Your Warehouse Has AI. Your GTM Is Still Reactive. Here's Why.
It's a tempting thought for any sharp GTM Engineer: "We have BigQuery AutoML and Snowflake Cortex. We can build our own predictive models."
While technically true, this DIY approach hides a mountain of high-cost, low-leverage work that keeps you trapped in a reactive loop, maintaining systems instead of engineering growth. The reality of building and productionizing your own predictive GTM models is a costly detour.
The Hidden Factory: The Feature Engineering Nightmare
Before you can even touch an AutoML tool, you need to feed it the right features. This is where the real pain lies. It's a manual, never-ending process of:
- Writing Brittle SQL: You're forced to hand-code hundreds or thousands of lines of complex SQL to define and calculate every behavioral feature—"time to activate," "frequency of key feature usage," "active users in the last 7 days."
- Endless Maintenance: These SQL models are incredibly fragile. Every time your product team ships a change or an event schema is updated, your models break. You're pulled from strategic work to spend your days debugging and fixing a tangled mess of technical debt. [1]
This is the foundational problem we solve. Our no-code journey and milestone modeling framework is your feature engineering engine. It replaces the endless cycle of writing and maintaining brittle SQL with a resilient, scalable semantic layer, giving you back your most valuable resource: your time.
The Unwanted Job: The MLOps Burden
Even if you solve feature engineering, you've now inherited a new, unwanted job: MLOps Engineer. A GTM Engineer's goal is to build automated revenue systems, not to manage the complex lifecycle of production machine learning. The DIY approach forces you to become an expert in:
- Model Training & Retraining: A predictive model isn't a "set it and forget it" tool. It needs to be constantly retrained with new data to avoid "model drift" and maintain its accuracy. This is an ongoing operational burden you have to manage.
- Deployment & Integration: Once a model is trained in AutoML, the scores need to be exported and piped back into the data warehouse. This is yet another data pipeline you have to build, monitor, and maintain.
- Cost & Complexity: AutoML tools can be computationally expensive and operate like a "black box," making it difficult to control costs and debug issues when they arise.
This is the strategic value we deliver. GrowthCues is "MLOps-in-a-box" for your GTM signals. We handle the entire lifecycle of the model—from training on the features you've easily modeled, to retraining, to writing the scores back into a clean, activation-ready table in your warehouse.
The GrowthCues Difference: Leverage, Not Just Features
A GTM Engineer's time is too valuable to be spent on data science plumbing. You're a force multiplier for the revenue team, and you need tools that give you leverage.
| DIY with AutoML | With GrowthCues | |
|---|---|---|
| Feature Engineering | Weeks of writing and maintaining complex, brittle SQL models. | Hours of defining journeys and milestones in a no-code framework. |
| MLOps & Maintenance | An ongoing operational burden of training, deploying, and monitoring ML models. | A fully managed, "set it and forget it" predictive engine. We handle the MLOps for you. |
| Time-to-Value | Months to get the first predictive scores into production. | Days to go from connecting your warehouse to having predictive scores ready for activation. |
| Outcome | A complex, custom-built system that becomes a new source of technical debt. | A resilient, scalable, and reliable source of GTM intelligence that frees up your engineering time. |
You don't need another complex tool. You need a resilient foundation for your descriptive metrics and a managed, automated path to the predictive intelligence your CRO is asking for. That's what we provide.