The state of lead scoring models and their impact on sales performance

Abstract

Although lead scoring is an essential component of lead management, there is a lack of a comprehensive literature review and a classification framework dedicated to it. Lead scoring is an effective and efficient way of measuring the quality of leads. In addition, as a critical Information Technology tool, a proper lead scoring model acts as an alleviator to weaken the conflicts between sales and marketing functions. Yet, little is known regarding lead scoring models and their impact on sales performance. Lead scoring models are commonly categorized into two classes: traditional and predictive. While the former primarily relies on the experience and knowledge of salespeople and marketers, the latter utilizes data mining models and machine learning algorithms to support the scoring process. This study aims to review and analyze the existing literature on lead scoring models and their impact on sales performance. A systematic literature review was conducted to examine lead scoring models. A total of 44 studies have met the criteria and were included for analysis. Fourteen metrics were identified to measure the impact of lead scoring models on sales performance. With the increased use of data mining and machine learning techniques in the fourth industrial revolution, predictive lead scoring models are expected to replace traditional lead scoring models as they positively impact sales performance. Despite the relative cost of implementing and maintaining predictive lead scoring models, it is still beneficial to supersede traditional lead scoring models, given the higher effectiveness and efficiency of predictive lead scoring models. This study reveals that classification is the most popular data mining model, while decision tree and logistic regression are the most applied algorithms among all the predictive lead scoring models. This study contributes by systematizing and recommending which machine learning method (i.e., supervised and/or unsupervised) shall be used to build predictive lead scoring models based on the integrity of different types of data sources. Additionally, this study offers both theoretical and practical research directions in the lead scoring field.

Enhancing Conversions and Lead Scoring in Online Professional Education

“…Lead scoring can improve overall business performance by lowering costs and increasing profits (Banerjee & Bhardwaj, 2019). Besides, GE Capital improved 30-50% of salespeople's productivity through automated lead scoring (Wu et al, 2023).…”

“…In this sense, identifying quality leads is a crucial intermediary step between lead generation and conversion. However, there is a lack of consensus or a weak theoretical foundation to support hot leads characterization and lead scoring practice (Wu et al, 2023;Espadinha-Cruz et al, 2021;Monat, 2011). Conventional lead scoring primarily depends on marketing professionals' and sales executives' experience and understanding (Jadli et al, 2022;Wu et al, 2023).…”

“…However, there is a lack of consensus or a weak theoretical foundation to support hot leads characterization and lead scoring practice (Wu et al, 2023;Espadinha-Cruz et al, 2021;Monat, 2011). Conventional lead scoring primarily depends on marketing professionals' and sales executives' experience and understanding (Jadli et al, 2022;Wu et al, 2023). The less statistically supported conventional lead scoring, such as manual scoring and demographic segmentation, may be insufficient in discerning quality leads, resulting in a low lead conversion rate (Zumstein et al, 2021;Banerjee & Bhardwaj, 2019).…”

“…Errors in evaluating lead quality can result in overestimating resource reservations, directing resources toward low-quality leads, and inefficiently organizing marketing and promotional endeavours (Nair & Gupta, 2021). Qualified leads that fail to result in timely sales often slip through, eventually becoming lost revenue opportunities (Wu et al, 2023;Jadli et al, 2022;Espadinha-Cruz et al, 2021). The lead score threshold inaccurately reflects customer behaviour, particularly regarding their interest level and purchasing intentions.…”

“…The lead score threshold inaccurately reflects customer behaviour, particularly regarding their interest level and purchasing intentions. In addition, the average of qualified leads is approximately 10.0% and only 1.0% -6.0% of qualified leads are converted (Gopalakrishna et al, 2022;Wu et al, 2023).…”