Let's say your store has the ability to target this promotion to any subset of your customers based on demographics and past purchases. That is to say, you have the ability to include or exclude specific customers from participating in the promotion.
Imagine there was a group of women who have demonstrated an affinity toward vapor cartridges by regularly spending at least $100 per month on this product category. The question is, should you include them in the promotion or not?
The argument in favor of including these women in the promotion is that they like vapor cartridges, so they are likely to redeem the promotion. The argument in favor of excluding them from the promotion is that they were likely to buy vapor cartridges anyway, so it's really more of a "discount" than a promotion.
So what's the answer?
The answer is that there is no one size fits all answer. The only scientific way to even approach the question is through trial and error.
Randomly divide the women into two groups;
Make the promotion available to half of them but not available to the other half;
Wait some period of time; and
See which group ends of spending more money down the road;
The caveat is that changes in spending will not be evenly distributed within the groups. That is to say, some of the women who received the promotion will go on to increase in spending, while others will decrease in spending, and others will be constant.
In an ideal world, someone from your team would roll up their sleeves and spend some time analyzing the data. Are there any similarities between the women who went on to increase in spending? What do they have in common? And how could we know, for future promotions, who to include and who to exclude?
In the real world, this process is extremely time consuming, and there may not be anyone on your team with the bandwidth and acumen required to perform the analysis. Furthermore, any lessons that can be learned from this exercise will be limited in scope. That is to say, whatever insight you're able to derive about women who like vape cartridges will not help you when it comes time to market to men who like edibles. Finally, its extremely uncommon for customers to only ever be exposed to a single promotion. So measuring the effectiveness of a given promotion normally will require you to first develop the competency to distinguish the effects/influences of one from promotion from the effects/influences of another promotion.
Clearly it's not realistic to do this much math by hand. But the truth is, even with the aid of a computer, without the right framework it will be an exercise in frustration.
Leaflet takes the complexity out of this process. Leaflet is a first of a kind promotional intelligence platform. Leaflet continually looks across time to identify the critical success factors that result in high value customer relationships, and automatically applies what it learns to create promotions that are personally optimized to each individual customer.