The Engagement Engine That Can
Somehow, every product org we interact with today is both drowning in data and starving for insights. Even in the best cases, where every department has access to customer touchpoints and behavioral info, these companies still struggle to have a unified view of the customer journey.
The result? Fragmented experiences, duplicated efforts, and missed opportunities to drive engagement. The solution isn't just better tools; it's rethinking how we orchestrate customer data across the entire organization.
Welcome to Beyond The Click by Balboa Solutions. In today's issue we're looking at the enterprise engagement engine for Product Ops, including:
What it is
How it works
How to get one started
Let's dive in.
Fueling The Engine 🚂
Product leaders need a fundamental shift in how they think about customer touchpoints. We call this the "engagement engine." Rather than treating in-app communications, sales outreach, customer success interventions, and support interactions as separate activities, companies with engagement engines orchestrate all customer experiences as a unified system.
But doing this take the right tools...and more tools (often) means more fragmentation. A typical enterprise today has:
Marketing clouds connected to CRMs
Product analytics platforms (e.g. Pendo) powering in-app messaging
Customer success platforms handling proactive communications
...all of which are collecting customer data and triggering outreach independently.
The challenge becomes...who owns this orchestration? 🎻
There's no single answer to this question. We've seen three different models work in different places:
The Cross-Functional Committee 👥
Pros: Leverages diverse perspectives; maintains departmental autonomy while creating alignment
Cons: Slower decision-making; potential for conflicting priorities when consensus breaks down
The Enterprise COE (Center of Excellence) 🏢
Pros: Creates consistent customer experiences; prioritizes initiatives based on overall business impact rather than departmental KPIs
Cons: Requires significant organizational buy-in; can face resistance from teams protective of their data and processes
The Executive Champion 🏅
Pros: Provides clear authority and rapid decision-making
Cons: Success depends mainly on the chosen exec's cross-functional influence and understanding of the entire customer lifecycle
The choice between these models should align with your organization's culture and existing power structures. Companies with strong collaborative cultures might succeed with cross-functional committees, while those needing rapid transformation may benefit from strong executive champions. The COE is sort of a middle ground between the two.
But org structure is just one part of the equation. You also have to align the way teams think about the data itself. 👇
Same Data, Different Questions 🔍
The great irony of modern product orgs is that customer data exists everywhere, but teams constantly struggle to get the insights they need.
One root of this issue is access. Product teams typically own user analytics, including data on feature adoption and engagement patterns. Meanwhile, customer success teams need this same data to build health scores and identify expansion opportunities, but they often find themselves having to make requests and wait for reports from Product.
But the deeper problem is not the data, but what we do with it. Different departments approach the same information with differing objectives:
Product: "Are users engaging with our new feature?"
Customer success: "How likely are they to renew?"
Sales: "Which behaviors show expansion readiness?"
Etc.
The data powering all these questions is identical (user events, session duration, feature interactions, etc.), but the analysis frameworks differ dramatically.
That's why strategic product leaders need to build their teams and culture around "catalyst questions" - data inquiries designed to drive specific, actionable outcomes, rather than just generating reports.
For example, instead of asking... 👎
"What's our NPS score?" (limited actionability)
...better questions include: 👍
"How does NPS vary by user role?
"What usage patterns do our promoters have in common?"
"What behaviors differentiate users who upgrade within 24 hours versus those who convert later (or not at all)?"
The key insight is moving from locally optimized questions (what helps my team hit our numbers) to globally optimized questions (what drives success for the customer and the business). This shift requires not just better question frameworks, but a shared definition of success across your organization.
So how do you shift this mindset in practice? You don’t need an overhaul. 👇
Getting started is easier than it sounds ▶️
Platforms like Pendo come with extensive button-clicking options that can paralyze teams. But despite the complexity of enterprise data orchestration, simply getting started with product analytics doesn't require massive transformation all at once.
The key is to build momentum through focused, high-impact initiatives that demonstrate value and create appetite for broader change. Waiting for perfect organizational alignment may mean missing the competitive advantage entirely. Successful implementations start small and then expand.
The most effective entry point?
A deep dive into adoption and retention for your most recently released feature. This approach works because:
New features generate organizational energy and cross-departmental interest
Stakeholders are more receptive to insights about recent launches
You see early-stage data in real-time (analyze who adopts the feature, how quickly they engage, where they drop off, etc.)
This initial analysis serves multiple purposes:
Closes the loop on your go-to-market flow
Reveals gaps in internal processes
Demonstrates the value of behavioral analytics to skeptical stakeholders
Often, teams discover their feature launch processes lack sufficient rigor or tracking, creating natural demand for more sophisticated analytics. This "trial run" provides a concrete entry point that builds both skills and organizational confidence. Once you've established value here, the method can be applied to existing features and broader user journeys.
Momentum > perfection 🎯
The path forward isn't about implementing the perfect engagement engine from day one. It's about creating small wins that demonstrate the power of unified customer data, building cross-departmental relationships around shared insights, and gradually expanding the scope of your analytics orchestration.
In an era where customer expectations continue rising and competitive differentiation increasingly depends on experience quality, the product orgs that crack this orchestration challenge will have a significant advantage in driving both customer success and business growth.
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