And the hits keep coming.
This story involves a tracking database. The database was tracking long-running campaigns, where the process was that a customer 1) contacted the company via customer care 2) at that point, was randomized on a by-campaign basis. Once there was customer activity, that customer was tracked for three months.
Here's where it gets tricky. On the next customer contact the treatment group was given the pitch again if they still qualified whereas the control group was automatically not given the pitch. That means in the treatment group the next contact generates a campaign-relevant data point whereas in the control group it doesn't. Remember the three month-tracking? After three months any control group customers are dropped out of the database, whereas treatment group customers that are still in contact with the company are still tracked. These are long-running campaigns. So the control group was composed of customers that had at most a three-month window to take the offer whereas the treatment group had a potentially unlimited time to take the offer. What a clever way to make sure the results are excellent!
I was once reviewing analysis of campaigns from this system. I was originally asked to make sure the T-Test formula was right, and poked around in the data a little. I saw a weird thing: the campaign results were a linear function of the control group size. The smaller the control group the better the results. I commented that they really shouldn't publish results until they had figure out what the Weird Thing was. Looking back, I can see how the database anomaly aboce could account for the effect. As time goes on, customers are going to be dropped out of the control group. Also, the treatment group will be given longer and longer to take the offer. So as time goes on, the control group numbers will fall and treatment group takes will rise.
So all the positive results that were being ascribed to the marketing system could have been due to the reporting anomalies.