Price Sensitivity: What is it & How to Reveal Untapped Profits

 

How do you decide what to charge customers for products or services? How much are customers willing to pay? In this article, we’ll discuss customer price sensitivity, how to measure it and how to discover untapped profits with the most effective techniques for price sensitivity analysis.

What is Pricing Sensitivity?

Price sensitivity refers to how changes in the price of products/services affect how many units are purchased. Or in other words, pricing sensitivity is the influence of changing price points on consumer purchasing behaviors.

The Law of Supply and Demand states that as prices go down, the quantity demanded goes up (and vice-versa). While this usually is the case in most markets, we can think of a few examples where setting prices too low can lead to lower quantity demanded (e.g., home prices, eye surgery, locks, and perfume).

In most cases, if you raise your price, some of your customers will leave you for less expensive options. Pricing sensitivity differs greatly across people. Even the same people can be very price sensitive for one type of product, but not very price sensitive regarding another.

Using the Formula to Determine Price Sensitivity?

Many of us have taken economics courses, where price sensitivity is formally quantified as Price Elasticity of Demand, which is usually a negative number. The equation for price sensitivity is:

%Change in Quantity Demanded / %Change in Price.

Why is Customer Price Sensitivity Important?

Understanding the price sensitivity for your product/service is key to maximizing profits.

If consumers (buyers) are less price sensitive toward your offering than for your competitors, you usually can maximize profits by raising prices. The opposite is true if consumers are more price sensitive toward your offering.

Either way, understanding your customer’s pricing sensitivity allows you to adjust prices accordingly and avoid leaving money on the table.

Importantly, segments of the market may be more price sensitive than others. If you can successfully segment the market with multiple offerings, you can experience higher profits. For example, via messaging and target advertising, you could direct a higher-end offering toward buyers in less price sensitive segments and a lower-end offering toward more price sensitive buyers.

Your long-term strategy also should factor into pricing decisions.

You don’t necessarily want to set prices to optimize profits just for the next quarter if the long-term result is decreased profits over the next five years. Initiating a price cutting war might lead to such results.

How to Measure Price Sensitivity

In the early years in the field of economics, price sensitivity (elasticity) was assumed to affect consumer behavior, but it wasn’t known how to directly measure it using questionnaires.

Over the last few decades, survey-based approaches for measuring and analyzing price sensitivity have been developed. Some are more successful than others. The common pricing questionnaire approaches include:

Monadic Pricing Tests

For the Monadic Pricing technique, we start by randomly dividing respondents into different equally-sized groups. We then show a product/service concept at a test price (which varies across groups) and ask how likely the respondent is to buy it at that price.

Monadic tests are useful, but they have limitations for price sensitivity analysis because they require large sample sizes and usually focus on only one product.  Well-designed monadic tests can include a competitive context—even presentation of product alternatives that resemble choice-based conjoint (CBC), where the test product’s price varies but the competitors are held constant.

Gabor-Granger Pricing Method

The Gabor-Granger method involves showing respondents a product/service concept at an initial (often randomly selected) price and asking respondents if they would buy it at that price.

If the answer is “yes,” we ask the question again at a higher price. If the answer is “no,” we ask the question again at a lower price. We repeat the process until the respondent stops saying either “yes” or “no”. (There are less mechanistic versions that involve randomly showing a higher or lower price, rather than always stepping up or down by one price level.)

Using the Gabor-Granger technique for price sensitivity analysis is problematic because respondents can see it as a transparent pricing “game,” and their answers can become patterned and may not represent very well what they’d do in the real world.

Other problems include: the initial price shown biases the results, the technique usually focuses on only one product, and it typically lacks competitive context.

Van Westendorp Price Sensitivity Meter

The Van Westendorp Pricing Sensitivity technique involves showing respondents a product/service and asking four questions:

• At what price would this product be so cheap that you would doubt its quality and not consider it?
• At what price would this product be a bargain—a great buy for the money?
• At what price would this product seem expensive, but you would still consider buying it?
• At what price would this product be too expensive for you to consider?

Either open-ended responses can be used, or a pre-defined wide list of prices. An optional extension was proposed in 1993 by Newton, Miller, and Smith that asks purchase intent questions (how likely are you to buy this product?) at the two middle prices.

The Van Westendorp Pricing Sensitivity approach may be useful in situations in which a product/service is truly new to the market and doesn’t have existing competitors.

However, it is problematic because the traditional data analysis is not grounded in robust theory, it usually involves examining just a single product concept, and it typically lacks competitive context.

Conjoint Analysis for Pricing Sensitivity Analysis

This widely used technique for price sensitivity involves showing respondents multiple sets of products/services where the brand names, prices, and often other features are systematically varied.  Using conjoint analysis for pricing research overcomes key weaknesses in the previously described approaches. The most common conjoint analysis approach is CBC (Choice-Based Conjoint) where respondents choose which product offering they’d prefer in each set, often including the ability to say “none”.

A statistical model (typically hierarchical Bayes multinomial logit) relates the presence of brands, features, and price levels to likelihood of choice.

Adaptive CBC (ACBC) and Menu-Based Choice (MBC) are more advanced forms of conjoint analysis and are also considered strong conjoint analysis approaches for pricing research and analytics for insights.

Conjoint Analysis is a stronger approach for price sensitivity than the previously mentioned techniques because it much more closely mimics how buyers see and choose products/services in the real world. The competitive context is established.

Rather than directly asking respondents to state a price, we indirectly discover price barriers and pricing sensitivity by observing how respondents choose in reaction to changes in price in the context of realistic market-looking scenarios.

Multiple, even up to thousands of product variations, can be studied at the same time and modeled for price sensitivity using the what-if market simulator.

Furthermore, not only can we simulate the price elasticity of demand for a product concept, but we can also simulate cross-elasticities—to see how changes in competitors’ prices affect quantity demanded for our offering.

Conjoint analysis is truly the better, and more realistic, approach when attempting to determine pricing sensitivity.

Can you use Real Sales Data for Price Sensitivity Analysis?

Sometimes, there are databases available with thousands of data points recording the volume sold for offerings in your marketplace together with their prices, promotional efforts, etc.

With such data in hand, it may seem that we can learn what we need about price sensitivity by fitting statistical models to real sales data.

The problem with real sales data is that usually there isn’t enough independent variation in the price fluctuations to be able to estimate the price sensitivity coefficients with enough precision. Moreover, you cannot use existing sales data to measure the price sensitivity for new products under development.

Can You Change Price Sensitivity for Your Product?

The good news is that there are various ways to reduce price sensitivity for your product. You can improve your brand image, do better messaging, improve your customer service, improve features in your product, or even create reference prices that make your product look less expensive by contrast. It’s common for firms to create gold, silver, and bronze versions of their product.

If your main desire is to decrease the price sensitivity for your product, you can formulate a gold version of it offered at a much higher price such that your main “silver” product looks less expensive by contrast.

Latent Class analysis can be used to analyze conjoint data to find segments of respondents who are less price sensitive and to optimize product features that attract and motivate them to pay a premium price for your offering.

Using Conjoint Analysis to Measure Price Sensitivity & Discover Untapped Profits

Conjoint Analysis is the most widely used and respected survey-based tool for measuring price sensitivity and predicting market success for both reformulated or new products.

With a well-designed conjoint study and its resulting market simulator, you can use search optimization tools to find the price that optimizes your profit, revenue, or market share.

Using conjoint analysis, you can find ways to capture untapped profits by changing price, optimizing product features, and exploring product line extensions. Sometimes even reducing the number of offerings in a product line can lead to profits.

We’ve seen many ways over the years that firms have used conjoint analysis to help them price their products and services to improve profitability. Here are a few examples:

• A leading storage media manufacturer fielded annual conjoint studies to estimate demand curves and price sensitivity for its brand versus key competitors. They used the results to track the effectiveness of their advertising, to measure brand equity, and to help them set their price premium versus the competition.
• Bob Goodwin of Lifetime products described how they used conjoint analysis to provide the data to convince a big-box retailer to allow them to set higher prices for their tables and chairs, whereas the big-box retailer was urging them to cut their prices.
• An entertainment company recently used conjoint analysis to help them design and set prices for a popular annual holiday event, reporting that the profits increased and attendance did not drop off.

Understanding your customers’ price sensitivity is crucial to a robust and successful pricing strategy. Without it you run the risk of over- or underpricing your product which may likely lead to diminished sales.

If you want to learn more about how pricing sensitivity research with conjoint analysis can help you create an effective pricing strategy and discover untapped profits check out our conjoint analysis software page, or schedule a product tour with one of our friendly and no-pressure product representatives.