RETAIL
Testing a retail decision
before it reaches the shelf.
Retail chains, brands sold in mass distribution, specialty chains: test your assortment, merchandising, private-label and banner-repositioning decisions on coherent populations of shoppers, before store deployment.
THE THESIS — CARDINAL PROMISE
A retail decision is not settled in the rationality of the shelf. It is settled in the instant when the shopper decides, between two products, which one to take, and for a reason they themselves do not know.
Our system lets you reconstruct that instant before deployment.
CASE STUDY: GENERAL-MERCHANDISE RETAIL CHAIN
“ How do you remove 40% of the SKUs in the breakfast aisle to launch a premium private label without driving away loyal customers? ”
Simulated on 2.8 million shoppers of the retail chain · 19 typologies of breakfast purchase behavior · 34 assortment configurations tested
The retail chain is looking to increase the value of its breakfast aisle: margins are structurally low, the space occupied is massive, and competition from national brands leaves little room to maneuver. The executive committee is considering cutting 40% of national-brand SKUs to introduce a higher-margin premium private label: the classic bet of modern distribution.
The risk is well known: customer studies show that 68% of shoppers in the aisle say they would switch retail chains if their favorite product disappeared. But what really happens? How many actually leave, how many stay, how many shift their purchase to the premium private label? Leadership must decide with a EUR 12 million industrial investment at stake.
What the system revealed
Stated intent massively overstates departure.
68% of shoppers say they would switch retail chains. In a 3-month behavioral simulation, only 11% actually do. The assortment disruption triggers friction, not a divorce. The large majority tries the private label, tests an alternative, or adjusts their habits. The critical threshold lies elsewhere.
The real risk is in the neighboring category.
The real danger is not losing the breakfast shopper. It is the spillover effect on adjacent categories: biscuits, hot beverages, spreads. Shoppers who experience frustration in breakfast also reduce their basket in neighboring categories for 8 to 12 weeks. The cumulative loss far exceeds that of the original aisle.
The rollout sequence changes everything.
An abrupt switch (week 0: everything changes) achieves 34% acceptance. A gradual rollout over 12 weeks (premium private label introduced in week 0, national brands progressively phased out between weeks 4 and 12) achieves 71% acceptance, and preserves the average basket in adjacent categories.
What Imagine All The People did that no one else could have done
Our system interviewed 4,200 synthetic shoppers in a simulated shelf environment, tested 34 assortment configurations in progressive variations, and projected effects not only on the target aisle but also on the 7 adjacent categories identified as sensitive to frustration. The “gradual switch + premium private-label signage” strategy dominates 26 of the 34 configurations.
No marketing study can simulate 34 configurations across 4,200 profiles. No in-store A/B test can measure the cross-category effect. No econometric model can project the weekly trajectory over 12 weeks.
- BREAKFAST SHOPPER RETENTION
- 66% → 89%with gradual sequencing
- CROSS-CATEGORY EFFECT AVOIDED
- +EUR 2.4Mof basket preserved
- ADDITIONAL AISLE MARGIN
- +18 ptsvs. historical assortment
APPLICATIONS
Three uses for retail.
Assortment & private label
Test an assortment overhaul, a private-label launch, a range discontinuation on differentiated shoppers. Anticipate retention effects, migration toward internal alternatives, and impact on adjacent categories.
Merchandising & store experience
Simulate a shelf reorganization, a customer-journey redesign, a store-concept transformation. Measure readability, friction, and induced circulation patterns: before the deployment investment.
Pricing, promotions, banner positioning
Anticipate reaction to a pricing policy, a promotional plan, a banner repositioning. Identify segments that accelerate, those that drop off, and calibrate the volume-margin balance.
METHODS
Three ways to test a decision.
Comparing what a synthetic-population simulation brings against existing methods.
| AI PERSONAS | TRADITIONAL SURVEYS | IMAGINE ALL THE PEOPLE |
|---|---|---|
| SCALE | ||
| 5 to 10 fixed personas | 500 to 2,000 respondents | Up to 10 million coherent individuals |
| DYNAMICS | ||
| Frozen characteristics | Pre-written questions, static answers | Agents that ask, probe and brainstorm |
| TIME HORIZON | ||
| Assumed current state | Snapshot of the present | Projection of future consequences |
| DEPTH | ||
| One synthetic answer | Aggregated statistical tables | Individual dialogue + collective analysis |
| DATA & SOVEREIGNTY | ||
| Limited demographic data · US public cloud | Personal data collected at scale · Public cloud | Zero personal data · Infrastructure in France · On-premise possible |
OTHER CASES
Three other decisions tested in the sector.
SPECIALTY CHAIN: CONCEPT REDESIGN
“How do you modernize the store concept of a home-improvement chain without losing the professional tradespeople who generate 40% of revenue?”
Simulated on 1.4 million customers of the retail chain · 17 profiles of relationship to the store
Tradespeople do not reject modernization; they reject the loss of operational reference points: aisles too wide, products grouped by use rather than by technique, staff perceived as “too generalist.” A concept that modulates modernization by zone (technical zone preserved, consumer zone modernized) retains both audiences.
- TRADESPEOPLE RETENTION
- 71% → 92%
- CONSUMER APPEAL
- +34 pts
E-COMMERCE: RETURNS POLICY
“How do you introduce return fees without hurting e-commerce conversion or customer satisfaction?”
6 pricing-policy scenarios tested · 28 dynamic follow-ups on moments of abandonment or dispute
Returns are not rejected as a principle: customers understand they carry a cost. What tips the balance is discovering the fee at checkout: fees announced at the end of the funnel = 47% abandonment. Fees announced on the product page = 12% abandonment. Upfront transparency changes everything, even at the same price.
- CART ABANDONMENT RATE
- 47% → 12%
- POST-RETURN NPS
- +18 pts
GROCERY: TRANSITION TO BULK
“Anticipating the evolution of the bulk aisle over 5 years in a general-merchandise chain.”
Projection over 60 months of shopper behavior evolution · 13 profiles of relationship to bulk buying
Bulk adoption is not linear: it moves in triggered waves — a public policy, a rise in packaging prices, a media event. The strategy that maximizes the trajectory is not the one that invests uniformly, but the one that prepares capacity to absorb the waves when they arrive. An aisle sized for 8% of shoppers, activatable to 22% within 6 months, is more profitable than one sized for 15% from the outset.
- 5-YEAR ADOPTION
- 6% → 21%
- COST OF OVERSIZING
- −EUR 3.8M
METHOD — RETAIL
A fifteen-minute study.
How a test runs on our system, from the brief to the delivered study.
1
You formulate your decision
A question in plain language, phrased the way you would phrase it in the executive committee. No formalism required.
2
The system builds the populations
Target segments are reconstructed from public aggregates and industry data. No personally identifying data enters the system.
3
The agents interview, discuss, brainstorm
The system runs the interviews itself, probes friction points, and proposes alternative scenarios you had not considered.
4
You explore the study
You talk with individual voices, challenge the reasoning, change the decision and watch the impact in real time.
Your next decision
Which decision do you want to explore?
Describe your need. We can point you to the right level of support.
