Suggestibilty: easily influenced by suggestion; a state, in which a person will accept the suggestions of another person. Suggestion can be powerful, particularly if a suggestion comes from your customer or your potential customer. I spent most of my career in tech as a product manager. The primary job of a product manager is to talk to customers to gather their requirements (their suggestions about what you should build and sell) and then translate those requirements into prioritized, product specs and a product roadmap. Since I’ve always worked in smaller tech companies, I would have one to two hundred customers at most to reach out to. A lot, but not impossible.
For larger enterprises, the calculus is much different. Companies can have thousands and thousands of customers. Now not every customer is created equally, but many of them have valuable insights that can help guide your product plans, improve interaction processes and even drive your messaging. But how do companies start? Many use market research and surveys as a way to capture these suggestions. As I have said before, these tools are extremely valuable as they give you a snapshot of what a sample, that represents your customers can think. But sometimes it is still hard to really understand what customers want because there is so much data captured using these approaches and reading it with the naked eye still forces the product manager or the developer to create some method to organize and prioritize this data. In addition, there is a relatively new source of customer suggestions, a source that can go well beyond the volume and detail you’d get out of a survey in social media. The social customer gives suggestions everyday and that daunting task of getting customer feedback and using it to drive product planning becomes even larger with this potentially massive source of customer requirements.
Unleashing Customer Suggestions is the Perfect Job for Text Analytics
So why do I bring up this topic? Well, it is the perfect problem for text analytics, and specifically Attensity’s unique brand of text analytics. So why is this a great opportunity for text analytics? Two interrelated reasons. First, verbatims are where the detail of the suggestions are – the details that give companies the insights of what to build and why it might be useful to the customer, and second because text analytics can help organizations organize, prioritize and truly understand these suggestions. So, why is Attensity’s approach the “right” approach to do this? Another two reasons, first, Attensity doesn’t require you to create rules or know what suggestions you are looking for – we uniquely find them without having to know what you are looking for – and in many cases, customer suggestions can be novel, so rules just plain old don’t work. Instead of rules, we automatically pull out suggestions in the form of what we call a “triple” – which ultimately is who, did what to whom, or who wants what and why, how, where and when! The triples look like this and are automatically categorized so users can be alerted to their occurrence and can report on them.
And that brings me to my second reason Attensity is the perfect application for pulling out customer suggestions, our automatic “voice” identification. With Attensity we are able to pull out all sorts of “voices,” which translates to an automatic identification of the different kinds of things people say from sentiment, to intent, to conditions (e.g., I would buy if…) to suggestions! We then apply voice tags to these comments and organize the data into categories of suggestions – providing an organized view of what could be hundreds of thousands, even millions of customer suggestions that come from a wide-range of customer feedback points. The example below shows a category of suggestions for website improvements. In this view, users can drill down to the specific suggestion. They can also see suggestions aggregated into charts – an example of a chart is included later in this post….
Suggestions Are In Your Customer Complaints
Now that we’ve established that text analytics can be very valuable in finding both known and not known suggestions and that finding these suggestions using our “voice tags” makes it easy, I would be missing something if I didn’t discuss the less direct suggestion (or depending on the customer, sometimes the more direct suggestion) a customer complaint! Customer complaints, no matter where they happen, are chock full of suggestions. Through text analytics, companies can get to the detail of a complaint, uncover top complaints and new problems, all of which can represent critical suggestions to what a company should build next. Where should an investment be made? Sometimes the biggest issues are the places to start. The chart below shows an example of the top issues with an unnamed cell phone manufacturer’s product. Top priorities for the next rev of the product are clear!
So if figuring out what your next best seller will be is important to your business – listen to your customers for the suggestions – they can help!
Photo credit: littlestar
