Measure Summit is virtual, but the energy in the chat was anything but. Analytics professionals logged in from around the world, the questions kept coming long after the session ended, and almost every follow-up conversation circled back to the same theme: the Data Manager API has arrived, and we have to be ready for it. (slides)
If you joined us live, this is the version you can drop into Slack in the morning. If you missed it, this is the short take, with the references and the architecture you need to act on it.
The 2026 KPI no-one talks about yet: Data Strength
Every measurement team I talk to has the same problem. Signal loss is accelerating. Third-party cookies, browser intelligent tracking prevention, ad blockers, regulation, consent gating and the natural attrition of gclid between ad click and offline conversion all conspire to thin out the picture you are trying to draw.
We have started referring to the response to this problem as Data Strength, and we believe it is the single most important measurement KPI for 2026. It has four dimensions:
- Volume: the percentage of possible signal you are collecting. Red flag: offline events that never make it back into the model.
- Quality: clean, consistent, complete data. Red flag: missing gclids on the sale.
- Durability: resilience to browser change, regulation and blockers. Red flag: no server-side GTM.
- Maturity: are you actually activating the data you collect? Red flag: only basic tagging in place.
Build all four and you maximise customer visibility, recover wasted signal and stop paying for clicks that never make it back into your bidding model. The Data Manager API is the mechanism Google has now given us to do precisely that. The question is how to wield it.
Two things landed in quick succession
Two recent moments have made Data Strength suddenly very tangible.
Earlier this year, the team at Stape released the Google Conversion Events Tag for server-side GTM. It is an open-source, well-engineered template that wraps the Data Manager API in a familiar GTM UI – everything you would want from a server-side tag, polished and available from the template gallery. Stape has done the analytics community a real service by getting this out so quickly, and it is rightly the default starting point for anyone working with the Data Manager API.
Then on 7 May 2026, Google announced significant enhancements to the Data Manager API itself. Lindsey Volta’s post for the Data Manager API team covers two parallel expansions:
- Google Ads store sales are now uploadable through Data Manager. In Google’s words, the new workflow “eliminates the need to create and monitor multiple offline jobs and replaces those jobs with a single API request.” Confidential matching, encryption for user identifiers and multiple items per event in CartData are now first-class features, subject to Google Ads account eligibility.
- Google Analytics event ingestion has been broadened to cover both web and app data streams. You can now “send any event to both your Google Analytics web and app data streams, provided they’re not reserved events.” Google positions the Data Manager API as “an alternative to Measurement Protocol for sending recommended and custom events directly to Google Analytics.” The unified schema covers app instance ID, event location and mobile device information, with routing determined by your Firebase App ID or Measurement ID.
There is also a quieter but very important “additional data source” expansion. Any event with a transaction ID can now be sent as an additional data source for your tag or the Google Analytics for Firebase SDK. Previously, this was restricted to purchase events on web data streams only.
Net-net: Data Manager is now a single ingestion point for Google Ads conversions, store sales, GA4 events and audience inputs. One API. One schema. One set of credentials. And with Stape’s template, a clean GTM-native way to talk to it.
A complete solution, not just a tag
The Stape tag is a brilliant tag. As a tag, it is comprehensive: well-engineered, well-documented and shipped under an open-source licence, with everything you would want for a server-to-server conversion upload. We deploy it with confidence and we recommend it without reservation.
What we offer at Duga is something broader in scope. A Data Manager API implementation that actually moves your numbers needs a great deal more than a properly configured GTM template. It needs:
- Solution design: matching the API’s affordances to your specific business, your data model, your consent regime and your destinations. Which events to send. Which identifiers to use. Which destinations to feed. Where the joins happen.
- Infrastructure build: the sGTM container, the Firestore session store, the Cloud Run processors, the BigQuery exports, the Shopify and CRM webhook handlers, and the IAM, service accounts and confidential matching configuration that hold it all together.
- Testing and proof of correctness: unit tests on the joins, validation runs against Validate Only on the API, BigQuery logging of every request and response, and a documented test plan that demonstrates the right gclid is reaching the right conversion at the right time.
- Change management: working with the marketing, paid media, CRM and engineering teams whose existing pipelines and reports will move when the new one goes live, so the transition is planned and not disruptive.
- Knowledge transfer: documentation, runbooks and walk-throughs so the system is owned, understood and maintainable by your team long after we step away.
Put plainly: Duga delivers the full consultancy and the full build, end-to-end, across the full range of Data Manager API use cases. Our heavily customised tag sits inside that build as the dispatcher at the end of the pipeline. At the centre of the architecture is a customised, consent-aware server-side GTM tag template with a Firestore session store integration. No third-party cookies. No Measurement Protocol fragility. No fleet of offline jobs to babysit.
What this gets you
Three things, all measurable.
- More signal. Offline sales, lead-to-sale journeys and audience membership changes are no longer stranded outside the model. Conversion rates and ROAS rise because the denominator is finally complete.
- Cleaner signal. Hashed PII and click identifiers are normalised once, then reused. Match rates in Google Ads improve. Audience accuracy in Google Analytics improves. The same is true for any downstream destination you add.
- More durable signal. When the next browser change, the next regulator or the next ad blocker arrives, you are not rebuilding the pipeline. The architecture is server-side, first-party and consent-aware by design.
What to do next
If you joined us at Measure Summit, thank you. The slides and the session recording should be appearing there, and they are worth catching up on for the diagrams alone.
When you are ready to act on it, we are ready to help. Book a consultation with Duga. You bring the question – can we attribute store sales properly, can our CRM update audiences in near-real-time, can we close the gap between ad click and offline conversion – and we deliver the complete answer: design, build, test, prove and hand over.
The hard parts are done. Don’t waste the signal. Build the strength.