I’m continuing my attempt to document, in a series of blog posts, the incremental improvements leading to today’s state of fitness-for-purpose analysis techniques. Previously in this series:
- Start with Net Promoter Score (NPS)
- Add customer narratives
Segmentation was the next logical step. If I could subdivide my product in to several modules, I would ask customers to fill out the brief two-question NPS+narrative survey for each module. This wasn’t a burden for customers as the number of modules was always small and the survey itself is extremely brief and simple.
The results were useful as I saw which part of my service earned a high NPS such as +0.7 and which got a disappointing -0.3 instead of only seeing a mediocre +0.2 from the overall survey. This information was immediately actionable as I knew where to focus my improvement efforts.
If any of this sounds very simple and obvious, that’s because it is! Yet, when I am a customer, I still see, even several years later, simple, no-segmentation, 1-2-question NPS surveys or pages after pages of questions about parts of my experience that have nothing to do with my satisfaction. Maybe it is not so simple after all.
Question 1. How likely is it that you will recommend this firm (service, product) to a colleague or friend? Scale of answers: 0 (very unlikely) to 10 (very likely).
Question 2. Why did you choose your answer to Question 1?
(blank space for the customer’s story)
Repeat this for every part or module of the product or service offering.
We can of course apply segmentation not only to our product or service offerings, but also to customer populations. This is not as easy as it sounds. If we use customer narratives as input into our segmentation process, we run the risk of our conclusions becoming circular logic: customers who tell this type of stories tend to tell this type of stories.
Using external (to our NPS survey) sources of information isn’t without problems either. Canadian organizational improvement coach Bernadette Dario provided a counter-example, which David J Anderson developed into a fictional, but realistic character, who has become known in the Enterprise Services Planning community as Neeta. Neeta is a modern professional woman and mother of elementary-school-aged children. She belongs to only one demographic segment, no matter how we define those. She is only one persona if we were to use personas as our customer research tool. Yet Neeta’s actual consumer behaviour reveals multiple personalities even when buying the same product. We want to understand these personalities, but neither the traditional demographic segmentation nor the more modern “agile” personas technique offer us a path to get there.
Resolving this challenge and further innovations were still ahead.
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