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Signal vs noise: what actually belongs on a journey map
You have seen the map. Somewhere around step four sits a number in a coloured box telling you that 34% of customers reported frustration there, and it makes the artefact feel like it was built on evidence rather than workshop opinion. Then you open the same map a month later and the figure has not moved a decimal place. The temptation is to call it stale, but is it really? In this article we will work through what separates a genuine signal from ordinary variation, which signals earn a place on the map, how to present them so they stay useful, and why even a good signal quietly stops being one.
What makes a Customer Journey Map valuable?
Google customer journey map and you'll see hundreds of different styles, different details, and arguably different purposes. It's a communication tool first and foremost, but its usefulness is purely based on what information is placed within it and to whom. We have been building journey maps for a very long time and there is a mix that seems to work for us when we ask the question about who is it for and why it exists. In the right format, it should be useful for many functions within an organisation, as well as useful for senior management and decision makers. We'll share what works for us and hopefully for your organisation as well.
Are CX Teams measuring the right things?
There was a time not long ago that all the craze was the Net Promoter Score (NPS). The allure was hard to resist: a single number that measured how likely customers would advocate an organisations USP to others in their network, and with that a link to potential growth. Whilst there are contentious debate on its effectiveness, what it did show was clear: CX needed other metrics because "happy" customers didn't give senior leadership any indication of likelihood to impact the bottom line. However, now with NPS under the microscope, the question is are we as CX practitioners measuring the right things? We'll explore some metrics in this article and some suggestions on others, especially in the world of AI.
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