THE EXPECTED VALUE OF APPLYING MACHINE LEARNING FOR PREDICTIVE LEAD SCORING BASED ON CUSTOMER CONTACT-FORM INPUT - A CASE IN THE B2B ENERGY SECTOR

Aug 4, 2026

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This thesis evaluates how machine learning predicts lead conversion probability at an early maturity stage using customer contact-form input. An empirical case study develops predictive models across two lead maturity stages in the lead funnel with automated machine learning, then quantifies the expected business value. Results support the use of machine learning for lead conversion prediction and show better predictive performance at later maturity. Findings also indicate that incorporating cost and benefit calculations improves profitability assessment.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages60

Reading Time2 h 31 min

AccessFree to download

Abstract

1. Introduction

2. Literature Review

2.1 Lead Management Framework

2.2 Machine Learning Models for Lead Management

3. Methodology

3.1 Business Understanding

3.2 Data Understanding

3.3 Data Preparation

3.4 Modelling

3.5 Evaluation

4. Results and Discussion

4.1 Results of the exploratory analysis

4.2 Results of the predictive analysis

4.3 Expected Value for the Models

4.4 Feature Importance

5. Conclusion and Recommendations

6. Limitations and Further Research

References

Appendix

A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Management from the Nova School of Business and Economics. THE EXPECTED VALUE OF APPLYING MACHINE LEARNING FOR PREDICTIVE LEAD SCORING BASED ON CUSTOMER CONTACT-FORM INPUT – A CASE IN THE B2B ENERGY SECTOR CARINA SELL Work project carried out under the supervision of: Dr. Rodrigo Belo (NOVA School of Business and Economics) Dr. Rachel Marie Harris-Lethaus (Partner Company) Fabian Fischer (Partner Company) Table of Contents Abstract ...................................................................................................................................... 1 1. Introduction ......................................................................................................................... 2 2. Literature Review................................................................................................................ 4 2.1 Lead Management Framework ......................................................................................... 4 2.2 Machine Learning Models for Lead Management ........................................................... 6 3. Methodology ..................................................................................................................... 10 3.1 Business Understanding ................................................................................................. 10 3.2 Data Understanding ........................................................................................................ 12 3.3 Data Preparation ............................................................................................................. 13 3.4 Modelling........................................................................................................................ 14 3.5 Evaluation ....................................................................................................................... 15 4. Results and Discussion ..................................................................................................... 17 4.1 Results of the exploratory analysis ................................................................................. 17 4.2 Results of the predictive analysis ................................................................................... 19 4.3 Expected Value for the Models ...................................................................................... 21 4.4 Feature Importance ......................................................................................................... 22 5. Conclusion and Recommendations ................................................................................... 23 6. Limitations and Further Research ..................................................................................... 24 References ................................................................................................................................ 26 Appendix .................................................................................................................................. 32 Abstract This thesis examines the performance of machine learning to predict lead conversion probability in an early lead maturity stage based on customer contact-form data. An empirical case study was conducted developing models at two maturity stages in the lead funnel using automated machine learning and their expected value for the business was calculated. The resulting models prove the suitability of machine learning to predict lead conversion and reveal that predictions are better in a later maturity stage. Furthermore, the findings suggest including cost and benefit calculations in the development is beneficial, as not all models are profitable despite good performance. Keywords: Predictive Lead Scoring, Lead Management, Expected Value Framework, Machine Learning, CRM Thi

FAQs

What problem does the thesis address in lead management?

It addresses how to predict lead conversion probability from customer contact-form input so leads can be prioritized within a B2B context where conversion rates are typically low.

How are machine learning models evaluated in the study?

Models are developed for two maturity stages in the lead funnel using automated machine learning, then assessed through exploratory and predictive analyses.

Why does the thesis emphasize expected value beyond predictive performance?

Because some models can show good prediction quality but still be unprofitable; cost and benefit calculations help determine whether model outputs create business value.

Aug 4, 2026

Upload by Violet