Turning loyalty data into a winning range
Sainsbury's wanted to be a quality children's bookseller, but the sales said otherwise. Book sales were sluggish, the range was inconsistent across store types, and the in-store experience wasn't working.
The brief had two clients. Sainsbury's needed a stronger commercial position in children's books. Penguin Random House needed to know where its own opportunity sat within that. Both had to be answered from the same data: loyalty card records, inventory and sales by store type, floor and shelf plans, and market benchmarks. None of it had been brought together.
I started with the diagnosis. Benchmarking sales and inventory against the market showed where Sainsbury's was losing ground: inconsistent curation, weak displays, and titles that didn't match what loyal buyers wanted.
The loyalty data then reframed the problem. The main buyer wasn't a parent browsing with a child. It was grandparents and family shoppers, buying for a predictable age range and choosing recognisable titles they could trust. The range wasn't built for them. Crossover data from stronger categories, especially children's clothing, showed where these shoppers already spent and how to position and place the range.
I worked with Insights, Sales and Finance to stress-test every recommendation before it was presented, and dropped what wouldn't land. The final proposal set out a defined target market, a curation framework built on recognisable titles and standard KPIs, and a supermarket-branded educational series with Penguin's educational publisher, offering an accessible price, broad appeal and direct-to-consumer potential for Penguin.
When loyalty, sales and market data are read together rather than held apart, the answer to a commercial problem is often already in the business.
This is the work of the Fractional Insight Lead: market and customer insight, triangulated with analytics and tied to commercial decisions.
Tell me where you're starting from. A short note is enough.
