Core Concepts of Customer Journey Analytics

How often have you wanted to master the latest trend in the digital market, 'Customer Journey Analytics', but struggled because you lack the essential foundational knowledge to grasp it?

How often have you heard people say 'Connections', 'Data Views' and 'Identity stitching' only to later wonder what they meant?

How many times have you read articles on CJA basics online but always found yourself confused because they used a lot of Jargons and keywords, without actually explaining anything?

If any of the three questions you just read have crossed your mind while learning CJA, then this post is for you! From grasping fundamental ideas to tackling advanced concepts, having a resource that provides clear analogies and real-world scenarios makes all the difference.

So, in this post, let's explore some essential concepts you should understand before stepping into the world of Customer Journey Analytics.

Person Identity Handling

This is how CJA identifies and tracks individual persons across different touch points in their customer journey. It integrates varied dataset sources and ensures all data is accurately attributed to the correct person.

But how does this work?

Imagine a big library where people come in and read books. Some people use their library cards (logged-in users), while others just walk in and browse (guest users). The library wants to keep track of what books each person reads, even if they don’t always use their card.

What is Person Identity Handling?

It’s like giving each visitor a unique library ID that helps track their activity across different visits, whether they use their card or not. This ID is like a special tag that follows them whenever they interact with the library (or in this case, a website, app, or service).

How Does Field-Based Stitching Work?

Let’s say a person first visits the library without a card, but later signs up for one. Normally, their past visits wouldn’t be linked to their new library card. But field-based stitching works like a smart librarian who realizes, "Hey! This is the same person who came in before!" and merges their past and new records under one stitched ID. This means:

• All their past and future visits are now tied together.
• The library gets a complete picture of what books they’ve read, even before they got a card.

Why Is This Important?

With this system, the library (or a business using CJA) can:

• Understand what books (or products/services) a person is interested in.
• Give better recommendations based on their full history.
• Provide a more personalized experience.

By using Person Identity Handling, businesses can see a full customer journey, whether they interacted while logged in or as a guest, allowing them to make smarter decisions and create better customer experiences.

How does field-stitching work?

Let’s say you visit a coffee shop multiple times:

• On your first few visits, you pay in cash and don’t sign up for their loyalty program.
• Later, you sign up and get a membership card that tracks your purchases.

How Does the Coffee Shop "Link" Your Past Visits?

Now, imagine the coffee shop has security cameras and receipts with timestamps. One day, an employee notices that you’ve been coming in for weeks before signing up. They go back, match your previous receipts to your new membership, and say:

"Oh! This person who bought cappuccinos before is the same person who just signed up! Let’s add their past purchases to their membership account!"

• Before signing up → Your visits were recorded but not linked to you.
• After signing up → They use clues (same phone number, payment card, or favorite order) to link old visits to your new account.

Now, How Does This Work in CJA?

• Before logging in → A user visits a website/app, but they’re treated as an anonymous guest.
• After logging in → They enter their email or some identifier (e.g., a customer ID).
• Field-Based Stitching steps in → It checks past events and looks for a match (like the coffee shop matching receipts).
• If a match is found → CJA merges all past guest activity with their logged-in profile, using a new stitched ID that represents their complete journey.

This way, businesses can track the same person across different interactions, even if they weren’t logged in before creating a full, connected customer story instead of treating them as two separate users.

Connections

Think of CJA Connections Like a Giant Puzzle

Imagine you are putting together a giant puzzle of a customer’s journey.

• Each dataset (website orders, in-store purchases, customer support chats, etc.) is like a separate puzzle piece with valuable information.
• However, these pieces come from different boxes (separate datasets from different platforms).
• To see the full picture of the customer, you need to combine all these pieces correctly, this is where Connections in CJA come in!

How Does CJA Build the Puzzle?

Merging Different Datasets into One Big Picture

Think of CJA as a super-smart puzzle solver that:

• Takes different puzzle pieces (datasets) and puts them together.
• Matches pieces correctly by using the Person ID (like a unique barcode for each customer).
• Ensures all pieces fit smoothly by aligning timestamps (down to the millisecond).

Example:

• A customer buys a laptop on a website (recorded in one dataset).
• The same customer buys a mouse in a physical store (recorded in another dataset).
• CJA merges these separate records into one view under the same customer ID.

Now, instead of two separate purchase records, you see a full journey of that person.

Why is the ‘Person ID’ So Important?

Think of the Person ID like a membership card number at a supermarket.

• Every time a customer buys something, they swipe their card.
• The system tracks all their purchases under one account, even if they buy from different locations.
• If a purchase is not linked to their membership card, it won’t appear in their purchase history.

This is exactly how CJA works, if an event doesn’t have a Person ID, it won’t be included in the final Connection!

Data Views

Think of a Data View Like a Menu at a Restaurant

Imagine you own a big kitchen (your Connection in CJA) that has all ingredients (raw data). You want to serve different types of customers, but each group wants different types of meals.

• The Data View is like a restaurant menu that decides what dishes to show based on your ingredients.
• Even though your kitchen has all the ingredients, you choose which ones to display in the menu for customers to order (which metrics & dimensions to show in reports).

How Does It Work?

• Data Views Decide What Data to Show : Let’s say your kitchen (CJA Connection) has everything: veggies, meat, pasta, spices, and sauces. But for Vegetarian Customers, you create a Vegetarian Menu that only shows veggie dishes. For Seafood Lovers, you make a Seafood Menu that only lists fish-based dishes. The same way, a Data View lets you pick and choose which parts of your data you want to analyze.
• Example in CJA: You may have a Connection with both website and mobile app data, but you create: A Website Data View (showing only website traffic) while a Mobile Data View (showing only app data).
• Data Views Allow Custom Rules (Like Restaurant Policies) : Your restaurant has rules, like “A table is considered empty if no one sits for 30 minutes.” Similarly, CJA Data Views let you define session settings (e.g., “If a user is inactive for 30 minutes, start a new session”). You can also set up filters, for example, only include customers from a specific country.
• One Kitchen, Multiple Menus (One Connection, Multiple Data Views) : Your kitchen stays the same, but you create different menus based on your audience. In CJA, your Connection stays the same, but you create different Data Views for different teams.

Example in a company:

• Marketing Team gets a Data View focused on “Campaign Performance.”
• Product Team gets a Data View focused on “Feature Usage.”

Projects and Analysis Workspace

Projects & Analysis Workspace is the core reporting interface of CJA, providing a flexible and dynamic environment for analyzing a wide range of metrics and dimensions. It allows you to drag and drop various components onto a project, enabling a fully customized view of your data. Additionally, you can create projects that incorporate panels from multiple datasets, facilitating side-by-side comparisons and helping you identify relationships between different data sets.

Understanding Projects & Analysis Workspace is fundamental to mastering CJA, as it serves as the primary interface for interacting with and interpreting customer data. It brings data to life, allowing you to uncover patterns and trends that shape customer behavior. Whether analyzing the customer journey, optimizing marketing campaigns, or diagnosing traffic drops, Analysis Workspace empowers you to visually explore and gain deeper insights into your data.

How is the CJA workspace different from Adobe analytics workspace?

Both Customer Journey Analytics (CJA) Workspace and Adobe Analytics Workspace serve as reporting and analysis platforms within Adobe’s ecosystem, but they differ significantly in their data architecture, flexibility, and use cases.

1. Data Sources & Architecture

• CJA Workspace: Built on the Adobe Experience Platform (AEP), CJA leverages Experience Data Model (XDM)-based datasets instead of traditional Adobe Analytics data. It allows you to combine multiple data sources (e.g., online, offline, call center, CRM, etc.), offering a more holistic, cross-channel view of customer journeys.
• Adobe Analytics Workspace: Uses traditional Adobe Analytics data models based on page views and events. The data is limited to web and mobile analytics, making it more focused on digital interactions.

2. Data Stitching & Identity Resolution

• CJA Workspace: Supports cross-device and cross-channel identity resolution, meaning it can unify customer interactions across multiple touchpoints. Identity stitching is done through Identity Graphs.
• Adobe Analytics Workspace: Lacks built-in identity resolution and treats different device interactions separately unless configured with Customer ID or People Metric in the Experience Cloud.

3. Data Processing & Attribution

• CJA Workspace: Allows real-time, event-based data processing across various channels, giving users the flexibility to analyze interactions beyond just web and app data. It also supports custom attribution models based on dataset relationships.
• Adobe Analytics Workspace: Uses pre-processed data with predefined attribution models (first-touch, last-touch, linear, etc.), making it less flexible for custom attribution scenarios.

4. Data Modeling & Flexibility

• CJA Workspace: Provides schema-based data ingestion, where analysts can define relationships between different datasets and use joins across multiple datasets dynamically in Analysis Workspace.
• Adobe Analytics Workspace: Relies on fixed data collection structures and variable-based implementation, limiting flexibility in modifying or integrating new datasets post-processing.

Use Cases

• CJA Workspace: Best for cross-channel, customer journey analysis, identity stitching, and multi-touch attribution across different datasets (e.g., CRM, offline sales, call center logs).
• Adobe Analytics Workspace: Best for web and mobile analytics, user behavior analysis, and marketing performance tracking within a single digital channel.

Hope this article was able to make use of simple analogies and terms to explain the core concepts that make up Customer Journey Analytics!

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