Introduction
Mobile app teams often need to understand which campaigns bring users, which touchpoints appear along the journey, and what happens after installation. Attribution can help answer these questions—but the answer depends on what data each platform makes available.
- Which campaign touchpoints were observed before an install or conversion?
- Did a social ad appear earlier in the journey, or was a search ad the last recorded touch?
- How much of the reported traffic is classified as organic, and how is that classification made?
- What can your available data say about the contribution of an influencer campaign?
When the data is incomplete, reports should show that uncertainty instead of promising a definitive view or assuming a fixed share of spend is wasted.
What is Mobile Attribution?
The Attribution Challenge
Imagine this user journey:
Day 1 (Monday)
Sarah sees your Facebook ad for your fitness app, taps the link, and visits the App Store but does not install.
Day 3 (Wednesday)
Sarah sees a later Instagram ad and taps again, but still does not install.
Day 5 (Friday)
Sarah searches for a fitness app, finds yours, and installs it.
Question: Which campaign should get credit for Sarah's install?
Last-touch report:
Google Search (100% credit)
What the report cannot show:
Whether earlier touches caused or assisted Sarah’s decision.
- Facebook introduced her to your app
- Instagram reinforced the message
- Google Search was the final nudge
How Attribution Reports Support Decisions
1. Optimize Marketing Spend
A last-touch report may credit Google Search in Sarah’s example and omit earlier recorded visits. A multi-touch report can show those eligible touchpoints, but neither report alone proves that any one channel caused the install or that shifting spend will improve results.
2. Compare Channel Reports Carefully
A channel report is only comparable when you know how conversions are counted, which touchpoints are eligible, and what attribution window is applied. Different models can allocate different amounts of reported credit to the same observed journey. Treat that allocation as a model output—not a “true” cost per install or a causal result.
The 5 Attribution Models Explained
1. Linear Attribution
How it works: Equal credit to all touchpoints
Best for: Balanced view when all touchpoints matter equally
2. First-Touch Attribution
How it works: 100% credit to first touchpoint
Best for: Understanding discovery channels and brand awareness
3. Last-Touch Attribution
How it works: 100% credit to last touchpoint before conversion
Best for: Direct response campaigns with short conversion windows
4. Time-Decay Attribution
How it works: More credit to recent touchpoints
Best for: Valuing recency and time-sensitive offers
5. U-Shaped (Position-Based) Attribution
How it works: 40% to first, 40% to last, 20% distributed to middle
Best for: A transparent position-based heuristic when the team accepts its assumptions; not a universal industry standard.
Getting Started with Lativiq
- Create and label campaigns in the Campaigns area, using names that make the source and placement understandable.
- Use the tracking links in the channels you want to measure, and verify that each destination works.
- Review the reporting and attribution options available to your account. Check the model, time window, and data coverage before comparing channels.
- For integrations, follow the supported requests in the API guide; do not assume an SDK method or event is available unless it is documented for your setup.
Conclusion
Mobile attribution is a measurement tool. Use it to understand the events and touchpoints you can observe, and be explicit about platform limits, consent, and uncertainty.
Key Takeaways:
- Last-touch reports may omit earlier observed interactions
- Multi-touch models can represent eligible measured touchpoints, subject to coverage and platform limits
- Choose a model whose assumptions fit the decision you need to make
- Respect user choices and platform privacy requirements
- Use experiments to test whether a channel or change drives incremental outcomes