How a grocery chain optimized price and assortment simultaneously
By combining demand elasticity with assortment optimization, the chain could make decisions that balanced customer flow and profitability — instead of optimizing one at the expense of the other.
An established Swedish grocery chain wanted to understand the relationship between pricing and assortment breadth in a key category. They had intuitions about which products drove store traffic and which contributed margin, but lacked quantitative evidence to prioritize.
Reflect conducted a combined study with two analytical components. First, a price simulation based on demand elasticity calibrated against actual point-of-sale data, showing how price changes on individual products affected demand across the entire category — including cross-product effects.
In parallel, a TURF analysis (Total Unduplicated Reach and Frequency) with four optimization algorithms identified the assortment that maximized customer coverage with fewer SKUs. The analysis showed that a significant share of the assortment had overlapping reach — they were reaching the same customers.
When the pricing and assortment analyses were combined, a clear pattern emerged: certain low-margin products were critical traffic drivers that could not be removed without losing customers. Other high-margin products had such low incremental reach that they could be phased out or replaced.
Results were delivered as an interactive report where the chain could test different assortment and pricing scenarios and see the effect on both reach and estimated category revenue.
Key takeaways
- Point-of-sale data provided calibrated demand elasticity per product
- TURF analysis revealed assortment overlap as a hidden cost
- Traffic-driving products were identified separately from margin products
- Interactive scenario tool replaced static recommendations