Despite this unforgiving environment, most grocers have, until very recently, tended to make promotional decisions based mainly on the 3 things: amount of available vendor marketing fees; history and tradition; and “gut instinct.” This is understandable since measuring grocery and retail data is a highly complex and labor-intensive endeavor. The changing and dynamic relationships between products and customers, in addition to the effects of pricing and promotions, make understanding and leveraging all this data a “humanly impossible” task.1
Promotion decisions are further complicated by ripple effects, including cannibalization and forward-buying. For example, putting “brand A” of soda on promotion may increase the sales of related products, such as salty snacks, but it’s also likely to cannibalize “brand B” of soda sales for that week and probably next week’s soda brand A sales, due to forward-buying.
Promotional Product Selection
With the help of Daisy, Harps analyzed years of collected transactional data, then simulated a mix of previously known and new, untried actions to find the many different ways a promotional decision may unfold. This enabled Harps to determine an optimal sequence of actions that would help them achieve the best long-term outcome.
Daisy’s A.I. enabled Harps’ merchandising and marketing personnel to rapidly analyze transactional data on a massive scale and simulate potential strategies, ultimately “supercharging” the process by which the company makes promotional planning and pricing decisions. As a result, Harps used A.I. to improve promotional effectiveness, meet the needs of their shoppers by keeping prices competitive, and increase revenue without added margin.
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