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Simulated consumer research for retail analysts

TL;DR

Retail comps come down to shopper behavior: how often people visit, what they put in the basket, and how they respond to price, deals and assortment. Most of that is invisible until the company reports.
Neon Blue runs simulated shopper studies on the questions that come up every quarter in the sector: traffic, basket, trade-down, holiday spend, promotions, loyalty and channel shift.
Each study returns answers by shopper segment (visit frequency, income, age, channel) as a labeled input to your model, never a stock call.
Results come as a report. An ongoing data stream and an API are in build. The first study is free.

Neon Blue builds simulated audiences of a retailer's shoppers, AI models that answer the way a defined group of people would, grounded in Neon Blue's own paid studies and AI-moderated interviews with real people. Retail analysts at hedge funds and investment firms use Neon Blue to answer the shopper questions behind a comp model, such as traffic, basket, trade-down and holiday intent, while the quarter is still open.

What consumer questions matter most for retail stocks?

The same handful of shopper questions drives most retail models, from mass and specialty to off-price and club:

Will traffic hold? Visits per shopper, especially among heavy shoppers. The full method is linked below.
Is the basket growing from price or from mix? Price is disclosed. Whether shoppers drop categories or trade to cheaper items is not. The full method is linked below.
Are shoppers trading down? Department to off-price, premium to value, brand to store brand. The full method is linked below.
How will the holiday quarter land? Who plans to spend more or less, on what, and where. The full method is linked below.
Do deals add trips, or only discount planned ones? The full method is linked below.
Is the loyalty app driving visits, or rewarding them? Loyalty and app methods are linked below.

Each of these maps to a variable page with the full method, linked below.

Which Neon Blue studies answer them?

Analyst question	Study to run	Audience	What comes back
Will traffic hold next quarter?	Visit intent vs last 90 days	Recent shoppers by frequency and income	Expected change in visits by segment, and where lost visits go
Is basket growth price or mix?	Last-basket rebuild and next-basket intent at today's prices	Recent shoppers by frequency	Add, drop or swap shares, and the first category dropped
Are shoppers trading down?	Prior vs current store and tier choice	Category buyers who changed	Trade-down share, stickiness and trade-back triggers
How will holiday spend land?	Planned spend vs last year by category and channel	Holiday shoppers by income	Spend up, flat or down, with timing and destination
Do deals add trips?	Deal response	Shoppers split by deal use	New trips vs swapped trips, and basket effect
Is the app lifting visits?	Open intent and conversion to trip	App users vs non-users	Extra trips credited to opens vs habit

How do you read retail traffic before the company reports?

Ask the retailer's recent shoppers how many times they visited in the past four weeks, then whether they expect to visit more, the same or less in the next three months, and why. Weight by visit share, so heavy shoppers carry the read. The method is on the foot traffic page, and the full comp build, traffic and basket together, is on same-store sales.

How do you tell price from mix in the basket?

Hold the price constant in the question and ask only about the basket. "At today's prices, will you buy the same as last time, add something, drop something or switch to a cheaper item?" Any change is mix. The average ticket and basket size page has the question set and an output layout.

How do you test a price increase or a deal before it shows in comps?

For a shelf or ticket price increase, ask shoppers what they would do at the new price: change nothing, buy less, trade down, visit less, or go elsewhere. Price increase tolerance covers the method. For club or paid membership fee hikes, use membership fee tolerance. For a deal or a coupon, ask whether it would bring an extra trip or replace a full-price one. A deal that only swaps trips lowers the basket without adding traffic.

What does a retail study look like?

Hypothetical: an off-price retailer gained shoppers while apparel prices were high. Prices are cooling, and the analyst wants to know whether those shoppers stay or drift back to their old stores before the holiday print.

Study question: "In the last year, did you start doing more of your clothing shopping at an off-price store? If prices at your old store stopped rising for the next three months, would you go back to shopping there as much as before, a bit more than now, or stay where you are?"
Audience: adults who bought apparel in the last six months, split by when they started shopping off-price, household income and age.
Follow-ups: how the off-price assortment compares, and what the old store would need to do to win them back.

Example output layout (illustrative, not a real result)

The shape of a Neon Blue shopper read. Cells are blank on purpose; a real study fills them.

Segment	Would go back fully	Would go back partly	Would stay	Off-price as good or better	What would bring them back
Moved in the last year, lower income	__%	__%	__%	__%	Coded themes with sample verbatims
Moved in the last year, higher income	__%	__%	__%	__%	Coded themes with sample verbatims
Long-time off-price shoppers	__%	__%	__%	__%	Coded themes with sample verbatims

How the analyst reads it: if recent movers rate the assortment as good or better and say they would stay, the gain is sticky into the holiday quarter. If they say they moved only for price and would go back if prices settled, the gain is at risk. The stay share goes into the traffic and share lines labeled as simulated.

Want this run on your own name? Neon Blue's first study is free: start a free first study.

When should retail analysts run these studies?

Inside the quarter, after a price change, a loyalty redesign, a major promotional event or ahead of the holiday peak, and before the quarter closes. A repeat read with the same questions each quarter gives a trend you can line up against reported comps. Pre-earnings consumer checks lays out the calendar.

What can't a shopper study see?

Operations: staffing, fulfillment speed, weather and calendar shifts.
Inventory and markdowns: out-of-stocks and clearance change the print without changing intent.
Unit growth: new openings, closures and remodels change the comp base, not shopper intent.
Treat each read as one directional input, not a settled number.

Questions about consumer research for retail analysts

What consumer data do retail analysts use?

Mostly card and foot-traffic data for what already happened, plus channel checks and expert calls. A Neon Blue study adds why shoppers are changing and what they plan to do next quarter. See how simulated consumer data differs from card panel data.

Can a simulated study cover a specific retailer's shoppers?

Yes. The audience is defined as that retailer's recent shoppers, cut by visit frequency, income, age and channel. You can name the retailer in the study.

Can it compare two retailers?

Yes. Ask shoppers of both, or ask one retailer's shoppers where else they go for the same trip. The answer shows which rival takes lost visits. Brand switching covers the method.

Does Neon Blue predict retail comps or stock moves?

No. Neon Blue answers the shopper variables in your model, such as visit intent or basket mix. It doesn't make stock calls or give price targets.

How do I start a free first study?

Bring the retailer, the quarter and the shopper question you trust least in your model. Neon Blue's first study is free. Start a free first study