Conjoint Analysis Is the Best Method for Optimizing Product Price in Pricing Research

Last updated: 09 Sep 2021

Restaurant menu boards above registers illustrating the price of different products

We all know how important it is to figure out the right price to charge.  After all, price is one of the key levers in marketing!   

Charging the right price lets you capture the value and profits your business has earned. So how do we determine an optimal product/service price? 

We could conduct in-market pricing research tests to vary our price and capture sales data from real paying customers, but this can be an expensive and risky route.  Plus, what your competitors do during your in-market test could foul up your pricing experiment.   

We could also analyze past sales data to calibrate the optimal price, if we have a database with a history of prices and sales volume for your product and competitors.  

But, it’s not that simple.  Existing data usually aren’t robust enough, with enough independent price changes to stabilize the kinds of predictive models needed to pinpoint optimal price points and optimize revenue or profit. 

So how do we best calculate optimal product price?  

Enter conjoint analysis. 

As we describe below, conjoint analysis is the best research method for pricing research and, as such, has become the most widely trusted survey-based approach for homing in on optimal price. 

Inferior Survey Research Methods for Measuring Price Sensitivity 

Survey research lets you test different prices and measure the price sensitivity for consumers and key market segments—before you go to market.   

But, unless the survey is realistic, respondents aren’t going to give you accurate data about how price motivates them.   

Consider the following popular traditional pricing research methods that have appeared over the years (neither of which has received as wide a following and trust as conjoint analysis).  

Van Westendorp Pricing Sensitivity Meter Chart

Van Westendorp’s Price Sensitivity Meter: 

The “four pricing questions” directly ask respondents to tell us the “Too Cheap,” “Cheap,” “Expensive,” and “Too Expensive” price points.  This approach lacks a robust statistical theory for finding the optimal price point.  Plus, there is no relevant competition or realistic context for the respondent to consider.  For brand-new to the world products without an easily established competitive context, this approach could be a good “qualitative” first step.  But, conjoint analysis is generally better.

Gabor Granger Pricing Method Chart

Gabor-Granger Pricing Method:

This approach involves asking respondents if they would buy a product at a given price.  If they say “yes,” then we ask the question again at a higher price.  If they say “no,” then we ask the question again at a lower price.  There are also multiple problems with this approach.  For example, no relevant competition is shown to provide adequate context.  Moreover, the price point we begin asking the respondent about strongly biases the outcome. 

Why Conjoint Analysis Is Better for Pricing Research than Other Survey-Based Pricing Approaches

A big weakness of the above approaches is they try to determine pricing for usually just one or a very few versions of the product concept; not hundreds or thousands of variations like conjoint analysis deftly handles. Conjoint analysis pricing research has become the most widely accepted and trusted method because the conjoint survey experience creates a more realistic environment where the respondent makes choices (and can price-compare) more true to what they see and do in the real world. Here’s an example conjoint analysis question:

A simplified conjoint analysis question for an example pricing research study. Four product concepts for smartphones

Conjoint analysis questions systematically vary the features shown and their prices and respondents pick which product they’d most likely choose in each carefully rotated scenario.   

Based on how respondents react to price and other feature changes, we can more reliably fit a model that reveals their price sensitivity (price elasticity) and willingness to pay (WTP). Or in other words, we can learn how quantity demanded changes with changes in price.  

Try an Example Conjoint Analysis Questionnaire for Yourself—Plus Real Data Results 

If you’d like to experience a conjoint analysis survey and see how it estimates price sensitivity curves, take our example conjoint analysis survey.  

The example conjoint study takes you through a sample conjoint questionnaire, asking about your food preference at a baseball stadium, and then lets you review the results based on the cumulative data from all survey responses gathered. 

Conjoint Analysis Market Simulator Predictions for Revenue/Profit Maximization 

Conjoint analysis has become so valuable over the decades for marketers and pricing managers due to the intuitive usefulness of the market simulator.   

The simulator is like a “voting machine,” where the manager can specify a competitive market scenario (involving the manager’s product vs. relevant competition) that interactively yields a market share type prediction (called “share of preference”).   

This market simulator can be in Excel, or Sawtooth Software also creates online or desktop versions.  You specify different prices for your product, run your market scenario simulation, and see the predicted share (share of preference). 

Simulate market share and optimize products in market simulators

The market simulator shows how raising or lowering price (relative to your competitors) changes the predicted share, revenue and profits.  To predict profits, you also need to tell the simulator how much it costs to produce your product.   

For example, here is a profit optimization curve as revealed by a conjoint analysis market simulator: 

Graph from a conjoint analysis market simulator showing a Relative Profit Curve

We can even use market simulators to search for optimal prices and features for tiered product line offerings, such as gold, silver, and bronze offerings. 

CBC (Choice-Based Conjoint)— A Strong Pricing Tool 

Because choice-based conjoint shows respondents sets of competing products with realistic features and price ranges similar to how buyers see and evaluate products in the marketplace, CBC is a very effective pricing research method.   

A strength of CBC is that we do not need to assume each brand has the same price sensitivity. Depending on the brand’s reputation and brand equity, price elasticity can and should differ.  

CBC’s experimental design permits efficient estimation of brand-specific price curves.  Measuring price sensitivity uniquely by brand can lead to more accurate pricing decisions and optimization. 

Improving Conjoint Analysis Results for Pricing 

Unmotivated respondents or bad actors (cheaters) are a problem in survey research.  This is especially a concern with pricing research.  If you have respondents who randomly answer conjoint questions, it can make it look like people are willing to pay (WTP) much higher price than real buyers would.   

Fortunately, conjoint analysis leads to an individual-level goodness-of-fit statistic to help you prune the bad actors.  You should also use speed checks and quality of open-end question checks to clean the data and obtain better pricing research data. 

Examples of Companies Using Conjoint Analysis for Pricing Research 

The popular Sawtooth Software Conferences give companies an opportunity to talk about how they use conjoint analysis for pricing decisions and optimizing profits/revenue.  Some recent examples include: 

Microsoft: Researchers at Microsoft’s peripheral division used conjoint analysis to figure out the right price to charge for improvements to their products.  They also demonstrated how conjoint analysis simulators can be used to optimize a product line involving multiple products. 

Procter & Gamble: P&G’s researchers compared conjoint analysis to econometric models they’ve built from real market purchase data.  On average, they found good correspondence between price sensitivity measured by conjoint analysis compared to real market data. 

Amazon: Their researchers in Amazon Devices have found that conjoint analysis can help them predict product launch success across multiple markets. 

Why Use Sawtooth Software for Conjoint Analysis?

Sawtooth Software focuses on providing gold-standard conjoint analysis tools and consulting since the mid-1980s. We offer the most respected and flexible conjoint analysis tools in the world. We also make it easy to get started, with responsive and authoritative technical support. Whether you are just getting started and want to use our streamlined online Discover CBC tool, or whether you are becoming more expert and want even greater power through our Windows-based Lighthouse Studio Platform, we’ve got you covered!