The Correlation Between Price and Consumer Preference
- $3 million in short term pricing opportunities.
- $10 million in long-term margin improvement opportunities
Case Study: Background
The client is a private equity company that manufactures and sells retail home design products. These products are sold through big box home improvement stores, smaller hardware stores, and independent home design and décor stores. The company experienced double digit growth in the last two years and is growing its eCommerce business, making products available to customers online directly through the company’s website.
The company, which offers thousands of premium SKUs was experiencing margin pressure and looking for assistance to transform its pricing strategy. Product differentiation was based solely on design, quality, and production cost. The dollar value of each unique design element to different types of customers (commonly referred to as customer segments) had never been considered. Uncovering this information was vital to an effective pricing transformation. However, with thousands of different products at play, how could value be accurately determined?
The company sold thousands of models, each with similar product features and functionality, varying only slightly by how the products look in regard to style, colour, material, shape, etc.
It was easy for the company to change one tiny detail of an existing model to create a new model, which is why they now produced thousands of different SKUs, which added to their pricing complexity.
What’s more, the company lacked integration between the design and production departments and as such, did not know what elements of the designs consumers really valued.
Consumers may have a higher willingness to pay for valued design elements, and thus indicate premium pricing opportunities for the company.
Building a Pricing Solution That Fits
The pricing transformation started by testing a hypothesis – was there really a need to create and produce so many different models of similar products? Further to this question, the team focused on identifying the design elements that consumers really wanted and understanding what they were willing to pay for each element. They also wondered if different customer segments shopped using specific retail channels, and if so, which products should be offered through each retail channel, and at what price?
A key take-away from the qualitative research was that consumers had a hard time visualizing what the product would look like. They couldn’t identify what design elements like style, shape, colour, material, etc.) they liked, without seeing a photo of the product with all attributes in place.
To account for this challenge, research methods were adapted. The research team presented images that reflected the different design elements in an adaptive manner. Participant’s choices of the most liked product images were shown across a series of stages, allowing consumers to accurately define and narrow their preferences for different products and then articulate the value they placed on each design element, moving from general to specific options.
The research team examined the SKUs, uncovering certain design elements that were common amongst the products. Based on this research, they then tested approximately 10 design elements, each with between six and eight different variations e.g., color as design element then as variations white, red, silver, oak, black, etc. These tests included qualitative research such as focus groups and interviews with consumers that provided information about their design preferences (traditional, modern, etc.), where they tended to shop (big box stores, online, small home design retailers, etc.), and more. The team looked for matches between product families and attributes with design preferences and preferred retail channels.
For quantitative research, the team used the findings to conduct an adaptive max diff analysis, which is essentially a best and worst ranking system and form of research that is conducted to understand the relative importance of each independent attribute and determine which attributes are perceived as most valuable to consumers. To do this, it became necessary to adapt the way this analysis is normally conducted. Because so many products and attributes were tested, there was no way to allow the customer to configure the product the way they wanted, with all their ideal attributes in place. Adaptations to this analysis uncovered which attributes added to the attractiveness of the product.
In the first stage of the adaptive max diff, the team used that information to categorize the products into families (with over twenty different styles per family) based on the information uncovered from the qualitative research. Participants were then asked to select the products they preferred most and least. The best styles were matched to different home décor tastes and all other products were eliminated from the testing pool.
In the last stage of the adaptive max diff, the team learned more about detailed differences in design elements. To do this, participants were given the option to see only the products they had identified as a preference during the conjoint analysis and were asked about the best and worst product pictures within that product family.
The team also used a price sensitivity meter, which is a survey that essentially asks, “how much is fair and is too expensive to pay for X product?” This is one of the simplest ways to get pricing insights. Researchers used Van Westendorp’s Price Sensitivity Meter to ask participants how much they were willing to pay for the products they preferred. With this information, they were able to identify the willing to pay (WTP) premium.
What was interesting, though is that participants were asked about pricing options as well as product preferences, which linked the price sensitivity meter to the data uncovered in the max diff and conjoint analysis in what was a triangulation of research methods. The price sensitivity meter method provided data about the best, middle and least preferred products.
Not surprisingly, participants were willing to pay more for what they liked compared to something they didn’t like, so that’s where pricing premiums for unique, well-liked attributes came into play.
Heat mapping was used as part of the research to record what, on the computer screen, captured a customer’s attention the most. One surprise that came out of the heat mapping was that customers liked seeing the brand name label on the product. Some products included this label and others did not. There was no strategy around using the label, it was done completely randomly, and in fact, the company had no idea that consumers preferred to see the brand’s name showcased. The research showed that the brand name reflected prestige and was well-liked by consumers. As a result, the company decided to include the brand’s label on all products moving forward.
“It is surprising that brands often forget to include the voice of the consumer in the product design process. To benefit from a customer-driven pricing approach and capture healthy margin rates, the first step is to consider the consumer’s voice.”
– Claudia Lopez, [Senior Research and Analytics Consultant, Iris Pricing Solutions]
Based on this research, the team was able to provide the company with recommendations to produce specific products for specific retail channels based on consumer preferences, perceived value, and shopping habits.
For example, the research showed that very design-focused shoppers were willing to pay a premium for specific attributes and were most likely to purchase from smaller, home décor retailers rather than big box hardware stores. More price sensitive shoppers were more likely to shop from big box retailers or online and were less particular about specific attributes.
Further, the company now knew how much each customer segment was willing to pay, and so used these WTP amounts to quantify each attribute, which gave the design and production departments the guidance they needed to focus on the products that produce the highest profits and recover the most money. In the end, the team narrowed manufacturing down to the highest profit products.
Ultimately, the company was able to put this methodology into a larger model for the company’s management team, to help guide product development and pricing in the future.
This pricing transformation provided $3 million in short term pricing opportunities.
Using the pricing model, the company could capture $10 million in long-term margin improvement opportunities moving forward.