How to conduct B2B pricing research: tools and advice from Adience

Why conduct B2B pricing research?

Price is a critical lever in the marketing mix. But it’s a difficult lever to pull and is often overlooked.

Companies often focus on product innovation or promotional techniques while failing to price their products effectively. 85% of companies recognize they have ‘significant room to improve’ in their pricing.

Generally, the problem is under-pricing rather than over-pricing. It is easier to see if your prices are too high – because sales will stall – than if your prices are too low.

Companies that under-price can leave millions on the table. To illustrate the point: a McKinsey study of 130 publicly listed companies estimated that if each company increased its prices by 1%, their EBITDA margins would increase by 22% on average.

Under-pricing can be particularly prevalent in business-to-business (B2B) markets. B2B customers tend to be more loyal to their suppliers. Moving suppliers can also be riskier, so not worth doing for a small price reduction.

Of course, it’s not as simple as just raising your prices. Once you have anchored buyers on a specific price, increasing that price will create an expectation that there will be an associated improvement in features. As a result, you may need to change the value proposition to justify the price hike.

Pricing research, which explores what customers are willing to pay for a product or service, is fundamental to pricing strategy. Pricing research enables businesses to:

• Leverage their brands to determine the optimal price point to maximize profit, revenue, or market share
• Maximize the real value in their products and services
• Understand the value of different elements of the proposition, so that decisions to change pricing align with buyer expectations

What are the different techniques for B2B pricing research?

In some markets, businesses can experiment with customers’ willingness-to-pay in real-life using A/B tests. In these experiments, companies set different price points for products and tracking demand at each price point.

Such experimental approaches allow businesses to gather real-life data rather than relying on hypothetical discussions.

However, price experiments are only possible in certain situations:

• They are better suited to online sales where pricing is dynamic and opaque
• They only work for existing products

And price experiments are often not practical in B2B markets. Instead, B2B organizations wanting to gauge buyers’ willingness-to-pay have to speak to the target audience.

Quantitative research is typically favored:

• Most pricing research techniques involve structured questions, which make more sense in the context of a quantitative survey than a qualitative interview
• Pricing is a critically important business decision. The more data you collect, the more confident you will be that you’re making the right decision

Some pricing research techniques are better suited to B2C research than B2B research (e.g., neuro-pricing), but there are still many methods for B2B researchers to consider, each with its benefits and drawbacks.

Van Westerndorp’s Price Sensitivity Meter

Peter van Westendorp, the Dutch statistician, believed that the price charged for a product runs on a spectrum that has two extremes.

At one end is a point below which the price is so low that serious concerns are raised about quality. At the other extreme is the point at which buyers feel that the price is unfairly or prohibitively high.

In his view, somewhere between these extremes is the optimum pricing point – that place where the buyer is comfortable.

To find this optimum pricing point, the researcher asks four open questions:

• At what price would this product begin to be so inexpensive that you would doubt the quality and not purchase it?
• At what price would this new product begin to be inexpensive?
• At what price would this new product begin to be expensive?
• At what price would this new product begin to be so expensive that you would never consider purchasing it?

The responses are plotted as follows:

The optimal price is the price point at which the Too Inexpensive and Too Expensive lines cross. Some of the other lines’ intersections provide researchers with an estimate of the lower and upper bounds of what customers are willing to pay.

Businesses gravitate toward the van Westendorp approach because it is simple, inexpensive, and quick to execute. As a result, companies often use it to conduct initial analysis before doing something more robust.

However, there are several flaws in the van Westerndorp technique:

• It is a theoretical technique that doesn’t reflect how people buy products. For example, it only focuses on prices, ignoring other factors in a purchasing decision (brand, features, competitor prices). As a result, it shows price sensitivity in a hypothetical scenario that doesn’t exist
• It is easy for research participants to ‘game’ the responses to persuade the provider to set an artificially low price. Companies who have used the technique in their research have reported that the ‘optimum price’ was lower than their manufacturing costs
• It is completely ineffective when a product is new or purchased infrequently. In these situations, buyers don’t have a benchmark to refer to, and their responses cover an unrealistic range
• Ultimately, the technique isn’t supported by economic theory. For example, there’s no logic as to why the ‘optimal price’ should be the point at which the ‘too expensive’ and ‘too inexpensive’ lines cross

Attempts have been made to improve the van Westerndorp technique. While they may improve the accuracy of the results slightly, they don’t solve the underlying issues with van Westerndorp.

Gabor-Granger

The Gabor-Granger technique tries to identify buyers’ demand for a product at different price points.

Unlike van Westerndorp, a technique that asks survey participants to invent prices, Gabor-Granger’s price points are pre-defined by the company doing the pricing research.

Research participants are shown the product at one of the pre-defined price points and asked how likely they would be to buy it. What happens next depends on their response:

• If they indicated they would buy the product at the initial price point, the price is raised, and are again asked how likely they would be to purchase the product at that price point
• If they indicated they would not buy it at the initial price point, the price is lowered, and the question is repeated

This process is repeated until the research finds the highest price the buyer is willing to pay. Once several interviews have been conducted, it’s possible to create a ‘demand curve’ that shows what percentage of customers will buy a product at different price points.

The optimal price depends on what you are trying to optimize. If you are trying to maximize market share, you should pick the price point that the highest percentage of participants will pay. Often a higher price point will lead to higher revenues or profitability.

Like van Westerndorp, Gabor-Granger is often used to do ‘quick’ pricing research because it is inexpensive and straightforward. And like van Westerndorp, it has several flaws:

• It is a theoretical technique that doesn’t reflect how people buy products. As a result, it shows someone’s willingness-to-pay in a hypothetical scenario that doesn’t reflect reality
• Because of the way the exercise is structured, it is easy for respondents to guess what the interviewer is doing and ‘game’ the responses to persuade the provider to set an artificially low price
• The initial price point ‘anchors’ respondents on a price, influencing their willingness-to-pay for other price points. For example, someone who is shown an initial price point of $10 is less likely to accept a price point of $100 than someone who is offered an initial price point of $1,000
• The binary ‘would buy/wouldn’t buy’ over-simplifies a complicated decision. For example, there may be a price point at which a buyer would start to consider making a purchase. In real-life, they could be convinced to pull the trigger at that price point under the right circumstances

Brand-Price Trade-off

One of the flaws common to both van Westerndorp and Gabor-Granger is that they are focused only on price. As a result, they ignore the other factors that influence decisions (e.g., brand, product features, competitor offerings).

Brand-Price Trade-off (BPTO) is, in some respects, an improvement over those techniques because it factors in brands. Not just yours, but also competitors’.

In a BPTO exercise, the research participant is shown a list of brands they might consider. Each brand has an associated price. The respondent selects the brand they’d be most likely to purchase given these prices.

The exercise is repeated, with one change – the price of their preferred brand has slightly increased.

If they select the same brand again, the price of the preferred brand is again slightly increased. This continues until they choose another brand or select ‘none.’

Once they select another brand, that brand’s price is slightly increased until it too is no longer chosen.

If ‘none’ is chosen before all brands have been selected at least once, the price of non-selected brands is lowered until they’re selected (or zero).

The exercise continues until all brands have been selected.

BPTO has a few benefits:

• As with van Westerndorp and Gabor-Granger, it is inexpensive and quick to conduct
• Unlike van Westerndorp and Gabor-Granger, it doesn’t just focus on price
• It helps researchers to estimate a brand’s value, including the price premium that the brand can justify

However, BPTO has a lot of the same downsides as Gabor-Granger. For example, it still doesn’t reflect how people buy products – product features and other trade-offs aren’t considered – and respondents can ‘game’ it.

Monadic price testing

In monadic price tests, respondents are randomly assigned to different groups. Each group is asked how likely they are to purchase a product (or products) at a defined price point. Each group sees different prices for the products they are shown.

By comparing how each group reacts to their given price point, you can determine which price point will be optimal.

This approach eliminates respondent bias by making it more difficult to ‘game’ the test:

• Respondents are only exposed to one price per product
• They are also unaware that price is the subject of the exercise

Another benefit is that the decision is based on product features, not just price, so the exercise is more reflective of actual purchase decisions.

However, there are some downsides to monadic price testing:

• You need a large number of respondents so that each sample group has enough responses. This is often not possible in B2B research
• Demographic variations can distort results, so you have to be very careful with sampling and randomization
• The technique ignores competitors’ prices, which can hugely impact the demand for your product

Regression

Sometimes, new pricing research isn’t needed, as the data already exists.

Many businesses conduct customer satisfaction studies. These studies typically include questions exploring customers’ overall satisfaction with their provider and questions exploring their satisfaction with the provider’s performance in specific areas (e.g., price, quality of service).

Regression analysis analyzes these two questions and determines which performance areas most influence overall satisfaction.

As long as the price is one of the performance areas, a company can:

• Identify how important price is relative to other decision-making criteria
• Determine customer types with higher or lower price sensitivity
• Track changes in price sensitivity over time

The advantage of regression analysis is that it uses existing data rather than requiring a separate research study.

However, there are several disadvantages:

• It’s a crude measure – it calculates price sensitivity, which isn’t the same thing as willingness-to-pay
• It doesn’t factor in product features or competitive brands

Choice-based conjoint

Choice-based conjoint (CBC) analysis is a statistical technique that simulates the decision-making process to identify which product attributes and tiers are most attractive. Doing so helps to forecast the impact of different structures on adoption and revenue.

You start by identifying the attributes that comprise a product or service. For example, a backpack can be described in terms of capacity, weight, color, brand name, style, price, and warranty.

Each attribute consists of several levels. In our example, for the ‘capacity’ attribute, the levels could be ’20 liters,’ ’30 liters,’ ’35 liters,’ ’40 liters,’ and ’50 liters.’

The conjoint exercise mixes the attributes and levels into many different product packages. Some of these may be packages that you may be considering launching. Others may not be. As long as the packages are possible in theory, they may be shown in the exercise.

Respondents are then shown 3-5 packages and asked which they would likely choose in a real-life decision. This approach is repeated multiple times with combinations of packages.

When the interviewee selects a package, they provide information about how much they value an attribute or level.

This information can be used to calculate:

• The relative importance of different attributes
• The relative importance of each level within an attribute
• How relative importance differs by company and individual type

The result is a ‘simulator’ that allows researchers to model how the market will respond when offering different packages or prices. You can then identify the optimal combination of packages to maximize market share or revenue.

CBC has many benefits:

• The exercise is more reflective of actual purchases than most pricing research techniques, as it takes into account more than just price
• The ability to simulate different choices provides clear guidance about where to focus
• Companies doing pricing research tend to be a lot more engaged in the outputs of CBC than other pricing research techniques

But it also has its drawbacks:

• It is time-consuming and tiring for the research participant, who has to proceed through multiple package combinations
• It is also time-consuming for the researchers, as a lot of time is spent designing the attributes/levels and on analysis
• It is expensive
• It can lead to an under-estimation of peoples’ willingness-to-pay, especially for low-price products
• The exercise itself is somewhat artificial. While people do make trade-offs between features when they purchase a product, they don’t necessarily break down a product into so many attributes and levels

Simultaneous Multi-Attribute Level Trade-Off (SIMALTO)

The SIMALTO technique was developed by someone who had worked in B2B markets, so it is suitable for B2B market research.

Respondents are shown different attributes that they may value in a product or service. For each attribute, they are presented with various levels that ascend in quality. Each level is assigned a notional value that people might attach to the feature.

They are then asked to indicate, for each attribute:

• Which level they receive at present
• Which level they would like to receive

They are then given points to spend improving their current product. The cost of each improvement is the difference in notional value between the level they currently receive and their desired level.

The number of points that a respondent can spend is limited, which forces them to allocate points to attributes and levels they genuinely value.

Price is not one of the attributes that can be ‘traded off.’ Instead, the ‘points’ act as a proxy for price. Indeed, respondents’ points can be converted to dollars as a guide to pricing.

There are a few advantages to the SIMALTO tool over another trade-off exercise like choice-based conjoint:

• The researcher does not need as many people to take part in the exercise
• It is more straightforward to use SIMALTO data to develop needs-based segments

However, there are also some disadvantages:

• Some research participants find the exercise to be too complicated, and it is challenging to keep them engaged, especially in an online survey
• It can be time-consuming to develop the list of attributes and levels

Best practices when conducting B2B pricing research

Start early

Pricing research allows you to understand the target audience’s willingness-to-pay for a product or service. Once you have this information, you can ‘design-to-price,’ building a product that meets the desired price thresholds without over-delivering or under-delivering.

Therefore, when developing a new product, it’s better to conduct the research as early in the process as possible.

Be clear on which pricing strategies, models, and tactics are in scope

There are many different pricing models that a company can adopt. Options include flat-rate pricing, usage-based pricing (or pay-as-you-go), tiered pricing, per-user pricing, per-active user pricing, per-feature pricing, freemium model.

Once a company has selected a pricing model, they can adopt various pricing strategies and tactics. Options include cost-plus pricing, value pricing, market penetration, market skimming, marginal cost pricing, product-line enhancement, odd pricing, captive pricing, prestige pricing, charm pricing, etc.

Not all of these approaches are relevant or appropriate for every company. For example, a company selling consumer goods may not want to consider a freemium model.

Pricing research should help you identify which pricing model, strategy, or tactic is most suited to your business. However, before you start the research process, you need to determine which approaches are not suitable, as this will impact what the research explores.

Choose the right research methodology

Pricing research tends to require quantitative surveys rather than qualitative interviews:

• Pricing is a critical business decision, and it is better to base those decisions on a large number of decision-makers
• Many pricing research techniques use multiple data points, which are best collected through a qualitative survey

In B2B research, quantitative techniques aren’t always possible due to the target audience being so small. Qualitative research can be used in B2B pricing studies, as long as the interviews include some structured questions.

Use the research to build meaningful personas or segments

It’s essential not to assume that all customers’ willingness-to-pay is identical.

Some customers will pay more than others, and they may be worth prioritizing. And certain segments’ willingness-to-pay is so low that they’re not worth selling to.

Pricing research allows you to develop different customer segments or personas, build a pricing approach that tailors price by segment/price, and maximize revenue or margin (depending on your objective).

For example, you could develop different product tiers to appeal to each segment.

Share the results with the sales team

Many companies educate their sales team about product features but don’t educate them about customers’ willingness-to-pay.

Sharing pricing insights with the salesforce allows them to sell more effectively, as they can better articulate the value proposition.

Once the price point is selected, don’t ‘set and forget’

A company should regularly review its pricing to ensure it is still optimal.

You don’t need to repeat the entire research exercise, but it’s worth reviewing competitor prices and sales data every year to evaluate if pricing is still optimal.