Case Study - Optimise assortment strategy for a leading european retailer

Overview

Our client, with over 1000 stores, is a leading grocery retailer. Their loyal customers have always appreciated the quality of their products and the associated service.

With the aim to create a sustained competitive advantage and offer better services to its customers, our client wanted to gain a deeper understanding of customer needs and product performance across diverse geographies.

To cater to evolving customer needs across diverse geographies, the merchandising teams have to optimise assortment mix across a wide range of store formats.

What we did

As part of the initial discovery, we worked with the merchandising teams to identify the high value decisions, and the data required to make these decisions.

In order to draw relevant insights data we have to process large amounts of data across sales, customer habits and product performance.

Traditional data architectures are not geared to process this scale of data and serve insights in near real time.

With the use of smalldata.ai’s fynd framework we were able to jumpstart the process of insights delivery.

Our team worked collaboratively with the client’s engineering team to create the data products necessary to support the initial use cases.

Within a short period of 3 months we enabled the buyers to evaluate product performance and make appropriate assortment choices with an AI powered insights platform.

The delivery process was set up to generate incremental business value at a cadence.
Our team worked with the client to identify and prioritise subsequent use cases to support new product selection, supplier evaluation & negotiation, analysis of evolving customer needs etc.

As part of the engagement we helped our client onboard new team members, trained them on using the new platform and currently they are equipped to drive improvements on their own.