6 cool ways AI can change customer journey mapping in the future

Key takeaways:

  • AI can analyze vast amounts of data in customer journey maps, identifying patterns and outliers to enhance insights.
  • It has the potential to provide context to data anomalies by considering external factors and correlations with other metrics.
  • Anonymized data could be used to generate industry benchmarks, offering users valuable comparisons and insights.
  • AI could assist in creating optimized customer journey maps by learning from existing maps and industry standards.
  • It can ensure consistency in communication by analyzing tone and style across various customer touchpoints, offering suggestions for improvement.

Everybody seems to speculate about how will AI change the world and everything in it. Today we’ll join the crowd and try to brainstorm a few possible changes and improvements artificial intelligence could bring to the customer journey mapping. 

Before we start, a little disclaimer: this article is highly speculative and whatever we write may be closer to the pipe dream than reality and therefore may not necessarily find its way to our customer journey mapping tool (although, who knows…). Our speculation also assumes that the customer journey map is created in a tool like ours where AI can be most effectively implemented.

1. Crunching the data

AI is capable of going through huge amounts of data without skipping a beat. If the customer journey map contains the data for the touchpoints/activities, AI will be able to crunch them and point out the outliers. Even better, the user could be able to instruct AI to look for specific patterns he’s interested in. This may replace, or at least significantly reduce the number of data analysts needed to mine the relevant information.

2. Giving data context

It’s nice to know how a metric behaves and be notified when it behaves differently than usual. What would be even nicer is also having a reason why. AI is already capable of processing new data in real-time, so it has access to the world events of the day and would be able to try to connect the metric’s unusual performance to some external cause that happened out there. Alternatively, it can look at all other metrics it has access to in the CJM and try to find a link with other metrics that are also up or down more than usual (domino effect) or simply a reason why the metric is down (website visits were 0, so it can be safely said the website was down, which explains 0 sales). Our CJM AI Assistant will be able to do something like this.

3. Using anonymized data for benchmarks

Now we’re getting to the good stuff: what if a CJM platform asked every user for permission to receive anonymized data and had AI provide other users either from the same industry or of the same business size (AI would evaluate the relevancy) with benchmarks? Until now, users have to either search for the performance data of similar companies for comparison themselves (and they usually only find very distilled versions) or have to simply guess whether their metrics are up to the standard or not. With this solution, the benchmarks would be provided by other users themselves. This would be of course only possible if the platform had a really big customer base that’s willing to anonymously share the data.

4. Help with creating the CJM

This one is also contingent on the company having a large customer (and therefore a large sample) base that’s willing to cooperate. AI could go through all the customer journey maps of the platform’s customers who were okay with anonymously sharing its outline and basically learn what an optimal CJM for every industry (or even for every niche) looks like. It could also use the metrics to evaluate which CJMs are well-made, so it doesn’t provide suggestions that are influenced by the poor CJMs. 

This could be a really big benefit for small, or even medium businesses, that would be able to “learn from the best”. AI would look at their current customer journey map and tell them what the industry leaders would add or do differently if they were in their shoes.

Note for #3 and #4: We realize the customers may be reluctant to share the data, even anonymously, that could help their competitors since the suggestions would be made based on the industry. To mitigate this, the industry element could be taken out and the suggestions can be made only based on the company size for example.

5. Consistency of communication

If there’s one thing AI loves the most, it’s probably text. Therefore, it would be a piece of cake for it to look at everything the company has published and is using in communication with its customers, and check whether the communication style is consistent. If there’s a super friendly tone on the website and super serious in the emails (or in one of the emails), it would spot and report it. Of course, it would be able to generate an alternative text that’s in tune with the company’s brand.

6. Defining personas

CJM without personas is like a one-size-fits-all sweater. From the sound of it, it should fit everyone, but in fact, it fits no one. Personas represent individual target groups of the company. The better the persona is defined, the easier it is to craft a custom customer journey for it. AI could help with properly defining the personas, and then also suggest ways to customize the journey in a way that would best resonate with the given persona (i.e. target group). When we take this idea to its logical conclusion, the user would create a customer journey for one persona, then tell the AI what other personas he wants to add, and the AI would create both the persona profiles and the tailor-made customer journeys for each.

Do you want to create an interactive and easy-to-use customer journey map with powerful touchpoints, personas, automatic KPI import & monitoring, and much more?

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