- “What is the best price for a product in this market?”
- “What text description of my product works best for certain customer types or selling channels?”
- “What product images or color schemes provide the highest conversation rates for these items and customer types?”
This article is the final of a three-part series on optimizing airline ancillary bundles based on customer preferences. In Part 1, we discussed data collection and customer segmentation methods. In Part 2, we met traveler Caroline and learned how recommendation and offer engines were used to present her with a set of personalized offers from which she found a desirable bundle for an anniversary trip to Spain with her boyfriend.
In this section, we will cover the use of experimentation engines to provide a “test-and-learn” framework for ancillary bundles that will further improve our marketing effectiveness, ensuring that travelers receive an offer that is both tailored to their preferences and priced competitively.
Experimentation Engine
Consumer expectations and market conditions change, so it is useful to have self-learning tools which can automatically adapt to evolving circumstances. Reinforcement learning techniques are often used for this purpose – this means that the artificial intelligence is put into a game-like situation in which it receives rewards (or penalties) for certain actions. Specifically, Sabre Labs has seen good success using multi-armed bandit (MAB) experimentation methods. Stated very simply, this is an alternative to the classic A/B testing methods where the AI continuously re-focuses its efforts on actions that yield a positive outcome. The name comes from a casino slot machine into which the gambler drops a coin, pulls a lever (or arm), and receives a payout (or not). In a multi-armed bandit methodology, the gambler plays several slot machines simultaneously – there are multiple “levers,” each yielding a different likelihood for success, and using machine learning we can narrow down which levers deliver the highest rewards.
Experimentation methods with multi-armed bandit have a variety of different business uses. First, it can be used as part of an automated framework; an example is Sabre’s recommender system which automatically assigns experiments, observes the outcomes and uses the results to improve the quality of its model-generated recommendations over time. Second, retailers may decide to run one-time experiments to help answer business questions such as: