Prescriptive Analytics for Allocating Sales Teams to Opportunities

Abstract

For companies with large salesforces whose sellers approach business clients in teams, the problem of allocating sales teams to sales opportunities is a critical management task for maximizing the revenue and profit of the company. We approach this problem via predictive and prescriptive analytics, where the former involves data mining to learn the relationship between sales team composition and the revenue earned for different types of clients and opportunities, and the latter involves optimization to find the allocation of sales resources to opportunities that maximizes expected revenue subject to business constraints. In looking at the overall salesforce problem, we focus on the interplay between the data mining and optimization components, making sure to formulate the two aspects in a jointly tractable and effective manner. We perform a sensitivity analysis of the optimization component to provide further insight into the interaction between prediction and prescription. Finally, we provide an empirical study using real-world data from a large technology company's salesforce. Our results demonstrate that by using these analytics, we can increase revenue by 15%.

Business Analytics for Sales Pipeline Management in the Software Industry: A Machine Learning Perspective

“…In this stage, salespeople take appropriate actions such as product demos and client meetings to maximize the likelihood of closing the sales deal. The primary goal is to ensure an increase in revenue and a growing customer base [27]. However, the qualification assessment is mainly influenced by personal judgement of the respective marketing or sales workforce.…”

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“…This belief of the interviewees is supported by the current literature. According to various authors (Bohanec et al, 2017;Kawas et al, 2013); Müller et al, 2018;Neubert, 2018), the use of business intelligence solutions that leverage artificial intelligence provides a significant impact of around 15% on revenues, productivity, and profitability, especially due to a higher efficiency of international learning (Stoian et al, 2017) and networking activities (Coviello et al, 2017;Vahlne & Johanson, 2017). According to Neubert (2018), the most important needs for digitalization are lead generation, client acquisition, and client retention.…”

“…Traditional data-driven and fact-based decision-making processes increase the productivity and profitability of companies by five to six per cent compared to their competitors (Bohanec et al, 2017;Neubert, 2018). Companies using prescriptive, analytics-based, machinelearning (ML) algorithms, for example, to compute the future attractiveness of international markets or to identify new business opportunities (Dedi & Stanier, 2016;Neubert, 2017a;Witten et al, 2016) increase their revenues by more than 15% (Kawas et al, 2013). However, such gains are only possible if international managers understand and are able to leverage the benefits of digitalization (Ransbotham et al, 2015).…”

An Information System for Sales Team Assignments Utilizing Predictive and Prescriptive Analytics

“…This models the perspective of each business unit rather than optimizing the sales force from a firm perspective. Kawas et al [16] present an approach for calculating headcount of different sales roles for sales opportunities. However, instead of assigning a specific sales rep, only sales roles where assigned.…”