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.

Before we get started working in CJA, even if you're not an analyst you can leverage the Data Insights agent to query CJA for you. From the Experience Cloud home page select the AI Assistant.

We first need to set the context, click on the context at the bottom of the chat prompt.

Select Source as Adobe Experience Platform and Sandbox as FAC (AUS5) and Data View as Frescopa Data View.

Ask the assistant: How have offline sales been performing over the last 30 days.

The agent has scanned the metrics available in CJA and needs to clarify what you mean by "offline sales". It returns the list of metrics it thinks might be applicable to your question. Select the number to use "offline orders".

The data is returned with a summary explaining the trend and a visual representation.

Ask a follow up question like: How does this compare to the previous 30 days

The data is returned again with a summary explanation and visualisation.

You can see the data in tabular form if preferred by clicking the table icon.

Let's see now how this looks across the country. Ask the assistant: break this down by city

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

Switch to Customer Journey Analytics using the solution switcher:

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.

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

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.

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).

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.

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.

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

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.

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.

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.



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

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

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

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.

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.

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.

Change the pathing dimension to Page Name.

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.

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

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

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

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.

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.

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.

Click to Create Frequency.

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.

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.

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

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.

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.

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.

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.

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.

Select Algorithmic



Update the lookback window the 14 days and click Apply

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.

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.