Feature Highlights

Smart Signals: Machine learning model for predicting conversion, expansion and churn

Yves Delongie
Yves Delongie
November 13, 2022
Smart Signals: Machine learning model for predicting conversion, expansion and churn

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:

  • The machine learning engine cannot be a black box! If operators cannot see what signals at which impact are being used, they will not trust it.
  • While recognizing flaw-backs in intuitive rule setting, one still wants to add manual signal rules to the engine’s signal model.
  • When products change, so does usage. And as such, operators want to get clear insights how signal impact changes over time, and when they even become irrelevant.

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.

How Smart Signals work?

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:

  • Lead (Free) β†’ Trial (Free) β†’ Freemium (Free) β†’ Starter (Paying) β†’ Corporate (Paying) β†’ Enterprise (Paying) β†’ Churn (Churn)
  • Lead (Free) β†’ Trial (Free) β†’ Trial Expired (Churn) β†’ Starter (Paying) β†’ Corporate (Paying) β†’ Enterprise (Paying) β†’ Churn (Churn)
  • Lead (Free) β†’ Freemium (Free) β†’ Starter (Paying) β†’ Corporate (Paying) β†’ Enterprise (Paying) β†’ Churn (Churn)

Next, from analysing and comparing segments of users/accounts that have converted (Free β†’ Paying), expanded (Paying β†’ Paying into higher tier) and churned (Free or Paying β†’ Churn) in the past, journy.io is able to identify and propose uniquely differentiating signals.

With a simple click on a β€˜βœ¨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.

Signals impact Scores that impact Health, Playbooks, and Segments

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 dependancies 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.

Smart Signals as part of a PLG motion

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.

Keep reading...

Built for SaaS

Changing the way you do business, case by case.

Success

Onboard. Monitor. Get expansion signals. Reduce churn, proactively. Grow.



Automatically detect churn & expansion candidates.



Accelerate onboarding and product adoption.



Align activities around 360Β° customer view, with health and onboarding scores.



Automated CS playbooks and collaborative inbox.

Sales

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.

Growth

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.

Product

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.

Check out our use cases 
Build revenue playbooks based on how people use your product
Stop crunching CRM records and data sheets once a month, to find risks and opportunities. Organise your workflows around real-time PQLΒ signals, playbook actions, and customer data from across your stack.

 Work in the tools you already know and use
 Give easy access to product data
 PQL intelligence and actions, the moment it happens
 All point'n'click, no SQL or coding needed
Check out our use cases 
Share Customer Data
Detect which leads are hot. Sell more with less effort.
Stop wasting time on unqualified leads. Look out for promising product qualified leads that trigger specific buying signals, and make more meaningful calls, knowing which features they're interested in.

 Get prioritized call lists, sorted by likelihood-to-buy
 See unified view of each account and their users
 Get cases with follow up tasks in your team inbox
 Get tasks and product data in your existing CRM
Check out our use cases 
health score
Easily test new PLG strategies and improve conversions
Try out new PQL conditions, assign different engagement variations, and monitor how conversions progress over time.

 See which features are most frequently used
 Quickly test new PQL conditions and actions
 Prove which engagement playbooks work best
 All point and click, no code required
Check out our use cases 
PQL Experiments
Onboard. Monitor product usage. Reduce churn, proactively.
Keep track of each individual user and account at scale, and get notified on expansion and cross-selling opportunities. Identify those who are at risk of churning, and pro-actively reach out to them.

 Get notified on churn risk and expansion opportunities
 Get cases with follow up tasks in your team inbox
 See all account and user activities in one place
 Collaborate with sales and marketing thru quick actions
Check out our use cases 
journy.io customer success use case
Convert more through hyper-personal automations
Target the right audience at the right time with the right message. Your existing automations get power-boosted with customer engagement metrics.

 Automatically add PQLs to engagement automations
 Get product usage data in your existing marketing tools
 Lower customer acquisition costs‍
Check out our use cases 
journy.io marketing use case

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