{"id":5169,"date":"2020-03-23T03:54:00","date_gmt":"2020-03-23T03:54:00","guid":{"rendered":"https:\/\/www.revuze.it\/?p=5169"},"modified":"2020-03-23T03:54:00","modified_gmt":"2020-03-23T03:54:00","slug":"proactive-customer-experience","status":"publish","type":"post","link":"https:\/\/www.revuze.it\/blog\/proactive-customer-experience\/","title":{"rendered":"Why should you favor proactive customer experience? (2020)"},"content":{"rendered":"\n

What is proactive customer experience?<\/h2>\n\n\n\n

The roots and DNA of customer understanding are reactive in nature. In general, as a brand you try to do something and understand if it worked or not. If sales are up, all good. If sales are down, now you start to dig in. Think about surveys and focus groups \u2013 you need to plan what to ask and who to ask before you gain a single insight about your declining sales. Once you get insights, they typically only trigger further investigations. <\/p>\n\n\n\n

This\nis why according to Bain and Company 80% of brands<\/a> believe they\ndeliver superior customer experiences, but only 8% of customers feel the same \u2013\nhence the \u201cCX gap\u201d or \u201cVOC gap\u201d.<\/p>\n\n\n\n

To make things worse, there are more feedback channels today, either direct to the brand like chat, calls, email or just to the general public like social media and online reviews, forums and blogs. According to IDG<\/a>, over 90% of the world data is soon to be made of such customer opinions. So, whatever didn\u2019t work thus far will for sure not work in a much more complex and fast-moving environment.<\/p>\n\n\n\n

This tectonic shift will force CX, VOC<\/a>, consumer insights<\/a> or whatever other name we call understanding our customers to become proactive and scalable.<\/p>\n\n\n\n

Why\nis CX reactive at all? Why do we have the CX gap?<\/strong><\/h2>\n\n\n\n

Because what is common to all existing solutions today that analyze\nunstructured customer opinions is that they heavily rely on human experts – data\nscientists, analysts, IT folks that train generic AI machines to search for specific\npatterns and expressions in customer opinions.<\/p>\n\n\n\n

Like with every other industry, once manual labor is involved it makes\nthings slower, more expensive, and in this case where experts need to use their\nimagination to predict how others express themselves on products or services \u2013\nit is also biased and inaccurate.<\/p>\n\n\n\n

Now if a person is setting up the CX machine to look for patters, how\ncan that machine be anything but reactive? The person can\u2019t predict a new trend\nor competitor, so he or she can only add these to the CX insights once there is\nenough evidence that this is mandatory.<\/p>\n\n\n\n

A couple of things that make things worse are accuracy and time to\ninsights:<\/p>\n\n\n\n