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Optimising lead qualification through machine learning: A customer data-driven approach
Applied Marketing Analytics: The Peer-Reviewed Journal,
10
(3),
255-270
(2024)
Abstract
Lead generation is the process of turning an outside person or business into a customer of the business. Traditionally, marketing personnel must conduct significant follow-ups in order to convert even one potential consumer. Converting bad client leads can cause businesses to burn through cash reserves. As a result of this, it is now necessary to develop an automated system that can correctly anticipate whether or not a lead should be explored (converted to a customer or not). In this study, an attempt is made to evaluate historical data for leads produced by other businesses in order to train and validate a machine learning (ML)/deep learning (DL) model and test it against real-world characteristics to categorise them as hot leads (convert to customers) or cold leads (failed leads). This can be achieved by employing ML algorithms, low code–no code libraries, such as PyCaret in Python, and can be used to make predictions regarding probable lead creation, propensity to convert generated leads and optimal actions on the leads by communications teams. Supervised ML algorithms such as logistic regression, decision trees, random forests and other models using a Python library were built to score leads for identifying potential conversions. With good and broad lead-scoring models in place, businesses can optimise their CTI actions on the basis of lead prioritisation and let go of non-prospect leads at the right time to cut costs and enable efficiency. The result of this study reveals that 52 per cent of the sample of 74,779 leads are cold leads and 48 per cent are hot leads that are sales qualified. The leads are qualified using the lead score matrix. This method can aid digital businesses to remove unqualified leads and manage leads better, and therefore improve the quality of the leads sent to clients. This, in turn, will improve conversion rates for individual customers. These increased conversion rates will enhance the business strategy of digital marketing firms.
Keywords: machine learning; supervised ML algorithms; logistic regression; decision tree; hot leads; cold leads
The full article is available to subscribers to the journal.
Author's Biography
Helen Josephine V L Dr Helen Josephine brings over 27 years of combined industry and academic experience. Dr Josephine holds an MCA, MBA and PhD in computer science. She has published more than 20 publications in Scopus and Web of Science-indexed journals and holds nine patents. Dr Josephine also supervises PhD research scholars. Her research interests focus on the applications of machine learning and natural language processing to solve business problems, particularly in areas like recommender systems and emotional intelligence.
Vasudevan Moorthy is an accomplished academic with advanced degrees, including an MA, MBA and PhD. He is an associate professor and the campus coordinator of the MBA programme at Christ University, Bangalore Kengeri Campus. Dr Moorthy's research interests are wide-ranging, encompassing sustainable consumption, retail marketing, services marketing and consumer behaviour. His expertise is evident in the research articles he has published in esteemed journals and his active participation in seminars, conferences and workshops.
Chandana Sai is a solution architect at Airtel Business in India, where she leverages her expertise to drive customised solutions for her clients. She has an MBA in business analytics from Christ University in Bangalore. Her research interests encompass a range of topics, including the intricacies of consumer behaviour, data-driven decision making, lead analytics, data visualisation and machine learning models. She is particularly focused on exploring how various factors in marketing campaigns influence consumer decision making in the field of business analytics.
Bindhia Joji is an assistant professor in the School of Business and Management at Christ University in Bangalore, specialising in business analytics since 2022. She previously served as Head of Department and Assistant Professor at St. Thomas College, Thrissur. Dr Joji holds a masters in computer applications and a doctorate in computer science. She has published and presented research in these areas and currently teaches business analytics, text analytics and DBMS.
Citation
Josephine V L, Helen, Moorthy, Vasudevan, Sai, Chandana and Joji, Bindhia (2024, December 1). Optimising lead qualification through machine learning: A customer data-driven approach. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 10, Issue 3. https://doi.org/10.69554/ETXF7251.
© Henry Stewart
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Publications LLP