Product UX Analysis: Finding Conversion Losses and Turning Traffic into Sales

If your website receives relevant traffic from paid search or paid social advertising relevant search queries, high-quality placements and targeting that accurately reflects your audience, but enquiries remain low, the problem is often not the advertising. Users may be dropping out before they convert.

Our job is to find these obstacles and remove them through precise, measurable changes.

The foundation of this work is conversion growth research. We cover the diagnostic stage and in-depth UX/UI interviews in a separate article, which explains our approach and process in more detail.

Below, we explain how we analyse user behaviour, develop hypotheses for improvement and test them with real traffic. This is a practical framework used in real projects.

We study real user behaviour using analytics tools such as Google Analytics:

• scroll maps;
• heatmaps;
• click and navigation maps;
• session recordings;
• analysis of key user journeys.

This helps uncover UX issues that may be hard to spot during a routine review but can prevent people from completing an action:

• an incomplete checkout;
• confusing page structure;
• gaps in the user journey;
• unclear or overloaded sections;
• weak calls to action;
• unnecessary steps;
• points where users get lost, hesitate or do not know what to do next.

This is especially important in e-commerce: details in product pages, filters, search and the cart can affect average order value, purchase frequency and overall sales.

We map the customer journey in data, from the first visit through product views, cart, enquiry and purchase. This shows where the funnel loses users:

• a high exit rate from the homepage;
• fewer visits to the cart;
• a low checkout conversion rate;
• bottlenecks within particular audience segments or devices.

This helps us prioritise hypotheses that can influence sales, instead of changes that only make a cosmetic difference.

We study detailed information about people who visit the website and complete—or do not complete—the desired actions:

• Demographic and social characteristics.
  Age, gender, location, occupation and times of activity.
• Devices and browsers.
  This helps us understand behaviour on mobile and desktop.
• Interests and topics.
  The subjects users engage with beyond the website.
• Affinity index.
  A measure of how much more interested a particular audience segment is
  in your product than the average user.

Our goal is to understand who actually converts and how that profile differs from the audience the business expected to reach.

This helps us:

• refine the value proposition around real audience motivations;
• adapt content and structure for the segments that already buy;
• identify high-potential segments and create more compelling offers for them;
• make the funnel easier and more natural for each user group;
• find growth opportunities that improve conversion without increasing the advertising budget.

We review:

• competitors’ value propositions in search results;
• offers and product ranges;
• page structures;
• UX patterns;
• content;
• service descriptions and product pages.

The aim is to understand which approaches work in the market: how competitors make their case, which interface elements help users find their way and which patterns build trust. We use these observations to shape our own hypotheses, not as templates to copy.

1. A record of where users get lost, hesitate or leave before converting, tied to specific pages, journeys and funnel stages.
2. A clearer picture of who converts, what influences their decisions and where business assumptions differ from actual user behaviour.
3. A prioritised list of hypotheses, ordered by their potential conversion impact and grounded in data and observation rather than assumptions.
4. Practical findings and recommendations for designing prototypes and preparing changes for testing.

We use Variocube in product analytics to test hypotheses and measure how changes affect conversion. It lets us run A/B tests without putting the live website at risk. We create alternative page versions, track events and compare the behaviour of real users under the same conditions.

1. Page structure.
   Which order of sections helps users take action sooner
2. Unique value propositions and offers.
   Which messages, benefits or promises capture attention and encourage people to continue.
3. Content and presentation.
   Which copy, visual cues, product cards or media help people understand the product.
4. User experience and interaction details.
   How changes to product pages, filters, search, forms, checkout and calls to action affect behaviour.
5. Emotional triggers and trust signals.
   Reviews, guarantees, badges and work examples—and which ones resonate with your audience.
6. How easy and quick it is to complete the journey.
   Can people move more easily and logically towards the cart, enquiry form or order?

Variocube helps us test hypotheses safely and quickly by changing page structure, offers, prompts, section order, product cards or checkout steps. Decisions are based on experiments, not guesswork.

1. Evidence of what works better.
   Data from real users.
2. A better understanding of user behaviour.
   Which element improved conversion, what made the page clearer and where fewer users dropped out after each change.
3. A measurable improvement in conversion.
   Each experiment shows how the number of cart visits, enquiries and orders has changed.
4. Behavioural signals that are hard to see without testing.
   More time spent engaging; more users noticing key sections; more completed journeys.
5. A growing body of knowledge about your website audience.
   Each test adds to our understanding of what influences people’s decisions
   in a particular market.
6. A systematic, predictable way to improve the website.
   A clear iterative process: research → hypothesis → test → result → next step.

We run every test to understand how users make decisions, what builds trust or creates doubt, and which elements help them move towards a cart, enquiry or order.

One test offers useful insight, but lasting impact comes from testing continuously. Each experiment sharpens the picture, accumulated data informs new decisions, and the website becomes more relevant and easier to use.

Structured testing is an ongoing process, not a one-off campaign. It turns a website into a more predictable and manageable sales tool. Each small improvement builds on the last, often producing more growth than a single major redesign.

Explore the agency’s services onservice page.