Authors: Praveen Prashant, Kelvin Heng, and Deepesh Naini
How we built a ML Driven voucher allocation engine to serve millions of customers across multiple geographies.
The Idea 💡
How can we use different vouchers to get more business from our customer base while keeping our costs low?
Gojek uses vouchers to achieve multiple business objectives.
For example, the objective can be to maximise food orders in Indonesia for a given week, while in another week it could be to maximise resurrection of churned users in Singapore
An ML Driven Multi-Objective Solution 💻 📊
Identifying the Persuadables
- The Persuadables: customers who make more transactions than if they were not targeted with the voucher ✅
- The Sure Things: customers who make the same number of transactions when targeted/ not targeted (i.e. zero incremental response) 😐
- The Lost Causes: customers who do not make transactions irrespective of whether or not they are targeted (i.e. zero incremental response) 💀
- The Do Not Disturbs: customers who make less transactions because they were targeted 🔻
We use historical data of customers to observe past effects of vouchers on them. This is a typical causal inference problem to measure effect (for example, incremental transactions) of treatment (voucher) for our customers.
However, the complexity increases since there are two response variables, predicted uplift in business objective and cost. So, we use a deep-learning based causal inference algorithm to produce both predictions simultaneously for all customers given a voucher. The “objective” in the problem formulation depends on the business use case (multi-objective).
These predictions are then fed into a knapsack optimiser to recommend treatments (vouchers) for each customer to maximise the business objective while adhering to the budget constraint. We chose the simple knapsack optimiser because of its fast processing, since there are millions of predictions.