Cross-Platform Multi-Channel Attribution in Marketing: Balancing Costs and Results Across Devices
Imagine you’re a traffic and analytics specialist at a SaaS company launching a new feature for your subscription platform. Your campaign uses multiple channels—push notifications, Google Ads, LinkedIn posts, email newsletters, YouTube videos, and affiliate partnerships—reaching users across devices like desktops and mobile apps. After a month, sign-ups increase by 25%, and 10% of users make a payment. But which channel and device drove the most value? Push notifications brought in users cheaply, but YouTube videos, though expensive, led to payments. Cross-platform multi-channel attribution helps you analyze the cost, impact, and synergy of each touchpoint, ensuring you balance marketing spend with the best results.
In this article, we’ll explore how end-to-end attribution across channels and devices helps you understand the cost-effectiveness of each interaction, from cheap traffic that drives registrations to expensive channels that secure payments. We’ll dive into attribution models, tools like Google Analytics 360 (GA360) and AppsFlyer Data Locker, and address challenges like discrepancies between systems. Using a SaaS example, you’ll learn how to optimize your marketing budget for maximum ROI.
What You’ll Learn
How cross-platform multi-channel attribution evaluates costs and results
Key attribution models for understanding touchpoint impact
How to use attribution to balance cheap and expensive traffic for better outcomes
Challenges of attribution discrepancies and how to address them
Why Cross-Platform Multi-Channel Attribution Matters
Modern campaigns involve multiple channels and devices, each with its own cost and impact. A user might see a cheap push notification on their phone, click a LinkedIn ad on their desktop, watch a YouTube video, and finally make a payment after an email. A recent eMarketer report found that customers engage with 5–7 touchpoints before converting, often switching between devices. Without end-to-end attribution, you can’t see how these touchpoints work together, nor can you assess their cost-effectiveness.
For traffic and analytics specialists, attribution across channels and devices is key to optimizing budgets. It helps you measure the cost of each interaction (e.g., $0.10 per push vs. $5 per YouTube view), understand their role in the customer journey (e.g., registrations vs. payments), and find the right balance between cheap traffic for scale and expensive traffic for quality conversions. Let’s follow a SaaS company’s campaign to see how attribution drives better results.
Attribution Models: Measuring Costs and Impact Across Channels and Devices
Attribution models assign credit to touchpoints in the customer journey, helping you evaluate their cost and impact across channels and devices. Before diving into the details, here’s a quick overview of the models we’ll cover:
First-Touch Attribution: Credits the first channel a user interacts with, ideal for identifying low-cost entry points.
Last-Touch Attribution: Credits the final channel before a conversion, highlighting what drives the end result.
Time-Decay Attribution: Gives more credit to touchpoints closer to the conversion, balancing timing and cost.
Position-Based Attribution: Assigns 40% credit to the first and last touchpoints, splitting the rest across middle interactions.
Data-Driven Attribution: Uses machine learning to assign credit based on actual impact, optimizing costs and results.
Now, let’s explore each model in detail through the lens of cost-effectiveness.
First-Touch Attribution: Identifying Low-Cost Entry Points
First-touch attribution gives 100% of the credit to the first channel a user interacts with, whether on a desktop, mobile app, or other device. It’s great for understanding what drives initial traffic at a low cost.
The SaaS team used first-touch attribution in Google Analytics 360 (GA360) for web data and AppsFlyer for app data. They found that 60% of users first engaged via push notifications, costing $0.10 per click with a click-through rate (CTR) of 3%. This cheap channel drove registrations, but most users didn’t convert to payments immediately, indicating push notifications were effective for scale but not for final conversions.
Key Metric: Push notifications’ cost-per-click ($0.10) and traffic contribution (60% of initial sessions).
Tool: GA360 for web, AppsFlyer for app data.
Last-Touch Attribution: Highlighting Conversion Drivers
Last-touch attribution credits the final channel before a conversion, regardless of device. It shows which channels drive the end result, even if they’re expensive.
Using last-touch attribution, the SaaS team saw that 65% of payments were attributed to YouTube videos, with a cost-per-view of $5 and a conversion rate of 8% (5% desktops, 3% app). While YouTube was costly, it drove high-value actions (payments), but this model ignored the role of cheaper channels like push notifications in starting the journey.
Key Metric: YouTube’s cost-per-view ($5) and conversion rate (8%).
Tool: Amplitude for cross-device conversion tracking.
Time-Decay Attribution: Balancing Timing and Cost
Time-decay attribution gives more credit to touchpoints closer to the conversion, with credit decreasing for earlier interactions. It’s useful for campaigns with longer journeys.
The SaaS team applied time-decay attribution and found that YouTube videos (closer to conversion) received 50% credit, while push notifications (earlier in the journey) got 15%. This model showed that while push notifications were cheap ($0.10 per click), their early role was critical for scaling traffic, and YouTube’s higher cost ($5 per view) was justified by its conversion impact. They adjusted email timing (mid-funnel, $0.50 per send) to bridge the gap, increasing payments by 5%.
Key Metric: Weighted contribution (YouTube 50%, push 15%) with cost analysis.
Tool: GA360’s time-decay model, integrated with AppsFlyer data.
Position-Based Attribution: Highlighting Key Touchpoints
Position-based attribution (also called U-shaped) gives 40% credit to the first and last touchpoints, splitting the remaining 20% across middle interactions. It balances awareness and conversion.
Using position-based attribution, the SaaS team saw push notifications (first touch) and YouTube (last touch) each get 40% credit, while Google Ads and emails shared the rest. This model highlighted the synergy between cheap traffic (push at $0.10) for registrations and expensive traffic (YouTube at $5) for payments, leading to a balanced budget allocation that improved overall conversions by 8%.
Key Metric: Push and YouTube each at 40% credit, with cost comparison.
Tool: GA360 for web, AppsFlyer for app attribution.
Data-Driven Attribution: Optimizing Costs with Machine Learning
Data-driven attribution uses machine learning to analyze all touchpoints, assigning credit based on their actual impact and factoring in costs.
The SaaS team used GA360’s data-driven model, integrating web data with app data from AppsFlyer Data Locker. The model revealed push notifications contributed 20% to conversions ($0.10 per click, 60% registrations), YouTube 35% ($5 per view, 65% payments), email 30% ($0.50 per send), and Google Ads 15% ($2 per click). This showed push notifications were cost-effective for scale, while YouTube drove high-value payments. Reallocating 10% of their budget from Google Ads to YouTube increased payments by 10%, improving overall ROI.
Key Metric: Contribution percentages (e.g., YouTube 35%, push 20%) with cost per interaction.
Tool: GA360 + AppsFlyer Data Locker for unified attribution.
Building End-to-End Attribution: Balancing Costs and Results
End-to-end attribution across channels and devices allows you to see the full customer journey, from low-cost traffic that drives scale to high-cost traffic that secures conversions. The SaaS team used GA360 and AppsFlyer Data Locker, unifying data in BigQuery to track users across touchpoints.
Their campaign showed how cheap and expensive channels work together. Push notifications ($0.10 per click) drove 60% of registrations, mostly on mobile apps, acting as a low-cost entry point. However, only 10% of these users converted to payments initially. Mid-funnel emails ($0.50 per send) re-engaged users across devices, and YouTube
