Fractional attribution
Fractional attribution is a method of assigning partial credit to multiple marketing touchpoints in a user’s journey. It helps provide a fuller picture of how different interactions contribute to a conversion.
What is fractional attribution?
Fractional attribution is a marketing attribution method used to assign credit for a conversion to multiple marketing touchpoints — across the whole user journey, not just one key moment.
Instead of focusing solely on one point of contact (like a last-click attribution model), it distributes the credit among every interaction a user has along the way. For mobile app marketers, this is a game-changer. You get a fuller picture of how users go from curious to converted, whether they’re installing an app or making in-app purchases.
Suppose a user sees an Instagram story ad, later clicks a Google search ad, and finally converts after seeing your app ad on an Instagram story.
With fractional attribution, each touchpoint would receive partial credit for the conversion. Consequently, you get a clear view of what’s really working — whether it’s the ad format or the channel — and can use that insight to make smarter budgeting and campaign planning decisions.
Types of fractional attribution
Under fractional attribution, several popular models use different approaches to distribute credit. Common types include:
Linear attribution
With linear attribution, every touchpoint in the customer journey shares the credit equally. It’s simple and works well for campaigns where each interaction plays an equally important role.
For example, if a customer hits five different ads before buying, each one gets 20% (100% divided by five) of the credit. This method gives you a nice overview of the entire journey, but doesn’t show you which touchpoints were real decision-makers.
Time decay attribution
Time decay attribution gives more weight to touchpoints closer to the conversion. It’s perfect for fast-paced campaigns or quick-turn promotions since it assumes those last few interactions had a bigger influence on the user’s choice.
For example, if someone clicks on an ad a week before buying, and then sees another ad the day before their purchase, that last ad gets more credit. This model is great if you want to pinpoint the interactions that nudged users over the line while still acknowledging the earlier steps.
U-shaped attribution
Also called position-based, U-shaped attribution favors the first and last interactions, with a smaller slice for any touchpoints in between. It’s ideal when the focus is on creating initial awareness and then pushing for the final conversion, while still recognizing those mid-funnel engagements.
For instance, with five touchpoints, the first and last might each get 40%, while the middle three share the remaining 20%. This way, you’re highlighting both the initial draw and the final push to convert.
W-shaped attribution
In W-shaped attribution, the first interaction, a significant middle touchpoint (such as lead capture), and the final interaction get most of the credit, with all others receiving less weight. This model is handy for longer, multi-step journeys, especially when you want to emphasize distinct stages — like early awareness, a mid-funnel milestone, and the final conversion.
For a journey with six touchpoints, the first, third, and last might each get 30%, with the rest sharing the leftover 10%. It’s useful for tracking engagement at different funnel points.
Custom attribution model
With a custom attribution model, you set the rules based on what matters most to your business — whether that’s specific goals, customer behaviors, or unique campaign elements. For example, a mobile app marketer might give more credit to social media interactions if they’re proven to drive conversions, while dialing down on display ads. Custom models are super flexible and let you tailor credit exactly how you want, but they need solid data and insights to back them up.
One step further with Markov chains
Markov chains are a nifty way to model sequences of events, where each event’s likelihood depends only on the one that came right before it. Here’s the gist: rather than looking at the entire history, they just consider the latest step. This makes them ideal for predicting user behavior in dynamic systems, where each interaction influences the next, like a marketing journey where each step nudges users along the path.
Unlike fractional attribution — which splits credit across multiple touchpoints by set rules — Markov chains take a probability-based approach. They assess each touchpoint by its likelihood of pushing the user to the next step. As a result, you get deeper insights into each touchpoint’s role in driving conversions, making this a handy method for optimizing complex journeys with multiple possible paths.
Benefits of fractional attribution
1 — Provides a full picture of what drives conversions
Fractional attribution doesn’t just stop at the first or last click — it gives you a complete view of all the touchpoints that help lead to conversions. With this, you can see which channels and moments actually make a difference, giving you insights into patterns you might otherwise miss. These deeper insights help you make smarter, more informed choices about where to focus your advertising efforts and spend.
2 — Optimizes engagement across the entire user journey
When you understand each touchpoint’s role and value, it’s easier to fine-tune the full journey — from first spark to final conversion. Fractional attribution lets you see what’s nudging your target users down the funnel, so you can smooth out the bumps and create a seamless experience.
The result? Campaigns that connect with users at every stage and ultimately lead to better conversions.
3 — Considers and leverages indirect influences
Some touchpoints might not directly seal the deal but still play a key part in moving users closer to action. Fractional attribution captures these indirect influences or helpers, showing their role in the overall journey. Say an awareness ad might drive users to search for your brand later — that’s value you’d want to see. This approach helps you recognize such smaller but essential contributions, so you can support them properly.
4 — Maximizes ROI with smarter budget allocation
By spreading credit across all touchpoints, you can clearly see which channels and campaigns give you the best bang for your buck. Using fractional attribution, you can spot opportunities to reallocate budget in ways that get better results. No more over-investing in that last-click moment — instead, you can distribute your budget effectively across the whole journey to maximize the impact of your marketing dollars.
How to implement fractional attribution
Here’s a detailed breakdown of the key stages to ensure your model is both accurate and insightful:
1 — Use your existing data
Start by leveraging your existing data to understand your app users’ journeys. Check out historical data across all your campaign channels — think social ads, paid search, display ads, and emails. Look for patterns showing the typical paths to conversion — like how often touchpoints happen, the time from first interaction to install, and the role each channel plays.
Your historical data will help you decide which touchpoints to include and prioritize the ones that matter most. This sets you up for a model that’s accurate from the start.
2 — Onboard the right tools
Fractional attribution needs advanced tracking tools that can handle the complexity of mobile marketing. Pick one that’s got multi-touch attribution capabilities and works seamlessly with ad networks and in-app events. For mobile app marketing, tools like AppsFlyer’s offer SDKs that capture in-app behavior and integrate with third-party networks.
When picking a tool, look for:
- Multi-channel tracking: Make sure it catches touchpoints across all channels — social, search, in-app ads, SMS, you name it.
- Cross-device/platform tracking: Find one that can track users across devices (from mobile to desktop).
- Customizable attribution models: Options to adjust or switch models (like linear to U-shaped) as you gather more insights.
3 — Build your measurement matrix
The measurement matrix is a crucial planning tool that outlines each touchpoint, defines key metrics, and establishes rules for distributing credit. Start by identifying the primary metrics that will reflect conversion events, such as app installs, in-app purchases, engagement events, and lifetime value (LTV).
Once you’ve got those, it’s time to choose an attribution model. Maybe time decay or linear feels right. Either way, you’ll want to set some weights for each touchpoint. For example:
- Social media ads: If these are great for top-of-funnel awareness, give them a bit more credit here.
- Search ads: They’re often solid for driving re-engagement and consideration, so they might deserve mid-funnel credit.
- In-app ads and push notifications: These can be crucial for conversions, especially in the lower funnel, so they might get more weight.
Using this matrix, you can ensure consistency in how you assign credit across touchpoints. This allows for a structured approach to evaluate which interactions truly impact your app’s user journey.
4 — Choose your attribution window
Selecting the right attribution window is all about capturing relevant touchpoints without giving too much credit to older interactions. For mobile apps, the ideal window can shift based on the campaign, how long your user journey typically is, and the type of app. For instance:
- Short windows (like 7–14 days) work well for fast-moving, transactional apps where users convert quickly.
- Longer windows (30 days or more) are better for apps that need more consideration, like finance or subscription services.
We recommend experimenting with different attribution windows and seeing how they affect your data. Over time, this can reveal the sweet spot for your app — one where you’re capturing meaningful interactions without extra noise.
5 — Perform incrementality tests
Incrementality testing lets you see the real impact of each touchpoint by isolating how much it actually influences conversions. The first step is to conduct an A/B test, where one control group sees certain ads or interactions, while another control group doesn’t. This way, you can measure the “incremental lift” that each touchpoint provides.
Take a retargeting campaign, for example. Run it for some users but not others. If the group exposed to the campaign has a higher conversion rate, it’s a clear sign that touchpoint is driving real value. These tests help you pinpoint which interactions genuinely move the needle on conversions and which might just be adding clutter.
6 — Apply machine learning for continuous model improvements
Once your fractional attribution model is up and running, machine learning can take things up a notch. It spots subtle patterns, adjusting credit distribution as user behavior changes. Think of it as a fine-tuning process — catching interactions or touchpoints that might not stand out at first, but actually play a role in many user journeys.
Consequently, your model stays in sync with seasonal trends, new channels, or shifts in what users respond to. For example, if a fresh ad format takes off, machine learning assesses its impact and adjusts its importance in your model — no manual recalibration needed. This way, your model is always accurate and in tune with real-world behavior.
Future trends for fractional attribution
Privacy and security-first mindset
As privacy regulations tighten, from GDPR to CCPA, fractional attribution will need to keep up. Collecting and tracking user data is getting trickier, and you’ll likely face new challenges in creating detailed attribution models. With privacy concerns on the rise, we’re moving toward aggregated data and consent-based tracking — solutions that protect user privacy without skimping on insights. Future models will have to strike a balance: accurate data that keeps user confidentiality intact.
AI and machine learning-powered attribution
AI and machine learning are redefining fractional attribution by making models smarter and more responsive. These tools help process vast amounts of data in real time, revealing intricate user patterns and fine-tuning credit distribution as behavior shifts. Predictive analytics adds another layer, letting you anticipate which touchpoints will likely lead to conversions. And, as AI evolves, so will fractional attribution. Think: dynamic, real-time adjustments that help you stay ahead.
Rising use of ad blockers
With ad blockers on the rise, tracking the full user journey isn’t as straightforward as it used to be. Many touchpoints are now hidden, pushing you to explore other ways of measuring engagement, like first-party data or contextual targeting.
Attribution models will need to work with what’s available, possibly blending data from non-intrusive sources with consented interactions. Fractional attribution will need to be flexible enough to account for missing data, possibly using predictive modeling to infer the influence of hidden touchpoints.
The role of hyper-personalization
As hyper-personalization creates more tailored user experiences, fractional attribution models need to keep up. These personalized strategies use customized messaging across channels, leading to unique user journeys that aren’t easy to generalize.
To capture these individualized paths, you can start using AI to spot trends in specific user segments. The focus ahead will likely be on measuring how well personalization works, helping you see which tailored approaches drive the best results.
Key takeaways
- Fractional attribution lets you see which touchpoints in your user journey actually drive conversions by spreading credit across multiple interactions. Instead of giving all the credit to a single touchpoint, it provides the full picture — from a user’s first spark of interest to the final action.
- This attribution approach helps you pinpoint the channels and campaigns that offer the best return on investment. By seeing where each dollar actually makes an impact, you can put your budget toward touchpoints that matter, rather than relying on just that final click.
- Several fractional attribution models — like linear, time decay, U-shaped, and custom models — allow you to distribute credit based on specific campaign goals and the unique characteristics of the customer journey. Each model offers a different view of how touchpoints contribute to conversions.
- Machine learning enhances fractional attribution models by continuously adjusting credit distribution based on user behavior changes. This helps the model stay up to date with seasonal shifts and emerging channels and trends.
- As privacy rules tighten, fractional attribution will need to adapt, too. Using aggregated data and consent-based tracking will keep your performance insights intact while respecting user privacy.
