Is Your Company Product or Service Centric? Do Your Customers Agree?

Mine their comments to find out.

In an ideal world, all companies would have a portfolio of excellent products delivered with remarkable service. Competition in the marketplace however dictates that company resources have to be allocated where they can be most effective. Understanding whether customers see your company as product-centric or service-centric does therefore matter.

Some companies have it easy: Apple for instance is (mostly) a product company. Airbnb is primarily a service company. But what about the retail side of Amazon (1)? What about banks, insurance companies, universities, or airlines? The line here is murkier, and all customers may not agree about the category. Moreover, ill-defined priorities increase the risk of a disassociation between the company internal view and its customers.

For instance, a company can internally think of itself as product-driven (and communicate in that manner), while the majority of its customers are focused on its service side. The divergence of these views can generate disaffection among customers who don’t feel heard or taken care of by the company.

Fortunately, there are steps one can take to understand what customers care about and align the strategic direction to the customers’ expectations. To do so requires stepping out of the ivory tower (academia is not alone in living in one) and listening to the voice of the customers.

Directly asking people what they would like can be inaccurate because of the stated vs. revealed preference dichotomy often observed in consumer studies [1]. In addition, customers also view the world through their own prism, leading the famous Henry Ford quote: “If I had asked people what they wanted, they would have said faster horses” (Henry Ford probably never said these words, but they became memorable nevertheless).

Reliable feedback can however be obtained from satisfaction studies, for instance when querying net promoter score (NPS). In that instance, customers are asked to give a numerical rating to a question such as “how likely are you to recommend company X”, and then are asked to justify/explain their rating. The collection of verbatim comments can then be analysed to provide valuable insight into perception customers have of the company.

A great way to obtain actionable insights is to perform automatic topic discovery on the verbatim comments. Among the best performing methods to do so is Latent Dirichelet Allocation (LDA). LDA is an unsupervised machine learning model that automatically parses every comment and group similar ones together into a pre-defined number of clusters.


Figure1: LDA example

In the case of satisfaction studies, numerical ratings can be used as a pre-filter to understand topics customers are satisfied/dissatisfiedwith. For instance, with NPS studies, promoters and detractors can be separated prior to performing LDA (effectively we run 2 LDAs, one on promoter comments and one on detractor comments) to illustrate what customers like and dislike.

As an example, we recently undertook the analysis of verbatim comments from NPS studies of a major client. We first separated the comments according to their promoter/detractor status (NPS score > 8 = promoter, NPS score < 7 = detractor) and performed LDA using Teradata Aster. The comments were clustered into 5 categories for both promoters and detractors. The key topics for each category is illustrated below:

Promoter Detractor
Personal Service All corporations are the same
Never had a problem I don’t know enough, I am new
Convenient, Easy Bad customer service
Customer service polite and helpful Difficulties with products
Good product experience Products too expensive


One immediately sees that the quality of customer service and amount of customer effort (2) dominate the conversations, indicating that satisfaction (high or low) is primarily linked to service rather than products. As a result, service improvements will have a greater impact than changes in the product portfolio. There is also an opportunity for the considered institution to change its perception by showing to its customers it is not the same as its competitors.

Verbatim comments offer a unique insight into users and customers due to being their own words. Carefully mined, these comments help define (or redefine) strategic priorities. Because focusing on products when customers care about service (or vice versa) will not make you successful in the long run.


1 personally I view it mostly as service-centric

2 In fact, customer effort score is probably a more predictive measure of loyalty and satisfaction than promoter score


[1] “A comparison of revealed preference and stated preference models of travel behaviour”, M. Wardman, Journal of Transport Economics and Policy, January 1988

Clément Fredembach is a data scientist with Teradata Australia and New Zealand Advance Analytics group. With a background in Colour Science, Computational Photography and Computer Vision, Clement has designed and built perceptual statistical experiments and models for the past 10 years.


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