Market Segmentation for Enterprise Services Planning


I realized after posting my article on Fitness For Purpose Score that it isn’t reasonable to expect readers to know the background and context that stimulated it. It isn’t reasonable that I assume readers are up-to-date with speeches I’ve given over the last two years covering Evolutionary Change, Fitness for Purpose and Enterprise Services Planning. So I felt some explanation of how we do market segmentation for ESP was in order to provide better context for Fitness For Purpose Score.

How do we know whether a change in our service delivery capability represents an improvement? This is the fair and reasonable question that should drive our decision making about how we manage, how we make decisions, and which changes we choose to invest in, consolidate and amplify. In evolutionary theory, a mutation survives and thrives if it is “fitter” for its environment [this is actually a gross simplification but it will do for an introductory paragraph on a related but different topic of marker segmentation.]

So how do we know whether or not a change to our service delivery capability makes it fitter for its environment? What do we mean by “environment” in this context? “Environment” is the market that we deliver into. So “fitness” is determined by whether the market feels our product or service and the way we deliver it, is “fit for purpose.” So to understand “fitness” to enable and drive evolutionary improvements, we first need to understand our market and what defines “fitness for purpose.” To do this we segment the market by customer purpose and the criteria by which they evaluate our “fitness for [that] purpose.” …

At David J Anderson School of Management, we create our market segmentation by clustering narratives about our customers. We do this by telling stories about them. The technique is a direct application of Dave Snowden’s technique from his Cynefin Framework. To explain this in our training and in the speeches I linked above, I tell the tale of Neeta, a fictional project manager, and mother of 4. Neeta is based on a real woman who works in the Canadian public sector and has considerable Kanban expertise. Neeta needs to order pizza for delivery to her office to feed her team who are working late against a deadline. On another evening the same week, she needs to order pizza for delivery to her home to feed her children who are hungry because she came home late. Neeta doesn’t represent one market segment, she represents two! The reason for this is that the purpose, context and fitness selection criteria are different in each of the two contexts.

When Neeta orders pizza for her children she needs: fast delivery – ideally within 20 minutes; she needs order accuracy – the kids only like plain cheese pizza; the non-functional quality doesn’t matter too much, the kids will eat cold pizza so long as it is cheese pizza; she needs a simple menu and predictable service; she wants delivery when promised because the kids need their expectations set and they are unforgiving; she also cares that the restaurant is clean and can be trusted to follow health and safety regulations; she may care whether or not they use organic ingredients because she is feeding her family.

When Neeta orders  pizza for her office her need are similar but some of the criteria vary and the threshold values are different: she needs delivery in up to 90 minutes; order accuracy is important but if one or two mistakes are made it won’t make a big difference; however, the non-functional quality matters, hot, tasty, pizza with gourmet flavors and exotic ingredients are required for these discerning geeks; it doesn’t matter if delivery isn’t as predictable as it might be, so long as they show up eventually – the team are busy; and yes, she still cares whether the restaurant meets health and safety legislation standards but organic ingredients probably aren’t so much of a concern.

In other words, Neeta decides whether she likes the pizza service and whether she will use it again, based on two different sets of criteria, depending on her context. This may lead her to use different service providers for each purpose if one provider can’t meet both sets of her needs. As a result, Neeta represents two segments, not one.

How would you know that Neeta represents two segments and not just one? Traditional demographic profiling wouldn’t give you this insight! Well perhaps she uses different credit cards or payment mechanisms depending on context? And the delivery address is different. So there are some obvious clues. However, the people in the business who know Neeta’s story are the people who took her telephone order, and the delivery boy who delivered the pizzas. It is these frontline staff who understand the customers best.

If you are to cluster customer narratives to determine segmentation, you need to bring frontline staff into the storytelling sessions. You need to listen for context, purpose, and selection criteria and create segments based on the affinity of these aspects of the market. Give each cluster a nickname. Recognize that an individual customer can appear in multiple segments depending on their context on a specific day and time.

The challenge of this for many companies is that the people who best understand the customer’s context, purpose and selection criteria are often the lowest paid, shortest tenured, highest turnover staff in the business. Foolishly, many companies undervalue, the value of customer-facing staff. Traditional 20th Century service delivery businesses take a transaction view of customer interaction rather than a relationship view. If you value repeat business and you value the insights that will enable your business to evolve and survive in a rapidly changing market then you need to value customer-facing people and involve them in your strategic planning.

Once you have the clustered narratives defining your segment, now select the segments you want to serve. This is a key piece of strategic planning. Which businesses do you want to be in? Which don’t you care about? Which do you want to actively discourage? Based on this you will develop the Fitness Criteria Metrics to drive your management decision making and evolutionary improvement.
Designing Fitness Criteria Metrics, choosing their threshold values, and making them your KPIs (Key Performance Indicators) will be the subject of my next post.

Leave a Comment

Your email address will not be published.
The following GDPR rules must be read and accepted:
We will process your personal data to post your comments in the blog if you indicate your consent by checking the corresponding box. David J Anderson School of Management is the data controller. You can withdraw your consent and exercise your data protection rights at any time by contacting us at info@djaa.com For more information review our Privacy Policy.