CRM, Customer Experience, Account Management, Product Usage, Customer Support Systems, NPS, CSAT. These Data Sources are Integrated seamlessly.
Our application, Retensity, automagically creates Your machine learning models. These models are specific to your company and include engineered features.
Retensity creates actionable insights. Our incredibly insightful reporting is powerful, but some customers choose to integrate our propensity scores, insights, and powerful predictors into their applications.
We believe the importance of Customer Success as a strategic team in every SaaS enterprise is increasing every day. That means you are busy and our job is to make your job easier. Our product is an intelligence and prediction application which uses your existing systems to arm Customer Success leaders with insights that improve retention and upsell results.
We help you fight churn using data! Our application is a retention intelligence and prediction application for Customer Success leaders. This does not replace your CRM, Customer Support, Account Management, or Customer Success applications. We leverage information from these and other enterprise systems (and Surveys) to create intelligence that allows you to identify account risks in time to build an action or 'save' plan.
This is not a templated approach. We know every single business and client is unique. Our entire approach is based on the confluence of multiple data streams from many different products across your enterprise. We can integrate with them all, easily!
We provide accurate and timely retention propensity scores based on Machine Learning Models which use many data sources within your company. We don't just provide you with scores, we tell you what those scores are over time and what has caused them to change. We call these "Powerful Predictors".
Using textual information from sources including support tickets, emails, and call transcripts, customer sentiment can be measured and analyzed. This is achieved through applying NLP techniques and our proprietary models to create customer sentiment scores. These scores have high utility value in predicting customer satisfaction, retention propensity, and are particularly useful in analyzing sentiment direction over a period of time.
Information from Net Promotor Score (NPS), Customer Satisfaction (CSAT), Customer Effort Scores (CES) are all important data sources and are important to the predictive accuracy of our Machine Learning Models.
We have learned that we can use the the same data that we are collecting for retention predictions and apply that data to identify the best time, and the right circumstances, to upsell your customers!
