The Pros and Cons of NPS
By Gemma JoyceJun 14
Published April 2nd 2019
They say that the best chefs are not as bothered about the food their customers eat as they are about the food their customers don’t touch – the stuff that ends up in the trash.
The food that gets left untouched might be boring, surplus to what someone can reasonably consume, or maybe even repulsive. It’s not consumed for a reason.
In the same way, I think it’s important to look at what people aren’t saying on social media, and why. What part of a debate or aspect of a product isn’t getting mentioned very often? Is it because people are uncomfortable, or are they just bored by something?
Here are some examples of the importance of blending data sets, especially when social conversation is sparse.
R Kelly is not a popular man at the moment. Conversation around him and his alleged crimes is particularly negative.
That said, there is a tiny subset of that conversation that uses the hashtag #ISupportRKelly – it’s made up of around 500 authors posting over the last year or so.
Here’s the thing. On Spotify, streams of R Kelly music are on the up. Thousands of people are supporting R Kelly via the platform, but they’re just not talking about it. While many are discussing muting the artist, others are quietly playing his songs more.
Of course, that’s not to say that everyone playing his music supports his actions, and there could be lots of factors involved (e.g. they’re just searching for his music to see what songs he had a part in). But it’s a clear example of how we don’t announce everything we do on social media, especially when it’s at odds with popular opinion. It’s a factor we should consider in every social data analysis, and it highlights the need to bring in multiple data sources to add further context to findings.
This doesn’t just go for highly controversial subjects like supporting artists with wonky moral compasses.
We behave differently when we’re under observation, and social media is effectively a public stage. We don’t tend to write about things we find embarrassing or shameful – that stuff tends to be addressed in private spaces, like impersonal search engines and doctor’s offices.
Silence on particular subjects doesn’t always mean we don’t have thoughts on them.
When people talk about your brand online, there’s probably some strong feeling behind it – complaints to customer service, or perhaps delight at a particular aspect of a product.
But what about when people aren’t talking about particular subjects? When there’s no data available, it doesn’t mean there are no takeaways – instead, I’d say a closer look is needed.
Let’s say you’re a large brand that regularly receives customer feedback around almost every aspect of the service you provide.
“Great customer service at the North Street McDonald’s from Kimmy. She helped me carry my baby’s pram up and down the stairs”.
“.@McDonalds these paper straws are inadequate. I just had to gulp down my milkshake without one.”
Now let’s say you’re mapping out your customer journey and you’re trying to work out how people feel about each aspect in a very granular way. How are people finding the on-screen ordering system, for example?
But when you look to the data, there’s just not much there. No strong feeling. No real opinion. It’s just fine – no need for comment. But ‘fine’ is not cool when you’re aiming to delight people at every stage of their experience with your brand.
Instead of inaction based on what people aren’t saying, there are plenty of improvements that can be made that can get people talking. For example, taking a look at the time it takes for a customer to find particular items and working to optimize this, or testing where deals and offers appear on screen to make it easier for people to save on their meal.
Small changes that make experiences more and more seamless can begin to inspire the positive conversation that just wasn’t there before. When experiences are delightful, people will let you know.
In both cases of silence due to discomfort and silence due to boredom, connecting different data sources with low volumes of conversation can reveal the insights that are missing from public social media platforms.
Blending data sets is all about getting a more complete picture of consumers, their preferences, and their actions.
With thanks to Ben Ellis for providing data for this post.