In setting up a product-led growth motion, manually defining detection conditions (aka signals) have reported to be the hardest thing to do. Our customers repeatedly told us that, while they wanted to start with intuitive rules they observed from product analytics, they mostly ended up being disappointed about the amount of false-positive being generated. They typically got those false-positives because of edge-cases, such as signups belonging to alternative ICP segments, or people already knowing your product and thus skipping onboarding steps. Also, there’s always the logic that some signals will only become relevant when matched with other signals...
We think the better way is to have the PLG platform continuously learn from any conversion, expansion and churn motion, and have it decide which signals to look at, and with which impact.
Yet, machine learning on its own won’t be meeting customer requirements. There are also the practical needs that were voiced:
Taking all above into account, we’ve built Smart Signals: a revolutionary machine learning engine that computes probability for accounts and users to convert, expand and churn, while providing clear overview of each signal and its impact.
journy.io starts with understanding the journey stages in which users and accounts can reside, at any given moment. These stages are characterized by being free, paying or churn. Here are a few journey examples, with their stage type:
Next, from analysing and comparing segments of users/accounts that have converted (Free → Paying), expanded (Paying → Paying into higher tier) and churned (Free or Paying → Churn) in the past, journy.io is able to identify and propose uniquely differentiating signals.
With a simple click on a ‘✨Smart Signal’ button, journy.io offers a list of all relevant Smart Signals.
Upon journy.io operators/administrators confirming above Smart Signals, or manually creating intuitive signals, conversion, churn and health scores will automatically be computed. Also the impact of these signals will automatically be finetuned over time, as users/accounts move along in their journeys and the ML model gets more trained.
As signals are being confirmed and our ML engine is computing conversion/expansion, churn and general health scores for each individual user and account, there are certain default dependencies that get influenced.
To start, health (good, normal, bad) is by default directly impacted by aforementioned scores. Also default playbooks for trial-to-paid conversion, expansion and churn risk detection heavily rely on these scores, and so does certain key segments that is used throughout the system.
The impact of changed and added signals to our ML engine is processed in such a way, that sudden fluctuations are avoided. And thus, downstream marketing, sales and CS workflows aren’t that easily affected when a new signal gets triggered or not.
As a product-led growth motion is all about automatically detecting signups that are likely to buy, to expand and to churn, signals are simply key.
You can easily implement and iterate on your intuitions, yet still leverage machine learning and data science to get those confirmed, or rejected. While journy.io was never intended to replace internal data science initiatives, it augments current data-driven processes. Many of our customers still leverage internal data science models and use our functionality on top of that work.
And in the end, even after considering all practical customer feedback and allowing flexibility to the ML model, we are still measuring and optimise workflows beyond what the ML model indicates, so you can be certain that you always operate by the best converting workflows possible.
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.
Connect journy.io to Segment to collect product traits and events, and send product-led growth (PLG) scores, signals, events and other metrics back to 400+ Segment destinations. In real-time.
Whether setting up workflows for freemium and trial signups to convert more, or for paying customers to be expanding towards a higher tier, or preventing to churn, being able to compare and optimise different workflows for maximum goal conversion is pretty much on every sales and CS leader’s list.
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.
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