customer journey analytics ai

AI and Customer Journey Mapping: Elevating the CX Strategy

• November 23rd, 2023

Peeking into the future of customer engagement, AI-enhanced customer journey mapping stands out, revolutionizing how businesses interact with consumers. This smart synergy is redefining personalized marketing strategies.

Discover how AI and Customer Journey Mapping go hand in hand to unveil intricate patterns in customer behavior, taking predictive capabilities to new heights. The use of AI forges a clear path in customer journey mapping, providing unprecedented insights that fuel Predictive Analytics in Marketing , and thus, allowing companies to anticipate needs and create hyper-targeted campaigns that resonate with each individual’s journey.

Understanding Customer Journey Mapping in the AI Era

Customer journey mapping is an indispensable tool, a visual representation that traces a customer’s footsteps from initial contact to eventual conversion and beyond. Now, weave in the transformative power of AI, and you get a dynamic process that not only depicts the journey but also breathes life into data, uncovering the rich tapestry of consumer interactions.

The Awareness Stage

Traditionally, the customer journey commences at awareness, where potential customers first encounter a brand. AI escalates this stage by analyzing vast amounts of data to determine how, when, and where a brand should make its grand entrance, maximizing impact. With intelligent AI for brand management , businesses can ensure that the first impression is not just seen but felt.

The Consideration Stage

As customers move into the consideration stage, AI steps in to skillfully guide them. Algorithms analyze past behaviors, preferences, and engagements, delivering personalized content that speaks directly to the heart of each customer’s needs, nurturing their journey with finespun precision.

The Decision Stage

Decision-making is no small feat. Here, AI becomes indispensable, offering predictive analytics to craft proposals that hit the mark. It’s like having a digital crystal ball, illuminating the path to conversion with greater certainty and helping brands to be proactive rather than reactive.

The Retention Stage

Loyalty is the ultimate prize, and AI does not stop at the sale. Machine learning algorithms assess post-purchase behaviors, cueing in on opportunities for upselling, cross-selling, and engaging in ways that turn one-time buyers into brand advocates.

Each moment in a customer’s journey is a chance to connect more deeply, and with AI for Real-Time Marketing Analytics , these connections are made increasingly relevant and timely. It’s a brave new world for customer journey mapping, and AI is the intrepid navigator leading the charge.

Key Benefits of Integrating AI into Customer Journey Mapping

• Enhanced Understanding of Customer Behaviors: AI-driven customer journey mapping allows for the deep analysis of customer behavior, uncovering patterns that are invisible to the human eye. This leads to much more effective targeting and personalization strategies.
• Accurate Predictive Analytics: Leverage AI to forecast customer needs and future behaviors, providing businesses with the power to anticipate rather than react. Engage customers at the perfect moment with the right message, driving conversions.
• Dynamic Personalization: By crunching vast datasets, AI offers on-the-fly personalization, giving each customer a unique experience tailored to their preferences, history, and real-time activity.
• Real-Time Decision Making: With AI, customer journey mapping evolves from a static to a dynamic tool. Business decisions can be made in real-time, responding to ever-changing market conditions and customer interactions.
• Increased Operational Efficiency: Automate routine tasks and analysis within the customer journey, freeing up valuable human resources for creative and strategic initiatives.
• Optimized Marketing Spend: By understanding and predicting customer behaviors, AI helps allocate marketing budgets more effectively, ensuring higher returns on investment.
• Revolutionized Content Delivery: AI’s role in content strategy is transformative, aligning content precisely with the stages of the customer journey for maximum impact and engagement. This is where AI in Video Marketing shines, providing dynamic video content that engages customers at just the right points in their journey.
• SEO Mastery: AI drastically improves search engine optimization, ensuring content surfaces at the top of search results at critical moments in the consumer journey. AI for SEO Optimization brings a competitive edge, making sure a brand is visible when it counts the most.

Top AI Tools for Enhanced Customer Journey Analysis

As we delve deeper into the potential of AI-enhanced customer journey mapping, the right tools can be pivotal. They bring a suite of capabilities that can meticulously dissect, analyze, and interpret consumer data, paving the way for powerful, actionable insights. Here’s a table featuring leading AI tools designed to elevate your customer journey analysis, complete with key features to cater to your strategic needs and respective price points for thoughtful budgeting. Witness firsthand how AI refines marketing automation and sentiment analysis to optimize customer interactions at every touchpoint.

Each of these tools offers a unique set of capabilities to assist marketers in not just mapping but mastering the customer journey with an AI edge. Whether you’re looking for an introductory platform or a more robust enterprise solution, there’s an AI tool out there to help you make data-driven decisions and enhance customer connections at scale.

Real-World Examples of AI-Powered Customer Journey Mapping Success

When theory meets practice, AI-powered customer journey mapping reveals its true potential. Countless companies across the globe are already reaping the lucrative rewards of integrating artificial intelligence into their customer journey analysis. Let’s dive into some compelling case studies that showcase the impact of AI on understanding and enhancing the customer experience.

Zalando: Personalization at Scale

Europe’s leading online fashion platform, Zalando, turned to AI to refine its customer journey mapping. The integration of AI solutions allowed for the creation of highly personalized recommendations, resulting in a surge in customer satisfaction and retention. By implementing AI in their social media marketing , they achieved a 95% accuracy rate in personalized offerings, which increased conversion rates significantly, proving the invaluable influence of AI in understanding and catering to customer preferences.

Netflix: Mastering Engagement with AI

Netflix’s recommendation engine is a triumph of AI-powered customer journey mapping. By analyzing immense datasets on viewing habits, the system offers tailor-made shows and movies, enticing users to binge-watch. The success is clear: customers enjoy highly relevant content, and for Netflix, this meant an impressive retention rate and a marked uptick in membership growth, showcasing how AI can revolutionize entertainment engagement.

Sephora: Revolutionizing Beauty Retail with AI

The beauty giant Sephora embraced AI to enrich its customer journey mapping, leading to a dramatic overhaul of both their in-store and online experiences. AI-backed tools like their Virtual Artist app, which employs facial recognition to allow users to try makeup virtually, have sky-rocketed customer interaction and boosted e-commerce sales. This AI initiative showcases the power of cross-channel marketing , where each touchpoint is an opportunity for enhanced connection with customers.

Each story is a testament to AI’s role in crafting journey mappings that don’t just track but also elevate every step of the consumer’s path. The results — increased engagement, soaring conversion rates, and customer advocacy — are a clear signal that leveraging AI is not just beneficial but essential for modern businesses aiming to excel in customer understanding and service.

How to Implement AI in Your Customer Journey Mapping Strategy

Ready to harness the power of AI in your customer journey mapping? This can seem like a daunting task, but fear not! Here’s a foolproof, step-by-step guide that talks you through the seamless integration of AI technologies with your existing strategies, ensuring you can navigate the complexities with confidence.

• Define Your Goals and Metrics : Begin by knowing what you want to achieve with your customer journey mapping. Is it to improve conversion rates, boost customer satisfaction, or reduce churn? Establish clear metrics to measure the impact of AI on these goals.
• Gather and Integrate Data Sources : Centralize diverse data sources to provide a comprehensive view of the customer journey. AI systems require a plethora of data to analyze and learn from; it’s critical to integrate CRM, social media, purchase history, and any other customer data points.
• Segment Your Audience : Use AI for Customer Segmentation to divide your audience into distinct groups based on their behaviors and preferences. Accurate segmentation is vital for the next steps of personalization and predictive analysis.
• Implement AI Tools : Choose AI tools that align with your business needs. Some platforms specialize in predictive analytics, while others excel in real-time personalization or operational efficiency. Comprehensively test these tools to ensure their compatibility with your data infrastructure.
• Personalize Customer Interactions : Leverage AI to create personalized messages and campaigns for different audience segments. Tailored communication strengthens the bond with customers and guides them smoothly along the purchasing journey.
• Monitor Real-time Customer Behavior : With AI-driven analytics, monitor customer interactions as they happen. This real-time observation helps in quickly identifying patterns or issues that could affect the journey and allows for immediate response.
• Optimize for Voice Search : Ensure your content is discoverable through voice queries by using AI for Voice Search Optimization . As voice-activated devices become more prevalent, accommodating voice search in your customer journey mapping is increasingly important.
• Analyze and Iterate : No AI implementation is perfect from the get-go. Regularly analyze your AI-enhanced customer journey mappings for insights, and refine your strategies and tools based on this data. Embrace a cycle of continuous improvement.

By integrating AI into your customer journey mapping, you unlock a new dimension of customer insights and predictive analysis. Remember, the key is not just to map the customer journey but to enhance it. These steps are iterative and ongoing – evolve your strategy as AI technologies and customer behaviors evolve, ensuring that your journey mappings remain as dynamic and insightful as the customers they represent.

The Future of Customer Experience: Predictions for AI Advancements

As we gaze into the crystal ball of customer experience, AI advancements stand at the forefront of innovation. We’re not just talking about incremental changes; we’re witnessing the dawn of a revolution in the way customer journey mapping is conceptualized and executed. AI is the maestro orchestrating a future where customer interactions are more intuitive, predictive, and satisfying than ever before.

Predictive customer journeys, powered by AI, will likely become even more nuanced and anticipatory. Imagine AI-driven visual marketing strategies so advanced that AI-Driven Visual Marketing tools not only produce content but also predict the types of visuals that will spur customer engagement at different journey stages. With AI’s help, these strategies will be tailored in real-time, based on the customer’s current emotions and behaviors.

Moreover, as AI becomes more ingrained in our lives and businesses, the industry will have to navigate the nuances of Ethical Considerations in AI Marketing . We’ll see a push towards transparent AI — algorithms that not only make decisions but can also explain them in a way that’s understandable to both marketers and customers alike. Privacy concerns and the respectful use of data will be paramount, with AI serving as a steward of consumer trust rather than a threat to personal privacy.

The future of customer journey mapping with AI looks bright, brimming with potential and ethical responsibility. As technology races forward, businesses will be compelled to adapt or be left behind, all the while ensuring that the customer remains at the heart of every AI-enabled experience.

Conclusion: The Synergy between AI and Customer Journey Mapping

Throughout our exploration, it’s become clear that the fusion of AI and customer journey mapping spells a transformative leap forward for customer experience. From the revelation of hidden behavioral patterns to predictive analytics and personalized real-time engagement, AI has supercharged the entire journey mapping process. It’s afforded businesses not just a map, but a GPS, dynamically updating and guiding the customer experience with uncanny precision.

Embracing AI-driven Email Marketing and leveraging AI for Competitive Analysis are just the beginning. Forward-thinking businesses that adopt AI tools are bound to gain a competitive edge through deeper customer insights and more efficient marketing strategies. In this rapidly advancing landscape, integrating AI into journey mapping isn’t just a smart move—it’s an essential step into the future of customer connection and understanding.

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Enhance CX at Scale With AI

Table of Contents

Customer journey management is one way to reap the benefits of artificial intelligence (AI) and machine learning. AI improves customer experiences (CX) throughout users’ interactions (journeys) with your brand. 

AI and machine learning algorithms process large amounts of customer data across social media posts, online reviews, transaction records and support logs, uncovering/revealing trends and patterns. You can use these insights to understand customer preferences, personalize marketing efforts, enhance customer support and tailor experiences that resonate with your consumers.

Understanding AI for Customer Journeys

AI enables your organization to predict consumer actions, anticipate their needs and proactively address friction points. Ultimately, AI-powered journey tools help boost consumer satisfaction, loyalty and overall business performance across various touchpoints and interactions.

What Is a Customer Journey?

A customer journey is a series of interactions that a customer has with a brand for any particular task or decision . Journeys occur during all six stages of the customer experience lifecycle:

• Discover  (research products and services)
• Buy  (purchase products or services)
• Onboard   and delivery (receive purchased products, activate cards or services, schedule medical appointments)
• Use and manage  (change account details, report suspicious behavior, receive alerts about suspicious behavior )
• Support   and resolve (pay bills, return a purchased product, schedule service appointments)
• Grow  (receive targeted offers, renew and upgrade contract)

The customer decides what actions to take at each step in the process, but the business has an opportunity to guide those decisions and behaviors toward the desired outcome.

The Importance of Mapping the Consumer Journey

Customer journey mapping is vital in creating simple, convenient experiences that drive loyalty and conversions. When you understand how consumers interact with your business at various touchpoints and analyze their interactions and behavior, you are in a better position to eliminate friction points and create customer experiences that are so easy they are forgettable in a good way . 

Leveraging Customer Journey Mapping and AI in Your Organization

Customer journey mapping automates data collection and analysis at scale, helping you get comprehensive insights rapidly. Your organization can also use predictive analytics to enhance the consumer journey.

Automated Data Collection and Analysis

Before AI, data collection and analysis were manual. You had to scour various systems and platforms to get an accurate overview of the customer journey. Now, AI makes data collection seamless, quick and automatic. AI capabilities deliver an overview of the consumer journey, identifying friction points and offering suggestions for improvement.

AI tools can extract accurate, meaningful insights from customer interactions that you can use to enhance journeys.

Predictive Analytics

Predictive analytics uses historical data, statistical algorithms and artificial intelligence (machine learning techniques) to identify the probability of future outcomes/events . Predictive analytics detects patterns in customer behavior to anticipate future behavior and needs, allowing brands to proactively meet those needs.

Predictive analytics allows you to tailor your offerings and deliver more personalized, efficient consumer journeys and experiences. You can use predictive analytics to make proactive decisions, helping you deliver positive interactions and effectively address pain points to increase satisfaction and reduce churn. 

Increasing Personalization With AI 

AI makes it possible to personalize content and offers at scale. AI algorithms improve  customer segmentation  by  analyzing customer data  to create more targeted segments. Better segmentation means better personalization of marketing campaigns—and more sales.

Enhancing Support With AI-Powered Chatbots and Virtual Assistants

AI -powered chatbots and virtual assistants can be used to deliver excellent customer support journeys.

Real-Time Consumer Support

AI chatbots provide 24/7 support without your organization needing to pay overtime costs. In fact, 64% of consumers agree that the best chatbot feature is the round-the-clock availability. 

Consumers can resolve problems quickly by using self-service guides and resources, giving customer service reps more time to handle complex queries. Virtual assistants can also support your employees by automating repetitive tasks such as sharing a knowledge article or resetting a password.

Customer Journey Touchpoint Integration

 You can integrate all your systems and platforms with AI and innovative technology . That means consumers can seamlessly transition between touchpoints without repeating themselves. And you can provide a frictionless, consistent customer journey experience across channels.

Improving Product and Service Recommendations

Machine learning algorithms can be used to tailor recommendations and promotions based on customers’ behavior patterns and content preferences. Netflix has credited its recommendation engine with saving more than $1B per year by keeping members from canceling their subscription.

Mitigating AI Risks and Issues

While AI can enhance CX, there are several potential drawbacks to consider:

• Privacy and security concerns: AI tech enables you to collect and process large amounts of data, which must be protected from unauthorized access and breaches.  
• Over-reliance on AI: Although AI can deliver valuable insights, it may not capture the full complexity of consumer behavior and emotions. 
• AI bias: AI algorithms are trained using historical data, containing inherent biases that can affect the accuracy of insights.

To mitigate risks and address AI issues , you can:

• Maintain human oversight and involvement in the decision-making processes.
• Implement robust security measures and delete any data you don’t need. 
• Comply with the latest privacy and security regulations .
• Use high-quality, diverse datasets to minimize biases.
• Continuously monitor and evaluate your AI system performance. 
• Partner with an experienced service provider to manage journeys.

Optimize Your Customer Experiences With AI

AI technology enhances your customer journeys and fosters loyalty, delivering exceptional experiences. With the right partner, you can reap the benefits of innovative technology while mitigating associated risks. CSG Xponent, our industry-leading customer experience solution, combines a customer data platform with customer journey analytics and customer journey orchestration to analyze current behavior, predict future behavior, and guide customers to the next best action. AI powers customer journey analytics, detecting intent and activating the next best action . In the contact center, CSG Xponent provides:

Automated self-service. Natural language processing analyzes customer responses to interactive voice response (IVR) prompts, identifying intent and needs. The decisioning system determines the most appropriate response or action based on customer information (intent, account information and behavioral patterns) and business rules or logic. Better self-service options mean fewer calls to live agents, reducing call volume and costs.

Intent-based call routing. the system determines callers’ intent by analyzing their IVR responses using natural language processing, then directs their call to the right agent group, reducing call transfers. The system provides a call transcript describing the problem to the agent during handoff, so customers don’t have to repeat themselves.

CSG has helped thousands of organizations for over four decades. Optimize your customer journeys with our simplified solutions and expert support. Contact us to learn more.

2024 State of the Customer Experience

Go beyond the buzzwords and learn about the new CX era. Learn what it is and what it’s not to understand how to craft the best strategy for your business.

Adobe unveils AI enhancements in Customer Journey Analytics

Image credit: Adobe Stock/Alex

When we first introduced Customer Journey Analytics (CJA) as a new offering in Adobe Analytics , our hope was to help organizations see their blind spots. Our teams had observed far too many examples of important decisions that were being made with incomplete data, or topline metrics that skimmed the surface when it came to an understanding of consumer behaviors. In the retail industry for instance, brands were having a difficult time grasping how online activities were driving in-store purchases.

Part of the issue, however, was that digital had long been considered a second fiddle. In recent months, that has changed substantially. The ongoing COVID-19 pandemic has moved us from treating digital as an afterthought into an all-digital economy. And even when we do return to some normalcy, aspects of this will persist. The growth in digital adoption will make it even more important for brands to understand how consumers engage across different channels.

In the last few months, through Customer Journey Analytics, we are beginning to see many organizations take their first steps. Adobe customers can merge physical point-of-sale data with signals from websites and apps. Call center insights are being brought together with loyalty programs and digital data. And Adobe Experience Platform has underpinned the effort, handling the intensive task of bringing disparate customer data sets together under a common language. It allows teams to act, taking immediate action to improve the customer experience.

Today, we are announcing enhancements in Customer Journey Analytics that will deliver deeper insights through AI, while freeing up resource constraints. The updates take advanced features that customers love in Adobe Analytics and makes them work on omnichannel data sets that go beyond the website. Through Adobe Sensei , our artificial intelligence technology, brands get access to time-tested algorithms (trained on massive data sets), producing insights that provide a better pulse on the quality of customer experiences and business performance. Below, we go through the details of new AI enhancements.

Anomaly Detection (available today)

As one of the most popular features in Adobe Analytics, Anomaly Detection has traditionally been used for website activity. Brands can use it to see areas of the web experience that drive or hurt sales. It allows teams to ramp up specific campaigns, or issue quick fixes in real-time.

We are now making Anomaly Detection available in Customer Journey Analytics. For the first time, users can get a better sense of the “hand-off” that happens between channels such as customer support and the mobile app. It is purpose-built AI that helps them identify pain points, or areas they can further refine the experience. For consumers, it means interactions that feel more connected and intuitive.

Anomaly detected in a call center

Contribution Analysis (sneak preview)

When teams see anomalies in their data set, some benefit from an additional analysis of the root causes. Consider a media company, who sees a significant dip in user engagement. With Contribution Analysis , they may see that a specific browser is the prime suspect, providing them a clue to investigate further.

We will be bringing this capability to Customer Journey Analytics as well. Consider a scenario where engagement with a loyalty program has been falling substantially. With Contribution Analysis on CJA, the team can better understand the disconnect between member benefits that their customers see online, versus what representatives are presenting in-person.

Intelligent Alerts (sneak preview)

In developing AI and machine learning features in Adobe Analytics, we realized early on that teams don’t know what they don’t know. There are signals hidden deep in data, that even skilled data scientists may not fully scope when they build their organization’s analytics practice. With intelligent alerts, part of the virtual analyst in Adobe Analytics, brands can rely on Adobe Sensei to do the heavy lifting—uncovering the “unknown unknowns.”

This is crucial to build into Customer Journey Analytics, given the uptick in data that teams will need to work with when they merge channels. Even without users having to ask, Intelligent Alerts on CJA will automatically surface insights that would have otherwise gone unnoticed. And as teams begin to engage with the alerts, the AI gets smarter and begins to personalize what is presented.

https://blog.adobe.com/en/2019/09/10/adobe-brings-photoshop-inspired-functionality-to-data-analysis

https://blog.adobe.com/en/publish/2019/10/08/customer-journey-analytics-omnichannel-analysis-really-can-happen-in-real-time

https://blog.adobe.com/en/2020/11/03/level-up-your-customer-experience-with-emotional-intelligence

How the AI-powered customer journey will transform digital experiences

2-minute read

Today’s consumers have high expectations of the customer experience, and they are equally willing to reward businesses who meet those expectations as they are to walk away from those who fail to deliver. To build loyalty, organizations must create delight at every stage of the customer journey. Many businesses are discovering that the key to delivering those relationship-building human experiences lies, ironically enough, in technology—specifically, in artificial intelligence.

For companies and customers, experience is everything.

Businesses are under pressure to deliver the experience customers want, when they want it, according to their individual needs and preferences. Consider the following results from recent surveys:

• 67 percent of consumers say their standard for a good customer experience is higher than ever.
• If an experience falls short of their expectations, 58 percent of customers show little hesitation in severing the relationship with the company.
• 84 percent of consumers will go out of their way to spend more money with businesses that offer great experiences.

Experience is everything, and a rapidly growing percentage of those experiences is digital— especially given the increased demand for contact-less services arising from the coronavirus pandemic. Technology enables optimized digital experiences, helping us design ever-more personal interactions, provide seamless service, and close the feedback loop. Whether the channel is phone, email, website, or text message, companies must deliver a consistent, high-quality experience to inspire the loyalty that keeps customers coming back over and over again.

To get insight into customers and drive business outcomes, companies can analyze a myriad of data sources. Most organizations have no shortage of data on their current and potential customers. However, translating that data into meaningful information can be challenging for two reasons: the data is disparate, and human behavior is complex.

Customer data is found in a huge number of sources—from product orders to support tickets to emails to social media messages to transcribed phone calls—in databases scattered across the organization. Analyzing terabytes of data and providing real-time actionable insights lies beyond the capacity of even the most capable human employee. Enter artificial intelligence.

Why now is the time to invest in AI as a competitive advantage

Unlike human employees, AI can access hundreds of sources simultaneously, extract the data needed for a desired task, and transform that data into insights. Using natural language processing (NLP), AI can understand human inputs—text and voice—and “listen” to outside sources (such as social media networks) as well as human conversations to identify opportunities and make recommendations. And it can leverage data to generate predictive analytics, which enables organizations to predict customer behavior and to take a more proactive stance (for example, in preventing customer churn).

Only recently has AI evolved to the point where it can “tame the customer data beast” to drive delighting experiences throughout the customer journey. Early iterations of AI for customer service were limited by an inability to handle complex situations and a frequent need for maintenance to accommodate new scenarios. Thanks to advancements in machine learning, today’s AI-based customer service applications can learn from a vast array of data resources, including unstructured data.

Due to the complexity and cost of developing AI models that can be used in multiple contexts, AI has historically been out of reach for mid-size companies. But now, with the incorporation of AI into tools like Salesforce, the benefits of artificial intelligence are no longer limited to large enterprises.

“ By 2025, as many as 95 percent of all customer interactions will be through channels supported by artificial intelligence (AI) technology. ” – Microsoft

The AI-powered customer journey

Artificial intelligence can empower organizations to raise the bar for customer interactions throughout the customer journey:

AI and the customer experience: more use cases

AI offers businesses advanced capabilities that can increase customer satisfaction, agent productivity, and strategic use of customer data. Here are just a few of the use cases that are proving successful in organizations of all sizes.

Personalization

AI can tap into all areas of a customer’s history—not just what they’ve bought, but also other products they’ve viewed, reviews they’ve posted, videos they’ve watched, even their public social media postings—for data that can reveal their needs and predict their behaviors. Machine learning can track customers’ activity to yield deeper insights and to enable real-time adjustments when behaviors change. These insights can be accessed by both human employees and AI-guided applications to create experiences that bespeak a true understanding of who the customer is and what they need.

For example, when a Netflix subscriber logs in, she’s immediately greeted with a list of recommendations based on AI-driven analysis of her past behaviors—not only the shows she’s watched, but also details such as the day of the week, the time of day, the device used, and possibly even the location for each viewing. Netflix has honed personalization to such a fine art that   80 percent of the shows users watch are discovered through the platform’s recommendation system .

Virtual agents

The category of AI that was born as programmed chatbots has evolved into sophisticated virtual assistants and, in the customer service world, virtual agents (VAs). With the rise of Siri, Alexa, and other virtual assistants,   interacting with conversational AI technology   has become a part of everyday life.

Thanks to recent advances,   today’s VAs can mimic human interactions more effectively and handle more complex issues . Organizations can offer 24/7 service-on-demand for a broad range of issues, and call center reps are free to focus their efforts on more complex, interesting cases. AI can also monitor virtual agent interactions to gauge user sentiments and route customers to human agents if anger or frustration is detected.

AI insights for agents

AI benefits organizations behind the scenes by offering powerful tools to support agents’ real-time conversations with customers. For example, if an agent is on the phone with a customer asking about payment plan options, AI can pull up knowledgebase articles about payment plans immediately, right on the agent’s screen. The agent has the information she needs to help the customer make an informed decision, and the customer doesn’t have to wait for the agent to click around looking for the right resource.

Agents can also benefit from AI in situations where information must be pulled from a variety of different sources. For example, if a customer calls with questions about a warranty, the agent will likely need product information, warranty information, the date of purchase, and other details to provide accurate answers. AI can fetch these details from their respective resources simultaneously, allowing the agent to focus on the customer instead of on hunting down each piece of information.

Discover trends with predictive analytics

AI algorithms can aggregate and analyze data to generate predictive models of future customer behavior. Organizations can leverage these models to, for example,

• Prevent churn by identifying “red flags” that may indicate a customer is considering leaving
• Leverage upselling and cross-selling opportunities at times when customers are most likely to buy
• Explore new markets based on shifts in customer demographics, geographic locations, and other indicators

At every stage of the customer journey, businesses have an opportunity to meet and even exceed expectations. The key to leveraging these opportunities lies in data, and AI-driven applications have the power to mine terabytes worth of information in real time to elevate the customer experience. Ironic as it may seem, technology holds the key to building a more personal experience and inspiring the loyalty that keeps customers coming back over and over again.

Alexis Greenwood is a manager in the Logic20/20 Digital Transformation practice, focused on offerings development and innovations. In her experience as a business systems analyst, she enabled change through development of low-code platforms, including Salesforce and ServiceNow, custom applications, virtual assistants, and a variety of tools including ERPs, ITSM tools and CRMs.

Follow Alexis on LinkedIn

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The Impact of AI on Customer Journey Mapping and Management

U nderstanding and optimizing the customer journey is paramount for delivering exceptional customer experiences. With the advent of artificial intelligence (AI), customer journey mapping has evolved significantly, offering new opportunities and challenges. Let's take a look at some of the pros and cons to consider before deciding if, when, and how AI can be beneficial in designing customer experiences.

Pros of Using AI in Customer Journey Mapping:

Data-Driven Insights

AI has the remarkable ability to process and analyze vast amounts of data quickly and accurately.  For CXers, the process of aligning unstructured data into a cohesive customer journey map can be a complex and time-consuming task. AI excels in processing vast amounts of unstructured data from various sources, including customer feedback, social media, and user reviews. For CXers, this means the ability to efficiently extract valuable insights from text, audio, and visual data. AI can identify patterns, trends, and hidden opportunities that may not be apparent through manual analysis, a capability that allows CX professionals to gain deeper insights into customer behaviors and preferences. This data-driven approach enables more precise customer journey mapping, helping you tailor your strategies to meet customer expectations.

Personalization at Scale

One of the significant advantages of AI in customer journey mapping is the ability to personalize interactions at scale. AI algorithms can segment customers based on their behaviors, demographics, and preferences, allowing you to deliver tailored content, recommendations, and offers to individual customers. This personalization enhances customer satisfaction and engagement, increasing retention rates and sales.

Predictive Analytics

AI-powered analytics can generate predictive insights by identifying potential pain points, opportunities, or issues in the customer journey. Researchers can use these insights to proactively address customer needs and expectations, ultimately leading to better user experiences.

AI can also predict future customer behavior by analyzing historical data and trends. This is invaluable for CXers, as it enables them to address potential issues and opportunities in the customer journey proactively. Predictive analytics can help to optimize touchpoints, streamline processes, and anticipate customer needs, ultimately leading to better customer experiences.

Pattern Recognition

AI algorithms can identify patterns and trends in unstructured data that might be challenging to discern manually. Researchers can gain a deeper understanding of the customer journey by recognizing recurring themes and behaviors in customer interactions. This enables more accurate and nuanced mapping that can guide improvements in the overall user experience.

Rapid Analysis

AI's ability to process data at high speeds can significantly expedite the research process. CXers can quickly analyze large datasets, saving time and resources. This rapid analysis facilitates timely decision-making and agile adjustments to the customer journey, ensuring that it remains relevant and effective.

Automation of Routine Tasks

AI can automate repetitive and time-consuming tasks, improving CX by quickly and efficiently solving customer issues or questions. For example, chatbots and virtual assistants can handle customer inquiries, ticket routing, and basic problem-solving, improving response times and reducing operational costs. This automation ensures that customers receive prompt assistance and support, enhancing their journey.

Cons To Consider When Using AI in Customer Journey Mapping:

Data Privacy Concerns

AI collects and analyzes customer data, raising concerns about data privacy and security. Customers are becoming more cautious about how their data is used, and any mishandling can lead to trust issues. CX professionals must strike a balance between personalization and respecting customer privacy, complying with data protection regulations like GDPR and CCPA.

Ethical Concerns

AI's ability to analyze large datasets can raise ethical concerns, especially regarding privacy and consent. CXers must be mindful of these concerns and ensure that the use of AI in customer journey mapping aligns with ethical guidelines and regulations.

Lack of Human Touch

While AI can provide efficiency and automation, it may lack the human touch that some customers value. The absence of genuine human interaction can make customers feel disconnected or undervalued, potentially negatively impacting the customer journey. Striking the right balance between automation and human interaction is crucial.

Loss of Context

AI may struggle to capture the nuanced context of customer interactions, particularly when dealing with unstructured data. The human touch is often required to interpret the subtleties, emotions, and non-verbal cues that can profoundly impact the customer journey. AI may miss these critical elements, potentially leading to incomplete or inaccurate journey maps.

Initial Learning Curve

Incorporating AI into the research process can be challenging, especially for researchers who are not familiar with AI technologies. Learning to use AI tools effectively and integrating them into existing workflows can take time and effort. Additionally, AI algorithms need to be given time to “learn,” so results and efficiencies are not instantaneous.

Initial Implementation Challenges

Incorporating AI into customer journey mapping can be challenging, especially during the initial stages. It requires an investment in technology, training, and data infrastructure. CX professionals may encounter resistance from employees who fear job displacement due to automation. A well-thought-out implementation plan and change management strategy are essential to address these challenges.

Dependence on Quality Data

AI is only as good as the data it is trained on. The AI's output may be similarly flawed if the input data is biased, incomplete, or inaccurate. CX researchers need to ensure that the data they feed into AI systems is of high quality to avoid misinformed decisions based on misleading insights.

Overreliance on AI

While AI can provide valuable insights, CX professionals should be cautious not to over-rely on AI-generated data and recommendations. Combining AI-driven insights with human expertise is essential to make informed decisions. For example, our customer journey management solution JourneyTrack incorporates AI-generated recommendations if requested by users, but users can determine the temperature - meaning how “creative” the generated ideas are, and they ultimately decide whether or not those recommendations and accompanying actions are included in the future state journey map. AI can provide valuable information, but it cannot replace the critical thinking and creativity of CX professionals.

Impact on Customer Journey Management

The incorporation of AI into customer journey mapping has a significant impact on customer journey management. It can lead to more efficient, data-driven, and personalized customer experiences, ultimately resulting in higher customer satisfaction, loyalty, and increased revenue. However, it also introduces challenges related to data privacy, human touch, and initial implementation hurdles.

CX professionals must carefully navigate these pros and cons, ensuring that AI complements their efforts rather than replaces them. An effective approach involves integrating AI as a tool to enhance the customer journey, harnessing its strengths while preserving the human touch and maintaining the highest standards of data security.

In conclusion, AI has the potential to revolutionize customer journey mapping and management, and tools like JourneyTrack can help. For CX professionals, the key lies in harnessing the power of AI while understanding and addressing its limitations, thereby delivering superior customer experiences and staying ahead in the competitive world of customer experience management. For more insights and examples of brands that harness the power of AI, check out this Harvard Business Review article .

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A Guide to Customer Journey Analytics: How to Analyze & Improve Customer Journeys

Customer journey analytics help product, marketing, and customer success teams improve the customer experience so that users can achieve their goals and the companies – their business objectives.

In the article, we explain:

• What a customer journey and customer journey analytics are.
• How customer journey analytics differ from customer journey mapping.
• The benefits of customer journey analytics.
• How to conduct the analysis.

We also examine a few product analytics tools to help you analyze your customer journeys.

Let’s get right to it!

• The customer journey is all the actions and steps that the customer takes when interacting with the product, from the moment they discover it till the end of the business relationship.
• Customer journey analytics is about tracking , analyzing, and interpreting these actions to identify opportunities for improvement .
• Thanks to customer journey analytics, teams can better understand how customers behave at different touchpoints and where they experience obstacles. Such insights help them predict customer behavior and prioritize customer experience initiatives to increase satisfaction , retention, and profitability.
• Customer journey mapping is only a part of the customer journey analytics process. Creating customer journey maps for different user personas is, in fact, one of the first steps.
• After the maps are ready, use analytics and feedback tools to gather quantitative and qualitative data about user interactions with their product and their experience .
• Funnel analysis helps you identify friction points in the customer journey, while path analysis can highlight specific behaviors that lead to either customer churn or success.
• Increasingly more tools enable teams to extract actionable insights from qualitative feedback .
• Having identified issues, implement the necessary changes to make the customer journey frictionless and help users achieve their goals. Examples of such changes include SEO optimization of your content, tweaking your onboarding , or providing self-service support.
• A/B and multivariate testing are two popular experimentation methods that allow you to choose between different versions of the same design or microcopy .
• Want to learn more about Userpilot’s customer journey analytics features? Book the demo!

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What is a customer journey?

In SaaS, the customer journey is a sequence of stages that the customer goes through, from the moment they find out about the product until they become paying customers, and beyond.

And beyond?

Yeap! For a SaaS business to thrive, it needs to be able to retain customers and leverage them for new customer acquisition , for example, via referrals and word-of-mouth recommendations.

What is customer journey analytics?

Customer journey analytics is the process of collecting, analyzing, and interpreting data on the customer interaction with the product throughout its lifecycle .

Its primary goal is to gain an understanding of customer behavior inside the product and identify opportunities to improve the customer experience to advance product and business goals.

Customer journey analytics vs customer journey mapping

Customer journey analytics and customer journey mapping are 2 interconnected but different processes.

In a nutshell, customer journey mapping is the process of outlining all the steps in the customer journey along with user actions, feelings, challenges, and so on.

As such, it’s an essential part of customer journey analytics. It’s impossible to accurately analyze progress along the user journey if you don’t know what the journey looks like. It would be like trying to plan your overseas holidays without a map of the places worth visiting.

The benefits of customer journey analytics

Customer journey analytics are essential for customer and product success .

First of all, it allows you to better understand how customers use the product and what they value most about it. Such insights can help you prioritize your product initiatives to make your product more useful.

It doesn’t stop there.

Customer journey analytics enables organizations to improve the customer experience by identifying and addressing user pain points and friction in the customer journey that stops them from realizing the product value .

Streamlined user experience and personalized interactions translate into higher customer satisfaction , retention, and loyalty .

Higher retention means also higher customer lifetime value , but that’s not the only way customer journey analytics can help you improve your revenue stream.

Marketers can use customer journey data to optimize marketing campaigns and identify the best customer acquisition channels , which reduces CAC. Further down the funnel, savings can be made by reducing the load on the customer support teams .

And don’t forget about the extra income from cross- and upsell opportunities that customer journey analytics can help you identify.

How to do a customer journey analysis?

Customer journey analysis is a journey itself. Let’s look at the key steps it involves.

Adopt the right analytics tools

In the context of digital products, all customer interactions with the product and the brand happen in the digital space. So to be able to track and analyze them, you need the right tool stack.

What tools exactly do you need?

The main ones include:

• Product analytics platform – to track user behavior inside the product.
• Web analytics tool – to keep a pulse on web traffic and website engagement.
• Heatmapping and session recording tools – for detailed analysis of user interactions.
• Social media monitoring tools – to listen to what your existing and prospective customers have to say about your brand.
• Survey tools – to collect customer feedback via multiple channels.
• Journey mapping tools.

On top of that, you need a CRM or CDP platform to integrate the data in one place.

Map your entire customer journey

Once you have all the toys, I mean tools, in place, let’s start by mapping out the entire customer journey . As mentioned above, you need to know exactly what you’re analyzing.

What information does the map include?

• The main stages of the journey – like awareness, consideration, interest, sign-up, onboarding & activation , adoption and use, retention , advocacy & expansion .
• The touchpoints where user interactions happen – e.g. the paid ads, the landing page , the sign-up form , etc.
• User actions at all the touchpoints along with their feelings and challenges.

To help you structure customer journey mapping, tools like Miro or Canva have ready-to-use templates. Use them as you will have a few of these to create – one for each user persona!

Collect customer data

Next, it’s time to collect the data for the journey analysis.

This includes quantitative data , gathered via web and product analytics tools . For example, you’re most likely to track user behavior on your website as well as product usage data inside the product.

When it comes to qualitative data , it’s mostly user feedback . This can come from different sources:

• In-app surveys .
• Email surveys.
• Usability testing sessions.
• Interviews and focus groups.
• Interactions with the customer-facing teams.
• Social media & forum posts.

Analyze data

As the data starts flowing in, you can start analyzing it. You can do it from multiple angles.

Analyze drop-offs in the customer journey with funnel analysis

Customer journey analysis often starts at a high level with funnel analysis . This allows teams to identify the general areas where users experience friction .

How does it work?

In the funnel chart, each stage of the journey is represented by one bar. As the users gradually drop off, the bars get narrower. This is normal unless you can see that lots of users drop during a particular stage.

If that’s the case, it may be an indication that they’re facing roadblocks that stop them from progressing. That’s where you need to conduct a more granular investigation to find the root cause of the friction.

Analyse the main customer journey map with paths

One of the more granular analysis methods is path analysis.

In short, this allows you to map out all user actions inside the product at each stage of the journey.

There are 2 main applications of this kind of analysis. You can use it to:

• Analyze the paths of the users that drop off to find out why they go astray.
• Analyze the paths of the power users to find the happy paths that others could replicate.

Analyze customer experience with surveys

When it comes to qualitative feedback , the analysis is a bit more time-consuming. That’s because it may be difficult to extract actionable insights from the captured data.

Fortunately, more and more products offer tools for automating qualitative data analysis .

For example, with Userpilot, you can tag the qualitative NPS responses to spot trends in user sentiment more easily, and we’re working on more advanced survey analytics that will allow you to cut through the noise and get the insights you need to make informed decisions.

Make improvements to the customer journey

Using customer journey analytics only makes sense when you act on the insights . So having analyzed all the data, it’s time to implement necessary changes to the customer journey.

What these changes look like depends a lot on the issues you’ve encountered.

For example:

• If you’re struggling to attract new customers, you may make changes to your content marketing strategy or invest in SEO.
• To convince prospects it’s worth giving you a shot, you can showcase case studies and testimonials from successful customers.
• Users aren’t signing up for the free trial ? Optimize the landing page UI and if they’re dropping off during the sign-up , you may need to simplify the process.
• If there’s an issue with activation or adoption, interactive walkthroughs , and onboarding checklists could be a solution.
• To boost account expansion , contextual in-app messages , like tooltips , can nudge users in the right direction.

You get the drift.

Test different flows

How do you know that the changes will improve customer experience and drive the desired behaviors?

You don’t until you try them out with real users. Doing this with a whole user population, however, can be risky, so it’s a good idea to run some experiments and validate your ideas before the total rollout .

A/B testing is one possible way to do it.

Let’s imagine you’re onboarding flow needs improving because it fails to activate enough users. You tweak your onboarding checklist and release it to half of the users from a previously selected sample. For the other half, you trigger the old version and compare their performance.

In Userpilot, you can also compare 2 new versions head-to-head or manipulate a range of variables in the multivariate tests .

Customer journey analytics solution for leveraging customer journey analytics

As mentioned before, analyzing a customer’s journey is much easier with the right tools. Let’s look at 3 different ones that will cover most use cases.

Userpilot is a product adoption platform that offers advanced analytics features as well as feedback collection and user engagement tools.

This means you can use it to collect and analyze both qualitative and quantitative data and act on the findings to improve the user experience and drive conversions.

Its analytics capabilities include:

• Customer segmentation .
• (Custom) event tracking.
• Code-free feature tagging and heatmaps .
• Funnel and path analysis.
• Retention and trend analysis .

When it comes to feedback, you get:

• In-app survey template library.
• Visual editor for easy customization.
• NPS dashboard .
• AI-powered automatic localization .

The engagement functionality includes:

• UI patterns and flows ( tooltips , modals , slideouts, driven actions, banners , and hotspots).
• Interactive walkthroughs and onboarding checklists.
• Resource center for self-service support.

Woopra is a real-time customer analytics service used by businesses to track and analyze the way users interact with their websites, applications, products, and services across multiple devices and customer touchpoints.

Its functionality enables teams to map and analyze customer journeys , helping them to identify critical paths , drop-off points, and opportunities for optimization to improve the customer experience.

Google Analytics

Google Analytics is a well-known free analytics platform. While its latest edition, GA4, supports in-app event tracking, many teams still use it for web traffic analysis.

With GA, you can analyze:

• How users find your website (organic, paid, referral).
• Which pages do they visit and in what order?
• What actions they perform on each page.

Customer journey analytics play an important role in creating frictionless customer experiences, increasing customer satisfaction, and driving business outcomes.

If you want to find out more about Userpilot and its customer journey analytics capabilities, book the demo!

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Customer Journey Analytics: Definition, Tools, & Examples

Learn how customer journey analytics helps you measure the ROI on your customer experience initiatives. Use it to boost revenue, reduce churn, and improve CX.

Customer journey analytics (CJA) is the process of analyzing the entire customer journey through customer data points, then strategizing ways to improve the overall customer experience (CX) . Customer journey analytics is a holistic process that makes customer journeys measurable and helps you identify insights and actions to delight and retain your customers.

Key takeaways

• Customer journey analytics is the process of examining critical data pertaining to user journeys to make informed decisions on improving the customer experience.
• Some of the benefits of customer journey analytics include the ability to measure the ROI on CX initiatives and improve the customer experience through the insights it provides, as well as reduce churn and boost revenue.
• Measuring customer journeys is a process that involves creating user journey maps, determining and collecting data pertaining to those journeys, and defining success metrics and milestones along the way.

What is customer journey analytics (CJA)?

Customer journey analytics is the gathering and analyzing of data that pertains to customer behavior to improve the customer experience. Analysis happens over multiple customer touchpoints and channels over a period of time and measures the impact of behavior on business outcomes.

While gathering customer data, you can collect all user actions in one central database with an associated timestamp. Information is collected through a unique identifier on what the user is doing and who the user is.

Using this information, it’s possible to map out customer journeys and then analyze the customer engagement levels for each journey to understand what’s going well and where customers might be getting stuck. The analysis helps to optimize these customer journeys and also measures the performance of your CX initiatives.

Example insights from customer journey analytics

Customer journey analytics provides deep insights to improve the customer experience. Some examples include:

• Customer journey analytics makes it possible to analyze the path customers take to resolve customer queries. Some insights derived from this analysis might include what types of issues can be resolved by self-service channels and which ones need human interference. This information makes it possible to optimize self-service channels, improve the operational efficiency of resolving customer queries, and enable customer support staff to focus on more complex customer issues.
• For ecommerce companies, customer journey analytics makes it possible to view the steps a customer takes while completing a purchase, which can help identify any barriers to completing the transaction. You might find, for example, that a common frustration while completing a purchase is having to create a user account. You can then use this insight to A/B test a guest checkout option and see if it removes the friction.
• Customer journey analytics can help you identify channel-specific insights to improve the customer experience. For example, you might find that users on your mobile app drop off at a higher rate than users on your website. This insight could indicate that you need to rethink parts of your mobile experience.

Benefits of customer journey analytics

There are several benefits of customer journey analytics, including:

Measuring the ROI on CX initiatives

In a CX survey, 20% of respondents said that measuring customer experience initiatives was one of the key challenges being faced by organizations in the U.S. Customer journey analytics solves this problem by making it possible to measure the ROI on CX initiatives.

Product managers and CX designers can use customer journey analytics to connect data and numbers to customer journeys. For instance, CJA can compare the revenue generated from two user journeys to figure out which user journey leads to more revenue for the business, making it possible to shift to the journey that’s most beneficial.

Customer journey analytics makes it possible to understand how the customer interacts with the product, how their behavior changes when modifications are made to the customer journey, and how your product metrics are impacted because of this changed user behavior.

Improving the customer experience

By visualizing user journeys built by customer journey analytics tools, it’s possible to identify bottlenecks people might face in your product, minimize them, and make smart product suggestions based on their past behavior.

For example, with customer journey analytics, it’s possible to diagnose that users of an ecommerce app fail to make a purchase during the last step of the transaction, especially when using the app for the second time. They might be getting stuck since they don’t remember what they bought the last time, so CJA makes it possible to autosuggest items based on what the customer purchased on their last visit. Reminding customers about their past transactions can improve the overall shopping experience by helping users purchase with ease.

Customer journey analytics tracks data in real time. This enables you to analyze customer behavior while the customer interacts with your website or product, build user journeys around that behavior, then find opportunities to make improvements to the customer experience.

Reducing customer churn

Since customer journey analytics helps predict customer behavior based on their past actions, it can also identify customers at risk of leaving the product or website, enabling you to take action to retain at-risk customers.

Upon identifying at-risk customers, it’s possible to use CJA to personalize interactions with them and help them feel more positive about the brand, so they’ll change their minds about leaving. In a CX study, 58% of respondents stated that their organization had seen significant increases in customer retention as a result of using customer analytics.

Boosting revenue

By helping you identify the customer journeys that result in a purchase, it’s possible to boost revenue using customer journey analytics. You can further optimize these journeys by reaching out to customers in real time with relevant communication like upsell and cross-sell offers.

CJA can also help identify your best-performing channels by monitoring customer behavior across different touchpoints. So if your website is performing better than your app, you can focus on optimizing the revenue from your website while making improvements to your mobile app.

Steps for measuring customer journeys

Step 1: create or capture user journey maps.

Before you start measuring a customer journey, it’s important to first create a user journey map. You can create this journey map using prototyping tools or Post-it Notes based on the actions users take. For example, if you want to measure the journey of playing a song on a music app like Spotify, you need to create the entire journey map so you can visualize it.

In this example, the user journey might consist of the following steps: going to the Spotify website, downloading the app, creating a login, searching for a song, and then playing the song. You’d need to write these steps out in the form of a “journey” or use a tool that captures these user actions and creates the journey maps for you.

The goal, in this example, is to play a song on the app. And the journey to that goal consists of several steps along the way. You need to view the journey across different channels since, at the end of the day, your user is trying to accomplish a goal and it doesn’t matter whether they do it via your mobile app or your website.

Step 2: Determine the data you need to collect at each step of the customer journey

To measure a customer journey, you need to define the critical data points you need to measure for that journey. For instance, in our Spotify example, here are some data points that might be relevant to help quantify the journey:

• Number of attempts to download the app
• Time it takes to download the Spotify app
• Time it takes to create a user login
• Number of attempts to play a song

Collecting all of the data in one centralized place makes it easy to access it and pick the relevant bits of data needed along with their timestamps. The data required will depend on the journey you decide to measure.

For every part of the customer journey, there’s an action that your brand wants the user to complete. Every journey has a goal, and there are milestones to achieving that goal. It’s important to define both the goal and the milestones along the way, so you know what to measure and what success looks like.

With the example of playing a song on Spotify, some success metrics or milestones would be:

• Downloading the Spotify app
• Creating a user login
• Playing the first song
• Subscribing to the Spotify service
• Sharing the first playlist
• Renewing the subscription

Think of milestones as critically important steps in the process of achieving a goal. Make a list of these steps, so you can have clearly defined milestones.

Step 3: Analyze the customer data

The next step is to identify your data sources and capture behavioral customer data across the customer journey. Once you’ve collected data, you can start analyzing it and measuring key metrics along critical flows of the journey. You’ll be able to gauge where customers are spending their time, what’s causing them frustration, and which behaviors lead to revenue-generating outcomes. Using this information, you’ll be able to measure the customer journey effectively and figure out how to improve it.

Customer journey analytics tools

Using the Journeys feature on Amplitude , it’s possible to discover what’s making users convert or drop off. With Journeys, you can:

• View step-by-step breakdowns of the paths taken by converted and dropped-off users.
• Uncover the paths most likely to accelerate conversion.
• Identify what your users do if they don’t convert.
• Understand the friction points in your customer experience and develop a strategy to fix them.

By defining the start and endpoints of the journey, the Journeys feature within Amplitude helps you see what percentage of paths converted and what percentage dropped off.

There is a range of other customer journey analytics tools available, including:

• Acoustic Analytics
• Adobe Analytics
• Google Analytics
• Salesforce Journey Builder
• Webex Experiment Management

Learn more about each of these customer journey analytics tools on a review site like G2 .

• What are the biggest challenges currently being faced by your organization in terms of customer experience? Statista
• Performance enhancements resulting from the use of customer analytics in the United States as of March 2018 . Statista

See what customer journey analytics looks like in our self-service demo , or get started with journeys using your own data in a free Amplitude account .

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More best practices.

Customer Journey Analytics Documentation

Welcome to the Adobe Customer Journey Analytics documentation home page. Find self-help, support, and tutorial information with our array of product and technical documentation for Customer Journey Analytics (CJA).

Get started with introductory guides and learn how to best implement CJA for your business. If you’re already an experienced user, explore our in-depth videos and tutorials to connect your channel data and gain vital insights to help improve performance. Explore our full range of Adobe Customer Journey Analytics docs today.

Guides lists-documentation-1

• Customer Journey Analytics Guide Learn about Adobe Customer Journey Analytics and how to use Analysis Workspace with data from Experience Platform.
• Overview Learn how Customer Journey Analytics lets you use Analysis Workspace with data from Experience Platform.
• Getting Started Understand the prerequisites and workflow required to implement Customer Journey Analytics.
• Media Analytics Implement Adobe Analytics for Streaming Media. Includes the Media SDK and the Media Collection API.

Tutorials tiles-tutorials-1

• Understanding Customer Journey Analytics Learn how to do multi-channel analysis on any of your Experience Platform data sets.
• Architecture and Integrations Get a walkthrough of the architecture of Adobe Customer Journey Analytics, including how it connects to and integrates with Adobe Experience Platform.
• Working with Data Learn how dataset schemas are translated into variables in Adobe Customer Journey Analytics, as well as how CJA handles very high cardinality.
• Blueprint: Customer Journey Analytics Unify and analyze data and customer behaviors from across the customer journey
• See All Tutorials Customer Journey Analytics

Release Information lists-release

• Customer Journey Analytics Release Notes

Related Resources lists-resources-1

• Experience Platform Community Forum
• Experience Platform Support
• Customer Journey Analytics Courses

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Customer Journey Analytics: A Comprehensive Guide

February 6, 2024   •   6 min read

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Individual touchpoint analytics vs. journey analytics – what should brands track? Overall journey analytics. Period.

Many customers churn saying their overall customer experience with the brand was “unsatisfactory” despite reporting satisfaction in their last interaction (just before defection).

Most companies today focus on reporting, but few on analytics. However, in order to deliver authentic, consistent experiences, one needs more than plain reporting. Customer journey analytics not only state what happened during the customer journey but also uncover why it happened and what to do now. Astute root cause analysis drives strategic decision-making, which impacts business growth and ROI positively and tangibly.

If you are losing customers to disjointed customer journeys, this comprehensive guide is what you need. It covers customer journey analytics end to end – from definition to implementation and best practices. Let’s dive right in.

What is customer journey analytics?

Top benefits of leveraging customer journey analytics, how to build customer journey analytics, how can teams take advantage of customer journey analytics data, customer journey analytics vs. customer journey mapping: core differences , deliver authentic and personalized customer experiences with sprinklr.

A customer journey is the set of interactions a customer undertakes across touchpoints to accomplish a task with a brand. Given the diverse paths a journey can take as customers hop from channel to channel, identifying which paths can come in the way of customer satisfaction and conversion can give real value to brands, and this can be accomplished via customer journey analytics.

Customer journey analytics goes a step ahead of customer journey mapping since it not only maps customer touchpoints and interactions but also highlights the impact of each element on the customer’s final decision, allowing brands to take proactive measures to minimize friction and move customers toward the desired action.

On a high level, customer journey analytics pinpoints the successes and areas for improvement, unlocking the following benefits for brands: 

Reveals deep customer insights : Customer behavior data is fodder for journey analytics, and it reveals complex patterns in customer behaviors that brands can utilize to their advantage. For example, a telecom company watching customer interactions might discover that a specific customer is more responsive to company communication in a specific timeframe, which they can then use to send promotional messages.

Pinpoints churn points and triggers: Unless a customer takes the time to fill in a customer survey , one can’t really pinpoint why they churn. That’s where customer journey analytics plays a pivotal role in capturing churn signals in real time. Deep analysis reveals triggers for different customers, allowing you to pivot and keep them in the funnel.  

Enables strategic decision-making: CX strategists and analysts evaluate the impact of their campaigns and initiatives more confidently with concrete numbers and actionable insights, removing the guesswork from decision-making.

Say you intend to invest in a pricey advertising campaign to multiply festive sales. However, customer journey analytics recommends organic outreach based on past results, which gives decision-makers the confidence to make this move.

Customer Experience Management (CXM): Be Present, Be Engaged, Be Human@Scale

Customer journey analytics and customer experience management are related concepts since they both aim to understand and optimize interactions between customers and brands.

However, customer experience management is a broader concept that encompasses the start-to-finish customer lifecycle, aiming to manage and elevate the customer experience by optimizing at all levels - product, service, process and support. It is a strategic and holistic approach that spreads delight and boosts customer retention .

While a robust analytics tool can yield journey data, you need a unified customer experience management platform to help with CXM. A unified customer experience platform like Sprinklr enables brands to be omnipresent on traditional and modern channels, pulling in and processing terabytes of unstructured customer data to weave a narrative around customer experience.

Unlike duct-taped point solutions, Sprinklr’s unified solution ties together all your customer-facing functions, including research, insights, marketing, sales and support, passing data and insights from one function to the next as the customer progresses along the journey.

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In order to leverage customer journey analytics meaningfully, approach it strategically using the steps below:

Step #1: Define your goals

Right at the outset, determine what it is you want to achieve by building customer journey analytics.  

Do you want to see the impact of blog engagement on subscriber list growth? 

Or do you want to find out the success rate of your retargeting ad campaign? 

Or are you keen to know the effectiveness of your self-service tools like knowledge base , FAQ chatbot or customer community? 

Step #2: Map the customer journey

Next, map the customer journey to be analyzed. A typical journey map contains the buyer’s entry point, buying process, subsequent touchpoints, pain points, emotions and exit points.

For example, if a customer opens a support chat on your website and they are directed to dial your call center or asked to repeat their details to multiple people, the journey is from chat to call center to exit. The pain points responsible for exit are insufficient customer self-service and siloed data.

Step #3: Gather relevant data

Your journey analytics tool should gather two data types – user data and interaction data. User data is information about the user, including demographics, behavioral traits and contact information. Interaction data is how the user interacts with your touchpoints, such as website engagement, conversion rate etc.

For gathering data, use customer data platforms (CDP) that attach unique IDs to each customer-facing platform, capturing variables such as device, browser, operating system, visitor log and historical information. This helps enable accurate attribution and build unique user views.

Types of customer journey analytics reports

Reports are the building blocks of analytics, and they are of many types:

Journey reports depict the buyer’s journey from point to point, highlighting friction and fall-off with reasons therein. They also analyze – and compare – the outcome of improvement strategies.

Attribution reports pinpoint touchpoints/events/products/campaigns that led to successful conversion. For example, product marketers can use attribution reports to identify feature updates that lead to maximum upsells.

Cohort reports depict growth in terms of product/feature usage. For instance, a product company can chart its growth by tracking the usage of a particular feature.

Retention reports track how long a user stays logged in or returns for the next session after performing a specific task.

Also Read: Customer Service Analytics – A Comprehensive Guide

Step #4: Identify improvement areas in the journey

Upon analysis of gathered data, you will uncover gaps and improvement areas in the customer journey. Mending these will help optimize the customer experience, conversion rate and revenue. You might discover new pain points during your analysis. Ensure these are updated on the map.

For example, if you own an e-commerce website experiencing drop-off at checkout, analyze the causal factors, which can range from limited payment options to stringent return policies. Your customer journey analytics tool should be adept at root cause analysis, pinpointing the trigger driving away customers.

Dive deeper: Try building a customer experience map using this guide.

Step #5: Test improvement strategies

Returning to the e-commerce example, your customer journey analytics tool can recommend improvement strategies such as using live chat to control cart abandonment or relaxing return policies. It should also allow A/B testing these strategies, tracking and comparing outcomes to drive strategic action.

Organizations can leverage customer journey analytics data in myriad ways. Let’s consider three common scenarios below:

Scenario #1: An e-commerce company wants to improve its digital customer experience to increase online sales and customer satisfaction.

Application: The team leverages customer journey analytics to analyze the entire online purchasing process. This includes identifying the most common customer paths, pinpointing drop-off points in the sales funnel and understanding the factors that lead to successful transactions. With these insights, the team can optimize the website, streamline the checkout process and personalize online interactions to create a smoother and more engaging digital customer experience .

Scenario #2: A telecommunications company aims to reduce customer churn and enhance customer loyalty.

Application: The team uses customer journey analytics to map the customer lifecycle, from onboarding to usage patterns and potential points of dissatisfaction. By analyzing customer interactions with various service touchpoints, the team can identify patterns associated with customers likely to churn. This insight enables the implementation of targeted retention strategies, such as personalized offers, proactive customer support or exclusive loyalty programs, to retain customers and improve overall customer satisfaction .

Scenario #3: A marketing team wants to optimize its multichannel campaigns to improve customer engagement and conversion rates.

Application: Customer journey analytics is employed to track and analyze customer interactions across different marketing channels, including social media, email and online advertising. The team identifies the most effective touchpoints and sequences in the customer journey, understanding how customers move from awareness to conversion. With this information, the team can allocate resources to the most impactful channels, personalize marketing messages based on customer preferences, and optimize the timing and content of communication to maximize the effectiveness of their campaigns.

Interesting Read: How Personalized Should Marketing Be?

Both customer journey analytics and customer journey mapping play crucial roles in understanding and improving the customer experience, but they differ in their approaches and objectives. 

The modern enterprise is struggling with torrential customer data flowing into disparate customer touchpoints, impairing the delivery of insights to teams and processes where they matter most.

A unified customer experience management platform like Sprinklr offers granular and holistic customer journey analytics from 30+ channels, along with AI-powered optimization recommendations. By bringing your customer data and interaction data under one roof, Sprinklr enables real-time reporting and decision-making necessitated by the volatile business landscape and evolving customer aspirations. 

Try Sprinklr for Free

Frequently Asked Questions

Traditional analytics measure each customer service metric without giving a holistic account of the start-to-finish customer journey, whereas customer journey analytics treats the entire journey as a variable and analyzes it holistically, yielding emotions, pain points and actions at every touchpoint in the journey.

Customer journey analytics uses two data types – user data and interaction data. User data is information about the user, including demographics, behavioral traits and contact information. Interaction data is how the user interacts with your touchpoints, such as website engagement, conversion rate etc.

Real-time analytics allows businesses to track interactions as they happen, enabling timely intervention and customer experience optimization based on customer behaviors. Customer journey analytics generated this way are more authentic and accurate.

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Article Author

Bhavna Gupta

Guest Contributor

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A complete guide to customer journey analytics.

13 min read Customer journey analytics can help you to nail down exactly why your customers behave the way they do and tie your customer experience efforts to financial outcomes. Learn how to use customer journey analytics for improved CX with our ultimate guide.

What is customer journey analytics?

Customer Journey Analytics is the process of understanding the impact of every interaction a customer has with your business.

Often, customer journey analytics starts with a customer journey map , which is presented as a graph, flow chart, or other visual that documents each stage of the relationship between a customer and a brand.

However, instead of just charting their customer journey on a map, customer journey analytics takes a further step to analyze what effect each interaction has on your customers’ decisions.

Further information is overlaid to help analyze how each interaction drives customers toward the end goal.

Customer journey analytics can include analysis of:

• Customer needs
• Emotional highs and lows
• Key metrics per step in the journey
• Customer satisfaction scores , customer effort scores , and other survey results

Customer journey analytics can help you to direct your customers’ attention and resolve any pain points that stop them from taking desired actions. It helps you to augment your customer experience and develop a customer journey that not only gets customers to where you want them to go, but helps them connect to the journey itself.

Learn the analytics and ROI on customer journey management in our free course. 

Customer journey analytics vs. customer journey mapping

Many brands have a broad sense of their customer journey but haven’t optimized it by creating a comprehensive customer journey map or analyzing what affects their customers’ experience.

Customer journey analytics and customer journey mapping are complementary but different processes. Here are the main ways in which they are distinct, and how they work together.

What is customer journey mapping?

Customer journey mapping is the process of laying out the end-to-end journey in a clear way. Creating a map of every touchpoint your customer will experience means you can see what steps your customers take to reach the end goal of a purchase, signup, or other action.

Often, journey maps are documented at the process level. For example, an insurance provider would map the claims process, and a bank would document the new account process.

Some common components of customer journey maps include:

• The process being evaluated
• The stages of the journey
• Critical customer interactions and touchpoints
• Representative customer quotes
• Key customer expectations
• Metrics like satisfaction score, mention volume, NPS
• Trends in topics related to this part of the journey

Our ultimate guide to customer journey mapping can help you to draft your first customer journey map or optimize one you have already.

How do you use customer journey analytics with customer journey mapping?

As we’ve already explained, customer journey analytics is the process of gathering as much information as you can from every part of the journey and analyzing the journey for pain points and successes.

Understanding which parts of the journey function as planned and which obstacles are in the way of your customers’ progress means you can take action to ensure they complete their journey as you intend.

Benefits of customer journey analytics

There are several benefits to completing customer journey analytics. From better understanding your customers’ behavior to a better ROI for your customer experience , customer journey analytics gives you better insights and a more informed strategy for improvement.

Your brand becomes more customer-centric

Understanding the customer journey allows your company to be more customer-centric . It allows you to closely evaluate the activities, expectations, thoughts, and feelings of your customers . You learn what they like and dislike, how to move them through your buying cycle, and how to satisfy and retain them . When journey mapping is complemented with customer journey analytics it helps you understand the priority for your customer experience initiatives.

Your business becomes more unified

In addition, with the right focus, customer journey mapping and customer journey analytics break down internal silos. They empower you to streamline services across departments. Not only that, but they help to align everyone by providing a common understanding of the customer experience. Employees get greater visibility into what happens upstream and downstream of their interactions with customers, letting everybody provide a more consistent, high-quality experience.

You can find track issues as they happen

With a sophisticated customer journey analytics platform, you can pinpoint issues in real-time. You can test new approaches and see their influence on your customer experience and bottom line with analytics that update as quickly as you need them.

You see direct and indirect feedback in one place

Explicit feedback – for example, the information you gather through surveys – is easier to pinpoint to specific interactions customers have with your brand. The customer has an experience and directly after, you request input.

Implicit feedback is more complex to understand. This type of data might include operational data such as sales numbers, or it might cover social mentions, what your customers say on the phone to your care center, third-party reviews, and more.

Understanding how your audience thinks, feels, and acts in response to customer interactions without directly asking them might seem impossible, but with tools such as conversation analytics , you’re able to link your customer journey to this type of customer data.

See how Qualtrics CustomerXM enables customer journey analytics

An example of using customer journey analytics

Customer journey analytics can be used to understand the impact of sub-journeys limited to single processes – such as opening a new account – or the entire digital customer journey .

Below is an example of how you can use customer journey analytics to chart the success of each journey.

Resolving a customer satisfaction issue for a specific sub-journey

Let’s take a printer business that provides hardware to its customers. The brand has realized that the repair sub-journey is currently leading to low Net Promoter Scores (NPS) and a higher cost to serve per customer.

The journey

First, the brand needs to chart the customer journey. It looks like the below:

• A customer has an issue with their printing device
• They call the customer care center to schedule a repair
• The service agents arrive at their place of residence
• The repair is made

However, there are other ways this journey might unfold. For example:

• The service agents arrive at their place of residence but the customer is not present
• The repair cannot occur, so the customer has to call again to reschedule the repair
• The repair is made at a later date when the customer is present

The analysis

Overlaying the NPS scores on this latter journey, the company realizes that the NPS score drops when the customer has to reschedule the repair. Asking the customer to go through the same process once again to rebook their appointment is causing customers to feel less satisfied with their experience.

Using natural language processing (NLU), the team can also see that there is a more negative sentiment expressed in the open text question they have added to the NPS survey. With the additional calls to the care center, the cost to serve each customer also increases.

The resulting action  

The brand decides it’s best to provide other means to customers to book their appointments at a time to suit them. Offering customers a self-service booking system that they can access via their mobile on an app or through the website gives the customers more control over when their appointment occurs. Adding a facility to reschedule any booked appointments for a more convenient time and accentuating this with push or text notifications when the repair team is on their way can help to see if this reduces the instances of missed repairs and reduces the impact on the customer care center .

With customer journey analytics in place, the brand team can see if this improves NPS scores at the same points in the customer journey, and measure in financial terms the impact of actions taken for improved customer experience .

How to use customer journey analytics

Customer journey analytics provides the insight you need to successfully manage your customer’s journey. From lowering customer churn to helping you predict customer behavior, putting a customer journey analytics solution in place will help you to leverage your customer behavioral data for financial success.

But how do you start using customer journey analytics? Below is the outline of the actions you’ll need to take.

1. Map your customer journeys and aggregate data

First, you need to create a customer journey and aggregate the customer data that you already have. Good customer journey analytics tools will be able to do this for you, cutting down the time your team needs to spend sourcing data from third-party locations, customer service chat logs, and survey results.

Competent customer journey analytics software will also be able to track data in real-time, allowing you to build a comprehensive map that reacts to current customer behavior . It should also be able to draw data from numerous sources, helping you to break down traditional business silos and understanding customer interactions from all business angles: sales, marketing, and more.

Learn the five competencies for customer journey mapping

2. Analyze your customer behavior and data

Once you have your customer journeys mapped out and your data collected, you can link specific interactions to particular customer behavior, survey results, social media comments , and more. You’ll need a customer journey analytics solution to be able to link all of this data together in an efficient way.

3. Take action informed by data-led insights

Customer journey analytics provides you with the ability to see cause and effect, as well as providing you with concrete steps to change specific interactions or the entire customer journey. When customers react badly to specific processes or interactions, you can test how changes in your customer journeys affect their future decisions.

Not only that, but you can coordinate your teams across your business to work on customer satisfaction with their experience, based on the data you’ve analyzed. For example, if customers are led to purchase through your marketing but aren’t happy with their purchase, they will deal with your marketing , sales, and customer care teams. Understanding what specifically caused a problem for them means you can inform each team of actions they can take to improve.

How customer journey analytics can improve your customer experience

Brands often hit a wall when trying to measure customer experience . Charting your customers’ often nebulous sentiment and which actions have an impact on customer experience can be difficult without the right tools to hand.

Understanding the return on investment for specific actions taken for customer experience is difficult for a number of reasons:

• Data is siloed or overwhelming
• Business departments work separately with a lack of oversight
• Actions aren’t based on data
• There isn’t a way to track the impact of actions on customer experience

Qualtrics CustomerXM allows you to see the value of customer journeys with rich data analysis, provided through conversational analytics . With natural language understanding, Qualtrics is able to provide you unrivaled insights into customer emotions, sentiment, and more to paint a complete picture of friction points and their rationale. Powered by feedback from multiple areas of your business, you are able to create a plan of action with a tangible effect on your customer experience and business outcomes.

With a deeper understanding of customer behavior, your brand is able to not only understand the return on investment of your actions but develop a customer experience that delivers results. Extending your customer lifetime value , increasing customer satisfaction, and reducing customer churn becomes easier when you understand the triggers for the behavior.

Learn how to take action on customer journey management with our free online course

Related resources

Customer Journey

How to Create a Customer Journey Map 22 min read

B2b customer journey 13 min read, customer interactions 11 min read, consumer decision journey 14 min read, customer journey orchestration 12 min read, customer journey management 14 min read, customer journey stages 12 min read, request demo.

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11 Best Customer Journey Analytics Software in 2024

Customer Journey Analytics Software is a crucial and adaptable tool that offers insights into customers’ paths. As we navigate through 2024, the demand for powerful analytics solutions has intensified, prompting the emergence of innovative software tailored to meet diverse business needs.

There are countless companies offering Customer Journey Analytics Software, each engineered to improve customer engagement. These software solutions use algorithms and data analytics techniques to create a customer journey map from awareness to conversion. Businesses gain holistic insights into customer behavior that transcend individual touchpoints by aggregating data from various sources, such as websites, mobile apps, social media, and offline interactions.

Customer Journey Analytics Software enables organizations to create customized experiences that deeply resonate with their target audience. Businesses can optimize marketing strategies, refine product offerings, and enhance overall customer satisfaction by identifying pain points, preferences, and moments of delight along the customer journey.

What is a Customer Journey Analytics Software?

Customer Journey Analytics Software is a specialized tool designed to track, analyze, and visualize customers’ interactions and experiences with a brand across various multiple customer touchpoints throughout their buying journey. These touchpoints may include online platforms such as websites, apps, and social media and offline channels such as physical stores or customer service centers.

The software aggregates data from multiple sources, such as website traffic, social media engagement, email interactions, and transaction history, to provide businesses with a comprehensive understanding of how customers navigate through different customer journey stages. By scrutinizing this data, companies can pinpoint patterns, trends, and pain points within the customer journey. It empowers us to make informed, data-driven decisions to enhance marketing strategies, refine product offerings, optimize customer service, and enhance the customer experience.

Customer Journey Analytics Software typically offers data visualization dashboards, predictive analytics, segmentation capabilities, and integration with other business systems to provide actionable insights that help businesses enhance customer satisfaction and loyalty and drive revenue growth.

Uses of Customer Journey Analytics Software

Customer journey analytics software serves many purposes for businesses, such as enhancing their understanding of customer behavior and improving overall performance. Here are several essential uses:

Identifying Pain Points:

Analyzing customer journeys helps pinpoint pain points and areas where customers may encounter obstacles or dissatisfaction. This customer journey analysis insight enables businesses to address issues, improving customer satisfaction and loyalty promptly.

Personalizing Customer Experiences:

Understanding the customer journey enables businesses to tailor experiences based on individual preferences and customer behaviors. Personalized interactions, such as customized recommendations or targeted promotions, can significantly enhance customer churn, engagement, and loyalty.

Improving Product Development:

Customer journey analytics provides valuable feedback on customer interactions with products or services. This unify data can inform product enhancements, feature updates, and new product development initiatives to better meet customer needs and expectations.

Enhancing Customer Service:

Analyzing the customer journey helps identify pain points in the customer service process and areas for improvement. Businesses can use this information to streamline support processes, increase responsiveness, and deliver better customer service experiences.

Measuring Customer Satisfaction:

Customer journey analytics software allows businesses to track key metrics related to customer satisfaction, such as Net Promoter Score (NPS) or Customer Satisfaction Score (CSAT). Monitoring these metrics over time provides insights into overall customer sentiment and helps gauge the effectiveness of customer experience initiatives.

Customer journey analytics software is valuable for businesses seeking more profound insights into customer behavior, optimizing processes, and delivering exceptional customer experiences.

11 Best Customer Journey Analytics Software

Embarking for optimal customer engagement and satisfaction requires the right tools. In modern business, Customer Journey Analytics Software is the cornerstone for understanding and enhancing every step of the customer experience.

Here, we present a curated list of eleven leading solutions, each offering unique insights and capabilities to decode customer behavior, refine marketing strategies, and elevate overall satisfaction.

1.QuestionPro:

QuestionPro offers a comprehensive suite of tools for collecting, analyzing, and acting on customer feedback throughout their journey. It provides advanced survey capabilities, feedback management, and analytics to understand customer sentiment and behavior.

• Advanced survey creation and distribution capabilities.
• Real-time analytics for tracking customer feedback.
• Journey mapping tools to visualize the customer experience.
• Integration with CRM systems for seamless data management.
• Customizable reporting dashboards for actionable insights.
• AI-powered sentiment analysis to gauge customer satisfaction.

Limitation:

• Advanced features may require more effort and resources to be effectively used.
• May experience occasional bugs or glitches in the platform.

Pricing: Most popular plans start at $99 per month, with pricing tiers available for businesses of all sizes. Custom pricing options are available for enterprises with specific needs.

2. Insider:

Insider is a platform that combines AI-driven predictive analytics, segmentation, and personalization to optimize customer journeys. It helps businesses deliver individualized experiences at every touchpoint to increase engagement and conversions.

• Predictive analytics to anticipate customer behavior.
• Personalization engine for tailored customer experiences.
• Multi-channel journey tracking across web, mobile, and offline channels.
• A/B testing for optimizing marketing campaigns.
• Customer segmentation is based on behavioral data.
• Automated workflows for personalized communication.
• Limited customization options for analytics reports.
• Challenges in integrating with specific third-party platforms.
• Higher pricing tiers may be prohibitive for smaller businesses.

Pricing: Offers custom pricing based on each organization’s specific needs and scale. Pricing is available upon request.

3. HubSpot Marketing Hub:

HubSpot Marketing Hub offers a robust CRM platform with integrated marketing, sales, and service tools. It enables industries to track interactions, automate marketing campaigns, manage sales pipelines, and provide personalized support, all in one place.

• Comprehensive CRM functionality for understanding customer interactions.
• Email marketing tools with automation capabilities.
• Landing page and website analytics for tracking visitor behavior.
• Lead scoring to prioritize high-value prospects.
• Social media monitoring for understanding customer sentiment.
• Integration with third-party apps for seamless data flow.
• Limited depth in analytics compared to dedicated analytics platforms.
• Challenges in tracking complex customer journeys across multiple channels.
• Certain advanced features may require additional paid subscriptions.

Pricing: The starter plan starts at around $50 monthly, with additional features available in higher-tier plans starting from $400 monthly.

4.  Contentsquare:

Contentsquare is a digital experience analytics platform that captures and analyzes customer interactions across web and mobile channels. It offers insights into customer behaviors, conversion pathways, and content effectiveness, enabling optimization of digital experiences.

• Heatmaps and session replays for visualizing user interactions.
• Behavior analysis to identify conversion barriers.
• Mobile app tracking to understand cross-device journeys.
• Journey attribution modeling for measuring campaign effectiveness.
• Customer journey segmentation based on user personas.
• Collaboration features for sharing insights across teams.
• May require significant training to utilize its capabilities thoroughly.
• Challenges in accurately attributing conversions across multiple channels.
• Integration limitations with some third-party tools.

5. Mixpanel:

Mixpanel is an advanced analytics platform focused on product and user behavior analytics. It helps businesses track user interactions, analyze engagement, and optimize product features to drive growth and retention.

• Event tracking to monitor user actions in real time.
• Funnel analysis to specify drop-off points in the conversion process.
• Cohort analysis for comparing user groups over time.
• Retention analytics to measure long-term customer engagement strategies.
• Experimentation tools for A/B testing and feature rollouts.
• Cross-platform tracking for unified customer insights.
• The higher learning curve for users new to analytics platforms.
• Limited support for specific data sources and integrations.
• Certain advanced features may require additional technical expertise to implement.

Pricing: The starter plan starts at around $24 monthly

6. Glassbox:

Glassbox is a digital experience analytics platform that captures and replays website and app sessions to understand user behavior and identify issues. It provides insights into customer journeys, conversion funnels, and usability challenges.

• Session replay technology to visualize user interactions.
• Error detection and troubleshooting for improving user experience.
• Compliance monitoring to ensure data privacy and security.
• Machine learning algorithms for anomaly detection.
• Customer struggle analytics to identify friction points.
• Integration with digital experience platforms for seamless data exchange.
• Potential privacy concerns related to user data collection and recording.
• Limited support for specific compliance requirements, such as GDPR.
• Integration challenges with some third-party systems.
• Steeper learning curve due to complex features and functionalities.

Hotjar provides tools tailored for website and user feedback, including heatmaps, session recordings, and surveys. These tools offer valuable insights into user interactions with websites, enabling businesses to pinpoint areas for enhancement within the customer journey.

• Heatmaps and click tracking to understand customer behaviors.
• Feedback polls and surveys are used to gather user opinions.
• Conversion funnel analysis to identify areas for improvement.
• User session recordings for detailed insight into interactions.
• Behavior analytics to uncover trends and patterns.
• Integration with popular platforms for seamless data sharing.
• Limited flexibility in customizing feedback forms and surveys.
• Advanced analytics features may be overwhelming for users new to the platform.
• Some users may experience performance issues with large volumes of data.

Pricing: Pricing starts at around $39 per month for the Basic plan, with additional costs for higher-tier plans offering more features and higher traffic allowances

8. WebEngage:

WebEngage is a customer engagement platform that combines marketing automation, personalization, and analytics to drive user engagement across channels. It helps businesses deliver relevant messages and experiences based on user behavior and preferences.

• Omni-channel journey orchestration for consistent customer experiences.
• Behavioral segmentation for targeting specific user groups.
• Campaign personalization is based on user actions and preferences.
• In-depth analytics dashboard for monitoring key metrics.
• Limited scalability for larger enterprises with complex data requirements.
• Challenges in accurately tracking cross-device customer journeys.
• Customization options for analytics reports may be restricted.

Woopra is a customer journey analytics software that tracks and analyzes user interactions across multiple touchpoints. It provides real-time insights into customer behavior, segmentation, and engagement to optimize marketing and product strategies.

• Unified customer profiles for holistic insights into individual journeys.
• Real-time analytics for instant feedback on customer behaviors.
• Behavioral segmentation for targeted messaging and campaigns.
• Journey reports to visualize and analyze user paths.
• Integration with third-party tools and platforms for data enrichment.
• Predictive analytics for forecasting future user actions.
• Limited support for certain advanced analytics features.
• Reporting capabilities may lack depth compared to more comprehensive analytics solutions.

Pricing: Basic plans start at $999 per month.

10. Salesforce:

Salesforce is a leading CRM platform that offers a wide range of customer journey analytics tools capabilities. It empowers businesses to monitor interactions, automate marketing initiatives, tailor communications, and deliver cohesive customer experiences across every interaction point.

• Comprehensive CRM functionality for managing customer relationships.
• Customer journey mapping to visualize touchpoints and interactions.
• AI-powered analytics for predictive insights and recommendations.
• Integration with third-party apps and data sources for a unified view.
• Customizable dashboards and reports for monitoring key metrics.
• Limited out-of-the-box analytics capabilities compared to dedicated analytics platforms.
• Challenges in integrating with certain third-party systems.
• Customization options may require additional technical expertise.

11. SurveySparrow:

SurveySparrow is a robust survey and feedback management platform designed to assist businesses in gathering, analyzing, and responding effectively to customer feedback. It offers advanced survey features, customization options, and analytics to understand customer sentiments and preferences.

• User-friendly survey builder with customizable templates.
• Multi-channel survey distribution via web, email, and mobile.
• Advanced reporting features for analyzing survey responses.
• NPS (Net Promoter Score) tracking to gauge customer satisfaction.
• Automated follow-up surveys based on user responses.
• Integration with CRM systems for syncing survey data.
• Customization options for survey design may be somewhat constrained.
• Integration options with specific CRM platforms may be limited.
• Some users may find the interface less intuitive compared to other tools.

Pricing: Basic plans start at $39 per month and $59 for the starter. There are also other various plans for teams and enterprises.

These software solutions provide diverse features and functionalities to assist businesses in comprehending, assessing, and refining customer journeys. Their collective goal is to enhance customer engagement, satisfaction, and retention through data-driven insights and optimizations.

Why QuestionPro can be the Best Choice

QuestionPro is not just another customer journey analytics software. It’s a premier solution that stands out for its unique features and benefits:

Robust Survey Capabilities:

With its advanced survey creation and distribution features, QuestionPro enables businesses to gather comprehensive feedback at every customer journey touchpoint, facilitating a deeper understanding of customer preferences and pain points.

Real-time Analytics:

The platform offers real-time analytics capabilities, empowering businesses to monitor and analyze customer interactions as they occur. This enables timely decision-making and the ability to adapt strategies to evolving customer needs.

Journey Mapping Tools:

QuestionPro offers sophisticated journey mapping tools that enable businesses to visualize the entire customer journey. These tools help identify key touchpoints, optimize pathways, and enhance the overall customer experience.

Integration with CRM Systems:

Seamless integration with CRM systems ensures customer data is centralized and easily accessible. It enables businesses to leverage customer information to enrich their understanding of the customer journey.

Customizable Reporting Dashboards:

The platform offers customizable reporting dashboards, allowing businesses to tailor insights to their needs and objectives. This flexibility enables stakeholders to focus on key metrics and effectively derive actionable insights.

AI-powered Sentiment Analysis:

QuestionPro leverages AI-powered sentiment analysis to gauge customer satisfaction and sentiment across various touchpoints. It enables businesses to proactively address issues and capitalize on opportunities to enhance customer experiences.

QuestionPro’s comprehensive feature set, real-time analytics capabilities, and focus on enhancing customer experiences make it a top contender for businesses seeking to optimize their customer journey analytics efforts.

Customer journey analytics software provides businesses with the tools to navigate this complex landscape effectively. By harnessing the power of data, these solutions offer invaluable insights into every facet of the customer journey, spanning from the initial interaction through the conversion phase and beyond. Real-time analytics capabilities allow businesses to stay agile, responding to interactions as they happen and adapting strategies accordingly.

Organizations can cultivate more meaningful and personalized customer interactions by leveraging customer journey analytics software. By pinpointing pain points and areas ripe for improvement, businesses can fine-tune their products, services, and marketing endeavors to align closely with the desires and expectations of their target audience. This enhances customer’s journey, satisfaction and fosters stronger brand loyalty and advocacy.

In essence, customer journey analytics software enables industries to harness the full potential of customer data, facilitate more strategic decision-making, and ultimately deliver standout experiences that distinguish them in today’s competitive marketplace.

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What is Customer Journey Analytics

Published: June 28, 2023

A customer doesn’t just mindlessly purchase a product or service. They go through an entire journey, from discovering your brand, to purchasing your product or service, to sometimes recommending it to someone else. 

To make sense of your customer’s journey, you’ll need to leverage customer journey analytics.

Every business, startup or enterprise — in any industry — needs to understand how customers interact with their brand. Insights gathered from customer journey analytics can help, while leading to increased customer lifetime value, customer loyalty, and revenue growth. 

In this blog post, we cover the following:

What is customer journey analytics?

• Customer Journey Stages
• Customer Journey Analytics Benefits

Customer Journey Analytics Software

• Customer Journey Analytics vs. Customer Journey Mapping

Customer journey analytics is a collection of data that helps you to understand how your prospects or customers behave, engage, and convert along the customer journey. 

Customer journey analytics often begins with a customer journey map , which is a visual representation of every step the customer goes through with your business. Then, it applies data on how your customer behaves throughout different phases of that map,  to help you assess the effect your customers’ journey has on your business, or what’s holding customer’s back from completing that journey and purchasing a product

Customer Journey Analytics Steps

1. outline a customer journey map..

Create your customer journey map using HubSpot’s template

The first step to customer journey analytics is creating a customer journey map. A typical customer journey map includes the following: the buying process, user actions, emotions, pain points, and solutions. The customer journey map is the foundation for further analysis.

2. Identify the right analytics tools.

To accurately conduct customer journey analysis, you'll need the right tools. 

A good customer journey analytics tool will monitor, track, and analyze data like website data, conversion data, and detail data across multiple channels.

Customer data platforms (CDPs) also play a role in supporting customer journey analytics. The platforms assign unique IDs to your website and app to build single customer views, which can include information such as location, browser, device type, operating system, historical transactions, and visitor logs.

3. Collect your data.

A robust analytics platform should enable you to collect data on customer behavior. Data can be broken down into two main buckets: user data and interaction data. 

• User data: Provides context on a user and their traits. Data can include email, age, industry, and occupation.
• Interaction data: Gives information about how a user interacts with your product or service.

4. Analyze data.

Data in itself is not meaningful without analysis. The purpose of customer journey analytics is to make sense of the data and extract insights that can inform your business strategy. 

For example, an e-commerce company might identify, through analysis, that requiring customers to create an account to complete a purchase leads to the customer not completing the purchase — a solution could be implementing a guest checkout option.

5. Update customer journey map.

Using the insights you’ve gained, you can now update the customer journey map accordingly. For example, you might add additional pain points uncovered through data analysis, like requiring customers to create accounts to complete a purchase.

6. Use customer journey analytics to test new strategies.

The next step is to figure out how to enhance the customer journey experience. Testing new strategies like adding a guest checkout option, making the account creation process faster with fewer steps, and sending abandoned cart emails are all examples.

Benefits of Customer Journey Analytics

By leveraging customer journey analytics, you'll be able to improve your customer’s experience with actionable insights, while unlocking benefits like:

Better Understanding Customers

By gathering and synthesizing data, you will better understand what aspects of the buyer’s journey lead them to purchase a product or service, or not. For example, an e-commerce company might learn that customers that come from a specific social media platform are more likely to buy, or discover that certain audience demographics or affinities are more likely to become leads.

Pinpointing Where You’re Losing Customers

Not all customers follow through, and unless they fill out a survey, it can be difficult to figure out why they churn. By leveraging customer journey analytics, you can pinpoint where you’re losing potential customers. 

For example, a business can lose potential customers during channel or device transitions. A prospect  might start filling out a form on a mobile device but choose to complete it on a laptop. If information entered is lost, the potential customer might not take the time to complete the signup process.

Optimizing and Solving for Prospects

With a better understanding of customers’ pain points and the reasons behind them, you'll be able to figure out how to improve and strategize around an accurate customer journey.

Improve ROI

Are your investments in customer experience worth it? By using customer journey analytics, you’ll be able to measure ROI for customer experience initiatives. From there, you can streamline, remove, or cost cut initiatives that don’t benefit your bottom line, or double down on the aspects of the buyer’s journey that do.

For instance, if you run an incredibly expensive advertising campaign that doesn’t yield the same level of new customers or purchase page visits as unpaid or more in-house content, you can aim to save money on ads and focus on the more affordable strategies that actually earn you money.

1. HubSpot Marketing Hub: Advanced Marketing Reporting

Get started with customer journey analytics

HubSpot Marketing Hub is equipped with robust customer journey analytics capabilities and tools, which can map data around conversions, leads, deals, and website engagements around different stages of the customer journey. 

The Advanced Marketing Reporting tool also enables you to  attribute every customer interaction to revenue, analyze conversion rates and time between nurturing path steps, and provides further data to help you build informed strategies that can improve ROI and purchase rates.

2. Content Square

Content Square captures UX, performance and product, and content data throughout the customer journey. The platform also enables you to visualize metrics so that they are easily digestible. You will be able to get insights like bounce rate and number of lost conversions, and dig deeper to pinpoint why.

3. Google Analytics

Google Analytics is a widely used website analytics software that enables you to track user behavior on different platforms, including mobile applications. Features like daily traffic reporting give you insight into what visitors are engaging with. Plus, its Analytics Amplifier can combine Google Analytics and HubSpot data .

Customer Journey Mapping vs. Customer Journey Analytics

Customer journey analytics and customer journey mapping are often confused with each other. Although they’re complementary,  customer journey mapping visually presents customer journey stages from start to finish, while customer journey analytics offers data about a  customers’ interactions in each stage.

Customer journey maps often include the following:

• The buying process: By pulling data from places like CMS and prospecting tools, you will be able to figure out what goes into a customer’s purchasing process. 
• User actions: This part of the customer journey map details the actions the customer takes throughout their journey.
• Emotions: Emotions help color your understanding of how your customer is feeling and reacting as they go through their journey with your business.
• Pain points: Adding pain points to your customer journey map gives you a comprehensive picture of the challenges your customer may experience.
• Solutions: Figuring out solutions can help your customers experience fewer pain points. The data and insights you’ve gathered can help inform your solutions.

Customer journey analytics delves deeper. The customer journey map is the “what” and customer journey analytics is the “why” because it organizes customer or prospect data around each stage.  

Here’s an example of how customer journey analytics works in HubSpot Marketing Hub:

HubSpot’s Advanced Marketing Reporting Tool  

Customer Journey Map vs. Analytics Example: Let’s say your business offers CMS tools and your ideal customer, a graphic design firm, finds you through a targeted Instagram ad.

In the customer journey map you’ve built , your target customer considers using your CMS tools to build a new website that showcases their strengths. They schedule a demo before trying the free version and are initially excited, but become frustrated with the limited design elements offered by the free version. Their biggest pain point quickly becomes lack of versatility. They then look into purchasing the paid version or go to a cheaper competitor.

With customer journey analytics, you’ll apply real-time data to that map: From journey mapping, you’ve identified the steps your customer often takes  and their common pain points. A strong customer journey analytics tool can then collect, aggregate, synthesize, and visualize data to help you make sense of your customer’s actions and see if your mapping and journey-based strategies work. 

For example, data might show how your customer is interacting with your product. 

A good Customer Journey Analytics platform combines data like user data, survey results, and website analytics, you can gain a comprehensive view of why your customer is experiencing those pain points and consequently address their concerns.

Cultivate an Impactful Customer Journey

In order to remain competitive, it is important to understand and create strategies to enhance the customer’s journey. Customer Journey Analytics is just one component. Other key steps include creating buyer personas , mapping out the customer journey , and continuously updating strategies based on data.

To get started with improving the customer journey, learn more about HubSpot’s marketing solution Marketing Hub .

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• Customer Journey Analytics

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ADOBE CUSTOMER JOURNEY ANALYTICS

Your customer data has a story to tell. We give you more context, so you get more story.

Adobe Customer Journey Analytics gives you more: real-time analysis of both online and offline data, privacy-ready profiles, and access for teams across your organization. Now, everyone has the insights they need to create more connected customer journeys.

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Unify data from any channel — online and offline.

The streaming data collection of Customer Journey Analytics follows customers in real time as they move from online to in-store to customer call centers and more. Then it unifies the data into a single customer profile and a comprehensive customer journey. The fully correlated data gives you unlimited breakdowns of any data elements so you can uncover deeper insights — all without writing SQL.

Dig deeper into connected data

Visualize the entire customer journey.

A simple drag-and-drop interface with flow analysis lets you map customer journeys in seconds — and get insights in minutes, not months — so you can understand how customers move across your websites and apps, and their in-person experiences. Analyze their behaviors with rule-based modeling and algorithmic attribution to understand your most valuable touchpoints, and recognize trends by comparing customers with shared characteristics using cohort analysis.

Dig deeper into end-to-end visualization

Go deeper with the help of AI.

Give marketers, analysts, and content creators tools to apply AI to their everyday tasks. Customer Journey Analytics applies AI to improve segmentation and attribution, help you identify causal relationships between customer interactions, and improve anomaly detection so you can quickly take action on outlier events that could impact your business. It also helps you weigh metrics and evaluate action plans so you can grow or streamline your business.

Dig deeper into AI-driven insights

Make insights accessible across the organization.

Now, everyone in your organization can deliver experiences based on a complete picture of customer interactions. Our intuitive, collaborative user interface lets teams easily curate, share, and schedule reports. Executives get real-time snapshots right on their phone with the Analytics Dashboard. And the patented data governance tools powered by Adobe Experience Platform, such as role-based access, give teams what they need to provide amazing experiences while protecting customer privacy and policies.

Dig deeper into data-driven decisions

Discover your most valuable customers.

Quickly discover new, high-value customers using real-time insights from Customer Journey Analytics. Publish customer audiences from specific events or campaigns or create new customer segments based on filters like actions, journeys, and events — all with flexible dates and look-back windows. Then immediately activate those audiences through Adobe Journey Optimizer or Adobe Real-Time CDP. Close the loop from audience generation to activation and impact by analyzing the performance of specific audiences over time directly in Customer Journey Analytics.

Dig deeper into insights activation

Adobe is leading the charge in real-time customer analytics.

Adobe is named a Leader in its Forrester Wave: Customer Analytics Technologies Q2, 2022 report, citing robust data visualization tools, a user-friendly interface, and seamless integrations.

Read the report