Position Based Attribution: E-Commerce Explained

Learn how position-based attribution can help your e-commerce business understand which marketing channels are driving the most sales.

Understanding Position Based Attribution

Attribution refers to the process of assigning credit to the marketing and advertising touchpoints that contributed to a conversion. Attribution models serve as a rulebook that outlines how credit gets allocated amongst the touchpoints. Hence, an attribution model determines the effectiveness of your marketing channels and ROI.

Attribution is a crucial aspect of any marketing strategy, as it helps you understand which channels are driving the most conversions. Without a proper attribution model, it can be challenging to determine which marketing efforts are worth investing in and which ones are not.

What is Position-Based Attribution?

Position-based attribution is an attribution model that gives credit to marketing touchpoints around the beginning, middle, and end of a customer journey. It ensures that appropriate credit is assigned to the touchpoints that contribute the most value and influence customer decisions. This model typically gives the first and last touchpoints equal credit, with 40% allocated to each, while the remaining 20% is split between the intermediate touchpoints.

Position-based attribution is an excellent compromise between the first-click and last-click attribution models. The first-click model gives all the credit to the touchpoint that initiated the customer journey, while the last-click model gives all the credit to the touchpoint that led to the conversion.

The Importance of Attribution in E-Commerce

Attribution is crucial to understanding the effectiveness of your marketing efforts. It helps you recognize which marketing channels are the most successful in attracting and engaging customers. As a result, an effective attribution model will enable you to optimize your marketing ROI by allocating your resources to the channels that have the highest impact on your bottom line.

In the world of e-commerce, attribution is even more critical as customers interact with multiple touchpoints before making a purchase. For example, a customer might see an advertisement on social media, visit your website, and then make a purchase after receiving an email promotion. Without an effective attribution model, it can be difficult to determine which touchpoints were the most influential in the customer's decision-making process.

Comparing Position Based Attribution to Other Models

There are several models of attribution used in e-commerce, each with its unique set of advantages and limitations. Some of the most common models include first-click, last-click, linear, and time decay.

The first-click attribution model gives all the credit to the touchpoint that initiated the customer journey. This model is useful for understanding how customers first interact with your brand, but it does not provide a comprehensive view of the entire customer journey.

The last-click attribution model gives all the credit to the touchpoint that led to the conversion. This model is useful for understanding which touchpoints are the most effective at driving conversions, but it does not take into account the other touchpoints that contributed to the customer's decision-making process.

The linear attribution model gives equal credit to all touchpoints in the customer journey. This model provides a comprehensive view of the entire customer journey, but it does not take into account the varying levels of influence that each touchpoint may have had on the customer's decision-making process.

The time decay attribution model gives more credit to touchpoints that are closer in time to the conversion. This model takes into account the fact that touchpoints closer to the conversion are likely to have had a more significant impact on the customer's decision-making process. However, it may not accurately reflect the influence of touchpoints that occurred earlier in the customer journey.

While these models can be useful in their own way, position-based attribution is a great compromise as it offers a comprehensive view of the customer journey while taking into account the varying levels of influence that each touchpoint may have had.

How Position-Based Attribution Works

The First and Last Touchpoints

As previously mentioned, a position-based attribution model allocates 40% of the credit to the first and last touchpoints of the customer journey. The first touchpoint could be a Google search or social media ad, while the last touchpoint typically represents the point of conversion, such as completing a purchase or filling out a lead-gen form.

Allocating Credit to Intermediate Touchpoints

The remaining 20% of the credit gets distributed amongst the intermediate touchpoints. These touchpoints could be anything from a retargeting ad to an email marketing campaign. The idea is to ensure that no critical touchpoint goes unnoticed or uncredited in the customer journey.

Customizing Position Based Attribution Weights

While the 40-40-20 rule is an effective starting point, businesses can customize the attribution weights to suit their unique marketing strategies. For example, you might want to allocate more credit to content marketing channels such as blogs and webinars, or to offline activities such as events and trade shows.

Benefits of Position-Based Attribution

Comprehensive View of the Customer Journey

By taking into account multiple touchpoints across the customer journey, position-based attribution offers a comprehensive view of the impact of both online and offline activities. This allows businesses to make data-driven decisions and optimize their marketing efforts.

Improved Marketing ROI

Attribution models like position-based attribution can help businesses to understand which channels are driving the most conversions, and therefore, reallocate their resources effectively. It means they can stop spending money on less effective campaigns and focus solely on the channels that are most likely to impact sales positively.

Enhanced Decision-Making for Marketing Strategies

Position-based attribution can help marketing executives to make better decisions surrounding their marketing efforts. By continuously analyzing the customer journey and making the necessary optimizations, they can better understand which channels to use and how to align their marketing campaigns with customer needs.

Implementing Position Based Attribution in Your E-Commerce Business

Choosing the Right Attribution Software

There are several software applications available in the market designed to help businesses implement attribution models. Before selecting a software, you must establish your objectives, understand the attribution models available, and choose the right tool that aligns with your business needs.

Integrating Position Based Attribution with Your Marketing Channels

Ensuring the successful integration of your attribution software with your marketing channels is crucial. You must have a thorough technical understanding of the software you choose, and how to integrate it with your existing ecosystem.

Analyzing and Adjusting Your Marketing Efforts

The most critical aspect of implementing position-based attribution is the continuous analysis and optimization of the attribution model. By monitoring the customer journey and adjusting the credit allocated to each touchpoint, businesses can optimize their marketing efforts and drive sales.

Conclusion

Position-based attribution is a powerful tool that enables businesses to gain a complete understanding of their customer journey and allocate credit to marketing touchpoints that most influence customer decision-making. By understanding this model, businesses can optimize their marketing strategies and drive ROI. It is essential to consider the unique needs of your business and allocate credit accordingly. As you integrate this model into your marketing strategy, you'll gain a better understanding of the marketing channels that drive sales and how to optimize and reallocate resources for maximum return.

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