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.
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.
To conduct a correct B2B PLG motion, you need to act on both accounts and users. First, proper product-qualified accounts (PQAs) must be identified, to then engage with the best possible users (PQLs) from within these specific PQAs. We're today introducing relatioship properties to improve that process.
While product-led growth (PLG) and sales-led growth (SLG) may seem like they are at odds with each other, they can actually work together to capture full business' potential.
Changing the way you do business, case by case.
Detect which signups are most likely to buy. Sell more with less effort.
Automatically surface product qualified leads.
Prioritize PQLs call lists and engage with quick actions.
Add tasks and full PQL context to existing CRM and other engagement tools.
Automated sales playbooks and collaborative inbox.
Onboard. Monitor. Get expansion signals. Reduce churn, proactively.
Automatically detect churn & expansion candidates.
Accelerate onboarding and product adoption.
Align activities around 360° customer view, with health and onboarding scores.
Automated CS playbooks and collaborative inbox.
Build revenue workflows, based on how people use your product.
Use machine learning to uncover new sales opps.
Add slow accounts to nurturing campaigns.
Optimize engagement playbooks for maximum conversion.
Leverage any data without needing engineering.
See which impact your product features have on revenue and churn.
Analyse feature importance, usage and impact.
Build key product metrics without SQL, nor coding.
Easily create customer segments based on any product interaction.
Comply to GDPR and CCPA.
Create your free account and start driving a product-led growth strategy with the tools you're already using.