Here is a common scenario faced by restaurant marketers. A restaurant marketer has developed a fantastic offer that is increasing traffic and net sales, but he or she is not sure about true incrementality. Questions often asked include: “Are we rewarding our loyal customers who would visit us anyway?” “Are we just pulling forward or cannibalizing visits from a future date?” “Is the offer driving future sales?” These are tough questions to answer. Most of the measurement tools measure sales attribution, i.e., a restaurant sends 1,000 offers, and those offers drove $100,000 in sales. These tools do not get to true incrementality.
Take a look above—this chart measures the impact of a “$10 off $30” offer to a casual-dining restaurant. It shows the visits of two groups of customers. The test group (in blue) received the offer and the control group (in red) did not. The black line shows when the offer was received by the test group. Look at how closely the two groups match each other in visit frequency before the offer, and how they differ once the offer is received. While tradition says you need to establish a hold-out group prior to launch of any campaign in order to measure these kinds of changes in behavior, Fishbowl has leveraged a sophisticated statistical algorithm that allows us to identify naturally occurring test and control groups, regardless of pre-formed hold-out groups. It establishes that we have the right control group that looks exactly like the group that receives the offer after the campaign is in flight. In this case, we found the promotion lead to a sharp increase in visits (21 percent) and in gross sales (30 percent), generating significant lift above the control. Also, because this was an incremental sales lift, the data makes a strong case for re-running the offer—much stronger than just using the sales attribution approach.
Another nice thing about doing statistical matching instead of doing a hold-out group is that you do not have to stop sending offers to any of your customers. For the situation below, you can test an offer after it has already been in market, or analyze historical offers. One key element of success is ensuring we have some way of connecting the customer to their transactions. In this case, we utilized payment and POS data. But other means may include loyalty program transactions—via traditional methods or via mobile app, as well as dining reservations or online ordering. And since we have access to this kind of data from multiple sources, Fishbowl can apply this learning to a restaurant's full marketing channel, including eClub, SMS, or Loyalty programs. Guest analytics and data science are invaluable in measuring incremental sales grounded in statistical analysis and predictive modeling.
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