Get started analyzing data with Coworker Chat

Set up Adobe CX Enterprise Coworker Chat to analyze your Customer Journey Analytics data views or Adobe Analytics report suites, then follow a working example. For an overview of what you can do with Coworker Chat, including use cases, skills, and best practices, see Analyze data with Coworker Chat.

Before you begin, learn the Coworker Chat interface and configuration options, then ensure Coworker is connected to Customer Journey Analytics or Adobe Analytics and the relevant data views or report suites.

Before you begin

Data access and permissions

Coworker Chat inherits permissions from Customer Journey Analytics or Adobe Analytics. You can access only those data views, report suites, dimensions, metrics, and segments available to you in Analysis Workspace.

Interface and configuration options

Before you use Coworker Chat with your Customer Journey Analytics or Adobe Analytics data, learn how to sign in and manage configuration options for the following features:

For more information, see the Coworker Chat UI guide.

Verify that Coworker Chat is connected to Customer Journey Analytics

In Coworker Chat, verify that Coworker is connected to Customer Journey Analytics:

Connect to the right data view or report suite

A data view is a container in Customer Journey Analytics that determines how data is interpreted. A report suite is a container in Adobe Analytics that holds the data collected from your sites and apps.

You might have access to various data views in Customer Journey Analytics or report suites in Adobe Analytics. Each might contain different dimensions and metrics that Coworker can use when analyzing data.

Decide which data views or report suites you want to use

Tell Coworker the types of questions you want answered, and ask it which data views or report suites you have access to that provide that information. You can also set your data view or report suite as a preference in memory.

You:

I’m interested in learning where customers drop off in the customer journey. Which data views in Customer Journey Analytics do I have access to that would be able to answer this question for me?

Coworker Chat response:

You have access to three data views. The Customer lifecycle data view contains the following dimensions and metrics, which would be best for answering your question.

You:

Great, let’s use that data view.

Coworker Chat response:

Okay, I’ll use the Customer lifecycle data view to answer future questions in this chat session.

Add a data view or report suite preference in Memory

Coworker Chat contains a memory feature that allows you to provide it with access to information that spans across all chats. It’s a good practice to add your preferred data views or report suites as preferences in Coworker’s memory.

Example: Find where customers drop off

You can ask Coworker Chat to use your data to analyze any business question.

For example, as a marketing manager, merchandiser, or growth lead, you might want to understand where customers abandon the path to purchase, why they did, and what can be done to drive higher conversion rates.

The types of questions you can ask

You don’t need to know what a “fallout visualization” is, or which metric goes in which slot. Instead, describe the outcome you care about in plain language:

The conversation

1. Start with the business question

You:

Where are we losing people on the way to purchase?

Coworker Chat response:

Notice that Coworker Chat chooses the right visualization, sequences the touchpoints, and even adds recommended next steps.

This is a funnel question — let me run the conversion funnel to see where drop-off happens on the path to purchase.

Here’s where you’re losing people:

The big finding: cart to purchase is your biggest leak

28,463 people add items to their cart but never complete the purchase. That’s an 83.6% cart-to-purchase drop-off rate — by far the largest single point of failure.

What this tells you about priorities:

Recommended next steps:

Which of these would you like me to dig into next? The channel breakdown would tell you directly which marketing actions are bringing you “almost-buyers” versus actual buyers.

2. Drill into the leak

You:

Okay, let’s break down by marketing channel

Coworker Chat response:

There’s a custom Marketing Channel dimension. Let me pull the funnel by that, then calculate the per-channel conversion rates and identify where each channel leaks.

The verdict: the leak is not channel-specific

This is a really revealing finding. Here’s the per-channel funnel breakdown:

Key takeaways:

What this tells you strategically

The marketing channels are doing their job — they’re all getting people interested and adding to cart at similar rates. The problem is downstream of marketing: your checkout and payment flow is where the money is leaking.

Recommended next investigations:

Which angle would you like to explore next?

3. Keep investigating

You:

Interesting. Let’s try breaking down by device type.

Coworker Chat response:

Found it: mobile checkout is broken

The funnel is identical across devices until checkout — then mobile falls off a cliff:

The smoking gun

The browsing and add-to-cart experience is fine on mobile (identical ~45% browse rate, ~55% add-to-cart rate). People are equally interested and engaged. But something in the mobile checkout flow is killing conversions.

The revenue opportunity

If mobile checkout converted at the same rate as desktop, you’d gain ~1,991 additional purchases — a 35% increase in total purchase volume from fixing one experience.

What’s likely happening

The data pattern (equal engagement, catastrophic checkout drop) is a classic signal for:

Recommended next steps:

Which would you like to explore, or is this enough evidence to take to your UX and checkout team?

4. Turn it into a decision

You:

So what should I do?

Coworker Chat response:

Based on what the data shows, here’s where I’d focus:

Want me to save this as a project so you can track it, or set an alert if mobile cart-to-purchase drops below a threshold?

What happened

In four plain-language questions, Coworker helped us:

Analyze in Customer Journey Analytics

After Coworker creates a visualization, you can open it in Analysis Workspace for deeper analysis and granular control. The visualization opens in a new Analysis Workspace project in Customer Journey Analytics.

To open a visualization in a new Analysis Workspace project:

Next steps

For more use cases, the skills that Coworker Chat uses to analyze your data, and best practices for writing prompts, see Analyze data with Coworker Chat.