Purchase Intent: Definition, Scale & Examples

Purchase intent measures how likely someone says they are to buy a specific offer under stated terms. A five-point scale shows the strength of that intention, but its positive share is not a sales forecast. This guide explains the question, calculations and follow-up evidence needed to choose a concept for a trial.

Example: comparing two sparkling-tea concepts

Lumen is a hypothetical sparkling tea offered as a six-pack at $15 in shoppers’ usual grocery store. The team is choosing a concept for a product trial. It needs the full answer distribution and reasons, rather than one headline score.

Concept A leads on positive intent, 50% versus B's 45%, while B leads on definite intent, 25% versus A's 18%. Which pattern fits your next investment? “Definitely would” and “probably would” describe different strength of intention, so the summary you choose changes the descriptive ranking.

The complete distributions below show the difference. Use them to separate a broad trial proposition from a strong-fit audience hypothesis, then examine the reasons and purchase opportunity that could justify either. The scores show which concept to examine more closely. They do not tell you how much you will sell.

The question fixes the offer and buying window

Use the same complete offer and question for each concept.

Imagine this six-pack of Lumen sparkling tea costs $15 at the grocery store you normally use. How likely would you be to buy it within the next four weeks?

Offer “definitely would,” “probably would,” “might or might not,” “probably would not,” “definitely would not” and “not applicable.” Follow with the reason and the product they would buy instead. Someone who rarely shops in that channel should not be forced into the same denominator as an eligible buyer.

100 eligible responses per concept

Two summaries, two descriptive rankings

A: 50% positive · 18% definite

B: 45% positive · 25% definite

The five-point distributions show how the ranking changes with the summary you choose. Each contains 100 responses (100%); the percentages are descriptive intent, not a sales forecast.

A's positive share includes definite and probable intent

For A, the first table contains 18 definitely-would, 32 probably-would, 24 “might or might not”, 16 probably-would-not and 10 definitely-would-not answers. With 100 eligible responses, the counts are also 18%, 32%, 24%, 16% and 10%.

The top-two-box share is (18 + 32) / 100 = 50%. The top-box share is 18 / 100 = 18%. Both calculations are correct, but they emphasize different strength of intention. Keep the full distribution so the reader can see that difference.

B leads when the summary uses definite intent

Concept B has 25 definitely, 20 probably, 25 “might or might not”, 18 probably not and 12 definitely not responses. Its top-two score is 45%, lower than the first concept's 50%, while its top-box score is 25%, higher than 18%.

The descriptive ordering changes with the threshold: B has fewer positive answers and more definitely-would answers in the example. That alone doesn't establish a stable audience tradeoff. If these were independent random human samples of 100 each, approximate 95% normal-approximation (Wald) intervals would be −8.8 to +18.8 points for A-minus-B top-two intent and −4.3 to +18.3 points for B-minus-A top-box intent. Both include zero, before multiple-comparison adjustment. Investigate reasons and segments as hypotheses. These human-sampling calculations don't describe synthetic-model uncertainty. See NIST's two-proportion guidance. Our sample size worksheet separates planning one share from powering a difference between independent human groups.

Stated interest needs an opportunity to become a purchase

People may like an idea but never encounter the offer, switch away when a competitor discounts, or discover that the experience does not meet expectations. The study also makes attention and consideration easier than many actual shopping situations.

A car survey found buyers outside its positive group

In a study published by NBER in 1966, F. Thomas Juster linked July 1964 intentions with households' reported new or used car buying at a January 1965 follow-up. The automobile analysis contained 395 classified households.

Source: Juster's NBER book, Tables 3 and 5. Other groups are combined by subtraction. The book's No group uses its six- and twelve-month classification.

The 26 definite/probable intenders provided only 13 of 67 reported buyers (19.4%). These are self-reports from selected historical follow-up cases, during a period with automobile supply disruption.

For Lumen's broad trial comparison, follow every eligible intent group. A prototype aimed at B's definite intenders can investigate that audience, while the full A/B comparison needs outcomes across the original groups.

Calibration requires appropriate observed evidence. If you have historical concept studies and subsequent market outcomes, compare them carefully within relevant categories, instruments and launch conditions. A universal multiplication factor such as “intent × 0.5” is not justified merely because it makes the estimate look conservative.

Synthetic intent adds a further distinction: the response is simulated. Neither the count of generated responses nor a polished probability label converts it into independent human purchasing evidence.

Rejection, inapplicability and missing answers differ

The distributions above contain 100 valid eligible answers per concept. Real instruments often have other response states. Keep those outside the five-point distribution until you define the base.

Keep valid negative answers in the denominator when calculating the positive share. Report people outside the grocery scenario separately rather than forcing them to state an intention. Show the eligible assigned group, valid scale answers and exclusions so readers can reconstruct each summary.

Reasons tell you which condition to change

For a diagnostic follow-up, “probably would, if my store stocks it” identifies availability; “definitely would not because I dislike sparkling drinks” identifies category rejection; and “might or might not until I know sweetness” identifies missing offer information. These meanings call for different actions even though all can affect the positive share.

Keep each reason, alternative and occasion connected to its intent answer. Adding a missing sweetness description clarifies the existing tea; changing the recipe creates a different offer. Show which proposed change addresses the condition behind the answer.

A disagreement between scores is a useful question

A contains 32 “probably would” answers; B contains 20. B contains 25 “definitely would” answers; A contains 18. The next analysis should explain those two concentrations. Are A’s conditional buyers waiting for a stocked outlet, or merely open to many drinks? Are B’s definite buyers attached to a routine the launch can serve? The displayed counts contain no linked reasons, so both explanations remain hypotheses.

For a small prototype serving one recurring occasion, investigate B’s definite buyers first. Progress B only if their reasons identify that occasion, the audience is reachable and the offer can meet expectations. For wider paid trial, investigate A’s additional conditional buyers and whether the business can supply what they need before buying.

Choose that objective before seeing which summary favors the team’s preferred concept. If neither pattern connects to a feasible audience and occasion, keep both candidates or revise the offer. Switching summaries afterward changes the question instead of resolving it.

A pilot can challenge the provisional choice

The next pilot should keep the offer people evaluated: the same quantity, price and information, available in a defined channel. Record who had an opportunity to purchase and the outcome over the stated window. That makes differences between consideration, checkout and paid trial interpretable.

Choose a pilot comparison that can overturn the provisional choice. For the recurring-occasion route, record paid trial and whether use returns in that routine. For the broad-trial route, record the actual opportunity to buy and paid trial under the complete terms. Suitable historical calibration can inform planning; without it, keep the survey distribution and observed outcomes separate.

Ten unknown outcomes leave the rate unresolved

Suppose one unchanged Lumen offer is available to all 100 eligible households for the same four-week window. Paid-trial outcomes are known for 90: 20 bought and 70 didn't. Ten outcomes remain unknown.

Report 22.2% with its 90-household base. For all 100, the known facts permit 20–30% until the ten outcomes are resolved. This is a worst-case range from missing outcomes; the earlier 95% intervals describe hypothetical human sampling. They answer different uncertainty questions.

Keep the original intent categories beside confirmed buyers, confirmed nonbuyers, unknown outcomes and actual purchase opportunity. A missing follow-up is not evidence that someone rejected or bought the offer. Resolve that gap before learning numerical conversion weights for A and B.

The next investment decides which intent pattern matters

Treat A's apparent breadth and B's apparent strength as hypotheses for different briefs.

Ask for the full intent categories, reasons, alternatives and purchase conditions for the first audience the business can serve. Recommend the concept that fits the chosen objective and name what would change the choice. The illustrative intervals leave the small overall gaps unresolved; keep that uncertainty beside the recommendation.

A completed recommendation can therefore be modest and useful: develop a candidate for a defined prototype, revise a condition that blocks consideration, or take both viable offers into a focused pilot. “A wins 50% to 45%” omits the very tradeoff the team needs to decide.

Sapien can compare simulated intent with comprehension, audience differences and objections, then recommend whether to revise the offer or investigate it in a defined trial. Use our concept-testing guide to prepare consistent questions or discuss a study with Sapien around the audience, purchase conditions and next investment you need to choose. If the unresolved variable is price, begin with our pricing research guide.