Attribution modeling: Which type is right for you?

Many of us have been there—lost in the jumble of digital marketing, trying to make sense of shifting consumer behaviors while ensuring our budgets aren’t wasted on ineffective campaigns. These issues often stem from ineffective attribution modeling and the inability to accurately track and measure the impact of various touch points along the customer journey.

Without a clear picture of how each touch point contributes to the overall customer experience, marketers risk making decisions based on incomplete data. This can lead to misallocated budgets, missed opportunities, and ultimately a failure to effectively connect with customers.

Investing in a suitable attribution model provides the clarity needed to navigate these complexities, helping to identify touch points that drive conversions and are valuable components of the customer journey. By focusing on the right touch points, you can gain valuable insights that enhance decision-making and improve overall marketing performance.

This article will dig deep into the different attribution models available and help you choose one that aligns with your unique business goals.

What are the different types of marketing attribution models?

Broadly, there are two main types of marketing attribution models: single-touch and multi-touch.

1. Single-touch attribution models

In this model, the entire credit for a conversion is attributed to a single touch point. As much as this sounds pretty simple and direct to measure, single-touch attribution models don’t offer the complete picture of how the customer progressed through their journey.

There are three main types of single-touch models.

Last-touch attribution model

Last-touch attribution is one of the simplest models of all. It gives complete credit to the final touch point before a conversion, ignoring all the other interactions that led up to that moment.

While last-touch attribution is easy to implement and understand, it has some serious limitations. To start with, it doesn’t account for the complex, multi-channel nature of modern customer journeys. A customer may have seen your ad on social media, clicked through to your website from a search engine, and finally converted after receiving an email promotion. Last-touch attribution would only give credit to the email, ignoring the crucial role played by the other channels.

Moreover, last-touch attribution modeling can lead to shortsighted optimization decisions. If you only focus on the final touch point, you may over-invest in channels that are good at closing the deal but neglect those vital in building brand awareness and guiding your audience through the consideration stage.

First-touch attribution model

On the opposite end of the spectrum is first-touch attribution. In this model, the first interaction gets all the conversion credit, regardless of what happens after that.

First-touch attribution modeling can help you understand the channels that are most effective in generating initial awareness and interest. However, it fails to account for the numerous touch points that follow and ultimately lead to a conversion.

Imagine a scenario where a customer first learns about your brand through a social media ad but doesn’t convert until months later after being retargeted with multiple other touch points. First-touch attribution would credit the social media ad—the first touch point—even though it played a partial role in the final decision.

The last non-direct touch attribution model

The last non-direct touch attribution model assigns 100% of the conversion credit to the last touch point in the customer journey that wasn’t a direct visit to the website. A direct visit is when a user types the URL directly into their browser or uses a bookmarked link to access the site.

In this model, the last non-direct touch point receives all the credit for the conversion, while direct visits and any previous touch points are ignored. This approach works on the assumption that the last non-direct interaction played the most significant role in influencing the customer’s decision to convert.

Here’s a simple example:

• Day 1: A user discovers your website through an organic search result and browses your products.
• Day 3: The user sees a retargeting search ad and revisits your products.
• Day 6: The user directly visits your website by typing the URL into their browser and completes a purchase.

In the last non-direct touch attribution model, the search ad would receive 100% of the credit for the conversion since it was the previous non-direct touch point.

This model is helpful for understanding which non-direct channels are most effective at driving conversions and optimizing your marketing efforts accordingly. However, it doesn’t account for the impact of direct visits or the cumulative effect of previous touch points in the customer journey.

2. Multi-touch attribution models

Multi-touch attribution (MTA) is the most sophisticated and comprehensive attribution modeling system. It considers all the touch points along the customer journey and assigns credit based on their estimated impact on the final conversion.

The beauty of multi-touch attribution is that it provides the most accurate and nuanced picture of how your marketing channels work together to drive conversions. It uses advanced algorithms and machine learning to weigh the influence of each interaction based on factors like the type of touch point, the time elapsed since the interaction, and the customer’s behavior after the touch point.

This is also why MTA is the most complex and resource-intensive model to implement. It requires a robust data infrastructure, advanced analytics capabilities, and a deep understanding of your customer journey. However, if you’re ready to invest in the long-term growth of your brand by accurately understanding how each of the touch points performs, then MTA can be extremely valuable.

There are different types of multi-touch attribution models available, each working on a different principle.

Linear attribution model

Linear attribution is the diplomatic middle ground between the first-touch and the last-touch models. It gives equal credit to every touch point along the customer journey, regardless of their order or perceived importance.

The advantage of linear attribution is that it acknowledges the contribution of every touch point, not just the first or last.

However, linear attribution has its drawbacks. By treating all touch points equally, it fails to account for the varying levels of influence each one may have had on the final decision. Casual social media interactions will receive the same weight as a targeted email campaign with an attractive discount for a price-conscious customer, even though the latter is more likely to influence a conversion directly.

Best-suited for: 

• Brands looking for a simpler attribution model that takes all the touch points into account
• Brands with long, complex customer journeys where it’s nearly impossible to properly attribute channels that drive conversions

Time-decay attribution model

Time-decay attribution is based on the idea that the touch points closest to the conversion are the most important. It assigns more credit to recent interactions and less to those that occurred further in the past.

However, time-decay attribution can undervalue the importance of early-stage interactions that generate awareness and consideration. It may lead marketers to over-invest in bottom-of-the-funnel tactics at the expense of top-of-the-funnel initiatives that feed the pipeline.

Best-suited for: 

• Businesses with shorter sales cycles, where the final interactions are more likely to be the tipping point for a conversion
• Products or services with high purchase frequency, as it emphasizes the touch points that drove the most recent conversions

Position-based attribution model

The position-based or U-shaped attribution model is a hybrid model that tries to strike a balance between first-touch and last-touch attribution. It usually awards 40% of the credit to the first and last touch points and distributes the remaining 20% equally among the middle interactions.

This model recognizes the importance of the bookends of the customer journey—the initial interaction that sparked interest and the final one that sealed the deal. At the same time, it doesn’t completely ignore the touch points in between that nurtured the relationship and kept the customer moving forward.

Best-suited for: 

• Businesses with mid-length sales cycles and a mix of awareness-building and conversion-focused tactics 

Note that it may not be ideal for very short customer journeys, where the 40/20/40 split may not accurately reflect the relative impact of each touch point.

W-shaped attribution model

The W-shaped multi-touch attribution model assigns credit to three critical stages of the customer journey: the first touch, the lead creation stage, and the final touch. Such a holistic approach helps marketers better view the marketing touch points and their contributions to the final conversion.

While the first and last interaction with a customer is apparent, identifying the lead creation stage in the middle can be tricky. The lead creation stage refers to the activities that capture the customer’s attention and nurture their interest—like website visits, form submissions, or clicks on promotional content.

By assigning credit to each stage, the W-shaped MTA helps marketers optimize their campaigns better and allocate resources across the marketing funnel more efficiently.

Best-suited for:

• Brands with longer sales cycles, like those selling high-value products or services

Custom attribution models

There are also a few more sophisticated types of MTA models that marketers can use to define their own weighted rules based on their unique business goals and customer journey. Here are two custom approaches to attribution modeling:

• Algorithmic attribution: This model uses statistical modeling to determine the relative impact of each touch point based on historical data and predictive analytics.
• Data-driven attribution: This model relies on large volumes of data and machine learning algorithms to continuously refine the attribution model based on real-world outcomes.

Best-suited for:

• All businesses, since it uses a tailored approach that can be customized specifically to fit into a brand’s customer journey, goals, and requirements

How can you choose the right attribution modeling approach for your business?

With so many attribution modeling approaches to choose from, how do you know which one is right for your marketing strategy? The answer depends on various factors, including your business goals, customer journey, data capabilities, and resources.

Let your long-term business goals be your guide

As you weigh the different attribution models to pick from, keep your long-term business objectives as the focal point. Think about what you want to achieve a few years or decades down the line—and the corresponding metrics you need to track to keep your goal in sight.

Are you focused on driving brand recognition and awareness, or are you more concerned with bottom-of-the-funnel conversions? 

For example, if your primary goal is to generate and nurture leads over time, a W-shaped or simple linear attribution model may be more appropriate. On the other hand, if your goal is to optimize for immediate product sales or bookings, a time-decay or position-based model may be a better fit.

Choose a model that closely aligns with your customer journey

The attribution model you choose should reflect your customer journey’s unique twists and turns.

Consider its nuances: How many touch points do your customers typically encounter before converting? Which channels and tactics are most influential at each stage of the journey? Do you have a long, complex sales cycle, or do most conversions happen in a single session?

If your customers tend to have short, straightforward journeys with only a few touch points, a simple model like linear attribution may be sufficient. However, if your customers have long, complex journeys with multiple touch points across various channels, a more sophisticated model like a W-shaped or custom multi-touch attribution may be necessary to capture the finer details of their behavior accurately.

Assess your data capabilities to support attribution modeling

The accuracy and effectiveness of the attribution model depend largely on quality, efficiency, and data collection capabilities.

So think about the infrastructure you have: Do you have the necessary tracking and reporting infrastructure to capture all the relevant touch points and customer interactions to support the attribution model of your choice?

Some attribution modeling methods require more granular data than others, like custom multi-touch attribution—which needs a wealth of data points to build a comprehensive picture of the customer journey. Meanwhile, last-touch attribution only requires data on the final touch point before conversion.

Although advanced multi-touch attribution modeling methods may provide the most accurate and actionable insights, they also require significant investment in data infrastructure, marketing analytics expertise, and ongoing management. So if you don’t have the resources to support such a model, you may need to start with a simpler model and gradually evolve your approach as your data capabilities improve.

What does a successful attribution model look like?

A successful attribution model should be accurate, reliable, and provide meaningful insights into marketing performance. It should take into account multiple channels, different user behaviors, and touch points while adjusting for seasonality.

That said, here are three underlying principles that every attribution modeling process should address, regardless of your domain, customer behavior, sales length, or any other external factors.

The attribution trifecta: 3 fundamental principles to remember

A proper attribution model should be built on three fundamental principles to form the foundation of your marketing analytics methodology:

1. Holistic measurement: Capturing data at every touch point along the customer journey, from initial awareness to final purchase, to gain a comprehensive view of the conversion path
2. Data-driven insights: Employing advanced analytics and machine learning algorithms to identify patterns and trends in customer behavior and determine the relative impact of each touch point on the final outcome
3. Actionable recommendations: Translating captured insights into specific, actionable recommendations for optimizing marketing campaigns, such as adjusting ad spend or refining creative messaging

The essentials of an attribution model

To effectively implement these principles, a proper attribution model should include the following:

• Your attribution model should leverage machine learning algorithms to process vast data, identifying patterns and trends that human analysts might miss. It should be able to handle multi-touch attribution, assigning appropriate credit to each touch point based on its influence on conversion.
• A robust attribution model should be able to adapt to different attribution windows and adjust the lookback period to match their specific sales cycle—whether it’s a few days for fast-moving consumer goods or several months for high-value B2B services.
• It should have cross-device and cross-channel tracking to stitch together user interactions across various devices and platforms to create a unified customer profile.
• Real-time data processing is another must-have. The model should update continuously, providing marketers with up-to-the-minute insights to inform quick decision-making and agile campaign adjustments.
• Importantly, a proper attribution model shouldn’t exist in isolation. It should integrate seamlessly with other marketing tools and data sources—including CRM systems, ad platforms, and analytics tools—to get a holistic view of marketing performance.

With these elements, a proper attribution model becomes more than just a measurement tool. It becomes a strategic asset that drives marketing efficiency and business growth.

A peek into Yelp’s attribution toolkit

With these core principles in mind, Yelp can help you customize a multi-touch attribution model that works for your unique business. Plus, with Yelp’s tools you can gather the data you need to track your touch points.

Yelp’s attribution toolkit has the following  components.

1. Conversions API: tracking conversions through first-party data

The Yelp Conversions API helps businesses compare first-party data with Yelp campaigns, making it easier to track conversions that happen anywhere—like in-app purchases and sales through a CRM system

Using this Conversions API, businesses can create an end-to-end view of their Yelp campaigns, ensuring the right Yelp interactions and ads are attributed.

2. Pixel tracking: monitoring the entire sales funnel

Pixel tracking is a powerful tool that captures granular data on user behavior across the sales funnel. By placing a small piece of code on a website, businesses can track key events like page views, clicks, form submissions, and purchases. Yelp Pixel helps identify traffic sources, measure conversion rates, and attribute conversions to specific touch points.

3. UTM tracking: connecting the dots

In addition to pixel tracking, Yelp also supports Urchin Tracking Module (UTM) parameters to track the performance of your marketing activities across various channels and touch points.

UTM parameters are bits of code added to URLs to identify the source, medium, campaign, and other elements of your traffic. Here’s what a URL with UTM parameters looks like:

https://www.example.com/landing-page?utm_source=yelp&utm_medium=cpc&utm_campaign=easter_sale&utm_content=banner_ad

Let’s break down each UTM parameter:

• utm_source: Identifies where the traffic is coming from (e.g., Yelp, Google, Facebook)
• utm_medium: Indicates the marketing medium (e.g., cpc, email, social)
• utm_campaign: Specifies the name of your marketing campaign (e.g., easter_sale, summer_promo)
• utm_content: Describes the specific content that led to the click (e.g., banner_ad, text_link)

A business can use UTM parameters to track the entire customer journey by tagging each touch point with a unique UTM code. For example, if a customer interacts with five different marketing channels before making a purchase, each channel can be tagged with specific UTM parameters to identify its role in the journey. By analyzing these UTM codes, businesses can see the exact path the customer took—from the initial click on a social media ad to the final purchase on the website—allowing for precise attribution of each touch point’s impact.

When you combine data from the Yelp Conversions API with pixel tracking and UTM parameters, you get a complete view of the customer journey and the impact of each touch point.

4. Third-party tools: expanding attribution capabilities

Yelp collaborates with a growing network of third-party partners to enhance its attribution capabilities. These partnerships enable businesses to leverage advanced tracking tools, analytics platforms, and data management solutions to gain a more comprehensive understanding of their marketing efforts and customer journeys.

For example, you can use:

• Cuebiq to track the in-store visits resulting from customer interactions with Yelp offerings
• Telmetrics to track the number of customers who call stores after engaging with Yelp ads
• Neustar to track the customer journey from Yelp to purchase across different channels.

Choose an attribution model that reflects your reality

Ultimately, the suitable attribution model for your business is one that aligns with your goals, reflects your customer journey, leverages your data capabilities, and fits within your resource constraints. It’s an ongoing process of experimentation, refinement, and adaptation as your business evolves and your marketing strategy matures.

As you fine-tune your approach to attribution modeling, consider how it fits into your broader performance marketing strategy and study the distinct journeys your customers take.

To dive deeper and gain insights into nuanced variations in customer journeys across different channels and strategies, explore this guide on performance marketing solutions.