Google Analytics Support for Leading Financial Services provider

Hookflash was engaged to support a large financial services provider with ensuring their Google Analytics set-up was accurate, comprehensive and robust. In addition, a critical part of the engagement was ensuring this data was usable for marketing performance reporting and forecasting purposes. Hookflash was engaged to do conduct this work across five different websites that sat under the brands portfolio.

Throughout 2024-25 the brand was replatforming and review their cookie consent platform. This represented a great opportunity to redesign their site data structure, focusing on future custom modelling and advanced insights, rather than just a lift-and-shift migration. Privacy and cookie restrictions led to reduced data visibility, while challenges arose from the complexities of GA4's new data model. 

Their goal was to centralise all website data in BigQuery, run custom models, and democratise data in Tableau. They reached out to Hookflash for our expertise in complex GA4 tracking setups, server-side Google Tag Manager (GTM) implementation, and BigQuery knowledge

To achieve the objectives in the previous section, we went through iterations of the below process for all five brands: 

• Lead discovery sessions with individual teams to understand stakeholders’ expectations from web data. Mapped business wide reporting and activation requirements.
• Designed web event data structure and measurement plan based on the requirements, and bearing BigQuery GA4 data structure and future modelling in mind
• Worked with developers and implemented dataLayer on site and configured best practice tracking set up in GTM
• Set-up server-side GTM and moved third-party tags onto server-side GTM container using GA4 data stream
• Set-up Advanced Consent Mode, mainly to use the unconsented pings in BigQuery for custom modelling
• Set-up Enhanced Conversions and passed on user provided data to Google Ads to improve conversion modelling
• Use BigQuery GA4 exports to build tables in BQ as data sources for Tableau web data environment. This allows the brand to build site-specific channel attribution models. 

By delivering a more robust, detail analytics implementation, we were able to support the brands with:

• Understanding drop-off rates at a more granular level, and by different product types
• Customise their own attribution model in Google Cloud to fine tune the outcomes based on the data we saw
• Modelling unconsent user data in BigQuery, effectively replicating Google's own consent modelling, but more accurately for this brand
• Passing more accurate data back to Google Ads for optimisation - resulting in more efficient Google Ads spending