---
title: Customer Journey Analytics
slug: ucx-syd-2026/customer-journey-analytics
docTags: 
createdAt: 2025-05-12T00:49:48.217Z
---

# Scenario

As an Analyst at Frescopa you are tasked to leverage to look for insights on how Frescopa customers are behaving across business across channels to inform the marketing strategy. 

# Access 

Your login credentials should be available on your desk including your lab participant number.   

Login via [https://platform.adobe.com](https://platform.adobe.com)

Once authenticated switch to **Customer Journey Analytics** using the solution switcher: 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/VfCpxDYRz1tG59-nUpMU6_image.png)

# CJA Introduction

First we'll start by creating a new project from a pre-built template.  This will provide you with some initial insights while giving you an isolated area to explore the data further.  

Under **Workspace -> Templates -> Demo APAC templates **and select the **Frescopa Data View **on the right side. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/OuRTbzLGQ5iqSKs8Q8LDy_image.png)

Hover over the **Frescopa (Template) **card and click to **Use template**

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/XaIvQS5Rjqfhux852Ds5w_image.png)

Take note of the following UI components:

**Panel** - Each dashboard/project is made up of one or more panels

**Data View** - Controls what data is available to the report - keep in mind this includes multiple datasets that can span multiple channels and data sources. 

**Date Range** - The date range controls what data is displayed in the panel 

**Left Rail** - Provides access to all of the components required to build your reports.   You will find menu's for panels, components, visualisations, guided analyses and a table of contents for the current dashboard. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/rCaWJ3BupkRm42tguMDCZ_image.png)

For this exercise we will walk through the first panel together.  

You can see in the first Trend Line visualisation the new containers used by CJA:  **Events, Sessions **and** People.  **If you're familiar with Adobe Analytics you can think of these as being equivalent to Hits, Visits and Visitors - but remember in Customer Journey Analytics we're grouping data based on a person identifier, not a device (or cookie).     

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/GZl5Xp6wCpGq0bdhCL5pn_image.png)

Based on the forward looking date range defined for this panel, Customer Journey Analytics has provided some automatic **forecasting** to predict how the data will continue to trend over the coming weeks.   You can leverage forecasting for:

**Planning**: By analyzing trends and predicting future outcomes, you can identify potential opportunities and threats and adjust your strategies accordingly. 

**Budgeting**: By forecasting future revenue and expenses, you can ensure that teams have adequate funds to cover their costs and invest in growth opportunities.

**Risk management**: By anticipating future events and trends, you can take proactive steps to mitigate risks and minimize potential losses.

**Resource allocation**: By predicting future demand, you can ensure the necessary resources are available to meet customer needs and avoid stock-outs or overstocking.

**Performance evaluation**: By comparing actual results to forecasted results, you can identify areas for improvement and make necessary adjustments. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/i1LoDNOIV7GToYFzEXKK-_image.png)

Since the event data ingested to CJA comes from multiple sources not just your website and app we can combine many different types of metrics into the same dashboard.  Scroll down to the expanded metrics table. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/Xq7dOlEAFhEFY8_Q68Efs_image.png)

Open the components menu and drag the calls metric to add it to your tables columns.

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/VRtpP8njf5GK1QX1rE9Cz_image.png)

Each of these metrics come from one or more source systems.  Search for **interaction channel **in the components menu and drag it onto the table to **replace **the week dimension.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/HVE62NFNb31X_kB41i360_image.png)

Some metrics will be specific per channel, for example the **Calls** metric comes from the **Call Center** dataset, others will be shared across multiple source systems, for example the business metrics for **Total Orders **and **Subscriptions**. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/f6uotJ_LVReOzUeG32oHX_image.png)

:::hint{type="info"}
Like Adobe Analytics, Customer Journey Analytics **does not use sampling**. It processes all ingested data at query time, allowing for full-fidelity analysis across massive datasets without approximation.   

There are also **no** **cardinality limits** allowing for any unique value to appear and be counted.  
:::

Since each of these source datasets are stitched at the person level, we can start to analyze things like audience size per channel and devices per person. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/wgGYS-6HeLJgWjKP_j4eC_image.png)



Now before we start our adhoc analysis let's save the project.   Click on **Project -> Save.**  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/_xTGsw0pCIi9tLZySuuhJ_image.png)

Give your project a unique name by including your lab number or name.   Then click **Save**. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/lIakMVetcSKkFGDB6S3JG_image.png)

:::hint{type="success"}
Now we've covered some basics, let's take a closer look at Frescopa's recent results and check whether we can see any changes to customer behaviour since the competitor launched last month. 
:::

# Data Insights Agent

Scroll down to the next panel **Frescopa Sales Performance Summary **to see some of Frescopa's key performance metrics summarized. 

We can see from this report that business is looking good, cancellations are trending up which isn't great though.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/btscDURc1AWlRURjiL_oL_image.png)

Let's use CJA's **Data Insights Agent** to analyse how the in offline (ie. in store) sales are performing and whether there have been any recent changes.    

To access the agent, open the AI Assistant by clicking the icon in the top right. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/U_aHJesapQesj_h4SnvjG_image.png)

Ask AI Assistant to: **trend offline sales from the last 30 days **and hit enter. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/n9gaGDeLRrxdZkN5Gs0rG_image.png)

The assistant has added a new panel to our project and added a line visualisation and freeform table.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/XruFOrESaS1juZpTO0FXD_image.png)

I actually want to look at order numbers though not revenue.

- Ask AI Assistant to: **replace "Offline Revenue" with "Offline Orders" **

The data in the generated visualisation is updated to use offline orders metric not revenue. This metric represents our instore orders.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/V-3WWVa6UkK40bb0WItJQ_image.png)

We're looking for changes in recent purchasing behavior so let's see how the orders compare to last month. 

- Ask AI Assistant: **compare this with the previous 30 days**

The visualisation has been updated with a new column showing the previous time period.   

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/w7pORq7-4WobGCnJ5uXZT_image.png)

:::hint{type="warning"}
The agent is still learning and might not get the answers right every time, if you see a result that's unexpected, use the thumbs up and thumbs down feedback buttons to provide context on what went wrong and help us to improve the accuracy of the agents results. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/6XBJtDE5AwLHyUroWtRUl_image.png)
:::

I want to reduce the granularity in my table from daily to weekly.  

- Ask AI Assistant to **show this by week instead of day**

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/wOocFzvegtFu6_q4sYM18_image.png)

Let's check now whether these numbers vary depending on the location or age group of the customer. 

- Ask AI Assistant to **change week to city**

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/ITWQBKX8PaWdupaQTR84e_image.png)

We can see that yes, offline orders are up month on month across all major cities.  

:::hint{type="success"}
With Data Insights Agent, business users can use natural language prompts to quickly and efficiently answers questions about their data. It builds relevant visualizations in Analysis Workspace using components and data from the currently active data view.   This can save time and effort  building out new visualisations. 

[Read more here](https://experienceleague.adobe.com/en/docs/analytics-platform/using/cja-overview/cja-b2c-overview/data-analysis-ai)
:::

# Analysing the Journey

Now that we've generated some quick insights using the Data Insights Agent, let's take a look at the how customers are behaving across channels.

Scroll back up to the panel labeled **Analysing the Journey **

:::hint{type="info"}
***Hint: **you can use the table of contents on the left rail to find and change panels quickly. *

::Image[]{src="https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/0-Q7GEIHzB-Rwy3ZX8pFm_image.png" size="30" width="307" height="716" position="flex-start" showCaption="false"}
:::

Here we can see the overlap of people interacting across channels.  The **Channel Overlap ** venn diagrams show the overlap of customers engaging on the website or mobile app while also calling the call center or visiting a store.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/91he1ZEIDp2thPKPPisQr_image.png)

The Flow visualisation is a powerful way to understand customer journeys through your website or app.  Now with CJA you can leverage the same visualisation to discover the journey your customers are taking across all of your channels.

Drag a **Flow **visualisation from the **visualisations **panel.  Drop it under the venn diagram. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/HHHJnnIThizQoslHZP-Sw_image.png)

For this example we can analyse which pages customers are visiting on the website before they call the call center.  

Search for **Call Reason **in the ends with field, click in to see the options and select the **Online Shopping Support **reason.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/z-0ZgnRXIpvx_opD87o6x_image.png)

Change the pathing dimension to **Page Name.     **

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/uXnONzgMXCJkIpoWmiOo3_image.png)

Click **Build.   **We can now see which pages a customer is viewing on the site in the lead up to a call.   You can use this analysis to optimize those pages and potentially deflect some of these calls back to self service channels. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/w4A_s6V3wBcR7U0HYzVHl_image.png)

Now, given the increase in offline orders, let's take a look to see how many of these customers are first researching online before buying in store.  

Scroll to the **Purchase Fallout **fallout, update the label to **Research online, buy instore.**

Drag the **Product Views** metric and add it as a touchpoint in our fallout. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/7Wp7lLHVNby1dQAYHjVXz_image.png)

Drag the **Offline orders **metric and add it.  We can now see how many known customers researched products online before purchasing offline.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/-7rwOTiJG0p_zwGjaZ8ft_image.png)

Let's take a look at how this behavior compares across segments.   Drag the **Age: Younger**, **Age: Older** and **Gold or Platinum Tier** segments and add them to the fallout. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/aef8jmkQryIbABIiy7PKy_image.png)

Interestingly we can see that this behaviour is more common with a younger demographic, and our gold and platinum tier members.  A targeted online only offer might help to promote online sales.

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/TaCN61HFy-hrtGFu9JTfX_image.png)

:::hint{type="success"}
With these kind of visualisations and connected customer data we can run analysis of fallout and fallthrough that accommodates complex user journeys crossing any number of channels, dimensions and metrics.
:::

# Guided Analysis

Let's look now for any changes to the frequency with which customers are purchasing.   We can use one of the new Guided Analysis options to quickly group event data by how often events occur in your product (on your website, app or any channel).   

We are using purchasing events in this case, but you could consider analysing the frequency of mobile app logins vs website logins or any other action you are expecting users to complete on a regular basis across your channels. 

Drag the **Frequency **analysis onto your dashboard.

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/Mm-TjDnR9fBl7-3EYUkSa_image.png)

Click to **Create Frequency.**

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/7c3s3jugzAAeMQ1qiNIZp_image.png)

The vertical axis of this analysis contains buckets that represent the frequency of the event. The horizontal axis measures the number of users or sessions for each bucket.

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/UU7pgjXzgrjWEU6TqNB9y_image.png)

Let's update the events we want to analyse from any event to orders based on their fulfillment type.  We'll analyse delivery orders vs in store pickup.  

Change **Any Event **to **Total Orders, **then filter based on **Fulfillment Method.   **Select all values except **in-store pickup.  **

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/m0kfLydiK9Wfj3bIMep9W_image.png)

Add a second event for **Total Orders **with** Fulfillment Method -  Instore pickup**

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/jDDaC6jN41mV_t6WokUA__image.png)

The frequency can be counted as Users (or People), Sessions, Percentage of users or Percentage of sessions. The denominator for percentage-based metrics in this analysis is users or sessions who did the selected events, not all active users of the product.

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/FIYJMrnlVTHujJVbbT1ld_image.png)

We can see that customers ordering via delivery have a much higher purchase frequency than those using In Store Pickup. 

Change the label to **Delivery vs Instore Pickup **and click to **Save **the panel and add it to your dashboard.

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/zhxqckD-BHYCnnqk3nnn3_image.png)

:::hint{type="success"}
**Guided Analysis **allows users—marketers, product managers, and analysts—to explore customer behaviour across channels using intuitive, question-driven workflows. It’s designed to simplify complex analysis and accelerate insights without needing deep technical expertise.   
:::

# Marketing Performance

Let's take a quick look now at the marketing channels which have had the most impact historically on new subscriptions which are key to Frescopa's recurring revenue.  This will provide insight on where we should focus our upcoming marketing effort and budget. 

Scroll down to the last panel in your dashboard.   We can see here a quick visualisation to show the top performing channels for both new subscriptions and total revenue. 

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/LfTsx0Egd9MI4yN3Yu3mv_image.png)

:::hint{type="info"}
The "Marketing channel" dimension differs from the "Interaction Channel" dimension we used earlier.  The marketing channel represents the channel the customer came from when landing on the website or app.   It is typically derived from campaign parameters attached to digital ads or marketing messages like emails.  
:::

If you scroll down to the data table which enables these visualisations, you can see we also have summary data from Meta and Google to understand how many impressions, clicks and the cost for these paid channels for the reporting period.  This data is collected as daily aggregates and can be tied to the person level event metrics through shared Campaign Id's collected when a customer clicks through from an Ad.  

With this summary level data we can start to report on advertising metrics like cost per click, return on ad spend and cost per aquisition across not only online but offline channels as well.  

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/u-3xHlfeOru_PFIb8rmXq_image.png)

Currently Frescopa is using a last touch attribution model, this means they assign 100% of the credit for the conversion to the last channel that the customer came from, but what if the customer sees more than one marketing message?    CJA supports 11 different attribution models, including Algorithmic attribution which uses machine learning and statistical modelling to distribute credit across multiple touchpoints.   Right click on the Total Revenue column and select **Compare attribution model.   **

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/GCQIjoEI0kSpiMgOSoVdx_image.png)

Select **Algorithmic**



![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/vCVAm3qEM0NebslJKNnBG_image.png)

Update the **lookback window** the 14 days and click **Apply**

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/eDQten9_MfdFEjey5vLd8_image.png)

We can see the credit applied to our top 3 channels have all been reduced and greater weight applied to channels we otherwise might have deprioritized.

![](https://api.archbee.com/api/optimize/1Jp0iolWv35Z_mkI7oUiT/lgfs9XEsis7Tdi5qQX1Zc_image.png)

:::hint{type="success"}
Adobe's Attribution IQ allows customers to understand the full impact of their marketing efforts whether the conversion happens online or offline. 
:::

# Summary

Customer Journey Analytics empowers organisations to see the full customer journey across every channel - online and offline.  CJA unifies fragmented data into a single, intuitive interface, enabling even non-technical users to uncover deep, actionable insights with drag and drop ease.   With Data Insights Agent we're democratising access even further building reports through natural language prompts.  Whether you're optimising campaigns or troubleshooting customer pain points, CJA delivers clarity, agility, and precision that traditional analytics tools simply can't match.

:::hint{type="success"}
Congratulations, you've completed the exercise to gather insights using Customer Journey Analytics!   
:::