Pricing Research Blog

Are Your Price Increases Causing Customer Confusion and Resentment?

Consumers are feeling the deep squeeze as inflation rates remain the highest they’ve been in 40 years.

It’s impossible to tune into any media outlets without receiving a barrage of warnings about ever-rising prices. Companies large and small continue to strain shoppers’ wallets, causing consumers to seek smarter shopping solutions, to switch brands, and to cut back and do without. With prices rising as much as 10% or more depending on the category, many brands are experiencing a decline in sales volume as consumers grapple with this ongoing challenge.

During tough economic times, companies may often seem insensitive to their customers, causing a loss in trust and loyalty. Sweeping price increases paired with decreasing value cut deep. There can, however, be a more artful approach to pricing that can soften the blow for consumers while still managing profitability for the company. Of course, pricing evaluation can be complex, so where should your company start?

Cream of the crop companies utilize pricing analytics that wholly examine their customers’ expectations along with willingness and ability to pay. Do you know which of your products or services offer key value that your competitors don’t? Have you clearly identified those where consumer price sensitivity is higher vs. those where their sensitivity is lower? Are you personalizing your promotions across your customer base to make the right offer to the right consumer based on these factors? If not, you may be missing out on opportunities to build customer trust and devotion during a time when many are struggling.

We have observed that as inflation increases, companies are passing on cost-of-goods increases to consumers, usually without increasing perceived value of their products (i.e., without providing “more” for the money). This promotes consumer confusion and resentment. Where is the additional value to justify the increase in price?

Before updating your products or communications, it’s important to vet any changes to pricing and perceived value. There are several pricing analytics methods available. We’ll touch on two broad categories where some exciting new developments can help: Econometric Demand Modeling and Choice Modeling (trade-off exercises).

Econometric Demand Modeling. This method capitalizes on the large body of well-grounded economic and econometric theory. With sufficient data, the method delivers high-quality, unbiased estimates of price elasticity. This technique relies on secondary data, such as web analytics, employment rates, weather patterns, company sales data, competitive products and pricing data, promotion dates, and even media-mix information.

Demand modeling has some disadvantages. Price elasticity is not projected reliably to price points outside the price range in the historical data. Rapidly changing conditions mean that historical data may not reflect current markets. The growth of e-commerce, for instance, dramatically changes the nature of demand and impacts price elasticity.

Choice Modeling. Survey-based trade-offs overcome some of the drawbacks of econometric demand modeling. An experimental design (a plan that dictates which products and features are shown and at which prices) is used to create survey screens. These tasks replicate real-world buying scenarios where a respondent considers a set of competitive alternatives with pricing and makes a choice about what, or how many, to buy.

When a representative sample of respondents has completed the survey, the data is used to estimate a choice model. The choice model is also an econometric model, with one major difference—it does not rely solely on the past, but instead attempts to predict the future. Model results are used to estimate a demand curve and price elasticity. Different pricing strategies can be assessed by changing the price and forecasting the impact on margin and customer acceptance.

There are many flavors of choice models and implementations available. For example, some companies are using regular tracking research to assess promotion efficacy by customer segment with trade-off analyses. Developments within choice modeling showcased recently may increase manufacturers’ ability to identify product features that promote value and optimize price.

• Incorporating constraints consumers face when they make purchases. Constraints can include budget, time, and effort, among other things. Allowing consumers to state and use a budget for making their purchase decisions during the exercise is an interesting add-on to the choice model. When the budget is used to adjust the choice model, the price elasticity results seem more reasonable and realistic. There isn’t sufficient evidence to fully support this development but it seems promising.
• Assessing brand equity and “premiumness” on an ongoing basis. Simply adding a choice model that incorporates brand, product, and price to a brand tracker can yield a wealth of insights, such as which brands can command price premiums and which cannot. Furthermore, aspects of the product and category experience can be evaluated for their influence on price perceptions. Because this information is gathered on an ongoing basis, companies can track their premiumness (and potential factors that promote it) over time.

Now more than ever, successful companies realize that their pricing strategy requires constant management. This is often most successfully implemented in a cross-functional environment with strong analytics, such as those described here. Additionally, these actions can bolster strategy:

1. Obtaining a solid foundation through good qualitative research to feed strong analytics. Better understanding of rational as well as emotional decision making can be teased out in preliminary qualitative research and will provide much more effective inputs into your choice modeling analytics. Don’t forget our earlier comments on price increases, perceived value, and the danger of customer resentment toward your brand.
2. Ensuring that your analytics are a dynamic piece of learning. Using a simulator tool based on the choice modeling analytics will allow you to make adjustments to your pricing strategy for months to come as the market and your competitors change. This extends the life of your analytics investment by allowing you to maintain a competitive edge.

Working across your teams with an expert analytics partner can help identify ways beyond price increases that could soften the blow for your customers while meeting the profitability goals of your company. It’s very natural for consumers to see prices rising, get angry about it, and then look for someone to blame. Don’t let that be your brand.

Elizabeth Horn

Senior VP, Advanced Analytics

Beth has provided expertise and high-end analytics for Decision Analyst for over 25 years. She is responsible for design, analyses, and insights derived from discrete choice models; MaxDiff analysis; volumetric forecasting; predictive modeling; GIS analysis; and market segmentation. She regularly consults with clients regarding best practices in research methodology. Beth earned a Ph.D. and a Master of Science in Experimental Psychology with emphasis on psychological principles, research methods, and statistics from Texas Christian University in Fort Worth, TX.

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