As a consumer insights manager at a national retailer, you are responsible for understanding the wants and needs of your customers. You’ve followed the sage advice of the Net Promoter Score (NPS) experts, and you’ve implemented an online NPS survey capturing “Would recommend..” that allows customers to leave open-ended comments.
In our prior posts we’d discussed how to join internal “structured” data such as retail location, transaction date/time, payment type, check amount, etc. to your survey results, segmenting the NPS scores to identify the customer segments requiring the most attention. But how do you make sense of a 100,000+ open-ended customer comments?
You can segment your open-ended or “unstructured” customer comments using your “structured” data in the same manner as your NPS scores. For example, we could focus our review of customer comments to the customers paying with a credit card within the Eastern region reporting an NPS score of 50 or less. Unfortunately, national retailers will likely find that they still have hundreds to thousands of comments to review even within very focused segments.
We can use text analytics to help make sense of survey comments without having to read every individual comment. Term frequency (TF), or the percentage in which a selected term occurs, helps gain an understanding of the important concepts underlying the comments, just like you would use the mean of a series of numbers to gain an understanding of a numerical series. Term frequencies can be conveniently analyzed using a selectable tag cloud graphic as we’d illustrated in a previous post.
When TF analysis is combined with segmenting the comments using our “structured” data segments, we can compare the term frequencies between various segments to gain an understanding of the relative importance of key concepts within each segment.
We can further refine term frequency analysis by normalizing our TF terms using inverted document frequency (IDF) analysis. TF-IDF analysis goes a step beyond TF analysis by considering both the frequency of occurrence of a term with a individual comment (TF) as well as term’s frequency of occurrence within either all comments or a segment of comments.
We can then use TF-IDF analysis as the starting point for determining the relative similarity of comments. This additional step allows us to call out a small set of comments, for example, the top 20 comments, that are the most representative of the larger group. These top 20 comments serve as an “executive summary” of the overall set of comments, summarizing the key topics and issues without the need to individually review the entire series of comments.
We would welcome the opportunity to help you to reveal the hidden insights within your NPS survey results. Please follow us on Twitter http://www.twitter.com/mTAB to receive updates to our ongoing survey analysis series as well as more information pertaining to our mTAB survey analysis service.