The DOI matches its Crossref record.
Prescriptive Analytics for Allocating Sales Teams to Opportunities
Abstract
For companies with large sales forces 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 sales force 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 sales force. Our results demonstrate that by using these analytics, we can increase revenue by 15%.
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Stochastic optimization focuses on decision making under uncertainty.
“In this sense, it focuses on decision making under uncertainty (Bertsimas & Kallus, 2014;Kawas et al., 2013).”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Kawas et al., 2013
In the mixed integer linear programming application for allocating sales teams, the prescriptive analytics algorithm incorporates predictions derived from mining historical selling data to capture the behavioral relationship between sales team size/composition and revenue for different clients and opportunities subject to business constraints.
“In this case, the prescriptive analytics algorithm incorporates predictions derived from mining historical selling data in order to capture the behavioral relationship between the size and composition of the sales team and the revenue earned for different types of clients and opportunities subjected to the business constraints.”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Kawas et al., 2013
Mixed integer linear programming is suitable in prescriptive analytics applications such as allocating sales teams to sales opportunities to maximize business revenue and profit.
“Mixed integer linear programming differs from linear integer programming in that only some of the variables are constrained to be integers, while other variables are allowed to be non-integers and hence is suitable in prescriptive analytics applications such as allocating sales teams to sales opportunities with the aim to maximize business revenue and profit (Kawas, Squillante, Subramanian, & Vars”
From Prescriptive analytics: Literature review and research challenges (2020) · cites this paper as Kawas et al., 2013
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Research works printed but not linked: 14
- 10.1017/CBO9780511804441 · DOI · Paper page
- kawas, 2013, Optimization under uncertainty for prescriptive sales-force analytics, Working Paper
- 10.1109/SSP.2012.6319855 · DOI
- linoff, 2011, Data mining techniques: For marketing, sales, and customer relationship management, Indianapolis
- 10.1007/s11002-010-9111-4 · DOI
- 10.1147/JRD.2012.2216314 · DOI
- reese, 2013, How Sales Has Evolved since the Mad Men Era
- 10.1109/SOLI.2012.6273573 · DOI
- 10.2753/PSS0885-3134280203 · DOI
- 10.1287/mnsc.45.10.1307 · DOI
- 10.1287/mnsc.26.3.242 · DOI
- 10.1287/serv.3.1.82 · DOI
- 10.1147/sj.464.0797 · DOI
- 10.1016/j.cor.2012.10.007 · DOI
Cited by
Sorted by citations
Prescriptive analytics: Literature review and research challenges
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.04.003 · Cited by 501
“…to be integers, while other variables are allowed to be non-integers and hence is suitable in prescriptive analytics applications such as allocating sales teams to sales opportunities with the aim to maximize business revenue and profit (Kawas, Squillante, Subramanian, & Varshney, 2013).”
Prescriptive analytics for human resource planning in the professional services industry
Berk, L., Bertsimas, D., Weinstein, A. M., & Yan, J. (2019) · European Journal of Operational Research · doi:10.1016/j.ejor.2018.06.035 · Cited by 59
An information system for sales team assignments utilizing predictive and prescriptive analytics
Bischhoffshausen, J. K. V., Paatsch, M., Reuter, M., Satzger, G., & Fromm, H. (2015) · 2015 IEEE 17th Conference on Business Informatics · doi:10.1109/cbi.2015.38 · Cited by 22
Metadata
- Authors
- Kawas, B., Squillante, M. S., Subramanian, D., & Varshney, K. R.
- Affiliations
- IBM Research - Thomas J. Watson Research Center (US)
- Venue
- 2013 IEEE 13th International Conference on Data Mining Workshops
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 211-218
- ISSN
- 2375-9259 (ISSN-L)
- OpenAlex
- W1969016862
- Cited by
- 28
- References
- 0
- Citations by year
- 2026: 2 · 2024: 4 · 2023: 2 · 2021: 4 · 2020: 3 · 2019: 4 · 2018: 6 · 2017: 1 · 2015: 2
- FWCI
- 1.05
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE ... International Conference on Data Mining workshops (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Social Sciences › Decision Sciences › Management Science and Operations Research › Supply Chain and Inventory Management
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- sales force management, sales opportunities, prescriptive analytics, predictive analytics, data mining, revenue maximization, sensitivity analysis
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