the new oil
Why your team should be spending more time worrying about your internal data and systems in order to drive scale
August 3, 2025
Most CS leaders are chasing AI solutions while sitting on a data goldmine they can't access.
They buy tools that promise to predict churn, identify expansion opportunities, and automate workflows, only to find that the outputs are garbage.
Here's what I learned from 90 days of AI research: AI is only as intelligent as the data you feed it.
And your data? It's trapped in systems that don't talk to each other. Usage data lives in Mixpanel, financial data in Salesforce, support tickets in Zendesk, meeting notes in Gong, and health scores in Gainsight. You have countless systems, but zero unified view.
The most underrated investment a CS leader can make right now isn't another AI tool. It's building a customer data warehouse.
Most SaaS companies start with a purpose: solve a real problem, serve customers, and build something meaningful. But somewhere along the way, internal complexity takes over. Silos form. Metrics multiply. Culture drifts. And suddenly, teams are working hard — just not on what matters.
The Purpose Code is a field guide for leaders who’ve felt that shift and want to fix it.
Imagine you built that unified view. Here's why it changes everything:
Context is everything. AI can't spot risk signals when it only sees fragments. Support tickets, usage drops, and stakeholder changes in different systems mean you miss the patterns that predict churn.
Historical patterns predict future behavior. AI needs 12+ months of unified history to learn what "normal" looks like. Without longitudinal data, every prediction is a guess.
Real-time triggers need real-time data. By the time you manually compile data from seven systems, the moment to intervene has passed. AI needs live feeds to be proactive.
Personalization at scale needs unified profiles. You can't deliver individualized experiences to 1,000 accounts if each CSM interprets data differently. AI needs consistent, clean data structures to work its magic.
The technical part — Snowflake or BigQuery, ETL pipelines, data refreshes — is straightforward.
The strategic part is what matters.
It forces you to answer critical questions:
Which signals actually predict outcomes?
What data deserves real-time monitoring?
And how do we structure data for AI use cases we haven't even imagined yet?
Map every customer touchpoint across their lifecycle. Document which system captures that data. Identify the 20% of data points driving 80% of your insights.
Build your warehouse to unify those first.
The companies winning with AI in Customer Success aren't the ones with the fanciest models. They're the ones who built the boring infrastructure to feed those models complete, clean data.
Your data warehouse isn't just storage. It's the foundation for every AI-powered insight you'll generate for the next decade.
The best time to build it was three years ago. The second best time is now.
Before your competitors figure this out.
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We’re grateful you choose to read each week.
When you’re ready for more, there are a couple ways we can help:
» Cover Your SaaS is a financial literacy course for go-to-market leaders. Grab your copy here.
» Promote your product and services to over 4,000+ senior SaaS Sales, Marketing, and Customer Success pros by sponsoring our twice-weekly newsletter and podcast.
