Machine Learning Making It Easier To Analyze CX

By focusing on experiences at scale, companies enable their digital teams to quantify and make improvements to customer experiences quickly and efficiently.

Improving the quality of customer experiences online has, historically, proved to be a challenge for marketers. Merely measuring them was difficult.

Think about it. Businesses must track a number of different metrics–from conversion rates and load times to Net Promoter Scores and more custom KPIs–and combine them across websites, online services, and apps in a way that reflects how different audience segments behave and feel.

Not only was this a lot of manual work, but there was significant room for error in how customer experience was reported. What weighting, for example, should you give to conversion rate as opposed to customer feedback when it comes to working out the quality of customer experiences? If someone buys a scarf online, does this automatically mean she had a good experience? Or did she just really want that scarf? What’s more, only one in 26 people actually give feedback when they’ve had a bad experience. The rest just leave.

But times are changing, and the maturation of machine learning is allowing businesses to automate the analysis of digital experiences. For example, companies today can capture experience data that reads the digital body language of users–the speed and angle at which they move a mouse, how they scroll and rotate the device, how frequently they tap, and much more. Post-capture, these vast digital experience datasets are algorithmically harvested to deduce user state of mind, be it engaged, frustrated, or confused.

This immediate insight into how users feel–without having to ask them a single question–completes the story of how a website or app is performing. Indeed, business intelligence dashboards today are furnished not only with revenue, technical, and operational metrics, but now with universal, easily understandable customer experience data, too.

By focusing on experiences at scale in this way, companies enable their digital teams to quantify and make improvements to customer experiences quickly and efficiently–no longer through trial and error, but through precise scientific measurement.

If your company isn’t embracing machine learning to automate the difficult work of experience analysis, you can be sure your competition is. It’s time to get started.