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Distinguishing between potential long-term customers and brief inquisitive visitors is key to attaining successful lead conversions stemming from web navigation, writes Stephan Kudyba, Martin Tuchman School of Management associate professor of business analytics and MIS, in InformationWeek. By implementing initiatives that incorporate Software as a Service (SaaS), subscription- and cloud-based apps that create data through user touch points or interactions, businesses can apply the power of data science in a more tailored fashion.

While the utilization of SaaS has grown dramatically in the digital era, Kudyba writes that it’s not enough to just collect data-based metrics without valuable analysis of the behavioral attributes of qualified leads. And that process, he notes in his commentary, “is far from cookie cutter” and “largely entails the ‘art’ of data science.”

“The ‘art’ of data science refers to having the creativity to know where and how to apply analytics techniques. The analytics methods of data science can be used to extract insightful information, however these methods need to be directed at processes and data resources in ever new and complex ways,” Kudyba pointed out. “The process of better identifying and understanding customer leads in SaaS requires the insights to know what data variables are being generated through user touch points, and also identify existing descriptive data variables of those users. There’s no script to tell analysts what’s out there.”

Descriptive data variables may include job type or job title, company type and skill base, which augment behavioral attributes such as time of use. For stakeholders in the analytics process, focusing on the “art” of data science can both enhance targeted marketing to new customers and assist sales personnel in how to best manage current ones.

“This approach is gaining momentum simply by the introduction of new technology platforms across industry sectors that involve a licensing and subscription base for users. SaaS is really centralized hosting of business application technology which involves high-growth areas such as the cloud and can facilitate processes such as analytics, office computing, gaming, CRM — the list goes on,” summed up Kudyba. “This approach enables providers to better understand users in general.”

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