Adobe Customer Journey Analytics powers next-generation data flexibility

A long-awaited product has finally launched, and the marketing team has created a robust integrated marketing campaign that includes various messages and offers in email, search, social, new web content, videos, and more. A few days later, you, as an analyst, are asked to create a set of reports that will provide the executive team with daily performance updates on the campaigns, ultimately connecting the various interactions to purchases – both online and in-store.

You open your analytics tool, and as you sift through the data, you notice several inconsistencies in how the tracking codes were applied across and within the channels. Something is clearly wrong with how the data is coming in, and it’s going to impact both your campaign and marketing channel reporting.

What do you do?

• Option 1: Make do with the data you have, and cobble reports together as best you can.
• Option 2: Work with channel owners to update the live tracking codes going forward.
• Option 3: Create a new processing rule that adjusts the digital data as it is collected, on a go-forward basis. Request IT or engineering to reprocess and reingest any offline data affected.
• Option 4: Spend a significant amount of time creating manual workarounds to get the data into a format that can be analyzed or presented.

Depending on the severity of the issue, you may consider these four options, but each comes with its own challenges. What if as an analyst, you could make necessary changes to the data in the system of analysis, without the need to adjust your collection or reprocess the data? What if you had the ability to change the way data appears in your reporting all by yourself, and it was easy and instant?

Today, I want to introduce an industry-first capability called derived fields within Adobe Customer Journey Analytics. Derived fields empower users to apply rule-based logic to make on-the-fly updates to their data that is instant, retroactive, and non-destructive to the underlying data, reducing costly and time-intensive cycles for updates.

That means analysts can make complex changes to data in just minutes to a few hours. And the rules apply to historical data, not just data going forward, with no processing or engineering support needed.

Transform data on the fly with Customer Journey Analytics

Customer Journey Analytics is technology built specifically for continuous customer analysis. It helps users unlock omnichannel insights at a person level — no SQL or IT queue necessary — so anyone responsible for the customer experience can visualize and analyze the customer journey in full context.

With data flexibility as a core pillar, Customer Journey Analytics integrates data from various platforms and sources. It also provides real-time data manipulation and non-destructive historical data restatement in ways that standalone digital analytics and BI tools cannot.

Introducing derived fields

Derived fields allow you to perform complex data manipulations through a customizable rule builder. Advanced functions within the rule builder help you to find and replace values, create case when logic, concatenate or parse URLs from existing data to create new fields, and much more. You can then use the derived fields you create as metrics or dimensions in Analysis Workspace with all the standard visualization and filtering capabilities available.

Derived fields can save teams a significant amount of time and effort, compared to transforming or manipulating your data in locations outside of Customer Journey Analytics. This capability empowers the core users and analysts to take control of their data and make the changes they need, when they need them, to better answer the questions most critical to the business.

How to implement derived fields in Customer Journey Analytics

You can create derived fields within a data view. A data view is a container specific to Customer Journey Analytics that lets you determine how to interpret data for specific business groups and analysis use cases. It provides a customized view of your data, where you can specify all the dimensions and metrics needed for analysis with settings such as session timeout, persistence, and attribution.

Within data views you can:

• Change a metric to a dimension and vice versa — use a string field as a metric or a numeric field as a dimension.
• Create multiple metrics with different attribution models or lookback windows from the same underlying data field.
• Overwrite display names with friendly names.
• Apply formatting to data such as a decimal, time, percent, and more.
• Automatically include or exclude certain values within a specific field.
• And now, create derived fields using a rule-builder for more complex manipulations.

Any settings that you select or change in a data view are run at report time

they are retroactive and non-destructive to your data. In other words, changes appear instantly, can be iterated on in real time with preview capabilities, and do not permanently change your underlying data. This allows for flexibility as you test and compare various reporting settings and build derived fields to learn what works best for you and your business.

Use cases for derived fields

Let’s go back to the marketing campaign dilemma. Data has been coming in for several days with different variations of tracking codes and isn’t landing right for analysis. Pulling together an overall campaign report — let alone a marketing channel view — is going to be challenging and time-intensive, especially if you must create a manual workaround.

There are two core use cases for derived fields that will fix this specific problem.

1. Data cleanup
   
   Objective: Remove or fix errant data to provide clean reporting.
   
   Pain point: Data is subject to errors, whether that be misspellings, incorrect tracking codes, missing or added spaces, nuances from the specific platform collected, abbreviations and more. In our example, it appears that several marketing channel owners mistyped a few tracking codes, so the problem isn’t isolated to one area.
   
   With derived fields: Those existing tracking codes are easily updated and the data previous captured aligns to the proper tracking. Derived fields make data easy to align, combine, fix, or remove with easy-to-configure rules. Users can employ find and replace and look up functions to replace errant values, fix incorrect formatting or spelling, and ensure data accessed by end users is clean and easy to interpret.
   
   Value: Costly cycles and updates can be avoided for minor fixes, increasing speed to insight.

Now that the tracking codes are showing up properly, you can now tackle your marketing channel reporting using a prebuilt template within derived fields.

2. Marketing channel reporting
   
   Objective: Simplify marketing channel reporting to provide a clear view into performance.
   
   Pain point: Marketing channels and subsequent campaigns use a variety of tracking codes that are not automatically summarized at the channel level. Web analytics tools have processing rules that can be applied, but data is still subject to classification errors and new processing rules only apply going forward. In our example, the tracking codes look good now, but rolling everything up by channel is a major pain.
   
   With derived fields: Rules can be created for each channel based on URL parsing and case when (or if, then) statements that roll channels and codes into an aggregate view. In this example, instead of dozens of “soc:XXXX” data points, you can see the performance of all paid social campaigns for the product launch in one view. You can add as many rules as needed to get your channel reporting in order.
   
   Value: Simplified reporting for better analysis, attribution, and campaign optimization. No impact to underlying data.

In just a few hours, you took data that was full of errors and difficult to use and turned it into clean, usable reporting that accurately shows the impact of the launch campaigns. There was no support needed from IT or engineering, and you didn’t have to ask your marketing team to make updates to live channels. A real win all around.

Unlock more value from your data with Adobe Customer Journey Analytics

Derived fields are just one incredible capability that supports the analytics evolution and growing need for better data flexibility. By tapping into the power of on-the-fly transformation within Customer Journey Analytics, analysts can ensure data is clean, usable, and valuable in helping the business make decisions and optimize customer experiences.

Customer Journey Analytics helps organizations to empower anyone responsible for the customer experience to visualize the customer journey in full context, across all data channels — even offline. Those omnichannel insights can be delivered within seconds that previously required weeks, months, or simply weren’t even possible with generic and outdated approaches. Only Customer Journey Analytics customers can take advantage of unique insights to activate and optimize engagement across the entire customer journey, fueling contextual approaches to acquisition, retention, cross-sell or upsell, and personalized marketing campaigns — all at scale.

Learn more about Customer Journey Analytics and how deep insights can transform your business.