Online surveys are a popular tool for gauging customer satisfaction, but when only a small percentage of customers actually respond, drawing meaningful conclusions can be difficult.

One of the problems with surveys--even those that achieve a high response rate--is they provide only a snapshot of customer data. This is why "Big Data" has become such a hot buzzword in business: when you're able to track mountains of customer behavior over time, that's when you can discover truly useful insights. This is particularly true for software-as-a-service (SaaS) businesses that can measure customer usage on a continuous basis.

"That's an incredibly valuable signal, because you're getting that signal every single day," says Don MacLennan, chief executive officer of Bluenose, a San Francisco-based company that uses predictive analytics to help businesses with issues such as attrition and customer engagement. 

So how do you turn heaps of data into valuable business intelligence? Below MacLennan offered three tips for what to look for when your end goal is to increase customer satisfaction and drive revenue.

Find at-risk customers.

If you recently sold 500 subscriptions of your software to a large company, seeing how many of that firm's employees have actually adopted your product can be extremely meaningful. "If they haven't adopted it, you're unlikely to achieve a renewal, in which case you should use that adoption data as a way to mobilize and manage that risk," MacLennan says. In other words, this is an early signal that you need to step in and proactively address what's turning people away instead of waiting to the point where you're reacting to unhappy customers.

Get to know what kind of help your customers need.

In addition to measuring how often customers use your product, look into where customers are struggling with usage. Do they need assistance? Could you provide help in an online video tutorial? "By knowing each customer in very specific terms, you can obviously be very purposeful in terms of how you engage them," MacLennan says. 

Identify frequent customers and turn them into advocates.

A deep data dive can also be valuable in helping you identify frequent users of products. "Would they be potentially interested in becoming an advocate and help me promote my product to the rest of the world because they're so happy?" MacLennan asks. These customers are also more likely to purchase more of a product or buy sister products--something you need to know to maximize the amount of revenue you get from these individuals.  

While companies have been trying to address problems with customer usage and engagement for decades, many of the techniques used in the past have been too costly to do so efficiently, MacLennan says.

"We're at this inflection point where the underlying data storage technologies are so cheap we can now solve this business problem in a cost effective way."