Understanding Multi-Touch Attribution in Amazon Marketing Cloud

Key Attribution Models in Amazon Marketing Cloud

AMC provides various attribution models that you can use depending on the insights you wish to derive from your campaigns. Let’s explore four key models: First Touch, Last Touch, Position-Based, and Linear.

1. First-Touch Attribution

First-touch attribution assigns 100% of the conversion credit to the first interaction a customer has with your ads. This model is particularly useful when you want to measure which campaigns are most effective at capturing initial interest and driving awareness.

Example: Imagine a customer searching for products on Amazon and clicking on a Sponsored Brand ad. Over time, they see additional ads from the same advertiser, but the first interaction was crucial in introducing the product. In a first-touch attribution model, this Sponsored Brand ad receives 100% of the credit for the eventual purchase.

Benefits:

• Great for understanding which ads work best at the top of the funnel.

• Helps marketers optimize campaigns focused on awareness and early-stage engagement.

Limitations:

• Ignores subsequent interactions that could have influenced the purchase decision.

2. Last-Touch Attribution

The last-touch attribution model gives all the credit for the conversion to the final interaction before a customer completes the purchase. This model is valuable when you want to identify which touchpoint played the most direct role in converting the customer.

Example: A customer clicks on a series of Sponsored Display ads while browsing Amazon, but they ultimately make the purchase after clicking a Sponsored Product ad right before checkout. In this case, last-touch attribution would assign 100% of the credit to the Sponsored Product ad.

Benefits:

• Ideal for identifying which campaigns are most effective at closing sales.

• Highlights the ads that directly lead to conversions.

Limitations:

• Overlooks the earlier interactions that may have influenced the customer’s decision.

3. Position-Based Attribution

Position-based attribution splits the conversion credit between the first and last touchpoints, typically assigning a higher weight to the final interaction but still recognizing the role of the initial interaction. By default, AMC assigns 30% of the credit to the first touchpoint and 70% to the last touchpoint, but this can be customized to fit your strategy.

Example: Suppose a customer’s first interaction is a click on a Sponsored Display ad (awareness stage), followed by additional engagements, and the final click occurs on a Sponsored Product ad just before purchase. In a default position-based attribution model, the first touchpoint (Sponsored Display) would get 30% of the credit, while the last touchpoint (Sponsored Product) would receive 70%.

Benefits:

• Captures both top-of-the-funnel (awareness) and bottom-of-the-funnel (conversion) efforts.

• Flexible - advertisers can adjust the credit split based on their objectives.

Limitations:

• Mid-funnel interactions (e.g., ads that build consideration but are neither first nor last touchpoints) may receive no credit.

4. Linear Attribution

In linear attribution, every touchpoint in the customer journey receives equal credit for the conversion. This model gives you a balanced view of how each interaction contributed to the sale, making it ideal for campaigns that span multiple touchpoints over a longer sales cycle.

Example: A customer clicks on a Sponsored Brand ad, later interacts with a Sponsored Display ad, and finally converts after clicking a Sponsored Product ad. In a linear attribution model, each of these touchpoints would receive an equal share (33.3%) of the conversion credit.

Benefits:

• Provides a complete picture of the customer journey by recognizing the role of every interaction.

• Useful for long, complex buying cycles with multiple touchpoints.

Limitations:

• Doesn’t account for the varying influence of different touchpoints. For example, the first or last interaction may have played a more significant role in conversion.

Custom Attribution in Amazon Marketing Cloud

One of the key benefits of AMC is the ability to customize attribution models based on your unique business goals. While default models like first-touch, last-touch, and position-based provide valuable insights, custom attribution lets you fine-tune the weight assigned to each touchpoint, offering more control over how you analyze your campaigns.

Position-Based Attribution Customization Example:

Suppose you're running a brand awareness campaign that’s followed by conversion-focused ads. You could adjust the attribution model to assign 50% of the credit to the first touchpoint and 50% to the last touchpoint, giving equal importance to both brand awareness and conversion. Or, if the campaign’s primary focus is conversion, you could assign a larger portion of the credit to the last touchpoint, perhaps 70%, while the first touchpoint receives 30%.

Customizing Lookback Windows:

AMC also allows advertisers to adjust the lookback window, which determines how far back in time a touchpoint is considered for attribution. For example, the default attribution window in AMC is 14 days, but if your typical sales cycle is longer, you can extend this period to ensure no important touchpoints are missed.

Time-Decay & Custom Weighted Attribution Models

In addition to the standard models, AMC allows the creation of time-decay or custom-weighted attribution models through SQL queries. In a time-decay model, points that are closer to the conversion event get more credit, and points that are earlier in the chain get less credit.

This method is especially helpful in the following scenarios:

• When consideration cycles are long

• For high-value transactions

• For seasonal campaigns

Through the creation of custom weighting logic, brands can make attribution more aligned with actual buying behavior instead of being constrained by predefined models.

How to Use Multi-Touch Attribution to Optimize Your Amazon Ads

Multi-touch attribution models in AMC help advertisers optimize campaigns at different stages of the funnel. Here are some strategies for leveraging these insights:

• Using First-Touch Data: If first-touch attribution shows that certain ads are excelling at capturing attention, you can allocate more budget to awareness campaigns.

• Leveraging Last-Touch Attribution: Last-touch attribution helps identify which ads close the sale, allowing you to double down on conversion-driven campaigns like Sponsored Product ads.

• Position-Based Strategy: Use position-based attribution to balance your ad spend across brand-building and sales-driving activities.

• Linear Attribution Insights: Apply linear attribution insights to ensure that each touchpoint in the customer journey is contributing value, leading to a more cohesive and effective marketing strategy.

• Multi-touch attribution in the AMC can also be used for New to Brand (NTB) analysis. By breaking down journeys by first-time versus repeat buyers, advertisers can see which touchpoints are most successful at acquiring new customers.

For instance:

• Upper-funnel campaigns for Sponsored Display or DSP may acquire NTB.

• Sponsored Products may have a heavier hand in repeat business.

This information enables brands to optimize acquisition and retention approaches while budgeting more effectively across the funnel.

Practical Examples of Multi-Touch Attribution in AMC

Example 1: Balancing Brand Awareness and Conversion

An advertiser runs a brand awareness campaign using Sponsored Brands and follows up with Sponsored Products ads for retargeting. Position-based attribution helps the advertiser see that the initial brand awareness campaign played a key role in driving traffic, while the Sponsored Products ad ultimately led to the sale.

Example 2: Last-Touch Attribution for Conversion-Heavy Campaigns

A retailer focusing on sales drives heavy traffic with Sponsored Display ads but finds that Sponsored Product ads are closing most conversions. By using last-touch attribution, the retailer can optimize Sponsored Product ads to increase conversions while refining top-of-the-funnel tactics.

Example 3: Linear Attribution for Complex Sales Journeys

A high-end product with a longer consideration period sees customers interacting with multiple ad formats before purchasing. Linear attribution provides a balanced view of how each ad format (Sponsored Brands, Sponsored Display, Sponsored Products) contributes, helping the advertiser fine-tune the entire campaign.

Example 4: Cross-Channel Attribution Across DSP and Sponsored Ads

The advertiser may find that the DSP display impressions are often early in the customer journey, while the Sponsored Products complete the sale.

With AMC, the brand can examine:

• The impact of DSP on Sponsored Ads conversions downstream

• Cross-device exposure patterns

• Assisted conversion paths

This cross-channel insight helps to ensure that the upper-funnel DSP spend is not underestimated when assessing performance based on last-click attribution alone.

Audience Overlap & Incrementality Testing

Audience Overlap Analysis can be even more effective when integrated with incrementality testing methodologies. By understanding shared versus distinct audience segments, advertisers can design test and control groups to assess incremental lift.

For instance:

• Suppressing overlapping audiences from one of the campaigns can help isolate incremental effects.

• Integrating overlap insights with Amazon DSP test tools can help determine if the campaigns are driving new demand or simply re-engaging existing audiences.

This ensures that the budget is allocated to drive incremental growth, not redundant exposure.

How Can Hector Help You?

Hector’s powerful reporting dashboard simplifies accessing AMC insights, eliminating the need for complex SQL queries. With an intuitive interface, it delivers actionable insights on Multi-touch Attribution that empower you to make timely, informed decisions.

If you’re new to AMC, Hector can help you start by setting up your own AMC instance.

Conclusion

Multi-touch attribution in Amazon Marketing Cloud offers advertisers the insights needed to fully understand how their ads impact customers at various stages of the purchase journey. Whether you’re focused on awareness, consideration, or conversion, the flexibility of AMC’s attribution models - First Touch, Last Touch, Position-Based, and Linear, allows you to measure and optimize your campaigns effectively. By leveraging the right model and customizing it to suit your business needs, you can make data-driven decisions that improve performance across your advertising strategy.

By implementing multi-touch attribution in AMC, you’ll gain a holistic view of your customer’s path to conversion. This will allow you to optimize campaigns more effectively and drive better results overall.