Product-led Growth

The importance of real-time data in a PLG/PLS motion

Yves Delongie
Yves Delongie
August 30, 2023
The importance of real-time data in a PLG/PLS motion

Speed is everything. In the realm of Product-Led Growth (PLG), where the user's journey takes center stage, the significance of real-time data cannot be overstated. The evolution of PLG has not only revolutionized how products are marketed and adopted but has also transformed the entire business-consumer dynamic. Amidst this transformation, the decreasing cost of integrating live platform data has emerged as the game-changer, propelling companies to make instant informed decisions, enhance user experiences, and ultimately drive real-time growth automations such as onboarding and retention outreach.

So, whether you're a startup navigating the PLG landscape or an established enterprise looking to fine-tune your product-led strategies, join us as we uncover the transformative power of real-time data in propelling your PLG journey to new heights.

The different options of collecting data: streaming vs static; real-time vs delayed.

When it comes to gathering data from various sources, we've traditionally classified them into two primary categories: event streaming object sources and static data sources, which encompass cloud-based applications and data warehouses. These distinct sources possess unique characteristics when it comes to the methodologies of data collection and querying technologies.

Event streaming object sources are renowned for their ability to promptly identify specific objects and transmit events in real-time as they unfold. For instance, a SaaS platform aiming to track screen views generates individual events for each view, which are swiftly processed by the data collector (such as with minimal delay right after reception. This seamless and rapid event processing characterizes a close-to-real-time data collection approach.

Conversely, static data objects primarily operate through query-based interactions. While the data collector occasionally receives webhooks from these sources to signal the availability of new data, the actual data retrieval is typically executed through data polling. This procedure involves the data collector periodically querying the source for updates. The frequency of these polling intervals can range from a few minutes for applications with limited API rate limits to several hours for more extensive data warehouses. Consequently, the data acquisition from static sources introduces a delay in the data delivery.

Here’s an example of how Segment —a Customer Data Platform acquired by Twilio some years ago— allows its customers to configure Data Warehouse Sync Frequency:

Segment CDP about Data Warehouse Sync Frequency

The speed at which data is collected, whether in real-time or delayed, fundamentally shapes the scope of actionable insights and playbooks that can be practically derived from that data. The timeliness of data acquisition significantly influences the types of strategies and decisions that can be executed by the data's availability.

Both real-time and delayed data play distinct roles in catering to various types of applications. For analytical purposes where insights from the last 24 hours are not critical, sourcing data from a data warehouse is a highly viable option. On the other hand, for Product-Led Growth (PLG) strategies, there is a clear need to acquire data as events unfold to capture the immediacy of actions.

Typical ETL and reverse ETL process.

How speed of data collection influences different customer stages.


When it pertains to acquiring new users on a SaaS platform, the crucial factor lies in the speed at which you can deliver a precisely tailored ICP-targeted message. This must be accomplished before a potential lead's attention span wanes—a window typically spanning just a few seconds. Any form of delay beyond this fleeting moment invariably leads to the unfortunate loss of that lead.

A prime illustration of this concept would involve a scenario where a potential lead displays significant interest in specific features. In response, a timely popup emerges, extending an invitation to the user for downloading a use case that impeccably aligns with these favoured features.


The initial phase of an onboarding sequence, particularly when facilitated through in-app messaging, demands a real-time approach for optimal effectiveness. Receiving this crucial message even just an hour or two later can lead individuals to persist with their current tasks, deferring the subsequent onboarding steps to a later time.

Later reminder messages within a same onboarding step are relatively less time-sensitive compared to the initial message; allowing a span of 1 or 2 days is generally acceptable!


When the task involves pinpointing promising trials poised for conversion, the ability to take immediate action offers distinct advantages. Employing a conversion playbook facilitates a dynamic in-app scenario where the objective is to steer these high-potential trial users toward new features—particularly those that require payment—while they remain actively engaged within the application. This approach hinges on timely intervention, ideally occurring while the user is still immersed in the app experience, rather than deferring these interactions by a couple of hours.

Once the user concludes their session, the strategy pivots to follow-up emails, potentially incorporating enticing discounts as well. This subsequent stage carries a more relaxed timeframe, allowing for the dispatch of emails even after a short delay. This dual-pronged approach capitalizes on the urgency of real-time engagement and the more flexible nature of post-session interactions, strategically catering to the diverse timelines of user engagement and optimizing conversion prospects.


When an account is poised for expansion, either due to nearly exhausting its total licenses or encountering feature limitations within the platform, initiating contact within a timeframe of around 24 hours is generally sufficient. In this particular phase, the immediacy of real-time data isn't a pressing necessity.


While a user might have been contemplating churning for a while, as indicated by their prolonged absence from the platform—a significant signal in itself—there are instances where they could engage in a flurry of activities within a remarkably short timeframe. For instance, they might explore the ‘danger zone’, proceed to export their data, and then return to the danger zone with the intent to delete their account.

In such scenarios, the swiftness of sending a well-timed retention sequence becomes paramount. The faster this sequence reaches them, the higher the likelihood that they can still be persuaded to reconsider their intentions. This critical juncture demands real-time data due to the very nature of the user's actions and the necessity for an immediate response to potentially salvage their engagement.

Real-time data capturing and distribution through a customer data platform.

Conclusion: real-time vs delayed data in PLG.

In the majority of Product-Led Growth (PLG) scenarios, the preference for real-time data over delayed data is resoundingly evident. With the democratization of costs associated with obtaining real-time data, any lingering uncertainty regarding the data speed category that a fledgling company should prioritize has largely dissipated.

However, when addressing more established and sizable enterprises, where the conventional approach has involved relying on data warehouses to house comprehensive customer and product data, transitioning entirely to a new real-time framework could potentially prove excessive. In this context, channeling resources into achieving real-time insights, particularly concerning product usage, remains a prudent recommendation. This targeted investment acknowledges the changing landscape without necessitating an unwarranted overhaul in situations where a more nuanced approach can achieve a same desired outcome.

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