Reinforcement Learning for Market Entry Strategy

Entering new product markets is a primary growth mechanism for many firms. However, choosing the sequence and timing of market entrances is a nontrivial problem for which artificial intelligence (AI)-based approaches remain underexplored. The complexities of these market entry decisions include uncertainty over future market demands and costs as well as the impact on each firm of the decisions of the potentially many other firms present. These decisions are complex enough that a complete theoretical analysis of the situations is infeasible, suggesting the possibility of using other techniques, like AI.

In this work, we introduce the Market Entry Game (MEG) to model market entry decision-making and apply existing reinforcement learning (RL) techniques to the problem of determining effective strategies for this game. Using a MEG simulator, we show that RL-based agents outperform rule-based agents in their ability to maximize capital through their market-entry decisions. Our results show that AI can make effective market entry decisions and uncover novel insights in strategic management.

See the code here!

See the full paper here!