Case Study: The Digital Lighthouse A UK-based paid media agency managing 30+ brands across ecommerce, retail, and DTC

Full-Stack Analytics Engineering for Brands That Demand Accuracy

We help ambitious brands and agency teams engineer reliable, scalable analytics foundations — from standardized tracking and server‑side tagging to warehousing, modeling, governance, and BI.

Onboarding time reduced to under 3 weeks Unified event schema rolled out across all managed properties 10–15% improvement in attribution accuracy 99.99% pipeline uptime across BigQuery, Airbyte, and Prefect

The Challenge: Fragmented Analytics Foundations

TDL managed a growing portfolio of brands, each built on different tools, tracking approaches, and historical decisions — creating operational friction and inconsistent outcomes.

Fragmented tracking across brands visualization

Root cause: Fragmented Tracking

No unified event schema or identity strategy — making measurement inconsistent across brands and channels.

Result: Slow Onboarding

Each rollout required custom tracking work instead of reusable foundations — increasing time-to-value.

Unreliable analytics pipelines visualization

Result: Unreliable Pipelines

Brittle ingestion and transformations created breaks and drift — reducing confidence in reporting and decisions.

Disjointed analytics and media tools visualization

Result: Disjointed Tools

Analytics, attribution, CRM, and paid media operated in silos — with no shared definitions or governance.

Scaling analytics maturity challenges visualization

Result: Scaling Limits

Without a repeatable standard, maturity varied by brand — slowing progress across the portfolio.

The root issue was fragmentation — it cascaded into onboarding delays, unreliable reporting, tool silos, and scaling limits.

Solution

Analygo designed and implemented a shared analytics foundation for TDL — enabling consistent data collection, governance, and activation across the entire brand portfolio.

When our team joined this engagement, the challenge wasn’t fixing individual tracking issues — it was fixing the system that kept creating them.
Instead of solving analytics brand by brand, we designed a shared foundation that could support the entire portfolio: consistent measurement, reliable attribution, and repeatable onboarding for new brands.
This foundation was built as an operating system for analytics — one that scales with the business while remaining flexible to each brand’s needs.

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We applied our CORE methodology—Collect, Organize, Refine, Expose—to break the project into clear, testable phases. This allowed us to stabilize tracking foundations first, standardize the event architecture, and validate data integrity before any activation or reporting.

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Full-Stack Analytics Engineering for Brands That Demand Accuracy

The Challenge: Fragmented Analytics Foundations

The root issue was fragmentation — it cascaded into onboarding delays, unreliable reporting, tool silos, and scaling limits.

Solution

Frequently Asked Questions

How did you approach complexity in this project?

What was the starting point when you joined the project?

How did this integrate with the client’s existing tools?

Did this require changes to the website or backend?

How did you ensure data accuracy before activation?

How did you handle privacy, consent, and compliance?

How long did it take before the data was trustworthy?

Was this built for reporting, activation, or both?

How did you avoid over-tracking or noisy data?

Is this approach scalable to other brands or markets?

Can this setup be reused for future projects?

How hands-on was your involvement during implementation?

What made this project successful compared to past attempts?

How does this differ from a standard analytics setup?

Who is this type of implementation best suited for?

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