In the first part of this session, I’m going to be reviewing findings from a recent survey that we are conducting, the Adobe Digital Measurement Maturity Assessment. The survey ties into an analytics maturity model and framework that I’ve developed for aspiring data-driven companies, which I introduced at last year’s Summit. If you’re at all curious about how your company stacks up against the industry in terms of web analytics maturity, I’ll encourage you to participate in the survey (soon if you’d like to be included in the results we’ll share at Summit).

The second part of the session will feature Ken Seiff (EVP, Direct and Omni-Channel) and Cindy Lincks (Director, Analytics) from Brooks Brothers. In this panel discussion format, Ken and Cindy will be sharing their insights from the data-driven transformation that they’ve been leading at this prestigious retailer. Look forward to some great insights into how their organization has been embracing a more analytical and testing-focused approach.

In the final part of the session, we’ll be focusing on peer-focused roundtable discussion on various digital governance topics. It’s a great opportunity to network with other web analytics professionals and share challenges as well as best practices. Every year the roundtable discussions make this session extremely popular (what do you mean you don’t want another PowerPoint presentation?). Typically, I’ve provided the discussion questions each year, however, this year I’d love to hear the top-of-mind burning questions you’re dying to ask your peers at Summit. Please send me any questions that come to mind, and I’ll include the most compelling ones in the materials for the roundtable discussions.

See you at the Adobe Digital Marketing Summit 2012!

Session Description:

Digital governance: Defining a strategic roadmap for data-driven success

For many companies, organizational challenges—not technical issues—impede the success of analytics programs. Unclear strategies, internal politics, budgetary issues, and resource limitations can limit data-driven effectiveness. Successful companies have made an investment in more than just the analytics tools by backing the people and processes behind them. Learn from a panel of online analytics experts about the challenges and triumphs they’ve experienced in fostering data-driven success within their organizations. In this session:

• Discover how Adobe’s new maturity model can help your company determine where it thrives and has potential opportunity for growth
• Discuss ideas, challenges, and best practices with peers from other organizations

This session is for marketing executives sponsoring implementations, managers navigating challenges within implementations that extend beyond technology, and key stakeholders looking to drive the maturation of their organization.

In the complex online world of today, having a single website is no longer an option.  We may have different websites for our different brands, geographies, subdomains, properties, or categories.  We may want to track departments separately even though they are on the same website.  Often, we will have several microsites created for us by our partners.  Organizations will want to track all of the visitors coming to all of the sites.  However, this may present several challenges from a web analytics perspective as our visitors “change channels” on our websites.  In order to understand the challenge is it important to ask ourselves these questions.

• Do our visitors crossover between the different sites?
• How do we want to treat visitors who crossover?
• Do we want our marketing campaigns to be effective across the different sites?
• Will our shopping cart share products from multiple sites?

Site Crossover

Our first item of business is to identify whether or not our visitors from different sites will overlap.  For example, if we have a site in the UK as well as in Japan, visitors may not readily jump from one to another.  However, if we have multiple brands on our site and it is easy for visitors to move back and forth between brands, we will want to have a sound tracking architecture in place.  It goes without saying that there should be a standardized data design across the board, even if only for a subset of data points.  One other consideration is whether or not each of these different organizations (brands/geographies/departments) will need to run analysis separately or together or both.  If there is no need for separate reporting, there is no need to introduce unnecessary complexity.

If we have identified the need for crossover tracking between sites, the next step is to determine how we will treat visitors who cross site boundaries.  It is imperative to have a report suite architecture and matching visitor identification strategy that allows for appropriate tracking.  There are several options available for your report suite architecture:

1. Use a single report suite which allows for visitors and their paths to be tracked uniformly across sites.
2. Multiple report suites can be used to allow for separation of reporting by site.
3. The best of both worlds can be had with Multi-suite tagging*.  This allows for click-stream data to be sent to more than one report suite at a time.  This can be accomplished via a comma-separated list of report suite id’s or by using a VISTA rule.

Marketing Channels and Campaigns

How does our multi-site paradigm affect our marketing strategy?  Do we want marketing campaigns and channels to be effective when changing sites?  If we have an overall marketing effort, perhaps it makes sense to analyze our marketing at the global level.  However, if our marketing is decentralized, it may make sense for us to pass marketing information back and forth between individual sites to enable crossover campaign tracking.  One caution: copying the campaign information to subsequent crossover site visits could potentially overwrite previous campaigns that lead directly to the individual site.

One additional data point I would recommend analyzing is the originating sister site.  In essence, this allows us to track intra-site traffic and gives us insight to visitor behavior that is invaluable.  This could be tracked in conjunction with existing marketing campaigns or separately.

Adobe Consulting Services* has plugin solutions that allow us to easily accomplish both of these objectives.

Shared Shopping Cart

Perhaps the most important consideration in crossover analysis for internet retailers is how the shopping cart and purchase data are collected.  Does each of our products logically fit into one of our department/brand report suites?  If so, is it possible to shop for products from each of these departments/brands in the same checkout experience?  If the answer is yes, we will want to carefully consider our report suite architecture.  Multi-Suite Tagging is a great way to get the information into multiple report suites.  However, sending all products may introduce non-matching products into our department/brand report suites.  The best way to handle this scenario is to use a pre-processing (VISTA) rule to remove products that are not applicable.  Adobe Engineering Services* can outline a plan to ensure the appropriate products are represented in the appropriate report suites.

Having multiple sites in your portfolio introduces complexity into any analytics practice, not only from a data collection standpoint, but from an analysis standpoint as well.  Understanding visitors who “change channels” is critical to your online optimization efforts. Choosing the right strategy and architecture is vital to enabling successful analysis and optimization.

* Your Account Manager should be consulted regarding the Multi-Suite Tagging, Adobe Consulting Services, and Adobe Engineering Services.

Have specific questions about Adobe SiteCatalyst?  Want to track a data point on your website, but not sure where to start with the implementation?  Follow me on Twitter @sitecattips Please feel free to leave a comment here or send me an email at adobesitecatalyst (at) adobe.com

I had to learn this lesson the hard way. I worked for a large group of websites who used 6 different systems for basic analytics reporting alone. I led a team to dive into the different systems and understand why they reported different things and to figure out which one was ”right.” After losing months of time and almost losing complete faith in our data, we discovered some really important hard won lessons. We learned that the use of the data is paramount, that there is no one view or right answer, that variance is almost completely predictable once you learn the systems, and that we would have been far better served spending that time on how to use the data instead of why they were different.

I want to help your organization avoid the mistakes that we made. The truth is that no matter how deep you go, you will never find all the reasons for the differences. The largest lesson learned was that your organization can be so caught up in the quest for perfect data that they forget about the actual value of that data. To make sure you don’t get caught in this trap, I want to help establish when and if you do have a problem, the most common reasons for variance between systems, and some suggestions about how to think about and how to use the new data challenge that multiple reporting systems presents.

Do you have a problem?

First, we must set some guidelines around when you have a variance problem and when you do not. When you have systems designed for different purposes, they will leverage that data in very different ways. No systems will match, and in a lot of cases, being too close represents artificial constraints on the data that is actually hindering its usability. At the same time, if you are too far apart, then that is a sign that there might be a reporting issue with one or both of the solutions.

Here are two simple questions to evaluate if you do have a variance “problem”:

1) What is the variance percentage?

Normal variance between similar data systems is almost always between 15-20%.
For non-similar data systems the range is much larger, and is usually between 35-50%.

If the gap is too low or too large, then you may have a problem. A 2% variance is actually a worse sign then a 28% variance on similar data systems.

Many groups run into the issue of trying too hard to constrain variance. The result is that they put artificial constraints on their data, causing the representative nature of the data to be severely hampered. Just because you believe that variance should be lower does not mean that it really should be or that lower is always a good thing.

This analysis should be done on non-targeted groups of the same population (e.g., all users to a unique page.) The variance for defendant tracking (segments) is going to always be higher.

2) Is the variance consistent in a small range?

You may see variance be in a series of 13, 17, 20, 14, 16, 21, 12 over a few days, but you should not see 5, 40, 22, 3, 78, 12.

If you are within the normal range and you are in the normal range of outcomes, then congratulations, you are dealing with perfectly normal behavior and I could not more strongly suggest that you spend your time and energy on how best to use the different data.

Data is only as valuable as how you use it, and while we love the idea of one perfect measure of the online world, we have to remember that each system is designed for a purpose, and that making one universal system comes with the cost of losing specialized function and value.

Always keep in mind these two questions when it comes to your data:

1) Do I feel confident that my data accurately reflects my users’ digital behavior?

2) Do I feel that things are tracked in a consistent and actionable fashion?

If you can’t answer those questions with a yes, then variance is not your issue. Variance is the measure of the differences between systems. If you are not confident in a single system, then there is no point in comparing it. Equally, if you are comfortable with both systems, then the differences between them should mean very little.

The most important thing I can suggest is that you pick a single data system as a system of record for each action you do. Every system is designed for different purposes, and with that purpose in mind, each one has advantages and disadvantages. You can definitely look at each system for similar items, but when it comes time to act or report, you need to be consistent and have all concerned parties aligned on which system is the one that everyone looks at. Choosing how and why you are going to act before you get to that part of the process is the easiest fastest way to insure the reduction of organizational barriers. Getting this agreement is far more important for going forward than the dive into the causes behind normal variance.

Why do systems always have variance?

For those of you who are still not completely sold or who need to at least have some quick answers for senior management, I want to make sure you are prepared.
Here are the most common reasons for variance between systems:

1) The rules of the system – Visit based systems track things very differently than visitor based systems. They are meant for very different purposes. In most cases, a visit based system is used for incremental daily counting, while a visitor based system is designed to measure action over time.

2) Cookies – Each system has different rules about tracking and storing of cookie information over time. This tracking will dramatically impact what is or not tracked. This is even more true for 1st versus 3rd party cookie solutions.

3) Rules of inclusion vs. Rules of exclusion – For the most part, all analytics solutions are rules of exclusion, meaning that you really have to do something (IP filter, data scrubbing, etc.) to not be tracked. A lot of other systems, especially testing, are rules of inclusion, meaning you have to meet very specific criteria to be tracked. This will dramatically impact the populations, and also any tracked metrics from those populations.

4) Definitions – What something means can be very specific to a system. Be it a conversion, a segment, a referrer, or even a site action. The very definition can be different. An example of this would be a paid keyword segment. If I land on the site, and then see a second page, what is the referrer for that page? Is it the visit or the referring page? Is it something I did on an earlier visit?

5) Mechanical Variance – There are mechanical differences in how systems track things. Are you tracking the click of a button with an onclick? Or is landing on the previous page? Or is it he server request? Do you use a log file system or a beacon system? Is that a unique request or added on to the next page tag? Do you rely on cookies or are all actions independent? What are the different timing mechanisms for each system? Do they collide with each other or other site functions?

Every system does things differently, and as such these smaller changes can build up over time, especially when combined with some of the other reasons listed above. There are hundreds of reasons beyond those listed, and the reality is that each situation is unique and each one is the culmination of the impact of these hundred different reasons. There is no way to ever get to the point where you can accurately describe with 100% certainty why you get the variance.

Variance is not a new issue, but it is one that can be the death of programs if not dealt with in a proactive manner. Armed with this information, I would strongly suggest that you hold conversations with your data stakeholders before you run into the questions that inevitably come. Establishing what is normal, how you act, and a few reasons why you are dealing with the issue should help cut all of these problems off at the pass.

We’ve more than doubled the capacity of our developer Tech Track this year at the Digital Marketing Summit! We have more labs than ever, more sessions than ever, more Dev Gurus and developer evangelists than ever to help you get top value out of Summit. Our Tech Track sessions will cover topics ranging from ongoing innovations in social media, extension points of CQ, measurement best practices, and implementation of new products such as TagManager.

With each Lab or Breakout Session you attend, you will gain:

• Hands-on experience working with Adobe’s products, tools, and technologies.
• Code samples and deployment-ready code assets.
• Best practices for implementation and innovation.
• Interaction with your developer peers.

The Tech Track promises to deliver actionable value and assets that you can take back to your office to deploy rapidly. Looking forward to seeing you in Salt Lake!

Each Developer Lab and Session is taught by our top-talent developers and leaves you with new knowledge and code.

This may seem like a really inane question, but think about it for a second. We are thinking breathing beings, how can we possible not be as smart as our trashcan?

First, let’s establish how we would measure this. We like to pretend that how smart someone is goes from nothing to absolute, but it doesn’t. Having no knowledge and doing nothing is far better than using bad knowledge, or thinking you have answered something correctly when you haven’t, or over reacting. We are fallible, and as such, we make bad decisions from time to time. Poor judgment, biases, and misinformation actually detract from an outcome, where zero knowledge does not have an impact. The scale for how smart you are is not one of 0 to 10, but one of -10 to 10, with 0 being the middle or neutral point in the absolute scale.

Your trashcan does not offer any knowledge, it does not react, and it does not have biases. It does not push its agenda, nor is it influenced subconsciously by the agenda of others. It is not wired to want to rationalize its own actions or to want to prove the value of its actions to itself or others. It is not impacted by Maslow’s hierarchy any more than it is by fear, or greed, or lust, or any of the other ways that we are wired to be influenced. Anything that goes into it, it can dump out just as easily. It doesn’t reject knowledge, nor does it change to fit the mood of the room. It does not provide any value and it has no knowledge, so it will always be stuck at 0 on that scale.

Now human beings are capable of amazing things, we have built great monuments, civilizations, history, art, cars, we have done it all. We have also had war, greed, genocide, hate, bigotry, and believe many crazy things. Those are the end points of all we are capable of, but I am not referring to the theoretical but this moment. You can be anywhere from the greatest level (10) to the lowest delusion (-10). We all constantly move up and down that continuum with each action we take, but do you know where you are at any given time?

How do we process information and how does it impact the decisions we make? How does it impact our view of ourselves and the world?

People like Philip Zimbardo and Robert Levine have shown that we are wired to look either at the future, or the present, or the past, but we fail to look at multiple perspectives. We get too caught up in reacting today, or planning for the future. We are full of biases and self-delusions, and even worse, knowing this in no way stops them from changing how we view our world around us. We know that in the past, we have made mistakes, and in the future, we will make them, but how do you know that right now you are making a mistake? We lose perspective, and because of this the meaning of data we use to make decisions changes constantly. We fail to balance what we are doing with where we go. To make up for this, we make assumptions, we rationalize, we ignore data, and we find things to confirm what we want. We are so wired to only confirm what we do that we ignore a majority of the information from the world around us.

This impacts everyone.

So I ask you, right now, not tomorrow or 20 minutes from now, are you smarter than your trashcan?

You can’t answer with what you are capable of, nor can you answer with what you have done in the past. In the here and now, how do you know that the answer is a positive? How do you know if you are currently adding value, or removing it. Are you really doing the right thing? Or are you just using misinformation, biases, and self-delusion to convince yourself that you are above 0, while all those other people are below it? Are you letting those biases rule what you view and letting you think you are smarter than the trashcan? Or are you really making an impact? Is your impact real, or is it hubris?

The only way to really make sure that you are improving your chances to be above zero is to put in place a system that limits the impact of those biases and gives you insight into your own decisions. You have to be humble enough to put a system in place to measure your decisions in context, away from any in the moment manipulations, and that allows you to know the efficiency of your choices. You’re never going to be perfect, but it’s up to you to make sure that you aren’t just calling trash gold.

Adobe TagManager is the quickest, easiest Tag Management System to deploy. We’ve made it so by leveraging our existing SiteCatalyst tags present on many, many pages throughout the web. Those tags are valuable in a number of ways:

• SiteCatalyst tags are capturing a superset of data as compared to any other tags on sites; the amount of granular conversion data shared is immense.
• They have prebuilt extension points (plugins and modules) for data integration with other systems and custom configuration to suit your business needs.
• They are already in place.

I’ve explained this in previous blog posts, but I think it’s worth highlighting again. The means by which we make it possible to turn your SiteCatalyst tag into a container is through what is historically referred to the s_code.js file. This is the measurement library where you configured your SiteCatalyst implementation. It contains plugins, modules, mappings, and other areas of customization. None of that is lost, you simply move it to the TagManager container. Then you replace your s_code.js file with a loader to point to the container we host. Finis! You can manage your measurement configuration and any other tags you want through Adobe TagManager’s UI and deploy it through our 3-stage deployment process without waiting on a full IT build and release cycle!

So, if you’ve already got the data you want exposed to your tag, that tag can become a tag container that orchestrates the firing of all tags on your site… and it can be enabled in a few minutes without deploying a new tag to each page on your site, why not do it?

In the first of these, I want to introduce the N-Armed bandit problem as it is really at the heart of all testing programs and is a fundamental evaluation of the proper use of resources.

The N-Armed Bandit problem, also called the One-Armed bandit problem or the multi-armed bandit problem, is the fundamental concept of the balance of acquiring new knowledge while at the same time exploiting that knowledge for gain. The concept goes like this:

You walk into a casino with N number of slot machines. Each machine has a different payoff. If the goal is to walk away with the most money, then you need to go through a process of figuring out the slot machine with the highest payout, yet keep as much money back as possible in order to exploit that machine. How do you balance the need to test out the payouts from the different machines while reserving as much money as possible to put into the machine with the greatest payout?

Which one do you choose?

As we dive into the real world application of this concept, it is important that you walk away with some key understandings of why it matters to you. An evaluation of the N-Armed bandit problem and how we interact with it in the real world leads to two main goals:

1) Discovery of relative value of actions

2) The most efficient use of resources for this discovery and for exploitation

The N-Armed bandit problem is at the core of machine learning and of testing programs, and does not have a one-size fits all answer. There is no perfect way to learn and to exploit, but there are a number of well known strategies. In the real world, where the system is constantly shifting and the values are constantly moving it gets even more difficult, but that does not make it any less valuable. All organizations face the fundamental struggle in how best to apply resources, especially between doing what they are already doing and exploring new avenues or functional alternatives. Do you put resources where you feel safe, where you think you know the values? Or do you use them to explore and find out the value of other alternatives? The tactics used to solve the N-armed bandit problem come down to how greedy you try to be and about giving you ways to think about applying those resources. Where most groups falter is when they fail to balance those two goals, becoming lost in their own fear, egos, or biases; either diving too deep into “trusted” outlets, or going too far down the path of discovery. The challenge is trying to keep to the rules of value and of bounded loss.

The reason this problem comes into play for all testing programs is that the entire need for testing is the discovery of the various values for each variant, or for each concept, against one another. If you are not allowing for this question to enter your testing, then you are always only throwing resources towards what you assume is the value of a change. Knowing just one outcome can never help you be efficient. How do you know what value you could have gotten by just throwing all your money into one slot machine? While it is easy to convince yourself that because you did get a payout, that you did the right thing, the evaluation of the different payouts is the heart of improving your performance. You have to focus on applying resources, and for all groups there is a finite amount of resources, to achieve the highest possible return.

In an ideal world, you would already know all possible values, be able to intrinsically call the value of each action, and then apply all your resources towards that one action that causes you the greatest return (a greedy action). Unfortunately, that is not the world we live in, and the problem lies when we allow ourselves that delusion. The problem is that we do not know the value of each outcome, and as such need to maximize our ability of that discovery.

If the goal is to discover what the value of each action is, and then exploit them, then fundamentally the challenge is to how best to apply the least amount of resources, in this case time and work, to the discovery of the greatest amount of relative values. The challenge becomes one purely of efficiency. We have to create a meaningful testing system and efficiencies in our organization, either politically, infrastructure, or technically, in order to minimize the amount of resources we spend and to maximize the amount of variations that we can evaluate. Every time we get side tracked, or we do not run a test that has this goal of exploring at its heart, or we pretend we have a better understanding of the value of things via the abuse of data, we are being inefficient and are failing on this question for the highest possible value. The goal is to create a system that allows you to facilitate this need, to measure each value against each other, to discover and to exploit, in the shortest time and with the least amount of resources.

An example of a suboptimal design for testing based on this is any single recipe “challenger” test. Ultimately, any “better” test is going to limit your ability to see the relative values. You want to test out your banner on your front door, but how do you know that it is more important then your other promos? Or your navigation, or your call to action? Just because you have found an anomaly or pattern in data, what does that mean to other alternatives? If you only test or evaluate one thing by itself, or don’t test out feasible options against each other, then you will never know the relative value of those actions. You are just putting all your money into one slot machine, not knowing if has a higher payout then the others near it.

This means that any action that is taken by a system that limits the ability to measure values against each other, or that does not allow you to measure values in context, or that does not acknowledge the cost of that evaluation, is inefficient and is limiting the value of the data. Anything that is not directly allowing you the fastest way to figure out the payouts of the different slot machines is losing you value. It also means that any action that requires additional resources for that discovery is suboptimal.

If we have accepted that we have to be efficient in our testing program, we still have to deal with the greatest limiter of impact, the people in the system. Every time we are limited only to “best practices” or by a HiPPO, then we have lowered the possible value we can receive. Some of the great work by studiers of probability, especially by Nassim Nicholas Taleb, has shown that for systems, over time, the more human level interaction, or the less organic that the system is allowed to be, the lower the value and the higher the pain we create.

Comparing organic versus inorganic systems:

We can see that for any inorganic system, one that has all of those rules forced onto it, over time there is a lot less unpredictability then what people think, and that there is almost a guarantee of loss of value for each rule and for each assumption that is entered into that system. One of the fastest ways to improve your ability to discover the various payouts is to have an understanding of just how many slot machines are before you. Every time that you think you are smarter then the system, or you get caught up in “best practices” or popular opinion, you have forced a non-organic limit into the system. You have artificially said that there are less machines available to you. This means that for the discovery part of the system, and the best thing for our program and for gaining value, that we must limit human subjection or rules, in order to insure the highest amount of value.

An example of these constraints is any hypothesis based test. If you are limiting your possible outcomes to only what you “think” will win, you will never be able to test out everything that is feasible. Just because you hear a “best practice” or someone has this golden idea, you have to make sure that you are still testing it relatively to other possibilities, nor can you let it impact your evaluation of that data. It is ok to have an idea of what you think will win going in, but you can not limit yourself to that in your testing. That is the same as walking up to the slot machine with the most flashy lights, just because the guy next to you said to, and only putting your money in that machine.

Everyone always says the house wins, and in Vegas that is how it works. In the real world, the deck may be stacked against you, but that does not mean that you are going to lose. Once you understand the rules of the game and can think in terms of efficiency and exploiting, then you have the advantage. If you can agree that at the end of the day your goal is to walk out of that casino with the largest stack of bills possible, then you have to focus on learning and exploiting. The odds really aren’t stacked against you here, but the only way to really win this game is to be willing to play it the right way. Do you choose the first and most flashy machine? Or do you want to make money?

What is predictive analytics? What is predictive marketing? Do I need a statistician on my marketing staff? These questions and others related to analysis and digital marketing are increasing in frequency. Since it is relatively easy to collect data, especially in the online space, digital marketers have a wealth of data points to describe their online customers and site performance. Wouldn’t it be great if we could reduce the data to its most relevant points and use it to not only describe, but prescribe and perhaps predict? Enter predictive analytics.

What is Predictive Analytics?

Predictive analytics is the practice of using data mining and statistical modeling to describe what is happening in your organization and estimate potential outcomes. As a result of predictive analytics, organizations are able to forecast revenue, define attribution models, and identify customer segments and score them on their likelihood to complete a desired action.

Predictive analytics is not new. Organizations have been using statistical modeling and data mining for years. It is used in fraud detection, revenue modeling, and even in HR hiring models. It is also used in direct mail campaigns – identifying households most likely to respond to the marketing message. The discipline is usually found in the Business Intelligence or IT groups within an organization, but is making itself known in the digital marketing space.

What is Predictive Marketing?

Predictive marketing is the practice of applying data mining and statistical modeling to optimize marketing efforts. For example, segmenting and scoring customers and online visitors based on a propensity to complete a desired action or defining attribution models based on campaign success to assist with budgeting and planning.

The challenge with digital marketing is the need to respond quickly. Most organizations have a BI or IT team churning through data and creating models to describe behaviors and outcomes, but the results may come after the opportunity. The web moves fast and digital marketers need to be able to respond just as fast. Having predictive capabilities within the digital marketing group enables marketers to not only analyze data quicker, but smarter. The result: improving the efficiency to act.

For example, it is the end of the quarter and online product sales are low. A quick statistical analysis takes in previous sales activity and behavioral data and identifies visitor groups and behaviors that are most likely to purchase given products. Then a targeting campaign with specific product and messaging to these groups is launched to provide more relevant promotional data rather than a generic campaign.

Do I need a Statistician?

Remember that high school or college math class? The one where you spent hours working on a homework assignment, only to come in the following day and have the teacher say “Now we are going to learn how to do it by computer”.  The ability to calculate the standard deviation of a distribution by hand does have its benefits. But when it is crunch time and decisions need to be made, I am going to turn to the computer. Make sure you have the right resource for the job.

Having someone on the staff who understands the nature of the data and statistics will help. It does not need to be a statistician, but someone with the ability to analyze large data sets and perform statistical and econometric analysis. A plus would be someone with those skills and who understands how the business works, but those people are rare. I suggest having more than one person to bring various skills together, or supplement with consulting.

In Summary

Many people think predictive analytics is a black box, or crystal ball. Really it is just math and science. It is important to differentiate that it is not a crystal ball telling you the future and what to do next. It is compass pointing you in the most likely direction. The accountability to act is still up to you. Unlike a crystal ball or black box, you can take apart a compass to understand how it works. Similarly, you can break a statistical model into smaller components to understand the math and assumptions driving the outcome, which will help inform you of the decisions to make and the actions to take. The idea is to take the guesswork and subjectivity out of the decisions that need to be made and become a little more educated and efficient with action.

A look at some of the key trends:

Facebook is finally on every major brands marketing plan: While Facebook rapidly expanded its user base from the very beginning, it was relatively slow in gaining traction with advertisers. In 2011, that has changed. It was, from our perspective, a watershed year for Facebook – a time when the site went from being an experimental medium to a medium that appeared on every major marketer’s media plan.

Facebook ad spend continues to accelerate as marketers begin to understand social ROI: Our recent research shows that Facebook ad spend is nearly 3% of biddable online ad spend. However, we expect this number to increase to 5% of all online ad spend within a year, specifically in verticals such as retail where Facebook currently has a relatively small footprint.

The main reason for Facebook’s slow adoption in the retail category has been its perceived inability to show a direct response to marketers’ call for ROI. However, this will soon change. Why?

1 - Based on our client data, between 40-60% of all transactions that start on a Facebook ad end in a different channel. So, in a last-click world (which about 70% of retailers currently use), the ROI on Facebook campaigns looks poorer than it really is.

2 - We have seen glimpses of evidence that show that when brands interact with their fans, the offline store traffic increases. In other words, online interaction on Facebook seems to lead to more monetization offline.

3 - We have seen that consumers interacting with Facebook apps tend to buy products more frequently and of a higher value.

4 –Facebook has a massive data asset that only continues to grow. This is perhaps the most important component driving this change.

The bottom line is that the effect of Facebook on purchase behavior and revenue is often indirect and long-term. Today’s direct response metrics don’t capture these effects correctly and thus do not show proper revenue impact. The ability to measure the value of Facebook on direct response will change as more marketers implement marketing solutions that offer cross-channel measurement, analytics and optimization.

New ad formats that leverage the social element of Facebook will accelerate social ad spend: There are two features of social marketing that give it a significant advantage over any other channel:

1 - The social element. When a friend endorses a brand, product or a service there is a much higher propensity for others to buy or endorse it, as well. However, we believe that the social element is still underleveraged for marketers on Facebook. Apart from Sponsored Stories (which do remarkably well on Facebook), and reaching friends of fans, we have not seen new innovation in this area. We believe there is huge potential in making ads and apps even more inherently social so as to gain more free “earned media”.

2 - Facebook’s massive data asset is relatively untapped. The company has one of the richest sources of consumer data on the planet, but for understandable privacy reasons, it has not enabled marketers to leverage it. Yet, we believe that in due course innovation will emerge that balances privacy expectations and regulations with ways to provide marketers with rich data beneficial to industry and consumers. The innovation will come in the form of new ad formats, segmenting capabilities and advanced reporting.

Facebook will be the catalyst for a major media mix shift: Many analysts ask if Facebook budgets are eating into search and display budgets. So far, it is not, and we believe that will continue to be the case. Facebook’s budgets have and will continue to come from TV and print. There are two reasons for this:

1 - Consumer’s behavior when they see ads on Facebook is a lot like when they see ads on TV. They initially respond well and strongly but when they see the same ads again the response rates drop significantly. The same applies to apps. Thus, the best-executed marketing strategies on both TV and Facebook have the following common elements – a short but intense campaign to maximize reach, frequency and word of mouth; a pulsed media buying strategy so consumers are not saturated with the same ads; and very strong, creative ads that encourage people to look, engage and interact with the brand.

2 - The other reason is the way in which people engage with different media. Many TV viewers are on Facebook simultaneously. We have seen this with some of the fan pages of TV shows that we manage – peak hours of engagement coincide with the times that the show is broadcast. So, if a brand wants to reach out to an audience of a particular show they should advertise on Facebook, too. This is unlike search where the core purpose is to find a product in the most efficient way possible and then get off the search engine’s ecosystem.

What does this means for marketers? Facebook’s IPO will increase pressure on the company to monetize its traffic much more efficiently. One should expect to see new ad formats and targeting capabilities being released. Additionally, one can also expect to see increased ad buying on the channels as more marketers look at social ROI through a broader and, in our opinion, more correct lens. Finally, expect to see more coordinated TV and Facebook campaigns as well as a shift of media dollars away from TV and print to Facebook.

Dr Siddharth Shah
Director, Business Analytics
Adobe

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