Machine Learning: Actionable Insights into Today's Cross-Device Reality
Billions of signals, one real person behind them. This white paper explains how ROQAD turns noisy, anonymised device data into a reliable identity graph – using supervised machine learning to decide which devices belong to the same user, and the metrics that prove the graph actually works.
7 pages · PDF
Download the white paper (PDF)
What you'll learn
From big data to smart data
How cookies and mobile ad IDs (IDFA, AAID) become nodes in an identity graph, and why the raw signals are so noisy.
Why machine learning beats hand-written rules
How deterministic data (e.g. hashed emails) trains a probabilistic classifier to score device connections.
Matching at scale
A two-step approach (smart candidate selection, then probability scoring) that makes 10 million devices – roughly 50 trillion possible pairs – workable.
How to judge a cross-device graph
Overlap, deduplication and enrichment, plus precision, recall and F-score, and how tuning the threshold suits reach vs. attribution use cases.
Inside the paper
- 01Big Data in a Nutshell
- 02Machine Learning
- 03Testing Cross-Device Matching Solutions
- 04Final Words
