Target the ‘moveable middle’ through advanced analytics to maximize marketing spend.
This is where more advanced analytic methodologies, like ‘Net-Lift’ or ‘Uplift’ modeling can help.
Uplift modeling is actually best executed through the use of an initial test, where the target group is randomly broken into two parts: one group gets the offer and the other group gets nothing. After this initial test is completed, uplift modeling analyzes the data and identifies which of those customers only purchased because of the offer, and which customers did not need the incentive to make a purchase. This not only makes your marketing efforts and campaigns more efficient, it also allows you to see a higher return on investment while enabling you to take these learning and refine further for future campaigns.
It’s also important to note that you don’t want this group to be selected using an existing model because it will bias the selection of customers toward those you know are already likely to be a good customer.
Uplift modeling enables companies to maximize their marketing spend—and the ROI of campaigns—by targeting offers to only those customers that need it. There will always be customers who are extremely likely to respond, and customers who are extremely unlikely to respond. Where we identify and focus on the ‘moveable middle’, we can build meaningful loyalty experiences.
Advanced analytics can enrich—or devalue—any customer relationship.
Of course, as analytics continue to play an increasingly significant role in the customer experience, more advanced techniques will allow for enhanced personalization, more impactful campaigns, and maximized marketing spend. But as data and analytics capabilities continue to evolve, they must always revolve around the customer experience, creating meaningful interactions that foster long term loyalty and advocacy.