Data-Driven Sales Leads Prediction for Everything-as-a-Service in the Cloud

Abstract

A cloud platform website, offering a catalog of services, operates under a freemium business model or a free trial business model, aggressively marketing to customers who have previously visited. In such a cloud platform or service business, accurate identification of high profile customers is central to the success for the business. However, there are several limitations of existing approaches because of the following challenges: (1) heavy customer traffic flows, (2) the noise in user behaviors, (3) a lack of collaboration across stakeholders, (4) class imbalanced customer data (few paying customers vs. high numbers of freemium or trial customers), and (5) unpredictable business environments. In this paper, we propose a data-driven iterative sales lead prediction framework for cloud everything as a service (XaaS), including a cloud platform or software. In this framework, from the BizDevOps process we collaborate to extract business insights from multiple business stakeholders. From these business insights, we calculate service usage scores using our RFDL (Recency, Frequency, Duration, and Lifetime) analysis and estimate sales lead prediction based on the usage scores in a supervised manner. Our framework adapts to a continuously changing environment through iterations of the whole process, maintains its performance of sales lead prediction, and finally shares the prediction results to the sales or marketing team effectively. A three-month pilot implementation of the framework led to more than 300 paying customers and more than $200K increase in revenue. We expect our scalable, iterative sales lead prediction approach to be widely applicable to online or cloud business domains where there is a constant flux of customer traffic.

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