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Attribution Habit #6

The Models Help You Make Sense of your Data

You don't need to be a data scientist to understand how to understand attribution. However, it is really important to have a solid grasp on the difference between true marketing reports and attribution reports. 


Most of us come into doing Marketing Attribution after a long history of doing standard marketing reporting. We know landing page views, unique visitors and email clicks. All things that are somewhat definitive.

There needs to be a mental shift in order to make sense of your attribution reports. We can’t just report on the data related to marketing touches. In order to determine their value, we have to make some assumptions about how to the define value of a specific or group of marketing touches.

Let’s continue with our sample from the Habit #5 email - “Should we invest more in paid search?” To jog your memory, here is our real world set of data.



Deeper Question

Attribution Model

Does paid search create leads?

Simple report of the number of leads created by Google Paid ads

Are the leads created  good leads?
Aka - do these leads drive revenue?

Lead Creation Model - Applies ALL future revenue from those leads to Google Paid ads as that was the channel that created the leads.

How does this channel impact revenue across the board?

Even Split Model - Considers the revenue for each customer and applies an evenly weighted amount of revenue to each touch. (Example, 4 touches means that each touch would have 25% of the revenue as part of the equation)

Imagine we spent $24,500 on Google ads. How will we determine if we should invest more in Google Paid ads?

For the Lead Creation model we only want to look at deals that originated with leads that came from Google Paid ads. This means we only care about the revenue from Customer A.