Customer journey analytics provides an in-depth understanding of key touchpoints in the customer journey, increasing visibility and facilitating targeted improvements. In this blog, we'll explore how journey analytics work, the value your brand can derive from this approach, and the important metrics to analyze customer journeys.
Key Takeaways
Customer journey analytics = visibility across touchpoints
Maps 5 key stages: Awareness → Advocacy
Connects multi-channel data for deeper insights
Critical for Web3 growth: wallet actions = user behavior
Metrics like CLV, CES, CSAT, and session time guide strategy
Drives cross-team collaboration through data-backed mapping
What is customer journey analytics?
Customer journey analytics is the process of tracking and analyzing customer interactions across channels to optimize strategies and improve customer satisfaction.
By uncovering insights into customer behaviors and interests, product and marketing teams can make data-driven decisions to enhance the overall customer experience.
Customer analytics capture data on customer behavior and interactions from multiple channels
Customer Journey Analytics helps product and marketing teams gain a comprehensive view of customer behavior through:
Understanding customer needs and pain points
Identifying critical touchpoints
Monitoring key metrics at each stage
Incorporating satisfaction and effort scores
Customer Journey Stages
The user journey map connects the entire lifecycle of a customer's interaction with a product or service, from initial awareness to becoming a loyal advocate. Here are the key stages:
Create a customer journey map to show the stages customers go through when engaging with a brand
Awareness: Prospects become aware of a need or problem and seek solutions. Educational content, such as white papers and e-books, tends to resonate better than direct product advertising.
Consideration: Customers compare offerings and engage with blogs, success stories, and webinars to make informed decisions. The tone shifts from educational to addressing specific consumer issues.
Decision/Purchase: Customers are ready to buy, often through direct interaction with sales or support teams. Effective sales processes and showcasing successful case studies can give brands a competitive edge.
Retention: Post-sale engagement is critical to maintaining customer relationships. Strategies include loyalty programs, regular updates, and ongoing communication to enhance the customer experience.
Advocacy: Positive experiences can turn customers into brand advocates who recommend the brand to others. Referral bonuses, loyalty programs, and responsive feedback channels can encourage advocacy.
Effectively navigating each stage can increase customer satisfaction, loyalty, and advocacy by capitalizing on specific opportunities and resolving specific challenges.
The benefits of customer journey analytics
Top 10 benefits of customer journey analytics for product and marketing teams
Customer journey analytics provides several key benefits that enhance a customer experience (CX) strategy:
Insight into customer behavior and experience
Customer journey analytics enables product teams to collect and analyze data across all customer touchpoints, providing deep insight into customer behavior. This analysis can help identify successful aspects of the customer journey and areas where customers struggle.
Improve operational efficiency
Customer analytics improve operational efficiency. For example, it can show which issues can be resolved through self-service channels and which require human intervention. This allows customer service agents to focus on more complex problems, resulting in better resource allocation and cost efficiency.
Measure the ROI of CX initiatives
Customer journey analytics enables brands to understand how customer interactions with products or services impact business outcomes, such as revenue generation. It allows for the comparison of different user journeys to determine the most effective ones.
Improve customer retention and reduce churn
User analytics predicts customer behavior and identifies at-risk users, enabling proactive retention strategies through personalized interactions, ultimately reducing churn.
Revenue optimization
By pinpointing and optimizing profitable customer journeys, customer journey analytics helps brands identify the best-performing channels to focus on and maximize revenue.
Behavioral segmentation
Going beyond traditional methods, customer analytics helps behavioral segmentation, enabling targeted marketing campaigns that align with customer experience goals and key business outcomes such as revenue, cost, and retention.
Advanced data analysis capabilities
Sophisticated customer analytics tools can reduce the time required to prepare and analyze data, empowering businesses to answer more questions on their own and freeing analysts to focus on more complex insights. This democratization of data leads to better decision-making across the organization.
Streamline customer interactions
Customer analytics helps eliminate unnecessary touchpoints in the customer journey, reducing friction and improving the overall customer experience. It identifies specific points where customers experience the most difficulty, allowing organizations to prioritize these areas for improvement.
Identify and resolve CX issues
Advanced customer journey analytics tools can pinpoint the root causes of customer experience issues. This allows teams to fix immediate issues, prevent future problems, and improve customer satisfaction.
Enhance digital and customer support services
Customer journey analytics is important for understanding customer goals and the effectiveness of digital channels. It enables the implementation of targeted strategies to enhance digital offerings and streamline complete customer experiences, ensuring a harmonious blend of customer contentment and operational cost-effectiveness.
The role of customer journey analytics in customer journey mapping
Customer journey analytics provides data-driven insights that optimize the mapping process. When integrated, analytics help products prioritize customer experience initiatives by highlighting critical aspects of the journey.
This integration also fosters cross-team collaboration, breaking down internal silos. Analytics facilitates collaborative, data-driven decision-making, resulting in impact decisions that benefit the customer journey.
How to analyze Customer Journeys?
Here are the key steps to analyze customer journeys:
Top 10 benefits of customer journey analytics for product and marketing teams
Identify touchpoints and define interactions
First, organize customer touchpoints by journey stages. Examples:
Awareness: Clicking a link from another website
Consideration: Watching a product demo video
Conversion: Creating a premium account
Service: Calling with customer support
Advocacy: Sending a referral code to a potential user
Measure how customers interact with each channel
Connect all interaction channels (social media, email, website, app) to customer analytics. Compare engagement rates and track user journey on the website with relevant metrics aligned with your goals.
Establish an attribution program
Implement multi-touch attribution to understand the touchpoints that lead to conversion. Define key events and user characteristics for your model, along with the channels that generate this data.
Identify where and why customers churn
Use surveys, customer support interactions, and session analytics to uncover reasons for churn (e.g., frequent errors). Customer journey analytics can also identify churn indicators.
Use your data to create a customer journey map
Use data from customer analytics to create visual maps that illustrate customer processes, needs, and perceptions. Maps can span the entire experience or focus on specific stages, such as product feature adoption.
7 metrics to analyze the customer journey
7 Key metrics to analyze the customer journey
The most common metrics involved with customer journey analytics include:
Customer Satisfaction Score (CSAT)
A Customer Satisfaction Score (CSAT) measures user satisfaction with your product or service, typically on a scale of 1 to 10.
Calculation: Average CSAT = Sum of Satisfaction Scores ÷ Total Number of Responses
Customer Lifetime Value (CLV)
CLV reflects the total revenue you expect to earn from a customer throughout their relationship with your company.
Calculation: CLV = (annual revenue per customer x customer lifetime in years) - customer acquisition cost
Customer Effort Score (CES)
CES measures how easy or difficult it is to do business with you through surveys in which customers rate the ease of use on a numerical scale.
Calculation: CES = Sum of Effort Scores ÷ Total # of Responses
Session/Activity Time
Tracking average session time helps you identify trends or outliers in app usage (e.g., drastic drops may indicate bugs or outages, while a steady decline may signal declining engagement).
Calculation:
Session duration = time the user leaves the app - time the user enters the app
Average session time = total session time ÷ number of sessions
Bounce Rate
The bounce rate is the percentage of single-page sessions, where a visitor leaves your site after viewing only one page, out of the total number of sessions.
Calculation: bounce rate = (single page sessions ÷ all sessions) x 100
Open Rate
The open rate measures the percentage of emails that were opened out of all emails sent in a campaign.
Calculation: open rate = (number of emails opened ÷ number of emails sent) x 100
Conversion Rate
Conversion measures the percentage of people who take the action you've asked them to take. If 100 people see your CTA to download an ebook and half of them do so, you have a 50% conversion rate.
Calculation: Conversion rate = (# of users who took a specific action ÷ # of total users) x 100.
6 Web3 customer journey analytics examples
Understanding how wallets interact across your dApp is key to growth in Web3. Here are powerful user journey analytics examples tailored to Web3 teams:
1. Onboarding Funnel Analysis
Track how wallets go from first connect to first transaction. Useful for: DeFi apps, NFT marketplaces, gaming platforms.
Example steps:
Wallet connects to the platform
Verifies identity or signs a message
Completes first action (mint/swap/stake)
Metrics to analyze:
Conversion Rate: % of wallets that complete onboarding
Customer Effort Score (CES): How easy users rate the onboarding process
Session/Activity Time: How long does it take from wallet connect to the first transaction
Bounce Rate: % of wallets that connect but drop off before taking action
2. Campaign Attribution Journey
See how wallets discover you and what makes them convert. Useful for: Growth teams running ads, referral links, or Farcaster frames.
Example steps:
Clicks a UTM-tagged link
Visits a landing page or Web3 form
Joins the allowlist or signs up
Takes onchain action (mint, stake)
Metrics to analyze:
Open Rate: % of campaign messages or emails opened
Conversion Rate: % of wallets that move from click to onchain action
Customer Lifetime Value (CLV): Value of wallets acquired from each source
CSAT: How users rate the signup or campaign experience
3. Product Usage Journey
Uncover what top wallets are doing inside your app. Useful for: dApp teams looking to improve UX or drive retention.
Example steps:
Signs in
Performs primary action (stake, borrow, play)
Engages with governance or referrals
Returns and reuses features
Metrics to analyze:
Session/Activity Time: Average duration of wallet engagement
CLV: Long-term value generated per active wallet
CSAT: Satisfaction with core product features
Feature Conversion Rate: % of wallets using a key feature who take further action
4. Cross-Chain User Journey
Follow your users as they move across chains and protocols. Useful for: Cross-chain platforms, rollups, bridge products.
Example steps:
Initial transaction on Ethereum
Bridge to Base
Uses a protocol on Arbitrum
Returns to Ethereum
Metrics to analyze:
Conversion Rate: % of wallets that activate on the destination chain
CES: How easy users found the bridging experience
Bounce Rate: % of wallets that initiate a bridge but never use the app post-bridge
Session Time: How long do cross-chain journeys take end-to-end
5. Community-to-Onchain Journey
Turn engaged followers into onchain power users. Useful for: DAO tooling, community growth teams.
Example steps:
Engages on Farcaster or Telegram
Fills a Web3 form
Mints a badge or votes in governance
Becomes an active contributor
Metrics to analyze:
Conversion Rate: % of community members taking onchain action
Open Rate: % who interact with newsletter or community announcements
CLV: Value generated by wallets from community channels
CSAT: Feedback after community onboarding or governance interactions
6. Grant / Incentive Campaign Funnel
Analyze how wallets interact before, during, and after a drop. Useful for: Protocols running incentive programs or retroactive rewards.
Example steps:
Sees a campaign on X or a newsletter
Completes eligibility
Claims a token or a grant
Starts using the platform
Metrics to analyze:
Open Rate: % of wallets that open eligibility or claim emails
Claim-to-Conversion Rate: % of wallets that claim and use the platform
Retention via Session Time: Do they return post-claim? How long do they stay active?
CSAT: Sent after claim or initial usage to evaluate satisfaction
CLV: Compare airdropped wallets vs. organic wallets over time
Enhance your web3 customer experience with Formo
Formo is a user journey analytics tool that enables Web3 product and marketing teams to understand customer behaviors, sentiments, and buying journeys. By integrating customer feedback from Web3 forms and analyzing onchain activity, Formo facilitates the creation of actionable insights to improve the customer experience, tailor marketing strategies, and drive product-led growth.
Customer journey analytics tools help brands connect customer behavior to key business metrics, enabling data-driven decisions that optimize the customer experience and deliver tangible results. Aligning insights with the KPIs that matter most, you can track ROI, refine the journey, and develop a customer-centric approach that drives customer satisfaction, loyalty, and long-term success.
Further sources:
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Additional FAQs
1. How is customer journey analytics different from traditional web analytics?
Traditional web analytics focuses on page views and sessions, while customer journey analytics connects touchpoints across channels to understand the full customer experience.
2. What tools are commonly used for customer journey analytics?
Tools include Mixpanel, Adobe Analytics, Google Analytics 4, FullStory, and specialized Web3 platforms like Formo.
3. Can customer journey analytics be used for B2B companies?
Yes. B2B businesses can track account-level journeys, map long sales cycles, and identify high-impact touchpoints for conversion.
4. What’s the biggest challenge in implementing customer journey analytics?
Unifying data from multiple channels and systems into a single view is often the hardest part.
5. How often should I update my customer journey map?
Ideally, every quarter, or whenever you launch a major feature, marketing campaign, or experience update.
