Reading customer comments is a serious business. Ultimately, it has to be the best way of understanding what your customers think about your organisation - and it is essential that comments get to the right
people - the ones that can actually change or improve customer experience.However, there are some drawbacks to manually classifying the comments:
- It's not scalable: Some of our clients have hundreds of customer comments a day. It takes only seconds to read and classify each one, but it still takes resources
- Interpretation varies by person: People classify comments in different ways. And what may be urgent to one person is mundane to another.
- Customer comments are variable: It can take some head-scratching to understand what customers are referring to. We regularly see multiple issues in a single comment. Sometimes positive and negative sentiments are expressed together. Or positive comment, "zero" Net Promoter score.
This unpredictability means that it's almost impossible with the technology of today to do pattern recognition, and automate the task (although we are working on this in CustomerGauge Labs).So we have a better method, recently productised in CustomerGauge. We call it by various names: "Customer Self Select"
or "Automatic Root Cause Analysis"
but like all good solutions it's simple and customer focused. The trick is to make the survey smarter. We ask the customer to highlight the issues that bother them (or delight them) the most. With a few simple clicks, the customer chooses the issues you should prioritise. We then direct the results into a series of "buckets" containing issues relating to Logistics, Product, Service etc. We call these "Level 1" as they are the high level matters to focus on.
Below each Level 1 issue are more detailed reasons that provide more detail. For example, Delivery issues might be related to lateness, damaged goods or returns - and it's important to separate out what drives the customer score. These are the Level 2 issues. Customer comments provide context and detail.Depending on the initial customer rating the reasons can change, and are easily edited in the CustomerGauge administration tools.The real magic comes in reporting. We now have a quantitative number of issues, selected by customers themselves. We then organise into Level 1 and Level 2 reports, and add Net Promoter Scores together with other customer data - for example order value or segment information.We build up the data and allocate to individuals, so the Logistics manager automatically receives a report on issues relating to his/her department. These can later be assigned to projects (more in a later post).The newest report is our Waterfall Analysis.
This shows a bridge between Promoters and Detractors to break down the Net Promoter Score by Level 1 issues. It works by aggregating Net Promoter Scores in each issue, and weighting them on by the number of issues selected. It's a simple way of visualising the impact of key elements in your business on your Net Promoter Score.
In the presentation we explain how this is all put together. Download it in a PDF
3.2Mb)And then try the survey
to see how our Self Select system works.We are happy to explain how it is all put together, and demonstrate how our clients are already making great progress with this tool. Revolution is a strong word, but we see this making significant improvements in some sites due to its simplicity of implementation and interpretation. Contact us to arrange a webex and see more.
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