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Customer recommendation: key drivers identification
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Customer recommendation: key drivers identification

The world of ISPs is highly competitive. Customers trust, above all else, other customers’ recommendations. By understanding which criteria drive customers recommendations, an ISP can beat the competition while maintaining the same level of spending.

Industry

Marketing

Project Duration and Effort

One week

Type of Prediction

Binary Classification

Customer Benefits

  1. Improve both customer retention and customer acquisition.
  2. Speed, once the tool is in place, TADA’s analysis takes a few minutes.
  3. A high efficiency in the predictions of customers’ recommendations.
  4. No need to be a skilled statistician to find the key impacting factors among the 123 types of information collected.

Problem to solve

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.

TADA’s Machine Learning approach can help predict and understand what aspects of the service makes a customer willing to provide a recommendation.

Objectives

  • Understand what triggers a good customer recommendation.
  • Optimise the whole customer experience in order to obtain it.
  • Monitor continuously this criteria to keep the key customer happiness criteria outstanding.

 

It poses the following customer relationship question: 

Can customer satisfaction be engineered and optimised?

Solution

The TADA predictive models’ are accurate in predicting customer recommendation in 79% of cases (R2 of 79%, a MAPE of 15%).

This means that in most cases, customer recommendation was well predicted by the model.

What is of high value to the ISP is why does a customer make a recommendation? What are the key criteria influencing this recommendation?

TADA has selected the following six main criteria out of the 123 available in the dataset:

  1. The quality of service of Internet installation, it accounts for 21% of the customer’s decision according to TADA,
  2. The support for teleworking, it accounts for 20% in the customer’s decision according to TADA,
  3. The quality of the internet access, it accounts for 19% in the customer’s decision according to TADA,
  4. The customer service satisfaction, it accounts for 18% in the customer’s decision according to TADA,
  5. The customer satisfaction with the wifi, it accounts for 17% in the customer’s decision according to TADA,
  6. The network coverage, it accounts for 4% in the customer’s decision according to TADA.

Moreover, the what_if feature (Live Predict) has shown to this ISP that the quality of the service provider who installs the internet access is relevant, up to a certain threshold. Beyond a customer satisfaction of 60/100, it does not make a difference anymore for the customer to recommend this ISP. Since this ISP was working solely with a single service provider in order to secure a continuous level of quality well above 60, it became clear that he could diversify its pool of service providers. This in turn allows for the scaling of its operations and the scaling of this ISP’s revenue.

Live Predict

Customer Benefits

  1. Identify hidden patterns that drive customer satisfaction and get actionable results.
  2. Speed, once the tool is in place, TADA’s analysis takes a few minutes.
  3. A high efficiency in the predictions of customers’ recommandations.


No need to be a skilled statistician to find the key impacting factors among the 47 types of information collected.