Field Note #002: Lifecycle Leverage Starts with Inputs you Can Trust
How reliable inputs enhance personalization, build trust, and drive growth
Growth leads, PMMs, lifecycle folks... seem to be wrestling with the same challenge these days: they're personalizing on top of signals they don’t really trust. There's no shortage of data. But ask a team, “Would you bet your onboarding flow on this signal?” and you’ll get a shrug, or a nervous laugh.
Too many systems are built around what’s easy to track, not what’s meaningful. Too many articles skip straight to branching logic without questioning the foundation. Too many dashboards give the illusion of insight.
This note is a response to that pattern. It's for anchoring personalization efforts in signals that actually move the needle because they’re observable, repeatable, and meaningful.
This piece is focused solely on signal validation. Operationalization, tooling orchestration, and data governance each deserve their own treatment and will be covered separately.
At Shopify, when we first built the lifecycle programs in 2018, we had no shortage of signals: product usage, support pings, click paths, time in-app. We built flows on top of them that looked smart on paper.
Still, something kept missing.
We started asking a different question, not “what can we track?” but “what holds up under pressure?” What behaviours are truly correlated with user success, ideally causal, and tied to the outcome the user actually came for?
The best signals had a few things in common: directly observable, deliberate, and predictive of future value, not just surface activity.
We dropped the rest. Simplified the systems. Anchored flows to fewer, stronger signals.
It worked. More clarity, less guesswork. More trust in what we were shipping. Impact followed. But even then, a harder truth came up: even good signals don’t always tell you what they seem to.
That’s when we had to confront the next layer.
Signal ≠ Intent.
Understanding the Signal–Intent Spectrum
A crucial distinction often overlooked is that signal does not equate to intent.
Signals represent observable behaviours. Intent reflects the user's internal objectives. While they may align at times, assuming they always do can lead to misinterpretations.
For instance, a click might signify interest or it could indicate confusion. Utilizing a feature might demonstrate value or result from a misclick. Therefore, we view signal and intent as points along a spectrum, not as interchangeable terms.
Zero-party data: information directly provided by users (preferences, needs, goals), this most accurately reflects true intent.
First-party behavioural data: offers insights but requires validation and context.
Second-party and inferred third-party signals: provide directional guidance but should be approached cautiously.
Effective personalization involves layering these data types, starting with declared needs and refining based on observed behaviours.
Personalization as an Evolving Process
At Shopify, we recognized that personalization isn't a static logic map. It's a dynamic system, a continuous feedback loop.
Signals evolve. Customer expectations change. New behaviours emerge.
So, every user journey we crafted required periodic review, auditing, and adaptation. The most effective personalization systems are those that learn; monitoring successes, failures, and shifts to make necessary adjustments.
Validating signals serves as the foundation. The feedback loop ensures ongoing integrity.
My Current Approach to Signal Evaluation
I don't subject every signal to a rigid checklist; instead, I triangulate.
Before anchoring any user journey to a behavioural input, I assess it from multiple perspectives:
Is the signal directly observable without relying on inferred models?
Does it appear consistently across both quantitative and qualitative data sources?
Has it been observed across various user cohorts, not just a single high-performing segment?
Can it be corroborated through multiple channels (e.g., system logs, user interviews, support tickets)?
Does the signal hold significance within the user's broader context (segment, timing, recent actions)?
Where does the signal fall on the intent spectrum—declared, inferred, or ambient?
Signals that satisfy these criteria tend to drive meaningful impact. Others aren't disregarded but are reserved for exploratory analysis rather than active deployment thru tests.
Distinguishing between exploratory and operational signals is a critical decision for any lifecycle team.
Since adopting this approach, our most successful user journeys have been the simplest, not due to reduced personalization, but because they are grounded in authentic, repeatable user behaviours.
This clarity has been impactful 🚀
Identifying the Right Signals for Your Business
Before finalizing signals for activation or personalization, consider the following:
What is your core value moment? Identify the action(s) that signify when a user begins to derive lasting value
Which signals consistently precede this moment? Analyze behaviours of your most retained users to uncover patterns leading up to conversion, activation, or expansion
Are these signals directly and consistently observable? Be cautious with inferred or proxy data unless there's proven correlation with desired outcomes
Do these signals manifest in both product usage data and user feedback? Ideally, high-value behaviours are evident in system logs and customer interactions
Are you confident enough in these signals to base a lifecycle campaign on them? If not, they may not be ready for personalization efforts
Can you enhance these signals with zero-party data? Directly ask users about their intentions to validate behavioural inferences
Tools Facilitating Scalable Signal Validation
Validating signals can be labour intensive, but advancements in AI and automation are streamlining the process:
Product Analytics Tools: Platforms like Heap, Mixpanel , and Amplitude help identify recurring user paths and potential friction points.
Customer Data Platforms (CDPs): Tools such as Twilio Segment and RudderStack consolidate user data across systems, simplifying signal triangulation.
AI Platforms: Solutions like Pecan AI or Cortex detect subtle yet significant patterns, highlighting early indicators of conversion or churn.
Zero-Party Data Tools: Services like Typeform , Jebbit , and in-app surveys empower users to directly communicate their needs and preferences.
While not panaceas, these tools contribute to building adaptive, responsive systems.
Data Sourcing Strategies by Personalization Layer
Zero-party (Declared): Utilize for onboarding processes, preference-based targeting, and long-term segmentation.
First-party (Behavioural): Apply to lifecycle timing, retargeting efforts, and in-product prompts.
Second-party (Partner-shared): Leverage for insights into adjacent upsell or cross-sell opportunities.
Composite Signals: Combine patterns (e.g., repeated use, integrations, invitations) to develop high-confidence activation models.
Signal Confidence Evaluation Framework
A practical lens for identifying the right signals to personalize against.
The below aren’t definitive answers: they’re examples to show how I think about signal quality in a PLG context. Use this table to map your own user behaviours to lifecycle stages, validate their reliability, and define how (or whether) to personalize based on them (hopefully thru controlled experiments if you have the volume and the means).
This list isn’t about chasing every datapoint. It’s about grounding personalization efforts in behaviours that consistently signal real progress, intent, and value.
When we did that, everything downstream got simpler: targeting, messaging, automation. Not because we added more personalization, but because we added better inputs.