Add-ons are one of the most reliable levers B2B SaaS companies have for growing revenue without launching an entirely new product. Launching 4K capabilities for your video platform? A new AI-powered feature? A new marketing module? Packaging innovation as an add-on gives existing customers a reason to spend more — improving net revenue retention — while giving new customers another reason to choose you in the first place.
But launching an add-on raises two questions that are easy to underestimate: what uptake should we expect, and what price actually maximizes revenue and profitability? Get the price wrong and you either leave money on the table or price yourself out of adoption entirely.
Gabor-Granger price testing is one of the most efficient tools for answering both questions — particularly for a single, well-defined add-on like a new AI capability, rather than a full repackaging of your product.
How Gabor-Granger Price Tests Work
In a Gabor-Granger test, each respondent is shown a single product or feature and asked how likely they would be to purchase it at a specific price, usually on a 5- or 7-point scale ranging from "extremely unlikely" to "extremely likely." Based on that answer, the respondent is then shown a different price — typically higher if their initial response was positive, or lower if it was negative — and asked the same question again. This continues across a short ladder of price points, usually three to five, until the respondent's interest drops off or the ladder runs out.
Because each respondent answers at multiple price points, a single sample can generate a full purchase-intent curve across the entire price range being tested, rather than requiring a separate group of respondents for every price point the way monadic testing does.
Why It Works Well
Gabor-Granger is simple to design, simple to field, and simple to explain to stakeholders who aren't researchers. Respondents answer a single, intuitive question repeated across a short price ladder, and the output — an uptake curve across price points — translates directly into a revenue and profitability model.
It's also efficient. Because each respondent provides data at multiple price points, Gabor-Granger typically requires a smaller sample than monadic testing to generate a reliable price-response curve for a single offer.
It's particularly well-suited to pricing a specific, well-defined add-on or feature launch — exactly the kind of decision SaaS teams face when rolling out something like a new AI capability.
Where It Falls Short
Gabor-Granger evaluates price in isolation. It doesn't ask respondents to weigh price against competing features, brands, or alternatives the way conjoint analysis does, so it isn't well-suited to bundling or packaging decisions involving multiple attributes.
Showing the same respondent several prices in sequence can also introduce anchoring and order effects — an initial high price can make a subsequent lower price feel like a bargain (or vice versa), which can bias the resulting curve if the price ladder isn't carefully designed.
And because it asks respondents to evaluate purchase intent for a specific offer, it depends on respondents actually understanding what they're being asked to pay for — a bigger risk than it sounds like for genuinely new innovation. More on that below.
When to Use Gabor-Granger Price Tests
- You're pricing a single, well-defined add-on or feature — a new AI module, a 4K upgrade, an add-on marketing tool — rather than a full package with several features to trade off.
- You have a manageable set of price points to test. A short, well-spaced price ladder (typically three to five points) is enough to build a usable demand curve.
- You need a fast, easy-to-field study. The single-question format keeps fielding time and respondent burden low compared with a full conjoint exercise.
- You want a demand curve, not just relative importance. Gabor-Granger is built to answer "how much uptake at this price," which maps directly onto revenue and adoption modeling.
- You're already running — or have already run — a conjoint study. Gabor-Granger is a strong complementary study alongside conjoint: conjoint identifies the right feature set and its relative importance, and Gabor-Granger fine-tunes the specific price point for a specific add-on once the packaging decision is made.
Key Considerations When Designing a Study
A Gabor-Granger study is simple to run, but a handful of design choices determine how useful the results actually are.
- Screener questions and quotas. Make sure respondents actually resemble your target buyer, and set quotas by segment (company size, current plan tier, role) so you can read results by segment rather than just in aggregate — especially if segments differ meaningfully in size or willingness to pay.
- A few well-placed introductory questions. Capturing a handful of segmentation and behavioral questions up front — current plan, usage intensity, prior interest in the feature area — makes it possible to explain differences in the price-response curve across segments afterward, rather than being stuck with only an aggregate result.
- Educate before you price. Innovation is often genuinely new, and respondents may not yet understand what an AI feature, a 4K upgrade, or a new module actually does for them. Consider a short explainer video or written description before the pricing questions — an uninformed purchase-intent answer isn't a useful one.
- Price ladder design. Space price points widely enough to move the needle on purchase intent, but not so widely that the ladder feels arbitrary, and think carefully about the starting price, since anchoring can shape everything that follows.
- Decide your objective before you see the data. The price that maximizes modeled revenue isn't always the price that best serves the business. A much higher uptake at $149/mo can mean better long-term retention and a stronger installed base than a lower uptake at $249/mo, even if the top-line revenue math looks similar on the day of launch. Know which objective you're optimizing for before the curve tells you what it thinks you should do.
Try it yourself. The simplified exercise below mirrors a real Gabor-Granger price ladder for a new AI add-on. Answer honestly and see where you land, then see how individual answers like yours would roll up into an uptake-and-revenue curve across the tested price points.
Turning the Curve Into a Pricing Decision
A Gabor-Granger study won't hand you the right answer on its own — it will tell you how demand shifts across a price range, and it's up to your team to decide what to optimize for. Pair the resulting curve with your NRR, MRR, and retention goals, and, where the decision also involves feature trade-offs, pair the study itself with a conjoint exercise to get the full picture: what to build, how to package it, and what to charge for it.
Used well, Gabor-Granger turns a guess about add-on pricing into an evidence-based demand curve — and a much more confident answer to what uptake to expect and what price to launch at.
