Case studies

Work that delivered a measurable outcome.

Every project starts with a specific business problem. These are three of them — the situation, what we built, and what changed.

eCommerce · Analytics

Mid-size Irish eCommerce retailerDelivered in 8 weeks

Attribution rebuild for a €15M eCommerce brand.

The situation

The company was running paid campaigns across Google, Meta, TikTok, and affiliate networks. Attribution was last-click only. The marketing team was optimizing spend based on a model that ignored 60% of the customer journey. They had no idea which channels were actually profitable.

What we built

GA4 implementation with server-side tagging (Google Tag Manager server container)
Custom attribution model built in BigQuery using full path-to-purchase data
Unified Looker Studio dashboard consolidating spend, sessions, and revenue by channel
Weekly automated report delivered to the marketing and finance teams

What changed

3.4×

Improvement in attribution accuracy

€180K

Misallocated ad spend identified in month one

23%

Reduction in customer acquisition cost over 6 months

FinTech · Automation

Irish payments processing companyDelivered in 6 weeks

Automated compliance reporting for a payments business.

The situation

The risk and compliance team was spending three days per week manually compiling regulatory reports from five different data sources. Errors were common. An upcoming audit had flagged the manual process as a risk. The team had no bandwidth left for anything except reporting.

What we built

Automated data pipeline using n8n connecting all five source systems
Centralised PostgreSQL data store with schema validation and error alerting
Compliance dashboard with real-time regulatory metrics and anomaly detection
Scheduled daily report delivery with PDF export for regulatory submissions

What changed

67%

Reduction in reporting time (3 days → 4 hours)

Zero

Compliance incidents in 18 months post-implementation

40%

Risk team time redirected to strategic work

SaaS · Data Science

B2B SaaS company (project management tools)Delivered in 10 weeks

Churn prediction model for a SaaS business losing 4% per month.

The situation

Monthly churn was running at 4.2%. The customer success team was reactive — they only heard about at-risk accounts when a cancellation came in. Product usage data existed but sat in a separate database that no one in CS had access to. There was no early warning system.

What we built

BigQuery data warehouse unifying product usage, billing, support, and CRM data
Churn prediction model using 45-day behavioural signals with 78% precision
Customer health score dashboard for the CS team, updated daily
Automated Slack alerts when an account crossed a risk threshold

What changed

4.2% → 2.8%

Monthly churn reduced in 4 months

45 days

Earlier visibility into at-risk accounts

~€280K

Estimated ARR retained in year one

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