Lead management optimization using data mining: A case in the telecommunications sector

Highlights

• Lead management is at the forefront relying on data to provide a customized offer to customers.

• Lead management is ineffective due to lack of data quality and the absence of lead follow-up.

• There is a lack of literature applications of data mining techniques in lead management.

• A methodology based on data mining to classify and segment leads is proposed.

• A case study was conducted in a telecommunications firm.

Abstract

The growing competitiveness of the market has put pressure on companies to improve their customer relationship management strategies. In an era where mass marketing techniques are inadequate, lead management is at the forefront to provide a customized approach to customer acquisition. For this, lead management depends on the correct selection of leads and decision making on what type of approach to take to satisfy the requirements of customers. However, currently, firms are faced with massive quantity of data regarding customers and prospects. Data mining is a solution to cope with this problem, providing a robust approach to massive quantity of data and its complexity. In literature, there is a lack of documented applications of these techniques in lead management. In this sense, we propose a methodology that aims to improve efficiency on the distinct maturity stages of leads management. Also, the methodology aids in support the decision-making regarding the segmentation of leads. This research suggests the application of data mining techniques in the optimization of leads management processes, from capture to conversion, with the objective of improving customer conversion effectiveness. A case study was conducted in a telecommunications company. It was possible to implement the proposed method to estimate the probability of conversion for each lead. With this, was possible to segment the offer to each type of lead.

Introduction

In recent decades, competition in the telecommunications sector has become increasingly fierce and challenging. In view of geographical restrictions, the difficulty of market growth and the increasing demand from consumers, telecommunications companies are moving their approach from price-based to non-price-based competition, focusing their strategies on customer relations (Guifang & Youshi, 2010). Increased market share or product or service innovation remain ways for these companies to achieve competitive advantage. To answer to this priorities, the study of consumer behavior and preferences is becoming an emerging trend in literature (Hitka, Pajtinkova-bartakova, Lorincova, Palus, Pinak, Lipoldova, Krahulcova, Slastanova, Gubiniova, & Klaric, 2019). Several studies dedicate to the design of customized solutions, improving relationship management and innovation by personalizing the offer and the consumer experience (Gupta, Wasid, & Ali, 2017).

The search for innovative solutions, aligned with the market trends, resulted in the idealization and adoption of customer acquisition strategies such as lead management. Lead management is a customer acquisition strategy aimed in systematic registration and processing of information about customer interest in a company offer (Deszczyński, 2016). Several researchers approach lead management as an innovative strategy for acquiring new customers (e.g. (D’Haen et al., 2016, Deszczyński, 2016, Ohiomah et al., 2019, Sabnis et al., 2013)). Lead capture occurs through the distinct marketing channels (e.g. stores, web campaigns, telemarketing, etc.) that register a set of information about a potential customer's interest in a particular product or service. The challenge for companies is to know which lead to contact, how and when to contact them (Ohiomah et al., 2019).

Although leads are at the forefront of contacting a company's potential customers, the lead management process is frequently ineffective. Also, the conversion process from a lead into a customer is still unclear. Poor lead management systems, lack of lead’s quality and absence of follow-up by salespeople are some of the key problems of lead conversion (Monat, 2011, Ohiomah et al., 2019). Even in situations of a highly interested customer (i.e. hot leads), inadequate lead management conducts to the loss of a customer. This happens due to not immediately identifying the lead to contact or how to meet the customer demands. Ohiomah et al. (2019) argues that marketing efforts in lead acquisition are worthless if leads are not properly managed. Though, there is still no common knowledge of how to make the most of available information generated during lead acquisition.

The application of Data Mining techniques in the area of Customer Relationship Management (CRM) is an emerging trend in the literature. These techniques allow assisting in the client's management processes, from capturing prospective clients (Hwang, Jung, & Suh, 2004) to managing the client's relationship. This involves the study of the behavior of loyal customers (Ha and Yang, 2013, Tong et al., 2017) and customers who may abandon the product or service (Brmez and Znidarsic, 2019, Sabbeh, 2018). If applied to lead management, these methods could be used to estimate the conversion propensity of each lead, and thus, manage resources more efficiently.

The main research questions we raise on this research are: “how data mining techniques can assist in improving the conversion probability?”; and “how data mining techniques can assist in the decision making in the lead segmentation process?”. Accordingly, we focus on the development of a predictive model to support the process of lead management. The research was conducted to demonstrate the applicability of data mining techniques in this type of problems. We proposed a propensity and segmentation model based on the information hidden in the lead data. The ultimate goal is to provide a solution that will support the decision-making process of selecting the leads to contact and the sales approach.

The article is structured as follows: in Section 2 a literature review is presented on the key subjects customer relationship management, and lead management; in Section 3 we propose a methodology to enhance lead conversion by applying data mining techniques; in Section 4 a case study is presented; last, in Section 5 we present the main conclusions to our research.