Ad agencies choose BigQuery to drive campaign performance
Agency Development Manager
Agency Development Manager
Advertising agencies are faced with the challenge of providing the precision data that marketers require to make better decisions at a time when customers’ digital footprints are rapidly changing. They need to transform customer information and real-time data into actionable insights to inform clients what to execute to ensure the highest campaign performance.
In this post, we’ll explore how two of our advertising agency customers are turning to Google BigQuery to innovate, succeed, and meet the next generation of digital advertising head on.
Net Conversion eliminated legacy toil to reach new heights
Paid marketing and comprehensive analytics agency Net Conversion has made a name for itself with its relentless attitude and data-driven mindset. But like many agencies, Net Conversion felt limited by traditional data management and reporting practices.
A few years ago, Net Conversion was still using legacy data servers to mine and process data across the organization, and analysts relied heavily on Microsoft Excel spreadsheets to generate reports. The process was lengthy, fragmented, and slow—especially when spreadsheets exceeded the million-row limit.
To transform, Net Conversion built Conversionomics, a serverless platform that leverages BigQuery, Google Cloud’s enterprise data warehouse, to centralize all of its data and handle all of its data transformation and ETL processes. BigQuery was selected for its serverless architecture, high scalability, and integration with tools that analysts were already using daily, such as Google Ads, Google Analytics, and Data Hub.After moving to BigQuery, Net Conversion discovered surprising benefits that streamlined reporting processes beyond initial expectations. For instance, many analysts had started using Google Sheets for reports, and BigQuery’s native integration with Connected Sheets gave them the power to analyze billions of rows of data and generate visualizations right where they were already working.
If you’re still sending Excel files that are larger than 1MB, you should explore Google Cloud.
Kenneth Eisinger, Manager of Paid Media Analytics at Net Conversion
Since modernizing their data analytics stack, Net Conversion has saved countless hours of time that can now be spent on taking insights to the next level. Plus, BigQuery’s advanced data analytics capabilities and robust integrations have opened up new roads to offer more dynamic insights that help clients better understand their audience.
For instance, Net Conversion recently helped a large grocery retailer launch a more targeted campaign that significantly increased downloads of their mobile application. The agency was able to better understand and predict their customers' needs by analyzing buyer behavior across the website, mobile application, and their purchase history. Net Conversion analyzed website data in real-time with BigQuery, ran analytics on their mobile app data through the Firebase’s integration with BigQuery, and enriched these insights with sales information from the grocery retailer’s CRM to generate propensity behavior models that accurately predicted which customers would most likely install their mobile app.
WITHIN helped companies weather the COVID storm
WITHIN is a performance branding company, focused on helping brands maximize growth by fusing marketing and business goals together in a single funnel. During the COVID-19 health crisis, WITHIN became an innovator in the ad agency world by sharing real-time trends and insights with customers through its Marketing Pulse Dashboard. This dashboard was part of the company’s path to adopting BigQuery for data analytics transformation.
Prior to using BigQuery, WITHIN used a PostgreSQL database to house its data and manual reporting. Not only was the team responsible for managing and maintaining the server, which took focus away from the data analytics, but query latency issues often slowed them down.
BigQuery’s serverless architecture, blazing-fast compute, and rich ecosystem of integrations with other Google Cloud and partner solutions made it possible to rapidly query, automate reporting, and completely get rid of CSV files.
Using BigQuery, WITHIN is able to run Customer Lifetime Value (LTV) analytics and quickly share the insights with their clients in a collaborative Google Sheet. In order to improve the effectiveness of their campaigns across their marketing channels, WITHIN further segments the data into high and low LTV cohorts and shares the predictive insights with their clients for in-platform optimizations.
By distilling these types of LTV insights from BigQuery, WITHIN has been able to use those to empower their campaigns on Google Ads with a few notable success stories.
- WITHIN worked with a pet food company to analyze historical transactional data to model predicted LTV of new customers. They found significant differences between product category and autoship vs single order customers, and they implemented LTV-based optimization. As a result, they saw a 400% increase in average customer LTV.
- WITHIN helped a coffee brand increase their customer base by 560%, with the projected 12-month LTV of newly acquired customers jumping a staggering 1280%.
Through integration with Google AI Platform Notebooks, BigQuery also advanced WITHIN’s ability to use machine learning (ML) models. Today, the team can build and deploy models to predict dedicated campaign impact across channels without moving the data. The integration of clients’ LTV data through Google Ads has also impacted how WITHIN structures their clients’ accounts and how they make performance optimization decisions.
Now, WITHIN can capitalize on the entire data lifecycle: ingesting data from multiple sources into BigQuery, running data analytics, and empowering people with data by automatically visualizing data right in Google Data Studio or Google Sheets.
A year ago, we delivered client reporting once a week. Now, it’s daily. Customers can view real-time campaign performance in Data Studio — all they have to do is refresh.
Evan Vaughan, Head of Data Science at WITHIN
Having a consistent nomenclature and being able to stitch together a unified code name has allowed WITHIN to scale their analytics. Today, WITHIN is able to create an internal Media Mix Modeling (MMM) tool with the help of Google Cloud that they’re trialing with their clients.
The overall unseen benefit of BigQuery was that it put WITHIN in a position to remain nimble and spot trends before other agencies when COVID-19 hit. This aggregated view of data allowed WITHIN to provide unique insights to serve their customers better and advise them on rapidly evolving conditions.
Ready to modernize your data analytics? Learn more about how Google BigQuery unlocks the insights hidden in your data.
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