Take a survey form like this. We have all been asked to complete these at the end of a conference or workshop and they almost always contain a section where one ranks an element of the event on a sliding scale corresponding to a satisfaction number. Maybe we had a bad experience and scored “ Likelihood to re-attend our events” as 2 = Quite Poor.
Luckily there is usually a free text box as well, often left blank but when completed represents the richest source of customer feedback one could wish for. This is were we get comments like:
“I would have preferred demonstrations to be longer and more detailed to explore features more thoroughly.”
“I really didn't really understand the benefit or applications of it.”
Two points that can be quickly resolved to improve the chances of re-attendance. And that might be all that is necessary unless there are many thousands of responses, too many to read, process, and distil anything meaningful with a high degree of confidence.
So jumping from this low frequency example to something more voluminous, say an on-line customer satisfaction survey or call centre logs where it is not unusual to have to process tens of thousands of records, without text mining all that can be done is evaluate the scores.
Say you are a high street retailer concerned about losing market share so decide to run a survey containing a free text box that says: “Tell us what we can do to improve our products and services”.
Well this is a genuine story and here's some real comments we got back:
“I suffered terrible service the previous visit so stayed away ever since.”
“The online service was awful. The website just wouldn't let me place orders, repeatedly. The customer service help, just wasn't any help. Was left with the feeling that you really just didn't want my money.”
“Absolutely terrible, placed order in plenty of time for delivery when it arrived the contents were not useable due to insufficient packaging.”
“I was quite disappointed by a staff member in the local store who was very rude and miserable and when I asked for a new rewards card was told they were too busy to do this.”
And on the positive side:
“The products was just as delightful, scrumptious and definitely worth every penny and the boxes have many uses.”
“Very good, excellent discounts and a good variety of products.”
In this example we got nearly 3.5K snippets like this and a great way of processing these is to automatically read each statement, classify it as expressing positive, negative sentiment or indeed both and assign a strength value to this statement. Then determine the features of the statement to which the sentiment is directed, so packaging, service, website, etc. and create some simple charts to convey the results like so.
We hope you agree this is impossible to do with the scale ranking element of the survey alone and only by processing the comments in free text do we get a enlightening and sometimes stunning view of customer experience.