Adobe Analytics ships with a robust set of pre-built dashboards, but for financial services companies that need to join web behavior directly with transactional data, dashboards alone aren't enough. The Adobe Data Feed — a raw, hit-level export of every user interaction — unlocks a different tier of analysis entirely, at the cost of significantly more engineering effort to make it usable.

Here's how Bajaj Tech.AI implemented and processed Adobe Data Feed for an online financial marketplace, and what it took to turn roughly 1,200 raw attributes into usable business insight.

A dashboard tells you what happened in aggregate. A data feed lets you ask a question nobody built a dashboard for.

Why Go Beyond Adobe Analytics Dashboards?

The Adobe Data Feed complements Adobe Analytics' pre-built dashboards by enabling capabilities dashboards can't provide on their own:

• Custom analytics: For more granular, customized analysis than a standard dashboard view allows.
• Integration with transactional data: Seamlessly combining Adobe data with internal data warehouses for a genuinely unified view of the customer.
• Analytical modeling: Using the raw data feed as an input to build advanced machine learning models, which pre-aggregated dashboard data can't support.

What Makes Adobe Data Feed Difficult to Work With?

Despite its benefits, working with Adobe Data Feed presents real, practical challenges:

• Data complexity: With around 1,200 attributes, Adobe Data Feed can be difficult for even technical users to navigate without a clear plan for which fields matter.
• Integration issues: Loading and integrating the feed with existing systems like CRMs or data warehouses requires real data engineering skill, not a simple import.
• Performance considerations: Datasets often run into the gigabytes, demanding efficient processing and retrieval strategies rather than naive full-table scans.
• Granularity: Data is provided at the hit level and requires aggregation to the page, visit, or visitor level depending on the specific business question being asked.
• Documentation and support: Adobe's documentation exists, but understanding and applying it to derive genuine business insight can still be a significant undertaking.

None of these challenges are unique to Adobe — any hit-level analytics export presents the same trade-off between raw granularity and practical usability.

How Was Adobe Data Feed Implemented for a Financial Marketplace?

Step 1: Configuring the Data Feed

Setting up the feed in Adobe Analytics (via Admin → Data Feeds → Add New Data Feed) requires configuring a name, the relevant report suite, an email for completion notifications, a feed interval (hourly or daily), start and end dates or a continuous feed option, and an optional processing delay for when the data feed files should arrive.

Step 2: Setting Up the Destination

Adobe recommends Amazon S3 for scalable, cloud-native integration, though FTP and SFTP are also supported. A typical S3 configuration specifies an account name and type, access and secret keys, and an S3 path — for example, routing data directly into a lakehouse's bronze layer. Compression format (zip or gzip) and an optional manifest file can also be configured.

Step 3: Processing the Feed

Given the large size of the dataset, efficient processing and storage are essential — naively loading the full raw feed into a query engine every time isn't sustainable at gigabyte scale. Our data warehouse was built on AWS Redshift, using AWS Glue for ETL and S3 for storage. The processing workflow moved through three stages: raw ingestion into an S3 “Bronze” bucket, transformation into an S3 “Silver” bucket (converting to Parquet format, adding derived columns like UTM parameters and a hit_date field, and partitioning by hit_date), and finally querying via Redshift Spectrum, where the Parquet files in S3 are accessed as external tables and joined directly with transactional data for reporting.

The three-stage bronze-to-silver-to-queryable pattern isn't specific to Adobe data — it's the same lakehouse discipline applied to any high-volume external feed.

What's Inside the Adobe Data Feed File?

Adobe Data Feed is delivered as a zip file containing a raw data feed file with logs of every user interaction, a column header file with metadata for the raw columns, and dimension files that enrich the raw data with contextual information.

Before deriving any insights, two filters are essential: excluding unnecessary hits by keeping only rows where exclude_hits = 0, and filtering hit sources by excluding rows with hit_source values of 5, 7, 8, or 9 to remove specific, non-relevant data sources.

What Key Insights Can Be Derived from the Raw Feed?

• UTM attributes: utm_source, utm_medium, and utm_campaign can be derived from the first, second, and third values in the post_campaign field respectively, with utm_term from the fifth value.
• Product: The product name can be extracted from the second part of the product_list field, a semi-colon separated string.
• Event list: The event_list array can be broken down and each event ID joined with its corresponding dimension table to obtain readable event codes.
• Unique visit and visitor counts: A combination of post_visid_high, post_visid_low, visit_num, and visit_start_time_gmt generates a unique visit ID for accurate session tracking, while post_visid_high and post_visid_low alone identify unique visitors.
• Time spent on pages: Calculated by subtracting the timestamp of the first hit on the current page from the first hit on the next page, using an SQL window function.

Each of these derived metrics turns a raw, hit-level log into something a business analyst can actually query — which is the entire point of investing in the processing pipeline in the first place.

Key Takeaways

• Adobe Data Feed's roughly 1,200 attributes require a clear filtering and aggregation strategy before they become usable
• A bronze-silver-queryable processing pattern, using S3, Glue, and Redshift Spectrum, handles the feed's scale efficiently
• Deriving UTM attributes, unique visit IDs, and time-on-page metrics from the raw feed unlocks analysis dashboards can't provide
• Filtering out excluded hits and irrelevant hit sources before analysis is a small step that prevents meaningfully skewed results
• Feed-level data is what makes advanced use cases — like the SEO and Customer 360 analytics built on our Data Lakehouse — possible in the first place

Conclusion

Using Adobe Data Feed to complement Adobe Analytics is genuinely complex, but it provides valuable insight into user behavior — visit patterns, time spent on pages, and more — that helps infer user intent in ways a standard dashboard can't. By following a structured processing pipeline, businesses can move from a 1,200-attribute raw export to insights that actually inform product and marketing decisions.

This kind of processing pipeline sits on the same Data Lakehouse architecture we built for a financial marketplace, and reflects the broader data engineering discipline behind projects like our OBT Data Warehouse case study.

Looking to unlock deeper insight from your own analytics data feeds? Connect with our experts to explore the right approach for your organization.