<?xml version="1.0" encoding="UTF-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>adtribute Blog</title><description>Attribution, BI, and operational data for fast-growing e-commerce.</description><link>https://www.adtribute.io</link><language>en</language><atom:link href="https://www.adtribute.io/blog/rss.xml" rel="self" type="application/rss+xml"/><item><title>The Reason Why Most Ad &amp; Conversion Tracking Tools Are Not Working</title><link>https://www.adtribute.io/blog/why-most-tracking-is-broken</link><guid isPermaLink="true">https://www.adtribute.io/blog/why-most-tracking-is-broken</guid><description>E-commerce brands and performance marketers spend millions on advertising and thousands on tracking tools. Most of those tools miss 30 percent or more of the sessions they&apos;re supposed to measure. That is not an attribution problem. It is a tracking problem.</description><pubDate>Wed, 27 May 2026 12:00:00 GMT</pubDate><category>tracking-attribution</category><author>Daniel Busch</author></item><item><title>Meta CAPI Optimization: How to Improve New Customer Acquisition with Better Signal Quality</title><link>https://www.adtribute.io/blog/meta-capi-optimization-new-customer-signal-quality</link><guid isPermaLink="true">https://www.adtribute.io/blog/meta-capi-optimization-new-customer-signal-quality</guid><description>Past a certain volume, every additional conversion event teaches Meta&apos;s algorithm almost nothing. Sending every purchase to CAPI may be training the optimizer to find more of your existing customers. The fix is a small server-side filter.</description><pubDate>Fri, 22 May 2026 12:00:00 GMT</pubDate><category>tracking-attribution</category><author>Daniel Busch</author></item><item><title>Missing the Semantic Layer: Why Your AI Agents Are Confidently Wrong</title><link>https://www.adtribute.io/blog/why-your-ai-agents-are-confidently-wrong</link><guid isPermaLink="true">https://www.adtribute.io/blog/why-your-ai-agents-are-confidently-wrong</guid><description>AI-on-your-data demos look impressive but produce output that is confidently wrong in the parts that matter. The LLM is not failing at math. It is guessing what your metrics mean. The fix is a semantic layer, defined before the agent ever connects.</description><pubDate>Wed, 27 May 2026 12:00:00 GMT</pubDate><category>ai-agents</category><category>business-intelligence</category><author>Daniel Busch</author></item></channel></rss>