As a customer experience professional, I’m hyper-focused on finding ways to improve the customer journey, and I’m always looking for tools to help me analyze customer insights.
For me, AI tools are at the intersection of those two initiatives, especially when it comes to building out or improving the customer journey.
I started looking into leveraging AI to build my customer journey map, and it has me excited about the potential insights, areas of opportunity, and even recommendations that AI tools can surface for me.
In this article, I’ll give you an overview of what I've learned about leveraging AI for customer journey mapping. We’ll look at using machine learning to process large amounts of data, surface themes and patterns, and even use sentiment and data points to predict future behaviors,
No matter what role or industry you’re in, this article is chock-full of tips that you can apply to your current customer experience strategy.
Note: While I reference specific machine learning tools in this article, you can apply the prompts I’ve included to any tool that you’d like.
Table of Contents
- What is AI-powered customer journey mapping?
- How to Create a Customer Journey Map With AI
- ChatGPT Prompts for Customer Journey Mapping
- Testing It Out: How I Created a Customer Journey Map With AI
- Helpful AI Tools for Customer Journey Mapping
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What is AI-powered customer journey mapping?
It’s no secret that AI is transforming the way businesses understand their customer needs and subsequently how they translate those needs into the customer journey.
Through machine learning, AI tools can quickly do things that would typically require a large amount of manual effort for a human to complete, such as analyzing vast amounts of data (both qualitative and quantitative) to uncover patterns, predict customer behaviors, and surface themes and insights.
For example, a traditional customer journey map creates a visual of how customers move from one stage to the next with your business. AI enhances this process by:
- Processing large volumes of data from multiple touchpoints.
- Identifying hidden patterns and correlations.
- Predicting future customer behaviors.
- Personalizing the journey in real-time.
- Providing actionable insights for optimization.
How can AI improve the customer journey mapping process?
If you’ve worked with AI, you know how valuable it can be when you need help with the heavy lifting! This is especially true for the journey mapping process, which can be time-consuming and requires a lot of data to get accurate results.
AI helps with those two pain points by analyzing data and surfacing trends — both of which would be really time-consuming to do manually.
According to a , completing a traditional customer journey map could take . That's not including the time it takes to collect and synthesize customer feedback, which is one of the most important parts of the process.
The journey mapping process is time-consuming due to four main factors:
- Quantitative data — website analytics, social media, customer service logs, sales data, etc.
- Qualitative data — insights from different departments, customer interviews, survey feedback, etc.
- Data analysis — identifying patterns and insights is often a manual, time-intensive task.
- Visualization — it takes significant effort and skill to create a visually appealing and easy-to-understand map.
Here are some other use cases for AI in the customer journey mapping process, according to the experts I spoke with:
- Designing marketing/sales/CS processes for engagement along the customer journey.
- Architecting workflows/automation for data management and outreach campaigns.
- Analyzing customer sentiment across multiple touchpoints.
- Personalizing customer experiences in real-time.
- Predicting future customer behavior and needs.
- Defining and outlining the customer journey.
- Discovering the right conditions for upsell and expansion.
I’ve personally used AI to analyze customer feedback and surface trends and themes, and it’s such a huge time saver. I use this data to directly impact the customer experience within the scope of my role. What would have taken me hours now takes a matter of minutes, and I’m always surprised at what themes it uncovers.
What are the limitations of using AI to create a customer journey map?
While AI is exciting and it feels like it’s being used everywhere and included in every tool these days, it’s still relatively new and is not without flaws. The results won’t be perfect and they absolutely require a human to verify them.
, CEO of , has used AI to develop journey maps and feels it's not quite ready for prime time yet.
A big challenge with creating a journey map using AI is that “it doesn't serve any user well,” he says. “AI can produce overly complex maps cluttered with unnecessary information or may generate overly simplistic, generic maps that fail to provide valuable insights. These journey maps frequently require extensive revision, and during this process, gaps in the journey become apparent.”
However, where AI can be useful (with some caveats) is in providing insights that contribute to a better journey or influence the journey itself (though a UX professional is still essential to the creation process), he explains.
Here are some real-life examples he shared:
- Summarizing qualitative insights to highlight key steps and pain points can be helpful, but the data must be rich and well-curated.
- Segmenting audiences based on specific criteria and analyzing their behavior has improved, but it still largely remains within the realm of analytics rather than journey mapping.
- Offering personalization suggestions is valuable, yet it's merely one component of a broader journey.
- Engagement across touchpoints can influence outcomes within a journey, but it doesn't define the journey itself.
By using AI as a partner in creating your journey map instead of a replacement, you can get the best of both worlds. Let’s look at how to do just that.
How to Create a Customer Journey Map With AI
Leveraging AI tools can be really fun — don’t get discouraged by the learning curve. I recommend treating the process as an experiment, and know that you’re likely going to work through a few iterations before you land on the perfect final product.
AI tools continue to roll out at a rapid speed, and we’re starting to see AI more and more in the customer success and customer experience space. In the example below, is a tool that helps synthesize customer data to create a personalized journey in a matter of seconds.
This is just a preview for now — we’ll go deeper into the tools here in a bit. But before we get there, let's go over a few foundational steps you’ll need to take to leverage AI when building your customer journey map.
Step 1: Define your objectives.
The #1 rule of any project is to define what you want to achieve. Without clear goals for your map, all of the exciting insights you uncover can create a distraction and potentially cause the scope of your project to balloon.
Narrow down what you want your map to accomplish, like one of the following example focus areas:
- Identifying and addressing customer pain points.
- Enhancing overall customer satisfaction.
- Identifying new upsell opportunities.
- Boosting customer retention.
- Increasing conversions.
Now more than ever, providing an excellent customer experience is critical. a direct positive impact on revenue growth, customer retention, and customer satisfaction for companies that invest in creating (and implementing!) customer journey maps.
In my role, I tend to focus my customer journey efforts on addressing customer pain points while enhancing overall customer satisfaction. In my experience, this often leads to a boost in retention and expansion. However, your goals may be to increase retention first and foremost, or even to increase conversions.
Need help defining your objectives and deciding how to measure them? This is a great place to start experimenting with AI.
How to implement AI at this stage: Based on what you want to know or further define, try out different prompts to surface your objectives and find focus areas for your customer journey map. Here's an example prompt below I tried with Claude.
Pro tip: Be as specific as possible with your request. The more information you give the model, the better your responses will be!
Step 2: Gather customer data.
Your customer data is critical to creating a functioning customer journey map. Start by gathering all the relevant customer data across your different data systems. While the types of data you need will depend on your specific business (and your goals), some commonly used data includes:
- Customer service data — help tickets, chat logs, knowledge base usage, etc.
- Purchase history — purchased orders, abandoned carts, returned items, etc.
- Email marketing data — emails opened, links clicked, unsubscribe rates, etc.
- Social media interactions — direct messages, mentions, engagement, etc.
- Direct feedback — surveys, customer satisfaction, product reviews, etc.
- Website analytics — page views, heat maps, session duration, etc.
- Referral data — organic search, paid ads, direct referrals, etc.
- Closed/lost or churn analysis — feedback on closed/lost deals, timing and reasons for churn, etc.
Pro tip: AI tools are only as useful as the data you feed them. is important, so make sure your data is recent and good quality. Check for things like empty cells, duplicate values, or inaccuracies before inputting the data into the model.
Important: Never put your sensitive company data into a public AI engine. , and other companies offer business licenses that ensure data security, and I also recommend toggling off the option to “train the model” when you’re using sensitive information.
In my role, I want to know where customers are getting stuck or why they’re churning. So I’ll look at things like feedback from our Voice of the Customer program, NPS surveys, support ticket themes, and of course, churn analysis.
Loading all of those different datapoints into the model then allows you to ask it to find themes, areas of opportunity, and even ask it for recommendations.
If you’re a small business or a startup, you may not have an easy way to gather this type of customer data. Don’t let this deter you, and instead, just start with what you do have, like your . You could even export things like customer reviews or transcripts from customer calls and load that into the model.
How to implement AI at this stage: Once you‘ve gathered all of the data you’ll need, you can dump it into Claude or ChatGPT and try something like the prompt below. You’ll notice my questions are specific, which helps ensure I get personalized responses.
Use AI-powered tools to integrate this data into a cohesive dataset.
Thinking about manually merging datasets like you see in the above example makes my palms sweaty, and I prefer to lean on data analytics tools where I can. Thankfully, AI-powered data integration tools can help overcome this challenge by automatically consolidating data from multiple sources.
Step 3: Analyze the data with machine learning.
Leveraging machine learning to analyze your data is where I personally think the magic happens. Machine learning algorithms can identify patterns, segment customers, and highlight key touchpoints in the customer journey.
Pro tip: While the example I include below has nice quantitative data points, I also use AI to do this for me with qualitative data. AI can easily surface themes, insights, and even make recommendations for me when I upload a list of support tickets or customer pain points.
You could consider making one big spreadsheet with different tabs of qualitative data and dump that into an AI model. For the example below, I ask the model to also include previously submitted qualitative data in its analysis, as this is how I would do it.
It’s worth noting that there are also much more advanced tools out there at your disposal, especially if you are part of a large-scale organization that requires a large amount of data to be analyzed at once. But if you’re just starting out, or you need to be scrappy, this approach will get you where you want to go.
Step 4: Use NLP to analyze customer feedback.
This is perhaps my favorite part in this process. You can also use natural language processing (NLP) to analyze customer feedback and other qualitative data points. Doing this allows you to really understand the customer, including their sentiment, at different stages of the journey.
For example, you can use AI to analyze the sentiment of customer feedback, categorize feedback into themes, discern customer intentions, and predict future customer behaviors. All of these tasks provide critical insights into your customers and their preferences.
Pro tip: In my role, I leverage AI to analyze customer pain points and support ticket themes, and then I ask the model to make specific recommendations based on the customer journey stage. For example, I may ask the model what training gaps exist in the onboarding stage of the customer journey, and then I’ll ask what type of content it recommends we create for customers based on the feedback it’s ingested.
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Step 5: Visualize the data with AI tools.
I envy those who are excellent with data visualization, but alas, I am not one of those people. Thankfully, I can lean on AI visualization tools to help me create a dynamic, data-driven representation of the customer journey.
Your customer journey is designed to be a visual map that highlights key touchpoints, pain points, and opportunities.
, director of client services at , recommends using a tool like at this stage. I was pretty blown away with how quickly this tool created a simple customer journey map flow chart.
Step 6: Validate with human insight.
As with any AI tool, you'll want to make sure you have a human validate the findings. While AI is pretty incredible, nothing beats the human experience.
You also have to be sure that the statistics and references the model gives you are verifiable. For instance, I’ve had ChatGPT give me great stats, but unfortunately, it was unable to locate the source, making them unusable for my articles, and likely hallucinations.)
While that‘s not ideal for writing an article, it can be downright harmful if you’re relying on this to build your business and influence the customer experience. In order to get a high-quality output, marry the AI-driven insights with feedback from your customers and your client-facing teams.
Pro tip: Not sure where to start with a customer journey map? Check out .
Don't forget that the customer journey continues post-purchase. Check out our for more insights and strategies.
ChatGPT Prompts for Customer Journey Mapping
For this article, I thought the best way to start building a customer journey map would be to ask ChatGPT a few discovery prompts. Asking the model questions helps to get the creative juices flowing, and it also allows me to quickly and easily surface key information.
Here’s an example prompt and the corresponding response from ChatGPT:
I’ve also compiled some additional prompts that can help with this process and save you a ton of time.
- Identify the key stages in a typical customer journey for [your industry].
- What are common pain points customers face when interacting with [your product/service]?
- What objections do my customers have before buying?
- List potential touchpoints between a customer and [your brand] throughout their journey.
- How can we measure customer satisfaction at each stage of the journey?
- What metrics should we track to evaluate the effectiveness of our customer journey?
- Suggest ways to personalize the customer experience at different touchpoints.
- How can we use customer feedback to improve our journey map?
- What are potential obstacles that might cause a customer to abandon their journey?
- Identify opportunities for upselling or cross-selling in the customer journey.
- How can we streamline the onboarding process for new customers?
- Suggest ways to personalize my post-purchase onboarding and support.
- What customer interactions can we implement to increase customer loyalty?
- How might customer needs and customer expectations change throughout their journey?
- What are effective ways to gather customer feedback at different stages?
- How can we use AI to predict potential customer churn points?
- What are the key differences in the journey between new and returning customers?
- How can we create a more emotionally engaging experience throughout the journey?
- How can we drive customer loyalty? List points for improvement in our process.
- What are potential triggers that move a customer from one stage to the next?
- How can we better align our marketing efforts with the customer journey?
- What role does customer support play in the overall journey, and how can it be improved?
- How can we use AI to create more accurate customer personas for our journey map?
- What external factors, such as economic fluctuation and seasonality, will influence my customers' buying decisions?
- What are the most common drop-off points in our [email nurture sequence, website, etc.], and how can we address them?
- Where is automation least effective in my customer journey — where do our customers need the most one-on-one contact?
- What data should we start collecting now to get actionable and accurate predictive analytics on our customers later?
- What trends are going to influence our customers' needs and buying decisions?
- Which of our customer segments are underserved, and what are suggestions for filling the holes?
- What data should we be collecting now to accurately analyze our customer journey 6 months from now?
I mentioned earlier how important it is to be specific when using AI, and this rings true in this style of prompt as well. By including your industry, company name, specific objectives, or concrete goals, you can help the model provide more robust and tailored responses.
Pro tip: If you’ve ever wished you could create your own GPT for your company, you’re in luck. With a ChatGPT plus membership, you can create a custom GPT trained on your business data and use it as a tailored AI knowledge base for your business.
Testing It Out: How I Created a Customer Journey Map With AI
I wanted to try building a customer journey map with AI for myself, so I set out to make one based on the examples I’ve been sharing so far.
Here's a simple prompt that I tested out using Claude, and since I’d been using Claude already for this article, it referenced my previously mentioned data and objectives to create the output.
I have to admit that the response and output Claude produced went above and beyond what I expected. Instead of just providing a list or table for me in the conversation interface, Claude actually created an artifact that included a color-coded journey map broken out into stages with corresponding touchpoints, pain points, enhancement opportunities and key metrics to track.
While this response is a great starting point, I still want my journey map to be more of a visual diagram and less of a list or document-style experience.
To do this, I took the data and loaded it into Whimsical Diagrams GPT. While I had to work with it for a bit and submit some additional clarification prompts, it eventually formatted the journey map into a traditional flowchart for me (which is what I was looking for).
Below is a snippet of that chart showing the awareness stage.
While I was able to get a finished product eventually, it did require multiple steps and some tweaking, which I suppose is to be expected. However, this process was much easier than the first time I created a customer journey map all on my own, where I had to manually type everything into a digital whiteboarding tool.
While I would need to do some more refining to get the map into the shape I want, I think this would make a great V1 to take to internal stakeholders and use as a conversation point.
Pro tip: Make sure you’re not creating this customer journey map in a vacuum. Your customers and your cross-functional colleagues should be involved in the iteration process.
What’s next? Based on your findings, leverage AI to help you come up with ideas for how to solve gaps and improve the experience.
Here are a few prompt ideas:
- “I’m making a customer journey map for my [industry] business, and I have gaps in the [consideration stage]. What are some ways for me to fill this stage of my customer journey map?”
- “I have a [industry] business and want to improve the [specific metric] in the purchase stage of my customer journey. What are two ways I can do this?”
- “My customer journey map suggests creating an ROI calculator to enhance the awareness stage. Can you create one for me that I can download as an Excel document? Here are the specific data points we’d like to use to help customers calculate ROI.”
Helpful AI Tools for Customer Journey Mapping
While ChatGPT is a key player in my daily workflow, I definitely consider it more of a “generalist.” In contrast, there are many AI tools on the market that are purpose-built to feel more like a “specialist.” I’ve pulled together a few great specialty tools to help your customer journey mapping work.
1.
If you’re looking for a one-stop shop in the AI customer journey mapping tools department, MyMap.AI may fit the bill. MyMap.AI helps you create a customer journey map using a conversational format (much like our friends ChatGPT and Claude).
What I’m most impressed by is the ability to upload data, analyze it, and generate the journey map — all in one place.
Key features:
- The ability to import various types of data, which the model then analyzes.
- Based on the analyzed data, the model will create a journey map for you. (I like not having to use two separate tools for this!)
- The tool searches the web for trends and insights to ensure your map is up to date.
- You can easily edit, export, and sharethe created journey maps with others.
2.
Taskade offers a ton of great work management features, like managing tasks and team collaboration. They also have a user journey map generator (powered by AI) that’s a great tool for brainstorming and visualizing the customer journey map.
Key features:
- Integration with other project management tools.
- Real-time collaboration capabilities.
- AI-driven journey map creation.
- Customizable templates.
3.
UXPressia empowers companies to turn research into customer journeys quickly and leverages business and industry insights in multiple areas of their product. UXPressia uses AI to uncover potential pain points, suggest improvement opportunities, and offer the user tailored mapping guidance based on their unique goals.
Key features:
- Persona-based mapping, which allows you to quickly and easily generate journey maps for different personas.
- Leverage industry-specific insights in customer pain points, stage-specific recommendations, and journey map templates. (This could be great for companies that are part of an industry where customers traverse the same journey, like hospitality or manufacturing.)
- Customization options for journey maps, from variables like icons and graphics to charts and more.
4.
Created by TheyDo, instantly converts customer research into journey maps packed with actionable insights — and saves you hours worth of manual work. For example, you can input your text-based research (think everything from sticky notes to surveys) to create a customer journey map tailored to customer feedback. I’m a big fan of this!
They also have a solid list of , meaning that you can bring in those surveys seamlessly, as opposed to uploading them into the Journey AI system manually.
Key features:
- Standardize and scale customer journey mapping and management.
- Creates customer journey maps in a matter of minutes.
- Intuitive, easy-to-use editor.
- Robust integrations that make data import an easy process.
Personalize your customer journey with AI.
As I researched these AI tools, I found myself excited about the impact that AI can have on the journey mapping process. I uncovered a lot of great opportunities to use AI in this process that I hadn’t originally considered — things like asking the model what data collection gaps or improvement opportunities exist based on the data I input.
I really like the idea of leveraging the model for the discovery stages in the customer journey mapping process, versus just using it to create the final map. By doing this, the model already has your goals, KPIs, data, and other key context ready to apply to the customer journey map, creating a more tailored and robust end result.
While any LLM can be a huge help in this process, my ideal tool would be one that could analyze my data and create a journey map for me, all without leaving the interface. (Even better if it integrates with my data sources so I don’t have to do a manual upload!)
I hope this article shows you a few of the ways that you can use AI to help with some of the “heavy lifting” involved in creating a customer journey map. By leveraging AI in this process, you can surface insights and themes, predict your customer behaviors, and make tailored recommendations that align with the goals of your customer journey map.
For the best results, don’t forget that using AI in this way works best when combined with human insight. While today’s models are pretty impressive, nothing beats your human expertise.
Editor's note: This post was originally published in October 2024 and has been updated for comprehensiveness.
The State of Customer Service Report
Unlock essential strategies for exceeding customer expectations and driving business growth in a competitive market.
- Exclusive insights from worldwide CRM leaders
- Analysis of modern customer behaviors
- Closer look at the AI opportunity in CRM
- Strategies for staying agile in 2024 and beyond
Download Free
All fields are required.
You're all set!
Click this link to access this resource at any time.
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