If knowing your customer is the route to success in business, predictive analytics can provide the map.
For all the ways that big data and analytics can be used to boost the bottom line, none delivers a better return on investment than analytics used to retain (and further monetize) existing customers. Data from Pacific Crest’s 2015 Private Software-as-a-Service (SaaS) survey shows that it costs an average of $1.18 to generate $1 of revenue from a new subscription customer. Acquiring that dollar through upsell or renewal revenue from existing customers comparatively is much cheaper: 28 cents and 13 cents, respectively.
Shreesha Ramdas, CEO and co-founder of customer management platform vendor Strikedeck, writes in CMSWire that predictive analytics is the key to controlling customer churn.
“The first step is to know which customers are likely to churn, and which ones are likely to renew and/or expand,” he says. “There are many factors that indicate churn: drops in product usage, increases in support ticket volume, degrading sentiment in customer communications.”
However, Ramdas writes, predictive analytics is only as good as the people interpreting the data. “Predictive analytics are based on complex models that consider many ‘variables’ that may or may not be independent,” he says.
This is where experienced data scientists and analysts can prove invaluable because a line-of-business worker with no analytics training could base decision regarding a product or strategy on a misreading of data. A knowledgeable data analytics pro can flag misleading conclusions and avert data-derived decision-making blunders.
As with all technology, predictive analytics is a tool that must be used in conjunction with — and not to replace — human judgment. If you want a pretty deep dive into the revenue impact of predictive analytics on customer retention, check out this post by Dr. Eric Siegel, conference chair of Predictive Analytics World.