Effective Implementation of Predictive Sales Analytics

Abstract

Sales managers are unlikely to reap the benefits of implementing predictive analytics applications when salespeople show aversion to or lack understanding of these applications. For managers, it is essential to understand which factors mitigate or exacerbate these challenges. This article investigates these factors by studying the implementation of an application that predicts customer churn. Using 9.7 million transactions from a B2B company, the authors develop a predictive model of customer churn, implement it in a field experiment, and study its treatment effects using causal forests. Furthermore, the authors manipulate one specific mitigation strategy proposed by prior literature: the fostering of users’ realistic expectations regarding the accuracy of an algorithm. The results show that the effectiveness of the churn prediction application strongly depends on customer characteristics (most importantly the predicted churn probability and prior revenue) and salesperson characteristics (technology perceptions, abilities, and selling orientations). Fostering realistic expectations only improves the effectiveness of the churn prediction under very specific circumstances. Two follow-up stimuli-based experiments conceptually replicate key results of the field study. Therefore, this paper helps build theory on predictive sales analytics and provides specific guidance to managers aiming to increase their return on analytics investments.

The Future of Research on International Selling and Sales Management

“…This can be extended to figuring out ways to exploit and implement sales analytics (e.g., Habel, Alavi, and Heinitz 2023) to predict international customer behaviors and outcomes. Along these lines, with the unfolding of different types of artificial intelligence (AI), such as generative AI, research can examine how managers can use machine learning algorithms to reveal unknown information about their international customers’ personalities, values, or churn estimates (Habel, Alavi, and Heinitz 2022). Development and implementation of international sales enablement.…”

Harnessing Predictive Analytics for Strategic Foresight: A Comprehensive Review of Techniques and Applications in Transforming Raw Data to Actionable Insights

“…In the ever-evolving marketing landscape, predictive analytics has emerged as a powerful ally, reshaping how companies engage with their audience [31]. The potential of predictive analytics within the marketing sphere is widereaching, offering a dynamic and data-driven approach to marketing decision-making and strategy development [1]. Smart data analysis allows marketers to discard useless data and use only valuable insights to power their sales and marketing strategies [31].…”

“…Predictive analytics is pivotal in accelerating lead prioritization for marketers [9]. It empowers them to swiftly identify the most promising marketing prospects with a high degree of certainty that these leads will ultimately convert [1]. By leveraging the insights from predictive analytics, marketers can make data-driven decisions that optimize their lead management processes, ensuring their resources are channeled toward prospects with the highest conversion potential [9].…”

“…In today's rapidly evolving digital landscape, where data is generated at an unprecedented pace and volume, organizations face a compelling challenge: how to harness this torrent of information for strategic advantage [1]. Data analytics, particularly predictive analytics, has emerged as the definitive solution to this challenge, serving as a transformative force that empowers decision-makers with actionable insights [2].…”

Guest editorial: Value-creating sales and digital technologies

“…While there are initial conceptual and industry-specific studies on how digitization affects the transformation process from product-centered to value-creating business models (Bustinza et al, 2018;Kamalaldin et al, 2020;Tronvoll et al, 2020;Vendrell-Herrero et al, 2017), prior research is mostly silent on the role of digitalization for value-creating sales. Although the sales literature offers more advanced insights into how digitalization affects the sales function (Ahearne et al, 2005;Habel et al, 2023aHabel et al, , 2023bHohenberg and Homburg, 2019;Rapp et al, 2010), it remains limited as numerous important questions remain unanswered (Singh et al, 2019). Thus, sales organizations operating or aiming to transition to valuecreating business models should particularly benefit from greater clarity on how digital technologies can engender a sales organization's creation of customer value by the sales function.…”