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There’s no doubt that customer feedback is key to improving customer experience, and organizations that prioritize customer surveys and user feedback have a greater chance of reducing customer churn and improving customer retention. Simply collecting data is not enough, though. Customer feedback analytics help to transform raw data into insights using analysis like Key Driver Analysis, NLP (Natural Language Processing), predictive analytics, and segmentation analytics. Together these types of data analysis provide a more holistic understanding of customer experience across the customer journey as well as actions that can be taken to improve CX.

Too often organizations feel that simply collecting customer feedback and highlighting that they conduct customer surveys is sufficient to improve customer experience. However, a feedback survey is only as good as the actions and improvements it inspires! Organizations that conduct customer satisfaction surveys with the sole aim of completing them will fall short. Plus, while ratings are very important elements in predictive analytics, collecting open-ended responses offer powerful insights, too. Those who don’t have time to read through every single open-ended answer (most people!) benefit significantly from Natural Language Processing, or NLP. This kind of customer feedback analytics offers important insights while saving time.

Simply put, a customer satisfaction survey has two main goals: To find out how satisfied customers are and to learn what can be done to make them more satisfied. Customer feedback collection is extremely important in improving real-time customer experience as well as powering the future: predictive analytics, prescriptive analytics, and NLP can all help to drive future CX. If you’re interested in predictive analytics marketing, predictive sales, and predictive churn models, you’ll need to include questions that enable these reporting options. At the very least, your customer satisfaction survey should include a customer satisfaction question (no surprise!). Beyond that, at least one open-ended follow-up question will help you to learn from Natural Language Processing. You may also choose to include a series of key driver rating questions to enable Key Driver Analysis, which will offer insights on areas of opportunity for improvement. Finally, including classifying questions like demographics or product type can make it possible to conduct segmentation analytics to understand how insights vary across different groups of customers.

NLP, or Natural Language Processing, is a type of data analytics that helps to uncover insights from participants’ own words. In customer experience management, it’s especially useful to hear directly from customers because simply asking for their ratings may not provide full insights. Consider NLP in customer service feedback: While customers may give good ratings overall, NLP may uncover that support team members are consistently speaking too quickly. Good info! Natural Language Processing can identify both content and sentiment from responses, making it easier for CX leaders to learn how customers feel about different touchpoints in the customer journey as well as micro-moments that really matter. Because it’s hard to predict what everyone thinks and it’s very challenging to read every single open-ended response, NLP makes it possible!

Natural Language Processing is a great tool to use in customer feedback analytics because it simplifies analysis of lots of open-ended customer feedback. While it may be very insightful to read every single customer survey response, such an approach isn’t necessarily practical at a larger scale. NLP makes it possible for organizations to uncover customer sentiment and to identify the aspects of their experience that have the biggest impact on CX – as well as customer churn models. Using NLP to study customer survey results, NLP customer reviews, and more customer feedback can help to uncover unexpected results beyond the predictive analytics based on key drivers and other CX metrics.

Predictive data analytics is an important tool in customer experience management. Whether you work with a dedicated predictive analytics platform, any of a range of popular predictive analytics softwares, or a spreadsheet and elbow grease, predictive analytics can turn customer feedback into business insights. Predictive business analytics help organizations to prioritize resources and to design strategic plans that meet customer needs, compete in crowded marketplaces, and anticipate future growth requirements. To use the example of a software company, user feedback can help the company to understand how much customers like using the platform, but feedback surveys that include key driver and open-ended questions will enable the company to figure out how to refine their product roadmap, to fine-tune their support processes, and to effectively market and sell their offerings. When combined with NLP and other CX metric analysis, predictive analytics help orgs to identify which improvements will have the greatest impact on their future business, therefore informing business decisions and strategy.

A customer survey is a smart and strategic way to collect customer feedback at any stage in the customer journey. While customer surveys power NLP, predictive analytics, and other customer feedback analytics, it’s important to remember that convenience is one of the most important drivers of success in customer satisfaction surveys and other customer surveys. Therefore, the ideal customer survey is both easy for participants to complete and for administrators to analyze. After all, if no customer feedback is collected, there will be no customer feedback analytics – and if admins can’t turn the feedback survey data into insights, the entire project will be pointless. Customers should see one or more CX metric questions, like CSAT or NPS, an open-ended follow-up question, and perhaps a grid of key driver ratings. A customer survey that gets to the point will also get more responses! Internally, it’s useful to include some classifying details like product type or customer location to power segmentation analytics, churn models, prescriptive analytics, and more. Customers don’t need to see these details. Instead, they should be filled in through data population, customer directories, or other automated methods.

If your organization collects customer feedback, you already know that there are three possible outcomes. One, you find out that everyone is happy. Great news – and no action required. Two, you find out that some people are unhappy, but you don’t know why. Bad news – and no action possible. Three, you find out that some people are unhappy, but you know why. Not exactly the best news, of course, but better to find out what’s wrong than to have no idea. Along with Key Driver Analysis, Natural Language Processing, and predictive analytics, customer segmentation analytics can make all the difference between a worthless customer feedback project and a meaningful set of valuable insights to improve your business. To use a banking example: If you knew that some customers were unhappy with their recent branch visit but you didn’t know which branch they visited, you can’t do anything about the problem. Segmentation analytics ensure you can get to the root of any issues you uncover – where they’re happening, when they pop up, who’s involved, and maybe even what you can do to fix them.