User behaviour analytics | Use case | Bloonde Soluciones Tecnológicas

Your website or application receives visitors — but do you really know how users interact with it? Where do they click? At what point do they abandon the checkout form? What elements go completely unnoticed? Without behaviour data, design decisions are a shot in the dark.

A user behaviour analytics system turns every visit into actionable insight: heatmaps that show where attention is focused, funnels that reveal where conversions are lost, and A/B tests that validate every change before it goes live.

At Bloonde we implement UX analytics solutions for SMEs that want to improve their conversion rate with data, not opinions. Typical results: 15–30% conversion increases within the first 3 months of optimisation.

Heatmap and conversion funnels for user behaviour analytics

The problem it solves

Most companies measure web traffic (how many visits, where they come from), but not on-site behaviour. They know how many people arrive, but not why most of them leave without converting. Redesigns are done once a year based on internal opinions, not actual usage data.

Without visibility into user behaviour, usability issues persist for months: frustrating forms, invisible CTAs, checkout flows that generate abandonment. Every day without data is money lost on conversions that never happen.

Low conversion rate with no diagnosis

The website generates traffic but few conversions, and nobody knows exactly why or at what point in the flow users drop off.

Redesigns based on opinion

Design changes are decided in meetings with subjective opinions instead of real usage and behaviour data.

High form abandonment rate

Users start registration or checkout processes but abandon them halfway. Without data, there's no way to know which field or step causes the friction.

Key elements nobody sees

CTAs, offers, or important features go unnoticed because they're poorly placed or designed — but without heatmaps, it's impossible to detect.

Changes without validation

Improvements are rolled out to the website without measuring their real impact. There's no way to know whether a change improved or worsened the experience.

How it works

We implement your UX analytics system in 4 phases:

01

UX audit & event tracking

We analyse your website or application, identify the pages and flows critical to conversion, and set up tracking for relevant events (clicks, scrolls, forms, navigation).

02

Analytics tool implementation

We deploy heatmaps, session recordings, conversion funnels, and on-site surveys — all integrated with your existing analytics so no historical data is lost.

03

Analysis & friction diagnosis

We analyse the collected data to identify the most impactful friction points: where users are lost, what causes confusion, and what improvement opportunities exist.

04

A/B testing & continuous optimisation

We propose improvement hypotheses, validate them with A/B tests, and measure the real impact of each change. A continuous optimisation cycle driven by data.

Key benefits

Increased conversion rate

By identifying and removing friction points, conversion improves by 15% to 30% within the first months of optimisation.

Data-driven design decisions

Every design or content change is validated with real behavioural data, eliminating subjective debates.

Full visibility of the user journey

Heatmaps, recordings, and funnels that show exactly how users navigate and where they encounter problems.

Change validation with A/B tests

Before rolling out a change across the entire site, you test it with a fraction of traffic and measure its real impact.

Lower customer acquisition cost

Improving conversion means every visit is more valuable. The same traffic generates more sales, effectively reducing CAC.

Objective improvement prioritisation

Data shows which improvements will have the greatest impact, enabling you to prioritise the UX roadmap with evidence-based criteria.

Technologies we use

Hotjar / Microsoft Clarity

Heatmaps, user session recordings, and on-site surveys to understand qualitative behaviour.

Google Analytics 4

Event tracking, conversion funnels, audience segments, and navigation flow analysis.

Google Optimize / VWO

A/B and multivariate testing platforms to validate improvement hypotheses with statistical significance.

Tag Manager (GTM)

Centralised management of all tracking scripts without needing to modify the application's source code.

Looker Studio / Power BI

Custom UX analytics dashboards with conversion, engagement, and behaviour metrics by segment.

Case study: SaaS with a problematic onboarding

Scenario

A B2B SaaS platform with 2,000 monthly sign-ups was only converting 12% into active users. The team suspected onboarding was the problem but had no data to confirm it or identify which step in the process was failing.

Challenge

Identify the friction points in the onboarding flow and increase the new-user activation rate without changing the product's functionality.

Solution

We implemented Hotjar for onboarding session recordings, set up a detailed funnel in GA4 for each step of the process, and launched in-app surveys for users who dropped off. We identified 3 key friction points and fixed them with sequential A/B tests.

+22 pts

Activation rate

-45%

Onboarding abandonment

-60%

Time to activation

+15 pts

New-user NPS

Frequently asked questions

Is it legal to record user sessions?

Yes, as long as GDPR requirements are met: informing users in the privacy policy, not recording sensitive data (passwords, banking details), and anonymising personal information. The tools we use comply with these standards by default.

How much traffic do I need for A/B tests?

For statistically significant results, we recommend a minimum of 1,000 weekly visits to the page being tested. With less traffic, optimisations are based on qualitative analysis (heatmaps and recordings).

Does it affect my website's performance?

The impact is minimal. The tools we use load asynchronously and are optimised so as not to affect load times. We monitor performance before and after implementation.

Can I use this on a mobile app?

Yes. The analytics tools work on web as well as native and hybrid mobile applications. We adapt the configuration to your product's environment.

How often are A/B tests run?

We recommend 2–4 week cycles per test, depending on traffic volume. In an active optimisation project, 2–3 tests run simultaneously across different areas of the site.

What metrics are typically improved?

The most common ones: conversion rate, bounce rate, time on page, form completion rate, activation rate (in SaaS), and cart abandonment (in e-commerce).