Retail trades greatly benefit from price promotions (promos), i.e., temporary price reductions. The marketing literature on the topic is vast, mainly under the heading “Trade Promotion Optimization”, but not much has been produced on the optimization of the schedule of promotions of brands or items on long time horizons. There is a rich offer of commercial packages to support these decisions, despite the rather poor coverage in the optimization literature of the many, intertwining operational constraints that characterize actual applications.
This work proposes a model for retailer chains, focused beyond transactional trade promotion management. It considers both manufacturers, who provide products to sell, and retailers, who are responsible for sales to the consumers. Input data to the model are derived from statistic analytics based on historical data, and yields the expected baseline and the uplift (ratio between the average sales volume with and without a promotion) for each promo in each time period, together with the different contributions to the uplift: cannibalization, halo, promotional dip, forward buying, etc. Building on this, we propose a mathematical model of the effectiveness of a promotion plan in the horizon of interest.
Scheduling a promotional plan consists in establishing the calendar, the discounts, and the mechanics of promotions over the time horizon, where the mechanics are possible means to enhance the simple price reduction, such as leaflets, hostesses, gifts, extra-display, etc.
A solution of the promotion scheduling problem determines the number of promotions in the plan, and for each promotion the starting and ending period, the sell-in and sell-out discounts, and its mechanics. A promo calendar specifies or permits to compute: sell in dates, sell in estimated volume, sell in promo price, promo mechanics, sell out dates, sell out estimated volume, retailer promo cost, retail price and retail margin.
The main constraints we included in the model are: minimum distance between promotions, maximum duration of each promotion, maximum number of promotions, minimum distance between promotions with the same discount or mechanics, maximum number of promotions with the same discount or mechanics, cost of the promotions in relation to the allocated budget, forbidden promotions in some periods.
We developed a full mathematical model and we compared different solution methods to solve instances of the promotion planning problem of real world complexity and size, including MIP, Dynamic Programming, Constraint Logic Programming and a matheuristic algorithm, namely a F&B heuristic, which is essentially an extension of beam search.
(Joint work with Marco Antonio Boschetti)