Retail Analytics Engine

Use Baskets to Decide What to Promote, Not Guess

Analyse product affinities to grow AOV and margin with smarter cross-sell and bundles.

Review My Basket Performance Discuss a Cross-Sell Strategy
affinity_matrix.py — Analysis
Affinity Score
0.87
↑ Strong Correlation
Margin Lift
+14.2%
Bundle Optimization
def calculate_lift(item_A, item_B):
# Analysing 1.2M transactions...
Product: Organic Coffee Rule #102
Attach: Oat Milk High Conf.

The Problem

Most product and promotion decisions still rely on intuition:

Cross-sell rules are generic (“related products”) and not grounded in purchase patterns.

Promotions are set at category level, with little view of attach rates or halo effects.

Planograms and recommendation slots ignore real co-purchase behaviour.

Finance cannot clearly see which bundles build margin vs. destroy it.

System Output:

Result: wasted promotion spend, flat basket size, and limited improvement in contribution per order.

Business Outcomes

Increase basket size and AOV by promoting products that are actually bought together.

Improve margin by designing bundles and promos that lift profitable combinations, not just volume.

Strengthen category roles by clarifying traffic drivers, margin builders, and attachment SKUs.

Inform pricing and layout with data on substitutes vs. complements at SKU level.

Market Basket Analysis Services

Transaction & Product Data Foundation

Use clean, structured data as the base for all basket insights.

Service deliverables:

  • Standardised transaction and line-item tables with customer, store/channel, and time dimensions.
  • Product hierarchy alignment (category, subcategory, brand, pack size, variant).
  • Data filters and eligibility rules (e.g., excluding returns, extreme outliers).
SCHEMA: RETAIL_TXN
transaction_id BIGINT
sku_variant VARCHAR
cust_segment ENUM
unit_price DECIMAL
VALIDATED

Affinity and Association Modeling

Quantify how products are actually bought together.

Service deliverables:

  • Association rule mining with support, confidence, and lift metrics.
  • Identification of key roles: anchors, add-ons, gateway SKUs.
  • Co-occurrence matrices by segment, channel, and season.
Apriori Analysis
Support (Freq) 0.45
Confidence 0.82
Lift (Strength) 2.4x

Cross-Sell, Bundling & Promotion Strategy

Turn patterns into concrete merchandising and marketing actions.

Service deliverables:

  • Cross-sell rule sets for onsite recommendations, email, and POS.
  • Bundle and kit design using profitable, high-lift product combinations.
  • Promotion scenarios: discount logic and expected halo effects.
Logic Configuration
IF Basket contains 'Running Shoes'
THEN Suggest 'Performance Socks'
Predicted Lift +18.5%

Activation & Testing

Deploy Market Basket insights into existing tools and workflows.

Service deliverables:

  • Integration of rule sets into ecommerce recommendation engines.
  • Segment-specific cross-sell strategies (new vs repeat).
  • A/B and multivariate test design to compare different rules.
Rules
Engine
Cart
Live Sync Active

Measurement & Continuous Optimisation

Track the commercial impact and refine the rules over time.

Service deliverables:

  • Dashboards for basket size, AOV, margin per basket, and attach rates.
  • Periodic recalibration of association rules as assortment evolves.
  • Playbook updates based on proven lift, not just model metrics.
AOV Growth
$84.50 +12%
Q4 Peak

Why Rudder Analytics

Commerce-focused

Deep experience with SKU-level data, promotions, and retail P&L.

Technical depth

Association rule mining, segmentation, and pricing analytics on a modern data stack.

End-to-end delivery

From data engineering and modeling to dashboards, tests, and activation in your tools.