Finding the optimal price is like cracking a secret code—it requires precision, confidence, and the right tools in order to unlock a successful strategy. These decisions can have resounding impacts on both revenue and demand, but there are proven techniques to help decode pricing for any product. With advanced data modeling and creative survey design, you can illuminate customer demand and tease out price sensitivity. In this piece, we’ll talk about the different research methodologies that can help provide the right insights you need to confidently optimize your pricing strategy.
Pricing research at a glance
Finding that magical price point is all about balancing two competing goals:
- Capturing adequate value by setting a price high enough to be both profitable and sustainable
- Ensuring accessibility by setting a price low enough to encourage both trial and word-of-mouth buzz.
While this may seem like a tightrope, successfully walking it will allow you to maximize both revenue and adoption right out of the gate. Going too low or too high will run you the risk of leaving money on the table or turning away potential buyers.
So where’s the sweet spot? The right market research can help you crack the consumer psychology code.
Here are some research methodologies that can be used to help determine your pricing strategy:
- Simple pricing questions that gauge interest at different price points
- Elaborate quantitative modeling to estimate demand curves
- Creative survey techniques like monadic testing and paired comparisons
- Price sensitivity meters to reveal price thresholds
In addition to your primary research, it’s also wise to gather intel on how your competitors are pricing their items—including substitute products. This helps ensure you come to the table with category norms and conventions in mind. Make sense? Ok, let’s now take a stethoscope to the proverbial safe of pricing strategy.
Gather your intel
Obviously, doing your own primary research is crucial. But we can’t crack the pricing code in isolation. That’s why you may want to start by gathering intel on competitors, substitutes, and category norms. Let’s go over some of the most important areas of investigation.
Competitor pricing
- What pricing tiers exist in the market? Where does most activity focus?
- How are competitors positioned on price?
- What discounts or sales do they offer and how often?
- Have there been any recent price changes? Why?
Category context
- What is the average price range for this product category?
- Are certain price points considered standard?
- Does pricing signal quality perceptions?
Substitutes and alternatives
- For comparable substitute products, what is the typical pricing?
- What are customers’ price expectations and tolerances?
- How might adjacent categories influence perceptions?
Scanning the landscape can help equip you with the right context needed to define pricing strategies. Not only can you find clues based on established expectations and industry norms, but you can leverage them to inform the way you position your product.
Find the optimal price point
Willingness to pay pricing questions
By far the most straightforward way to gather pricing insights is to directly ask your customers about their willingness to pay (or WTP). For example, a question asking something like "How much would you be willing to pay for X?" This can be implemented either as an open-ended question or a question with multiple options or ranges to choose from.
However, we advise against this approach. Respondents are often unwilling or unable to indicate the upper limit of what they would pay (in other words, the optimal price point) when asked in this way, as their motivation to be accurate is in direct conflict with the rational desire to get the best deal possible.
Instead, we often recommend one of the following methodologies to more accurately determine optimal price point:
- Monadic price test
- Price ladder/Gabor-Granger
- Van Westendorp (or Van Konan)
- Choice-based Conjoint
In the rest of this article, we’ll break down the pros and cons of each of these options in detail but here is a helpful reference guide.
Monadic price test
A Monadic design is a smart way to remove bias when directly asking about pricing to ultimately determine purchase intent. In this setup, respondents will only see and evaluate one price point. This can help minimize bias by focusing the respondent on only one pricing option and allowing them to assess their likelihood to purchase at that price. It largely eliminates the rational desire to lowball as the onus is not on the respondent to suggest a price, but instead to react to a price. In a monadic test, we typically split a population into, for example, three different randomly assigned groups; A, B, and C. In Group A’s survey, they are exposed to the product at one price level, Group B sees a totally different price, and Group C sees yet a third. This controlled exposure produces a demand curve used to discern price sensitivity.
Let’s use toothpaste as an example and look at how this would work in a monadic survey design.
Say you have three survey variants:
- Group A sees your toothpaste at $2.50
- Group B sees your toothpaste at $2.80
- Group C sees your toothpaste at $3.10
By exposing each group to different prices, you’ll get a clean, un-anchored read on purchase intent at each price point to build a comprehensive price sensitivity and demand curve. This method also saves space for additional questions about preference, brand perception, and more.
In addition to a straight purchase intent setup as described above, a monadic price test can also be implemented in a shelf test setup - the measure of interest becomes product choice instead of product purchase likelihood, but the analysis is otherwise the same.
Pros:
- Low respondent bias—monadic design eliminates respondent bias towards pricing because respondents only evaluate ONE price point. They are unaware of the other prices being tested and evaluated by other respondents.
- Results of monadic studies are often pretty clear-cut and provide strategic direction without ambiguity.
Cons:
- Requires a larger sample size than other approaches because of the monadic design that necessitates a minimum number of cases per cell.
- As a result of the larger sample, monadic studies can be more expensive.
Price Ladder and Gabor-Granger
Price Ladder and Gabor-Granger are approaches that are conceptually similar to a monadic price test but the primary difference is that respondents don't evaluate just one price point. Instead, they’re asked to indicate their purchase intent on a scale or select their purchase choice between multiple products—with statically priced competitors and the product of interest—at various price points. By charting changes in both purchase intent and estimated revenue per category purchasers for each price point tested, we can identify the price point that maximizes market share or revenue, and examine price elasticity.
Here’s how each of these tests works:
With price ladder, respondents start by evaluating the highest price point: “How interested would you be in purchasing this product, if it cost [highest price point]?” If they are willing to purchase, they are discontinued from the exercise, but coded as a “YES” for each subsequent lower price point. If they are not willing to purchase at the highest price point, they are shown the next highest price point and asked to provide their purchase intent once again, and they repeat this exercise until they proceed through the entire price range.
In a Gabor-Granger exercise, respondents are randomly assigned to evaluate one price point: “How interested would you be in purchasing this product, if it cost [randomly assigned price point]?” If they’re willing to purchase, they’ll be shown a higher price point. If not, they are next shown a lower price point.
Both of these exercises are seen as variations on a monadic price test but are generally considered inferior to the more “pure” monadic test. The advantage is that they require a far smaller sample size. If sample costs are a concern, price ladder is recommended over Gabor-Granger.
Pros:
- Requires a much smaller sample size than monadic price testing, while allowing you to produce the same share, revenue, price elasticity metrics that a monadic test does.
Cons:
- Price ladder: Respondents may “figure out” the test after a few price points and may eventually indicate that they would purchase simply to exit the ladder. To combat this, consider fielding a price ladder among category purchasers or those who show a baseline interest in the product, as opposed to a general population sample.
- Gabor-Granger: Evaluating a lower price point may “spoil” a respondent for higher price points. Respondents may rationally be inclined not to endorse a higher price point after seeing a lower one, as they would prefer the lower price point. However, it’s possible that if only presented with the higher price point, their purchase interest would still be high. In other words, Gabor-Granger may invite price comparisons that can impact the data in undesirable ways.
Van Westendorp
There’s the price sensitivity meter, developed by Dutch economist Peter Van Westendorp in the 1970s. In a Van Westendorp exercise, survey respondents are asked four questions:
- At what price do you think the product/service is priced so low that it makes you question its quality?
- At what price do you think the product/service is a bargain?
- At what price do you think the product/service begins to seem expensive?
- At what price do you think the product/service is too expensive?
Here, the aggregated responses will reveal price boundaries and ideal value perception from the standpoint of the market—effectively cracking the code of pricing norms built into your consumers’ psychology.
Taking it a step further, aytm's Van Konan Price Optimization Model layers revenue and profit views onto the traditional Van Westendorp model, if one can provide assumptions for cost of production, TAM (total addressable market), etc. Our Van Konan takes the traditional Van Westendorp price model to the next level, allowing add-ons for Max Revenue, Max Sales Frequency, and Max Profit calculations.
Pros:
- aytm's Van Konan Price Optimization Model layers revenue and profit views onto the traditional Van Westendorp model, if one can provide assumptions for cost of production, TAM, etc.
- Responses to each question are plotted as cumulative percent line graphs. By looking at where specific lines intersect, we can identify an optimum price point as well as a Range of Acceptable Prices.
Cons:
- Assumes that the category has a price below which it is so cheap you would question quality; this is not necessarily a fair assumption for every category.
- For emerging categories, luxury, and tech, VW can yield low-ball estimates. Consumers do not always have the best sense of what they would actually be willing to pay for emerging products AND they may vastly underprice expensive products.
Conjoint
Finally, choice-based conjoint (CBC) is one of the most flexible options for price testing. CBC allows you to assess market share at various price points, as well as revenue per number of consumers and price elasticity. With Conjoint, you can also assess the importance of price relative to other product qualities, as well as the monetary value of specific features and components. Finally, conjoint allows you to test the pricing of your product in the context of the competitive set, understanding trade-offs and choices consumers are likely to make when your product is priced competitively vs. too high or too low. To conduct a successful conjoint study, you should test a range of price points. In addition to price, you must test at least one other product variable, since CBC is an experiment that requires a minimum of two variables under investigation.
Pros:
- Conjoint is very robust and can assess many pricing scenarios, including changes in competitor pricing and the impact that would have on share for the brand of interest's own product.
- Conjoint is an efficient use of sample, particularly compared to a monadic price test. Although larger sample sizes are preferred for conjoints with large designs, some CBCs can be run with as few as 400 respondents.
Cons
- Conjoint can come with a higher price tag depending on the complexity of your design and the particular pricing scenarios under investigation.
- The importance of price is sensitive to the specific values and range you test, so it's important to test a range that balances realism with your ability to examine price cliffs, elasticity, etc.
Analyze and apply the code breakers
Cracking the pricing code is an ongoing project. Once you’ve gathered pricing insights through your research, how do you interpret the clues to set an optimal price? Let’s take a look.
Listen carefully
When analyzing demand and revenue potential curves, look for where purchase intent and/or revenue potential either plateaus or drops off. This is a pretty good indication of where you begin to lose consumers or profits due to pricing being either too high (demand side) or too low (revenue side).
For sensitivity meters like Van Westendorp, shoot for a launch price around the “good value” perception marked by your respondents. Remember, the goal is to find the peak of consumer value perception and demand before hitting any prohibitive prices.
Set the price
So what should you consider when you actually go to set your price?
- Seek to land in that value that balances demand and revenue generation
- Make sure to take production costs and business goals into account
- Always leave a little room for future discounts and promotions
- Consider seasons and holidays when timing your launch and adjust accordingly
The bottom line? Listen to your insights—they’ll suggest an ideal price range and you can refine based on business context.
Always iterate, always optimize
Returning to what we said at the top of this section: Revisit pricing periodically to find ways you can improve. Don’t treat initial pricing like the final word—it’s just a starting point. With every product release cycle—every market shift and every updated promotions calendar—you need to be ready to gather new intel and refine your approach.
Final thoughts
Pricing a new product is a high-stakes puzzle. Too high or too low, and you’ll have massive consequences. However, there are proven techniques to help decipher an optimal pricing strategy. By taking a “code cracking” approach, you’ll be able to leverage insights to find that sweet spot.
Moving forward, blend primary research learnings with your unique knowledge of the competitive landscape. Analyze your results diligently and iterate based on feedback. But don’t feel like you have to do it alone. In fact, aytm can help you implement best practices in your pricing research. We have a variety of pricing solutions available on our platform and a team of experts who can jump in to provide guidance and ensure your surveys help point you in the right direction.
Editor's note: This post was originally published in 2011 but updated in 2023 for accuracy and comprehensiveness.
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