Skip to content

Can Customer Satisfaction Surveys Be Predictive? Absolutely!

Are Customer Satisfaction Surveys Predictive

It has become fashionable in some circles to bash customer satisfaction surveys. Those who share such sentiment offer critiques that often revolve around online customer surveys being less valuable because of low response rates, feedback that isn’t actionable, gaming or manipulation, or the difficulty of tying survey results to business benefits.

While there is some truth to these criticisms, each can be overcome by applying the right strategies and avoiding common pitfalls of survey programs, especially for B2B companies.

Another contention I’ve seen being floated by some people is that surveys are “backward-looking” and, therefore, less actionable. In my experience, this notion is usually put forth by those selling an alternative, and their argument holds even less water than the criticisms above.

This notion is usually put forth by those selling an alternative, and their argument holds even less water than the criticisms above

Why? Because, in reality, customer satisfaction surveys are very much forward-looking, providing valuable insights on how companies can improve their products and services in the future - to better cater to their customers' needs.

When properly acted upon, the feedback from surveys drives continuous improvement that leads to more loyal customers who:

  • Stay longer
  • Buy more
  • Spread positive word-of-mouth
Gain Predictive Insights

Forward-Looking Questions in Your Customer Satisfaction Survey

The argument that customer satisfaction surveys are backward-looking stems from the fact that the questions posed often ask customers how satisfied or dissatisfied they are with a product or service. Anytime someone is asked about how they feel about something, their response is based largely on their personal experiences, which, by definition, are historical. However, that doesn’t mean their feedback has less value. Quite the contrary, much of what your customers share in a survey – good or bad – can and should be used to improve customer experiences in the future.

Additionally, many surveys we design and administer for our clients include questions asking the contact what they are likely to do in the future or what they’d like the company to focus on going forward. In fact, two of the most widely used questions in online customer satisfaction surveys are:

It is also quite common to include versions of open-ended questions like:

  • “What enhancements would you like us to focus on?”
  • “How can we better serve your needs going forward?”

These are just a few examples of survey questions that are forward-looking, eliciting feedback that B2B leaders use to prioritize roadmap items, enhance customer support, refine the approach of the customer success team, and make investments that will benefit both their current and future customers.

Gain Predictive Insights

Satisfaction Surveys Help Address Issues Before They Escalate

Customer satisfaction surveys also serve as a proactive tool that companies use to identify and address potential problems before they escalate into larger issues. One way to do this is to identify common customer frustrations and model them to anticipate potential future issues, thereby proactively addressing issues before they escalate into larger problems.

Utilizing Predictive Analytics

This is where predictive analytics comes into play. Predictive analytics is already quite common in the world of customer experience. Qualtrics defines it as

“The art of using historical and current data to make projections about what might happen in the future.”

While some predictive analytics rely on steady streams of data being collected and ingested into complex statistical models, they don’t have to. Let’s take a look at some simple constructs and how our clients use them as more as “cause-and-effect frameworks.”

Predictive CSAT Survey Example

Let’s say you work at a SaaS company, and you have “relationship” survey and transactional support survey response data from a strong cross-section of your customers, including several key contacts at those accounts (i.e. budget holders, decision-makers, influencers, power users). Associated with your survey data is information about how often your customers use support, what channels they use, how satisfied they were with each support experience, etc.

In this example, you’re also able to look at other meta-data associated with your customers, such as how long they’ve been customers, how large or complex the customer is, where they are located, and how often they use your software. All the “conditions” noted above are quite easily attainable and fairly common across our B2B client base.

With the use of some simple regression analysis, you’re able to identify a pattern that indicates those customers who have more complex environments and that are based in the EU are meaningfully more satisfied when phone support is used than other similar customers that rely on chat for support.

This finding is supported by unstructured (verbatim) feedback in response to open-ended questions in your survey. You’ve also heard similar comments from your Customer Success, and Support teams, who have also been told by customers that relying on chat for support has proven frustrating. You might even see an early trend showing increasing churn among large, complex customers in the EU.

All of the above is presented to the leadership team and action is taken to improve the support experience in chat. What does that mean for your customers? It means a better experience for those customers you currently work with, as well as future large customers in the EU that are likely to complain less than they might have, thanks to the enhanced chat experience for support. Overall satisfaction and retention for that customer segment (and possibly others) are likely to go up as well.

Simple modeling was used to address a specific trouble spot and address a customer pain point across a certain cross-section of your customers. This relatively simple example clearly shows how even ‘backward-looking” customer satisfaction survey data can be used to improve the customer experience in the future. A similar approach can also prove extremely effective by modeling your most enthusiastic Promoters and identifying what they love about their experience that leadership can strive to replicate elsewhere.

Gain Predictive Insights


In addition to leveraging survey feedback to mitigate issues and stay abreast of changing sentiment, satisfaction surveys also help create a more loyal customer base simply by demonstrating a company's willingness to listen to feedback and make changes to improve the customer experience. When customers see that their feedback is being heard and acted upon, they are more likely to feel valued and appreciated by the company. This can lead to increased loyalty and repeat business, as customers are more likely to stay with a company that they feel cares about their needs and is committed to continuous improvement.

These are just a few examples of how online customer satisfaction surveys are often used by B2B companies as predictive tools for the benefit of their customers and their own businesses. With that said, it’s also important to consider that while surveys can be powerful tools, there are other ways to gain insights into how your customers feel and what they would like from your company going forward. Your survey data can be supplemented using tools that track user behavior, feedback from customer communities, strategic Customer Advisory Boards, in-depth interviews, and now, AI and “big data” platforms. Some or all can be used together to gain even deeper and more reliable insights. But we’ll save that for a future article!