Retail & Consumer Analytics
As of 2026-08-16
Retail (Retail/CPG block, 12%,) is high-volume, high-velocity, low-margin, daily-cadence data — engineer for volume and speed, not elegance. KPIs: sell-through, GMROI, like-for-like sales, basket size, markdown effectiveness — always framed as the availability-vs-markdown trade-off. SAP CAR holds the transactional POS grain and powers forecasting/availability; Datasphere builds cross-functional retail data products + external signals; SAC delivers merchandising/store-ops dashboards. Bypassing CAR's retail structures is an anti-pattern. Omnichannel is a data-unification problem (BDC's pattern). Tier the volume (CAR grain operational, aggregate for trends); match latency to the decision. CAR fluency + merchandising literacy is the rate-setting combination.
What you will learn
- Explain why retail analytics is a volume-and-speed discipline, not a boardroom BI exercise, and where SAP CAR fits versus a generic warehouse
- Apply the availability-vs-markdown trade-off to frame sell-through, GMROI, and markdown-effectiveness KPIs
- Recognize the anti-patterns that make retail analytics slow, expensive, or margin-blind — and how to avoid them
- Position CAR/Datasphere fluency plus merchandising literacy in your day-rate conversation
Retail and consumer analytics serves the Retail and CPG demand block, one of the largest verticals for SAP analytics work in Europe. Retail is the discipline of high-volume, high-velocity, low-margin data: point-of-sale transactions arrive by the million every day, margins are thin enough that a single markdown-timing decision measurably moves the P&L, and the operating cadence is daily, sometimes hourly, never monthly. The analytics discipline that wins in this vertical is built for volume and speed first, boardroom elegance second — a beautifully designed dashboard that updates once a week is worthless to a merchandiser who needs to decide today whether to mark down a line before the weekend.
The KPIs that run a retail business
Prerequisites
- Review core concepts first: C087, C083, C046
Outcomes
- Understand the core concepts behind retail & consumer analytics
- Apply Retail in a typical SAP analytics engagement
- Explain the core architecture and decision points for Retail & Consumer Analytics
- Apply a repeatable implementation pattern in a 15-minute lab format
Full module available to members. The full module adds: the decision framework · the end-to-end scenario walkthrough · the KPI scorecard · the anti-patterns · the code blocks · the knowledge check · the diagrams.