Kalyber logo
Case Study

Multi-Brand Portfolio — AI Tag Intelligence

How AI optimized tag management and performance for a multi-brand web portfolio.

AI-led tag governance keeps multi-brand analytics fast, trustworthy, and aligned with business KPIs.

Data & Analytics Modernization

We built a tag intelligence layer that audits GTM, simulates performance impact, and keeps analytics teams confident in every launch.

AnalyticsInstrumentationQuality

The Challenge

Each brand site had evolved with its own marketing stack. Hundreds of Google Tag Manager entries piled up, slowing pages and eroding trust in analytics across marketing, product, and IT teams.

  • Over 250 tags across GTM containers with duplicates firing on every page across brands.
  • Tag sequencing errors quietly broke conversions and inflated channel attribution.
  • Conflicting scripts from retargeting tools triggered layout shifts and slowed Core Web Vitals.
  • HubSpot forms and microsite embeds each shipped bespoke snippets, creating overlapping events and consent issues.
  • Marketers lacked visibility into tag usefulness, making audits manual and reactive.
  • Data gaps degraded ad optimization and forced teams to double-check every report.

Approach

We stood up an AI-led governance layer that continuously audits GTM, classifies tags by purpose, and simulates performance impact before scripts hit production. Automation keeps marketing agile while engineering retains guardrails.

Automated GTM export and AI classification that recommends keep/remove/consolidate actions by tag purpose.
Anomaly detection that watches GA4, HubSpot, and paid media events and alerts teams when tags spike, break, or go dormant.
Performance simulation that maps script latency to Lighthouse data and suggests async/defer or server-side migrations.

Engagement Roadmap

  1. Weeks 1-3 — Pull GTM container JSONs, train the tag classifier on historical data, and deliver a prioritized cleanup roadmap.
  2. Weeks 4-8 — Implement naming conventions, trigger guardrails, and launch the AI copilot that surfaces tag health insights to marketers.
  3. Weeks 9-14 — Optimize load performance, migrate heavy pixels to server-side GTM, and integrate dashboards into the marketing ops runbook.

Impact

  • Page load (LCP) improved from 3.8s to 2.2s across flagship landing pages.
  • Tag inventory trimmed from 250+ entries to 90 governed tags with clear owners.
  • Conversion event accuracy climbed from 82% to 97%, restoring trust in campaign reporting.
  • Marketing ops recaptured 15+ hours per week previously spent on manual QA.
  • GTM container errors dropped to zero thanks to automated sequencing checks.

Why it Matters

Tag management shifted from a black box to a transparent, self-healing system. Marketing now iterates without fear of breaking analytics, while IT uses the AI audit trail to sign off on every script with confidence. HubSpot form submissions, Marketo programs, and ad platforms now share the same governed taxonomy, so every team speaks the same measurement language.

Back to allReview the blueprintReview Data Backbone Services