Simulation of a Customer Loyalty Program
Imagine your company had implemented one of the worlds most successful customer loyalty programmes, with millions of customers participating.
Your programme offers different tiers of membership which are reached according to the customers activity level within a rolling, fixed time period.
Over the years the rules of your programme have become quite intricate and difficult to follow. Also there has been a shift in the way customers use your service offering and the demographics of your customer base is changing.
In short: you want to simplify the rules of your loyalty programme and adjust the reward system.
The Challenge
The challenge is: you only have one shot in getting this right.
Any change in the programme is going to affect your customers in some way or another - you must ensure that customers and their status in the membership programme aren't negatively affected by the changes.
Of course you also want to make sure your bottom line isn't negatively impacted.
The Solution: Simulation To The Rescue
Thankfully, our client had a large set of GDPR-compliant historical data about their customers service usage and about their status history in the loyalty programme.
Based on that data, we set up a simulation to evaluate the potential effect of new rules, membership points and membership tiers.
The simulation helped our clients to identify a rule set and point systems that ensured all key objectives of the updated loyalty programme could be met.
Be built the simulation using Mathematica and the Wolfram Language, because this environment provided us with all necessary capabilities right out of the box, making the development of the simulation very efficient and ensuring that we could later transfer it to our client easily without any extra technical dependencies.
In particular, the Wolfram computational platform provided us with:
- Mechanisms to transform data that was extracted from a data warehouse and load it into the simulation.
- Find patterns in the input data and simulated result data.
- Setup the necessary data structures and simulation rules and equations using both statistical approaches and rule based approaches
- Run the simulation and display and export the results.
- Predict future customer behaviour based on probabilistic machine learning algorithms.
Our Expertise
Simulating business models and market strategies
Cross-Industry-Standard-Process for Data Mining (CRISP-DM)
Generating business value from advanced analytics
Target Group
Recent Blogposts
Meetup: AI for Enterprise Decision Making
Oliver Grasl - 2021-02-10Ten Key Principles of Digital Transformation
Max Erdrich - 2021-01-24Agile Transformation Processes
Ognjen Manojlovic - 2021-01-19How To Deal With Requirements in Flawed Projects
Igor Tunjic - 2020-08-02Training Artificial Intelligence to Play the Beer Game
Oliver Grasl - 2020-05-31Email newsletter
Subscribe to our newsletter and stay up to date about the latest trends and news.
Name
Subscribe to our newsletter and stay up to date about the latest trends and news.
