*Written by Tara Patoile, Adobe data & insights practitioner*

The Bite-Sized Breakdown

Sorting Out the Story: Customer Journey Analytics Tables Just Got Way More Useful This Month

Today on the Tarabytez unofficial podcast that is definitely still a blog post, we’re talking about a few deceptively small updates to Adobe Customer Journey Analytics that quietly solve some very real reporting headaches for Marketing Operations and Revenue Operations teams.

No dramatic product launches.
No AI hype cycle theatrics.

Just… tables that finally behave the way analysts want them to.

And if you’ve ever spent an hour explaining to an executive why a table screenshot “doesn’t quite show the whole picture,” these updates are about to make your life easier.

The Eternal Analyst Struggle: Telling the Data Story

If you work in Marketing Ops, your job is rarely just pulling numbers for stakeholders. Your real job is translating customer behavior into something stakeholders understand.

Questions like:

Which campaigns actually drive pipeline?
What combination of channel + device + campaign drives conversions?
Where are customers dropping off between digital and offline touchpoints?

The problem is sometimes the data knows the answer… but the table layout doesn’t. March’s updates to Customer Journey Analytics are all about fixing that.

Let’s break them down.

1. Multiple Dimension Columns in Freeform Tables

Let’s start with the biggest usability win: You can now add up to five dimension columns in a single freeform table in Analysis Workspace.

Previously, you were stuck with one dimension column and a bunch of breakdown gymnastics.

Now? You can put multiple dimensions side-by-side.

Each row acts like a combined dimension value, meaning the system evaluates the combination of dimensions together when calculating metrics.

In other words:

Instead of:

| Campaign | Conversions |

You can now do something like:

And suddenly the story becomes obvious.

How Marketing Operations Can Use This

Let’s say your CMO asks:

“Which campaign and channel combinations actually produce pipeline?”

A Marketing Ops analyst could build a table like:

Now stakeholders can see patterns instantly:

Example insights:

Paid Search converts better on desktop
Paid Social drives mobile traffic but lower pipeline
Certain campaigns perform better in specific regions

Instead of stitching together multiple reports, the relationship between dimensions becomes visible immediately. When you're trying to tell a customer journey story across web, CRM, and offline events, this becomes incredibly powerful.

2. Sort Tables by Multiple Columns

Next up… something analysts have been patiently waiting for: You can now sort freeform tables by multiple columns simultaneously.

Before this update, sorting was single-column only.

Meaning if you sorted by Revenue, you lost the ability to maintain grouping logic like:

Campaign
Channel
Region

Now you can define sorting priority across multiple columns, and CJA will respect that order.

Think of it like layered sorting in Excel or SQL.

Why This Matters for Data Storytelling

Sorting doesn’t immediately equal just formatting… it determines how stakeholders interpret the data.

For example, Marketing Ops could sort like this:

Marketing Channel
Campaign
Pipeline Revenue

This lets leadership quickly see:

Which channels perform best
Which campaigns within those channels drive pipeline
Which campaigns are underperforming

Instead of a chaotic list of numbers, the report becomes structured narrative.

You’re not just showing data. You’re guiding the reader through it.

3. Full Table Export Improvements (Coming This Month)

The final set of updates this month focuses on something every RevOps team eventually needs: Getting the data out of CJA and into the rest of the stack.

Customer Journey Analytics already allows full-table exports containing millions of rows to cloud destinations like Snowflake, S3, or Azure for downstream analytics.

March introduces several improvements to make that workflow smoother.

Key enhancements include:

1) Improved Export Configuration

Creating and managing exports is becoming easier to configure, helping teams operationalize recurring data exports.
This is particularly useful when:

Delivering regular datasets to BI tools
Syncing data into warehouse environments
Feeding ML or attribution models

2) More Flexible Table Structures

Full table exports support tables with:

Multiple dimensions
Multiple metrics
Combined dimension rows

Which means the exports reflect the same analytical structure you're using inside Analysis Workspace.

3) Larger and More Detailed Exports

Customer Journey Analytics has expanded the limits for exported tables, allowing up to 10 dimensions and 10 metrics in a full table export.

For Marketing Ops teams that need to share data with:

Finance
Data science
BI teams

This removes the need to create multiple exports just to include all the context.

Real Marketing Ops Example: Reporting the Customer Journey

Let’s bring it all together.

Imagine you’re responsible for reporting on lead-to-revenue performance across online and offline interactions.

Using these new capabilities, you could build a single report like:

Where Event Type could include:

Web Form Submit
Webinar Attendance
Sales Meeting
Closed Won

With multi-dimension tables and multi-column sorting, you could quickly see patterns like:

Webinars driving pipeline primarily via email campaigns
Paid search driving high lead volume but lower opportunity conversion
Certain campaigns performing differently across devices

Then export the entire dataset using Full Table Export and push it into:

BI dashboards
Revenue forecasting models
Customer lifecycle analysis

Suddenly the same dataset powers:

Executive dashboards
Marketing performance reporting
Data science models

All from one table.

Why These Updates Matter More Than They Appear

None of these updates are flashy, but they fix something analysts deal with constantly: the friction between the question and the answer.

The more flexible your tables are, the easier it becomes to:

combine dimensions
sort insights logically
export structured data for deeper analysis

Ultimately, that’s what Marketing Ops and RevOps teams are trying to do: Turn messy customer behavior into clear business insight.

Final Byte

If you spend your days inside Analysis Workspace, these updates are the kind that quietly improve everything:

Better dimensional storytelling
Cleaner reporting structures
Easier data exports

Not revolutionary, just… finally practical.

And honestly? We’ll take that any day.

Reference Links for Light Reading

Adobe Customer Journey Analytics Release Notes — Latest Updates - The official Adobe release notes summarizing newly released and upcoming capabilities for Customer Journey Analytics, including enhancements to Analysis Workspace and reporting features.
Multiple Dimension Columns in Freeform Tables — This documentation explains how analysts can add multiple dimension columns to a single freeform table to analyze combined dimension values and reveal relationships between attributes like campaign, device, and region.
Filtering and Sorting Freeform Tables in Analysis Workspace — This guide describes how to filter and sort data in freeform tables, including the ability to sort by multiple columns to better structure reports and highlight insights.

Adobe Finally Fixed the Most Annoying Thing in Customer Journey Analytics Tables