Retail Analytics
The Retail section provides store and category-level sales analytics linked to loyalty program activity. It gives commercial and category teams visibility into which product categories, SKUs, and store locations generate the most loyalty engagement — and how cart behavior changes when customers are actively participating in loyalty mechanics.
This module answers: which categories and stores benefit most from our loyalty program, and where is the promotional investment having the biggest retail impact?
Use Cases
| Use Case |
|---|
| → Analyze cart size and category mix for loyalty members vs. non-members, by store. |
| → Identify which product categories attract the highest redemption activity (points or coupons). |
| → Compare store performance on loyalty KPIs to prioritize in-store training or campaign investment. |
| → Track top-selling SKUs purchased by loyalty members during promotional periods. |
| → Measure the uplift in category revenue during a campaign window vs. the baseline period. |
| → Export category-level sales data for CPG partner reporting or internal category reviews. |
| → Understand which time slots and days of week generate the highest sales and customer traffic. |
| → Track private label share within each product category and its evolution. |
| → Identify cart affinities to build better cross-selling strategies. |
Dashboard Groups
Retail Analytics is organized into five groups, each covering a different analytical lens:
| Group | Description |
|---|---|
| Sales | Core sales metrics: overview, period evolution, growth contribution, time slots, price index, private label, and promotions |
| Categories | Category-level analysis: contribution, supplier/brand performance, affinity, transferability, won/lost, and hierarchy |
| Products | SKU-level analytics: evolution, classification, concurrency (bought together), and substitutability |
| Stores | Location performance: detail by store, category KPIs by store, and assortment |
| Budget & ROI | Store-level ROI reporting for loyalty investments |
Common Filters
Most Retail dashboards share a common set of filters. Specific dashboards may add additional filters:
| Filter | Description |
|---|---|
| Base date period | Primary analysis window (max 12 months) |
| Compared date period | Period to compare against (defaults to same period previous year) |
| Audience | Customer segment to filter the analysis |
| Taxonomy | Product classification system used to group categories |
| Product Categories | Filter to specific categories within the selected taxonomy |
| Supplier / Brand | Filter by supplier or brand type (all / own brand / supplier brand) |
| Store categories | Group stores by category (e.g. format, region) |
| Stores | Specific store locations |
| Carts | All / Identified / Unidentified tickets |
| Consider only comparable stores | Exclude stores that did not have sales in both periods |
| Country | Filter by country |
| Chart granularity | Month / Week / Day of year / Day of month / Day of week |
:::tip Filter Saving All dashboards support saved filters. Configure your date range, taxonomy, store group, and audience once, then save for one-click reuse in future sessions. :::
How It Works
The Retail section consolidates transaction data from the Streaming API (POST /carts/realtime) with loyalty program event data (points, coupons, rewards). Data is stored in BigQuery and refreshed daily.
Product and category classification is derived from the item-level data sent in cart payloads. If product data is missing or category fields are not mapped, those items will appear as "Uncategorized" — use Data Quality to monitor and resolve data mapping gaps.