Greed based efficient reward disbursal
Submitted by Ajeet Jha (@ajeetjha) on Tuesday, 21 May 2019
Session type: Full talk of 40 mins
Reward disbursal strategies are important to attract sufficient customer time for users to experience the product. These rewards when disbursed to random(or all) customers generate huge cost and mild incremental return. Sometimes, such reward disbursals tend to annoy customers and eventually make them less likely to react to these rewards.
In this talk we will try to understand possible solutions using machine learning algorithms to predict influence of reward treatment on customer’s purchasing behavior to make effective decision at choosing whether to reward a customer or not for a purchase he/she has made. While minimizing reward disbursals, we need to make sure gross returns stay close to that if we would have rewarded everyone. This helps us save expenses to spend on other avenues for acquisition and engagement. We will also discuss in detail, the process of production deployment of this user-based solution for efficient reward disbursal.
- Objective and Scope of problem
- Identify greedy users
- Selective reward disbursal and budget reduction
- Limited range of reward variant
- Data collection over experimentation cycles
- App journey and progression data
- Data segregation of control and treatment
- Feature Engineering
- Traditional approach
- Capturing and modelling for response
- Model per reward variant approach
- Training purchase probability models over control and treatment variants
- Build and tune heuristic to estimate influence of treatment on purchase
- Single model approach
- How to make sure that model will work for next experiment cycle
- Out-Of-Time validation of trained model
- Production Deployment of user-based solution
- Model deployment pipeline [Ad-hoc]
- Reward disbursal engine [ML Platform, self sufficient reward disbursal system]
- Performance metrics
- Incremental Response Rate
- Savings, expected vs achieved gross return, incremental cost
I am Ajeet, Data Scientist at Phonepe, India’s payment app. Over 6+ years of experience, i have tried my best to observe evolution of Indian startup culture and Indian population responding to products.
I have chosen to speak about this problem because, while dealing with huge scale or smaller budget, in either of the case it is very important to disburse reward intelligently, capturing sufficient customer time for user to experience the app. We achieve this by understanding each user on the scale of greed towards reward and disburse the reward amount best suited for the user.