Applying AI To Customer Surveys
Using NLP To Extract Additional Insights From Customer Surveys
Many executives, particularly in smaller or non-technology companies, assume that AI does not apply to them. However, even with relatively small amounts of data, AI can quickly deliver commercial benefits in certain areas.
For example, anyone who conducts Customer Serveys can use Natural Lanuage Processing (NLP) techniques to undercover key insights that are hard to uncover with manual processing alone. Benefits have been achieved by applying NLP techniques to applications with as few as 380 customer surveys
Most Customer Surveys rely upon numeric ratings to understand what customers are thinking. Comments are also captured and these often contain key insights into the drivers of the ratings. However, because these comments are “free text”, extracting insights from these comments manually is a time-consuming and subjective process.
By using NLP techniques, the “free text” comments can be converted into meaningful data. This data can be combined with the numeric ratings to identify additional key insights into the customer feedback.
For example, suppose you are interested in understanding what factors are driving the overall Net Promoter Score (NPS) ratings in your survey. Conventional techniques would be to analyse each question and look for correlations between the ratings on this question and the overall NPS score.
However, by examining the text comments, it may be possible to identify additional drivers of the NPS score. For example, it may be that customers who use certain words/phrases (e.g. “loyalty”) or mention certain products/services that you offer, are more likely to be “Net Promoters”. These insights would be hard to identify manually, even with a relatively small number of surveys.
Identifying these values or products can be useful in determining what actions to take in order to improve your NPS and hence drive higher customer loyalty.
Using Machine Learning To Identify Different Categories of Customer
If you have captured the demographics of the customers completing the surveys, Machine Learning techniques can be used to identify different categories of customer. Alrthough this can also be done with more traditional analytical techniques, such techniques often fail to identify non-obvious relationships.
For example, suppose that want to identify the characteristics of customers who give a negative rating for a particular product or service that you offer. One way is to guess what those characteristics are and check each one. This is the traditional analytical method.
However, by developing a Machine Learning model, it can “learn” what those characteristics automatically, just by examining the data. This can include not just the numeric ratings ( as would be the case in the traditional analytical approach), but also the insights obtained from the NLP.
Identifying these categories of customer allows you to tailor specific actions to the rights customer groups, improving customer satisfaction and increasing customer loyalty.
Learn More About Applying AI To Customer Surveys
For a free consultation on how to apply AI to gain more insights from your customer surveys, please contact us by emailing firstname.lastname@example.org