Uncovering Value Gaps in Product Analytics

We have come to a sobering realization, and we are calling it: the product analytics ecosystem has failed to deliver on its lofty promises. While we've made strides, the gap between our tools capabilities and the real needs of businesses remains significant.

‍

Remember the rallying cry? "Be more data-driven!" They said. "Your data is full of insights; you just need to dig!" They promised. Well, we've been digging for years, and what have we unearthed? A mountain of reports, funnel charts, and vanity metrics that scratch the surface of true business impact.

‍

The harsh truth is that our product analytics tools have become just another data silo. We've mastered the art of tracking website traffic, conversion rates, and even user retention. Still, when it comes to answering the questions that can drive actions or help us discover the root cause – the ones that drive growth and profitability – we're left high and dry.

‍

Sure, we can tell you exactly where users drop off in a funnel or which cohort has the best Week 4 retention. Those are important pieces of information, undoubtedly. But ask us to connect those metrics to actual business outcomes or customer lifetime value, and suddenly, we're scrambling to piece together data from multiple sources. I know about metric loops, metric trees, metric maps, etc., but aren’t they just semantic relationships defined on a piece of paper?!

‍

In reality, the implementation in production is like trying to solve a jigsaw puzzle where half the pieces are missing, and the other half are from completely different sets. And we are not trying to solve this for one glorified, advertised use case; we want a sustainable solution.

‍

Let's look at some simple questions that still stump us despite our arsenal of "advanced analytics" tools:

‍

1. Which product features are driving our most valuable customers to upgrade their subscriptions?
2. How does our onboarding process impact long-term user engagement and revenue?
3. What's the true ROI of our latest product feature, considering user acquisition, marketing campaigns, conversion, and retention?

‍

Be honest, are we asking for a lot here? The questions are less modern than your so-called- modern data stack! These questions are now old. They're the bread and butter of product strategy, yet our vaunted analytics tools leave us fumbling in the dark.

‍

The irony is palpable. We've built an entire ecosystem of tools promising to help us make data-driven decisions, yet we're still stuck in first gear, endlessly churning out reports that barely influence decision-making. We've become so desperate for "insights" that we'll slap that label on any marginally interesting data point, making it a beauty lies in the eye of the beholder phenomenon, hoping it'll justify our bloated analytics budgets.

‍

Let's face it: most of us use these fancy tools as a shield, hiding behind charts and graphs to justify our decisions and organizational roles. We've accepted our fate as glorified bean counters, too afraid to admit that our "insights" aren't nearly as business-critical as we've been claiming.

‍

The product analytics and marketing analytics industry has quadrupled in size over the last decade, with thousands of tools promising to solve the same problems. Yet here we are, still unable to help a non-technical business user join event data with customer information to answer basic questions about user behavior and business impact. 

‍

My eyes hurt looking at that 6x6 diagram of tiny product icons barely visible. There were 9932 marketing technologies out there in 2022 as per chiefmartec.  I am not advocating for consolidation here, but despite so many tools, why are folks still matching data values from one platform to another and losing trust?

‍

The scope of product analytics has expanded. We're no longer just monitoring and extracting data; we're expected to drive user activation and business growth. But our tools haven't fully adapted to these evolving needs.

‍

Three key challenges persist:

‍

1. ‍Data Silos: Our product analytics tools often operate in isolation from other crucial data sources like CRM systems and financial databases. This fragmentation hinders our ability to see the full customer journey.

2. Limited scope: While our tools excel at showing "what" happened, they struggle with the "why" and "what next." We need more than monitoring; we need actionable insights or automated actions.
3. Difficult ROI Measurement: Quantifying the value of a dashboard or funnel report remains challenging, especially when insights don't directly translate to business actions

‍

The industry is aware of these shortcomings. We've seen attempts at solutions - composable CDPs, metric standardization, semantic layer, being data warehouse native, etc. Even the big players are pivoting. The darlings of product analytics are now rebranding to "digital analytics experience” or "analytics for everyone." It's like watching a game of musical chairs but with buzzwords.

‍

We need a fundamental shift in how we approach product analytics. With our success with LLMs, we have an opportunity to completely reimagine the solution. Currently, we're left with a nagging feeling that we're not asking the right questions, not because we lack curiosity but because our tools aren't flexible enough to support our inquiries. Rather than expanding our horizons, they have inadvertently limited our creativity and willingness to test our hypothesis end-to-end.

‍

Business teams still find themselves tethered to engineering for deeper insights or basic tasks like setting up A/B tests or creating nuanced marketing segments based on user behavior. Nobody wants to spend more time convincing the engineering team to help them implement a pipeline that joins data from two different sources when they have already spent time configuring a bulky enterprise tool for the team that was supposed to set them free of ad-hoc requests. 

‍

We need tools that break down data silos, not create new ones. We need analytics that connects product usage directly to business outcomes. We need insights that drive automated actions, not just pretty visualizations that gather dust in forgotten slide decks, Slack channels, or Emails as alerts.

‍

So, where do we go from here? The future of product analytics is uncertain, but one thing is clear: we can't keep doing what we've always done and expect different results.

‍

This isn't about abandoning product analytics. It's about evolving it to meet the real needs of businesses today. Whatever the answer, We've come a long way, but we've got an even longer way to go. And who knows? Maybe in another year, we will be writing a very different article, maybe mocking this very piece.

‍

Until then, we're just another group of people with opinions – expensive, data-laden opinions, but opinions nonetheless.