Picking the right attribution model for your organization depends on a number of considerations. This article explores a methodology and some general best practices.

## Step 1: Exploratory Analysis

NOTE
This analysis needs to happen before you pick an attribution model.

This phase consists initially of understanding customer behavior and defining conversion metrics. Based on the conversion metrics, tools like Data Feeds (for raw data) or Analysis Workspace facilitate your understanding of

• The number of customers who are touching different marketing channels before converting
• The proportion/distribution of these behaviors

For example, if 50% of customers touch 3 channels before converting, is there any interaction among those 3 channels?
You could then do upper- and lower-funnel analysis to expand your understanding.

### Upper-funnel analysis

Upper-funnel analysis channels are used to create brand or product awareness. For example, the goal of most TV ads is brand awareness. You might use the “Time decay” attribution model, since people will forget about your TV ad over time.

### Lower-funnel analysis

The purpose of this step is to validate your hypotheses.

Example 1

Suppose your hypothesis is: “My First-touch channel has more impact on conversion than my last-touch channel.”

In this case, you would then use the “Inverse J-shaped” attribution model to test this hypothesis. This model gives 60% of the credit to the first touch point.

Example 2

Suppose your hypothesis is: “In our industry (such as travel industry), the attribution window is 60 or 90 days, not 30 days, because customers do a lot of research before buying a product.”

In this case, you would change your lookback window to 90 days.

## Step 3: Use Algorithmic attribution

If you don’t yet have an attribution model that provides satisfactory answers to all of your questions, you can use algorithmic attribution. Because it is very hard to validate a large number of possible hypotheses and combinations, algorithmic attribution uses built-in algorithms to allocate credit across dimension items.

## Other considerations

• You might need to use the services of a data scientist instead of relying on Analysis Workspace alone.
• You can rely on raw data, as in Adobe data feeds.
• Consider using Customer Journey Analytics, for example, if you want to consider your Impressions data.
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