Merchandise Decisions Grounded in Data, Not Opinions
Rudder Analytics builds merchandise views that show which SKUs earn their place—by revenue, margin, and working capital impact.
Where Merchandise Decisions Typically Break
Most retailers and brands have transaction data, but not a reliable merchandise view. Problems usually look like this:
Range and depth decisions driven by intuition and legacy lists.
Sell-through and margin reports that vary by system or team.
Markdown and promotion impact hard to separate from baseline demand.
Inventory ageing reports that don’t account for product roles or lifecycle.
"The business impact is direct: lost sales from stockouts, excess stock on low-value SKUs, higher markdown cost, and longer planning cycles."
The Business Case for Merchandise Analysis
Increase full-price sell-through by backing the right SKUs, categories, and ranges.
Reduce excess and obsolete inventory by identifying low-velocity and misaligned items early.
Improve margin and GMROI by guiding pricing, promotion, and markdown decisions.
Shorten buying and range-planning cycles by giving teams trustworthy, ready-to-use views.
Core Merchandise Analysis Capabilities
Merchandise Performance Analytics
Understand how each product contributes to sales and profit, not just volume. Rudder designs models and dashboards that:
- Measure sales, margin, GMROI, and sell-through at SKU, style, category, and brand levels.
- Separate performance by channel, region, and store cluster.
- Distinguish structural underperformers from items blocked by stock or exposure.
Category Performance: Footwear
Assortment Heatmap (ABC)
Assortment and Range Analytics
Align the assortment with demand patterns and strategic roles. The service includes:
- ABC and contribution analysis across SKUs, categories, and brands.
- Cluster analysis to group stores, regions, or customer segments with similar behaviour.
- Range rationalisation views showing duplication, gaps, and long-tail drag.
Lifecycle and Markdown Readiness
Model how products move from launch to end-of-life. Rudder builds views that:
- Classify SKUs by lifecycle stage and velocity.
- Track weeks of supply versus target for each lifecycle phase.
- Highlight SKUs drifting toward markdown risk well before season-end.
Product Velocity Curve
Price Elasticity Model
Price, Promo, and Basket Impact on Merchandise
Measure how pricing and promotions affect merchandise performance, not just short-term volume. This includes:
- Price realisation and elasticity indicators by product and category.
- Comparison of promo vs non-promo performance and halo effects.
- Basket-driven views that show attachment SKUs and trade-up potential.
Store and Channel Mix Analytics
Give each channel and store a merchandise view tailored to its role. Rudder structures analytics to:
- Compare assortments and performance between online and offline channels.
- Evaluate store clusters by demand profile, not just sales totals.
- Surface reallocation opportunities between locations and fulfilment nodes.
Channel Contribution
The Team on Merchandise Analysis
Merchandise Analytics Consultants
who understand category roles, planning rhythms, and retail P&L.
Data Engineers
who design pipelines and warehouse models for high-SKU, multi-channel environments.
Data Scientists and Analysts
who build clustering, elasticity, and demand models where required.
Use Cases
Fashion and Lifestyle Brand
High markdown spend and frequent stockouts in key sizes.
Size-curve analytics, lifecycle tagging, and store-clustered assortment views.
Fewer lost sales in key SKUs and reduced margin erosion from late markdowns.
Omnichannel Retailer
Online and store teams running separate, conflicting merchandise reports.
Unified merchandise warehouse, shared KPIs, and BI layer across channels.
Aligned decisions on range, depth, and allocation with shorter planning cycles.
D2C Brand with Rapid SKU Expansion
Range proliferation and unclear contribution by product.
Contribution and ABC analysis, basket-led roles, and exit criteria by SKU group.
Leaner assortment with stable or higher revenue and lower working capital.
Maturity Path for Merchandise Analysis
Stabilise and Baseline
- Consolidate key data sources.
- Define KPIs and core merchandise views.
Build and Embed
- Implement the warehouse, BI architecture, and core dashboards.
- Integrate into planning and review cadences.
Optimise and Automate
- Add advanced models (clustering, elasticity, basket analysis).
- Automate more of the reporting and planning inputs.
