The Question

Click-and-collect has become a core omnichannel capability in grocery retail, offering customers convenience while helping retailers reduce delivery costs. Yet adoption varies widely across shoppers and stores.

Which factors most reliably predict click-and-collect (C&C) adoption in grocery retail, and how should channel strategy change when demographics are weak predictors?

Context

This analysis used basket-level transaction data from 2018–2024.

The dataset contained 97,624 baskets from approximately 55,000 shoppers, derived from 2.45 million item-level records.

Traditional demographic segmentation provided limited predictive power, making it difficult to prioritize store-level omnichannel investment.

The objective was to build a predictive framework that generalized to future periods, allowing the business to identify where C&C adoption would scale most effectively.

Key Insight

Behavioral signals and store context were far stronger predictors of adoption than demographic attributes.

Key findings:

• Channel context strongly influenced adoption probability
• Store effects remained significant even after regularization
• Demographics were directionally consistent but comparatively weak predictors

This suggests omnichannel growth should be driven by shopping behavior and operational context, not static audience segmentation.

Strategic Impact

• Shifted targeting strategy from demographic personas to behavioral signals

• Identified stores with structurally higher omnichannel adoption potential

• Enabled channel-specific messaging and promotions based on shopping patterns

• Improved planning for C&C capacity and store-level rollout prioritization

• Created a reusable feature engineering framework for omnichannel adoption modeling

Privacy Note

Values and retailer identifiers have been anonymized to preserve confidentiality while maintaining analytical relationships.