Improving incentive policies to salespeople cross-sells: a cost-sensitive uplift modeling approach

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dc.coverageDOI: 10.1007/s00521-024-10051-2
dc.creatorVairetti, Carla
dc.creatorVargas, Raimundo
dc.creatorSánchez, Catalina
dc.creatorGarcía, Andrés
dc.creatorArmelini, Guillermo
dc.creatorMaldonado, Sebastián
dc.date2024
dc.date.accessioned05-01-2026 18:06
dc.date.available05-01-2026 18:06
dc.description<p>In this study, we present a novel cost-sensitive approach for uplift modeling in the context of cross-selling and workforce analytics. We leverage referrals from sales agents across business units to estimate the individual treatment effects of incentives on the cross-selling outcomes within a company. Uplift modeling is employed to predict relationships between salespeople that should be encouraged based on the probability of successful cross-selling - defined when a customer accepts the product suggested by sales agents. We conducted experiments on data from a Chilean financial group, evaluating both statistical and profit metrics. Exploring various machine learning classifiers for predictive purposes, we observed a significant improvement over the current approach, which exhibits an uplift below 0.01. Finally, we show that selecting the best classifier with profit metrics results in a 31.6% improvement in terms of average customer profit. This emphasizes the importance of defining an adequate compensation scheme and integrating it into the modeling process.</p>eng
dc.identifierhttps://investigadores.uandes.cl/en/publications/05bda631-00ca-49fd-b88d-98a8afe3b71c
dc.languageeng
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourcevol.36 (2024) nr.28 p.17541-17558
dc.subjectBusiness analytics
dc.subjectCost-sensitive learning
dc.subjectCross-selling
dc.subjectUplift modeling
dc.subjectWorkforce analytics
dc.titleImproving incentive policies to salespeople cross-sells: a cost-sensitive uplift modeling approacheng
dc.typeArticleeng
dc.typeArtículospa
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