# What Is Predictive Attribution?
What's the difference between predictive and retrospective attribution and how does it work? read this article to find out!
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## The difference between predictive and retrospective attribution
Most attribution models in the market, like multi-touch and data-driven, use a retrospective approach. They analyze the touchpoints after a conversion occurs, applying rules to decide which touchpoints are most significant. For instance, the U-shape model emphasizes the first and last touches, and the Shapley value calculates an average contribution.
However, in a cookieless environment, tracking the full user's journey is impossible. GDPR, [cookie and privacy restrictions](/blog/articles/marketing-attribution-common-challenges) as well as [cross-device limitations](/blog/articles/marketing-attribution-101) make these retrospective methods less reliable, as they depend on complete tracking for accurate attribution.
That’s why a more privacy-focused approach - predictive attribution is becoming more popular. It doesn't rely on tracking the full user journey like traditional methods, making it better suited for today's cookieless world.
## What is predictive attribution?
Predictive attribution uses statistical models and machine learning to identify which marketing actions will likely result in a conversion. It analyzes user behaviour, a key advantage in the cookieless world, where the focus is shifting from detailed user-based analytics to broader, privacy-centric approaches.
For example, it involves 'what-if' scenarios. These help predict what might happen if, for example, you spend more on ads in a particular channel or campaign.
### Benefits of predictive attribution
* **Understanding user behaviour:** Predictive attribution goes beyond surface-level metrics like clicks or sales. It analyzes behaviour trends to help marketers see how their ads engage with users. For example, Oxford Summer Courses using AI-driven attribution easily identified less engaged countries aiming to optimize their marketing efforts. [Read the full case study here](/resources/success-stories/oxford-summer-courses-ai-driven-attribution-case-study#Example).
* **Data-driven budget allocation:** Predictive attribution helps in allocating marketing budgets more effectively. By identifying which channels or activities have the most significant impact, marketers can invest more wisely, ensuring a higher return on investment.
* **Cookieless and privacy-centric:** Predictive attribution doesn't rely on tracking the full user journey like retrospective models, making it well-suited for today's cookieless world.
## SegmentStream’s predictive attribution and optimization solution
SegmentStream relies on first-party data and machine learning to give a fuller picture of how paid media contributes to sales, moving past traditional methods that depend on cookies.
It offers two main solutions:
### **1. Incremental attribution**:
SegmentStream analyzes user behaviour to measure incremental ROAS across digital campaigns without relying solely on cookies. [Learn more here.](/measurement-engine/cross-channel-attribution)
### **2. AI-driven Marketing Mix Optimization**:
The platform provides AI-driven budget recommendations to achieve an optimized marketing mix. [Learn more here.](/measurement-engine/marginal-analytics)
