Optimising lead qualification through machine learning: A customer data-driven approach

Helen Josephine V L, Vasudevan Moorthy, Chandana Sai and Bindhia Joji
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Helen Josephine V L: Associate Professor, Business Analytics, Christ University, India
Vasudevan Moorthy: Associate Professor, Marketing, Christ University, India
Chandana Sai: Manager, Enterprise Solution Architect, Airtel Business, India
Bindhia Joji: Assistant Professor, Business Intelligence, Christ University, India

Applied Marketing Analytics: The Peer-Reviewed Journal, 2024, vol. 10, issue 3, 255-270

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 (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2024
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