Shelf and pack decisions
Where a product sits in the planogram, how a new pack reads next to its neighbors, what happens when the price moves or the favorite is missing. Trade and category teams decide these all year.
Virtual shelf simulator for shopper research
A new pack, a planogram, a price change, an out-of-stock. Shopper Insight Lab puts participants in a virtual aisle with a cart, a budget and three missions, and records where the mouse lingers, what goes into the cart, what comes out and what gets bought. In 5 to 8 minutes per session, in a browser.
Illustrative data. The scenes play on their own.
The work
Planogram, new pack, price change, out-of-stock. Each one changes what happens in front of the shelf, and most are decided with what shoppers declared in a survey.
Where a product sits in the planogram, how a new pack reads next to its neighbors, what happens when the price moves or the favorite is missing. Trade and category teams decide these all year.
The survey shows the packs and asks which one the person would buy. The observed route is a physical mock store or an in-store test, which takes weeks, needs a location and works with a small sample.
In a survey, people answer what sounds right. At the shelf they grab the usual brand, trade down when money is short and walk past what they never noticed. The method that observes this is too heavy for everyday decisions, so those get made on declared data.
Participants shop your category in a browser, with a cart, a budget and three missions. The platform logs attention, additions, removals and the final choice, and the dashboard reads them by attention, choice and trade-offs, the swaps people make when the budget tightens or a product is missing.
Ask shoppers which pack they prefer and you get an opinion. Hand them a cart and a budget and you get a purchase.
What it answers
Each view of the dashboard exists because it answers a question that category, trade or packaging teams ask before touching the shelf.
Attention heat mapmouse and time
A heat map over the shelf, from the top shelf to the bottom, built from mouse movement and the time spent on each product. Attention share by product shows how much of the shelf time each item earns.
Attention × Choice4 quadrants
Every product lands in one of four quadrants: Champions, Attracts but does not convert, Brand loyalists and Invisible. A pack that is seen and left on the shelf is a different problem from a pack nobody saw.
Reduced budgetmission 2 of 3
In the second mission the budget is cut and the participant adjusts the cart. You see what leaves first, what is protected, and how the average ticket and the items per cart move from one mission to the next.
SubstitutionsA → B
In the third mission the favorite product is unavailable. The dashboard lists the most frequent substitutions, product by product, so you know which neighbor inherits the purchase.
Additions vs removalsper product
Every addition and every removal is logged. A product that is often added and often removed is being considered and losing at the last step, something a table of final choices would hide.
Post-purchase questionnaireafter the behavior
Only after shopping does the participant answer: reason for the choice, how hard it was to decide, importance of price, alternatives considered, satisfaction and influence of the budget. The declared answer is read next to the observed behavior.
Method
The participant spends 5 to 8 minutes in a flow that feels like a short game. The researcher receives behavior organized by mission.
The Cassi.ai team configures the study with you: category, products, prices, planogram and missions.
Your shelf is built for your study and is never reused for another client.
The participant opens a link, accepts the consent terms and picks an avatar. Sessions run in Portuguese, English or Spanish, and a desktop is recommended.
Sessions are anonymous. Nobody is identified.
Each mission states the task and the budget before the shelf opens: free choice, reduced budget, out of stock.
Every participant goes through the three missions in the same sequence.
The participant browses, adds and removes products, watches the budget and checks out. The platform logs mouse movement, time on each product and decision time.
Attention here means mouse and time. It is never called eye tracking.
Reason for the choice, difficulty, importance of price, alternatives considered, satisfaction, budget influence, recommendation and an open comment.
The questions come after the behavior, so they do not steer it.
KPIs per mission and three tabs, Attention, Choice and Trade-offs, with the heat map, the four quadrants and the substitutions.
Every reading shows the number of sessions behind it.
What is behind it
Each number below describes how the product works. None of them is a promise about your result.
For shelf decisions that come too often for a mock store and matter too much to settle with a declared survey.
Questions
Shopper Insight Lab is a Cassi.ai product for shopper research. It is a virtual shelf purchase simulator: the participant shops a virtual aisle as they would in a real supermarket, with a cart and a budget, and the platform observes the behavior in place of asking about it. The researcher reads the result in an insights dashboard.
The participant accepts the consent terms, chooses an avatar, reads the mission brief, shops at the shelf with a cart and a budget, answers a post-purchase questionnaire and finishes. A session is estimated at 5 to 8 minutes, a desktop is recommended, and the flow is native in Portuguese, English and Spanish.
Free choice asks the participant to buy snacks for the week within the budget. Reduced budget cuts the budget and asks them to adjust their choices. Out of stock makes their favorite product unavailable and asks them to find an alternative. The three missions run in sequence, so the same person is observed under three conditions.
Attention is measured from mouse movement over the shelf and from the time spent on each product, together with decision time. It is a behavioral indicator recorded in the browser. The platform never calls it eye tracking, and the dashboard labels it as mouse and time based attention.
The simulator shows comparative behavior at a controlled shelf: which product earns attention, which one is chosen, what is swapped when the budget tightens or a product is missing. It supports shelf and pack decisions, and it never presents simulated behavior as a sales forecast.
The Cassi.ai team configures each study together with the client: the category, the products, the prices, the planogram and the missions. A shelf belongs to the study it was built for and is never reused for another client.
KPIs for participants, average ticket per mission, items per cart, average time at the shelf and the share of participants who went over budget. Three tabs, Attention, Choice and Trade-offs, bring the heat map over the shelf, attention share by product, the four attention and choice quadrants, budget influence, the most frequent substitutions and additions versus removals per product.
Sessions are anonymous. The platform records behavior at the shelf, such as mouse movement, time on each product, products added and removed and the final choice, plus the answers to the post-purchase questionnaire. It never identifies the participant.
Tell us the category and the decision: a new pack, a planogram, a price move, an out-of-stock. We show how the study would run in the simulator, from the mission brief to the dashboard.