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<\/a><\/p>\nI 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.<\/p>\n
In this article, I\u2019ll give you an overview of what I’ve learned about leveraging AI for customer journey mapping<\/a>. We\u2019ll 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,<\/p>\n No matter what role or industry you\u2019re in, this article is chock-full of tips that you can apply to your current customer experience strategy.<\/p>\n Note:<\/em><\/strong>\u00a0While I reference specific machine learning tools in this article, you can apply the prompts I\u2019ve included to any tool that you\u2019d like.<\/em><\/p>\n Table of Contents<\/strong><\/p>\n <\/a> <\/p>\n It\u2019s no secret that AI is transforming the way businesses understand their customer needs and subsequently how they translate those needs into the customer journey.<\/p>\n 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.<\/p>\n 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:<\/p>\n If you\u2019ve 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 <\/strong>and requires a lot of data to get accurate results.<\/p>\n AI helps with those two pain points by analyzing data and surfacing trends \u2014 both of which would be really time-consuming to do manually.<\/p>\n According to a Nielsen Norman Group survey<\/a>, completing a traditional customer journey map could take days or even weeks<\/a>. 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.<\/p>\n The journey mapping process is time-consuming due to four main factors:<\/p>\n Here are some other use cases for AI in the customer journey mapping process, according to the experts I spoke with:<\/p>\n I\u2019ve personally used AI to analyze customer feedback and surface trends and themes, and it\u2019s 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\u2019m always surprised at what themes it uncovers.<\/p>\n While AI is exciting and it feels like it\u2019s being used everywhere and included in every tool these days, it\u2019s still relatively new and is not without flaws. The results won\u2019t be perfect and they absolutely require a human to verify them.<\/p>\n Erik Karofsky<\/a>, CEO of VectorHX<\/a>, has used AI to develop journey maps and feels it’s not quite <\/em>ready for prime time yet.<\/p>\n A big challenge with creating a journey map using AI is that \u201cit doesn’t serve any user well,\u201d he says. \u201cAI 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.\u201d<\/p>\n 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.<\/p>\n Here are some real-life examples he shared:<\/p>\n By using AI as a partner<\/em> in creating your journey map instead of a replacement, you can get the best of both worlds. Let\u2019s look at how to do just that.<\/p>\n <\/a> <\/p>\n Leveraging AI tools can be really fun \u2014 don\u2019t get discouraged by the learning curve. I recommend treating the process as an experiment, and know that you\u2019re likely going to work through a few iterations before you land on the perfect final product.<\/p>\n AI tools continue to roll out at a rapid speed, and we\u2019re starting to see AI more and more in the customer success and customer experience space. In the example below, Journey AI<\/a> is a tool that helps synthesize customer data to create a personalized journey in a matter of seconds.<\/p>\n Source<\/em><\/a><\/p>\n This is just a preview for now \u2014 we\u2019ll go deeper into the tools here in a bit. But before we get there, let’s go over a few foundational steps you\u2019ll need to take to leverage AI when building your customer journey map.<\/p>\n 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.<\/p>\n Narrow down what you want your map to accomplish, like one of the following example focus areas:<\/p>\n Now more than ever, providing an excellent customer experience is critical. Data shows<\/a> a direct positive impact on revenue growth, customer retention, and customer satisfaction for companies that invest in creating (and implementing!) customer journey maps.<\/p>\n 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.<\/p>\n Need help defining your objectives and deciding how to measure them? This is a great place to start experimenting with AI.<\/p>\n How to implement AI at this stage: <\/strong>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.<\/p>\n Pro tip:<\/strong> Be as specific as possible with your request. The more information you give the model, the better your responses will be!<\/p>\n 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:<\/p>\n Pro tip: <\/strong>AI tools are only as useful as the data you feed them. Data optimization<\/a> 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.<\/p>\n Important:<\/strong> Never<\/span> put your sensitive company data into a public AI engine. ChatGPT<\/a>, Google Gemini<\/a> and other companies offer business licenses that ensure data security, and I also recommend toggling off the option to \u201ctrain the model\u201d when you\u2019re using sensitive information.<\/p>\n In my role, I want to know where customers are getting stuck or why they\u2019re churning. So I\u2019ll look at things like feedback from our Voice of the Customer program, NPS surveys, support ticket themes, and of course, churn analysis.<\/p>\n 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.<\/p>\n If you\u2019re a small business or a startup, you may not have an easy way to gather this type of customer data. Don\u2019t let this deter you, and instead, just start with what you do have, like your CRM<\/a>. You could even export things like customer reviews or transcripts from customer calls and load that into the model.<\/p>\n How to implement AI at this stage: <\/strong>Once you\u2018ve gathered all of the data you\u2019ll need, you can dump it into Claude or ChatGPT and try something like the prompt below. You\u2019ll notice my questions are specific, which helps ensure I get personalized responses.<\/p>\n 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.<\/p>\n 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.<\/p>\n Pro tip:<\/strong> 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.<\/p>\n 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.<\/p>\n It\u2019s 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\u2019re just starting out, or you need to be scrappy, this approach will get you where you want to go.<\/p>\n 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.<\/p>\n For example, you can use AI to analyze the sentiment of customer feedback<\/a>, 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.<\/p>\n Pro tip:<\/strong> 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\u2019ll ask what type of content it recommends we create for customers based on the feedback it\u2019s ingested.<\/p>\n 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.<\/p>\n Your customer journey is designed to be a visual map that highlights key touchpoints, pain points, and opportunities.<\/p>\n John Suarez<\/a>, director of client services at SmartBug Media<\/a>, recommends using a tool like Whimsical Diagrams’ Custom GPT for Flow Mapping<\/a> at this stage. I was pretty blown away with how quickly this tool created a simple customer journey map flow chart.<\/p>\n Source<\/em><\/a><\/p>\n 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.<\/p>\n You also have to be sure that the statistics and references the model gives you are verifiable. For instance, I\u2019ve had ChatGPT give me great stats, but unfortunately, it was unable to locate the source, making them unusable for my articles, and likely hallucinations.)<\/p>\n While that\u2018s not ideal for writing an article, it can be downright harmful if you\u2019re 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.<\/strong><\/p>\n Pro tip: <\/strong>Not sure where to start with a customer journey map? Check out our templates here<\/a>.<\/p>\n Don’t forget that the customer journey continues post-purchase. Check out our <\/em>Post-Sale Playbook<\/em><\/a> for more insights and strategies.<\/em><\/p>\n <\/a> <\/p>\n 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.<\/p>\n Here\u2019s an example prompt and the corresponding response from ChatGPT:<\/p>\n I\u2019ve also compiled some additional prompts that can help with this process and save you a ton of time.<\/p>\n 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.<\/p>\n Pro tip:<\/strong> If you\u2019ve ever wished you could create your own GPT for your company, you\u2019re 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<\/a> for your business.<\/p>\n <\/a> <\/p>\n 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\u2019ve been sharing so far.<\/p>\n Here’s a simple prompt that I tested out using Claude, and since I\u2019d been using Claude already for this article, it referenced my previously mentioned data and objectives to create the output.<\/p>\n 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.<\/p>\n 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.<\/p>\n 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).<\/p>\n Below is a snippet of that chart showing the awareness stage.<\/p>\n Source<\/em><\/a><\/p>\n 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.<\/p>\n 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.<\/p>\n Pro tip:<\/strong>\u00a0Make sure you\u2019re not creating this customer journey map in a vacuum. Your customers and your cross-functional colleagues should be involved in the iteration process.<\/p>\n What\u2019s next? Based on your findings, leverage AI to help you come up with ideas for how to solve gaps and improve the experience.<\/p>\n Here are a few prompt ideas:<\/p>\n <\/a> <\/p>\n While ChatGPT is a key player in my daily workflow, I definitely consider it more of a \u201cgeneralist.\u201d In contrast, there are many AI tools on the market that are purpose-built to feel more like a \u201cspecialist.\u201d I\u2019ve pulled together a few great specialty tools to help your customer journey mapping work.<\/p>\n If you\u2019re 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).<\/p>\n What I\u2019m most impressed by is the ability to upload data, analyze it, and generate the journey map \u2014 all in one place.<\/p>\n Source<\/em><\/a><\/p>\n Key features:<\/strong><\/p>\n 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\u2019s a great tool for brainstorming and visualizing the customer journey map.<\/p>\n Source<\/em><\/a><\/p>\n Key features:<\/strong><\/p>\n 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.<\/p>\n Source<\/em><\/a><\/p>\n Key features:<\/strong><\/p>\n Created by TheyDo, Journey AI<\/a> instantly converts customer research into journey maps packed with actionable insights \u2014 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\u2019m a big fan of this!<\/p>\n They also have a solid list of integrations<\/a>, meaning that you can bring in those Qualtrics<\/a> surveys seamlessly, as opposed to uploading them into the Journey AI system manually.<\/p>\n\n
What is AI-powered customer journey mapping?<\/strong><\/h2>\n
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How can AI improve the customer journey mapping process?<\/strong><\/h3>\n
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What are the limitations of using AI to create a customer journey map?<\/strong><\/h3>\n
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How to Create a Customer Journey Map With AI<\/strong><\/h2>\n
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<\/p>\nStep 1: Define your objectives.<\/strong><\/h3>\n
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<\/p>\nStep 2: Gather customer data.<\/strong><\/h3>\n
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<\/p>\nUse AI-powered tools to integrate this data into a cohesive dataset.<\/strong><\/h4>\n
Step 3: Analyze the data with machine learning.<\/strong><\/h3>\n
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<\/p>\nStep 4: Use NLP to analyze customer feedback.<\/strong><\/h3>\n
Step 5: Visualize the data with AI tools.<\/strong><\/h3>\n
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<\/p>\nStep 6: Validate with human insight.<\/strong><\/h3>\n
ChatGPT Prompts for Customer Journey Mapping<\/strong><\/h2>\n
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Testing It Out: How I Created a Customer Journey Map With AI<\/strong><\/h2>\n
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Helpful AI Tools for Customer Journey Mapping<\/strong><\/h2>\n
1. <\/strong>MyMap.AI<\/a><\/strong><\/h3>\n
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2. <\/strong>Taskade<\/a><\/strong><\/h3>\n
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3. <\/strong>UXPressia<\/a><\/strong><\/h3>\n
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4.<\/strong> Journey AI<\/a><\/strong><\/h3>\n
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