Glossary

Customer Retention Analytics

Business models are evolving. Companies can no longer sell a customer a product or service and move on. Whether it's subscription, utility, freemium or other ongoing pricing, companies are earning more and more of the "lifetime value" of the customer overtime.

As Marc Andreessen famously put it, every industry from healthcare to financial services to retail is being transformed into being mostly about software. And since the most efficient way to deliver technology now is via the Internet, every business is evolving into a SaaS business.

As customers interact with your business online, they leave a digital breadcrumb trail of information leading to insights on the value they ascribe to your business. It is imperative that you capture this data with a product analytics tool otherwise you risk missing out on key retention signals. Feature usage data, survey responses and even social media commentary can help your customer retention analytics and identify engaged versus disengaged customers. Unfortunately, siloed data sets, error-prone guesswork and manual workflows can leave companies blind to the actual customer retention analytics. Despite advances in big data technology for acquiring new business, the "state of the art" in terms of customer retention analytics for many companies is still elbow grease, hope and Microsoft Excel.

We think there's a pressing need to make customer retention analytics as data-driven and automated as customer acquisition. And that's why we created Gainsight.