by Johanna Tam , Chen Zhao

The Challenge

A global food and beverage retailer sought to improve their pricing strategy for a major North American market segment. The existing solution limited pricing strategy to a geography-based approach, excluding other key attributes that more directly influence projected price elasticity. They engaged Point B to build a new, data-driven pricing solution that would help optimize pricing across thousands of stores and lead to greater top and bottom-line financial improvement.

Setting the Foundation

Our customer had the data pouring in. They wanted to build a pricing strategy that was indexed against the projected price elasticity of its customer base. This method gives retailers insights into the pricing power it can exert without risk of losing its customers. Point B began by identifying and curating a variety of data sources that would provide a comprehensive view of each store location. Example data sets included competitor store locations, census data, and store point of sales data, among others. Developing this rich data set provided the foundation from which the analysis and insights would be generated.

Building a Data-Powered Pricing Engine

With the underlying data foundation set, our team then developed a machine-learning solution to help identify clusters of stores with similar attributes that drive price sensitivity, resulting in several store profiles of varying price elasticity. This data-driven output helped to codify – and make real – long-held assumptions that attributes such as urbanity, competitor presence and customer demographics were better indicators of price sensitivity than geography alone. This previously unattainable solution was now ready to power top and bottom-line growth.

Embedding Insights into Operations

In addition to curating this unique data set and developing the analytical models, our team also partnered with the business stakeholders to understand and plan for the operational impacts this new solution provided. Pairing the analytical output with a consideration for regular business operations led to quick implementation of the solution with minimal disruption to business partners and customers. This solution is projected to provide more than 20x return on investment within the first year of its operation.

Moving stores into new “pricing profiles” based on the price elasticity scores also put a few long-held pricing strategy beliefs to test (do not adjust product price more than two times per year, for example). To overcome potential reservations, our team developed measurement tools to validate these rules and identify opportunity to modernize the pricing strategy playbook used across the company.

Our customer is now equipped with reliable, iterative tools to help drive pricing improvement across their global store footprint. The data-driven analytics engine paired with business-conscious operations and implementation strategy has led to fast, scalable and impactful outcomes – driving faster, sustainable growth.