Integrating expert knowledge and multilingual web crawling data in a lead qualification system - D’Haen, J.; Van den Poel, D.; Thorleuchter, D.; Benoit, D.F. (Decision Support Systems, 2016)
Qualifying prospects as leads to contact is a complex exercise. Sales representatives often do not have the time or resources to rationally select the best leads to call. As a result, they rely on gut feeling and arbitrary rules to qualify leads. Model-based decision support systems make this process less subjective. Standard input for such an automated lead qualification system is commercial data. Commercial data, however, tends to be expensive and of ambiguous quality due to missing information. This study proposes web crawling data in combination with expert knowledge as an alternative. Web crawling data is freely available and of higher quality as it is generated by companies themselves. Potential customers use websites as a main information source, so companies benefit from correct and complete websites. Expert knowledge, on the other hand, augments web crawling data by inserting specific information. Web data consists of text that is converted to numbers using text mining techniques that make an abstraction of the text. A field experiment was conducted to test how a decision support system based on web crawling data and expert knowledge compares to a basic decision support system within an international energy retailer. Results verify the added value of the proposed approach. •A decision support system for lead qualification is developed.•It uses web crawling data augmented with expert knowledge as input.•Integrating expert knowledge increases the quality of the decision support system.•A real-life test validates these results.