How Analysis Goes Wrong: The Week in Awful Analysis – Week #9
How Analysis goes wrong is a new weekly series focused on evaluating common forms of business analysis. All evaluation of the analysis is done with one goal in mind: Does the analysis present a solid case why spending resources in the manner recommended will generate additional revenue than any other action the company could take with the same resources. The goal here is not to knock down analytics; it is help highlight those that are unknowingly damaging the credibility of the rational use of data. What you don’t do is often more important than what you do choose to do. All names and figures have been altered where appropriate to mask the “guilt”.
I have a special place in my heart for all the awful analysis that is currently being thrown around in regards to personalization. So many different groups are using personalization as the outward advantage prevalent with big data. No matter where you go, ad servers, data providers, vendors, agencies, and even internal product teams, they are all trying to talk about or move towards personalization.
This is not to say that personalization is a bad thing, I believe that dynamic experiences can produce magnitudes higher value then status experiences and have helped many groups achieve just that. What most surprises me however is the awful math being used show the “impact” of personalization from groups who have achieved absolutely nothing. I have lost count the number of times I have walked into and found one person or group talking about how personalization has improved their performance by some fantastic figure that it seems that the business should be doing nothing but thank them for their genius. The sad reality is that most of the analysis is biased and bad that in most cases the same companies are actually losing millions by doing this “personalization” practice.
Analysis – By putting in place personalization, we were able to improve the performance of our ads by 38%.
We have to tackle the larger picture here to evaluate statements such as above. Before we dive too deep into how many things are wrong with this analysis, we need to start with a fundamental understanding of one concept. There is a difference between the changing of content or the user experience and the targeting portion of that experience. In other words, changing things will result in an outcome, good or bad, and then targeting specific parts of that change to groups is also going to lead to an outcome. The only way that “personalization” can be valuable is if that second part of the equation is the one leading to a higher outcome.
1) Just to get the obvious out of the way, the analysis doesn’t tell you what the improvement was. Was it clicks? Visits? Engagement? Conversion? Or RPV? If it is anything but RPV, then reporting any increase has no bearing on the revenue derived for the organization. Who cares if you increased engagement by 38% if total revenue is down 4%.
2) The only way that “personalization” can be generating 38% increase would be if the following was true:
The dynamic changes of content raised performance by 38% to total RPV over any of the specific static content or content served in ANY other fashion.
In other words, if I would have gotten 40% increase by showing offer B to everyone, then personalization is actually costing us 2%.
3) Since most personalization is tied to content and the inherent nature of content changes is very high initial difference and then normalizing over time, what is the range of outcome? What is the error rate? The inherent nature of any bandit problem with would use causal data to update content means that you either have to act as quickly as possible, resulting in higher chance of error, or act slow and risk the chance of not responding fast enough to the market. In either case, performance will never be consistent.
Rather than continue to dive through each and every biased and irrational part of this analysis, I want to instead present two ways that you can test out these assumptions to see the actual value of personalization:
Set-up: Let’s say that you believe that 5 different pieces of content are needed for a “personalized” experience. In other words, you have a schema that will change content by 5 different rules.
The same steps work for anything from 2 rules to 200.
Option #1 (the best option):
Serve all 5 pieces of content to 100% of users randomly and evenly. Look at the segments for the 5 rules AND all other possible segments that make sense.
You will get 1 of two outcomes:
1) Each piece of content is the highest performing one for that specific segment and those are the highest value changes
2) ANY OTHER OUTCOME which by definition in this case results in more revenue.
Create dynamic logic in the tool, based on the 5 rules.
Create 6 experiences.
Each experience except the last shows each piece of content one at a time to all users (so content matching group A actually gets served to all 5 user definitions in recipe A). In the last recipe, then add the dynamic rules to the last experience.
If the last experience wins, then you at least know that the dynamic content is better than static content. If you are looking at your segments correctly, you will then also be able to calculate the total lift from other ways of looking at the content to the dynamic experience that you tested. If the dynamic experience is still the top performer, congratulations on being correct. If any other way works best, congratulations on finding more revenue.
In both of these tests, if something else won, then by doing what you were going to do or what you would otherwise report on IS COSTING THE COMPANY MONEY.
There are massive amounts of value possible by tackling personalization the right way. If you do rational analysis that looks for total value, then you will find that you can achieve results that blow even that 38% number out of the water. Report and look at the data the same way that most groups do though, and you are ensuring that you will get little or no value, and that you are most likely going to cost your company millions of dollars.