As James A. Garfield, the 20th President of the US, once quipped: “The truth will set you free; but first it will make you miserable.”
Who wants to voluntarily be miserable? This is the reason people struggle to come to terms with the truth — even though in the long term it leads to growth and learning.
I liken it to Neo’s choice of whether to take the Blue or Red Pill in this clip below from The Matrix. The Blue Pill will allow Neo to continue to live under the premise that all is well and good in the world, whereas the Red Pill will forever illuminate for him the truth, painful though it may be.
What does this have to do with retention marketing and customer data?
For us, the parallel is clear.
True data-driven marketing is about refusing to compromise on pseudo-accurate metrics and standard customer analytics.
Let me start by giving you two examples of pervasive pseudo-accurate metrics in measuring campaign success.
The first example is email open rates. Email open rates don’t even adequately measure email open rates, let alone campaign monetary success. You
can read about this problem more in depth in this Wikipedia article and this article from Smart Insights.
The second example is email revenue attribution metrics, which are, admittedly, a bit more accurate, in terms of evaluating the monetary success of a particular campaign. They still, however, fail to take into consideration customers who might have purchased the item without receiving that particular email.
The clearest indication of the success of a particular retention campaign is the amount of monetary uplift generated as a result of that campaign.
Unfortunately, many methods of measuring retention campaign uplift are inaccurate as well. Marketers often compare the week a particular campaign ran to the same timeframe for the previous week, month, or year. They then measure the difference in revenue between the two time periods and – voila! “Campaign success!” they declare.
These methods of measurement, however, fail to take into account the myriad of different circumstances between the two time periods. Perhaps a new competitor emerged or vanished. Maybe the economy perked up or tanked. Maybe it was the holiday season during one period, or the weather was terrific or terrible. (You get the idea.)
You can continue measuring campaign success with these quasi-accurate metrics if you want. That’s the blue pill – living life with a blindfold over your eyes, pretending that everything is fine and dandy.
Or you can choose to scientifically test your marketing campaigns, based on a test vs. control methodology. Your campaigns would be clean, each group – both test and control – having equal exposure to exogenous factors (economic, a new competitor, or the weather). The test group would be isolated from endogenous marketing offers or incentives (any other campaigns you send to customers), while the control group wouldn’t receive any campaigns whatsoever. Each group would be an accurate representation of your target group – giving you the ability to precisely measure the monetary uplift resulting from each campaign.
Can You Handle the Truth (in Your Customer Data Analysis)?
Let’s set pseudo-metrics and scientific testing aside for a minute. Before marketers can even get to the point of testing and measuring, they have to acknowledge true customer data analysis.
Although most companies today are using some form of customer analytics, it is important to distinguish between standard analytic practice andtrue customer analytics.
Standard analytics fail to take a holistic approach to customer behavior. If registrations are up this year by over 50%, that’s great, but what percentage of customers were active after registration? That increase isn’t so impressive if you’ve done deeper analysis to see the bigger picture – that most of those new registrations leave your site almost immediately and don’t come back.
Standard analytic practice also often ignores the dynamic behavior of a customer such as the different customer lifecycle stages. The activity of a new customer should be understood differently than the activity of a long-term active customer. True customer analysis takes into account these different types of dynamic customer behavior.
We often see our client’s visible disappointment when they take their old marketing plan and copy it into our retention automation platform. It’s not just that their marketing campaigns aren’t generating the results they thought they were (because they were using quasi-metrics and inaccurate testing methodologies) – it’s that with deep customer data analysis, they’re now forced to see it with their own eyes.
“What do you mean, I am losing 40% of my new customers in the first 24 hours after their first purchase?!?!”
“You’re telling me that within my VIP customer group, which consists of customers with the highest total purchase amount, are customers with the highest return rates who actually aren’t profitable?”
“How come our winning players (or players who won just once), with negative ‘Net-Revenue’ have a higher future value than losing players with a positive ‘Net Revenue’??!”
Sometimes, the truth hurts.
It’s human nature to suppress facts, to keep on chugging along with those old marketing campaigns. What marketer really wants to open a Pandora’s box? Especially when they don’t see a solution in sight?
Modern marketers now have the option of choosing the red pill, to stop suppressing facts about your customers and acknowledge their true behavior.
Acknowledge Your Customers for Who They Really Are
Only once you give up those quasi-metrics and start real customer data analysis can you start to learn from your past customer marketing campaigns and improve them in the future. You’ll start to discover your customers for who they really are and personalize your campaigns accordingly.
James A. Garfield might as well have been talking about retention marketing and customer data. True customer data analysis can indeed make you miserable. You may end up revamping your entire retention strategy as you starting seeing your customers for who they really are. The long-term rewards and benefits, however, will be well worth the pain and suffering as you begin to learn and improve as a marketer.
Pini co-founded Optimove in 2012 and has led the company, as its CEO, since its inception. With two decades of experience in analytics-driven customer marketing, business consulting and sales, he is the driving force behind Optimove. His passion for innovative and empowering technologies is what keeps Optimove ahead of the curve. He holds an MSc in Industrial Engineering and Management from Tel Aviv University.
