Market Segmentation Process

Segment the market around desired outcomes And discover new opportunities for growth

Outcome-Based Market Segmentation Process

Our outcome-based market segmentation process follows four steps: (1) collecting the required data, (2) choosing the segmentation variables, (3) clustering, and (4) profiling the clusters. Using the example of Motorola’s Radio Products Group, here is how our approach to market segmentation is executed.
Market Segmentation Process

Collect The Required Data

Unlike any other market segmentation process, the data required to create outcome-based segments is, in fact, the customer’s desired outcomes. Motorola found, for example, that radio users had nearly 100 desired outcomes when using radio products. They included minimizing the number of communications that can be intercepted by unauthorized parties, minimizing the likelihood of inadvertently making changes to the settings, and minimizing the number of communications that are misunderstood. Once captured, we designed a survey instrument, or questionnaire, and administered it to a large number of radio users that represented an accurate sample of the user population. The questionnaire was designed to capture and quantify the importance that users placed on each outcome and the degree to which each outcome was satisfied with the products currently available. Capturing both of these data points is critical in determining which desired outcomes will make the best segmentation criteria.

Choose The Segmentation Criteria

As part of our market segmentation process we do not use all the outcomes to generate the segmentation scheme. Instead, the only outcomes included in the clustering process were those that explained differences in what customers wanted to achieve when getting the job done. If everyone wanted to minimize device downtime, for example, then that outcome would not help to explain differences between customers.

Identifying what makes customers different is the key to a successful market segmentation process. We define these differences in a unique way. In contrast to traditional clustering methods which typically look at differences in the importance customers place on an attribute, our approach looks for differences in the opportunity for improving the satisfaction of a desired outcome. This opportunity variable, which was also introduced in Turn Customer Input Into Innovation, HBR January 2002, is calculated as [importance + (importance—satisfaction)] and enables us to quickly determine which desired outcomes are both important and unsatisfied, indicating where customers want to see improvements. It is these differences that power our market segmentation process and enable us to discover segments of opportunity.

To determine which outcomes explained customer differences, Motorola looked for outcomes that were important and unsatisfied to some members of the population, but not to others. Using statistical (factor) analysis to help with this evaluation, we helped Motorola determine that only 11 of the nearly 100 outcomes met this criterion.

Segment Through Cluster Analysis

We used clustering algorithms found in a commonly used computer-based statistical analysis programs to execute the clustering process. The clustering algorithms focused on the opportunity ratings given to the 11 selected outcomes and placed the respondents surveyed into a predetermined number of segments based on their responses. It is the use of the opportunity rating as the basis for segmentation that makes this market segmentation process truly unique, as the resulting clusters indeed identify segments of opportunity.

In the end, Motorola decided a 3-segment solution was best, as the segments contained 40%, 28%, and 30% of the respondents respectively. The clustering algorithm determined that this grouping did the best job of explaining differences in the way the surveyed respondent valued the 11 segmentation criteria.

Profile The Outcome-Based Segments

When the 3-segment solution was first generated, Motorola had no idea what held each segment together or what types of users they would find in each segment. We helped them understand the segments by profiling them, or applying descriptors to them. When the questionnaire was administered, in addition to the outcome-related questions, it contained over a dozen questions that would help Motorola understand what characteristics each segment possessed. The questions included the users age, job title, how they used the product, and what they used it for-industry classifications, frequency of radio use, geographic location, and several other important descriptors.

These types of questions are critically important to the development of the survey instrument, as they are instrumental in understanding segment content once the clusters have been identified. After analyzing the data, some of Motorola’s immediate conclusions were that Segment 1 valued privacy and security-related outcomes, often conducted covert operations from inside a vehicle, included federal and state police, security, and other individuals, consisted of younger users, and was likely found in urban areas. Segment 2, they concluded, was comprised of users that rely on their radios when involved in potentially life-threatening situations. They consisted of mainly firefighters, police and security personnel that often leave their vehicles to perform assignments, but must maintain vehicle contact at all times. Segment 3 was comprised of coast guard personnel, locomotive engineers, and other users that rely on radio communications throughout the day to carry out their job functions. In contrast to the other segments, their need for privacy and managing emergency situations was negligible. With the segments profiled, Motorola was ready to begin to use this new information as the foundation for its market and product strategy.

To learn more about our market segmentation process, download the Outcome-Based Market Segmentation white paper.