Retail / Multi-Brand E-Commerce

How Coop scaled CRO across 10 e-commerce brands.

A centralized CRO program improves testing quality and conversion outcomes across Coop's 10 e-commerce brands.

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“As a cooperative with multiple formats, we rely on data-driven decisions with real impact. DRIP now supports ten of our formats and has helped boost both ARPU and conversion rates. Their regular collaboration delivers valuable insights and a strong A/B test win rate.”

Timo Dunke

Digital Marketing Manager Personalisierung

Brands supported: 10
CRO system rollout: Portfolio-wide
Experimentation program: Ongoing

Partnership since 2023

The goal

Coop operates multiple e-commerce formats, each with different audiences and category dynamics. Testing quality and speed varied by format, which made it difficult to scale winning patterns or compare outcomes.

The goal: enable 10 Coop brands to scale A/B testing effectively and make data-driven e-commerce decisions across the group.

Our process

How every brand starts

Every collaboration starts with a structured research package: GA funnel data, customer reviews, heatmaps, click and scroll maps, and brand-specific insights. From this foundation, we develop and launch three initial A/B tests to validate opportunities and demonstrate impact.

Step 1

Research

Repeated friction from app reviews, grocery shopping, promotions, checkout and quality complaints is grouped into shared drivers: comfort, security, autonomy and progress.

Five feedback sources (app reviews, grocery orders, promotions, checkout, quality complaints) feed one finding: uncertainty about the promise, tagged comfort, security, autonomy and progress.

Step 2

Prioritization

Portfolio reviews decide which friction patterns become local tests and which local winners can be reused in other brands.

A friction pattern, promo price unclear, shows up in 4 of 10 formats; one format turns it into a local test.

Step 3

Parallel testing

Every brand works with the same hypothesis, QA, analytics and readout standards, and tests its own high-value user problem. Winning mechanisms and failed assumptions are visible to all brands.

Portfolio CRO scales when the process is shared and the hypotheses remain local. So far this has led to more than 100 planned experiments across Coop brands.

Timeline over four weeks with five tests running in parallel: promo labels, order check, app login, self-checkout and freshness info.

What the research found

Five recurring risks showed up across Coop brands: price fairness, order completeness, digital stability, procedural control and freshness trust.

#Purchase driverDriver scoreMentionsSentiment

Promo / Price Accuracy

Trust

Shoppers reacted strongly when offer claims and charged prices did not feel aligned.

Driver score96

Mentions78

Sentiment8% positive17% neutral75% negative

Online Order Completeness

Core functionality

Missing items and partial delivery created high-friction trust breaks.

Driver score94

Mentions74

Sentiment10% positive16% neutral74% negative

Website / App Stability

User experience

Slow pages, re-login loops, cart clears, and checkout failures reduced ability.

Driver score92

Mentions66

Sentiment12% positive18% neutral70% negative

Self-Checkout Controls

User experience

Controls needed to feel predictable and fair rather than accusatory.

Driver score90

Mentions61

Sentiment9% positive21% neutral70% negative

Freshness / Quality

Product quality

Freshness assurance mattered because online grocery removes physical inspection.

Driver score89

Mentions59

Sentiment22% positive18% neutral60% negative

What it meant for the roadmap

Routine shopping had to feel fast, reliable and controllable
Comfort, security and autonomy shaped the research across the portfolio.
Prices and complete deliveries decided trust
Gaps between the promise and the delivery shaped the buying decision.
Less uncertainty and fair processes worked best
Clear offer terms and predictable controls made the process easier to follow.
Visible category filters made access easier
Familiar shortcuts helped customers who already knew what they wanted.

Overall impact

Coop now runs CRO with a single operating logic across 10 e-commerce formats while preserving format-level flexibility.

The program improved test quality, increased organizational learning velocity, and raised confidence in data-driven decisions.

The takeaway

For multi-brand retailers, CRO impact scales when experimentation is operationalized as a shared system, not isolated projects.

A portfolio model enables faster compounding of learnings and stronger economics from each format's traffic base.

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