What did we look at?
Relational customer feedback data set with all open explanations on the rNPS for different brands from the telecom industry.
How did we do this?
Industry-specific text mining analysis applied to enrich the dataset with topics from the customer feedback.
What were the results?
- The analysis revealed that, in particular, price and quality of the network are important issues when it comes to the experience of a telecom brand. These are the biggest drivers of will/won’t recommend the brand.
- The analysis made it clear that the issues customers return in the customer feedback are different for every brand.
What will the company do with the insights?
With the analysis of the enriched data, the telecom brand is given a better interpretation of customer feedback in order to influence the rNPS in its favour and to take advantage of opportunities in relation to the competition.
- Setting up a market segment-specific customer approach
- Improve omnichannel brand proposition
What are the next steps?
- Broader use of text mining, such as text mining on product type (e.g. television, internet, fixed telephony, mobile). The combination of product type and subject gives more valuable insights.
- Text mining enrichment for market segments other than the consumer market (self-employed, SME, large/corporate) and text mining enrichment on other KPIs from the customer feedback.
More customer experiences