Jobs-Based Segmentation: Part 2

…Continues from Jobs-Based Segmentation: Part 1

At the most fundamental level, the difference between conventional market segmentation methods and the jobs-based segmentation approach is the primary unit of analysis for grouping customers. For conventional segmentation, the primary unit of analysis is the attributes of customers’ themselves. The primary unit of analysis for jobs-based segmentation is a job that customers are trying to get done.

However, the aim of both approaches is the same—to create customer segments that share a uniform set of needs that can be profitably satisfied by products and services. Let’s use this common premise as the basis for further contrasting jobs-based segmentation and conventional segmentation methods.

One key difference between these two approaches is how a “market” is defined. The conventional definition of a market is based on the product and service categories defined by solution providers. Jobs Theory, on the other hand, defines a market as an aggregation of all available solutions, both provider and non-provider, that customers regard as being able to satisfy their needs with respect to getting a job done. For this reason, the term customer-defined solution market or simply solution market is used rather than just term “market” to emphasize this distinction.

From a Jobs Theory perspective, customers segment themselves by the jobs they’re trying to get done and the needs they’re trying to satisfy with respect to getting those jobs done well. For this reason, customers do not constrain their search for solutions based on industry defined product and service categories.

This is important distinction because to create and maintain best value solutions, it’s imperative to know what other alternatives your products and services are competing with from the customers’ perspective. If this is not known, it’ll be unclear what differential value must be generated to keep a company’s offerings positioned as the best value among competing solutions.

Yet another key difference is the primary criteria used to define customer needs. Conventional segmentation approaches group customers according to similar characteristics and behaviors— collectively called attributes. Customer attributes often include a combination of demographic, psychographic, lifestyle and behavioral data, business classifications, among others.

The assumption is that individuals and organizations that share similar attributes will strongly correlate with a uniform set of needs for that group. The problem, however, is that correlation can’t really predict the value that customers want from solutions because their buying behaviors are often driven by more fundamental causal factors.

Using correlations as the primary basis for segmentation can result in customer segments that have significant variation around a set of needs. Such variation makes it very difficult for companies to create solutions that can profitably satisfy all the needs of customers in that group.

In contrast, the primary criteria used to define customer needs for jobs-based segmentation is the jobs that customers are trying to get done, not the attributes of the customers themselves. For any target job, moments of struggle and the circumstance causing that struggle are the primary basis for grouping customers into segments. Customer attributes are then used as secondary criteria to create job executor personas so that customers can be identified out these in the world based who they are and/or what they do.

Once customers are grouped according to these criteria for a target job, all the value targets associated with those particular job executors are a complete and precise set of needs for the segment. As such, those customers have a high degree of uniformity around the value they want from solutions to get a target job done better. Because innovation teams know in advance the value that these customers want, they can consistently create and maintain best value solutions at the lowest cost to the company.

Again, the common goal of segmentation is to identify groups of customers that have a high degree of uniformity around a set of needs. Therefore, the efficacy of any segmentation method relies on completely and precisely defining those needs. Yet, needs are often defined in ambiguous and incomplete ways.

They are defined as wants, benefits, motivations, bundle of satisfactions, requirements, problems to be solved, state of dissatisfactions, desire sets, preferences, desired outcomes, product attributes, functional goals, functional tasks, critical-to-quality specifications, among other definitions. Further, it’s often said that customers have articulated and unarticulated needs.

Now, let’s combine some of these perspectives into a more comprehensive definition. Customer needs are the functional and emotional benefits that customers seek via solution features that will solve the problems in their lives and businesses, where these needs can be articulated or unarticulated. However, it’s still not clear how to operationalize this definition of customer needs.

Questions arise such as – How do we consistently define benefits? Why do customers want those benefits anyway? How do we define problems? Are there other needs that aren’t necessarily problems? What’s the best way to ensure that all customer needs are captured whether they’re articulated or not?

This uncertainty is a big problem because it’s been well established that ambiguity around customer needs is the root cause of most innovation failures.

To summarize, conventional segmentation methods group customers together that share similar attributes. For each segment, the customers’ needs are then defined using a combination of methods like the voice of the customer, ethnography, lead user analysis, conjoint analysis, and focus groups, to mention a few. The aim is then to satisfy those needs better than competing solutions where “competing solutions” are typically limited to product and service categories defined by companies.

Regardless of which of these methods are used, conventional segmentation is inherently based on the assumptions of correlation because needs are defined AFTER customers are grouped according to similar attributes.

The Jobs-based segmentation method, on the other hand, starts with identifying an important job that’s not getting done well with products and services. Competing solutions are those defined by the customers’ solution market. Using the Jobs-to-be-Done Framework, a complete set of needs is FIRST captured for all customers trying to get that job done.

Customers are then asked to prioritize that set of needs which produces an exhaustive set of value targets. The moments of struggle indicated by undershot value targets and the circumstance causing that struggle is then used as the primary criteria to group job executors into a job segment(s). The other associated value targets for the segment(s) fall into place. Now the customer segment has a high degree of uniformity around the value those customers want from solutions to get that particular job done better.

Job-based segmentation is inherently predictive because causal factors rather than assumptions of correlation are used as a basis for grouping customers. This gives a company a significant advantage over competitors because they can anticipate the value that customers want—even before customers are aware of certain needs.

A company can quickly and efficiently enhance their existing offerings and create new offerings that can satisfy customer needs better than competitive alternatives at the lowest possible cost.