It is my pleasure to introduce Gareth Cartman. Gareth works with MS Dynamics CRM partner, Preact, and blogs frequently on tech, outsourcing & marketing issues.
When was the last time you had a really good customer experience? They stand out, don’t they? But do we shout about them?
While a good experience makes us more likely to remain loyal to a business or to recommend them discretely, are we likely to blog about it or write a review on that basis? More importantly, are we as likely to do so as we are when it’s a bad experience?
Bad experiences tend to get viral traction more easily than good experiences, which makes them a poor indicator of what good looks like.
So, if virality does not give us an idea of what good looks like, then what does?
For years, we’ve measured customer service in terms of metrics – how many touch points do they have to reach a resolution? How many rings before someone picks up?
I’ve always seen these metrics as diagnostic, as opposed to truly informative. Whether it’s through NPS, market research or any other means of understanding the quality of your customer service, the difference lies in your approach to obtaining that bigger picture.
In other words, you have the metrics, but what do you want to do with them?
The very idea that an NPS score (Net Promoter Score) is a single baseline figure frustrates me immensely. Whenever I hear a CEO demand that the NPS figure move upwards, it makes me want to cry.
The question I want to hear is “what does the underlying information tell us?”
Splitting and slicing your data tells you so much more. For example, what’s the NPS score of customers at risk or who are likely to lapse out of contract within the next 12 months? What’s the NPS score of big ticket customers?
I’ve seen examples of NPS scores being dragged higher by smaller customers, while the big ticket or lapsing customers are giving drastically different scores.
This is where NPS splits off into two different camps: the camp that says “it’s useless” because they only see a baseline customer loyalty metric, and the camp that says “it’s the most important metric you have” because you can split and slice it any way you want, in order to find where your customer service needs focus. I’m firmly in the latter camp.
When it comes to qualitative research, it is easy to pick out certain comments from others and hold them up as an example. It’s particularly interesting, though, when you analyse customer comments about your business from a combined qualitative and quantitative viewpoint.
Verint systems have done work on this in the past, creating an alert system of words that express frustration or anger. For example “bear with me”, “you people” and “you promised” immediately flag up signals that say “unhappy customer”.
Customers give off all kinds of signals, and they aren’t always recorded the same way. However, these lexical signals are important because they can give you a layer of intelligence that takes the qualitative into the quantitative.
That’s not always an easy thing to do – nor is it always a wise thing to do – but it adds a layer of data and intelligence on top of your customer interactions that can flag up problems quickly.
This harks back to my comment about diagnostics – what do those operational customer service metrics tell us about the big-picture customer loyalty scenario? Are we more likely to retain a customer if we answer the phone within two rings?
Of course, it’s never that clear, but by starting to map out these metrics alongside the more qualitative aspects that we can discern from NPS and surveys, we start to pinpoint areas of improvement with greater accuracy.
Customerthink.com came up with 15 metrics – and I’m sure there are many more being measured up and down the country.
The question is – to what extent do these metrics affect the potential for a customer to up sticks and leave – or vice-versa? And the next question is, which metrics work for your particular business?
So what does good look like? Is it your baseline retention figure, while every metric below it tells you how good came about? It may well be… for me, good happens when you look at customer service from every angle.
If you take NPS from a split and slice viewpoint rather than a ‘what’s my score’ viewpoint, you start to visualise the truth behind customer loyalty (or disloyalty). If you take the qualitative and find a way of quantifying it, then you start to uncover the hidden meaning of your conversations.
And once you know what your customers are saying, then you can focus on delivering great customer experiences. And if one of those happens to go viral, then that is just an extra bonus.
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