Content Marketing Attribution Models: A Beginner’s Guide

Attribution modelling is one of the most important aspects of content marketing. It helps marketers determine which pieces of content are driving results, which in turn helps them allocate time, effort, and budget more effectively. 

Yet, it can be challenging to know where to begin with attribution modelling, especially given the several attribution models to choose from. These models describe how credit for a conversion is distributed among content, and depending on the model you pick, different touchpoints may appear more or less influential in your analysis.

This guide breaks down the most common models, explains how they work, and offers guidance on selecting the right one.

The 3 Categories of Attribution Models

Broadly, attribution models fit into three categories: single-touch, multi-touch, and next-generation attribution. Let’s explore each of these below.

Single-touch attribution models

With single-touch attribution, all credit for a transaction or conversion goes to a single piece of content. The three most common single-touch models are:

First-Touch Attribution

This model assigns all credit to the first piece of content a user interacts with. For example, if a customer’s first touchpoint is a blog post, that blog receives full credit for any eventual conversion.

Last-Touch Attribution

This model gives all credit to the final piece of content a user interacts with before converting. For instance, if a user watches a product demo video just before converting, the video receives full credit, regardless of prior touchpoints.

Last Non-Direct Touch Attribution 

This model ignores direct traffic, which includes visitors who access your website by typing the URL directly into their browser’s address bar or by clicking a bookmark. In Google Analytics 4 (GA4), untraceable traffic—such as visits from Facebook ads or influencer links without Urchin Tracking Module (UTM) tags—is also classified as direct.

Instead, this model looks at the last traceable interaction, such as Search. 

Pros and cons of single-touch attribution models

First-Touch Models are useful for teams most interested in demand generation, and Last-Touch Models for those most interested in conversions. Their simplicity is what makes them so popular, as you can easily compare different types of content against a single, similar metric of success. However, they lack nuance and fail to fully capture the “customer journey.”

Multi-touch attribution models

Multi-touch attribution involves crediting multiple pieces of content for a single conversion. It’s a more complicated model to design and calculate, but in many cases—especially content marketing—it better reflects the customers’ actual journey.

That’s because most people don’t make a purchase or sign up for a service after seeing just one piece of content from a company. Instead, they may read a blog, visit a product page, view a white paper, and then make a purchase or sign up.

Examples of multi-touch attribution include:

Linear-Touch Attribution

Each piece of content a user interacts with is given equal credit for a conversion. For instance, if a conversion involves three touchpoints, each would receive a 33% share.

Position-Based Attribution

With position-based attribution, content is assigned credit based on its place in the customer journey. Three popular models are U-shaped, J-shaped, and W-shaped attribution, so named due to the shape these models produce on a graph.

With U-shaped attribution, the first and last pieces receive equal credit, with the remaining credit distributed evenly among the other points.

With J-shaped attribution, the last piece receives greater credit than the first, with remaining credit spread evenly among other points. Inversed J-shaped attribution does the opposite: the first touchpoint has the greatest credit, followed by the last touchpoint, and remaining credit split evenly among other points. 

With W-shaped attribution, higher credit goes to the first and last touchpoints and the lead-qualification touchpoint. Remaining credit is spread evenly among other points.

Time-Decay Attribution

Time-decay attribution assigns more credit to content closer to the conversion event, emphasising the influence of touchpoints that occur later in the journey.

Pros and cons of multi-touch attribution models

Linear-Touch Attribution is simple to calculate and easy to understand. It offers a fuller picture of the customer journey than single-touch models by crediting all interactions equally. However, it assumes that every touchpoint is equally influential, which often isn’t the case. 

Position-Based Attribution provides more nuance by assigning greater weight to specific moments in the customer journey. For example, U-shaped attribution emphasises the first and last touchpoints, while J-shaped attribution prioritises one over the other. This can offer useful insight into which stages are most impactful. The trade-off is that these models are based on fixed assumptions about what matters most—and those assumptions may not be accurate for all customers or campaigns.

Time-Decay Attribution gives more credit to content that appears closer to the point of conversion. This fits nicely with basic conceptions of a standard sales funnel, where early, top-of-funnel content is assumed to have less impact on the final purchase than later, bottom-of-funnel content. However, not all customer journeys follow a neat, linear path.

Next-generation attribution models

New tracking technologies have made it possible to apply attribution models in more advanced ways. Here are some of the most interesting examples:

Full-Path Attribution Model

This model considers the complete user journey, at least as much as it can be captured through data. It’s similar to the W-shaped position-based model, except that it looks at four key points: (i) first interaction → (ii) lead-generation → (iii) sales-qualification or deal creation → (iv) final conversion.

This requires integrating with a customer relationship management (CRM) tool, such as HubSpot, to accurately capture moments of lead generation (e.g., signing up for a newsletter or submitting a form) and deal creation (e.g., replying to a sales email).

This model works well for understanding how the marketing and sales processes work together to generate sales, especially in more complex marketing and sales cycles. 

Data-Driven Attribution Model

Also called machine-learning attribution modelling, this sophisticated model uses machine learning to examine previous customer and non-customer behaviour and determine which touchpoints and customers are most important for your company.

For example, suppose you work for a B2B SaaS (software as a service) company that uses both organic marketing and paid ads. You employ a machine learning algorithm to analyse your data and find that users who first discover your brand through a downloadable eBook before engaging with a paid ad are 15% more likely to convert. The algorithm redistributes credit accordingly, granting the eBook greater credit than it might otherwise receive.

Data-driven modelling is especially exciting for content marketers. It helps identify the particular roles content marketing plays in the broader marketing scheme. If the model determines that certain types or even individual pieces of content serve as a catalyst for sales won through other channels, that helps highlight their value in the overall sales cycle. Conversely, content that has little to no impact or even reduces the likelihood of a sale later on is a good candidate for optimisation or deletion. 

Pros and cons of next-generation attribution models

These models promise a better approximation of customer behaviour but require additional tools, technology, and knowledge to implement (although GA4 now comes with data-driven modelling built in, which is handy). Other models also benefit from being widely used and validated, whereas custom models require you to validate the model yourself.

How to Choose a Content Marketing Attribution Model

Each attribution model has its unique strengths and weaknesses, and each makes certain assumptions to help simplify analysis. Understanding these assumptions is crucial to selecting a model that provides meaningful insights. (This is often one of the first hurdles marketers face when trying to measure return on investment (ROI) from content.)

As with most things in marketing, finding the right model takes some trial and error. The goal is to pick one that fits your data and helps you make better decisions.

Here are a few core principles to guide you:

There’s no one-size-fits-all model

Every business operates within a unique context—your industry, audience, and goals will dictate the most appropriate attribution model. Longer sales cycles, typical of B2B businesses, will benefit more from multi-touch models like Position-Based or Full-Path. In contrast, shorter, direct-to-consumer brands may find any of the single-touch models sufficient to make decisions. 

Start simple, then layer in complexity

If you’re just getting started with attribution, jumping straight to a sophisticated model can be tempting. However, this can lead to operational strain and decision paralysis. Begin with what’s easiest to implement, and focus on mastering the basics of data collection and reporting before progressing to more sophisticated models.

As your business introduces new channels, expands into new markets, or changes its customer acquisition strategies, your model should evolve to reflect these changes, growing or shrinking in complexity accordingly. 

Match the model to your customer journey

Your chosen attribution model should align well with your customer journey. To do this effectively, you need a clear understanding of how customers move through your sales pipeline, which you can achieve by speaking directly to those who go through it. 

Generally, complex sales cycles (such as those in B2B SaaS) work best with advanced, multi-touch attribution models. Simpler sales cycles (such as those in B2C eCommerce) can often be captured through single-touch models.

Data-driven attribution modelling is exciting for just this reason: It essentially builds an attribution model from the customer journey itself. 

Align attribution with business goals

Your chosen model should also assign credit based on your overall goals as a department. If the focus is on demand or lead generation, models that emphasise early touchpoints will help identify opportunities. If conversion or sales is the priority, models that highlight later touchpoints will be more useful.

Additionally, different campaigns may have different focuses. If you were to simultaneously run a blog campaign focused on demand gen and a LinkedIn campaign focused on conversion, you might evaluate the former using a first-touch model and the latter using a last-touch model. Strategies that encompass the whole cycle and move audiences from Problem-Aware to Purchase could benefit from any of the position-based or next-gen models. 

Experiment, assess, and execute

Once you’ve mapped out your customer journey and considered your business goals, test different models to see how well they fit your data and expectations and what insights they yield.

Ultimately, you’ll need to select a model and stick with it. If you’re unsure where to start, it’s often best to start simple and build in complexity over time. For the most valuable insights, your model should remain consistent over time.

Conclusion

Attribution modelling gives content marketers the clarity to make smarter, more strategic decisions—showing what’s working, what isn’t, and where to focus next. While no model captures every detail of the customer journey, the right one will highlight the content that truly drives results.

Want to go deeper? Download Eleven Writing’s free eBook, Introduction to Multivariate Attribution for Content Marketing, or get in touch for a free consultation on setting up effective attribution reporting for your team.