Mimecast is a leading SAAS provider of cybersecurity and threat intelligence, specializing in email security and protection that was looking for a comprehensive dashboard of metrics to manage their partner engagement program.
An assessment of the current CRM and analytics data models and processes was performed.
Developed a scoring model for existing customers and collaborated with the Mimecast Analytics team to edit, improve and apply in various settings
The dashboard was created with filters and parameters that allowed the leadership team to analyze metrics across several dimensions.
Mimecast believed that with a better view into their data, they could expand their Annual Recuring Revenue (ARR). Because of this, Mimecast’s Channel Operations team wanted to develop a comprehensive dashboard that provided the leadership team with the metrics required to manage their partner engagement program, as well as report bookings and pipeline metrics to a singular location.
Archetype’s Data Science capabilities were leveraged to provide advanced analytics to Mimecast’s Sales Operations team, creating an account potential predictive model that ranked customers based on their potential ARR. Additionally, Archetype created a behavioral churn model to uncover unique relationships between customer behavior and churn. Natural Language Processing was also deployed in order to categorize free text responses from Mimecast's tech support detractor surveys, based on topic.
The models developed by Archetype provided Mimecast with more comprehensive of view of their customers ‘health’ and the ability to prioritize customers and prospects as part of their go-to-market strategy. The data models uncovered an unexpected inverse relationship of customer surges in competitor search activity and reduced likelihood of churn. In turn, this helped Mimecast identify pain points in their tech support processes, such as highlighting specific products shortcomings, support personnel knowledge deficiencies, or service level agreement policies that need to be addressed and corrected to mitigate negative customer sentiment.