Working with Noibu and AI

➡ Investigating an error and confirming the fix landed.

An error is affecting shoppers and you need the cause, not just the alert. The investigation typically moves from the error detail and Noibu's AI diagnosis of the root cause, to staging a fix for review and approval. Session replay in Noibu provides contextual evidence of the exact failure, and a post-release query can be used to check error trends after the fix ships to confirm it worked. Learn how it works.

➡ Finding where the checkout funnel leaks.

Conversion has moved and you need to know which checkout step is responsible. The analysis opens with the full funnel by depth alongside your cart value baseline and payment and delivery method mix, then compares each step against the equivalent prior period to separate a real regression from normal variation. Learn how it works.

➡ Standing up a live dashboard the whole team can open.

Your team needs current store numbers without going through you to get them. A saved dashboard re-runs its queries each time it is opened, so it reflects the store now rather than the state it was in when it was built. Learn how it works.

➡ Getting a daily brief pushed to you.

You want to be told when something needs attention, instead of checking a dashboard. Store Pulse can be scheduled as a recurring brief delivered to Slack or email, so the read on store health arrives before you go looking for it. Where you want more than the standard blocks — new priority errors in the last 24 hours, or a Core Web Vitals check against your baseline. Learn how it works.

➡ Deciding which products deserve more attention.

Some product pages get traffic without moving shoppers, and you need to know which ones and why. The analysis runs across products, collections, and product types — views, add-to-carts, purchases, and conversion rate — then drills into exit rate, scroll depth, and what non-converters click instead on the pages worth investigating. Learn how it works.

➡ Understanding which traffic actually converts.

Traffic volume tells you nothing about traffic quality, and channel-level reporting may hide where the gap actually is. Breaking traffic down by source and medium separates a shift in traffic mix from a real decline within a channel, and shows whether paid traffic bounces on the same landing pages organic traffic converts on. Learn how it works.

➡ Auditing page speed and verifying a performance fix.

A page is slow, or a layout-shift problem appeared after an app or theme change. Core Web Vitals are audited sitewide, broken down per URL and by device, and scored to surface the pages where traffic volume and severity overlap. Running the same check before and after a deployment date confirms whether the fix landed in the data rather than only in staging. Learn how it works.

➡ Watching a session replay to confirm a hypothesis.

Aggregate data tells you something is wrong but not what the shopper experienced, and that is usually what you need to explain the problem to the rest of the team. Rage-click sessions and highest-friction sessions are the most direct route to it, and merchants most often arrive here from an error investigation or a conversion failure. Replays render inline in the conversation, and deeper visual playback opens in the Noibu console.

➡ Mapping click and tap behaviour to inform design decisions.

A redesign decision rests on where shoppers actually click, which is rarely where the team assumes. Click-text analysis on a specific page, dead-click and rage-click identification, and page-group journey shapes ground the decision in observed behaviour. Pop-up interaction rates and the downstream effect of a navigation change are common follow-ups.