Optimizing online recurring promotions for dual-channel retailers: Segmented markets with multiple objectives

Online promotion helps enhance brand awareness and boost sales. Although it attracts customer traffic, an ill-conceived price promotion has serious repercussions because it disproportionately draws bargain hunters, results in profit erosion and causes operational chaos due to erratic demands. This research proposes a long-term optimization model to help dual channel (click-and-mortar) retailers understand the conditions necessary to promote products online across all markets. When partial markets are recommended, we investigate how to price and select the market portfolio for promotion in each time period. We develop a multi-objective evolutionary algorithm to efficiently solve complex and large-scale problems. Both theoretical analysis and numerical study show that the proposed model outperforms the conventional strategy of promoting online across the board. Due to its dynamic nature, the multi-period recurring promotion problem is difficult to address optimally. Our model is capable of planning for multiple periods, multiple markets, and multiple objectives to maximize long term profitability and competitiveness. Click-and-mortar retailers will find our approach extremely effective for maximizing profit, enhancing brand awareness, and improving customer satisfaction.