Attribution VS Trackingplan

Trackingplan's answer:

Trackingplan is used by brands and agencies including dentsu, Havas Media Network, Schneider Electric, El Corte Inglés, Cofidis, RIU Hotels & Resorts, Euronics, ISDIN, Baleària, Wpromote, Making Science and Awaze.

Trackingplan's answer:

In 2021, our founders faced a familiar frustration: broken analytics and unreliable data. Dashboards didn’t add up, key events were missing, and making decisions felt like guesswork.

Determined to fix it, we built Trackingplan; a tool to automatically monitor, validate data tracking, and catch issues before they impact business decisions. What began as a solution for ourselves quickly became essential for companies around the world.

Since then, we have been building the best observability layer for analytics data, sitting exactly where data breaks: the hit, the dataLayer, the SDK, the pixel, the CAPI payload, the consent state, the GTM release.

That’s where the truth of a number is actually decided, before it becomes the number everyone relies on.

But observability was only the foundation. Agency is the destination.

With AI, the question is no longer whether a system can act on your data. It’s whether the signal it acts on can be trusted.

Agents can now fix a tag, pause a campaign, rewrite an attribution model, or move your budget and bids. And the more autonomous they become, the more their decisions depend on the quality of the signal underneath them.

That’s where Trackingplan comes in: the observed state of your tracking, checked against what should be there, with a clear answer when reality and expectation don’t match. Because most AIs are trained to answer, ours is trained to tell the truth.

Trackingplan's answer:

Trackingplan is the only digital analytics agent that works where data is produced, not where it ends up. Instead of reading a warehouse or a dashboard after the fact, it observes every request a website, iOS and Android app, and server sends to 80+ analytics and ad platforms, along with the dataLayer, consent signals and tag manager releases behind them. That's where a number becomes true or false.

This gives the agent something general-purpose AI doesn't have: evidence. It learns each company's events, properties, and normal traffic patterns from live data, with no data model or tracking plan to set up. So it can tell broken tracking apart from a real change in the business, trace an issue to its root cause, and show the exact hits behind every answer. Teams ask questions in plain English, get reports and audits written for them, and receive results in Slack, Teams, email, Jira, or Claude.

Trackingplan's answer:

Against general-purpose AI (ChatGPT, Claude, Gemini connected to a warehouse): an AI can only reason about what it can see. A warehouse shows what eventually arrived. It doesn't show whether a pixel fired correctly this morning, whether consent changed, or whether a GTM release dropped a parameter. Generic AI fills those gaps with a plausible guess. Trackingplan answers from first-party observations of every hit, so it knows when the data is wrong and says so, with the evidence.

Against AI assistants built into analytics platforms (GA4, Adobe, Amplitude): each one only sees its own platform and assumes its own data is correct. Trackingplan sees every destination at once, so it can explain why Meta reports more purchases than GA4, or why one platform is missing a property the others receive.

Against traditional tracking QA and monitoring tools (ObservePoint, Avo and others): those tools depend on predefined crawls, test scripts or a tracking plan maintained by hand. Trackingplan learns the implementation from real traffic, monitors it 24/7 across web, apps, and server-side, and goes beyond alerts: it investigates, explains, writes reports, and runs audits on a schedule.

It's also fast to adopt: one tag or SDK, no data model to build, a 14-day free trial with no credit card, and pricing based on traffic rather than seats.

Trackingplan's answer:

Teams that depend on digital analytics and marketing data being right, at mid-market and enterprise companies with websites and mobile apps:

• Digital analytics teams, who need to know when a release breaks tracking before it breaks reporting.
• Paid media and performance marketers, who need conversions, UTMs and Meta CAPI or Google Ads signals to be accurate before bidding algorithms optimize on them.
• Compliance, legal and DPO teams, who need to catch PII leaks, consent breaches and undeclared cookies on every hit.
• Agencies and consultancies that manage analytics for many clients and want an always-on analyst for every account without growing headcount.

Common industries include ecommerce and retail, travel and hospitality, financial services, consumer brands, and media and marketing agencies.