EDA for Attribution Analysis: A Real-World Guide Using Pandas

EDA for Attribution Analysis: A Real-World Guide Using Pandas

Attribution is at the heart of modern marketing measurement, but it’s also one of the most complex. Understanding which channels, campaigns, or touchpoints truly drive conversions is challenging enough. Add in duplicate conversions, multi-touch journeys, and inconsistent data across platforms, and the problem becomes even more difficult.

That’s why Exploratory Data Analysis (EDA) is critical before jumping into advanced attribution models.

how I use Pandas to tackle EDA for attribution analysis,

In this article, I’ll walk you through how I use Pandas to tackle EDA for attribution analysis, including:

• Exploring multi-touch journeys

• Handling duplicated conversions

• Analyzing time lags between touchpoints

• Laying the groundwork for custom attribution modeling

1. Why EDA Matters for Attribution

Attribution models (first-touch, last-touch, linear, time-decay) are only as good as the data feeding them. Poor EDA can lead to:

• Overcounting conversions due to duplicated events

• Misattributing conversions due to session breaks

• Underestimating the influence of certain channels

Before modeling, I always run EDA to ensure the data reflects actual user behavior, not just platform quirks.

2. Exploring Multi-Touch User Journeys

First, I examine how users interact across multiple channels and sessions. Typically, I import raw logs from Google Analytics (via BigQuery) or CRM exports:

I check for:

• User identifiers: Are users consistently identified (e.g., Client ID, User ID)?

• Timestamps: Are event times consistent and correctly formatted?

Then, I sort by user and timestamp to build journey maps:

To visualize:

Questions I ask:

• How many touchpoints does an average user have before converting?

• Which channels typically appear first, middle, and last in the journey?

• Are there any channels that tend to appear only as a first or last touchpoint?

3. Handling Duplicated Conversions

One of the most common issues is duplicated conversion events, especially when pulling data from CRM systems or event logs. I look for:

• Duplicate order IDs or transaction IDs

• Same user ID with multiple identical conversion events in quick succession

Example:

I’ll decide whether to:

• Keep the first event only

• Deduplicate based on timestamp (e.g., 10-second rule)

• Aggregate revenue (if partial payments exist)

Key tip: Always verify the business logic with marketing or sales teams to understand whether duplicates represent valid partial conversions or system errors.

4. Analyzing Time Lags Between Touchpoints

Understanding time lag helps inform time-decay or position-based attribution models.

Using Pandas, I calculate time between touchpoints:

I’ll then analyze:

• Median time between first and last touch

• Average time from first touch to conversion

• Whether specific channels tend to have shorter or longer time lags

This helps answer:

• Are users converting quickly, or is there a long consideration period?

• Do certain channels speed up or slow down conversions?

5. Using Pandas to Build Custom Attribution Models

Once the data is clean, I often build custom attribution weights using Pandas.

Example: A simple linear model

Or a position-based model (40-20-40):

• 40% to first

• 20% to middle

• 40% to last

This sets up the dataset for advanced modeling or dashboard integration.

6. Common EDA Pitfalls in Attribution Analysis

• Ignoring session breaks: Users may leave and return days later. Group by sessions, not just users, if needed.

• Assuming perfect channel IDs: UTM tagging mistakes can split identical channels into multiple categories.

• Not validating timestamp logic: Time zones or duplicate event logging can create false patterns.

Always cross-check with business teams to align data cleaning with real user journeys.

7. Final Thoughts: EDA is the Foundation of Attribution

Attribution models are only as good as the data you feed them. A thorough EDA helps you:

• Understand user journeys

• Identify and clean duplicates

• Analyze time lags that inform model design

• Build customized, flexible attribution frameworks

Pandas makes this process fast, repeatable, and powerful.