In anticipation of his upcoming conference co-presentation, Building a Digital Marketing Data
Science Discipline at Cisco: Experiments to Excellence, at Predictive Analytics World Chicago, June 8-11, 2015, we asked Viswanath Srikanth, Senior Program Manager, Analytics at Cisco, a few questions about his work in predictive analytics.
Q: In your work with predictive analytics, what behavior do your models predict?
A: Our models predict for our digital visitors, topics of interest, lead potential, likelihood to consume a type of offer, and probable path through the site.
Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?
A: Predictive Analytics is used for understanding propensity to buy, amount of potential purchase as well as identifying leads. Identified leads are actively pursued by our outbound marketing teams (email, contact centers) and by our Sales teams. The lead potential is a very useful filter in prioritizing which leads to pursue, significantly cutting down on unproductive calls.
Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?
A: I cannot disclose the actual lift percentage here, but suffice it to say attaching scores to leads allowed for a double digit improvement in number of deeper conversations we had with potential customers (that is, took the next step along the journey).
Q: What surprising discovery have you unearthed in your data?
A: Perhaps not entirely surprising – but our digital visitors want better information, timely information and want to figure out solutions to their problems – and the more intuitively we can make our digital property in helping them get there, the more likely they are to engage in deeper conversations with us.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.
A: The focus of the talk will be on the journey that Cisco’s Digital team undertook in moving from an environment of experimenting in data science to making it an entrenched part of the business – and we hope the audience will be able to apply some of the successful aspects (and avoid the unsuccessful aspects) in their own business – both from a business process and modeling aspect.
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