Preference Analytics is sharing this important and useful article from Sawtooth Software about the use of needs-based customer segmentation for product development.
Needs-based customer segmentation is the cornerstone of modern product development. Segmenting your customer base and providing solutions that align with their needs is essential to compete in the market.
But how do you go about segmenting your customers based on their needs? The article reproduced below describes how to use a technique called MaxDiff in a survey and then apply a segmentation technique called Latent Class logistic regression to find groups with distinctly unique needs.
Preference Analytics has used choice-based methods, such as MaxDiff and Conjoint Analysis, to solve feature prioritization, product optimization, and pricing challenges for over two decades. We are long-time users of Sawtooth Software (a leader in providing conjoint analysis tools to the market research industry for over years).
Article Source: Sawtooth Software
Needs-based customer segmentation is one of the first steps in disciplined product development. But what is needs-based segmentation? Why is it a smart strategy to start with it? And most importantly, how to go about doing it?
Products and services are often a combination of features, each feature may be valued differently by customers. It is especially true for Software-as-a-Service products in which often hundreds of capabilities are bundled together and offered as “the product.”
Often, the product is then sold at “the price,” which is usually the reflection of the value it creates. The problem is that customers value the differentiating features very differently, due to their preferences, individual needs and ability to pay.
Imagine that there are five customer segments, each with unique needs and economic value.
Source: The Strategy and Tactics of Pricing by Th. Nagle and G. Müller
In the illustrative image above, the company set its (one) price based on which segment’s maximum willingness-to-pay maximizes contribution margin. In this example, segment C is the most profitable, and so the price will be set at $10.
The single-price strategy leaves money on the table. Segments A and B are willing to pay quite a bit more, and by paying only $10, they’re enjoying a ‘consumer surplus.’ Another problem is that a price-point of $10 misses out on nearly half of the market. Segments D and E aren’t interested in the product at $10.
Source: The Strategy and Tactics of Pricing by Th. Nagle and G. Müller
It is significantly more profitable to create a structure of prices that aligns with the differences in economic value and customer needs. Notice on the images, above, that by providing solutions to each segment, the company – in this example – would nearly double its profit contribution.
Financial success then depends on the company’s ability to identify distinct customer needs and then create solutions for those needs. Ideally, each solution will be set at a price that near-maximizes the segment’s economic value.
What are the best approaches to find unique customer needs?
Finding customer groups that are unique based on distinct needs is certainly a challenging task. The researcher may be familiar with various statistical techniques, yet applying the correct inputs to these methodologies is just as critical as picking the most appropriate clustering method.
When searching for unique needs, the most common approaches involve survey-based approaches. Survey-based clustering can quickly become over-complicated and complex. The more survey questions (dimensions) are included in the segmentation the less clear and often more confusing the segments become.
One of the increasingly popular segmentation methods uses a technique called MaxDiff. A MaxDiff is a survey-based method in which survey takers pick their favorite and least favorite item from a subset of a list of items. Survey-takers, during the MaxDiff exercise, typically go through eight to ten different subsets of items and make repeated choices of best/worst or most preferred/least preferred. The result of a MaxDiff exercise is a prioritization and scoring of all the items. However, the item scores are likely different by each survey-taker. The MaxDiff choices can be analyzed using a technique with a fancy name called Latent Class Multinomial Logit (or LC-MNL in short), which looks for patterns in the choices.
The image, above, shows a MaxDiff survey question. In this example, the survey-taker is asked to evaluate a CRM system based on feature desirability. The four items, shown here, are a subset of 20 product features. Using the LC-MNL technique, the researcher is able to find unique clusters of customers whose members have similar feature-needs within the segment but have different needs across the groups.
What are the advantages of using MaxDiff to segment customers?
One of the most challenging tasks in traditional, survey-question-based segmentation is to determine how many questions (variables) to use to segment customers. There is an irresistible desire in the research community to include more and more. “Surely, if we include Questions 10 through Question 18, we’ll be able to segment our customers better, right?” Wrong. The more questions (variables) one includes in segmentation, the more confusing and less clear the segments often become. Leading segmentation experts call it “the curse of dimensionality.”
A MaxDiff, however, can be thought of as “one conjoint attribute with many levels,” which avoids the complexity of many variables, each with different scales. A MaxDiff keeps it simple and clean – by having the variable components be the same type.
Keys to a robust MaxDiff Analysis
If MaxDiff analysis is used for segmentation, it is important that the items in the exercises are part of the same concept.
1. Avoid including items that are unrelated to the overall concept you’re measuring. As an example, if you’re measuring preference for product features, don’t include marketing statements or statements irrelevant to the product features in the exercise.
2. Avoid items that are obviously similar in nature, especially if you’re looking to segment the market using the MaxDiff exercise. As an example, again, if you’re measuring product features, don’t include ‘Ability to create custom landing pages,” and “The landing pages have 23 different color schemes.”
A thorough understanding of the segments
Finding the distinct segments and their unique preferences and needs is only step one in the process. These findings will allow product managers and marketers to structure and price the offering according to the segments’ needs. The research should include exercises to measure customers’ willingness to pay for the various features to assist in setting up the pricing structure for the unique offerings.
Hopefully, the segmentation will yield valuable insights into other aspects of the customer segments that will allow marketers to target the customers. For example, the size of the segment, the business size, location, etc… can prove to be invaluable for a marketer.
Using MaxDiff with a LC-MNL segmentation technique has been increasingly popular in the market research community.
The original article can be found on the Sawtooth Software site.
Please feel free to contact Preference Analytics to discuss your business objectives and research needs, and we can figure out whether the use of MaxDiff or Conjoint Analysis is the right tool to use for your situation. al@preferenceanalytics.com
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