For funds
Promotion sensitivity: how simulated consumer surveys estimate it
TL;DR
- Promotion sensitivity is how much a brand's volume depends on discounts, multibuys and app offers. When a company says it will "rationalize promotions" to protect margin, the open question is how much volume leaves with the deals.
- A Neon Blue study asks the brand's own buyers what they would do at full price: buy the same, buy less often, stock up only on deal, or switch to whatever is on promotion.
- The output is a deal-reliance split by segment and the share of volume at risk if promotions pull back. It is a labeled input for your volume and margin lines, not 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 company's customers, 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. For funds, a promotion study answers one model question: how much of this volume is bought only because it is on deal?
What is promotion sensitivity, and why does it matter to a model?
Promotion sensitivity is the gap between what people buy on deal and what they would buy at the regular price. It sits behind two lines that usually move in opposite directions:
- Margin. Fewer, shallower promotions lift price realization.
- Volume. Shoppers who only buy on deal either wait, stock up less or go to whichever rival is on promotion that week.
Management guidance tends to cover the margin side. The volume side is an assumption, and it is often the one that decides whether the quarter works. It is closely tied to price increase tolerance, because pulling a promotion is a price increase for the people who used it.
How does a Neon Blue study estimate promotion sensitivity?
- Define the audience as the brand's buyers. Recent buyers, cut by how often they buy and how often they say they buy on deal. Deal-led buyers and full-price loyalists need separate cells, since the answer is completely different for each.
- Rebuild the last purchase. Was it on promotion, a multibuy, a coupon or an app offer, or full price?
- Remove the deal. Ask what they would do if the next purchase were at the regular price with no offer.
- Ask where volume goes. Buy the same, buy less often, wait for the next deal, switch to a rival on promotion, or switch to private label.
- Weight by volume. Heavy buyers who buy only on deal carry the risk. A light buyer who would walk matters less.
How simulated audiences work explains how each simulated buyer is grounded in real people's answers.
What questions go into a promotion sensitivity study?
| Question | Model input | Why it matters |
|---|---|---|
| Was your last purchase on promotion, a multibuy, a coupon or full price? | Deal share of volume | Baseline reliance on deals |
| If there were no deal next time, would you still buy it? | Full-price conversion | The core sensitivity read |
| Would you buy the same amount, less often, or wait for a deal? | Volume timing | Separates lost volume from delayed volume |
| If a rival brand were on promotion, would you switch for that trip? | Deal-driven switching | Shows how much volume follows the deal |
| Do you stock up when it's on deal? | Pantry loading | Explains lumpy quarters around promo calendars |
| Do you use the brand's app or loyalty offers? | Owned-channel offers | Targeted offers can replace broad discounts |
What does a promotion sensitivity study look like?
Hypothetical: a beverage company has said it will cut promotional depth next quarter to lift price realization. The analyst's model assumes volume dips only slightly.
- Study question: "Next time you buy this brand, there is no multibuy or discount, and it is at the regular shelf price. Would you buy the same amount, buy it less often, wait for a deal, switch to another brand that is on promotion, or buy the store brand?"
- Audience: households that bought the brand in the last 60 days, split by purchase frequency and by whether their last purchase was on deal.
- Follow-ups: how much they would stock up at the next deal, and whether an app offer would keep them.
Example output layout (illustrative, not a real result)
The shape of a Neon Blue promotion read. Cells are blank on purpose; a real study fills them.
| Segment | Buy same at full price | Buy less often | Wait for a deal | Switch to rival on deal | Store brand |
|---|---|---|---|---|---|
| Heavy buyers, last purchase on deal | __% | __% | __% | __% | __% |
| Heavy buyers, last purchase full price | __% | __% | __% | __% | __% |
| Light buyers | __% | __% | __% | __% | __% |
How the analyst reads it: if heavy deal-led buyers mostly say "wait for a deal", volume moves between quarters rather than disappearing. If they say "switch to a rival on deal", the loss is real and goes to a named competitor's shelf. The analyst sets the volume dip by segment, labels it simulated, and compares it with reported volume at the print. Brand switching covers the rival side in more depth.
Want this run on your own name? Neon Blue's first study is free: start a free first study.
When should a promotion study run against the reporting calendar?
Run it as soon as management signals a change in promotional intensity, and before the change shows in scanner or card data. Good triggers are a stated plan to cut promotions, a rival starting a price war, a retailer pushing its own brand, or a shift from broad discounts to app-only offers. A second read mid-quarter tells you whether deal buyers waited or left. The timing logic is the same as in pre-earnings consumer checks.
Which sectors is this most useful in?
- Consumer staples: where multibuys and retailer promotions drive a large share of volume. See consumer research for staples analysts.
- Restaurants: where value deals and app offers decide traffic (see consumer research for restaurant analysts). Pair with foot traffic and average ticket and basket size, since a deal can lift visits while it lowers the check.
- Retail and apparel: where markdown cadence trains shoppers to wait.
- Subscriptions: where intro discounts end and the full price starts. See subscriber churn.
What are the limits of a survey-based promotion read?
- It can't see the promo calendar of rivals or retailers ahead of time. Ask about likely scenarios instead.
- People underplay how much they respond to deals. Read the difference between deal-led and full-price buyers, not the absolute share.
- In-store displays and placement change behavior in ways people rarely describe. The study covers the offer, not the shelf.
- Neon Blue tests simulated answers against real responses on attitudinal and intent questions, including price sensitivity. Treat the read as one directional input, not a settled number. How funds evaluate a new alternative data source covers how to trial it on names you know.
Questions about estimating promotion sensitivity
What is promotion sensitivity?
It is how much a product's sales depend on discounts, multibuys, coupons and app offers. High sensitivity means volume falls or moves to rivals when deals pull back.
How is it different from price increase tolerance?
Price tolerance asks about a higher list price for everyone. Promotion sensitivity asks about the people who were buying below list, which is often a smaller group carrying a large share of volume.
Can a survey tell lost volume from delayed volume?
Yes, if it asks. "Wait for the next deal" is delayed volume. "Switch to a rival on deal" or "stop buying" is lost volume. The split matters for which quarter takes the hit.
Does this predict a company's volume?
No. It gives a direction and relative size by segment for your volume and margin assumptions. Neon Blue doesn't make stock calls or give price targets.
How do I start a free first study?
Bring the brand, the promotional change, and the volume assumption you trust least. Neon Blue's first study is free. Start a free first study