Gabor-Granger pricing analysis – Alida Help Center

What is Gabor-Granger pricing analysis?

Determining the ideal price for your product can feel like a guessing game, but it doesn't have to be. Gabor-Granger pricing analysis is a research-backed technique that takes the guesswork out of pricing. It's a survey-based approach designed to help you understand how much your customers are willing to pay for a given offering or service, ultimately guiding you to the price point that maximizes your revenue.

Essentially, Gabor-Granger helps you measure price elasticity, which is how sensitive demand is to changes in price. By asking a series of structured questions, you can gain valuable insights into how many customers you might gain or lose at different price points. This information is crucial for making informed pricing decisions and ensuring your product is both competitive and profitable.

The analysis is based on a few core assumptions about consumer behavior:

Generally, customers prefer to pay the lowest possible price.
Given a choice, customers tend to avoid spending more.
Purchase intent decreases as the price increases.

While these assumptions seem intuitive, Gabor-Granger provides a systematic way to quantify these tendencies and turn them into actionable data.

Gabor-Granger analysis works by presenting participants with a range of prices and asking about their purchase intent at each price point. The key is the sequential nature of the questions. By starting at a midpoint and then moving up or down based on the participant's answer, we can pinpoint their price sensitivity. This process simulates a real-world purchase decision, providing valuable insights into how demand changes as price varies.

For instance, if a participant is willing to pay the midpoint price, they are then asked about a higher price. This helps us find the upper limit of their willingness to pay. Conversely, if they are unwilling to pay the midpoint price, we present a lower price to see if they are more price-sensitive. This iterative process allows us to map out a demand curve for the product.

How do I use Gabor-Granger?

Tip: Gabor-Granger questions are great at the end of concept tests. You can ask participants pricing questions about a concept, product, or offering that they have just spent some time evaluating.

Using Gabor-Granger involves asking a series of purchase intent questions at different price points. Typically, this is done using a Likert scale (for example, from "Very likely" to "Not at all likely").

Here's how it works in practice:

Define your price range: First, you need to establish a reasonable price range for your product. This could be based on market research, competitor analysis, or cost considerations.
Select five price points to test: Choose five price points within a range, ensuring they are spaced at consistent intervals.
Use the Gabor-Granger template provided in the Alida platform: The template includes everything you need for Gabor-Granger. There are five Single Choice questions featuring Likert scales, and there is survey logic designed to adapt to the participant's answers. Participants start with the midpoint price. Their subsequent questions depend on whether they indicated they were likely to purchase. This branching technique helps to narrow down the individual's price sensitivity.

For example, let's say you're trying to price a new noise-canceling headphone set. You've decided on a range of $100 to $500, with $100 increments. Your survey might look like this:

How likely are you to purchase these headphones for $100?
How likely are you to purchase these headphones for $200?
How likely are you to purchase these headphones for $300?
How likely are you to purchase these headphones for $400?
How likely are you to purchase these headphones for $500?

And each question might look like this:

How likely are you to pay $300 for these headphones?
Very likely Likely Not very likely Not at all likely

The survey logic would then guide participants through these questions based on their answers, efficiently determining their willingness to pay. Participants who answer "Very likely" or "Likely" to $300 are shown the same question for $400. At this point, some participants may drop off because they consider $400 to be too expensive. But if they are likely to pay $400, they are shown the same question for $500.

Similarly, participants who answer "Not very likely" or "Not at all likely" to $300 are shown the $200 question. The ones who are likely to pay $200 will not be shown the $100 question. The ones who would not pay $200 will then be asked if they are likely to pay $100.

While our example uses headphones, Gabor-Granger can be applied to a wide range of products and services. For a subscription service, prices could represent monthly fees. For a software product, they could be different license tiers. For a physical product, they could be various versions. A hotel might use Gabor-Granger to determine optimal room rates during different seasons. A software company could use it to price different feature packages. A restaurant could test pricing for a new menu item.

Considerations and limitations

Framing effects: How you frame the concept's properties, values, and benefits can influence responses. Exaggerating or inaccurately representing the value or benefits of the product can make participants more willing to pay a higher price. If you already know how a product or service will be showcased in-market, include the information emphasizing specific values and benefits. Otherwise, refrain from providing that information here.
Hypothetical bias: Since Gabor-Granger questions are hypothetical, there's a risk that participants' stated willingness to pay might not perfectly reflect their actual behavior in a real purchase situation. However, this can be mitigated by accurately framing the product.
Sample representativeness: As with any survey, the results are only as good as the sample. Ensure your survey participants are representative of your target market for accurate results.

How do I include Gabor-Granger in my Alida survey?

Use the Gabor-Granger template when creating a new survey:

Go to the Surveys app.
Click New Project.
Under Start from Template, click View Templates.
Locate the Gabor-Granger template and click Create.
Before the Gabor-Granger questions, include information about the product or service.
This information should appear before the Gabor-Granger exercise so participants have a chance to review it first. Add an image or a video along with a description of the product or service.
Edit the question text for each question. For example, in the sentence "How likely are you to purchase [INSERT ITEM] for [INSERT MIDDLE PRICE POINT]?" replace the bolded items with your product and price point.
If you update the names of survey questions, ensure you update the survey logic references as well.
If the Gabor-Granger questions are supposed to come at the end of a concept test, add the concept testing questions before the GG Mid-Tier Price Point question.

How do I analyze my data?

When you analyze your data, pay attention to the participants who are likely to pay at any price point—these are your potential customers. In your analysis process, you'll need to perform a rebasing step and figure out the number of customers at every price point as a percentage of total responses. Your analysis will focus on either the Top Box ("Very likely") or the Top 2 Box ("Very likely" and "Likely") responses. For the purposes of this example, we will be using the Top 2 Box for the rebasing, although Top Box works the same way.

Then, you'll need to plot the data on a curve. The curve will illustrate the drop-off of customers with each price increase. Within that curve, there will be a sweet spot that represents the optimal price point to drive revenue and return on investment.

Let's continue with our headphone example. After answering a series of Gabor-Granger questions, your participants have effectively segmented themselves into six different groups:

Those who are likely to pay $100 for the headphones
Those who are likely to pay $200 for the headphones
Those who are likely to pay $300 for the headphones
Those who are likely to pay $400 for the headphones
Those who are likely to pay $500 for the headphones
Those who are unlikely to pay any of the specified price points

You'll need to rebase the Top 2 Box at every price point. This analysis is performed outside of the Alida platform. To analyze your data:

Export your report data to Excel.
Tip: If you'd like to perform this analysis with weighted data, weight your data in reporting before you export it.
Note the total number of responses; you'll need this number for the subsequent calculations. (In the example screenshots below, the total number of responses is 140.)
Calculate the percentage for the highest price point. Look at the Highest Price Point survey question. Add the number of Top 2 Box responses ("Very Likely" and "Likely"), and divide that sum total by the overall number of responses.
Calculate the percentage for the second highest price point. Look at the Second Highest Price Point survey question. Add the number of Top 2 Box responses ("Very Likely" and "Likely"), and divide that sum total by the overall number of responses.
For the mid price point, look at the Mid Price Point survey question. Add the number of Top 2 Box responses ("Very Likely" and "Likely"), and divide that sum total by the overall number of participants.
For the second lowest price point, look at the Second Lowest Price Point and Mid Price Point survey questions. Add the number of Top 2 Box responses ("Very Likely" and "Likely") from both questions. Divide that sum total by the overall number of responses.
The reason you add the Top 2 Box responses from both questions is that, logically, someone who is willing to buy at the mid price point would also be willing to buy the same thing at a lower price.
For the lowest price point, look at the Lowest Price Point, Second Lowest Price Point, and Mid Price point survey questions. Add the number of Top 2 Box responses ("Very Likely" and "Likely") from all three questions. Divide that sum total by the overall number of responses.
The reason you add the Top 2 Box responses from all three questions is that, logically, someone who is willing to buy at the mid price point would also be willing to buy the same thing at a lower price, and at a price even lower than that.
After you have completed these calculations, you should have five data points, one for each price point. The highest price point should have the lowest number of customers. Conversely, the lowest price point should have the highest number of customers. These are the data points you'd plot on a curve.