Preprint Hypothesis Version 1 Preserved in Portico This version is not peer-reviewed

Customer Churn - Prevention Model – Unsupervised Classification

Version 1 : Received: 2 June 2021 / Approved: 3 June 2021 / Online: 3 June 2021 (13:34:28 CEST)

How to cite: GRIJALBA FACUNDO, J. Customer Churn - Prevention Model – Unsupervised Classification. Preprints 2021, 2021060118 (doi: 10.20944/preprints202106.0118.v1). GRIJALBA FACUNDO, J. Customer Churn - Prevention Model – Unsupervised Classification. Preprints 2021, 2021060118 (doi: 10.20944/preprints202106.0118.v1).

Abstract

The strategy of any organization is based on the growth of its customer base, and one of 6 its principles is that selling a product to an existing customer is much more profitable than acquiring 7 a new customer. However, this approach has several opportunities for improvement, since it usu- 8 ally has a totally reactive approach, which does not give opportunity to the areas specialized in 9 customer experience and recovery, to give an effective response for that moment, since the customer 10 is gone at the time of the intervention. This happens because usually a diagnostic analysis of cus- 11 tomers who have stopped buying products or services in a defined period, commonly three (3) pe- 12 riods or months, is performed. This paper challenges the way to face this problem, and proposes 13 the development of a complete solution, which does not focus exclusively on the prediction of 14 churn, as is usually done in the state of the art research, but to intervene in different interactions 15 that can be carried out with customers. The above focused not only to prevent customer churn, but 16 to generate an added value of continuous improvement in sales processes, increase customer pene- 17 tration, leading to an improvement in customer experience and consequently, an increase in cus- 18 tomer loyalty.

Subject Areas

Churn 1; customer churn; customer segmentation; churn prevention; predictive churn 21 model; recommendation system engine.

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