Data Virtualization Leader Utilized ML to Improve Renewals
Today, our renewal process can only go by value and the rest of the lower value renewals are done manually by outsourcing which leaves revenue on the table. If I can get the ability to assign renewal churn risk we can scale our renewal processes regardless of their value and increase renewal revenue.
Sales Leader, Renewals/Customer Growth
The client is a leader in virtualization products- specializing in corporate IT systems and cloud service providers. Their products are used in many enterprises across industries and are recognized for driving innovation. This is echoed in the client’s own company culture, which is keenly focused on fostering innovation and change and prompted their Renewals and E-commerce Teams to look to Machine Learning and Artificial Intelligence in the cloud as a means of showcasing innovation to help drive business benefits.
Our hi-tech customer has experienced rapid growth, with its products being used by many enterprises and cloud providers. This growth meant that certain processes such as renewals needed to be looked at from the perspective of achieving breakthrough productivity. The Sales Team also wanted an innovative approach for identifying high-value customers (before they actually became high-value customers). This would help build a core competency to help define a competitive edge for this type of customer, compared to their peers.
With these objectives in mind, this large virtualization products leader partnered with Pluto7 to transform and consolidate their data. They also looked to Google Cloud and Pluto7 to help them identify the right business use cases for driving transformational change. Using our deep Machine Learning expertise, we ran workshops with the customer to establish the root of their needs.
It was decided that the client would focus on the following two use cases to achieve the transformation they needed: Use service contract and subscription renewal data to categorize subscription customers by their risk for churn, and then use the same data to have a focused process for handling their renewals based on their risk rating. Use e-commerce and external data to identify purchases made by individuals, and from this, identify potential high value customers for the future (known as ‘diamonds in the rough’). Then, develop a customized customer relationship and marketing strategy for these customers.
Leveraging Google Cloud Platform, they used Google BigQuery to create a Digital Marketing Platform. Utilizing advanced analytics, Machine Learning and Artificial intelligence on top of their DMP, Pluto7 and GCP helped create predictions for customer churn risk for renewal subscriptions and also identify potential high value customers, per expectations. This laid the foundation for the customer to initiate the change management and planning activities needed to deploy such an innovative process change in production.
Predict customer churn & find out high value customers.
Working with Pluto7, Our customer was able to take an innovative mindset and apply it to their business, impacting use cases with the leverage of Google Cloud Platform and its components (BigQuery, Dataflow/DataPrep, Tensorflow and Machine Learning Engine). They were able to drive innovative process improvement and saw tangible business benefits like identifying customer churn rate up to an accuracy of 92% and 2-3 potential high value customers through the Diamonds in the Rough model.