What Is an Attribution Model? (With Types and Benefits)

By Indeed Editorial Team

Published June 2, 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Companies often prioritize developing marketing strategies, improving investment returns, and personalizing advertisement efforts for prospective clients. To achieve this, companies incorporate various strategies into marketing efforts, including attribution modelling. Understanding this system can help you learn how different businesses leverage it for more successful advertising campaigns.

In this article, we discuss the attribution framework, explore several types of models, detail the benefits of conversion rates, and highlight the limitations of this model.

What is the attribution model?

The attribution model or attribution modelling (AM) is an advertisement approach that evaluates which marketing channel interactions, also known as touchpoints, convert an online user to a paying client. Sales and marketing professionals commonly refer to touchpoints as types of interactions and locations with prospective clients. For example, a touchpoint may be clicking on a digital ad, adding an item to an online wishlist, or commenting on a post on social media.

This model helps companies learn where an audience becomes a customer on their consumer journey. A consumer journey involves the different interactions between an individual and a brand, leading to the purchase of the product or service. Generally, as the consumer journey is only complete following a purchase, conversion is an essential stage in the journey.

Related: What Is the Consumer Decision-Making Process? (A Guide)

8 types of attribution frameworks

Here are some common types of attribution frameworks for you to review :

1. First-touch attribution

Also called the first-click-first-interaction attribution, the first-touch attribution transfers complete conversion credit to a user's initial interaction with a brand before they're a paying customer. It's a single-touch concept that emphasizes one particular interaction the prospective customer has with a brand on their consumer journey. For example, if a potential customer views your social media page before browsing through your website and making a purchase, the social media page receives credit for causing the conversion.

This model provides simple data analysis and gathering, and it may be a favourable option for businesses with brief buying cycles or those that speedily convert customers. First-touch AMs may also enable businesses to improve leads for prospective customers interested in an offering or brand.

2. Last-touch attribution

This is also a single-touch attribution framework. It assigns the conversation credit to a prospective customer's final interaction with a brand before their conversion. For example, if a customer views paid search results, subscribes for a free trial through a website, and then converts, the free trial receives complete credit for the conversion.

Generally, last-interaction attribution is commonly more reliable than multi-touch models. This is because users may access several devices, employ multiple browsers, or clear cookies, making it challenging to follow their consumer journey. For example, collecting information about their consumer journey may be challenging if a potential customer employs advanced online security.

Still, you can commonly identify their last touchpoint before conversion. Last-interaction attribution is usually suitable for businesses with brief buying cycles or for prospective customers close to making purchases.

3. Linear attribution

Linear attribution is a multi-touch model that evenly divides conversion credit among all interactions during a consumer's journey. For example, a user visits a website, subscribes to the e-mailing list, and takes a website quiz before making a $70 purchase. Because the client had multiple interactions with a brand, the three interactions get 33% of the user's conversion credit. As a result, the blog, quiz, and newsletter each get a conversion value of $23.

This model factors every aspect of a brand's marketing strategy, and it's generally easier than other models to explain to stakeholders or clients. This is because it shows that each channel is equal. Despite this, it also makes it challenging to determine whether a channel outperforms other interactions in the conversion process. Some mediums are commonly more valuable than others, and linear attribution may overvalue some and undervalue others.

4. Time decay attribution

This is another multi-touch model that credits conversion to different interactions that occur before the conversion happens. While similar to linear attribution, it allocates different values to channels rather than distributing the conversion credit equally. The model gives higher conversion credit to touchpoints that occur closer to a potential customer's first purchase with a business. For example, if a user views an ad through social media and waits two weeks before visiting a website, the website receives higher conversion credit than the ad.

Time decay attribution is suitable for businesses with extended buying cycles, as it helps assess the customer's entire consumer journey. It may also help a business evaluate more practical strategies to develop engaging and lasting relationships with leads.

Related: What Is the B2B2C Model? (With Its Importance and Uses)

5. Last non-direct touch attribution

Last non-direct click attribution, or last non-direct attribution, is a single-touch model that disregards all direct interactions that occur before conversion. Direct interaction happens when a potential customer types the web address in their web browser or clicks on a website link from their browser bookmarks. Contrastingly, indirect interactions occur when a user reaches a website through a link from another site.

This model enables businesses to determine how customers discovered or engaged with it before purchasing the products. For example, a potential customer may discover a site from search engine results, bookmark it, and then use the link in the bookmark to purchase from the website. With a last non-direct touch, the model disregards data related to clicking on a bookmark on the website. Instead, the search engine results receive the user's conversion value. This model is suitable for businesses with extended buying cycles.

6. Position-based attribution

The position-based attribution is another multi-touch attribution framework. With this model, all potential customers' interactions with a business get conversion credit. As a result, the first and last touchpoints receive more credit. Many marketing analytics allocate 40% credit to the first and last interactions while evenly splitting the remaining 20% among intermediate touchpoints on the consumer's journey.

For example, a viewer may interact with a business in this manner, viewing a digital ad, visiting their blog, distributing a post on their social media, and downloading an article from the blog. This model attributes 40% of the credit to the initial digital ad and the last article, while the other interactions receive and share the remaining 20%. This position-based model may be suitable for brands with several interactions. It can also enable the brand to evaluate the particular touchpoints and their order of occurrence before conversion.

7. Algorithmic attribution

Algorithmic attribution, or data-driven attribution, refers to models that don't disclose how they collect or calculate data. These platforms commonly employ machine-learning strategies to allocate fractional credit over several interactions. The platforms typically prefer to withhold the interactions they factor in their algorithm or the conversion value they allocate.

A vendor's attribution algorithms may withhold a complete understanding of how, when, or why users become paying customers. Regardless, vendors' algorithmic models can streamline the business processes, as you don't require data collection or evaluation by yourself.

Related: Machine Learning Certification Guide and Career Options

8. Custom attribution

This model enables you to develop a personalized attribution framework suitable for a business. The custom model lets you determine which interactions are relevant for the attribution algorithms and their resulting conversion value. A custom model may offer a company the most accurate information concerning which touchpoints cause the highest conversions.

Benefits of AM

The attribution framework offers different marketing departments several benefits, including:

  • Better marketing strategies and campaigns: The attribution framework assesses which marketing channels, design elements, and messaging styles are likelier to convert users into paying clients. This helps a brand focus on developing and enhancing marketing tactics with the most conversion rates.

  • Improved return on investment (ROI): The attribution framework can assist a business in understanding the most practical marketing strategies or channels for converting leads into customers. Reaching the audience with timing and optimized messaging can improve a company's ROI rates.

  • Personalized channels and messaging for different potential customers: Most businesses have several types of users who have other preferred engagement methods and marketing channels. The attribution framework can help a business develop custom advertisement campaigns for unique groups in its target audience.

  • Cost-efficiency: The attribution framework can help businesses determine which campaigns or marketing channels offer the most conversions. Marketing teams can then re-evaluate or alter their budgets to emphasize their most effective marketing channel, reducing operational costs.

Related: A Guide to Marketing as a Career

Limitations of AM

Here are some challenges with the attribution framework for you to consider:

Collecting data across channels

The primary difficulty with AM is determining the necessary interactions and how much value each channel gets. This challenge is because many businesses employ different channels, platforms, and vendors for its marketing campaigns. You can manage this challenge by adopting an optimized data collection network.

Assuming a causal relationship

Some attribution frameworks assume that a specific interaction directly causes a customer to make a purchase. Despite this, there may be external factors or factors not in the model that prompt or influence the customer's purchase, such as a friend's referral. To manage this challenge, it's vital to adopt models that establish causal relationships.

Integrating offline data

Offline data regarding conversions, including storefront interactions or in-person networking events, can be challenging to include in the attribution framework. The analysis processes and data collection for offline data commonly differ from marketers' algorithms for digital data. You can implement a system that records offline contacts digitally and allows you to trace the appropriate casual relationship.

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