How does Proxima’s "Commerce Graph" differ from traditional Facebook or Google interest-based targeting?

The fundamental limitation of traditional "walled garden" platforms like Meta or Google is that their targeting is largely based on inferred intent—signals like what a user likes, who they follow, or what they search for. In a post-iOS 14.5 world, these signals have become increasingly "noisy" and less predictive of actual purchasing behavior. Proxima’s Commerce Graph takes a diametrically opposite approach by anchoring its intelligence in verified transaction data.

Instead of guessing that a user might be interested in "sustainable fashion" because they followed an influencer, the Commerce Graph tracks 73M+ unique U.S. shopper profiles across 2,000+ e-commerce brands, encompassing over $30B in transaction volume. This allows Proxima to see exactly what products a person bought, how much they spent (AOV), and how frequently they purchase within specific categories.

When you build an audience in Proxima, you aren't just targeting "interests"; you are targeting a "buyer DNA." This graph identifies cross-brand affinities—for example, knowing that customers who buy high-end cookware from Brand A are 4x more likely to convert on premium home organization tools from Brand B. By feeding these high-intent signals back into the ad platforms, Proxima helps the algorithms bypass the "learning phase" faster and focus spend on users with a statistically higher probability of becoming high-LTV (Lifetime Value) customers. This effectively creates an "unfair advantage" by using external market-wide data to solve the internal data blindness caused by privacy changes.