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Gaining insight into customer behavior through extensive data and advanced analytics holds great value for businesses, not only in marketing and selling to consumers but other businesses as well. Key to the process when it comes to business-to-business (B2B) interaction are machine learning and other emerging AI techniques, which can provide targeted and detailed information.

Stephan Kudyba, associate professor of business and MIS at Martin Tuchman School of Management, explores this topic with Thomas H. Davenport, distinguished professor of management and IT at Babson College, in Harvard Business Review. They note that where before traditional B2B insight activities rendered limited data, such as industry type and company size by revenue or staff, now neural networks and “deep learning” algorithms, as well as other machine learning methods, enable advanced searches that more finely identify and categorize potential business customers.

“An example of such a search involves a query to identify the executive teams/leadership of organizations,” Kudyba explained. “Systematizing this task relative to the existing content on the web that sources this information is noteworthy.”

In their article, the co-authors cite EverString Technology as realizing great success using AI-based analytics for B2B applications. The company provides its own B2B customers with augmented data by applying guided AI to a variety of web sectors, including site domains and employee digital footprints, that contain descriptive information about businesses. It also incorporates input from expert practitioners, described by Kudyba as “marketing and salesforce personnel who have domain knowledge of their customer space.” With AI, EverString ultimately produces thousands of customer data points that can be used to develop more data-driven approaches to B2B sales and marketing.

Martin Tuchman School of Management's Stephan Kudyba, associate professor of business analytics and MIS