It’s really a no brainer that Customer Satisfaction matters. Every IT or Business unit I’ve known, considers Customer Satisfatction, a very high priority and does strive really hard to ensure they engage very closely with their customers. To ensure every customer query or concern is serviced well within time. If one does go by the assumption that servicing customers in a timely manner will keep them really happy and satisfied, one would tend to focus more on Service Level Agreements.
It’s really a no brainer that Customer Satisfaction matters. Every IT or Business unit I’ve known, considers Customer Satisfatction, a very high priority and does strive really hard to ensure they engage very closely with their customers. To ensure every customer query or concern is serviced well within time. If one does go by the assumption that servicing customers in a timely manner will keep them really happy and satisfied, one would tend to focus more on Service Level Agreements.
To stay on top of your Service Level Agreements, metrics are defined and operational SLA related reporting infrastructure is implemented. What get reported up the management chain too would be the operational SLA reports. The teams that do not meet their SLA’s get punished and the ones who meet their SLA’s get praised for their work.
Is a 95% SLA performance truly worthy of praise? Most might say yes that means my customers are being serviced on time and are happy. Operational reporting that’s used out here to determine SLA adherence does tend to use just maybe around 30-40% of the overall data residing in your customer support system. Why is the remaining 60-70% data within such systems not analyzed at all?
Is the remaining 60-70% of your customer support systems transactional data, a GOLD MINE, which can help you uncover a lot of information about how your customers are thinking about you, your products and the services you are offering? Can this data tell you something which you never knew? In the context of Customer Satisfaction is this much more Business Relevant?
Yes it is and out here I am referring to the free text residing with Customer Call descriptions, notes and comments. Unstructured or free text data is in vast amounts in your customer support systems, mining this data and co-relating it with your operational reporting could give you much more business relevant information. It could give you better SIGNAL’s about your customer SENTIMENT. This could get you closer to getting a real good answer to your question “Are my customers Satisfied?”
Text analytics is nothing new and continues to use the same old word frequency distribution, pattern recognition, tagging/annotation, grouping of information and association of words/phrases within large samples of data using Natural Language Processing. With the Big Data, Social Web Wave, text analytics has suddenly got much more relevant and important. Technology, infrastructure and tools too have got more advanced and easier to use as compared to earlier years. When Text Analytics is used to derive customer sentiment, the results could be really fascinating.
Let’s look at some use-cases which text analytics, could help senior IT managers or Solution owners uncover about customer sentiment
- ~30-45% of the customers from within a particular business unit seem to be indicating, they would defer using a particular application until a new feature gets introduced, from comments like “I will wait”, “Not using currently” and ”Next Release?”
- 20% of an influential user group seems to be using words like “No Business Sense”, “Angry”, “Frustrated”, “Time and Again”, “Too complicated to use” and “I don’t really use this”. This does indicate negative sentiment around the core product or service and not around the level of support provided.
- 10-15% of customer queries do seem to be indicating the usage of “How to”, “Not Clear”, “Can’t understand”, “Not self-explanatory”, indicating the need to train or educate the business user group further
- There is a sense of excitement as nearly 20% of a power user group seem to be closing their queries with words like “Wow”, “Amazing results”, “Could not even image this is possible”, ”Solved a huge problem”
There are a number of open source and commercial tools available to perform such analysis. The ones that I have seen and used are pretty impressive “Lexalytics”, “Attensity” and many more. I am not at all trying to recommend any tool here, all perform really well. I would look up to some of the readers to share your insights on this subject too, your observations around what has worked for you and your customers.
Finally, I end this with, use your data to its fullest potential, the ANSWERS lie within it. Don’t miss out on your real GOLD MINE.
