How to model retail store performance in SAP Analytics Cloud

Retail reporting lives and dies on a handful of KPIs that have to be comparable across very different stores. Build the model right in SAP Analytics Cloud and the comparisons just work; build it wrong and your conversion rate hits 4,000%. Here is the structure.

The KPIs to model

Revenue, average basket, footfall, conversion rate, gross margin, and like-for-like (LFL) growth cover most of store performance. The catch is that they aggregate differently: revenue and footfall sum, while conversion and basket are ratios that must be recomputed at the displayed level, never summed.

The aggregation trap

Conversion rate is transactions over footfall. If you store the rate per store-day and let SAC sum it, a hundred rows of 3% become 300%. Either set the measure to AVERAGE, or — better — model transactions and footfall separately and define conversion as a calculated measure. Average basket works the same way (revenue over transactions). The full rule set is in our guide to choosing the right aggregation.

Dimensions

Model a Store dimension, a Region, a Product category, a Channel (in-store, click & collect, online) and the time dimension. This lets you rank stores, compare regions and split online from physical, all from one model.

Like-for-like done correctly

LFL compares only stores open in both periods, so it cannot be a naive year-on-year of total revenue. Flag comparable stores with an attribute and compute LFL as a calculated measure over that subset — otherwise a new store opening inflates your growth and hides a real decline.

Inventory sits next door

Stock on hand is a balance, so it needs a LAST aggregation on time — the opposite of revenue. If your model also carries inventory, see modeling stock and supply chain in SAC.

Start from a built model

The Store performance & footfall template comes with these KPIs and the correct aggregations already set. Explore more Retail templates or compose your own with the generator.