Can’t I Just Use Personas Instead of a Customer Segmentation Framework?
Perhaps your organization is like a Chicago-based firm we spoke with in early August that works with customer personas to guide app development, with no clarity around a customer segmentation framework.
The organization is investing millions of dollars in its mobile app without knowing the size of the segments that the personas fictionally represent, or even if the personas they are using are representative of a meaningful customer segment.
We believe the benefits of the customer segmentation framework are compelling for both B2C and B2B organizations, and that personas should be informed by the most strategic customer segment targets.
Benefits of Customer Segmentation Framework
A well-done quantitative customer segmentation framework will illuminate distinct groups or segments of customers who share common needs and motivations about the product or service category.
The framework will provide clarity into the size of the group in terms of people or households, and also in terms of product spending, usage and profitability.
The segmentation also should require that customer segments created through clustering-aligned motivations and psychographics do translate to distinctive behaviors.
The segmentation provides a landscape of all the customer segments in the market, including those that the organization may not be reaching with the current brand strategy. For example, one of the consequences of using the persona-based-only approach at the Chicago company is overlooking a legacy customer segment who prefers to interact in-person and with paper forms, rather than using the mobile app.
The Chicago company’s app is a choice to walk away from the in-person, paper-using customers’ needs and preferences. That means there is an opportunity for another company to serve these overlooked “Traditionalist” consumers that don’t fit into the persona this organization is targeting.
Dog Food B2C Customer Segmentation Framework Example
Where I live in Southern California, dogs are prominently seen as part of everyday life, and we are a dog-friendly culture. While other states like West Virginia may have a higher percentage of dog owners than California, it’s a fair guess that West Virginia dogs are not going out to sit-down meals at restaurants with their owners as often as California dogs. It’s not surprising that a brand like Just Food for Dogs (with retail stores dedicated to dog food) is headquartered in Irvine, California.
A classic customer segmentation landscape example that we used for years at The Cambridge Group is for dog food. In this example, the critical insight is that the customer’s relationship with his or her dog translates to the choice of dog food. There were six segments in the market, representing a range of relationship. Three of these segments are:
- Dog as child segment: At one extreme, there is a customer segment who skews to older, smaller households who views the dog as a (spoiled) child. This dog child, who is often a smaller breed, deserves the very best dog food on the market, typically with human food qualities, and in small containers.
- Dog as family member segment: This second segment views the dog as a valued family member who fits into the family’s active, healthy lifestyle. They skew to families with children under 18 at home, and to golden retrievers as pets. As a family member, the owner wants to feed the dog convenient, healthy food to support its active lifestyle.
- Dog as farm implement segment: As may be expected, this segment skews to farm households. The choice of food is one that provides basic nutrition at a good price, often in a very large container.
B2B Customer Segmentation Framework
It’s common in B2B segmentation to find multiple decision makers and influencers in a single customer organization, and also to encounter intermediary B2B customers who highly influence the end customer’s decision.
A classic example of multiple B2B decision makers comes from Miller Heiman’s manufacturing sales example that identifies four typical constituents:
- Technical end users
- Bid managers
Their data suggests that there are 6.4 buying influences for each B2B deal. As a practical matter, the B2B customer segmentation framework needs to take this into account.
As an example of the decision influencer, while an insurance broker may represent multiple brands of insurance for the same need, the reality is that they are unlikely to divide their business perfectly evenly among the brands. The end customer of the insurance broker is more likely to walk away with a product from the brands they work with most regularly than a less frequented brand.
Frame of Reference for Customer Segmentation
The correct frame of reference is a very important consideration for the customer segmentation framework. For instance, Silk evolved its frame of reference from soy milk to other plant-based milk options, such as almond milk. In this case, the customer segmentation framework we designed initially for soy milk was versatile enough to allow the expanded frame of segmentation in support of Silk’s move into almond milk.
In cases where the frame of reference is in question, we recommend exploring this with customers to understand their definitions as well as substitute products and services. For instance, “household chores” was the frame of reference that consumers used to describe the specific domains of bathroom, kitchen and floor cleaning. In this case, we investigated multiple frames of reference quantitatively before arriving at an integrated customer segmentation framework for household chores.
When building a customer segmentation framework, a useful starting point is to identify the possible segmenting dimensions for the frame of reference. In the early stages, we recommend developing a more inclusive set and avoiding judging and eliminating dimensions too quickly.
In our practice, we then investigate these segmenting dimensions using any available data, and ideally solicit customer input on the dimensions (through interviews). The dimensions then form the basis of research questions that have the potential to create the quantitative customer segments.
Of course, some dimensions may not be statistically differentiating, and will be eliminated later in the process. It’s also important to avoid highly-polarizing dimensions that are unrelated to the topic at hand. For instance, on a packaged cookie segmentation, an attitude related to cigarette smoking was inadvertently included in the dimensions that created the segmentation. This proved to be highly polarizing and needed to be excluded, and the segmentation rerun.
Once your team is satisfied that you’ve got a solid set of dimensions and a fact-based understanding of how a range of customers think about the dimensions, you are ready to go into the next step to develop a hypothesized segmentation.
Getting Started: Hypothesized Customer Segments
We also recommend creating a hypothesized segmentation before the research is fielded. This is not meant to be the “answer;” it is meant to push the thinking.
Some questions to consider when developing the hypothesized segmentation:
- Who are the heaviest users and/or needers of the product or service category?
- Who are the infrequent users or rejectors?
- What motivates each segment?
- Which segments are high and low on the segmenting dimensions?
- Is there any evidence of this segment’s existence from data?
- As the team looks over the hypothesized segments, is there a group missing? For instance, the paper-based Traditionalists segment was missing from the mobile app firm’s personas and hypothesized segmentation.
- Based on available data, how large are the hypothesized segments, and how much of the category spending and profit do they command?
To develop a robust customer segmentation, follow an open-minded and fact-based approach. Allow time with iteration on multiple segmentation solutions, frames of reference and segmenting dimensions. Push the thinking at the beginning with a hypothesized segmentation.