Case Study: Looking for a way to become more proactive around churn and upsell opportunities, CompleteCSM predicted churn and expansion opportunities with 90%+ accuracy, proven via regression testing, setting them up to spend their time where it matters most.
CompleteCMS Solutions Used: Meeting Intelligence & Customer Intelligence
Primary Talend Business Systems: Gainsight, Salesforce, and Pendo
Results: Predicted renewal, churn, and expansion with 90%+ accuracy in a 10 account set where the outcome was known by Talend and confirmed by CompleteCSM.
Talend is a global leader in data integration and data integrity that is changing the way the world makes decisions through its Data Fabric platform. Every day, it transforms, cleanses and qualifies terabytes of data on behalf of its 6,500+ customers, and yet it was virtually in the dark with respect to what its own usage and interaction data was telling it about its customers’ propensity to churn, renew or expand. Without the dedicated data infrastructure and team to leverage that data, it was looking at the daunting task of hiring the data scientists, engineers, and analysts to build the capacity itself. In CompleteCSM, it found the perfect, proven partner and technology to capture, model, and operationalize its sentiment and propensity data at scale..
Bridge data from its enterprise systems. Like many enterprises, Talend has a complex technology stack that includes multiple business platforms and ecosystems. To get at the heart of the customer sentiment behind the thousands of support tickets, Salesforce interactions and Gainsight communications, as well as the Pendo consumption and seat utilization data, required the ability to reliably integrate numerous types of structured and unstructured data, at high velocities and volumes.
Operationalize deep sentiment analytics at scale. As a data company, Talend understood the value of specialization, especially with respect to the training and optimization of predictive and propensity models. Even with a top-tier data team in place, it might never gain the deep domain expertise or broad dataset needed to effectively mine its interaction data, correlate it with usage patterns, model good and bad customer profiles, and scale it across its entire 6,500+ company client base.
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