Attribution in a PLG World: How to Measure Marketing's True Impact

Your latest campaign drove 5,000 signups. Marketing automation shows impressive open rates. Yet when you report to the board, you cannot definitively prove which efforts actually drove revenue.

This is not a failure of execution. It is a structural problem.

The Attribution Crisis Facing Product-Led SaaS

Traditional B2B SaaS companies selling through sales teams have long relied on HubSpot's marketing automation backbone. For 15 years, it has powered lead capture, email nurture sequences, and funnel reporting that culminate in a sales conversation. The attribution model is straightforward: marketing generates leads, sales converts them, and everyone sees the connection.

But product-led growth (PLG) fundamentally rewrites this playbook. Users discover your product organically, sign up without speaking to anyone, explore features during a trial, and upgrade within the product itself. Marketing still drives awareness and consideration, but the conversion moment happens inside an application your marketing stack cannot see.

HubSpot, built for marketing-led funnels, lacks the granular in-app event tracking required to understand what drives PLG conversions. When a user upgrades after discovering a core feature, inviting teammates, or hitting a usage threshold, that critical revenue moment occurs in a blind spot. Your CRM knows who bought, but not why they bought or how your marketing actually influenced that decision.

As one B2B SaaS company discovered during an attribution audit, this disconnect resulted in a loss of visibility into $500,000 in misattributed revenue. They had been investing heavily in digital ads, while in-person events, which were completely undervalued in their first-touch model,  were actually driving their highest Annual Contract Value (ACV) deals.

The question is not whether marketing drives value in PLG. The question is whether you can prove it.

Why Traditional Attribution Models Collapse in Self-Serve Journeys

First-touch and last-touch attribution models dominate B2B marketing because they offer simplicity. Credit the initial touchpoint that brought someone into your world, or credit the final interaction before they purchased. Easy to implement, easy to report.

These models fracture the moment your product becomes the primary conversion driver.

The In-Product Conversion Black Hole

In sales-led models, the funnel progresses linearly:

1. awareness →
2. consideration →
3. decision →
4. sales conversation →
5. closed deal.

Every stage produces trackable events in your marketing automation platform. Email opens, content downloads, demo requests... each action flows through systems designed to capture it.

PLG inverts this structure. Users enter your product during the consideration stage through a free trial or freemium tier. They explore independently, discovering value through hands-on experience rather than sales conversations.

The decision to convert happens based on product interactions: completing onboarding, adopting core features, inviting collaborators, or hitting feature limits  that traditional marketing tools never see.

When attribution relies solely on pre-product touchpoints, you are measuring awareness and top-of-funnel activity while remaining blind to the actual conversion drivers.

You might attribute a conversion to the blog post they read three months ago, while completely missing that they upgraded immediately after inviting their third team member.

Cross-Device and Self-Service Complexity

PLG users interact with products across multiple devices and sessions before converting. They might discover your product on mobile, sign up on desktop at work, explore features at home on a laptop, and finally upgrade on mobile. Each device switch breaks traditional tracking cookies and attribution chains.

Additionally, the self-service nature of PLG means users control their journey timing. They research on their schedule, trial when convenient, and convert when they perceive sufficient value. This extended, asynchronous journey renders simplistic touchpoint models inadequate.

A user might interact with your brand across six months, dozens of sessions, and multiple contexts before converting. Yet, first-touch attribution would credit only their initial blog visit, while last-touch attribution might attribute everything to a direct website visit on the day they upgraded.

The Multi-Touch Illusion

Multi-touch attribution seems like the solution. Distribute credit across all touchpoints to acknowledge the full journey. In practice, it introduces new problems for PLG companies.

Standard multi-touch models still operate primarily on marketing touchpoints—ad clicks, email opens, website visits. They do not naturally incorporate the product usage events that actually drive PLG conversions.

A multi-touch model might assign 20% credit to an initial ad, 30% to a webinar, and 50% to a retargeting campaign while allocating zero value to the onboarding milestone completion that actually triggered the upgrade decision.

Without integrating product analytics data into your attribution framework, even sophisticated models remain incomplete.

The Product-Marketing Data Chasm

The attribution problem is a symptom of a deeper organisational divide: marketing teams and product teams operate in separate data universes.

Marketing's World: Campaigns and Contacts

Marketing automation platforms like HubSpot excel at tracking the external journey. They capture how users discover your brand, which content resonates, what campaigns drive signups, and how messaging influences early-stage behaviour.

Your marketing team can segment audiences, personalise communication, and optimise top-of-funnel conversion rates with remarkable precision.

This data answers: Who is engaging with our marketing?

Product's World: Features and Behaviours

Product analytics tools like Amplitude, Mixpanel, and Heap track the internal journey. They reveal which features users adopt, where they encounter friction, what usage patterns predict retention, and which behaviours indicate expansion potential. Your product team can identify activation moments, optimise user experiences, and prioritise features based on actual behaviour.

This data answers: How are users finding value in our product?

The Question Neither Can Answer Alone

The question that drives business outcomes requires both perspectives: Which marketing efforts drive users who activate, engage deeply, and expand?

Marketing cannot answer this without product data. They can drive signups, but cannot determine which campaigns attract users who actually become valuable customers versus those who churn after a brief trial.

Product cannot answer this without marketing data. They can identify successful user behaviours, but cannot trace those users back to the campaigns, channels, or messages that brought them in.

This chasm manifests in tangible ways. Marketing celebrates a campaign that drove 1,000 signups, only to learn later that 90% churned without ever activating.

Product optimises onboarding for better activation, but marketing continues investing in channels that bring in users unlikely to benefit from those improvements.

Revenue Operations tries to forecast based on signup volume when conversion rates vary wildly by source, a variable they cannot accurately track.

Misalignment between product and marketing teams creates friction beyond attribution. Marketing might promise features in campaigns that product has not prioritised.

Product might launch capabilities marketing never hears about, missing promotion opportunities.

Strategy diverges, goals conflict, and both teams optimise their respective funnels while the business suffers from a broken end-to-end experience.

The Path Forward: Unified Attribution for PLG

Solving PLG attribution requires bridging the product-marketing data divide through technical integration and organisational alignment. Three foundational changes unlock visibility into marketing's true impact.

1. Integrate Product Analytics with Marketing Automation

The technical solution begins with connecting your product analytics platform directly to your marketing automation system.

When product events flow into HubSpot, Customer.io, or your marketing platform of choice, you gain the ability to track the complete user journey from first touch through activation, engagement, and expansion.

A user's progression from initial signup through core feature adoption to paid conversion becomes visible as a unified timeline rather than fragmented data across disconnected tools.

Implementation typically involves:

Choose Your Product Analytics Foundation: Amplitude, Mixpanel, or Heap. Each offers robust event tracking capabilities. Your selection depends on your product complexity, data volume, and team technical capabilities. For most B2B SaaS companies scaling to 500 employees, Amplitude provides the right balance of power and usability.

Define Critical Product Events: Identify the 10-15 in-product behaviours that truly matter. These are not every possible action—they are the milestones that indicate value discovery, engagement depth, and expansion signals.

Examples include: completing onboarding, first use of core feature, inviting a team member, hitting a usage threshold, and exploring premium features.

Establish Bidirectional Data Flow: Product events must flow into your marketing platform to enable campaign triggers and segmentation. Marketing attribution data should flow back into product analytics to enable source analysis. Tools like Segment simplify this by serving as a customer data hub that syncs events across your stack.

Create Custom Objects in HubSpot: Standard HubSpot contact properties cannot capture the depth of product usage data. Build custom objects for product usage, feature adoption, and expansion signals. These objects maintain relationships to contacts and companies while storing granular behavioural data.

One critical consideration: HubSpot's architecture, designed for marketing and sales workflows, cannot replace dedicated product analytics. It complements your analytics stack by making product data actionable for marketing campaigns and sales workflows. The integration creates a unified view without forcing one platform to perform functions it was never designed for.

2. Shift from MQLs to Product-Qualified Leads (PQLs)

Traditional marketing-qualified leads (MQLs) measure top-of-funnel engagement: content downloads, email interactions, and website visits. These signals indicate interest but reveal nothing about product fit or conversion likelihood in PLG models.

Product-Qualified Leads represent a fundamental shift in how you identify sales-ready opportunities.

Four Types of PQLs in PLG:

Free Users Demonstrating High Engagement: Users on free or trial plans who exhibit usage patterns associated with paid customers. They have completed onboarding, adopted core features, and returned consistently over multiple sessions. Their behaviour indicates they perceive value even before paying.

Hand-Raisers Requesting Sales Assistance: Self-serve users who proactively request enterprise features, security documentation, or procurement support. These users have qualified themselves through product experience and now signal readiness for a sales conversation.

Users Hitting Feature or Usage Limits: Freemium users who encounter restrictions designed to encourage upgrades. They have attempted to access premium features, exceeded usage allowances, or tried to add team members beyond free tier limits. The product experience has created natural expansion moments.

Silent Converters with Expansion Potential: Users who upgraded through self-serve workflows without sales interaction but display behaviours suggesting further expansion opportunity. They might be heavy users of specific features, have organisational domains indicating company size, or show usage patterns associated with customers who typically expand.

Implementing PQL scoring requires collaboration between marketing, product, and revenue operations teams. You must define the specific behaviours and thresholds that constitute each PQL type, implement tracking for those signals, establish workflows to route different PQL types appropriately, some to automated nurture, others to sales, and continuously refine your scoring model based on conversion data.

The biggest mistake companies make is implementing PQL tracking without organisational readiness. Your sales team must understand that PQLs represent a different conversation than traditional MQLs.

These prospects have already experienced your product. They do not need education about what you do; they need help understanding how you fit their specific context, addressing procurement or security requirements, or exploring advanced use cases.

3. Implement Custom Event Tracking for End-to-End Visibility

With product data flowing into your marketing platform and PQL scoring active, the final piece creates campaigns and workflows that respond to product behaviour.

Five Critical In-Product Events for Marketing Leaders:

Sign-up and Onboarding Completion: Track not just account creation but progression through onboarding steps. Users who complete onboarding within 24 hours exhibit different engagement patterns than those who take a week. Marketing can trigger personalised sequences based on onboarding velocity and completion rates.

Core Feature Adoption: Identify the 2-3 features that, when adopted, predict long-term retention. For a project management tool, this might be creating a first project and inviting a collaborator. For an analytics platform, it could be connecting a data source and building a first dashboard. When users adopt these features, marketing can reinforce success with use case content and advanced tip sequences.

Team Invitation and Collaboration: Viral growth depends on users inviting teammates. Track invitation sends, acceptances, and collaborative activity. Marketing can encourage this behaviour through targeted messaging, recognise successful advocates, and accelerate multi-user adoption through network effect campaigns.

Conversion Events (Free-to-Paid): Capture not just that someone upgraded, but the context around the decision. Did they convert immediately after hitting a usage limit? Following a specific feature interaction? After receiving a promotional email? This context enables optimisation of both product limits and marketing conversion campaigns.

Account Inactivity and Churn Signals: Proactively identify disengagement before users churn. Track absence of key activities over defined periods, declining usage trends, or specific behaviours associated with churn risk. Marketing can launch win-back campaigns, offer additional resources, or flag accounts for customer success intervention.

With these events tracked as HubSpot custom events, you can build sophisticated automation:

• Segment users by product engagement level for differentiated messaging
• Trigger workflows based on product milestones rather than just time-based sequences
• Personalise email content dynamically based on feature adoption
• Alert sales when PQLs take specific high-intent actions
• Create lifecycle stages that reflect the product journey, not just marketing funnel position

This transforms marketing from broadcasting generic messages to orchestrating contextual experiences that respond to how users actually interact with your product.

Tools and Technology: Building Your PLG Attribution Stack

The technical architecture supporting PLG attribution combines specialised tools, each serving distinct purposes within a unified system.

The Foundation: Product Analytics + Marketing Automation

Product Analytics (Amplitude, Mixpanel, Heap): These platforms serve as your source of truth for user behaviour within the product. They track granular events, enable cohort analysis, calculate retention metrics, and identify behavioural patterns that predict outcomes.

Amplitude generally offers the most robust features for B2B SaaS companies, including strong SQL support, flexible event taxonomy, and powerful segmentation capabilities.

Marketing Automation for PLG (Customer.io): While HubSpot has dominated B2B marketing automation for 15 years, its architecture centres on email marketing and lead nurture for sales-led motions. Customer.io, conversely, was built specifically for lifecycle marketing in product-led companies.

It excels at event-triggered messaging across email, push notifications, and in-app messages. Its data model naturally accommodates product events, making it superior for PLG-specific nurture campaigns and behaviour-driven communication.

CRM for Sales Assist (HubSpot Sales & Service Hubs): Even in PLG models, some deals require sales assistance, particularly enterprise contracts, complex implementations, or high-value expansions.

HubSpot's Sales and Service Hubs provide deal management, sales enablement, and customer success workflows. When positioned as the sales-assist layer rather than the marketing automation engine, HubSpot complements Customer.io effectively.

This is not one-or-the-other. The optimal PLG stack combines Customer.io for lifecycle marketing and in-app engagement with HubSpot for sales-assist deal progression and customer success management.

The Integration Layer: Customer Data Platform

Segment or Reverse ETL (Hightouch, Census): Moving data between product analytics, marketing automation, and CRM creates integration complexity.

Customer Data Platforms (CDPs) like Segment serve as central hubs that collect events once and distribute them to multiple destinations.

This ensures consistency across tools, reduces implementation overhead, and provides flexibility to add or change downstream platforms without reconfiguring every source.

Reverse ETL tools like Hightouch achieve similar outcomes by syncing data from your data warehouse to operational tools, giving you maximum control and enabling custom business logic before data reaches destination platforms.

The Visibility Layer: Business Intelligence and Dashboards

Databox + Custom BI: With data flowing across your stack, you need unified visibility. Databox connects to multiple sources, including HubSpot, Customer.io, Amplitude, Google Analytics, and Stripe, and presents KPIs in consolidated dashboards. This allows marketing leaders to monitor top-of-funnel metrics, product engagement, and revenue outcomes in a single view.

For companies requiring deeper analysis, custom Business Intelligence layers built on data warehouses (Snowflake, BigQuery) with visualisation tools (Looker, Tableau, Mode) provide flexibility for complex queries, custom attribution models, and executive reporting that spans the entire revenue operation.

The BI layer transforms disparate data points into strategic insights:

• Which campaigns drive users with the highest lifetime value?
• What product behaviours correlate with expansion revenue?
• How does feature adoption vary by acquisition channel?

The Reality of Implementation

Building this stack is not trivial. It requires technical implementation, cross-functional alignment, and ongoing maintenance. The typical timeline spans 8-12 weeks for initial setup, followed by continuous optimisation.

Many B2B SaaS companies, particularly those scaling rapidly toward 500 employees, lack the internal expertise to design and implement this architecture efficiently. They can hire data engineers, marketing operations specialists, and revenue operations leaders... or they can adopt pre-built solutions that package the architecture, methodology, and best practices into a unified system.

From Tools to Operating System: The ARISE Revenue OS Approach

Individual tools solve specific problems. An operating system solves the systemic challenge.

Most PLG attribution "solutions" amount to recommended tool combinations: "Use Amplitude for product analytics, Customer.io for messaging, HubSpot for sales, and Databox for reporting." This approach provides components but leaves companies to:

• Design the data model connecting these systems
• Build integration logic and maintain sync rules
• Create attribution frameworks from scratch
• Establish workflows and automations
• Train teams on new processes
• Optimise continuously based on learnings

Each company reinvents the wheel, makes predictable mistakes, and spends 6-12 months reaching basic competency.

ARISE Revenue OS takes a different approach: it is a packaged operating system with pre-built data models, automations, and GTM playbooks that make standard tools behave as one integrated product rather than a loose collection.

The Opinionated Data Model: Rather than building custom objects and properties from scratch, Revenue OS installs a standardised revenue data model inside HubSpot. This model has been battle-tested across dozens of B2B SaaS companies operating PLG and sales-assist hybrid models.

It includes custom objects for product usage, lifecycle scoring logic, product event mapping, expansion signal tracking, and ARR calculation. From day one, product usage, trials, contracts, and revenue speak the same schema across your organisation.

Pre-Engineered Automations and Workflows: Instead of creating workflows one by one through trial and error, Revenue OS ships with ready-made lifecycle automations.

PQL scoring rules automatically route opportunities to appropriate channels. Expansion trigger alerts create tasks for customer success managers or trigger nurture sequences in Customer.io.

Churn risk workflows activate playbooks combining automated outreach and human intervention.

These are not generic templates—they embody the ARISE methodology's Assess → Research → Ideate → Strategise → Execute framework, ensuring marketing, sales, and customer success see the same journey and operate from shared definitions.

Unified Attribution and Reporting Out-of-the-Box: Because the data model and automations are standardised, Revenue OS delivers end-to-end dashboards immediately. Track users from first touch through activation, free-to-paid conversion, expansion, and net revenue retention without weeks of configuration.

The Business Intelligence layer comes pre-mapped, providing PLG attribution and revenue insights the moment data starts flowing rather than after months of custom development.

Methodology Baked into the System: Revenue OS is not just a technical stack recommendation. It digitises the proven ARISE GTM methodology, which outlines:

• How to run experiments,
• Test PLG levers,
• Tie feature adoption to revenue,
• Implement reverse trial structures,
• Create in-app upgrade loops,
• and apply MEDDPICC to sales-assist motions.

The system encodes these playbooks so teams can execute without reinventing processes or making costly mistakes.

Think of it as the difference between buying lumber, nails, and tools versus buying a prefabricated house. The materials might be familiar, but one comes already designed, integrated, and proven to work as a complete system.

Real-World Impact: Attribution Clarity Drives Strategic Shift

A B2B SaaS company we worked with faced a familiar scenario: marketing reported strong top-of-funnel performance, product showed healthy activation rates, yet leadership could not confidently connect marketing spend to revenue outcomes.

The attribution audit revealed $500,000 in misattributed revenue. Their first-touch model credited digital advertising with driving their largest enterprise deals. Detailed analysis showed these high-ACV customers actually entered through in-person events: conferences where they experienced product demos, met the team, and built relationships before signing up.

Digital ads played a role in the journey, typically as later-stage touchpoints when customers researched specific features. But the advertising campaigns received credit while event marketing received minimal investment.

This insight transformed budget allocation. The company expanded its event presence, enhanced booth experiences with hands-on product access, and implemented systematic post-event follow-up workflows.

They maintained digital advertising at reduced levels with revised goals: maintaining top-of-mind awareness and catching in-market searchers rather than primary acquisition.

The CMO's "aha moment" was not just correcting misattribution. It was gaining a holistic view of how the entire organisation contributes to revenue. She could now show the C-suite how marketing channels, product features, sales motions, and customer success activities interconnect to drive customer acquisition, expansion, and retention.

Attribution became a strategic asset rather than a reporting headache.  Cash flow projections improved because the team understood which channels generated quick-payback customers versus those requiring longer nurture.

Retention modelling became more accurate because they could correlate acquisition source with product engagement patterns. Expansion forecasting incorporated signals from both product usage and the sales pipeline.

Attribution clarity did not just prove marketing's value; it enabled smarter investment across the entire go-to-market organisation.

Stuck in an attribution rut? Struggling to figure out who creates what and why? Book a time with our team and learn more about our business intelligence reviews by filling out the form in the footer of our website.

Frequently Asked Questions

What is the biggest challenge in measuring marketing impact for PLG SaaS companies?

The core challenge is that traditional attribution models track only pre-product interactions (ads, emails, website visits), while conversions in PLG happen inside the product based on self-serve experiences that marketing platforms cannot see.

Without integrating product analytics with marketing automation, CMOs remain blind to which campaigns drive users who actually activate, engage deeply, and convert, versus those who sign up and immediately churn.

How do Product-Qualified Leads (PQLs) differ from Marketing-Qualified Leads (MQLs)?

MQLs measure top-of-funnel engagement signals like content downloads and email clicks that indicate interest but not product fit.

PQLs represent users who have demonstrated value discovery through actual product usage, completing onboarding, adopting core features, inviting teammates, or hitting usage limits.

PQLs are significantly stronger conversion predictors because they reflect hands-on product experience rather than just marketing engagement.

Can HubSpot alone handle attribution tracking for product-led growth?

No. HubSpot's architecture centres on marketing funnels and sales processes, lacking the granular event tracking required for PLG attribution. It cannot track in-app behaviours, feature adoption, or usage patterns that drive self-serve conversions.

However, HubSpot becomes powerful when paired with product analytics (Amplitude, Mixpanel) and lifecycle marketing tools (Customer.io). The optimal approach uses HubSpot for sales-assist deal management while Customer.io handles product-triggered lifecycle campaigns.

What are the essential product events marketing teams should track?

Five critical event categories provide attribution visibility:

(1) sign-up and onboarding completion, revealing early engagement;

(2) core feature adoption, indicating value discovery;

(3) team invitation and collaboration, showing viral growth potential;

(4) conversion events from free to paid, capturing upgrade context;

(5) inactivity patterns, identifying churn risk.

Tracking these events in your marketing platform enables segmentation, targeted campaigns, and lifecycle workflows that respond to actual product behaviour rather than time-based assumptions.

Why do first-touch and last-touch attribution models fail in PLG?

These single-touch models attribute conversion credit to one isolated interaction while ignoring the self-serve product journey where actual conversion decisions occur.

First-touch might credit a blog post read months ago, while last-touch might credit a direct website visit on upgrade day, both missing that the user converted immediately after inviting their third teammate or hitting a feature limit. PLG requires multi-touch attribution that incorporates in-product events as primary conversion drivers.

How long does it take to implement proper PLG attribution?

Building the technical stack, integrating product analytics, marketing automation, CRM, and BI tools, typically requires 8-12 weeks for initial setup.

Organisations then spend 3-6 months optimising workflows, refining PQL definitions, and calibrating attribution models based on conversion data.

Companies adopting pre-built solutions like ARISE Revenue OS significantly compress this timeline, achieving operational attribution in 4-6 weeks, as the data models, automations, and playbooks are pre-engineered.

What is the difference between marketing attribution and product attribution in PLG?

Marketing attribution tracks how external touchpoints (ads, content, campaigns) influence customer acquisition. Product attribution tracks how in-app behaviours (feature adoption, usage patterns, engagement milestones) drive conversion and expansion.

PLG companies need unified attribution that connects both: which marketing channels bring users who exhibit valuable product behaviours, and which product experiences convert users who entered through specific marketing campaigns. Neither perspective alone provides complete insight.

How does poor attribution hurt alignment between product and marketing teams?

Without shared visibility, product teams optimise for activation and engagement without understanding which marketing sources deliver quality users, while marketing optimises for signup volume without knowing which campaigns drive users who actually convert.

This creates dysfunctional cycles: marketing celebrates campaigns that bring users who immediately churn, product builds features marketing never promotes, and both teams blame each other for underperformance. Unified attribution creates shared metrics and mutual accountability.

Which attribution model works best for PLG: multi-touch, data-driven, or custom?

Multi-touch attribution that incorporates both marketing touchpoints and product events provides the most actionable insights for PLG companies.

Data-driven models that use machine learning to assign credit can work well once you have sufficient conversion volume (typically 1,000+ monthly conversions).

Most B2B SaaS companies scaling to 500 employees benefit from starting with time-decay multi-touch attribution weighted toward product events, then evolving to data-driven models as they accumulate more conversion data.

How can I prove marketing ROI when most conversions happen through self-serve?

Connect your product analytics platform to your marketing automation and CRM systems to track users from first marketing touch through activation, conversion, and expansion. Implement PQL scoring based on product behaviours.

Create custom objects in your CRM that store product usage data alongside marketing interaction history. Build unified dashboards showing how marketing channels influence user quality metrics: activation rates, time-to-value, conversion rates, and customer lifetime value—rather than just signup volume.