Property Analytics: Discovering your customer commonalities

Growth teams at SaaS companies need clear insights on who their customers are, and how they differ with e.g. those who didn’t convert. As such, by analysing data from across an entire stack, from CRM, Sales Engagement, Marketing Automation, Chat, Support Ticketing and other engagement platforms —combined 🙌— they are able to easily determine trends in customer segmentations and aggregations.

This gets particularly important when driving a product-led growth strategy, where comparing new signups with existing customers is key. Yet even in traditional sales-led environments, being able to confirm your ideal customer profile (ICP) assumptions with a simple click... feels like magic.

And where previously, this would have required expensive and data-engineering-heavy BI tools to be deployed, journy.io offers this out-of-the box at any subscription level. Simply connect all your apps, create a segment of accounts that e.g. converted the last 30 days, and see commonalities in cross-ecosystem properties for those accounts.

The importance of property analytics in a PLG motion

Product-led growth resolves around signals that allow to identify product qualified leads (PQLs) for acquisition, expansion and retention (fight churn). One of the key ingredients to automatically identifying such PQLs is a scoring algoritme named customer fit score. Or how well — within specific segments— do new signups resemble previously converted customers, by industry, by funding, or by basically anything a machine typically can learn. But more about our ML-engine later — I’m not yet supposed to talk about it 😉.

We thought that at least it would provide PLG growth engineers some great guidance in (for-now-still-) manually setting up PLG signals.

‍