Turning fragmented data into 15% growth.
Objective
Create a clear plan to integrate analytics and unlock 15% year-on-year growth.
Obstacle
Disconnected data systems, manual reporting, and no shared definitions of key metrics.
Outcome
A clear data roadmap, a prioritised investment case, and £267,000+ in identified annual savings.
Background
Digital Ventures operates a suite of classified advertising platforms connecting service providers and service users across the UK, Brazil, and Belgium. The business was growing at 9.8% year-on-year with an ambition to reach 15%, and the leadership team knew that getting there would require becoming a more data-centric organisation. They brought in 173tech to conduct a comprehensive review of their data infrastructure, run a half-day strategy workshop with teams across the business, and produce a prioritised roadmap for where to invest next.
Challenges
Complex Set Up: Digital Ventures had invested in data capability, but a fundamental misalignment had emerged between what the BI team was working on and what the business actually needed. The team was absorbed in operational concerns: capturing upstream data issues, maintaining consistency between systems, keeping pipelines flowing, whilst departments were requesting foundational outputs like executive dashboards, automated reports, and clear metric definitions. The result was a data function that was busy but not impactful.
Lack Of Answers: With 30 data sources, three visualisation tools, and no shared metric definitions, different teams were routinely arriving at different numbers for the same question, and 80% of analytical time was being spent on data gathering rather than insight. MySQL held a robust customer identifier, but it did not propagate to GA4, Intercom, payment processors, or safeguarding tools, making cross-system analysis take days of manual work. Marketing could not calculate Customer Lifetime Value. Customer Service could not link interactions to outcomes. Safeguarding could not build cross-system risk profiles.
Solution
Fix The Foundations: The starting point for our strategic review was to establish a more robust and scaleable infrastructure. This meant simplifying the architecture from near real-time streaming to a daily batch model, setting up clean BigQuery and dbt environments, and building a shared Data Dictionary with agreed KPI definitions across every business function.
First Insights Quickly: Whilst the business had a wide range of data sources that all needed to be integrated, we wanted to start off with something smaller and useful, revenue intelligence. This initial dashboard would give leadership visibility of financial performance, growth rate, service provider retention, ad performance, and platform health for the first time.
Impact
A Detailed Plan: We then laid out a detailed roadmap assessing the time and cost of different analytics initiatives weighted against the return on investment. With a Data Dictionary in place and a sequenced roadmap agreed, every team would be working from the same definitions, the same metrics, and the same strategic priorities, the foundation on which the step from 9.8% to 15% growth could actually be built.
Scaleable Infrastructure: The architecture review alone identified a 40% reduction in BigQuery costs (£17k+ in annual savings) by replacing frequent transformation cycles and eliminating Kafka infrastructure that the business did not need. Across the five teams reviewed, automated reporting would free over 60 hours of manual work per month, redirecting that time from data gathering to decision-making.
Clear ROI: On the marketing side, applying LTV-weighted attribution to a £750,000 annual spend was projected to reduce customer acquisition costs by at least 20%, representing £150,000 in annual savings. Combined with the infrastructure savings and the cost avoided by outsourcing rather than building internally, typically £100,000 or more in salary costs over a six-month internal build, the total identified value exceeded £267,000 per year.
Creating Value For Digital Ventures...
We evaluated data from across 30 different systems,
With 40% infrastructure cost reduction identified,
And an actionable plan towards data-driven decisions.
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