The next generation of CCaaS is here
Digital-first customer service, enterprise-scale voice support. Redefine customer service with an AI-powered platform that unifies voice, digital and social channels. Power channel-less interactions and seamless resolution no matter the channel of contact.
Customer Journey Analytics: A Comprehensive Guide
There is no room for failure when it comes to delivering customer experience. The modern customer is unforgiving toward brands delivering inconsistent, broken experiences, especially after the pandemic.
Customer journey analytics streamlines customer experience management by connecting individual interactions with business processes and outcomes directly. Dismantling data silos, it unifies customer data across the journey, transforming it into comprehensible intelligence that drives rapid response action across the board.
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
What is customer journey analytics?
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.
Top benefits of leveraging customer journey analytics
On a high level, customer journey analytics pinpoints the successes and areas for improvement, unlocking the following benefits for brands:
- Helps visualize customer journeys end-to-end: You get a helicopter view of the online and offline journey for customers with shared attributes. The parallel comparison highlights trends in customer behavior and the outcome of retention campaigns.
- 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.
How to build customer journey analytics
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.
How can teams take advantage of customer journey analytics data?
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?
Customer journey analytics vs. customer journey mapping: Core differences
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.
Aspect | Customer Journey Analytics | Customer Journey Mapping |
🎯Focus | Analyzing customer interactions and data to derive insights and patterns | Visualizing and documenting the entire customer journey, often using maps and diagrams |
🌐Nature of Activity | Involves data analysis, metrics and statistical evaluation for actionable insights | Primarily a qualitative activity, creating a visual representation of customer experiences |
🔢Data Utilization | Leverages data from various touchpoints to analyze customer behaviors and trends | Utilizes both qualitative and quantitative data to create a holistic view of the customer journey |
🕔Timing | Ongoing and continuous analysis of real-time and historical customer data | Typically, a one-time or periodic activity to map out the customer journey stages a one-time or periodic activity to map out the customer journey stages |
🚩Purpose | Understands, predicts and optimizes customer interactions to enhance experiences | Illustrates the customer's end-to-end experience, identifying pain points and improvement opportunities |
🔨Tools and Technology | Relies on analytics platforms, machine learning and data visualization tools | Built using visual tools, journey mapping software and collaboration platforms |
➕Output | Generates actionable insights for process optimization, personalization and customer engagement | Outputs a visual representation that aids in communication, empathy building and strategic planning |
🔘Scope | Can cover a wide range of customer interactions across channels and touchpoints | Focuses on a specific journey, mapping out the steps a customer takes in a particular customer service scenario |
Deliver authentic and personalized customer experiences with Sprinklr
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.
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.
