2016 Analytics 50 Honorees

Citrix Analytics 50 Submission

March Liao, Director of Advanced Marketing Analytics and BI
Citrix
Santa Clara, CA
Industry: Software

Business Challenge

Citrix is an industry recognized analytics pioneer in B2B marketing and has a very large installed base of customers. Sales reps have to call them one-by-one to make cross-sell or upsell offers. This process has two major drawbacks—first, product offerings may not be relevant to customers, thus decreasing customer satisfaction; second, sales reps have to make a large number of phone calls to land one deal, a very inefficient process.

Analytics Solution

As head of advanced marketing analytics at Citrix, Liao introduced a big data, cloud-based analytics platform to predict cross-sell and upsell. This tool, powered by a third party big data analytics software provider, takes the entire installed base data (existing customers), along with their purchase history and product ownership data, to match with external data it collects from the Internet cloud (including blogs, websites, news, press releases and social networks, etc.) and various commercial and government databases. Once matched, the tool automatically creates machine learning models to predict customers with the highest possibility to respond to specific product offerings. Only the customers with the highest probability of making a purchase are sent to sales reps through a native integration to Salesforce.com. This greatly increases sales reps’ productivity as they now convert more customers by making fewer phone calls.

Impact

The solution went live in mid-2016. Initial results showed dramatic improvement to business performance. The conversion rates of targeted groups are 250-350% higher than nontargeted groups. The total pipeline finances generated is also significantly higher. Because the solution provides a fully automated analytical platform, it is easy to scale, and easy to use by sales reps who may not have any data science background. Potential pipeline finances generated could go as high as $800 million per year once the tool is fully operationalized.