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Case Study

Crafting a One-of-a-Kind Media Planning Platform

London Media×PersonalyticsPersonalytics

London Media

London Media is a full-service media agency specializing in data-driven campaign planning and audience targeting for brands across Latin America. With a growing client base and increasingly complex data requirements, they needed a unified platform that could centralize media performance data and power smarter planning decisions.

Personalytics is their proprietary media planning tool — built from the ground up alongside Cabana Data to transform raw audience and performance signals into actionable planning intelligence.

London Media
Client
London Media
Product
Personalytics
Industry
Media & Advertising
Service
Reactive Data Analysis
Year
2024
Reactive Data Analysis

The Challenge

London Media was operating across multiple disconnected data sources — ad platforms, CRM tools, spreadsheet exports, and third-party audience providers. Campaign planners spent hours manually consolidating data before any meaningful analysis could begin.

The absence of a unified data layer made it impossible to build a reliable, real-time planning product. Personalytics needed a solid data foundation before a single feature could be shipped.

The team needed a partner who could design the data architecture from scratch, integrate heterogeneous sources, and deliver a reactive pipeline that could power a product used daily by media planners.

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Strategy

Our Approach

Rather than patching over existing chaos, we built the data infrastructure Personalytics needed to be a world-class product.

Data Strategy & Auditing

We mapped every data source London Media relied on — ad platforms, audience providers, CRM exports — and defined a consolidation strategy that eliminated silos without disrupting existing workflows.

Defining Data Architecture

We designed a cloud-native data pipeline on Google BigQuery, ingesting real-time and batch data from 8+ sources into a unified semantic layer that Personalytics could query reliably.

Decision Design & Outcomes

With clean, structured data in place, we modeled the audience and performance entities that drive planning intelligence — giving media planners the signals they need, when they need them.

Outcomes

Results That Moved the Needle

The Goal1 Platform

Unify 8+ fragmented data sources into a single, queryable layer powering all of Personalytics's planning features.

The Numbers24×

Reduction in time spent manually consolidating data — from days to hours, freeing planners to focus on strategy.

The AchievementReal-Time

Audience and performance signals now flow reactively into Personalytics, enabling live campaign adjustments without engineering bottlenecks.

Impact

World-changer Industry Impact

Beyond the technical delivery, this engagement redefined how London Media operates — and opened a new product category in the media planning space.

Personalytics launched as a fully functional product, with a data layer capable of scaling to thousands of campaigns.

Media planners reduced manual data work by 95%, shifting from spreadsheet consolidation to real-time platform interaction.

London Media gained a proprietary competitive advantage — a planning product no competitor offers in their market.

The pipeline architecture now supports multi-client deployments, turning a single project into a scalable product line.

Data governance and lineage were established from day one, setting the foundation for future AI and ML capabilities.

Reflection

Key Takeaway

Personalytics didn't just need a dashboard — it needed a data product mindset applied end-to-end. By treating the data layer as a first-class product component, London Media was able to ship faster, scale smarter, and compete on a fundamentally different level.

The lesson is universal: before you can build intelligent products, you need intelligent data infrastructure. That's what Cabana Data delivers.

A product is only as good as its data foundation.

Reactive pipelines turn static reports into living intelligence.

Data architecture is a product decision, not a backend detail.

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