Feature Highlights

Event Computed Properties: Transforming platform events into actionable triggers

Hans Ott
Hans Ott
September 13, 2022
Event Computed Properties: Transforming platform events into actionable triggers

Customer teams at SaaS companies need insights on what customers are doing on their platform! This is particularly true when driving a product-led growth strategy, but even in traditional sales-led environments, monitoring and seeing metrics in customer tools, is key to any business.

This typically involves asking product and dev teams to start creating counters, sums, averages and time-related calculations of most important features; and piping these results through to analytics and customer engagement tools, often over other media such as databases and even excel sheets/CSV files.

Of course, over time, when more features have been developed and customer teams need more A/B testing, more business metrics will be asked for, and eventually, bigger portions of expensive development resources end up maintaining a myriad of code for the sole purpose of providing insights.

The general need for no-code dashboards

While counters, sums and averages might sound trivial to implement, the difficulty in creating and maintaining the code often constitutes a burden on development personnel. They are the ones needing to deal with issues that are outside the scope of their application, from storing timeline feature events with appropriate metadata on both user and account level, over keeping processes up for regular re-calculation, to eventually sync’ing new results towards connected ecosystems apps (within proper API rate limits).

An easier and frankly better approach is to simply send raw user and account events with their metadata onto a secure data pipeline, the moment features are being used, and have a customer data platform (CDP) process that raw data into required business metrics. Such CDP will typically be operated by growth (marketing) engineers, without any need for development or SQL skills, through simple point-and-click workflows. Any maintenance in updating or creating new metrics will have no burden anymore on  development resources.

Here’s an example: Imagine you are building a B2B marketplace for companies to sell 2nd hand furniture and office equipment (to other companies). For each customer, you want to keep track of the amount of orders in the last 90 days and how much percentage increase that is vs previous 90 days. Rather than asking your developers to create these metrics and sync them to your Salesforce CRM, simply have them send an Order Placed event each time a purchase occurs. A marketing growth engineer will do the rest... basically filling in below form.

Of course, changing the time period to 30 days and getting the results in absolute values instead of percentages, won’t have any impact to the development team. Nor will changing from Salesforce to HubSpot CRM.

Metadata-based computed properties

Most often though, the metrics being asked for are a bit more complex than just counting events or calculating rate increases over periods of time. Most product- and sales-led business logic will actually resolve around processing metadata from those events and act on those results to determine product-led growth signals.

Continuing on previous example: Wouldn’t it make more sense to consider looking at actual amounts sold over the last 30 days? Or even by delivery method breakdown? That would translate into filling in the following...

Changing strategy and looking at averages or extremes (min. max) etc... won’t again (obviously) have any impact on development teams and can be defined by any data-driven marketeer with point-and-click skills.

And although the main focus on having these event metadata computed properties is to create growth motion signals and actions, and syncing them to customer apps, an additional advantage to development teams could be that they can query those computed properties back into their platform, thus avoiding the need to creating algoritmes themselves. The functions available are business-generic and include:

  • Counters and occurrences
  • Period over period increase/decrease
  • Daily/Weekly/Month averages
  • first/last seen dates
  • Count/Sum/Average metadata values
  • first/last seen metadata values
  • Most frequent metadata values
  • Min/Max metadata values
  • ...

All above is optionally related to a moving time window of e.g. Last 24h, 7d, 14d, 30d, ...

The importance of event computed properties in a PLG motion

Product-led growth resolves around being able to identify qualified leads for acquisition, expansion and retention (fight churn). Two major aspects in defining a product-led growth motions are customer fit and product behaviour. While it doesn’t need much time to explain why event computed properties are important for product behaviour, also customer fit scores often require event-related properties, mainly to segment contacts and determine fit conditions per segment.

Next to first creating computed properties, there’s also a clear need to directly include metadata in signal conditions, so to finetune PLG signals with business critical metrics.

All computed and native non-computed platform properties can eventually be made actionable in the customer tools you are using, by simply sync’ing them to custom fields. This way, you can start sending personalised email sequences and have all relevant data added to each customer record in your CRM.

Here’s our example in Close CRM email sequence:

Keep reading...

Built for B2B & B2C SaaS, simultaneously

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, expansion 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 PQL 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

Set up journy.io
in under one hour

Create your free account and start driving a product-led growth strategy with the tools you're already using.

Get Started 
journy.io white logo
© journy.io BV 2023 — All rights reserved.
The names and logos of third party products and companies shown on the website and other promotional materials are the property of their respective owners.