Driving SaaS Growth Through Product Usage Signals

Discussion with Khurram Chaudhry

Khurram Chaudhry brings more than 20 years of marketing experience, spanning brand management at Chevron, SaaS-focused roles at CureMD and ServicePath, and leadership in cybersecurity and AI consulting at Rogue Logics. His career reflects the shift from traditional advertising and field surveys to today’s data-driven, automation-led strategies. This perspective allows him to connect foundational marketing principles with the emerging practices that define modern RevOps. In this article, Chaudhry shares how SaaS leaders can unlock growth by weaving campaign orchestration into product usage data.

Turning Product Signals Into Seamless Campaigns

Winning a new signup is only the beginning. For SaaS providers, the real battle lies in how customers interact with the product after that first click. Every choice, whether activating a feature, stalling during setup, or ignoring a core capability, provides insight into customer intent. Acting on these signals early can mean the difference between retention and churn. Chaudhry stresses: “A lot of users quit halfway through onboarding. If you can tag what they are doing and what they are not doing, you can send them the right message at the right time. It’s about coaching them through, not letting them drop off.”

He adds that tracking non-usage is just as critical: “It’s not only about what they are doing, but also what they aren’t using in the product. That can tell you exactly where they might need help.” This approach transforms onboarding into a living process. By linking product usage to CRM data, companies can design responsive campaigns. For example, if a user fails to explore a key feature within three days, the system can automatically deliver a short guide or case study to encourage adoption. These nudges not only reduce drop-offs, but also reinforce the product’s value early in the journey.

Just as important is paying attention to what users avoid. A customer who browses dashboards without generating a report is silently asking for help. Instead of interpreting this as inactivity, campaigns can be tailored to showcase real-world use cases, peer success stories, or quick-start tips. This flips non-usage into a powerful indicator of where support is most needed.

Redefining Go-to-Market Strategies With Behavior-Based Insights

Traditional GTM strategies often prioritized broad targeting and lead scores. Chaudhry argues that this approach falls short in a product-led world. Today, success depends on observing how users behave inside the platform and adjusting strategies accordingly. “Instead of guessing who our best leads are, we can watch them in real time. A team that creates an account or invites members is sending a buying signal. That’s when you trigger a sales alert or offer a demo.”

The shift from marketing-qualified leads (MQLs) to product-qualified leads (PQLs) represents this change. An MQL may open an email or attend a webinar, but a PQL has demonstrated commitment through real product interaction. This distinction allows sales teams to focus on “hot” leads showing tangible intent, while marketing nurtures less active users with educational content.

By mapping usage signals against timelines, companies can deliver personalized prompts, whether a how-to guide, a customer story, or an invitation to a demo, that accelerate adoption without overwhelming users. The result is a GTM strategy that feels less like a funnel and more like a guided partnership.

Translating Usage Data Into Measurable Revenue

Vanity metrics like clicks and impressions may inflate dashboards, but they rarely predict revenue. Chaudhry underscores the importance of identifying product behaviors that directly correlate with upgrades and retention. “We found that users who created a report in the first week were three times more likely to upgrade. That’s a high-impact signal worth focusing on, compared to noise like simple logins or clicks,” he explains.

He points to AI as a game-changer here: “We used AI to find patterns, like which early behaviors led to upgrades and which led to churn. That allowed us to focus on the signals that mattered most and ignore the noise.”

By mapping usage events to financial outcomes, companies can pinpoint the high-value signals that matter. Activating an integration or inviting teammates reflects deeper commitment than simply logging in. Conversely, repeated logins without progress often predict churn. This clarity allows RevOps teams to double down on behaviors that create revenue while intervening when warning signs appear.

Equally important is shifting the tone of outreach. When customers stall, the solution is not aggressive sales follow-ups but supportive coaching. Offering practical help, whether through tutorials, contextual tips, or live guidance, turns potential churn moments into opportunities to build trust. 

Breaking Down Silos Between Marketing and Product Teams

Customers don’t see organizational charts, they see one company. Yet too often, marketing and product teams act independently, creating fragmented communication. Chaudhry has seen how this disconnect frustrates users and undermines adoption. He explains: “Customers don’t see marketing and product as separate. For them, onboarding is one process. If marketing sends an email at the wrong time, it can derail the instructions they’re waiting for from product.”

He also highlights coordination as critical: “I’ve been told by product managers not to send marketing emails during onboarding because it confuses users. They’re expecting instructions from us, not promotions.”

To fix this, companies need unified playbooks. Campaigns should complement the onboarding process, not compete with it. For instance, sending advanced feature guides before users finish basic setup can overwhelm them. Instead, event-based triggers aligned with product milestones ensure relevance and clarity.

Metrics must evolve as well. While return on ad spend (ROAS) offers a surface view, deeper metrics like hook rates provide insight into real engagement. If users watch only a fraction of a tutorial video, the content may need reworking. 

Scaling With Automation and AI-Driven RevOps

Scaling personalization has long been a challenge in SaaS. Chaudhry sees automation and AI as the enablers that make it possible without sacrificing quality. He describes AI as a tireless partner: “Think of it like a RevOps assistant that never sleeps. It watches what users do, predicts churn, and ensures we reach them with the right message at the right time.”

Automation handles the mechanics, ensuring consistent execution of workflows, such as triggering demo requests when teams are created. AI adds predictive intelligence, identifying patterns that signal churn or highlight likely upgrades. Together, they shift RevOps from reactive to proactive, allowing companies to anticipate customer needs.

AI also enhances acquisition strategies through lookalike models. By analyzing high-performing customers, tools like HubSpot and LinkedIn can replicate outreach to similar prospects, expanding reach while maintaining precision. 

Key Takeaways for SaaS Leaders

To close, Chaudhry’s insights remind SaaS leaders that growth comes from aligning technology, teams, and customer success around the right signals. These are not abstract ideas, but practical steps any company can begin implementing today:

• Track what users do, not just what they see. Feature activations and team invitations reveal intent more clearly than clicks or opens.
• Shift from MQLs to PQLs. Product-qualified leads create alignment between sales and marketing around real adoption signals.
• Map usage to revenue. Identify and prioritize the behaviors that predict upgrades, while coaching stalled users back on track.
• Unify messaging. Break down silos so marketing and product teams speak with one voice, guided by event-based triggers.
• Leverage AI for scale. Combine automation and predictive analytics to deliver personalized engagement at scale, while reducing churn and boosting acquisition.

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