Digital analytics is now more crucial than before, but many still struggle to grasp its true significance.
We live in an age where nearly every single interaction with digital product is tracked, provided with necessary consent. However businesses find it difficult to derive insights from this data and even harder to put it into action. Despite the advancements in tools such as Google Analytics, Adobe Analytics, Mixpanel and Amplitude over the years and the shift from reporting to data activation, the core obstacles remain unchanged.
Let’s explore the challenges that digital product analytics teams face in real world scenarios regardless of the sophistication of their tools.
We have a large amount of data, but we don’t know what to do with it.
This is the most common complaint from product managers and stakeholders. Digital Analytics Implementation team captures various events on web and mobile app, but there’s no clear line between all that noise and a strategic business decision.
❝The FinTech company possessed a large sets of digital behavioural data, but they find it challenging to answer a simple question as “Do push notifications influence users to use the budgeting tool repeatedly?”❞
We find it hard to determine what digital product success look like.
While business OKRs often sound clear on paper—grow revenue, improve engagement, reduce churn—it’s harder to reflect that clarity in digital product metrics. In reality, the numbers fluctuate constantly due to external factors (seasonality, marketing pushes) and internal changes (feature releases, bug fixes). Teams struggle to align on a single, reliable “north star” metric. Instead, they track multiple signals, each telling a different story. Without a baseline or forward-looking projection, teams get pulled in conflicting directions and lose confidence in what success really looks like.
❝A product team celebrated a 20% increase in onboarding completion—only to find retention dropped. They had optimized the wrong behavior.❞
We have messy data and low trust
Digital product data is inherently fragile. Unlike transactional or CRM databases with rigid schemas and tight access control, behavioral data collection relies on tags, SDKs, and instrumentation pipelines—often managed across multiple teams. Every code release, A/B test, or tag manager update is a potential point of failure. The result? Broken events, missing properties, inconsistent naming, and silent data drift. This erodes trust: stakeholders grow skeptical of dashboards, analysts waste time validating queries, and critical decisions stall due to data uncertainty.
❝An executive flagged a major drop in signups—only to learn the event name changed during a hotfix, breaking the dashboard logic.❞
Wait a second—your numbers don’t match!
A common source of frustration: digital analytics numbers rarely align. GA4 doesn’t match Adobe. Neither matches internal dashboards or CRM data. Add Google Ads and Facebook Ads into the mix, and the inconsistencies multiply. Attribution logic varies, event definitions differ, opt-in tracking and sampling further complicates the picture. The result? Stakeholders lose confidence, question every report, and deprioritize digital behavior data, in favor of “more reliable” systems of record. Analysts are left chasing discrepancies instead of driving insight—explaining again and again why numbers don’t align.
❝A product team was asked why GA4 showed 30% fewer signups than Salesforce. They spent a week reconciling differences—again.❞
Digital behavior data is hard to work with
Digital product data isn’t just big—it’s messy, complex, and structured for machines, not people. Every user interaction generates event-level data with hundreds of attributes, often nested or stored in arrays. Sequencing matters. Meaningful insights require joining sessions, flattening payloads, and transforming raw event stream data into usable formats. Analysts need advanced SQL or Python skills just to get a clean view. The result? Long lead times, analysis bottlenecks, and valuable questions left unanswered.
❝An analyst spent two weeks answering a simple feature usage question—just to untangle GA4’s nested event parameters and map them to real-world logic.❞
Hard to prove impact—and easy to overlook what matters
Demonstrating the value of a feature or journey improvement is difficult without controlled experiments or clear causal frameworks. But in practice, not every release gets A/B tested—especially when resources are tight. Even when engagement or click-through rates improve, it’s hard to quantify downstream business impact, particularly in traditional industries where digital is a supporting channel, rather than primary. As a result, mid-funnel behaviors like onboarding or education often get ignored in favor of top-line metrics (impressions, reach) or bottom-line financials. Add to that the complexity of nonlinear user journeys, and funnel insights are too often deprioritized or dismissed.
❝A mobile team launched dark mode. Usage spiked, but there was no experiment or control group—so leadership was unconvinced of the feature’s value. The analysis got deprioritized because the feature didn’t map clearly to quarterly revenue goals.❞
Insights don’t drive action
Even when analysis is solid, insights often get buried in decks, Confluence pages, or Jira tickets—never reaching decision-makers at the right time. In many teams, roadmaps are locked months in advance, leaving little space to pivot based on what the data reveals. Everyone is laser-focused on delivery. As a result, digital behavior insights are treated as a retroactive check, not a proactive input. It’s hard to integrate analytics into early design stages, and without clear dollar impact or urgency, even critical insights go unprioritized.
❝An analyst identified a key drop-off point in an application funnel, but it wasn’t prioritized because the insight lacked clear dollar impact and the delivery team already committed to another feature for the next 6 months.❞
Why this blog?
This blog is built around questions and challenges.
Each blog mirrors the kinds of questions product analysts are asked every day. You’ll learn how to answer them with hands-on code — but more importantly, how to think critically, communicate clearly, and link data to decisions.
Digital analytics is not a report. It’s a practice.
This blog aims to give you not just SQL or dashboards, but frameworks for defining success, telling stories with data, and building trust across product, marketing, and leadership.
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