Modern data warehouses which power production marketing and growth use cases, not just dashboards.
At a glance
Most data warehouses start off as a reporting layer, a source for dashboards rather than operational use cases. At Metron Growth, we specialise in changing this, using the data warehouse to personalise the user experience and optimise performance marketing as well as measuring performance.
To do this, we build production-quality data models in dbt using version control and unit tests to ensure reliability. Then we connect these models to the marketing technology stack using reverse ETL.
We also use the data warehouse and dbt as a foundation for machine learning, creating lead scoring and predictive LTV models as a feedback signal for ad platforms, optimising paid media campaigns.
Our approach
Attribution gets complicated fast. You need to take all your pageview data, sessionise it, identify the source of each session, resolve identity across multiple devices, connect this to conversion data then assign credit to each touch point. Then you need to join the adspend data and profitability or lifetime value estimates to calculate ROAS. And finally, calibrate it against the results of media mix modelling or incrementality tests.
The comprehensive attribution strategy triangulates from multiple sources. Media Mix Modelling uses regression to measure the incremental impact of each channel. It's often a good next step once you have multi-touch attribution in place.
Whilst MMP data usually forms the foundation of your mobile attribution reporting, larger advertisers need to build bespoke BI reporting on top. This enables you to account for data gaps, particularly on iOS, and to combine mobile, web and web-to-app data in one place.
Modern CDPs rely on a two-way integration with the data warehouse to enable more complex use cases. This can include enriching events in real time or incorporating additional data beyond the event stream into audience definitions.
If you want to optimise campaigns for offline conversions or predicted lifetime value, it's usually necessary to calculate these metrics in the data warehouse. We can help you both build the metrics and connect to the ad networks through a CDP or reverse ETL tool. This information is passed to Google's Enhanced Conversions for Leads or Meta's Conversion API.
AI agents are only as good as the context they have on the user. The data warehouse often contains rich information about the user, but it's not always optimally structured for use by AI agents. Our team specialises in transforming data and making it available to AI agents in a way that's secure and effective.
In industries with long sales cycles like SaaS or Fintech predicting the value of a lead early in the customer journey can be highly valuable: for measuring marketing performance, optimising campaigns and forecasting revenue. These machine learning models sit on top of your data warehouse.
Tooling
Opinionated choices, but tuned to existing infrastructure and team skills. No platform chosen on a demo.
The strategic question
The Data Warehouse has changed fundamentally in the past five years. Five years ago, Data Warehouses were a reporting layer used by BI teams. Now they're deeply integrated with the rest of the stack and powering a range of production use cases from optimising ad campaigns to powering AI agents.
This has all been enabled by a shift to treating data models like software. Adding CI/CD, version control and unit testing to ensure data quality.
Talk to an Expert about taking your data warehouse to the next level.
Garrett Scott
Head of Growth Marketing
“Cannot sing their praises enough. They brilliantly partnered with the team to execute a not at all easy project with almost no disruption, and a flawless rollout…can’t wait to see this continue to change the trajectory of Calendly.”
A modern data warehouse is the central store for your company data, sitting at the heart of the modern data stack. It pulls raw data from every source system into one place (usually Snowflake, BigQuery or Databricks), shapes it into re-usable data models and feeds a wide range of use cases. Data is used across the business: - analysis, executive reporting, marketing measurement, lifecycle marketing, personalisation, context for AI agents etc.
All Metron Growth teams use Claude Code with our proprietary AI skills and tools to accelerate our workflows and ensure consistency. These tools enable us to work at two to three times the speed of a traditional data team without sacrificing quality.
It all depends on your use cases. BigQuery is the simplest to set up but the least configurable and only runs on Google Cloud. Databricks is the most configurable, has extensive machine learning capability and is often chosen by teams who primarily use Python or Spark. Snowflake sits in the middle: it works with all the major cloud providers, it's easy to configure different sized clusters etc. and is most often chosen by SQL first teams (although it does support Python).
At Metron Growth we use a medallion architecture for all of our data warehouse builds. This organises warehouse data into three layers: bronze is a simple abstraction from source data, silver models are re-usable representations of your key business objects (e.g. users, purchases, products) and gold models provide use case specific transformation. This creates a very flexible architecture which avoids code re-use and can be easily extended. The full pattern is documented here.
How long a migration takes is completely dependent on the complexity and number of use cases. A small warehouse can be migrated in weeks, but a large one can take months. We aim to break up the work into modular work streams which each deliver value incrementally.
Reverse ETL takes modelled data from the data warehouse back into operational tools like a CDP (Twilio Segment), a customer engagement tool (Braze), a CRM system (Salesforce) or an ad network (Google Enhanced Conversions for Leads). This enables marketing, commercial and product teams to use data to adapt their work flows and the customer experience, not just for reporting. At Metron Growth, we do all of the data modelling and transformation in dbt to avoid errors and maximise observability.
Get started
30 minutes. No pitch deck.
Prefer email? contact@metrongrowth.com
