Conference Paper

Probabilistic Modeling of a Sales Funnel to Prioritize Leads

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Abstract

This paper shows how to learn probabilistic classifiers that model how sales prospects proceed through stages from first awareness to final success or failure. Specifically,we present two models, called DQM for direct qualification model and FFM for full funnel model, that can be used to rank initial leads based on their probability of conversion to a sales opportunity, probability of successful sale, and/or expected revenue. Training uses the large amount of historical data collected by customer relationship management or marketing automation software. The trained models can replace traditional lead scoring systems, which are hand-tuned and therefore error-prone and not probabilistic. DQM and FFM are designed to overcome the selection bias caused by available data being based on a traditional lead scoring system. Experimental results are shown on real sales data from two companies. Features in the training data include demographic and behavioral information about each lead. For both companies, both methods achieve high AUC scores. For one company, they result in a a 307% increase in number of successful sales, as well as a dramatic increase in total revenue. In addition, we describe the results of the DQM method in actual use. These results show that the method has additional benefits that include decreased time needed to qualify leads, and decreased number of calls placed to schedule a product demo. The proposed methods find high-quality leads earlier in the sales process because they focus on features that measure the fit of potential customers with the product being sold, in addition to their behavior.

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... The total score is built by the (weighted) sum of scores. Categories and scores are "hand-tuned by experienced members of the marketing or sales team" [8] and comprise monetary and qualitative aspects as well as expected developments of the customer. Depending on the categories the scorecard method can also be applied to prospects. ...
... The stages and their names are often customized by sales teams and terms have different meanings. To give an example from our study, in [7] the stages "suspects, prospects, leads, customers" are used, in [8] the stages are "awareness, leads, marketingqualified leads, sales-qualified leads, won". However, the underlying logic is the same: If a customer or prospect reaches the next stage because the contact person responded to some marketing activities such as an e-mail, the probability to win a deal increases. ...
... The authors of [24,23] and [8] in row (5) predict both, the win propensity and the expected sales in case of a won deal. For predicting the win propensity the authors used logistic regression and a boosted tree classifier respectively. ...
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... The total score is built by the (weighted) sum of scores. Categories and scores are "hand-tuned by experienced members of the marketing or sales team" [8] and comprise monetary and qualitative aspects as well as expected developments of the customer. Depending on the categories the scorecard method can also be applied to prospects. ...
... The stages and their names are often customized by sales teams and terms have different meanings. To give an example from our study, in [7] the stages "suspects, prospects, leads, customers" are used, in [8] the stages are "awareness, leads, marketingqualified leads, sales-qualified leads, won". However, the underlying logic is the same: If a customer or prospect reaches the next stage because the contact person responded to some marketing activities such as an e-mail, the probability to win a deal increases. ...
... The authors of [24,23] and [8] in row (5) predict both, the win propensity and the expected sales in case of a won deal. For predicting the win propensity the authors used logistic regression and a boosted tree classifier respectively. ...
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Field sales forces play an important role in direct marketing, especially for companies offering complex products, services, or solutions in the business-to-business context. A key task of sales representatives in operational planning is to select the most promising customers to visit within the next days. On an operative horizon, a key task for sales representatives is to select the most promising customers to visit within the next days. A strongly varying set of scoring methods predicting or approximating the expected response exists for this customer selection phase. However, in the case of field sales forces, the final customer selection is strongly interrelated to the tour planning decisions. To this end, we formalize variants of the profitable sales representatives tour problem as a multi-period team orienteering problem, thereby providing a unified view on the customer scoring and the tour planning phase. In an extensive computational study on real-world instances from the retail industry, we systematically examine the impact of the aggregation level and the content of information provided by a scoring method and the sensitivity of the proposed models concerning prediction errors. We show that the selection of a customer scoring and tour planning variant depends on the available data. Furthermore, we work out where to put effort in the data acquisition and scoring phase to get better operational tours.
... D'Haen et al. (2016) fed web crawling data into a Logistic Regression model, which generates a sales prospect list for selling energy retail plans. Duncan and Elkan (2015) used a three-class gradient boosted tree classifier to design two predictive lead scoring methods. Both methods were tested on sales data of two companies, and the experimental results highlighted the improvements in sales revenue when compared to a conventional rule-based approach (Duncan & Elkan, 2015). ...
... Duncan and Elkan (2015) used a three-class gradient boosted tree classifier to design two predictive lead scoring methods. Both methods were tested on sales data of two companies, and the experimental results highlighted the improvements in sales revenue when compared to a conventional rule-based approach (Duncan & Elkan, 2015). Benhaddou and Leray (2017) constructed a Bayesian network for lead scoring local companies based on news stories. ...
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... Those predictions can be used to carry out an optimal amount of customer interactions, associated with the individual leads (Yan et al., 2015). Probabilistic classifiers are trained to rank initial leads based on their probability of conversion to a sales opportunity, probability of successful sale, and/or expected revenue (Duncan & Elkan, 2015). Data mining models, based on ANN are used to predict the behaviour of customers to enhance the decision-making processes for retaining valued customers (Bahari & Elayidom, 2015). ...
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... Yan et al. propose a win-propensity approach based on modeling the interaction of users with the sales support system as Hawkes Processes [9]. Duncan and Elkan propose a pure probabilistic model [48]. Compared with these approaches, our approach is theoretically either superior in terms of predictive or explanatory power, and always superior in balancing predictive and explanatory power. ...
... Yan et al. propose a win-propensity approach based on modeling the interaction of users with the sales support system as Hawkes Processes [9]. Duncan and Elkan propose a pure probabilistic model [48]. Compared with these approaches, our approach is theoretically either superior in terms of predictive or explanatory power, and always superior in balancing predictive and explanatory power. ...
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... This means that the conversion to the next purchase funnel steps cannot be appreciated. Databases comparison on a digital footprint can only show averaged statistics, and the classical marketing research methods at the last stage of the purchase funnel give a very serious distortion [10,11,12,13]. ...
... In the context of professional social networks there has been work on identifying key decision makers within an organization (via declared user profiles) for the purpose of B2B ad targeting [24]. In the B2B marketing industry, identifying such leads [9] and key decision makers in an organization is widely seen as an effective approach for B2B marketing [24]. Compared to prior work in professional social networks (where users explicitly declare their roles in an organization), we do not focus on identifying generic decision makers in an organization. ...
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  • Chandrasekar Shaw
  • Gek Subramaniam
  • Michael E Woo Tan
  • Welge
Leakage in data mining: Formulation, detection, and avoidance
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  • Claudia Rosset
  • Ori Perlich
  • Stitelman