Sena adds the two figures the history omits: the price buyers accept and the point they move away.
Price optimization finds the price a product earns most at, across the range of prices buyers would accept. Three shortcuts leave the second half of that sentence unmeasured.
The curve gets built from historical volume at historical prices. Those prices were chosen by the business, so the curve describes decisions already taken.
The price comes from unit cost with a margin applied. That method produces a floor and leaves the ceiling open.
The model returns a single price. Acceptance shifts country by country and income band by income band, so one optimum overprices somewhere and underprices somewhere else.
Sena is the decision AI with access to real-world data. It records what buyers paid and asks the same buyers what they would accept, so the curve extends past the prices the business has already charged.
Buyers state the price they would pay and the price at which they move to something else, so the range is measured at both ends.
Acceptance splits by market and by buyer group, so a single figure resolves into the groups behind it.
Stated acceptance sits beside what the same buyers paid, so intention reads against a recorded purchase.
Price optimization finds the price at which a product earns the most, given what it costs to make and what buyers will pay for it. The cost side is knowable from inside the business. The acceptance side sits beyond it, and that is the half most models estimate.
| Half | Where it comes from | How it is usually handled |
|---|---|---|
| Cost and volume | The ledger, the plant, the contract. | Known to the decimal. |
| What buyers accept | The market, one buyer at a time. | Estimated from past volume. |
A demand curve fitted to historical sales can only describe prices the business has already charged. If a product has sold between two figures for three years, the curve is confident between those two figures and speculative everywhere else. The optimum frequently sits beyond the tested range, which is the part the history leaves open.
Four methods are in general use, and they differ in what they can see.
Unit cost with a target margin applied. Produces a floor, fast, and leaves the buyer out of it.
Price set against what rivals charge. Moves with the market and inherits its mistakes.
A curve fitted to past volume at past prices. Reliable inside the tested range and speculative outside it.
Ask category buyers what they would pay and where they would move, then set it beside what they paid. The curve extends past the prices already charged.
Price sensitivity analysis measures how far a price can move before buyers move. It returns three figures per market: the price treated as too cheap to trust, the price treated as fair, and the price at which buyers leave.
Four collected inputs set the acceptance side, applied per market.
| Input | What it answers |
|---|---|
| Stated preference | The price a buyer accepts, the price they treat as too high, and where they move. |
| Receipts | What the same buyer paid in the end, dated, so stated meets the record. |
| Store captures | What the product listed for that week, and what sat beside it. |
| Geo-verified photos | The shelf price and the promotion running against it, placed and timed. |
Three questions sit outside the model, and each one moves the answer it returns. A curve fitted to three years of one price band carries weight only inside it. A volume figure counts the buyers lost and leaves open which rival received them.
One price across eight markets is eight different decisions wearing one figure.
Real people share what they buy and prefer, under explicit consent. Sena captures it directly at the source, so every figure traces back to where it came from whenever a number comes under question.
Stated acceptance and the receipt behind it arrive from the same buyers, and every figure carries the market and the day.
Buyers name the price they accept and the price they leave at, so the decision runs against a range.
The competing line buyers name puts a price on the volume a rise would cost.
What buyers say they would pay sits next to what they paid, so a stated ceiling carries a check.
A price model is confident about cost and estimating about acceptance. Sena builds every figure from real-world signals captured when the question needs it, from a buyer naming the price they would leave at through to the receipt showing what they paid.
Records what buyers paid for this line and for the lines they consider against it, so trading down shows up in the next read.
Reads the shelf price and the promotion running against it off images captured in real outlets, so the price a buyer faced is the price in the read.
Signal arrives from the consumer network under explicit consent. The price a buyer would leave at comes from that buyer.
Unit cost, volume history, listing records, and margin targets join over 250+ integrations, so the cost half meets the acceptance half.
Every figure holds its market and its capture date, so a price decision opens back onto the buyers who set the band.
Past price files, elasticity records, and volume histories, covering every cycle the line has been scanned in.
Four teams read the same band, and each one needs a different cut of it before they can commit.
The price point. Needs the accepted band and the switching point, per market.
The price within the range. Needs this point read against the other packs.
The margin. Needs the headroom above cost that buyers will fund.
The shelf position. Needs what the line was priced against the week it sold.
The same band, read against the alternative each category actually moves to.
The shelf price buyers accept, and the competing item they move to above it.
The specification and price pairing buyers fund against the tier below it.
The counter price that holds, and the own-label point buyers switch at.
The monthly figure buyers accept before moving down a tier.
One mechanism, applied per market and per category. Each step is documented, which is what carries a price decision through a margin review.
Real contributors share what they buy and pay under explicit consent, naming the price they accept and the price they would leave at.
Sena reports the accepted range per market, with the competing line buyers name at the top of it.
The stated band runs against recorded purchases from the same buyers, so the ceiling carries evidence behind it.
Most price models are built from costs the business knows exactly and acceptance it estimates from its own sales history, so the curve is confident only where the price has already been. Sena measures the other half.
| Capability area | Typical setup | Sena |
|---|---|---|
| The demand curve | Fitted to past volume at past prices. | Extended by what buyers say they accept. |
| Above the tested range | Extrapolated. | Stated by buyers, with the switching point. |
| Who leaves | A volume figure. | Named buyers, with the line they move to. |
| Market resolution | One optimum for the line. | A band per market, since the ceiling differs by country. |
| The shelf price | Taken from the list file. | Photographed in the outlet, dated. |
| Stated against paid | Held in separate systems. | Read together, with the underlying records held by the provider. |
| Evidence in a review | A curve and a confidence interval. | Open any band onto the buyers who set it. |
Three situations where a stated band changes the price.
Run a proposed increase against the accepted band per market, so the markets that absorb it separate from the markets that resist.
Set the launch price against what buyers pay for the line they use today, and the price they say they would move at.
Read which buyers moved to a cheaper line in the category and what they paid, before the next price decision.
Price optimization sets one number. The four decisions below are what a revenue team asks around it.
Every revenue decision Sena answers, in one place
The four levers together
Coordinates price, pack, promotion and mix across a range, one altitude above this single point.
Explore revenue management 02The ladder across packs
Reads this price against every other pack in the range, since a buyer compares them at once.
Explore price pack architecture 03The figures underneath
Supplies the elasticity and realized price figures this decision runs on.
Explore pricing analytics 04What rivals charge
Places the point against what competing lines charge in the same market that week.
Explore competitive pricing analysisThe walkthrough takes one line in one market, builds the accepted band from what buyers state, and runs a proposed price against it while the team watches.
Tell us the line and the market, and we will build the band.