It takes time and effort to understand your customers. Customer journey analytics — aka using data to track, analyze, and visualize how your customers interact with your brand at every touchpoint in your customer journey — is your shortcut.
Armed with reliable data sources, such as customer feedback surveys and customer journey mapping, you can construct an effective customer journey analytics plan that helps you improve customer experience and better meet customer needs.
And that’s ultimately good news for your bottom line: Brands with stellar customer experience grow their revenue up to 8% above their peers in the market, according to Bain & Company analysis.
Keep reading for expert insights into user journey analysis; plus, find customer journey analytics examples to help guide your own strategy.
Key takeaways
Customer journey analytics is the process of collecting and analyzing customer data to understand their activity and behavior throughout the customer lifecycle. Companies can use this data to gain insights into how customers interact with their products, services, and brand and come up with ways to improve the customer experience.
Customer journey analysis includes:
This process often starts with customer journey mapping, “which stitches together multiple customer touchpoints into a unified view of each customer's journey from brand awareness to purchase,” says Nikki Zimmerman, Senior Lifecycle Marketing Manager.
This data can often be fragmented, which is something customer journey analytics can fix. “Rather than looking at channel performance in isolation (e.g., ppc, web, emails, etc), journey analytics allows you to understand the path (or journey) each customer takes that results in a sale,” says Nikki.
She adds that this technique helps you identify opportunities to lean into "free" channels (organic social, email, web) and rely less on paid channels. The ultimate goal is to make more revenue while reducing CPA.
Here’s an example of what your customer’s path might look like:
Here’s what that looks like visually: paid ad → product page → email signup → abandoned cart → SMS → purchase → repeat purchase
You can use journey analytics throughout this path. You’ll be able to calculate metrics such as click-through rate, time on page, email capture rate, cart abandonment rate, SMS click rate, and conversion rate using the data you gather from a journey like the one outlined above.
Some of these metrics will help you identify where friction occurs, such as poor page load speed that causes low time on page, or high shipping costs that cause customers to abandon their carts.
Then, you can use these learnings to make decisions about how to better engage your customers. For example, you might improve page load speed or show shipping costs earlier to address the roadblocks above.
Customer journey mapping is a visual representation of customer interactions throughout their relationship with your brand. It shows how your users interact with and experience your business from their first contact to their last. This allows you to identify potential areas of improvement, such as when customers are considering making a purchase or when they need help with a product.
You can create these maps from customer feedback surveys, focus groups, interviews, and other qualitative and quantitative methods.
Customer journey analytics and customer journey mapping are both important tools for understanding customer behavior and improving customer experience. However, they each serve a different purpose.
Customer journey analytics is quantitative; it’s used to collect and analyze customer data. Customer journey mapping is qualitative; it’s used to visually represent customer interactions. A customer journey map can be the first step in journey analysis.
Customer journey mapping is a roadmap of the hypothetical touchpoints you expect for your shoppers. It focuses on their entire experience, including their emotions and perceptions.
But user journey analysis is all about measurable data that shows you the actual path those customers take, which is especially important when reality veers away from your hypothetical plans.
Both are important for understanding customer behavior, but they should be used in combination to get a full picture of the customer experience.
This might be starting to sound like another ecommerce strategy you’re familiar with: marketing attribution.
There are some similarities: Both attribution and journey analysis take a close look at touchpoints throughout a customer’s relationship with your business. But customer journey analytics are about how users move through this relationship. Marketing attribution is about which specific channels actually drive conversions throughout this relationship.
Customer journey analytics offers a broader view of your customer relationships, while marketing attribution is a focused look at conversions.
These are perhaps even more effective when used together. Your customer journey data analytics reveal your true customer journey, and attribution tells you which parts of that journey are most profitable.
Customer journey analytics works by collecting and analyzing behavioral data from all customer touchpoints. Specific interactions are tracked to specific users, and large amounts of customer behavior data is collected, stored, and analyzed to help you make more strategic decisions.
There are several key components of this process to know about:
Customer journey analytics tools can deliver insights in a number of different categories that can help you make informed decisions about your marketing spend.
Here are some of the insights you can expect to get:
There are many benefits of implementing this analysis, including:
Even though customer journey tracking is a powerful tool, it’s not without its limitations. Here are some challenges to keep in mind when implementing such a system.
“The biggest challenge with journey analytics has always boiled down to data,” says Nikki. “The 2021 release of Apple iOS 15 forced marketers to build their own in-house analytics and performance measurement playbooks,” she adds, noting how she often needs to stitch together "influenced data" (like email open rates), with first-party data like clicks and conversions.
This process can result in a lot of data, and if you’re not sure where to start when applying it to decision-making. “Spend 10 minutes in Google Analytics and you'll be overwhelmed and end up with way more questions than answers,” says Nikki.
“Marketing data analytics is very nuanced. You really have to understand how to splice the data together in order to answer specific questions.” Her suggestion? “Start with a hypotheses and use the data to prove yourself wrong… rather than trying to prove yourself right.”
Triple Whale’s custom business intelligence tools can help make sense of your customer journey analytics so you can seamlessly apply your findings to your marketing efforts.
Other challenges include:
It’s not always easy to capture the offline touchpoints along your customer journey, such as encountering in-store displays or print advertisements in newspapers and magazines. Marketing mix modeling (MMM) is a more comprehensive approach that considers both online and offline channels in your customer journey.
You may end up with gaps in your unified user profiles. That can happen because of data silos, privacy regulations, and incomplete offline visibility. It can also be due to cross-device tracking issues that don’t seamlessly sync a single user’s behavior on, say, their smartphone and their desktop computer.
In addition to incomplete user profiles, you might also have overlap: If your analysis can’t integrate cross-channel touchpoint tracking, you might end up with separate or duplicated customer journeys for the same user.
Depending on which type of attribution modeling your business uses, you might be overly reliant on, for example, a customer’s last click in assigning credit for a given conversion. But this often neglects the nuances of a long or involved customer journey. A multi-touch attribution (MTA) model can help.
Because journey analysis itself is a complicated process, the tools that help you run this analysis can be a bit complicated, too. They can require a fair amount of money and team resources to set up and implement, risking missed or incorrect insights. Triple Whale’s Pixel takes the guesswork out of the process and can help you aggregate and optimize customer journeys with ease.
Ready to start analyzing your customer engagement journey? Here’s exactly what you’ll need to do.
There are many possible journeys a customer (or potential customer) can take with your brand, but the most important ones are those that affect your bottom line. To identify the journeys worth tracking and analyzing, you first need to identify your business goals.
Most likely, these will be the journeys that result in a conversion with wide enough margins for you to make a profit. But other businesses might have other objectives in mind, like email marketing signups, if they’re earlier on in building awareness around their brand.
Dive deeper into those revenue-driving journeys and map out each touchpoint a customer has with your brand along the way. This should start with their very first interaction with your business and end with conversion or another desired objective.
Here is where the right journey analysis tool will be key. Look for a platform with automatic data capture across all channels so you don’t miss anything due to human error. The best customer journey analytics tools help you sift through large amounts of data to identify patterns and trends in customer behavior.
You’ll also want a system that helps you centralize your data, rather than keeping it siloed in various places like your CRM and your support tickets. Unifying this information helps create that streamlined user profile that delivers the best insights into your customer’s experience.
When your data is unified, you’ll be better able to analyze cross-channel behavior among various customers and cohorts. This gives you a more holistic view of user behavior across touchpoints on various mediums, like social media or in-store interactions. This also helps you avoid misattributing credit for a conversion to a specific channel.
These insights will uncover hidden obstacles getting in the way of conversions. This might include a complicated checkout process or lack of clear calls to action, for example. Identify as many instances of friction as possible so you can start to make the path to conversion smoother for your customers.
Once you’ve identified those friction points, look for ways to improve on the problem areas. Implement these improvements based on data, not assumptions or emotions. It can help to start with one revenue-driving journey rather than making lots of changes to lots of journeys at once.
Track how your tweaks affect valuable metrics like revenue, conversion rate, and customer satisfaction scores. Then continue to make changes or apply these improvements to other journeys as needed. Customer journey analysis isn’t a one-and-done exercise; you should continue to optimize and iterate regularly to make sure you’re always giving your customers the best experience possible.
If any of this has felt a little overwhelming, rest assured you won’t be expected to figure it all out on your own. Much of the data collection and interpretation happens within various customer journey analytics tools and technologies, such as the following:
Looking ahead, customer journey analytics will make space for an even deeper understanding of the fact that customer journeys are not always linear, resulting in more real-time personalization. This will require more reliance on AI and machine learning to keep up with the sheer volume of data and decisions to be made, according to MarTech. But, as long as there’s appropriate human oversight, it can result in some pretty impressive results, such as personalized, real-time offers based on certain behavioral signals.
Nikki Zimmerman gives us some insights to this, stating that: “By mapping the customer journey beyond a sale (or a demo), you get into predictive modeling territory. For example, Target will start sending you flyers for pullups a few years after you created your baby registry. Sephora will start sending you anti-aging content on your 30th birthday… all because you signed up for the free birthday product.”
“Smart brands are leaning into capturing that first-party data up front to keep the conversation going beyond the purchase,” she adds.
With AI, real time journey mapping is way more accessible to smaller and mid brands who don't have the engineering resources (or budget). AI-powered segmentation will likely also help finetune brands’ ability to group customers based on past behaviors by analyzing large amounts of data quickly, but also based on intent and predicted actions and behavior.
Predictive modeling will likely gain popularity in other arenas, too, as more businesses try to anticipate their customers’ needs and desires. More brands will also likely turn to incrementality testing to measure the true effects of their marketing efforts compared to conversions that would have occurred anyway.
Customer journey analytics offers a wealth of insights to ensure customers have an enjoyable and seamless experience with your brand.
By combining this analysis with other valuable marketing tools such as attribution, you can effectively manage your customer journey in one place. Analytics tools can help you uncover your customers’ motivations and pain points, predict customer behavior, and optimize your marketing message and budget allocation.
Triple Whale’s intuitive dashboards and powerful data analysis tools give you the edge you need to track customer journeys and unlock the power of customer journey analytics. Book a free demo today!
The customer journey is important because it helps you visualize your customer’s experience with your brand at various touchpoints from awareness to conversion. You can use these insights to optimize your customer experience, improve retention, and boost profitability.
Examples of customer journey analytics include customer journey mapping, identifying friction points along the customer journey, cross-channel stitching, and customer data unification. Then, you can use the insights from this analysis to optimize your customer experience.
Data silos, privacy regulations, and tool complexity are all potential challenges of customer journey analysis.
Absolutely. You don’t need to be a large, established brand to learn more about your customer journey. Just keep in mind as a small business you may have fewer resources to devote to this analysis.
It takes time and effort to understand your customers. Customer journey analytics — aka using data to track, analyze, and visualize how your customers interact with your brand at every touchpoint in your customer journey — is your shortcut.
Armed with reliable data sources, such as customer feedback surveys and customer journey mapping, you can construct an effective customer journey analytics plan that helps you improve customer experience and better meet customer needs.
And that’s ultimately good news for your bottom line: Brands with stellar customer experience grow their revenue up to 8% above their peers in the market, according to Bain & Company analysis.
Keep reading for expert insights into user journey analysis; plus, find customer journey analytics examples to help guide your own strategy.
Key takeaways
Customer journey analytics is the process of collecting and analyzing customer data to understand their activity and behavior throughout the customer lifecycle. Companies can use this data to gain insights into how customers interact with their products, services, and brand and come up with ways to improve the customer experience.
Customer journey analysis includes:
This process often starts with customer journey mapping, “which stitches together multiple customer touchpoints into a unified view of each customer's journey from brand awareness to purchase,” says Nikki Zimmerman, Senior Lifecycle Marketing Manager.
This data can often be fragmented, which is something customer journey analytics can fix. “Rather than looking at channel performance in isolation (e.g., ppc, web, emails, etc), journey analytics allows you to understand the path (or journey) each customer takes that results in a sale,” says Nikki.
She adds that this technique helps you identify opportunities to lean into "free" channels (organic social, email, web) and rely less on paid channels. The ultimate goal is to make more revenue while reducing CPA.
Here’s an example of what your customer’s path might look like:
Here’s what that looks like visually: paid ad → product page → email signup → abandoned cart → SMS → purchase → repeat purchase
You can use journey analytics throughout this path. You’ll be able to calculate metrics such as click-through rate, time on page, email capture rate, cart abandonment rate, SMS click rate, and conversion rate using the data you gather from a journey like the one outlined above.
Some of these metrics will help you identify where friction occurs, such as poor page load speed that causes low time on page, or high shipping costs that cause customers to abandon their carts.
Then, you can use these learnings to make decisions about how to better engage your customers. For example, you might improve page load speed or show shipping costs earlier to address the roadblocks above.
Customer journey mapping is a visual representation of customer interactions throughout their relationship with your brand. It shows how your users interact with and experience your business from their first contact to their last. This allows you to identify potential areas of improvement, such as when customers are considering making a purchase or when they need help with a product.
You can create these maps from customer feedback surveys, focus groups, interviews, and other qualitative and quantitative methods.
Customer journey analytics and customer journey mapping are both important tools for understanding customer behavior and improving customer experience. However, they each serve a different purpose.
Customer journey analytics is quantitative; it’s used to collect and analyze customer data. Customer journey mapping is qualitative; it’s used to visually represent customer interactions. A customer journey map can be the first step in journey analysis.
Customer journey mapping is a roadmap of the hypothetical touchpoints you expect for your shoppers. It focuses on their entire experience, including their emotions and perceptions.
But user journey analysis is all about measurable data that shows you the actual path those customers take, which is especially important when reality veers away from your hypothetical plans.
Both are important for understanding customer behavior, but they should be used in combination to get a full picture of the customer experience.
This might be starting to sound like another ecommerce strategy you’re familiar with: marketing attribution.
There are some similarities: Both attribution and journey analysis take a close look at touchpoints throughout a customer’s relationship with your business. But customer journey analytics are about how users move through this relationship. Marketing attribution is about which specific channels actually drive conversions throughout this relationship.
Customer journey analytics offers a broader view of your customer relationships, while marketing attribution is a focused look at conversions.
These are perhaps even more effective when used together. Your customer journey data analytics reveal your true customer journey, and attribution tells you which parts of that journey are most profitable.
Customer journey analytics works by collecting and analyzing behavioral data from all customer touchpoints. Specific interactions are tracked to specific users, and large amounts of customer behavior data is collected, stored, and analyzed to help you make more strategic decisions.
There are several key components of this process to know about:
Customer journey analytics tools can deliver insights in a number of different categories that can help you make informed decisions about your marketing spend.
Here are some of the insights you can expect to get:
There are many benefits of implementing this analysis, including:
Even though customer journey tracking is a powerful tool, it’s not without its limitations. Here are some challenges to keep in mind when implementing such a system.
“The biggest challenge with journey analytics has always boiled down to data,” says Nikki. “The 2021 release of Apple iOS 15 forced marketers to build their own in-house analytics and performance measurement playbooks,” she adds, noting how she often needs to stitch together "influenced data" (like email open rates), with first-party data like clicks and conversions.
This process can result in a lot of data, and if you’re not sure where to start when applying it to decision-making. “Spend 10 minutes in Google Analytics and you'll be overwhelmed and end up with way more questions than answers,” says Nikki.
“Marketing data analytics is very nuanced. You really have to understand how to splice the data together in order to answer specific questions.” Her suggestion? “Start with a hypotheses and use the data to prove yourself wrong… rather than trying to prove yourself right.”
Triple Whale’s custom business intelligence tools can help make sense of your customer journey analytics so you can seamlessly apply your findings to your marketing efforts.
Other challenges include:
It’s not always easy to capture the offline touchpoints along your customer journey, such as encountering in-store displays or print advertisements in newspapers and magazines. Marketing mix modeling (MMM) is a more comprehensive approach that considers both online and offline channels in your customer journey.
You may end up with gaps in your unified user profiles. That can happen because of data silos, privacy regulations, and incomplete offline visibility. It can also be due to cross-device tracking issues that don’t seamlessly sync a single user’s behavior on, say, their smartphone and their desktop computer.
In addition to incomplete user profiles, you might also have overlap: If your analysis can’t integrate cross-channel touchpoint tracking, you might end up with separate or duplicated customer journeys for the same user.
Depending on which type of attribution modeling your business uses, you might be overly reliant on, for example, a customer’s last click in assigning credit for a given conversion. But this often neglects the nuances of a long or involved customer journey. A multi-touch attribution (MTA) model can help.
Because journey analysis itself is a complicated process, the tools that help you run this analysis can be a bit complicated, too. They can require a fair amount of money and team resources to set up and implement, risking missed or incorrect insights. Triple Whale’s Pixel takes the guesswork out of the process and can help you aggregate and optimize customer journeys with ease.
Ready to start analyzing your customer engagement journey? Here’s exactly what you’ll need to do.
There are many possible journeys a customer (or potential customer) can take with your brand, but the most important ones are those that affect your bottom line. To identify the journeys worth tracking and analyzing, you first need to identify your business goals.
Most likely, these will be the journeys that result in a conversion with wide enough margins for you to make a profit. But other businesses might have other objectives in mind, like email marketing signups, if they’re earlier on in building awareness around their brand.
Dive deeper into those revenue-driving journeys and map out each touchpoint a customer has with your brand along the way. This should start with their very first interaction with your business and end with conversion or another desired objective.
Here is where the right journey analysis tool will be key. Look for a platform with automatic data capture across all channels so you don’t miss anything due to human error. The best customer journey analytics tools help you sift through large amounts of data to identify patterns and trends in customer behavior.
You’ll also want a system that helps you centralize your data, rather than keeping it siloed in various places like your CRM and your support tickets. Unifying this information helps create that streamlined user profile that delivers the best insights into your customer’s experience.
When your data is unified, you’ll be better able to analyze cross-channel behavior among various customers and cohorts. This gives you a more holistic view of user behavior across touchpoints on various mediums, like social media or in-store interactions. This also helps you avoid misattributing credit for a conversion to a specific channel.
These insights will uncover hidden obstacles getting in the way of conversions. This might include a complicated checkout process or lack of clear calls to action, for example. Identify as many instances of friction as possible so you can start to make the path to conversion smoother for your customers.
Once you’ve identified those friction points, look for ways to improve on the problem areas. Implement these improvements based on data, not assumptions or emotions. It can help to start with one revenue-driving journey rather than making lots of changes to lots of journeys at once.
Track how your tweaks affect valuable metrics like revenue, conversion rate, and customer satisfaction scores. Then continue to make changes or apply these improvements to other journeys as needed. Customer journey analysis isn’t a one-and-done exercise; you should continue to optimize and iterate regularly to make sure you’re always giving your customers the best experience possible.
If any of this has felt a little overwhelming, rest assured you won’t be expected to figure it all out on your own. Much of the data collection and interpretation happens within various customer journey analytics tools and technologies, such as the following:
Looking ahead, customer journey analytics will make space for an even deeper understanding of the fact that customer journeys are not always linear, resulting in more real-time personalization. This will require more reliance on AI and machine learning to keep up with the sheer volume of data and decisions to be made, according to MarTech. But, as long as there’s appropriate human oversight, it can result in some pretty impressive results, such as personalized, real-time offers based on certain behavioral signals.
Nikki Zimmerman gives us some insights to this, stating that: “By mapping the customer journey beyond a sale (or a demo), you get into predictive modeling territory. For example, Target will start sending you flyers for pullups a few years after you created your baby registry. Sephora will start sending you anti-aging content on your 30th birthday… all because you signed up for the free birthday product.”
“Smart brands are leaning into capturing that first-party data up front to keep the conversation going beyond the purchase,” she adds.
With AI, real time journey mapping is way more accessible to smaller and mid brands who don't have the engineering resources (or budget). AI-powered segmentation will likely also help finetune brands’ ability to group customers based on past behaviors by analyzing large amounts of data quickly, but also based on intent and predicted actions and behavior.
Predictive modeling will likely gain popularity in other arenas, too, as more businesses try to anticipate their customers’ needs and desires. More brands will also likely turn to incrementality testing to measure the true effects of their marketing efforts compared to conversions that would have occurred anyway.
Customer journey analytics offers a wealth of insights to ensure customers have an enjoyable and seamless experience with your brand.
By combining this analysis with other valuable marketing tools such as attribution, you can effectively manage your customer journey in one place. Analytics tools can help you uncover your customers’ motivations and pain points, predict customer behavior, and optimize your marketing message and budget allocation.
Triple Whale’s intuitive dashboards and powerful data analysis tools give you the edge you need to track customer journeys and unlock the power of customer journey analytics. Book a free demo today!
The customer journey is important because it helps you visualize your customer’s experience with your brand at various touchpoints from awareness to conversion. You can use these insights to optimize your customer experience, improve retention, and boost profitability.
Examples of customer journey analytics include customer journey mapping, identifying friction points along the customer journey, cross-channel stitching, and customer data unification. Then, you can use the insights from this analysis to optimize your customer experience.
Data silos, privacy regulations, and tool complexity are all potential challenges of customer journey analysis.
Absolutely. You don’t need to be a large, established brand to learn more about your customer journey. Just keep in mind as a small business you may have fewer resources to devote to this analysis.
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Body Copy: The following benchmarks compare advertising metrics from April 1-17 to the previous period. Considering President Trump first unveiled his tariffs on April 2, the timing corresponds with potential changes in advertising behavior among ecommerce brands (though it isn’t necessarily correlated).
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