For the last decade, Customer Centricity is a buzz in many industries. Many technologies and new ideas have been implemented. However, there is still a gap. The investments and changes did not enable companies to measure the customer centricity. Also the degree of customer centricity is very hard to measure because it is correlated to individual customer satisfaction and product preference.
Therefore, marketing experts would have to analyse the lot of customer voices in granular detail and many different sources. Manual work or classic market research will only support this task in a limited way. Think about evaluating 1.000 customer voices and summarise them – on a daily basis!
Many of our customers recognised the value of Insaas and what we can add to the existing data and technology. We discovered over the course of several customer projects companies are interested in customer feedback but cannot easily apply it to their daily work. The results are often too heterogeneous. A list of topics, combined with a positive, negative or neutral sentiment cannot be related to customer centricity and customer satisfaction. Therefore, our approach is to label the reviews and cluster them into categories. For “Service”, it could look like this:
But this does not solve the problem to measure customer centricity. The definition of customer centricity is based on the value a product and service delivered to a customer according to Deloitte. But how can you define “value” to measure customer centricity? In the end, it is about the performance of service and product in the eyes of the customer. It is all about listening to the customer and understanding the “job to be done”. To put it in other words:
“A truly customer-centric organization takes a systematic approach, talking to customers to create a fact-based assessment of what customers want and which things they value over others.” (A customer centric approach to commercial excellence)
Therefore, the measurement of customer centricity focusses on two major categories: service and product. For those clusters, we defined four subcategories each to describe the two categories based on the research of Prof. Mischa Kolibius (IUBH). Like this, our software solution is able to apply the single voice to clusters like service and product including the sentiment.
The result will be a graph for one company in comparison to public feedback of competitors. We call this approach the “Customer Centricity Graph”.
In regard to this, we are working on the measurement of customer centricity for German car insurances. In a first step, we researched the public web to find sources where customers talk about their car insurance. We defined a list of the most popular insurance brands in Germany. We harvested more than 300.000 voices from 100 different sources, including aggregators, forums, blogs, portals and app stores.
In a second step, we started a research project with Ludwigs-Maximilians-University to predict entities and sentiment and calculate the score automatically. Our focus lies on the lemmatisation and correct classification of the context of each dataset. As a result, we will create a dashboard to measure and filter the graph according to the sources.
Like this, customer centricity will be measurable and might become a general KPI. We expect the Customer Centricity Graph will be relevant for industries like Insurance, Retail and Software and influence the optimisation of products and services. More results of our work will be published soon.