Admit it, we all do it. I’m talking about how whenever we post something online, we can’t help but check back later to see how it was received. Thumbs up, likes, retweets, comments, downloads, page views. We all love metrics, whether it’s just “did anyone like the picture of my cat I posted on Instagram yesterday” all the way up to complex reports about web traffic, journey flow, click-through rates, and all that good stuff it takes a data scientist to sift through. We have so much data available about customer interactions that the true meaning is often forgotten.

The problem is that most of the metrics record what someone did in the past — typically an interaction with your content by either clicking a button or following a link. They don’t tell us why the person did what they did.

And knowing why is the most important part of understanding the customer journey.

Getting to the why (and why not) of customer behavior

There is an excellent video from Adobe entitled Click, Baby, Click that shows how reacting to clicks without knowing what is driving them can lead to an incorrect interpretation of customer demand. If you haven’t seen it, I highly recommend watching it — it’s a fun lesson you won’t forget.

So if action-based metrics don’t provide the information you need, do time-based metrics give a better picture of what’s driving customer behavior? They are probably a step in the right direction, but they have the same underlying issue — they still reflect past actions. You may now know how long someone interacted with your messaging but not why. For instance, time-on-page can be a false indicator: is someone engaged because your content is good and they enjoy reading it, or is it so obtuse that they have to keep plowing through it to find the answers they want?

Most people come to websites or interact with apps for one of two reasons: to get answers to questions or to complete a transaction. So maybe we should be measuring how well we achieve those two things. Instead of having page-based analytics, shouldn’t we be focused on content and transaction-based analytics combined with search analysis and time reporting to determine how easily, or quickly, customers achieve their goals?

On top of wanting to know what people do during a customer engagement and why they do it, it’s equally important to know why someone didn’t do what you wanted them to do. Why is no one clicking on that beautifully designed call-to-action button? Why isn’t anyone finding high-value content that would help them? This is where tools like heat maps can help you track where people engage with your designs.

Understanding intent

So if the current metrics are a snapshot of past physical actions, how do you realign for a future where interactions migrate from the physical to the digital or to even more esoteric forms of interaction?

Think about the growing use of voice-based assistants such as Siri and Alexa. How will you measure audio interactions?

In many ways we already do, but for a different need. When you call a telephone helpline or get passed to a call center representative with a message that says “your call may be recorded for training purposes,” chances are high that training is low down on the list of why the call is being recorded. Call centers have long used technology to record, index, and analyze customer interactions not just for what was said, but also for the way it was said in terms of tone and inflection.

Sentiment analysis may drive the next generation of metrics for voice-assistant-driven interfaces, not only allowing you to understand what a customer asked for and wanted but also, with the application of machine learning, allowing you to start to understand not just how someone feels about an interaction but also what it was they were hoping to achieve in the first place.

Once you understand intent, as opposed to past actions, you can start to deliver predictive customer experiences and look forward instead of backward.

How can we help you?

The only true indication of a successful customer experience is whether you helped the customer do what they needed to do in a quick, intuitive, and helpful way? Did you make their day easier or answer their question?

The more you remove friction from the customer experience, the more likely those customers are to return and want to engage with you again.