What is Attribution Modeling in Marketing? 2025 Guide

No matter what kind of business you’re marketing, your potential customers likely go through a journey before investing in your product or service. They can interact with various marketing channels like paid ads, organic content, partnerships, search, and more.

The key to a long-term, sustainable marketing strategy is knowing which efforts drive the most conversions to best allocate your resources.

How do you know your best-performing channels? The answer lies in attribution modeling.

With attribution modeling, you’ll have data for insights on which channels drive the most conversions and which have the lowest Customer Acquisition Cost (CAC), Return On Ad Spend (ROAS), Return On Investment (ROI), and more.

In this guide, we’ll cover what you need to know about attribution modeling, including what it is, what different types of models there are, as well as challenges and solutions for your business.

Key Takeaways:

Single-touch attribution models offer insights for short sales cycles, but don’t provide meaningful data for longer customer journeys.

Multi-touch attribution models offer holistic insights; they’re most useful when using multiple marketing channels or have long/complex customer journeys.

Choosing the right attribution model depends on your sales cycle’s length and complexity.

What is Attribution Modeling?

Attribution modeling assigns conversion credit to different marketing touchpoints in your customer’s journey.

The point of attribution modeling is to identify the most effective channels and activities for driving conversions and customers to your brand, allowing you to optimize your marketing spend.

Imagine you placed a Google search ad for your SaaS brand.

A potential customer clicks the ad, reads your landing page, and decides to do more research.

Over a few weeks, the customer engages with your organic content, tries an interactive demo, or calls your sales team. They start a free trial, enjoy it, and upgrade to a paid subscription.

Your attribution model will tell you how much credit each touchpoint deserves for the converted customer—and which touchpoint is worth investing more into.

Attribution modeling has evolved from simplistic single-touch models to sophisticated multi-touch models, reflecting how buyers now use numerous channels and devices in the modern customer journey.

The multi-touch modeling approach is crucial for B2B companies. With multiple stakeholders, buying committees, and higher ticket prices, the sales cycles are longer and more complex. As a result, the attribution models B2B companies use need to account for these journeys.

Single-Touch Attribution Models Explained

Single-touch attribution models give all conversion credit to a single touchpoint in the customer journey.

The obvious candidates for measurement are first-touch and last-touch. There’s also the “indirect last touch” model, which excludes the direct click on a site or link, focusing on the last “indirect” touchpoint. But this model isn’t as popular as the other two—which we’ll cover below.

First-Touch Attribution

First-touch attribution gives 100% conversion credit to the first touchpoint in the customer journey. For example:

A lead clicks a Google search ad.
They browse your landing page.
They leave for a bit to do research (comparisons, reviews, etc.)
They return to the pricing page.
They convert to a customer.

In this scenario, the initial Google ad gets the conversion credit for attracting the customer. First-touch attribution is a good option to assess your brand awareness campaigns.

Last-Touch Attribution

Last-touch attribution gives 100% conversion credit to the last touchpoint in the customer journey. In the example above, instead of the Google ad, the conversion credit would go to the site’s pricing page.

This model is most useful when you want to measure which touchpoint instigates the final push to conversion.

The trouble with first-touch and last-touch attribution is that potential customers go on a journey—and these models can be too simplistic for measuring it.

Single-touch attribution models are best for specific insights on brand awareness, top-converting pages, and very short sales cycles.

If you’re an ecommerce store running a Black Friday campaign, first and last-touch can help determine where to invest for brand awareness and which pages convert customers.

Multi-Touch Attribution Models Explained

Unlike single-touch models, multi-touch attribution models assign conversion credit to multiple touchpoints in the customer journey.

This approach offers a more comprehensive overview of your customer journeys and is more accurate since each touchpoint influences customer behavior. The downside is that getting accurate data for multi-touch models is complex to implement.

There are five main types of multi-touch models: linear, time-decay, position-based, data-driven, and custom models.

Linear Attribution Models

Linear attribution models assign credit equally across all touchpoints in the customer journey. Let’s use the same scenario as we did for single-touch attribution:

A lead clicks on a Google search ad.
They look around your landing page.
They go away for a bit to do some research (comparisons, reviews, etc.)
They go back to the pricing page.
They convert to a customer.

Every touchpoint gets equal credit here—which seems like a “safe” or “fair” option. The main downsides to this linear approach are that it doesn’t account for influence, cost, and the reality of customer behavior.

Linear attribution gives the illusion of balance and completeness, when it actually flattens the nuances of how and why customers make decisions. If the customer goes away to do more research, how influential was the initial ad in reality?

The only useful scenario for linear attribution modeling is to help figure out which touchpoints customers use.

Time-Decay Attribution Model

Time-decay attribution modeling assigns more credit to touchpoints closer to conversion. While it does acknowledge every touchpoint, like the linear model, it more accurately portrays how initial touchpoints lose their influence over time.

Time-decay models are a little more complex to set up, but the two main steps are:

Defining the lookback window—which tells your model how far back to look for touchpoints prior to conversion.
Customizing the half-life—the half-life refers to the amount of time that should pass before the credit should half the original value, otherwise known as the decay rate.

The best use-case for time-decay modeling is for brands with long sales cycles—typically B2B companies. Since you can customize the half-life of time-decay models, those with long sales cycles can account for early touchpoints.

On the other hand, that means time-decay models are not well suited for short sales cycles, since not enough time might pass for the decay rate to mean anything.

Position-Based Attribution Models

Position-based attribution models assign a greater weight of conversion credit at specific touchpoints in the customer journey. The points at which you assign greater weight depend on the position shape you use. There are two main shapes: U-shaped and W-shaped.

U-shaped position model: Assigns credit in a U-Shape—which translates to 40% credit to both first and last touch, and 20% to all middle touchpoints.
W-shaped position model: Assigns credit in a W-shape—which translates to 30% credit to first, middle, and last touches, and 10% to in-between touches.

Both of these models offer a few key benefits, including spotlighting critical touchpoints while still valuing the in-between. Position-based models are also a useful compromise between the simplicity of single-touch models and the complexity of advanced, algorithmic attribution.

On the other hand, position-based models can overvalue those “bookend” touchpoints. If you have a lot of middle touchpoints, that 20% ends up getting spread thin.

These models work best when you have a short-to-medium sales cycle with lower cost transactions and a multi-step (but still manageable) funnel.

Data-Driven Attribution

Data-driven attribution, or otherwise known as Marketing Mix Modeling (MMM), models assign credit based on the actual performance of each touchpoint.

You’ll usually find that the “data-driven attribution” model refers to the default model in Google Analytics 4—which uses a machine-learning approach to spread credit across every influential touchpoint.

Marketing Mix Modeling achieves the same data-driven result, just with manual set up of your data foundation (or using a tool like The Attribution Platform).

If you take the machine-learning approach, your model will dynamically assign credit based on conversion patterns in real-time.

These models work best when your business generates a lot of data—enterprises or medium-to-large businesses—and you want to know how your touchpoints work together with smarter optimization recommendations.

On the other side of the coin, data-driven and MMM are data-dependent. This dependency means if you run a small business that doesn’t generate a lot of data, your model is unlikely to produce meaningful results.

Custom Attribution Models

Custom attribution models are what you build to tailor attribution specific to your business and goals. You structure it to assign conversion credit to touchpoints along the buyer’s journey without having to rely on off-the-shelf models that often create bias to typical touchpoints (like first-and-last).

You can think of custom attribution modeling like adjusting the settings to create a personalized experience rather than relying on default, factory settings.

Using a custom attribution approach can naturally become complex, but the steps you take are fairly straightforward:

Define your business goals and figure out what you’re really optimizing for. Are you looking for revenue growth, high-margin product conversions, faster conversions?
Select the right data sources and move beyond surface-level tracking.
Weigh touchpoints dynamically with engagement signals, time-decay, and assisted conversions.
Test, iterate, and adapt by comparing attribution insights with revenue, running controlled experiments, and using feedback loops.

As a more practical example of applying custom attribution modeling, ClickUp used this tailored approach in The Attribution Platform, creating a “marketer spend view” by integrating their data warehouse to help scale ad spend in the right places.

Within three years, ClickUp saw their Annual Recurring Revenue (ARR) jump from $4m to $150m.

Challenges and Solutions of Attribution Modeling for B2B Companies

B2C brands typically have shorter sales cycles—which means they can justify using single-touch models or simpler multi-touch models like linear or position-based attribution.

On the other hand, B2B sales cycles are a little more complex, and with these multi-touch journeys come unique challenges that attribution needs to address.

Long Sales Cycles

B2B brands typically have longer sales cycles with a web of touchpoints that interact with each other. These long cycles pose a challenge for standard attribution models because the default settings will often miss earlier touchpoints.

Missing out on those earlier touchpoints means your attribution model could be misinterpreting the earliest touchpoint it can see as being the first.

The solution to this challenge is using either a time-decay attribution model and tweaking the lookback window to suit your sales cycle, or building a custom attribution model that addresses the length of your cycle.

Multiple Stakeholders and Buying Committees

Another unique factor for B2B buyer journeys is how there are typically multiple stakeholders or buying committees involved in making purchase decisions.

As a result, your attribution model needs to address account-based marketing journeys, as opposed to focusing on individual user journeys. Standard attribution modeling (especially in GA4) doesn’t have this capability.

The solution to this issue is using a customer data platform (CDP) and aggregating shared identifiers (e.g., email domain) into a tool like The Attribution Platform, that has the ability to analyze attribution at the account-level.

When you have the account-level information, you can then drill into the details for identifying and weighting influential stakeholders. You can do this by assigning weighted score engagement by role and intent, for example:

Executive downloads pricing guide = high influence.
End-user attends webinar = medium influence.

You can use this type of logic using tools like HubSpot ABM features and run retrospective analyses to refine custom weighting over time.

High-Value Transactions

High-ticket B2B deals generally have more complexity, higher risks, and high rewards. The bigger the deal, the more important it is to know what actually works so that you can scale it, prove ROI, and avoid wasting budget.

When each conversion represents significant revenue, you need to be able to adapt your attribution model accordingly. Here are the steps you should take when you’re working with high-stakes:

Invest in granular data collection. Track all of your touchpoints, integrate offline interactions (more on this next), and unify data across platforms using a CDP.
Use multi-touch or custom attribution models. Single touch models would skew your data significantly, use time-decay or a custom attribution model.
Validate your attribution regularly. Back-test the model against historical high-value deals and A/B test different attribution models to compare insights.
Account for contextual factors. Include ABM-specific insights (influencers, buying stages) and adjust for external factors (e.g., market shifts or competitor moves).
Set up governance. Define clear KPIs (pipeline contribution, deal velocity, etc.) and review/refine the model regularly with cross-team input. Build live dashboards to keep insights actionable.

To help you balance individual lead attribution with account-level insights, you can attribute to the lead level first, using these insights for campaign performance. From there, you can roll up to the account-level for revenue reporting and sales enablement.

Incorporating offline and Cross-Channel Interactions

Regardless of whether your brand operates as a B2B or B2C business, you’ll still likely end up with offline and cross-channel interactions. But it is particularly important for B2B brands to consider them, since a lot of B2B or ABM strategies involve offline activities like sales calls, seminars/webinars, and conferences.

The challenge for any business is integrating these non-digital and traditional marketing methods into your attribution modeling. Here’s a step-by-step approach of how to do it:

Connect your touchpoints to a unique identifier, like email address, company domain, or a CRM ID. Make sure you have lead-to-account matching to map contacts to one account.
Manually log offline interactions or use automations. Make sure sales teams are logging calls, meetings, or events, or use scannable tools like barcodes or QR codes to log interactions at trade shows or conferences into your CRM automatically.
Normalize and categorize interactions. You can do this by using data tags such as “offline_event”, “sales_call”, “print_mail”, “webinar”, or “inbound_demo”. Keep these tags consistent across tools.
Assign weight or value to offline and cross-channel interactions. For a basic approach, you can assign these values manually based on qualitative analyses. Otherwise, use a tool like The Attribution Platform to unify offline and online data, and use a machine-learning approach to determine the true value of these interactions.
Integrate your systems for cross-channel visibility. Integrate data from sources like email service providers, paid ads, events and meetings, into your CRM tool, then use a CDP or data warehouse to centralize the data.
Visualize the full journey with unified dashboards. Using a tool like The Attribution Platform, you can visualize the full buyer’s journey that includes offline and cross-channel interactions.

With these dashboards, you can split attribution views by channel type, source, or cohort analyses, and apply cost data to figure out true ROI, ROAS, CAC, and more.

Selecting the Right Attribution Model For Your Business

Conversions represent revenue opportunities, and choosing the right attribution model based on your business needs is essential for figuring out which opportunities are most valuable.

Here’s a quick step-by-step framework for guiding your approach, plus how to measure if it’s working:

Define your goals.
- Do you want to optimize for ROI, lead quality, sales velocity?
- The model you choose should align with your goals and funnel complexity.
Map your customer journey.
- Is it short (ecommerce) or long and complex (B2B with multiple stakeholders)?
- Mapping the journey shows which interactions you need to capture and how much credit they should get.
Audit your data.
- Do you capture all online and offline touchpoints? Is your CRM connected? Are you using UTM parameters effectively? Can you unify the data?
- Clean, unified data is the foundation for any good attribution model.
Match your model to business type (at first).
- Ecommerce = Last-Click or Time-Decay model
- Lead Gen B2B = Linear or Position-Based model
- High-Ticket B2B = Custom Multi-Touch or Markov model
- ABM = Custom or Time-Decay model
Build and test your model.
- Start simple by using a pre-built attribution model at first, and then gradually test more advanced modeling, like machine-learning custom models.
- Make sure you use lead-to-account matching for ABM-based strategies.
Evaluate and iterate.
- Compare different model outputs and validate the data with sales feedback and reports.
- Make sure you review your data and models at least quarterly and focus on insights that drive action.

Using these steps, you should at least have an idea of where to start your attribution journey.

Measuring attribution effectiveness

Simply picking a model and running with it isn’t a good long-term strategy. You’ll need to measure your success to see if the model is working. Here are a few KPIs you’ll want to track to help you evaluate your model’s effectiveness:

Cost per Opportunity or Cost per Acquisition (CAC)—is your model helping you lower costs?
Return On Ad Spend (ROAS)—is your model helping you increase ROAS?
Lead-to-Customer Conversion Rate—are high-attribution channels converting well?
Time to Close—are better-attributed leads moving faster through the funnel?
Channel ROI—are you identifying and investing in high-return touchpoints?
Attribution Coverage—% of leads/opportunities with sufficient attribution data.
Marketing-Sourced Pipeline—how much revenue is influenced by marketing (per model)?

With these KPIs in hand, you can evaluate performance by:

Comparing model outputs (e.g., linear vs. algorithmic)
Validating insights with sales
Checking if attribution leads to better decisions (i.e., leads to growth)

Attribution is both a science and an art—it’s not perfect, but the right model should help you clarify your data into usable insights that help you scale smarter and faster.

Attribution Modeling Doesn’t Have to Be Hard

Once you know what each attribution model does and which situations they work best for, choosing the right one for your business should be straightforward.

But having at least a good idea of where to start—instead of winging it with the default options—is important to help you get meaningful insights that help your business, rather than potentially wasting resources in the wrong places.

Attribution modeling is also an ongoing process. As your business grows or evolves, your model should adapt with it. You may start simple, but feel the need to progressively implement more sophisticated approaches.

Wherever you are on your attribution journey, The Attribution Platform offers a comprehensive attribution solution that can grow with your needs.

If you’re interested in finding out more about how The Attribution Platform can help your business unify data and reveal the true ROI of your marketing efforts, book a demo today.

Attribution Modeling FAQs

How do I know which attribution model is right for my business?

Some attribution models lend themselves better to certain types of customer journeys. Single-touch models are better for short journeys, while multi-touch models are better for medium–long journeys.

How do I track offline channels in a multi-touch model?

You can track offline channels by manually logging offline interactions into a CRM that’s integrated into your attribution model. Otherwise, you can also use scannable tools to automate this process.

Can attribution modeling work for businesses with limited data?

Simple attribution models can work for any business—but those with limited data should avoid using data-driven or marketing mix modeling.