Setting the optimal price for home security services

Context & Objectives

Our home security client was considering adjusting its prices based on the actual value delivered to its customers and the competition benchmark. They turned to Agilytic to assess their customers’ price sensitivity and optimize price increase to avoid churn.

Approach

We worked alongside the in-house Business Intelligence team to build up price sensitivity models. We combined historical data from various systems, including:

• Customer usage

• Customer account details

• Previous price increases

We applied predictive modelling techniques to better understand users’ reactions to the price increase, identify key factors of price sensitivity, and cluster users into sensitivity segments.

Results

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 optimizing price increase at the customer level while maximizing loyalty.

Ultimately, our tool helped increase our client’s monthly revenue by 4%.