Case Study: New Product Introduction and Price Elasticity

Case Study: New Product Introduction and Price Elasticity

Building Materials Case Study

We often look at different market research techniques broadly across many industries but today we’re going to switch gears and focus on a specific industry, Building Materials. A large building materials company came to us as they were getting ready to launch a series of new products; one of them being a value-add roofing underlayment.

The client wanted to understand how consumers and the trade (roofing contractors and home builders) would make the trade-off decisions between recommending/installing standard felt underlayment and recommending/installing the new offering; with specific emphasis on modeling how a range of possible price points impacts the product choice.

In other words, they wanted to better understand the factors driving purchase decisions between the two products (standard & new) for both consumers and the trade; with a specific focus on the impact of price.

Price Elasticity Philosophy

Before we jump into how we attacked their problem, let’s first understand our price elasticity philosophy. Optimization Group’s research has shown that, in many cases, the “Best Price” is not necessarily the lowest price in the eyes of the customer. There are many factors influencing the perceived value equation; the Optimal Price balances elements of the value proposition over the perceived costs to arrive at a “Value Level” or expected value:

value_equation

Research Objectives

  • To determine the customer perceived price differential for our client’s roofing underlayment versus standard felt alternatives
  • To understand how attributes (e.g. product benefit statements, product feature statements, warranty, installation and performance advantages) impact price perceptions and therefore optimal pricing levels

Methodology

After careful consideration of the client’s objectives and possible product attributes and levels of pricing to be evaluated, we recommended a Choice-Based Conjoint (CBC) methodology. The CBC methodology is well-suited for studies where there are an uneven number of levels across the various attributes (material, installation attributes, durability claims, etc.) included in the design of the study.

For example, one attribute (i.e. Warranty) may have 5 levels (1-year, 2-year, etc.), while another attribute (i.e. Price) may have 7 levels ($5, $7, $10, etc.). Think of attributes as “buckets” or individual parts of an offer. Think of levels as the number of options under an attribute.

The CBC design is best implemented using as an online study. Using the CBC approach, respondents express their preferences by choosing products from sets of concepts rather than by rating or ranking them. We essentially ask them to do what they do in a real-life purchase situation; choose.

Each respondent was exposed to a series of choice sets which varied specific levels of each attribute (including the absence of a category in some instances). The CBC methodology allows us to control the inclusion and exclusion of specific levels of each attribute to maintain an internal logic.

An example choice set for designer dress suits is shown below. Notice how three of the choice sets include similar attributes (brand name, material, price) but different levels ($400, $425, $450)

example_choice_set

For more information on how this method works, check out: Conjoint Analysis Explained: What It Is and How To Use It

Output

The analytical output included attribute utility values (which measure the individual contribution of each attribute to concept acceptance) and utility values were calculated for each level of the attributes so that within a category we were able to determine the relative strength of each specific item. Most importantly, the analysis was used to generate simulated preference share, which enabled us to compare the appeal of specific bundles of attributes to each other.

In less geek-talk, we were able to identify the weight of each attribute (warranty, price, etc) and level under each attribute (1-year, 2-year, 3- year warranty, etc.). Then we used that information to compare the different bundles of attributes (warranty, price, etc.) by simulating preference share.

Key Takeaways

One of the things that makes building materials somewhat unique, is that they are often installed by someone other than the homeowner. So this particular manufacturer tested a range of product and service attributes among consumers AND among the Trade. We learned that these two different targets place value on different attributes. Consumers wanted a long warranty; the trade wanted a very comprehensive warranty (they were seeking reassurance that the new product would perform well on many characteristics). Consumers didn’t really care about the timing of the product delivery, but the trade wanted delivery dates cast in stone.

We have found that manufacturers usually have a good sense of what THEIR customer (the contractor/installer) is looking for because they sell to and service the trade every day. However, manufacturers may not have as good a view as to what the end consumer/homeowner desires. This learning can be valuable not only to the manufacturer, but also to the trade. Getting the entire value chain aligned is beneficial to all.

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