How Analysis Goes Wrong: The Week in Awful Analysis – Week #2
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. All names and figures have been altered where appropriate to mask the “guilt”.
What you don’t do is often more important then what you do choose to do.
This week for How Analysis Goes Wrong, I wanted to start covering a number of the many errors in rate versus value and how it can be used to pretend to know things that you really don’t. I figured I would start with the most obvious example there is, which would be “how much revenue comes from email”. So many times in the realm of optimization you are faced with having to stop people from applying resources towards actions that could never produce meaningful results. The most obvious of these is email.
Analysis: We want to optimize our email because 60% of our revenue comes from email.
There are a number of problems with this, but let’s tackle the analytics ones first, and then move on to the optimization ones.
1) You have no clue if 60% of you revenue COMES from email, you only can attribute 60% of revenue to email. The difference is this. In attribution, you can’t say what direction something happens, only that a group of people have a common trait (usually channel). You cannot in any say if email drives 60% of your sales, or if in the real world situation, the people who want to spend a lot of money on your site on a regular basis might be inclined to sign up for email.
2) It suffers from the graveyard of knowledge effect of not saying what the difference in performance of people with and without email are, especially since it is only looking at success of revenue and not all users.
3) It assumes that just because 60% of revenue comes from a group that optimizing that group is more valuable than any other group. Unless you know your ability to change their behavior and the cost to do so, you cannot ever make that gross assumption.
4) Statements like these are used for internal competitive evaluations of groups (paid, email, display, etc…). People are going to abuse data, that is a given, but the fact that someone who is responsible for optimization or analytics, the one person in the company who should be most concerned with the correct portrayal of data in a rational sense, is the one most likely to make a statement like this. Keep your data people away from politics!
I can go on, but I want to dive a little deeper into the evils of email testing. It is not that email testing cannot produce results; it is simply that the scale of those results and the cost to do so is so insanely high that there is no point in ever going down that path.
Here is some example math. If you are interested, this assumes a 20% higher action rate and RPV compared to an actual extremely larger retailers actual performance. It assumes a 10% margin on actions. Both of those are actually higher than the customer in question, but I wanted to over promise value to show how obscured optimizing email can be:
Open rates and all other metrics come from this article, but there are many other similar sources out there.
I usually share a story with people when we get to this point, which goes like this. I worked with a very large ticket reseller who had spent 2.5 years optimizing their email, and had been able to achieve a 120% increase through having 2 full time resources spend that time on nothing but optimizing their email. The total value in increased revenue they derived was around 600k, which sounded great.
My first week working with the customer, we went to the least political page, used existing internal resourced, did a simple real estate test, and that test was worth approximately 6 million.
Total time spent on conversation and setting that test up, 1 hour.
Future testing continued to show similar scale of results without even touching their most political pages. In 1 hour, we were able to show that they wasted 2.5 years and all those resources chasing a mythical dragon. The real punch line of this story is the reason they did all that work is because they “knew” that 72% of their revenue came from email.
Do not let this happen to you.