Welcome to part two of our three-part blog series about the future of digital marketing. If you missed “Changing Regulations & OS/Browser Tech,” be sure to check it out now. 

The most significant change in the modern marketing stack is the rapid transition to the first-party context. 

Brands typically download third-party javascript to send tags to data platforms. However, browsers now delete cookie identifiers at very short intervals, forcing brands to adopt new measurement standards, and quickly!

To assure the cookie maintains maximum expiry under ITP/ETP standards, it needs to be protected by the HTTPOnly Secure context. That is an expensive transfer of technical debt to the brand and puts a lot of pressure on existing IT/DevOps teams to support. Enter the Cloud.

Google Tag Manager – Server-Side

Google has released Tag Manager Server-Side (that we shorten to "sGTM"), a new competitor in the field of server-based tag management systems, including the more mature Tealium iQ or Segment. However, Google's offering includes some unique features worth considering: built-in integrations for the New Google Analytics, Universal Analytics, Google Ads and Campaign Manager.

As shown above, sGTM consolidates measurement using its Global Site Tag API. The gtag library is served through a brand's domain while being maintained by Google. Doing this creates consistency between platforms (including FB Conversions), reduces network and CPU load on the browser and dramatically improves security by minimizing data leakage.

Notably, the security improvement satisfies Intelligent Tracking Prevention standards and allows a two-year expiry on the HTTPOnly, Secure cookies. GTM Server can reconcile this ID with Google Analytics to create a resilient attribution key between a client and an analytics service.

The New Google Analytics – The Next-Generation Analytics Platform for App and Web

At long last, Google Analytics is upgrading to a new platform, built with next-generation capabilities in mind. They led with one of the most significant innovations in the product: BigQuery Export, offered in both streaming (real-time) and batch integrations.

A clickstream export to a cloud data warehouse enables advanced modeling and attribution. Google previously limited this capability to the more expensive Analytics 360 product, and while there are limits on the free version, access to clickstream data is invaluable. Remember, campaign, clickstream, and customer data are the pillars of Cloud for Marketing capabilities.

Adswerve has been hard at work preparing for the New Google Analytics, so you don't have to. Read all about it here.

Convergence of Clouds Leads to New Capabilities

We know that cloud platforms will play an essential role for both IT and Marketing organizations. IT has already adopted cloud-first strategies where they make sense and are becoming more comfortable with its security and administration. Marketers, meanwhile, have been using cloud platforms to power data-driven marketing tactics but have primarily been doing this behind SaaS offerings. 

Digital marketing and product teams are rapidly running point managing customer experiences, even for traditional brick and mortar companies. The modern marketer needs access to data at high speed, whose interfaces tie seamlessly to business data platforms at scale.

To deliver dynamic experiences, product teams need low-latency recommendations and decision systems. Retail and Finance teams need demographic, GIS data. BI and data teams want consistent APIs and tooling, and also support for CI-CD flows. Google Cloud is positioned well for all of these, having a history of building best-in-class platforms for IT and Digital Marketers. 

We can align Google Cloud and Marketing platforms and solutions to both the business teams that use them and your customers. The Cloud AI solutions are attractive because any or all of the teams can leverage them. Flexibility in a data platform can assure you get the most out of your existing investments and ensure that you can commit to realistic projects and timelines.

We can interpret this grid in many ways. In a silo, a customer intelligence story might begin by exporting their social listening analytics to BigQuery. Then widen that data using BigQuery Public Datasets, including census or weather data. They might want to purchase some of the commercial datasets to hone their insights and, finally, to mature into competitive analytics.

That's operating in a silo. Imagine if these teams were working together! What’s your story? We’d love to help. Contact us to get started.

Ready for Part 3? Check out the final article in our "What's Next for Digital Marketing" three-part blog series as we look into modeling and AI.