity.

When you try and tell stories, sometimes it is not a matter of just have as few things on the slide as you possibly can. You will a lot of the time. But, sometimes you simply have to show the complexity that lays just below the surface.

Here's a fantastic example of that… Don't scroll too far, take a look at the below slide, you are presenting it to a group of Sr. Leaders, does it communicate effectively the key point you are hoping to make with the three big numbers? Write down your thought's on a Post-It.

(The best way to learn is to come up with your answer, contrast it with the other person's. Regardless of who is right or better, understanding the contrast is where learning occurs.)

Ok. Got your thoughts down?

I love these three numbers. And, I'm often in pain when I think about how few businesses are structured around taking advantage of this. We still live in an world where we are optimize for single visit sessions. If that single visit is a success, hurray! If it is not, trash!

This is insane. Companies should optimize for the portfolio of visits (multiple!) leading to a business outcome.

The above slide, IMHO, sadly fails to expose the complexity in this user behavior/journey. That means, it becomes one of a parade of numbers that go by in a presentation with nary a change in business strategy. Because…. the Sr. Leader won't quite get it.

To be fair, the user behavior is described in text format. But, who is going to read it, or even understand it when you speak it?

We can fix the problem quite easily.

Dump the text. Show a representation of the user behavior. I would show each line at one time as I speak (84% click, describe, click 86%, describe, click 56%, describe), and here's how the completed slide would look…

It is the same thing that was described in the text. But, it's visualized simply yet.

It really brings home the point about how conversions are happening at the moment, and that as as a company we might be making insanely bad decisions by relying only on last-click conversion attribution models.

Yes, there is more stuff on the slide. But, it works much, much better.

If you have an audience that is even slightly connected to the digital business, and not Leaders who look at the digital data just once very two years, then you can actually go all out and truly how how users behave on the web…

While there is a lot more stuff on the slide, in each case, as you present it one number's behavior at a time, it does such a wonderful job of showing just how sophisticated, and unpredictable, user behavior it. It will kick off a really powerful conversation that will go well beyond attribution modeling and encompass everything from the campaign strategy, landing pages, abandonment rates and more.

Conversation. A good thing as Martha Stewart would say.

[To come up with the so what for the above user behavior, two posts for you: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models and Multi-Channel Attribution: Definitions, Models and a Reality Check.]

#6. Deeply resist, dislike, prettification for the sake of it.

This is another common trend when we have the opportunity to present data, we add unnecessary stuff to it. One of the most common forms of this is icons.

Here's a great example of this, check out this slide…

It shows searches in some country in 2019. This is of course a good idea. But, are you seeing the the data, or are the icons staring at you?

Admit it, you did not even notice that the core point of this set of data, as mentioned in the title, is to obsess about 2-in-1s (the most hyped and least successful product known to human-kind because no one on the planet needs this product, sorry, venting!).

Or, that we've not computed how much the impact of any change will be on the business. Ex: Is the 2-in-1 green because it will add the most to the bottom-line? (Actually, no.)

Or, that we have a perfectly good graph we can add numbers to in many clever ways to drag the eye to the growth in searches, but we don't above.

Or, the legend is the sub-title.

Or, any host of issues?

No.

Don't add crud to your slides, they distract from the story (besides costing you more time to find the crud to add than it took to create the graph!).

Simplify… let the data be the hero…

The legend usually works best at the bottom. There it is in red, but out of the way.

The graph is much bigger, now the hero of the story. The growth rates are at the bottom, by themselves, and will now work hard to drive the conversation.

There is one thing that's still missing. Outcomes. If the recommendation is to focus on the 20% grown in 2-in-1 searches, compute the business impact to back up your recommendation. When you compute outcomes, you'll quickly realize 2-in-1s are not going to have a business impact (you would go from query volume to current best CTR to conversion rates to AOV to total revenue or profit). Then, you can drive the leadership conversation to the best place for the business.

The most heartbreaking cost of the icons, and other such curd, is that we spend time on that rather than worrying about the story and if we have the why and the so what.

Here's an even better example of adding non-value added things to the story, along with, in this case, additional distractions…

From this day on you'll never use icons, so let's skip that part. The blue circles are gone! (I'm not even going to talk about how that is a terrible icon for a newspaper!)

Look at the slide.

Are both set's of numbers equally interesting? Are both of value?

Remember our principle #2, take everything away until you have the essential left.

Like me, you'll come to the conclusion that Screen Penetration (OMG, never, ever, never, never use the word penetration!), is not all that useful. There are some differences, but the numbers cluster close to each other.

What is interesting on the other hand, are the Average Hours Spent per Day.

So. Why not remove the icons and distracting extra data, and just focus time spent?

Here's what that might look like…

They are ordered from least to most hours per day. It might take a second, but the colors simply represent online and offline channels. You will now drive a much more focused conversation that might start with: "So if this is how our customers are spending their time with media, what does our advertising media-mix look like?" Make sure you have that ready on the next slide!

Even with the ugly template design, the company logos (yes, plural), and other elements that were on the original slide, the one above manages to survive negativity they add. The data still shines through because it is simply presented and is focused.

I'm often told that people like adding icons and distractions because the data they are presenting is boring. Icons are a way to make the slide more "interesting." You are an Analyst. Your job is to make the data interesting (by finding interesting things!). If you choose to be interesting by using icons, you might be limiting career growth because most Sr. Leaders are not stupid enough to find the meeting valuable because the data was boring but had great icons.

#7. Don't make a deal with the devil, stock photos are evil.

This is now a full blown design trend. Say as little as you can, have as little data as you can, just use an image that describes the essence of what you are trying to say. It was sourced in real pain. Slides with data were so full of data, so distractingly presented that Major Big Gurus recommended just using stock photos to communicate the key message.

Sadly, this is a terrible strategy for one simple reason.

Actually wait.

Here's one such slide, it follows every recommendation from Major Big Gurus… Look at it, what are you thinking about…

Are you thinking about the data, or why a young woman in her underwear, with perfect makeup, is on her computer so late in the night?

Are you trying to come up with things she might be doing?

You would not be the only one.

And, that's one big problem with stock photos. You lose control of the story.

Photos are very personal. We bring our biases, our life experience, our dislikes, our hidden emotions, and everything else to a photo when we look at it. Even people from the exact same background, in an isolated country, with the exact same education will look at the same photo differently. Pick a village in Iceland. Find five people with light colored skin. Who went to the same school. All of whom now work at a bank. Show them a stock photo. Ask them what thought it evokes. You'll get five answers.

Now think of how much more compounded this problem is in mixed audiences with location, education, ethnic, life path diversity.

This is why stock photos suck.

I'm not even going to touch the fact that most stock photos are poorly posed, unnatural and have other ill-advised elements. I'm not even going to touch that this new stock photos with one word on the slide craze actually often causes you to compromise on trends, segments or other such important baggage that data to Sr. Leaders has to carry.

Forget that. Just think of the one big problem I've identified for you.

Let's try this again….

Are you excited about the ecommerce or social media or AdWords possibilities on mobile phones because of the 70% number, or are you thinking that this couple is definitely getting divorced if late in the night they are sitting in their bed with their backs turned on each other? For the sake of human race, and their relationship, should they not be facing each other and having a conversation about their day?

Are you thinking about the data possibilities?

Let's try a different company, and a different example, this is so pervasive, it is not that hard to find these…

Aren't these people in a home already? If you insist on using a picture, should you not use one of some young hipsters searching for their home? Or, at least getting a pre-approval for a loan?

(Don't even get me started on a rant if 31% is higher or lower than in the past, or the two numbers are off different bases – something hard for people to catch, or… other data things.)

Or here's one that takes the cake… a profoundly dis-interested young (hipster-ish) man, thirsty, sitting on a park bench… representing data that is about how far away he is in his living room from various devices!

If you are wondering WTF, you would not be the only one.

Just think of how crazy this whole slide is.

Ignore the fact that the Analyst is implying that the reason we feel 2X more personal connection to a brand is because these devices are simply closer to us physically! #omgomgomg

Ignore the fact that the person who created this slide was likely born 50 years after those radios and TVs ceased to exit on planet earth. (If you want to denigrate other channels, don't let's your distaste be this obvious.)

Ignore all the other issues. I just want you to feel how terrible the whole use photos to communicate the emotion of your data point strategy is when you shine even slight light on it. Because, you can't control what people will read into your stock photos.

And, do I need to even tell you how profoundly sub-optimal this is….

No, I don't. Just bathe in it's awfulness.

And, promise me that you are not going to do this for as long as you live.

If you need one last push, let the philosopher Mr. Vince Vaughn make the case for the awfulness of stock photography…

You are an Analyst. Your job is to communicate the data story as simply as you can, but no simpler, to expose the why and shift the conversation to the so whats you've identified. Try and eliminate everything that is getting in the way of that. Including time you spend hunting for the optimal stock photos.

#8. Leverage proportionality for higher-impact stories.

This is one of my favourite stories. It is hugely impactful. It truly shows something amazing. Except. It does not.

There are many standard data presentation strategies you should use all the time. Like. Don't make the difference between 1.4% conversion rate seem extraordinarily bigger than 2.4% conversion rate (it might not be statistically significant!). Representing things proportionally is one that you should try and follow as much as you can. This story is a great example of that.

The smart Analyst is trying to show something important. It turns out, lots of people who search on Google, in this case, and visit your website, also visit your store where they also make additional purchases. If you want to think about accurately valuing your search investment, you should also take into account this offline behavior. So cool.

To prove this out, an amazing effort was undertaken where online ids were matched with offline behavior and offline outcomes were computed. As you can imagine, not everyone who visited the site could have their id properly matched (cookie issues, and more). No biggie.

This data is the resulting output. It follow the very common house metaphor to show the data… see if you can understand it…. do you think the behavior shown is valuable if this were your business….

If you are super into data, you might see how powerful the story is. If, like me, you are a normal person, you might not quite grasp how fantastically amazing it is. And, it is all the fault of how the data was visualized.

Here's a simple way to understand the problem. Look at the slide above. Only 21% of the click ids matched to the provider's cookie pool. But, does that yellow box show 21% of the blue box?

Nyet!

Continue the rest of your visual analysis in the same manner.

The house picture is broken. And, hence, in the end it fails to communicate that that 20 million is a big freaking deal!

Try this, and again when presenting we would have one color show up at a time so that you can control the story and build the suspense, instead…. we make sure we size the boxes proportionally…

Only 21% of the ids were matched to the cookie pool, of that less than half, 49%, were matched to people's accounts in the Store's CRM system, and of those only 22% had a conversion.

Even with such a small red box matched of all the blue people, the Store made $20 million offline!! It is a safe assumption that if the yellow box was bigger (better matching), and/or the red box was bigger (better matching), the real impact on store sales is much, much bigger than $20 million.

With the original visual, you don't quite communicate that in the worst case scenario the Store is making an additional $20 mil in offline sales. With the above slide, even with the tiny unreadable font, you do make that much more clear.

The conversation you power with the above version will immediately shift to the so what, in this case for the AdWords strategy. The house looked cooler I suppose, but it is a lot less effective.

Use these standard data visualization approaches to communicate more effectively to quicker internalization of reality.

#9. Look beyond the obvious, really look.

Very often at Analysts and Researchers we are so into the data, slicing and dicing it, and in trying to get something decent out of that work, that we fail to actually see the data. See as in really look at it. We have our table, our graph, our slide, but we don't look at this final output to see beyond the obvious things to find something a lot more interesting that might be lurking below the surface.

This is one such deceptively simple example, and I get to make fun of myself.

We all know that real pies are yummy, data pies are yucky. Two data pies are especially yucky.

Just look at this example where all decorations and design elements have been removed. Grab your Post-It again, take some notes on what you think is wrong with this slide, and what you might do differently.

Even if the fonts and numbers were larger, it is extremely difficult to compare the slices (despite three big shifts).

What makes the job harder is that there are too many segments, when there seem to only be a handful that are material.

I'm confident you ditched the pies immediately.

So, what was your fix?

Mine was simple. Just make a table. Why make things any harder?

Here's my delightful table…

You can see the names clearly, you can see the numbers! I even color coded the growth, and did not percentage percentages! (See rule #3 above.) And, I even reduced the number of data points.

And, you know how obsessive I am about this, I even have the so what.

I felt pretty good about this. Until I saw Lindsay's version.

Here it is…

She saw something that was not obvious, something right in plain sight, something absolutely amazingly critical.

There was a stark difference in performance of brands and resellers.

When you see Lindsay's graph, I'm sure, like me, you hit your forehead with a big Doh! Because that's the story. That's the big point.

And, it is important because it will lead to very different so what actions, a very different conversation would ensue, than if you'd used my fixed version.

Make sure you take time to look at your most important data. Really look at it to see what it is saying. Pattern yourself to look beyond the obvious. It is not easy, it takes time to get good at this. But get good you must – as Yoda would say it. :)

#10. When faced with a data puke, pick focusing facts!

Sometimes in life, you have no choice, you have to puke out a bunch of data.

Here's a good example of one such slide…

Ooof! That is a lot of data!

It actually does not matter how good or bad it is, there is no way anyone will actually understanding anything. Just look at all the trends.

When faced with this much data two things will happen: 1. People will stop listening to you and start wondering if their Ashley Madison profile will be discovered in the data leak. 2. Start asking you random questions about any random thing/trend that catches their fancy (How come so many more people have heard of Ram when compared to Jeep?).

Your goal in these data puking scenario is to get to a focusing fact very quickly, and remove the data puke from the slide.

I have a couple of go to strategies in these cases.

My favourite one is to get them focused on the competition. There are so many companies on the slide. I'll pick a competitor or two that our company/client is really worried about, and a competitor or two that I think they should really be worried about (great way to push Sr. Leaders beyond their blind-spots).

You show your data puke, click, eliminate everyone except those that fit the categories above…

The conversation will immediately go to the worried about/should be worried about competitors. Why are they at x and we are at y? What do we need to do differently to crush Toyota? OMG, how in the world did those newbies at Kia get to be so x at y?

All really good things. Quickly off the puke and on to the so what. Make sure you are ready with some recommendations there.

(A very small thing, but I love balance on slides. I hope you see that in all the after examples in this post. In the slide above, I moved the legend to be center-aligned with the graph. Small thing, but it taxes the audience's brain a little less.)

My second strategy when faced with a data puke is to pick a trend/thing as my focusing fact.

In this case, the most desirable outcomes are represented by the purple and the green, and hence I choose them…

Just add a white box, transparency set at 11% on the rest of the trends and you have yourself a discussion point. The conversation will focus on what our competitors are doing to be so much better at the purple or the green, and what we can learn from them.

(While that discussion is happening you'll have a panic attack as you realize that this data is quite likely suspicious because for that many people to say they love Chevrolet and will stand up for the brand seems quite illogical given who else is on that list! A good use of focusing facts rather than just looking at the whole data puke!!)

I'm sure you'll have your own go-to strategies for picking your focusing facts. What's important is to have an arsenal of these ready because you will have to use data pukes on some extremely rare occasions when Mercury and Pluto are in perfect alignment. Even on those rare occasions you'll be ready.

That's it, ten rules to help you simplify the story you are trying to tell with data, to help you bring focus to drive the conversation about what to do, powered by an obsession about business outcomes. It is hard to accept that the job of an Analyst is to move the conversation off the data as fast as you possibly can, but, trust me, it is. The rewards of that strategy are immense and gratifying.

We started the post with a cartoon by Tom Fishburne, let's end with one as well..

Admit it, you've not only said it but actually worked hard to find said "unbiased" data!

As always, it is your turn now.

Surely you don't agree with all the after versions. Which one do you dislike the most? And, to be fair, which one do you love the most? If you had to redo one of the before versions, care to share your after version? I'm confident some of you passionately believe in limiting the number of slides and blame PowerPoint, care to share what powers that passion? If you use stock photos or icons, why do you stick with them? If you had to add to my list of rules for storytelling, what would you add?

I would absolutely love to see your examples, hear your critique, listen to your praise, and alternative points of view. Please share them via comments.

Thank you. Go out and tell amazing stories with data!

Great Storytelling With Data: Visualize Simply And Focus Obsessively is a post from: Occam's Razor by Avinash Kaushik

You can skip this article and go straight to Peltier Tech Charts for Excel 3.0 to license your copy. From now until September 30, the 30th anniversary of Microsoft Excel, you can get $10 off using one of these coupon codes:

• NPTX30A10 to get $10 off the Standard Edition
• NPTX30B10 to get $10 off the Advanced Edition

If you have a valid license to any version of the Peltier Tech Chart Utility 2.0 (Advanced or Standard, Windows or Mac), you are eligible for a 50% discount, on top of the $10 discount above. Email Peltier Tech for a discount coupon.

If you have a valid license to any of the older standalone Peltier Tech Whatever Chart Utilities (Waterfall, Cluster-Stack, Box Plot, etc.), you are eligible for a 25% discount, on top of the $10 discount above. Email Peltier Tech for a discount coupon.

If you’ve licensed the Peltier Tech Chart Utility in the past month or two, email Peltier Tech and we’ll work out a fair price for an upgrade.

What’s New in Peltier Tech Charts for Excel 3.0

There are several major changes and a lot of minor ones.

First, the utility has been updated to run in Excel 2016 for Windows (out next week) and for Mac (out last month).

Second, the same add-in can run in both Windows and Mac. In the past I had to support two different files, because of incompatibilities between Windows VBA and Mac VBA. But I dug into all of these incompatibilities and found ways around them. Users can now license the utility once and use it on both platforms.

Third, I’ve added a feature to the advanced version of the utility that summarizes your chart’s data, lets you select some or all series in a chart, then moves or resizes their data ranges by a number of rows or columns or to a different sheet in a single operation.

I built it for myself, then decided people might like it, so I put it into the program.

Fourth, I’ve added one new chart type, Clustered Box Plots. This is a nice enhancement to box plots, allowing categories to be grouped and color coded to show patterns more readily.

What’s Still On Tap for Peltier Tech Charts for Excel 3.0

Although Peltier Tech Charts for Excel has been released, there are more things being done to it.

First, there is still a lot of testing to be done in Excel 2016 for Mac. A lot of bugs and inconsistencies have been addressed, but there are still incompatibilities, and there are features that need more development. Exporting charts as image files, for example, which is a victim of an increasingly harsh lockdown by Apple on its hardware.

Second, there are a lot more features, both chart types and other functions, under development. Tornado sensitivity charts, Gantt charts, panel charts, step charts, and run charts. Features to clean up charts, arrange charts and chart elements, extract chart data, quickly apply favorite series formats.

Third, under development are versions of this software that work directly in other Office applications, linking to your Excel data, but building charts in PowerPoint or Word.

License Peltier Tech Charts for Excel Now

Visit Peltier Tech Charts for Excel 3.0 to license your copy. From now until September 30, the 30th anniversary of Microsoft Excel, you can get $10 off using one of these coupon codes:

• NPTX30A10 to get $10 off the Standard Edition
• NPTX30B10 to get $10 off the Advanced Edition

If you have a valid license to any version of the Peltier Tech Chart Utility 2.0 (Advanced or Standard, Windows or Mac), you are eligible for a 50% discount, on top of the $10 discount above.

If you have a valid license to any of the older standalone Peltier Tech Whatever Chart Utilities (Waterfall, Cluster-Stack, Box Plot, etc.), you are eligible for a 25% discount, on top of the $10 discount above.

If you are a user of any previous Peltier Tech product, email Peltier Tech for your discount coupon.

The post Peltier Tech Charts for Excel 3.0 appeared first on Peltier Tech Blog.

The Goal: Chart side-by-side bars for each month

Here’s what I want: Revenue for each month for the year to date, vs. the same month last year. By including the months at the end of last year, I can see what’s coming if this year follows the same trend.

Step 1: Connect to the data

I’ll start with an Excel file that has a flat table of data with 3 columns:

• Month = Jan, Feb, Mar, etc.
• Year = 2014 or 2015
• Revenue = amount of revenue for the month and year in the first 2 columns

When I connect the Excel file to Tableau and go to my worksheet, I see Month and Year under Dimensions, while Revenue is under Measures. Tableau makes an educated guess where each item belongs, and this time it guessed right.

What’s not so easy to see is that Month is blue, while Year and Revenue are green. This will turn out to be important!

Step 2: Drag and drop Month and Revenue (or other measure)

I know I want revenue by month, so I’ll drag and drop the Month pill onto the Columns shelf, and the Revenue pill onto the Rows shelf. This gives me a bar chart by month, but it’s not quite what I want.

Notice that the bars for January through August represent the total revenue for both 2014 and 2015. I still need to separate the data by year.

Here’s where things can go wrong, if you don’t have a guide to warn you. Lucky you, here I am. “Danger, Will Robinson!”

Step 3: Convert the Year dimension from continuous to discrete

Before you drag and drop Year anywhere, you need to change its data type. Tableau made Year a continuous data type because it’s a number. For our bar chart, though, we need to change it to discrete.

If you’re not familiar with the terms continuous and discrete, just stick with me and you’ll see why this is important.

Under Dimensions, click Year and select “Convert to Discrete” as shown below.

Notice that Year has changed from green to blue, indicating that its data type is now discrete. “Discrete” means that a dimension can take a countable number of values, in our case, exactly two: 2014 or 2015.

“Continuous” means the dimension has an infinite number of values, e.g. from 2014 to 2015 and all the decimal numbers in between. Tableau will not make bar charts with a continuous dimension; it will make line charts instead. (Try it and see!)

Step 4: Drag and drop Year

Drag and drop the Year dimension onto the Columns shelf. Both Month and Year should be blue pills, indicating they are both discrete data types. And your bar chart should look like the one below. Almost there!

All that remains is to change the color for each year and do a little cleanup.

Step 5: Add color and clean up

Give each year its own color by dragging and dropping Year from the Dimensions list onto the Color field under “Marks”.

I’ve edited the colors to match the way Google Analytics shows YOY comparisons, with orange for last year and blue for this year. To edit colors, click the top right corner of the Year legend.

To clean up the unnecessary labels at the top and bottom of the chart, do two things:

1. At the top of the chart, right-click Month/Year and select “Hide Field Labels for Columns”
2. In the Columns shelf, click Year and un-check “Show Header”

The result should look like the chart at the top of this post.

Bonus Step: Add % Change to the tooltip

For year-over-year data, you may want to add the percent change from the previous year. It’s easy with Tableau’s table calculation feature.

I recommend adding percent change to the tooltip, which you’ll see when you hover over each bar in the chart.

First, drag and drop Revenue from the Measures list to the Tooltip field under “Marks”. Then click on the Revenue pill and select “Add Table Calculation”.

For the table calculation, choose “Percent Difference From”. Calculate the difference along Year, and display the value as a percent difference from Previous. Click OK.

To change number formatting, click the pill for that measure and select “Format”. To edit the wording of the tooltip, click the Tooltip field under “Marks”. And that’s it!

Have you run into any issues creating year-over-year or other side-by-side bar charts in Tableau? I’d love to know how you’ve created Tableau charts like this or related visualizations. Please share in the comments.

The post How to Make YOY Bar Charts in Tableau appeared first on LunaMetrics.

As +Stéphane Hamel says in his last comment, “this type of info is qualitative data expressed as a quantitative value, anecdotal at best, or based on a very limited data set.”

The numbers I show in my slide are based solely on my own experience of client implementations. That is, when my company has been hired to do the setup of Google Analytics – its a mix of a clean slate and fixing an existing bad/basic setups.

The explanation…

-50% improvement when implementing directly means…

no third-party development agency is involved. Without GTM this would involve producing documentation for each custom tracking code required. The internal web team then implements it. With GTM, this becomes a one-time request for the inclusion of the GTM code snippet, and then I pick it up form there – all within the GTM.*

-90% improvement when when working with a dev agency

is the same as above except this time my communication is via a third-party web dev agency. That generally means without GTM a lot more paperwork, explanations, followups and verification are involved i.e. more moving parts! So the saving is greater if the GTM is implemented.

*Of course, if implementing for an e-commerce site, the requests are more involved. So this is a generalisation for all client types. I estimate my client mix is 50/50 of e-commerce and non-ecommerce sites.

Original conversation and comments on Google+

Also see the post that sparked the debate: “7 Myths & Misconceptions about GTM Busted”: http://bit.ly/GTMmyths

© Brian Clifton for Successful Analytics - the new Google Analytics book, 2015. | Permalink | No comment | Add to del.icio.us
Post tags: GTM, tag manager

Take the following infographic recently published by Time as an example. The topic is important, “Why We Still Need Women’s Equality Day,” but notice how unnecessarily hard you must work to get the information and how difficult it is to compare the values that it displays and combine them into a sense of the whole.

This infographic provides eight measures of women’s participation in government. Each measure is expressed as a percentage of female vs. male participation. So why is each measure presented graphically in a different way? A single graphical form that makes the percentages of female vs. male participation easy to read and understand for all of the eight measures would work so much better. Also, given the fact that there is value in comparing the eight measures, why does the infographic arrange them vertically in a way that no computer or tablet screen could contain without scrolling? And even if all eight measures could fit on a single screen, because every one is expressed in a different manner, they still couldn’t be quickly and easily compared.

Has anything been gained by displaying the eight measures in these various ways? Some infographic designers would argue that by displaying the measures differently, visual interest has been increased, resulting in greater reader engagement. I suppose that there are people who might actually find this variety of expression engaging, but only in a way that draws them into the pictures independent of their meanings. Is someone who reads this article merely to enjoy the pictures with little concern for the content and its meaning an appropriate audience? Only if the journalist is trying to win a popularity contest among the disinterested.

Here’s the same story told primarily in graphical form, but this time it is clear, simple to read, makes comparisons easy, and brings the measures together is a way that makes the whole available at a glance.

A great deal has been gained through this redesign, but has anything been lost? Nothing other than meaningless, complicating, and distracting variety.

Isn’t it time that we demand of graphical journalists the same standards of effectiveness that we demand of their traditional counterparts? Journalism is journalism. Whether the story is told in words, pictures, or both should be determined by the nature of the information and the integrity of that information should always be respected.

Take care,

Can you reduce our cost-per-acquisition by one hundred percent? Can we spend one million dollars next fiscal year and expect to improve our results? We need results by next month, can you accomplish this task? Can you do all of these things simultaneously?

These are real questions we hear from prospective clients. They are great questions at heart, but do they really have answers? I mean real, honest answers. In the search department at LunaMetrics, we’re not in the business of making any guarantees. We wish we could but that’s just not our style.

We are in the business of making data-driven decisions and using that information to guide you toward the best possible strategy to help accomplish overall marketing goals.

Back to those questions

My first reaction to these sort of questions is almost always to ask additional questions in response. Is the data trustworthy? Is it clear how marketing channels work together? How can attribution add insight?

Paid search is far more complex than many CEOs or Marketing Directors are willing to accept.  Sometime a little tough love is in order.

Here are my actual, non-sugar-coated answers:

Given the dynamic nature of the internet, the way that people use search engines, and the way that any given website is designed, any guarantee regarding a reduction in cost per acquisition, whether its by 1% or 30%, would be misguided and does not set expectations in any appropriate manner.

Q: Can we reduce cost per acquisition by 30%?

A: Possibly. Probably.  But in achieving this goal, we might lose out on total conversions.  We want to make sure that we are maximizing potential based on various metrics to achieve the most profitable performance.

Q: Can we do it as we simultaneously increase spend by 440%?

A: Probably not, at least not right away. That’s why testing is important. To increase your spend by this level, we will need to add a tremendous amount of keywords and expand the targeting scope. This will naturally require an equally tremendous amount of research to find the most effective keywords.

Q: And, can we achieve all of these goals and be done by next week?

A: Not a chance in hell. Paid search is not a set-and-forget channel. It takes both an investment of time and money to achieve the best results.

Completing one objective in PPC will almost always impact another.  It’s really a matter of properly balancing your goals to find a complete strategy that meets, or comes close to meeting, the “ideal” scenario you have in mind.

How should I view my data?

What matters most is that you are interpreting your data correctly from the start. Sure, you have lofty goals for your business and that’s okay; however, maybe it’s time that you take a step back, broaden your view, and gain some new perspective on what the paid search channel actually does for your business.

We are here to build a strategy based on proper data attribution. We know from our experience that your approach will vary dependent upon your product or service. Investment banking is not the same as custom tee shirt design. Real estate sales does not equal medical services. Industrial product manufacturing is dramatically different than higher education.

If you don’t believe me, believe these Google attribution studies.

I mention these examples specifically because I have worked in all of these sectors, and the truth is that none of the strategies implemented here are remotely similar to the next. Tactically-speaking, there are similarities in how we optimize the accounts using match type optimizations, geographic targeting and the like, but the overall strategy is always dramatically different from one client to the next.

Formulating a strategic approach requires proper understanding of existing data as well as the target audience. In many of these examples there are always multiple touch points on the path to goal completion. If we ignore that one fact, we are doing your account a disservice.

What we can guarantee

I mentioned that we aren’t in the business of making guarantees, but if we were they’d look something like this (spoiler alert: not all of these a good):

We can conduct thorough goal analysis to have a better understanding of your existing data. You might look at your data and say “our cost per lead was $500 over the course of the past year.” I might look at your data and say, “your branded cost per load was $15 and your non-branded cost per lead was $3,000 over the course of the last year. You’re investing 80% of your budget on keywords that represent less than 10% of total leads generated. Maybe a last-click attribution model is not the right way to view this data. Maybe you should invest more heavily in awareness and engagement goals.”

We can perform cost analysis by running reports to identify wasted spend on keywords and targets that have proven to be ineffective over the life of the account. We work to eliminate this wasted spend in the immediate first steps of account optimization. Then we continue to work on this throughout the life of the account. This is one of those tasks that is always associated with testing and is never truly complete.

We can recommend advanced budget segmentation based on historical data that makes CPA goals more attainable within a specific segment of your budget. Essentially we are investing as heavily as possible in the known. We build a performance-oriented budget segment, then allocate the remaining budget specifically for the purpose of testing (the unknown). We optimize from there and make new budget recommendations along the way. We can do this at a high level or across individual products or services. Think of this budget as an investment in individual products or services instead of an investment in a particular channel.

We can expect that search volume will change over time. There are very few evergreen products or services. Demand for a particular product or service may inflate or deflate and your PPC team needs to be prepared to react. If search volume jumps from 10 to 1,000 searches a week your budget will almost certainly be underfunded. Conversely, if volume falls off the chart, you will notice a severe reduction in business generated from this channel. Be prepared.

We can expect impression share to change throughout the life of the account. If you are lucky enough to be in a market with zero competition, then a congratulations is in order. I’m willing to bet though that if you’re reading this right now, this statement doesn’t apply to you. Competition is consistently in flux in a PPC account, so you need to be prepared for the “other guys” to make an impact on your traffic at one point or another.

We can expect CPCs to change, as they happen to do. The AdWords auction is designed in such a way to increase competition among advertisers resulting in higher overall CPCs over long spans of time (if you have a problem with that notion, I would argue that Google has just as much right to increase its business profits as you do). The long-term effect of increasing bids will be lost traffic within your target budget and, ultimately, lost leads or sales that accompany a volume reduction.

We know that technology will evolve resulting in new outlets for users to search for your products. You need to continually evolve with the channel and test these advancements as they become available.

Next steps

The underlying goal here is to start thinking about your paid search account as an extension of your overall marketing plan. To often we find that clients and prospects want to throw money at the channel without first thinking about how it plays within the rest of there efforts.

Make it a point to understand what you should work toward achieving over a more significant span of time.  Set both realistic short-term goals and ideal long-term goals. There are really no guarantees given the volatile nature of search, so understand that it’s about using data to build a strategy that works toward making these goals a likely possibility in the long-run.

If this didn’t answer your toughest question let us know in the comments or feel free to contact us today.

The post How to Answer the Toughest PPC Questions appeared first on LunaMetrics.

We’re going to use a dummy (hidden) XY series on the chart, whose data labels will become the labels we want our axis to display.

We’ll start with two sets of data. The first (below left) is the data for the stacked columns, using simple counting numbers for the categories. The second (below right) is XY data where X is the list of values where we want our labels, and Y is zero.

Select the first set of data and insert a stacked column chart.

Copy the second range, select the chart, and use Paste Special (Home tab of the ribbon > Paste button dropdown > Paste Special), and select the following options: Add data as series, series in columns, series names in first row, categories in first column.

The new series is added as a third set of stacked bars, which don’t show up because their height is zero.

Select the added series by selecting the green bars and clicking the up arrow key. Click the menu key (between the right Alt and Ctrl buttons on most Windows keyboards) or hold Shift and click the F10 function key to pop up the context menu. Click Change Series Chart Type, and choose XY Scatter. This adds a set of markers along the bottom of the chart (I used blue circles in the chart below) and it adds secondary X and Y axes.

Format the scale of the secondary horizontal axis (top of chart) so it fits the data: min = -115, max = -50. Note that the blue circles are now aligned between the bars, where the labels will go.

Hide the secondary X and Y axes by formatting their label position as No Label, and their line color as No Line. This hides the labels and reduces the margin around the chart that previously held the labels.

Hide the primary horizontal labels by using a custom number format of ” ” (that’s right, a space surrounded by double quotes). This hides the labels but keeps the margin in place for the other labels we’re going to add.

Right-click the series of blue dots, and choose Add Data Labels. Excel adds the default Y values (zeros) to the right of the markers.

Format the labels so they are in the Below position, and so they show the X values instead of the Y values.

Finally format the series of dots so they use no markers. And we’re done.

This technique can be used to customize axis labels and add many other labels to your charts.

The post Column Chart with Category Axis Labels Between Columns appeared first on Peltier Tech Blog.

Part ONE (this page): 

1. Most of the web is junk, but web analytics can help
2. The two types of web analytics
3. The accuracy debate: On-site versus off-site metrics

Part TWO: On-site versus off-site analytics tools – how they work

Part THREE: On-site versus off-site analytics tools – what’s accurate?

1. Most of the web is junk, but web analytics can help

Imagine using the web to find exactly what you are looking for, instantly. By that, I mean actually discovering authoritative, reliable and accurate websites, with the information you need, in a quick and efficient way. Maybe the information you want is the best price from a respective retailer, resort reviews from real holiday makers or a news article from an expert that helps you understand what the financial crisis is actually about. The problem is that such sites are like gold nuggets – a valuable resource that is rare in a virtual world of poor ergonomics.

The truth is that the vast majority of the web contains poor quality content. Even for those sites that do have great content, often they are difficult to find (via a search engine) and the user experience is so poor, you simply wish to leave. Despite nearly 20 years of web development, most of us still waste dozens of hours a month trawling the web, weeding out sites that either have irrelevant content to our search query or where the user experience is just too frustrating. Unfortunately – similar to weeding – those sites don’t really go away. A Google search the next day can bring up the same poor results and the process starts all over again.

How web analytics can help

That’s where web analytics comes to the rescue – the part science (measuring it) and part art (understanding it) of assessing a website’s performance. Simply put, web analytics is for website owners to understand their online visitor behaviour with the purpose of improving it. Perhaps the marketing campaign is poorly focused; visitor expectations not met when they arrive on the website; the content displayed is out of date; the navigation system sucks; or an on-site search function returns no results (or worse still the same result) no matter what keywords you use. Web analytics tools help you identify these problems on your site, so you can fix and improve them i.e. get a better return on you digital investment.

Almost always the increase to the business bottom line by making such improvements is greater than 100%. That is, a doubling of revenue returned back. Often, it is significantly more – making web analytics a very exciting place to be.

Examples include:

• Highlighting top performing products, pages, campaigns (and poor performing ones to be culled)
• Identifying pain-points in sales funnels that is choking conversion
• Identifying high bounce rate pages – those where visitor look at only one page and then leave. Surely that must define a poor user experience…?
• Showing what results are delivered from your on-site search engine and what keywords your visitors use, including those that produce zero matches (when you know the data is there), and those returning generic PR pages when a specific search query is used.
• Segmenting the different visitor behaviour of your prospects, staff and customers so you can target the right ones.

2. The two types of web analytics

There are two types of web analytics data: off-site metrics and on-site metrics. As their names suggest, these relate to metrics that can be obtained irrespective of your website presence (off-site metrics) or those obtained when a visitor lands on your website (on-site metrics) respectively – see Figure 1.

Figure 1 – the two types of web analytics and the tools used for each*

3. The accuracy debate: On-site versus off-site metrics

The two types of web analytics tools have developed independently of each other. Partly because of the different methodologies involved, and partly because the stitching together of both types of data points (everyone is striving for the best of both worlds – e.g. valuable demographic information combined with actual visit actions!), is too difficult to achieve. The difficulty lies in the disparate data systems involved and the scary privacy implications of actually doing it.

Because of this separation within the web analytics industry, there are heated debates as to which method is more accurate…

But there is no accuracy debate – really…! My point is that accuracy is a moot discussion – having little or no practical relevance.

Instead…

Use on-site tools if you wish to count the activity on your website. Only such tools can do this effectively and accurately. Assuming a best practice implementation, data collected on-site can be very accurate for measuring your visitors, their journey through your site, their engagement, conversion to a customer/lead, and most importantly, their value to your business.

Use off-site tools to bring additional data to the table – not accuracy. This includes demographic, search engine query data (Google and other search engines remove this from searches) and competitor intelligence information.

Essentially you need both… Having on- and off-site analytics data is the key to success. That is, having both pieces of the jigsaw together provides a complete view of the performance of your site and where it fits within your organisation (its value) and your competitive landscape (benchmarking against others).

A unified tool/approach for combining both types of data is still some years away, but it is the direction the industry is heading. For now, if you have to focus on just one type of analytics, choose an on-site tool. There is so much to gain form understanding your actual visitors and customers.

*From a vendor perspective, the separation of methodologies is not as mutually exclusive as Figure 1 suggests. For example, Hitwise, comScore and Nielsen Netratings also have on-site measurement tools, while Google has the ability to provide off-site search query data to compliment their on-site tools.

…continue to PART TWO: “On-site versus off-site analytics tools – how they work”

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Post tags: ABCE, accuracy, accurate, audit, PART 1

Part ONE:

1. Most of the web is junk, but web analytics can help
2. The two types of web analytics
3. The accuracy debate: On-site versus off-site metrics

Part TWO (this page): On-site and off-site analytics tools – how they work

Part THREE: On-site versus off-site analytics tools – what’s accurate?

How off-site web analytics works

Off-site web analytics tools measure your potential website audience. They are the macro tools that allow you to see the bigger picture of how your website compares to others. There are two types of techniques that achieve this:

• Using panel data, or
• Using Internet Service Provider (ISP) data

Using Panel Data

Companies such as comScore and Nielsen Netratings use the panel method by recruiting participants using a combination of their website and the calling of prospective panellists. Their technique is to have monitoring software installed on users’ computers to measure their web activity.

Panel sizes vary, but range from a few thousand, to hundreds of thousands of participants, with the majority of these based in the US. For example, comScore reports 2 million participants worldwide with over 50 per cent of these based in the US. Most panel participants are home users, as these are not restricted by IT policies when it comes to installing tracking software (public access is screened out from comScore data). Similar to election polling, panel data is extrapolated (multiplied up) to provide an estimate of the behaviour for total web population.

An important advantage of panel data is that the analytics vendor knows who its panellists are. Valuable demographic information such as age, gender, ethnicity, income bracket etc. are available. The caveat to this method, is that websites you wish to measure must have sufficient visitors to show up above the ‘noise’ threshold and mitigate sampling errors. Think of this in terms of having a high signal to noise ratio. The threshold will vary depending on where most of your visitors connect from, as the sample size of panellists varies from country to country.

Using Internet Service Provider (ISP) data

As an alternative to user panels companies such as Hitwise (part of Experian), collect off-site visitor information by aggregating anonymous data provided by ISPs. This has the potential to offer much larger sample sizes than panels (Hitwise reports 25 million people worldwide, 40 per cent based in the US) and therefore a lesser degree of extrapolation is required, potentially resulting in greater accuracy.

Because this type of off-site tracking happens at the ISP/network level, all visitor types are represented, including home, work, mobile, educational and public access. The trade off is that this data is anonymous. Therefore demographic user data is not available.

How on-site web analytics works

On-site web analytics tools measure the actual visitor traffic arriving on your website. Examples include Google Analytics (which dominates the market place), Adobe Site Catalyst, WebTrends etc. They are capable of tracking the engagements and interactions your visitors have. For example, whether they convert to a customer or lead or not, how they got to that point (or where they dropped out), what they purchased and how much they spent, what campaigns they viewed and clicked on, and so forth.

Although there are several techniques to measure visitors on your site, the method used by the vast majority of vendors is the so-called ‘page tagging’ technique. This requires the placement of a small snippet (aka ‘tag’) of JavaScript code on your web pages that act as a beacon – capturing visitor information, storing these as cookies, then broadcasting this to data collection servers in real-time.

Access to data is different for each

A key difference between on-site and off-site web analytics tools is that on-site visitor data is only available to the website owner and the people he/she grants access to. Conversely off-site web analytics data can be obtained for any website – that is, you own website and your competitors – provided there is sufficient visit data.

…continue to Part THREE: “On-site versus off-site analytics tools – what’s accurate?”

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Post tags: accuracy, accurate, comscore, hitwise, measurement, off-site, on-site, PART 2, similarweb, web analytics

Part ONE:

1. Most of the web is junk, but web analytics can help
2. The two types of web analytics
3. The accuracy debate: On-site versus off-site metrics

Part TWO: On-site and off-site analytics tools – how they work

Part THREE (this page): 

8 recommendations for choosing a tool

As you can see form Part TWO, the differences in methodology and techniques for on-site and off-site analytics are significant and this leads to very different results – even for the same metric. For example, basic numbers such as the number of visitors a website receives, or the total number of pageviews etc. can vary dramatically when comparing these form the two different methods (differences of over 100% are common). The problem of non-matching numbers is a constant and exasperating headache for site owners, media buyers and marketers alike. So which method produces the more accurate data?

The truth is, all web analytics solutions have their limitations as shown in the table:

How accuracy can be improved

In Part ONE of this series I discuss the accuracy debate – or rather the misconception of it…

It is important to realise that different data collection methodologies bring different metrics to the table. It is these differences that are their strength as they help you build a bigger picture of the website in question. Think of it as building a jigsaw – one piece alone can be very misleading, multiple pieces together provide a clearer picture.

The solution to maintaining your sanity is to combine on-site and off-site web analytics data in a way that compliment each other, rather than providing conflicting data points. A unified tool/approach for combining both types of data is still some years away, but it is the direction the industry is heading. For now, if you have to focus on just one type of analytics, choose an on-site tool. There is so much to gain form understanding your actual visitors and customers.

Here are my recommendations for choosing a tool:

1. Use on-site metrics if you wish to know how many people visit your website, where they come from, which campaigns perform best, engagement and conversion performance (sales, lead generation). This information is so valuable to your organisation it should always supersede off-site data
2. Use on-site metrics if you wish to know the value of your visitors and the value of your content.
3. Use on-site metrics to measure funnel processes (e.g. cart checkout), abandonment rates, and navigational flow.
4. Use on-site metrics if you wish to know where your visitors are being referred from – which search engine, social conversation, ad, email or banner campaign etc.
5. Use off-site metrics when considering the launch or relaunch of a website. For example, what terminology and semantics are being used by potential customers on the search engines – e.g. blue widgets or blue gadgets?
6. Use off-site metrics to understand your visitor demographics as proportions of the total. Does this match your customer base?
7. Use off-site metrics to understand what websites your visitors go to just prior to yours and just after they visit your site.
8. When using off-site panel data, bear in mind that the data is more reflective of a US home audience. If your target is international or a business audience, use ISP data instead.

Do you have to battle with reconciling numbers from different tools, or never ending accuracy questions from senior management?

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Post tags: accuracy, accurate, comscore, hitwise, measurement, off-site, on-site, PART 3, similarweb, web analytics

Ted Cadsby is not a neuroscientist or a psychologist. He is a former banking executive with an MBA, seasoned with training in philosophy, who now works as a researcher, writer, and speaker on complexity and decision making. As such, his interest and work in decision making is not just theoretical. He is intimately familiar with the types of problems that we face day to day in our lives and especially in our work. In Closing the Mind Gap, by drawing on the work of many researchers from various disciplines, he’s managed to weave together into a coherent whole an understanding of the gaps that exist between our System 1 thinking abilities and the complex problems that we face, along with the steps that we must take to develop and apply our System 2 thinking abilities to solve these problems.

• In Part I, “Brains Coming into the World,” he describes how our brains evolved and the ways in which our cognitive abilities are bounded.
• In Part II, “Brains Sorting out the World,” he describes how our brains work—how System 1 thinking simplifies and satisfices in ways that served our ancient ancestors well and continue to serve us well when facing easy, day-to-day decisions, but how simplifying and satisficing fail when faced with complexity.
• In Part III, “The Brain-World Problem,” he explains that we live in two worlds: World 1, which is simple and easy to navigate using intuitive System 1 thinking, and World 2, which is complex and therefore beyond the reach of intuition.
• In Part IV, “The Brain-World Solution,” he explains that Systems Theory, the scientific method, and statistical thinking enable “metacognitive thinking”—a form of System 2 thinking—to deal with complexity.
• In Part V, “Brains and People,” he explains how complexity in our selves (psychology) and complexity in others (sociology) complicate the struggles that we face.

This book consolidates and articulates a great deal of content that you could find elsewhere in greater detail, but with much more time and effort. As I was reading it, it felt a little repetitive at times, but that might have been because I’m well read in this subject matter. I recommend this book to anyone who engages in complex decision making or supports those who do. This should be required reading for anyone who is embarking on a career in analytics. Even if you’ve been doing data analysis for years, this book will help you rebuild a foundation for analytical thinking that will make you a better analyst. It’s never too late to strengthen your skills in fundamental ways.

Take care,

Registration is now open for the Stephen Few’s two advanced U.S. workshops. If you’ve already read Stephen’s introductory books or attended his introductory courses and are looking for a way to improve your dashboard design or visual data analysis skills further, these workshops provide a a great way to do so:

• Signal: Understanding What Matters in a World of Noise in Berkeley, CA on Jan. 25-26, 2016
• Advanced Dashboard Design Workshop in Berkeley, CA on Jan. 27-29, 2016

Each of these workshops will only be taught once in the U.S. in 2016. Signal will also be taught in London on Mar. 9-10, Stockholm on Oct. 10-11, and Melbourne on Nov. 7-8, though registration is not open for these workshops yet. In addition to the U.S. workshop, Advanced Dashboard Design will also be offered in the Netherlands on May 23-25 and Melbourne on Nov. 9-11, but registration isn’t open for these workshops yet, either.

-Bryan