Something I've been thinking about for a few months has been how to measure the success of a change in your customer loyalty.
Most loyalty or customer retention apps would like to take credit for any additional sales, but in reality some of those would have happened anyways.
You'd need a large volume of repeat orders to be able to use statistics to prove the benefits (e.g. A/B tests, holdout tests). Even then, those would be less effective for changes lower down the funnel where the volume drops off.
It's almost like you have to fly blind on individual changes and only use wider metrics to guide your thinking. e.g. did Repeat Purchase Rate improve over last year? Is Average Order Value holding steady? This sort off thing my app compares for you automatically as part of its Insights System.
That makes it less about attribution and more about metric monitoring. In a world of imperfect data and rapid change, that might be a good thing.
Eric Davis
Optimize your promotion timing to save money and attention
Repeat Customer Insights will analyze a ton of customer behavior data for you, including their buying cycles.
If you knew exactly when the majority of your customers were ready to buy again, you can increase your orders and profit just by tweaking your message timing.