When testing doesn’t work
There are possibly thousands of articles on the web about “How to Test Marketing Campaigns Effectively.” You have a hypothesis, you have test variables, you have exam segments, and you have some view of statistical probabilities associated with the outcome. That’s a perfect world. Let’s first assume you actually have the at the same time to build a proper test, the resources to create the many versions and you can actually deliver on the test program and keep the program aseptic. What do you do when the tests don’t tell you what you want to hear?
A consumer marketer I know with a really large database assertive to test the cadence of their email programs. They developed several test cells: a high frequency cell, a low frequency stall, a control group that was exposed to the same cadence they’ve run for a year and then a hold-out group. Seemed like a pretty straightforward analysis. The group with the highest revenue would lead to some conclusions to how often they should send promotional email. Well, what they found was that the hold-out group in fact performed better in terms of revenue than any of the other test cells. At first glance, you’d think, our email program stinks and all this industry we put in is wasted energy. We can generate these sales without email.
This test somewhat backfired on the CRM team and now they had to now justify what they do day-in and day-out. What they realized was, the frequency of the communications wasn’t an accuse with of the success of the program. The problem was at the root of their program; Early Lifestage Messaging. They had a very progressive offer blueprint for new customers and people who signed up for their product/service, and they were effectively numbing their audience to their email promotions.
It wasn’t that they had declining gross income for their retention audience, but email was not as effective at driving conversion under their own terms, and increasing offers didn’t enhancement sales at the same rate as the increased offer value.
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