A Qualitative Method for Job-Based Segmentation
Companies often undershoot and overshoot the features and benefits of their products and services due to ambiguity around what different customers value. One reason for this is that conventional customer segmentation methods group customers based on a combination of demographic, psychographic, and behavioral data because this data is readily available and conveniently analytic. The data has to do with the attributes or characteristics of the customers themselves, not the jobs they are trying to get done. This is problematic because customers’ buying behaviors change a lot more frequently than their personal characteristics.
The fact is that customer characteristics are poor indicators of customer behavior because the data does not reflect why customers make the choices they do. Although attribute data may correlate with customer choice after the fact, the data can never really predict what products and services customers will prefer (Christensen & Raynor, 2003; Christensen et al., 2005).
Conventional segmentation schemes use attribute data to define the average customer, a necessary fiction to target the needs for a segment. However, customers’ buying decisions do not necessarily conform to those of the “average” customer. Statistically, there will always be significant variation in attribute data because the data are normally distributed for any given customer population. This means that innovation efforts to satisfy the needs of the average customer will undershoot some customers and overshoot others.
The greater the variation in attribute data around the average customer, the greater the undershooting and overshooting problem. For this reason, innovation efforts that rely on attribute-based segmentation schemes will be hit or miss. Successful innovation requires much more precision than this.
A more effective approach for the purpose of creating customer demand is to segment customers around the jobs they are trying to get done. Job-based segmentation uses job circumstance as the primary basis of segmentation because this is what ultimately drives customer behavior. Attribute-based data is used as a secondary basis to create job executor personas. Segmenting around a job to be done is a more precise way to characterize the value that customers want because they are grouped based on common job circumstance. Because of this, the customers’ perception of value is highly uniform and therefore will not vary significantly within a job segment.
Once job circumstance is understood for a group of customers, an innovation team can predict what products/services these customers will prefer to get a job done today and well into the future. Further, the team can anticipate how changes in customer circumstance could change the customers’ perception of value and how such changes could, in turn, affect customer choice. This kind of predictive power gives a company a significant advantage over competitors because they can anticipate the value that customers want — even before customers know what they want. With this knowledge, a company can enhance their existing products/services and create new products/services that offer superior value relative to competitive solutions.
Competitors that cannot anticipate the value that customers want are left to scramble in reactive mode, causing them to reach the market late with inferior offerings. Many of these competitors adopt “fast follower” strategies aiming to free-ride on successful innovations. However, such competitors usually get shaken out in the early stages of the value lifecycle because they cannot generate enough total value (customer value plus profit) to remain viable in the market.
Use the Job Segmentor tool concurrently while creating a Progress Map to identify a customer job segment(s). Job Segmentor uses multi-directional affinity grouping to reveal job executors who share the same or similar circumstances with respect to getting a job done. This is a straightforward yet highly effective way to segment job executors without using sophisticated quantitative segmentation methods that are time consuming and expensive.
If your company has a product/service with a large customer base and there seems to be a lot of job diversity indicators within the group or if the product/service is losing customers to a competitive solution, then you may want to consider performing this segmentation procedure as though you were creating a new product/service. If job segmentation results in two or more customer groups, then a Value Target Analysis for each group could reveal some lucrative innovation opportunities.
To begin, re-create the Job Segmentor template on the wall next to the Progress Map. You will need to assign a unique customer number to every individual interviewed — usually customer 1, customer 2, customer 3, and so forth. As you interview each individual to elicit customer value metrics for the Progress Map, ask them some additional questions for segmentation purposes:
- Duplicate the success outcomes for each individual on sticky notes and then place these sticky notes inside the “Job Executor Success Outcomes” box at the top of the Job Segmentor template. Ask each individual to rate the importance of each of their success outcomes from 1 to 9, where 1 is not important and 9 is extremely important; circle these rating numbers. Write the unique customer number in the upper right corner of each sticky note.
- Ask each individual about the situational factors that are related to the job-to-be-done and the context(s) that the job is executed. Reference the Job Segmentor template for specific questions to uncover these two aspects of job circumstance. Write this information on sticky notes and place them in the “Situational Factors” and “Job Contexts” boxes where they belong. Write the unique customer number on each sticky note.
- Profile each individual based on demographic, psychographic, and behavioral data. The trick is to use a minimum combination of these data to create a job executor persona that can be used to easily identify these individuals out in the world. Record this information on sticky notes and place them in the “Job Executor Personas” box. Write the unique customer number on each sticky note.
- After all individuals have been interviewed, group the sticky notes by similarity within each of the four boxes. Sticky notes that have the same customer number on them must move together as a block because they represent a whole individual. Clump these sticky notes together. Individuals that have similarities within each box are grouped to the left. Individuals that are not similar to others are outliers and are moved to the right. The same success outcomes are grouped together based on the following 3-point rating spreads: 1–3 or 4–6 or 7–9.
- Now, look for patterns where the unique customer numbers on the sticky notes line up vertically with horizontal groups containing the same unique customer numbers; this is the vertical affinity of similar groups. The customer number is used as a key to vertically group the groups within the four boxes. Because you’ll never get a perfect fit, you will need to do some interpreting. Further, expect that this exercise will be messy because that’s the nature of job segmentation, especially with a small sample size. What you are looking for is enough vertical similarity among the groups within the horizontal boxes to identify a job segment(s). That is, if certain groups line up vertically, then you have identified a job segment(s).
Christensen, C. M., & Raynor, M. E. (2003). The innovator’s solution: creating and sustaining successful growth (1st ed.). Harvard Business Review Press.
Christensen, C. M., Cook, S., & Hall, T. (2005). MARKETING MALPRACTICE: The Cause and the Cure. Harvard Business Review, 83(12), 74–83.
Jordan, M. S. (2018). Value Target Analysis: A Jobs To Be Done Methodology. INNODYN Research Publications.