The market of ISPs is highly competitive. Optimizing the Cost of Customer Acquisition is key to most players. Of course digital marketing is heavily used by ISPs to gain traction and momentum.
However, the cheapest and most effective means of acquisition is “word of mouth”. An ISP who keeps track of numerous data about its customers wants to understand which criteria contribute to a customer making a recommendation.
The goal is to “predict” which customers are going to recommend the ISP and to understand what criteria are key in impacting this recommendation.
The dataset used contains 4369 customers’ records. And for each record 123 information.
Some of the typical information collected are:
- Information about the quality of service provided by the partner who installed the internet access; reactivity, kindness, pertinence etc.
- Different criteria about the services provided by the ISP, how relevant the various services offered are with regard to the household
- The number of people living in the household
- The quality of the wifi in this household
So far, customer recommendations have been evaluated but not analysed nor anticipated.
Quaartz’ Machine Learning approach can help predict and understand what aspects of the service makes a customer willing to provide a recommendation.