What is an attribution model?

An attribution model refers to the methodology used to determine how credit for sales and conversions is assigned to different touchpoints in a customer’s journey. It helps in understanding which marketing channels or interactions contribute most to a purchase decision or conversion.

Benefits of attribution modelling

Attribution modeling is important for all businesses but especially so for eCommerce brands carrying out digital marketing strategies across multiple marketing channels.

Here are some of the main benefits:

  1. Insightful Decision-Making: It helps in understanding which marketing channels and touchpoints are most effective in driving conversions. This insight guides better allocation of resources and budget, optimizing marketing strategies for higher returns.
  2. Optimized Campaign Performance: By knowing which touchpoints contribute most to conversions, you can fine-tune your campaigns, focusing on the most impactful channels. This optimization can lead to improved ROI.
  3. Understanding Customer Behavior: Attribution models provide a clearer picture of the customer journey. Understanding how customers move through various touchpoints aids in tailoring marketing efforts to better match their behavior and preferences.
  4. Budget Allocation: It assists in allocating marketing budgets more effectively. By recognizing the channels that bring in the most conversions, you can prioritize spending on those channels to maximize results.
  5. Enhanced Personalization: Insights from attribution models enable personalized marketing efforts. You can deliver more relevant and timely messages to customers based on their interactions across different touchpoints.
  6. Improved Collaboration: Teams across marketing, sales, and other departments can better understand and appreciate each other’s contributions. This leads to improved collaboration and a unified approach towards achieving business goals.

Overall, attribution modeling is crucial in providing a comprehensive view of the customer journey, enabling businesses to make data-driven decisions and optimize their marketing efforts for better results.

Common attribution models used in eCommerce

There are a number of different types of attribution models used by marketing teams within eCommerce businesses.

Here are the most commonly used:

1. Last Click Attribution

Last click attribution, also known as last interaction attribution, is an attribution model that attributes 100% of the credit for a conversion or sale to the last interaction a customer had before making the purchase.

It gives all the credit to the final touchpoint in the customer journey, ignoring previous interactions.

Example: Let’s say a customer first discovers a product through an Instagram post, then later clicks on a Google ad but doesn’t make a purchase. Finally, they receive an email with a discount code and use that code to buy the product. In last click attribution, the entire credit for the sale would be attributed to the email that led directly to the purchase.


  • Simplicity: It’s straightforward and easy to implement and understand.
  • Clear Focus: Highlights the final step in the customer journey, providing a clear view of what directly led to the sale.


  • Ignores Previous Touchpoints: It overlooks the influence of other touchpoints that might have played a significant role in convincing the customer to make the purchase.
  • Incomplete Picture: Doesn’t consider the entire customer journey, leading to potential undervaluation of other marketing efforts.
  • Unfair Attribution: Often gives disproportionate credit to the last touchpoint, discounting the contribution of earlier touchpoints that might have initiated or nurtured the customer’s interest.

While the last interaction attribution model is simple, it might not reflect the true influence of various marketing channels throughout the customer journey, potentially leading to misallocation of resources and less effective marketing strategies.

2. First Click Attribution

First click attribution is an attribution model that attributes 100% of the credit for a conversion or sale to the first interaction a customer had in their journey towards making a purchase.

It gives all the credit to the initial touchpoint, disregarding subsequent interactions that might have influenced the sale.

Example: Imagine a customer first discovers a product through an influencer’s YouTube video. Later, they come across a Google ad but don’t buy right away. Finally, they search for the website on Google, click through from search results, and make a purchase. In first click attribution, the entire credit for the sale would be attributed to the YouTube video that initially introduced the product.


  • Simple and Clear: Similar to last click attribution, it’s easy to understand and implement.
  • Highlights the Entry Point: Emphasizes the initial touchpoint that potentially sparked the customer’s interest.


  • Ignores Later Influences: Disregards the impact of other touchpoints or marketing efforts that might have helped in converting the customer.
  • Oversimplification: Fails to consider the entire customer journey, potentially undervaluing touchpoints that played a role in nurturing or convincing the customer towards the sale.

First click attribution can oversimplify the customer journey by solely crediting the initial touchpoint.

While it offers a straightforward view, it doesn’t reflect the combined impact of various marketing efforts across the entire buying process, possibly leading to skewed insights and suboptimal allocation of resources.

3. Linear Attribution

Linear attribution is an attribution model that distributes equal credit for a conversion or sale across all touchpoints in the customer journey.

Unlike other models that prioritize specific touchpoints, linear attribution assigns an equal share of the credit to each interaction, regardless of its position in the journey.

Example: A customer discovers a product through a Facebook ad, then engages with the brand’s Instagram post, later receives an email with a promotion, and finally makes a purchase after visiting the website directly. In linear attribution, each touchpoint—Facebook ad, Instagram post, email, and direct website visit—receives an equal 25% credit for the sale.


  • Fair Distribution: Provides an even and fair representation of each touchpoint’s contribution to the overall conversion.
  • Comprehensive View: Considers the entire customer journey, acknowledging the impact of each interaction.


  • Lack of Specificity: Doesn’t differentiate between the effectiveness of various touchpoints; might undervalue more influential interactions.
  • May Overlook Critical Touchpoints: Treats all touchpoints equally, potentially giving less credit to pivotal moments that heavily influenced the purchase decision.

Linear attribution offers a balanced perspective by acknowledging the role of every touchpoint in the customer journey.

However, its equal distribution of credit might not accurately represent the varying impact of different interactions, potentially leading to less targeted marketing strategies.

4. Time Decay Attribution

Time decay attribution is an attribution model that assigns more credit to touchpoints closer in time to the actual conversion.

It acknowledges that interactions occurring nearer to the purchase decision often have a greater impact and therefore deserve more credit compared to earlier touchpoints.

Example: Suppose a customer interacts with a brand by clicking on a Facebook ad two weeks before making a purchase. Later, they receive a promotional email a few days before buying the product. In a time decay attribution model, the email might receive more credit than the Facebook ad since it was closer in time to the actual purchase.


  • Recognizes Timeliness: Reflects the idea that interactions closer to the conversion are often more influential.
  • More Accurate Than Linear Models: Provides a more nuanced view by considering the recency of touchpoints in influencing the purchase decision.


  • May Undervalue Early Interactions: Gives less credit to touchpoints that initiated or nurtured the customer’s interest but occurred further back in time.
  • Complexity in Implementation: Calculating the appropriate decay rate for different touchpoints can be challenging.

Time decay attribution acknowledges the significance of recent touchpoints but can undervalue the contributions of earlier interactions that played a crucial role in initiating the customer’s journey.

It strikes a balance between equal credit distribution and emphasizing the importance of proximity to the conversion event.

5. Position-Based Attribution

Position-based attribution, also known as U-shaped attribution, is an attribution model that assigns varying levels of credit to different touchpoints in the customer journey.

It emphasizes both the initial touchpoint that introduced the customer to the product or brand (usually the first interaction) and the final touchpoint that directly led to the conversion, while also giving some credit to intermediate touchpoints.

Example: Let’s say a customer discovers a product through an organic search (first touchpoint), engages with a social media ad, interacts with a retargeting ad, and finally converts after clicking on an email campaign (last touchpoint). In a position-based attribution model, the first and last touchpoints might each receive 40% credit, while the two intermediate touchpoints share the remaining 20% (10% each).


  • Balanced View: Acknowledges the importance of both the initial touchpoint that sparked interest and the final touchpoint that led to the conversion.
  • Reflects Customer Journey: Provides insights into how different touchpoints influence the customer’s decision-making process.


  • Complexity: Determining the appropriate distribution of credit across touchpoints can be subjective and complex.
  • Potential Oversimplification: Despite its attempt to balance credit allocation, it might still oversimplify the impact of various touchpoints.

Position-based attribution aims to offer a balanced view by giving credit to the first and last touchpoints while also recognizing the contributions of intermediary interactions.

However, the challenge lies in determining the appropriate weightings for each touchpoint, and it might still oversimplify the complexities of the customer journey.

6. Algorithmic Attribution

Algorithmic attribution is an advanced attribution model that utilizes sophisticated algorithms and data analysis techniques to assign credit to various touchpoints in the customer journey.

Unlike other models that use predefined rules, algorithmic attribution dynamically adjusts credit weights based on historical data, behavior patterns, and the specific context of each conversion path.

Example: In algorithmic attribution, machine learning algorithms analyze vast amounts of data to determine the contribution of each touchpoint. For instance, it might consider factors like the time spent on a webpage, the interaction depth with an ad, the sequence of touchpoints, and the type of device used to assign personalized and weighted credit to each interaction in the customer journey.


  • Data-Driven Precision: Utilizes machine learning and data analysis to derive more accurate and granular insights into the impact of each touchpoint.
  • Adaptable and Dynamic: Adjusts credit weights based on evolving patterns and changes in customer behavior, offering more adaptable and up-to-date attribution.


  • Complexity: Requires advanced analytics and technical expertise to implement and interpret the algorithms accurately.
  • Resource-Intensive: Analyzing extensive datasets and implementing algorithms can be resource-intensive in terms of time, computational power, and data processing.

Algorithmic attribution offers a more sophisticated and dynamic approach by leveraging machine learning algorithms to analyze multiple data points and patterns.

While it provides more accurate insights into the customer journey, its complexity and resource demands can be challenging for some organizations to manage.

Comparing attribution models inside Google Analytics 4

Here are simplified instructions for comparing attribution models in Google Analytics 4:

  1. Go to Google Analytics 4 and click on “Advertising” in the menu on the left-hand side. Then, select “Model comparison” under the “Attribution” section.
  2. By default, the report will show all conversion events from the last 28 days, using the default channel grouping.
  3. Start by choosing the date range and the specific conversion event you want to analyze.
  4. To focus on specific data, like a particular campaign, location, or device, click on “Edit comparison” at the top right of the report and add filters.
  5. Next, select the dimension (like campaign or geography) you want to analyze further. Then, use the drop-down menus to pick and compare different attribution models.

These steps will help you easily compare how different marketing attribution models impact your performance metrics in Google Analytics 4.


What is the best attribution model?

Determining the “best” attribution model depends on various factors, including the nature of your business, your specific marketing strategies, and your objectives.

There isn’t a one-size-fits-all answer, as each marketing attribution model offers different insights into your marketing performance.

However, here are a few considerations:

  1. Last Click or First Click: These models are straightforward but might oversimplify the customer journey. They are useful when your sales process involves quick, single-touch conversions.
  2. Linear Attribution: Provides a balanced view of all touchpoints but might not accurately represent the impact of each interaction.
  3. Time Decay: Recognizes the significance of recent interactions, suitable for businesses where quick decision-making is crucial, but it might undervalue earlier touchpoints.
  4. Position-Based: Offers a compromise between first and last touch, acknowledging the importance of both. It’s useful for understanding the beginning and end of the customer journey.
  5. Algorithmic Attribution: Offers precision by considering various data points, but it requires technical expertise and might be resource-intensive.

The “best” model often involves using multiple models in conjunction (otherwise known as multi-touch attribution models) or choosing one that closely aligns with your specific business goals.

For instance, if you’re focused on lead generation, a position-based model might offer valuable insights into initial and final touchpoints.

For eCommerce, a time decay model can often better reflect recent interactions leading to purchases.

It’s recommended to analyze and compare different models to gain a holistic understanding of your customer journey rather than relying solely on one attribution model.

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