Data Rich, Insight Poor: The Ecommerce Analytics Problem

The blind spot isn't missing data. It's too much of it.

Digital experience analytics is the practice of capturing and interpreting how real users experience a website — their behavior, the friction they hit, and the technical issues that shape the journey — so teams can connect what happens on-site to conversion and revenue. The problem for most ecommerce teams isn't a lack of this data. It's the opposite: too many tools capturing too much of it, with no single view that turns it into a decision. The result is a state Noibu co-founder and president Kailin Noivo calls “data rich, insight poor.”

That phrase surfaced in a recent conversation between Kailin and Rohit Nathany, who leads marketing, product, and engineering at the global DTC jewelry brand Mejuri. Their discussion mapped a problem almost every mid-market and enterprise retailer will recognize — and a way out of it.

For years, the assumption in ecommerce has been that better visibility means more instrumentation. Add an APM tool for performance. Add a heat-mapping tool for UX. Add an analytics platform to sit in the middle. Each team gets the tool it trusts. The outcome is rarely more clarity. It's more overlap.

As Kailin put it, you don't want dozens of different things pulling data from your site. Each one adds weight to page load, introduces conflict between third-party scripts, and — the part teams often miss — costs money to store, because storage volume is exactly what many providers price against. You end up paying to capture the same data multiple times, and paying again to keep it.

This is the coverage trap. More tools feel like more visibility. In practice, they fragment it. You can read more on that in the ecommerce visibility gap.

The real cost is speed of decision, not access to data

Here's the part that's easy to underestimate. When teams run parallel tools that each report slightly different numbers, the damage isn't just redundancy — it's paralysis.

Kailin's analogy: it's like leaving Miami one degree off course. Not a big deal at the start. But that one degree is the difference between arriving in Panama and arriving in Cuba.

When engineering spends most of its time questioning the validity of marketing's data, the effort goes into proving the data instead of solving the problem. As Kailin describes it, teams get dragged down because they spend weeks litigating the source of truth — and by the time they finish, they've forgotten why they started. The tools aren't the bottleneck. The lack of a shared source of truth is.

Silent failures: the revenue leakage no one reports

The most expensive issues aren't the ones that light up a dashboard. They're the ones that don't.

Rohit calls them silent failures — the slow revenue leakage that adds up over time. No customer files a ticket. No alert fires, because the issue lives in the gap between the tools you already own: it's not a hard error your APM catches, and it's not a behavior pattern your heat-mapping tool flags. It's both, and neither tool sees the whole thing.

Mejuri hit exactly this after replatforming to Shopify — a process Rohit compared to rebuilding a plane while flying it. The team expected the new stack to clear their monitoring problems. Instead, they inherited a familiar one: a firehose of alerts with no customer context, no commercial impact, and no way to tell which of thousands of issues actually mattered.

A spinning cart doesn't announce itself. Fewer than 1% of customers report anything when a site breaks — they just leave. The issue had been leaking revenue since migration, and it took cutting through the noise to a single prioritized signal to surface it.

In commerce, as Kailin notes, you don't care about 10,000 bugs. You care about your top eight. Getting from 10,000 to eight is the hard part — and it's why prioritization by revenue impact matters more than raw detection. See how Issues & Alerts ranks issues by revenue impact.

What good consolidation actually looks like

Consolidation is the obvious fix. But “fewer tools” isn't the goal — the goal is fewer tools organized around the right thing.

Rohit's framing is the clearest test: consolidate around an outcome, not around data types or org charts. At Mejuri, that outcome is sales per visit. Under that single metric, error monitoring stops being an engineering-only concern and becomes one input among several — alongside funnel analytics and session replay used by product designers. All of it ladders up to one number the whole team shares.

The alternative — tools weaponized across siloed teams, access withheld, numbers disputed — is where Kailin sees the most cultural drag. His recommendation: unify the teams that own the customer journey under one leader (the CTPO model), and give every team access to the full platform.

From investigation to action: Noibu's AI plugin

Consolidating the data is step one. The bigger shift in 2026 is what happens after the data is unified: turning “what's wrong” into “it's fixed” without a two-week detour through five dashboards.

That's the role of the Noibu plugin — Noibu's ecommerce data, connected directly into the AI assistant your team already uses. Instead of clicking through the console, a team member asks a plain-English question and gets an answer grounded in real shopper behavior. Three capabilities matter here:

The plugin is read-only across your Noibu data by default — it surfaces, analyzes, and prioritizes, and makes changes (like updating an issue's state, or shipping a fix through a connected tool) only when you explicitly approve them. Nothing acts on your store without a human saying yes. This is the “you approve, they ship” model: the AI does the investigation and drafts the work; the operator stays in control.

Four prebuilt agents

On top of on-demand questions, the plugin ships with prebuilt agents that run recurring ecommerce workflows and bring back drafted actions for approval:

The result is the shift Rohit and Kailin both point to: from reactive firefighting across disconnected tools to a proactive loop where the highest-impact work is surfaced, drafted, and shipped with a human in the loop. See how the Noibu plugin works.

Why this problem is hard to solve horizontally

There's a structural reason general-purpose tools struggle here. A horizontal platform that serves retailers, banks, and SaaS companies is optimizing for portability — a build-your-own-adventure toolkit flexible enough for everyone. That flexibility comes at the cost of an opinionated, ready-to-act product for any one vertical.

Ecommerce doesn't need a neutral toolkit. It needs a platform that already understands checkout, PDPs, PLPs, cart abandonment, and Core Web Vitals — and can connect a technical root cause directly to its conversion impact without a data team stitching it together.

This is the case for a purpose-built ecommerce analytics and monitoring platform. Noibu unifies site monitoring, experience analytics, and conversion growth opportunities in one view — issues prioritized by revenue impact, 100% session capture with ecommerce-specific signals, performance benchmarked against best-in-class retail, and release monitoring that connects every deployment to changes in stability, speed, and conversion. Not a toolkit to assemble. An opinionated platform teams work out of together — now with an AI plugin that turns that data into action. See why teams choose Noibu.

Frequently asked questions

Related topics

How much revenue is your ecommerce site silently losing to unreported errors?
What should ecommerce teams consolidate their monitoring stack around?
How does session replay with 100% capture differ from sampled session recording?

Stop drowning in data. Start acting on it.

Most ecommerce teams already have more data than they can act on. The advantage in 2026 won't come from capturing more of it — it'll come from finally connecting what's happening on your site to what it's costing you, in one place your whole team can trust, with AI that turns those findings into action.