Context & Objectives
Our client was considering adjusting its prices based on the actual value delivered to its customers and competition benchmark. They turned to Agilytic to assess price sensitivity of their customers and optimise price increase to avoid churn.
We worked alongside the in-house BI team to build up price sensitivity models. We combined historical data from various systems including:
Customer account details
Previous price increases
We applied predictive modelling techniques to improve understanding of users’ reactions to the price increase, identify key factors of price sensitivity and cluster users into sensitivity segments.
We used our model to develop a simulation tool used by internal teams. The tool was able to predict customers’ reactions to the price increase given their price sensitivity. Doing so, we allowed optimising price increase at the customer level while maximising loyalty.
Ultimately, our tool helped increase our client’s monthly revenue by 4%.