Executive Summary

WTWH Media operates a network of B2B publisher websites serving specialized industries like manufacturing, electronics, and engineering. With over 1,000 advertisers across their platform, they help companies reach targeted professional audiences through digital advertising and lead generation.

But tracking performance across hundreds of advertisers and multiple data systems created serious challenges. Data lived in separate places: Google Ad Manager for ad campaigns, Zoho CRM for customer relationships, and various other tools for different parts of the business. Giving advertisers clear reporting on their campaigns required manual work pulling data from multiple systems.

Soli & Co partnered with WTWH Media to build a unified data platform using Google BigQuery, dbt, and Lightdash. The platform brings together data from all their systems into one place, automates reporting, and gives both internal teams and advertisers better visibility into campaign performance.

The results changed how WTWH Media operates. Their customer success team can now access advertiser performance data instantly instead of waiting for manual reports. The company can analyze advertiser retention and identify which accounts need attention. And they built a foundation for scaling their business analytics as they continue growing.

‍

The Challenge: Managing a Complex B2B Publisher Ecosystem

‍

WTWH Media runs specialized websites that reach professional audiences in technical industries. Engineers looking for the latest electronics components, manufacturers researching equipment suppliers, and other B2B buyers visit these sites daily. Advertisers pay to reach these valuable audiences through display ads, sponsored content, and lead generation campaigns.

Managing over 1,000 advertisers across this ecosystem requires tracking numerous data points. Where did ads appear? How many people saw them? Which campaigns generated leads? What return did advertisers get on their spending?

The answers to these questions lived in different systems that did not talk to each other.

‍

The Data Silo Problem

‍

In early 2024, WTWH Media's data landscape looked like this:

Google Ad Manager (GAM): All display advertising data lived here. This system tracked impressions, clicks, and campaign performance.

Zoho CRM: Sales and customer relationship data lived in Zoho. This included which advertisers were active, their contract details, and sales pipeline information.

Lead Generation Systems: Various tools captured leads from content downloads, quote requests, and other conversion actions.

Financial Systems: Revenue and billing data lived in accounting software separate from advertising performance data.

David Miyares, leading WTWH Media's operations, saw the problems this created. His customer success team spent significant time pulling data from different places to answer basic questions about advertiser performance.

"We need to figure out what is the behavior of acquisition, retention, and churn on our advertisers," the team discussed during planning sessions. But answering that question required combining data from multiple systems that were never designed to work together.

‍

Limited Visibility Into Business Performance

‍

The data silos created several specific problems:

Manual Reporting Burden: Customer success representatives spent hours each week creating custom reports for advertisers by pulling data from multiple sources.

Inconsistent Data: Different systems sometimes showed different numbers for the same metric, making it hard to give advertisers confident answers about their performance.

No Advertiser Analytics: WTWH Media could not easily analyze patterns across their advertiser base. Which advertisers were most successful? Which ones were at risk of leaving? These insights remained locked in disconnected systems.

Limited Self-Service: Advertisers had to request reports rather than accessing their own data. This created delays and increased workload for the WTWH Media team.

Difficult Strategic Decisions: Business leaders struggled to make data-driven decisions about pricing, product offerings, and sales strategy without unified visibility into their business metrics.

‍

The Technical Complexity

‍

Beyond the business challenges, WTWH Media faced technical complexity. They already used Google BigQuery as a data warehouse, but data was not flowing into it consistently from all systems. Some data required manual exports. Other data arrived through fragile connections that broke frequently.

The team also dealt with the challenge of data quality. When combining data from multiple sources, matching up records correctly becomes critical. An advertiser in the CRM needed to connect to the same advertiser in Google Ad Manager and the lead generation system.

Marshall Matheson, working on WTWH Media's data initiatives, understood these challenges. The company needed a systematic approach to bringing all their data together in a way that was reliable, maintainable, and could grow with the business.

‍

The Solution: Building a Unified Analytics Platform

‍

In January 2024, WTWH Media engaged Soli & Co to design and build a comprehensive data platform. The vision centered on creating a single source of truth for all advertiser and business data.

The approach focused on using proven, market-standard tools that would give WTWH Media long-term flexibility. Rather than building custom solutions that only a few people could maintain, the team selected tools with active communities and strong track records.

Weekly touchbase meetings between the WTWH Media team (including David Miyares, Matthew Knapp, Jonathan Semkiw, Marshall Matheson, and DJ) and Soli & Co kept the project moving forward and ensured the platform met real business needs.

‍

The Technical Architecture

‍

The platform built on three core components:

Data Warehouse (Google BigQuery): All data flows into BigQuery, creating a central repository. This gives the team one place to query data from all systems without jumping between different tools.

Data Transformation (dbt): Using dbt (data build tool), the team built transformation logic that cleans, combines, and structures data for analytics. This tool lets data analysts write transformations in SQL and manage them like software code, with testing and version control.

Analytics Dashboard (Lightdash): Lightdash provides self-service analytics capabilities. Users can explore data, build charts, and create reports without writing code. The tool connects directly to BigQuery and uses the data models built in dbt.

‍

Connecting Data Sources

‍

A major part of the solution involved building reliable connections from various data sources into BigQuery:

Google Ad Manager Integration: Automated pipelines pull advertising campaign data from GAM daily. This includes impressions, clicks, campaign details, and creative performance.

Zoho CRM Sync: Advertiser and account data flows from Zoho into BigQuery, giving visibility into customer relationships alongside campaign performance.

Lead Generation Data: Lead capture systems feed conversion data into the warehouse, connecting ad campaigns to actual business results.

Financial Data: Revenue and billing information connects to advertiser performance, enabling profitability analysis.

Each integration needed careful attention to data quality. The team built validation checks to catch issues early and alert when data looks wrong.

‍

Building for Advertiser Analytics

‍

One key objective involved creating analytics capabilities focused specifically on advertiser performance. The platform needed to answer questions like:

• How is each advertiser performing across different campaign types?
• Which advertisers are growing their spend versus declining?
• What patterns distinguish successful advertisers from unsuccessful ones?
• How does advertiser retention vary by industry, campaign type, or account manager?

The dbt models organized data to enable this analysis. Clean dimensions for advertisers, campaigns, and time periods connect to fact tables of impressions, clicks, conversions, and revenue.

‍

Implementation: From Foundation to Analytics

‍

Phase One: Core Infrastructure

The implementation started with establishing the foundational infrastructure. The team set up BigQuery as the central data warehouse and configured dbt for data transformation workflows.

The first major integration focused on Google Ad Manager, WTWH Media's highest-volume data source. Building reliable pipelines from GAM into BigQuery required understanding the API, handling rate limits, and managing incremental data loads efficiently.

As one of the highest-value data sources, getting GAM data flowing reliably delivered immediate business value while proving out the technical approach.

Phase Two: Expanding Data Sources

Once the foundation proved solid, the team systematically added more data sources. The Zoho CRM integration brought customer and account data into the platform. This enabled joining advertiser relationship information with campaign performance data.

Lead generation data came next, connecting conversion events back to the campaigns that drove them. This closed the loop from advertising spend to business results.

Throughout this phase, data quality checks became increasingly important. As more data sources joined the platform, ensuring consistency across them required careful attention to mapping and validation logic.

Phase Three: Analytics and Visualization

With data flowing reliably into BigQuery and clean data models in place, the focus shifted to making the data accessible. The team deployed Lightdash and built out dashboard templates for different use cases.

Customer success dashboards gave representatives quick access to advertiser performance metrics. Business analytics dashboards helped leadership track key metrics like advertiser retention, revenue trends, and campaign mix.

The team also created advertiser-facing reports that could be generated automatically and sent on schedule. This self-service capability reduced the manual reporting burden significantly.

‍

Continuous Improvement and Knowledge Transfer

‍

Implementation was not a one-time project. Throughout 2024 and into 2025, the platform continued evolving based on new requirements and feedback.

An important part of the engagement involved knowledge transfer. Matthew Knapp and others on the WTWH Media team worked closely with Soli & Co to understand the platform deeply. This ensured WTWH Media could operate and extend the platform independently over time.

"The question is what frameworks can be taken over by WTWH resources," the team discussed during planning. The focus stayed on building something maintainable and understandable, not just functional.

‍

Results: From Fragmented Data to Unified Insights

‍

Operational Efficiency Gains

‍

The platform transformed daily operations for the customer success team. Tasks that previously took hours now take minutes. Need to check how an advertiser is performing across all campaigns? Pull up their dashboard. Want to see which advertisers are trending down? Run a retention analysis.

This efficiency freed up time for higher-value activities. Customer success representatives spend more time talking with advertisers about strategy and less time copying numbers between systems.

‍

Better Business Intelligence

‍

WTWH Media gained visibility into their business that simply was not possible before. Leadership can now analyze advertiser behavior patterns, understand retention dynamics, and make data-driven decisions about pricing and product offerings.

The ability to analyze advertiser retention proved particularly valuable. By understanding which advertisers stay versus leave, and what differentiates successful advertisers from unsuccessful ones, WTWH Media can take proactive actions to improve retention and focus sales efforts effectively.

‍

Foundation for Growth

‍

Perhaps most importantly, the platform created a foundation for continued growth. As WTWH Media adds new data sources, advertising products, or analytics requirements, the infrastructure can accommodate them.

The company no longer faces the constraint of fragmented data systems limiting what questions they can answer. The platform provides flexibility to evolve analytics capabilities as the business grows.

‍

Improved Advertiser Experience

‍

While much of the benefit accrues to internal teams, advertisers also benefit from faster, more consistent reporting. The automated reporting workflows deliver performance data to advertisers regularly without them needing to request it.

This proactive communication keeps advertisers informed and engaged with their campaigns. When they have questions, customer success representatives can quickly pull up detailed data to provide answers.

‍

Technical Sustainability

‍

The platform uses standard tools that the broader data community actively maintains and improves. This gives WTWH Media access to ongoing improvements in these tools without additional investment.

The team can also hire data analysts and engineers who already know tools like BigQuery, dbt, and Lightdash. This reduces training time and makes the platform easier to extend as the team grows.

‍

Key Lessons: What Made This Work

‍

Starting With the Biggest Pain Point

‍

The team wisely started with Google Ad Manager data, the highest-value and highest-volume data source. This approach delivered immediate value while proving out the technical architecture before expanding to other sources.

Starting with a big win built momentum and organizational support for the broader platform initiative.

‍

Building for Maintainability

‍

Throughout the project, decisions favored long-term maintainability over short-term speed. Using standard tools rather than custom code meant the platform would be easier to operate and extend over time.

This focus on maintainability paid off as WTWH Media's own team took on more ownership of the platform throughout the engagement.

‍

Regular Communication and Iteration

‍

The weekly touchbase meetings created a strong feedback loop. Rather than building in isolation, the Soli & Co team stayed closely connected to WTWH Media's evolving needs and could adjust the approach accordingly.

This communication also helped build buy-in across the organization. As more team members saw the platform delivering value, support for the initiative grew.

‍

Focus on Real Business Questions

‍

The platform did not try to be everything to everyone immediately. Instead, it focused on answering specific business questions that mattered to WTWH Media: advertiser performance, retention analysis, and operational reporting.

This focused approach delivered clear value rather than building a comprehensive but overwhelming system that no one uses.

‍

Investing in Data Quality

‍

The team dedicated significant effort to data quality checks, validation logic, and testing. This investment paid off in the form of trustworthy data that people actually use to make decisions.

When users can trust the numbers they see, adoption of analytics tools increases dramatically.

‍

Looking Forward

‍

WTWH Media now has a data platform that can grow with their business. As they expand their publisher network, add new advertising products, or acquire additional companies, the infrastructure can accommodate new data sources and requirements.

The platform also opens possibilities for more advanced analytics. With clean, reliable data and a solid foundation, WTWH Media can explore predictive modeling for advertiser retention, optimization algorithms for campaign performance, and deeper integration of analytics into their products.

For B2B publishers managing hundreds or thousands of advertiser relationships, the lesson is clear: unified data infrastructure creates competitive advantage. The investment in bringing data together pays returns through operational efficiency, better decision-making, and improved customer experience.

WTWH Media transformed their data operations from a constraint on growth into an enabler of it. They can now answer questions about their business that were previously impossible to address, and they built the foundation for continued innovation in their analytics capabilities.