Case Study: How a Dutch Marketing Firm Cut Analytics Costs by 40%

Introduction

Across the Netherlands, many companies are investing in an AI analytics platform to improve decision-making and stay competitive. But as data volumes grow, so do cloud costs, performance issues and operational complexity.

This data analytics software case shows how a Dutch digital marketing firm reduced its analytics spend by 40%, improved reporting speed and simplified its entire data architecture by partnering with a nearshore delivery team in Spain.

The challenge: rising cloud costs and slow analytics

The company relied on a mix of internal tools and third-party services. Over time, several problems appeared:

• Cloud and processing expenses rising every month

• Reports taking 12–24 hours to refresh

• Data duplicated across multiple systems

• Limited scalability during peak campaigns

• Minimal automation and no real use of AI

They needed an AI analytics platform capable of real-time processing, lower infrastructure costs and faster insight generation—without expanding their internal team.

The solution: an AI analytics platform built for efficiency, automation and cost reduction

Unimedia Technology designed a new AI analytics platform focused on speed, automation and long-term cost efficiency.
The goal was not only to rebuild their analytical environment, but to transform how the marketing team interacted with data.

A modern, modular and scalable architecture

We replaced their fragmented ecosystem with a unified data structure that:

• integrates multiple sources in real time

• eliminates duplicated storage

• scales automatically during high-traffic periods

This significantly reduced cloud usage and improved processing performance.

AI models applied to real marketing use cases

The platform integrated AI modules for:

• automated audience segmentation

• predictive conversion modelling

• multichannel attribution

• anomaly detection in campaigns

This allowed the marketing team to make decisions based on more accurate, real-time insights.

Fully automated data pipelines

We automated the entire data lifecycle:

• ingestion

• cleaning

• transformation

• aggregation

• reporting

This eliminated manual work and ensured consistent, reliable output.

A unified dashboard for all teams

A centralised dashboard provided:

• real-time KPIs

• advanced filtering

• AI-generated insights

• automated alerts

This simplified reporting and reduced the need for multiple analytics tools.

Cloud optimisation built into the platform

The new AI analytics platform was designed with cost optimisation as a core principle:

• reducing unnecessary workloads

• minimising storage costs

• using compute resources only when required

Savings were immediate and measurable.

Results: measurable impact and long-term savings

Before → After Summary

The cost reduction allowed the client to reinvest in acquisition, new channels and additional automation.

Cloud-Trim: identifying the hidden waste behind cloud spending

During the initial audit, we identified several inefficiencies:

• idle compute workloads

• duplicated databases

• oversized storage

These issues are exactly the type of cloud waste that Cloud-Trim detects and eliminates automatically.

Cloud-Trim:

• scans cloud environments

• identifies unused or duplicated resources

• applies automated optimisations

This ensured long-term savings even after the new platform went live.

Conclusion

This case demonstrates how Dutch companies can modernise their analytics operations and reduce costs by combining:

• a modern AI analytics platform,

• a flexible nearshore development model,

• and intelligent cloud optimisation using Cloud-Trim.

An approach that more and more organisations in the Netherlands are adopting to stay competitive without increasing their IT budget.