How to analyse the user journey and increase sales?

User journey analysis makes it possible to check how people really move from their first contact with a brand to a purchase or sending an enquiry. This is not about simply counting visits to the site, but about understanding where the user stops, what puts them off and what makes the next step easier. In practice, it is one of the most effective ways to improve sales without increasing the budget for acquiring traffic. The biggest uplift usually does not come from “more visits”, but from removing barriers at the most important stages of the funnel. To do this properly, you need to combine analytics data with on-site behaviour and real signals from users. Only then can you see which changes genuinely make sense from a business perspective.

What is user journey analysis?

User journey analysis is the study of the whole customer journey, from landing on the site to completing a goal, for example a purchase, sending a form or contacting a salesperson. Its purpose is to identify the moments when the user drops off, postpones the decision or loses trust. This makes it possible to improve specific parts of the process instead of guessing where sales below expectations come from.

In practice, several stages are examined: entry from a campaign or search engine, the first impression on the site, browsing the offer, the decision moment and completing the action. In an online store, this will most often be the move from the product list to the basket and payment. In a service company, it more often concerns the transition from a landing page to a form, a call or a quote.

This is not just an analysis of the website itself. You need to take into account the traffic source, the user’s intent, the device, the quality of the message, the ease of the form or checkout, and post-purchase actions. The same landing page may perform well for brand traffic and poorly for paid campaigns, because users arrive with a different need and at a different stage of the decision.

A properly carried out analysis ends with concrete recommendations to implement. You create a journey map, a list of issues, change hypotheses and a plan for tests or implementation work. This matters, because observation of drop-offs alone does not tell you much if you do not know what to improve and in what order.

The greatest value of this analysis lies in the fact that it shows what really affects conversion. Sometimes the problem is not price or low traffic, but a mismatched message, an overly complex form or a lack of information that builds decision confidence. First it is worth removing purchase friction, and only then refining the visual details.

What data is key in user journey analysis?

The most important data is the data that shows not only visits to the site, but also transitions between subsequent stages and the reasons for abandonment. The mere number of sessions or page views is not enough, because it does not show where potential sales disappear. It is worth measuring both the main conversion and the micro-conversions along the way.

The foundation is quantitative data from analytics. This includes, among other things, traffic sources, landing pages, CTA clicks, transitions to the product page, add-to-basket actions, form starts, validation errors, checkout abandonment and completed purchases. If you do not measure the intermediate steps, you only see the end result, but you miss the moment when the problem starts.

Equally important is the quality of the analytics implementation. Events must be configured correctly, naming should remain consistent, and goals need to be precisely defined. Otherwise, it is easy to draw the wrong conclusions, for example to consider a form effective when the system does not record some abandonments or mixes different types of conversion.

The second group consists of qualitative data, which makes it possible to understand where a given behaviour comes from. Session recordings, click maps, on-site surveys, sales conversations and support tickets show where the user hesitates, what they do not understand and what objections they have. A high drop-off at a given stage signals a problem, but only qualitative material makes it possible to establish what that problem is.

Data on the entry context is also important. A user from a product ad, from informational SEO and from an email behaves differently because they arrive with a different intent. That is why it is worth breaking the analysis down by channel, device, new and returning users, and offer segments. Putting all users into one bucket in a single report often hides the actual source of the conversion drop.

In a more mature analysis, you also need a data connection between marketing and sales, for example with CRM. This is particularly important where the purchase does not happen immediately and is only closed after a call, a quote or several contacts. In such a situation, you assess not only who filled in the form, but also which sources and which journeys lead to real sales, not just cheap leads.

What are the stages of user journey analysis and optimisation?

The stages of user journey analysis and optimisation include defining the business goal, verifying the data, mapping the real path to purchase, identifying barriers, prioritising changes, implementation and tests. Such a process only makes sense if you know which conversion is the most important: purchase, lead, registration or contact. This determines which intermediate steps need to be measured and where to look for the biggest losses.

At the beginning, it is worth organising the measurement. Check whether analytics correctly records traffic sources, CTA clicks, product page visits, add-to-basket actions, form starts and completion. Without proper measurement, it is impossible to distinguish a real problem from an error in the data.

The next step is mapping the user journey based on real behaviour, not the team’s assumptions. It is worth describing separately the journey of new and returning users, traffic from different devices and the main entry channels. The journey should be analysed separately for channels, devices and segments, because users arrive on the site with different intent and a different level of readiness to buy.

One then moves through the subsequent moments of the funnel. At the entry stage, what matters is the match between the ad or search result and the landing page, page speed, the first message and the credibility of the offer. Later, it is worth checking whether the user can easily find the product, price, terms, answers to objections and a clear next step.

The most serious difficulties usually emerge at the decision and completion stages. You need to assess whether the offer is clear, whether additional costs only appear at the end, whether the form requires too many fields, and whether the checkout does not add unnecessary steps. First remove the biggest sources of friction, such as an unclear offer, lack of trust and an overly complicated form, and only then refine the minor visual elements.

Numerical analytics alone are not enough, which is why the next stage is qualitative analysis. Session recordings, click maps, surveys, sales conversations and support tickets show at what moment and for what reason the user stops. This is where you can see whether the problem is an unclear message, hidden information, flawed process logic or simply a lack of trust.

At the end, it is worth arranging the backlog of changes by impact on sales, traffic scale, implementation difficulty and business risk. A good recommendation does not stop at “improve the page”, but identifies a specific stage, hypothesis and success metric. Every change should have a defined goal, hypothesis and indicator; otherwise, it is hard to assess whether it actually improved sales.

After changes are implemented, the work is not over, because another round of observation begins. You need to check the impact not only on a single screen, but on the entire funnel, from the quality of the entry all the way to sales and order or lead value. A local improvement, such as more clicks on the CTA, does not always translate into a better final result.

What tools should you use to analyse user behaviour?

For user behaviour analysis, it is worth combining quantitative, qualitative and sales tools, because only their combination gives a complete picture. One tool will show where users drop off, another will show how they move around the site, and a third will confirm whether those people actually buy or leave valuable leads. In practice, what matters is not the number of systems, but their correct implementation and a sensible joining of data.

• Web analytics and tagging – used to measure traffic sources, events, micro-conversions and funnel stages. The most important things here are consistent event names, correct goal configuration and a clear, sensibly structured reporting framework.
• Session recording and click map tools – show at which point the user gets lost, what they try to click and at what stage they abandon the process. They are a good complement to hard numerical data, but they do not replace analytics.
• Data from CRM, e-commerce and sales systems – allow you to verify which sources and paths translate into real revenue, rather than just traffic or empty form submissions.
• Campaign and visibility tools – make it easier to assess the intent with which the user lands on the site and whether the ad message or search result is consistent with the landing page content.
• User feedback – on-site surveys, sales conversations, customer support and bug reports help capture objections that are not visible in the charts alone.

The choice of tools depends on the business model. In e-commerce, data on product pages, basket, checkout, payments and abandoned stages are key. In lead generation, tracking forms, phone calls, lead quality and the sales team’s response time may matter more.

If the purchase process is long, last-click attribution often distorts the picture. A user may first enter via SEO, return from an ad, and only submit the form after an email campaign or a phone call. That is why it is worth combining marketing data with CRM and looking more broadly than just through the lens of a single session.

The most common problem is not a lack of tools, but poor implementation. Events are named badly, forms are not measured to completion, campaigns have inconsistent labelling, and ad data does not add up to the sales result. Even the best set of tools will not help if you do not know exactly what each event measures and how it links to the business goal.

In practice, it is best to start with a simple but complete set: web analytics, correct tagging, a behaviour observation tool and access to sales data. This setup is usually enough to quickly spot the biggest losses in the funnel and decide what should be improved first. Only later does it make sense to expand the tool stack with more advanced reporting or tests.

What are the most common barriers in the purchase journey?

The most common barriers in the purchase journey are a mismatched message, poor usability, lack of trust, too much friction in the form or checkout, and inconsistency between the traffic source and the offer. Users usually drop off not because they “do not want to buy”, but because something makes it harder for them to move on to the next step. In practice, the biggest losses appear where intent is high and the process requires too much effort. So first you need to find the stage with the biggest drop-off, and only then get to the cause.

A common barrier is already the visit to the site itself. The ad or search result promises one thing, and the landing page shows something else, in different language or without clear information about who the offer is for and what the next step should be. When the first screen does not confirm the user’s intent, the traffic is technically acquired, but from a sales perspective it is lost.

The second big problem is often unclear navigation through the offer. The user is unable to quickly find the price, variants, turnaround time, terms of cooperation or answers to common objections. If the customer has to “guess” the offer for themselves, they usually do not go any further, but go back to comparing other options.

At the decision stage, trust barriers have a particularly strong impact. A lack of reviews, unclear return rules, hidden costs, scant information about the company or overly vague promises reduce willingness to buy. This is especially important in services and higher-priced products, where the customer weighs not only the price, but also the level of risk.

A large share of sales is lost in the form and at checkout. Too many fields, the need to create an account, validation errors, limited payment methods, unclear delivery costs or a lack of visible process progress effectively increase abandonment rates. The closer you get to completion, the more simplicity matters: every unnecessary step acts like an additional psychological cost.

A separate category is made up of mobile and cross-channel barriers. The user often starts from an ad on a phone, returns from search on a laptop and only then buys. If the mobile version loads slowly, the CTA is not very visible, and the form performs poorly on a small screen, then a large part of the problem does not stem from the offer, but from the implementation.

In lead-generation models, what happens after the form is submitted can also be a barrier. When the sales contact is delayed, the response does not match the query, or the CRM does not correctly pass lead sources, site analysis will show only a fragment of the cause. The user journey only closes when you know whether the lead was actually handled and moved further along the sales process.

How should changes be prioritised in user journey optimisation?

Changes in user journey optimisation should be set according to their impact on conversion, the scale of the problem, ease of implementation and business risk. The point is not to improve everything at once, but to start with the areas that block the largest share of valuable traffic. The highest priority usually goes to problems at stages with high traffic and strong intent, for example the product page, cart, contact form or checkout. The most profitable changes are those where the loss is large and the user was already close to making a decision.

In practice, every proposed change is worth putting through four questions. How many users is the problem affecting, how strongly does it affect results, how much does implementation cost and is there a risk of worsening other stages of the funnel. This simple filter makes it easier to separate genuinely important fixes from those that look good in the backlog but add little from a business perspective.

Systemic problems usually have a higher priority than cosmetic ones. An example may be a landing page that does not match the campaign, no price, unclear CTA, an overly extensive form or incorrect tracking of key events. Changing the button colour only makes sense once the basic logic of the journey works smoothly.

Priorities are best set separately for traffic segments. Different improvements will be crucial for users from brand campaigns, different ones for traffic from comparison sites, and yet others for people returning from email. It is not worth averaging out the whole journey if different channels bring users in with different intent and at a different stage of the decision.

Good prioritisation always ties the hypothesis to a success metric. If you shorten a form, measure not only the increase in submissions, but also lead quality and the next stage of sales. If you change a product page, observe not only CTA clicks, but also add-to-cart actions and final transactions.

It is also worth taking real implementation constraints into account. Some changes require developer work, CRM integration, adjustments to the product feed or sign-off from several teams. That is why a sequence often works better: first quick fixes with a high impact, and only later larger implementations that require more time and testing.

After implementation, the priority does not disappear, it simply moves into the verification stage. You need to assess whether the change improved the whole funnel, and not just one local fragment. The most common mistake is to consider a change a success after one metric improves, even though final sales or lead quality has not increased.

What are the best practices in continuous user journey optimisation?

Best practices in continuous user journey optimisation include regular measurement of the whole funnel, working with hypotheses, quickly implementing changes with the greatest impact and constantly checking whether a local improvement actually translates into sales growth. The biggest mistake is treating optimisation as a one-off project rather than a process. User behaviour changes together with campaigns, the offer, seasonality, devices and traffic sources. What worked a month ago may already be reducing conversion today.

The first good practice is to tidy up measurement before you start interpreting results. If events are implemented incorrectly, forms do not report errors and campaign data does not connect with sales, the team is optimising on the basis of illusions. Without reliable data, it is impossible to distinguish a real problem from a measurement error.

The second practice is to analyse the journey separately for segments that genuinely differ in intent and behaviour. A user from a brand ad will behave differently, as will someone from a comparison site, and yet another will be someone returning from email or from direct traffic. The same applies to devices, because problems in the mobile checkout often do not appear on desktop.

The third practice is to measure not only the final outcome, but also the intermediate stages. A drop in sales alone says little if you do not know whether the problem starts at entry, while browsing the offer, at the form, or only at payment. Micro-conversions show at which stage the user stops moving forward.

• set one main conversion and a few micro-conversions for key stages,
• review results at regular intervals, for example every week or every two weeks,
• separate changes in traffic from changes in behaviour, so you do not confuse an input-quality problem with a page problem,
• combine quantitative and qualitative data, meaning reports from analytics tools with session recordings, surveys and sales feedback,
• assess the effect of changes across the whole funnel, not just on one screen.

Another good practice is assigning every change to a specific hypothesis. If the team modifies the CTA, offer layout or form length, it should clearly define which problem it is meant to solve, which metric should improve and in which traffic segment. This makes it possible to assess the result after implementation, instead of relying on the impression that “the page looks better”.

In practice, teams that first remove the biggest sources of friction and only then polish the details usually win more often. A mismatched message, no price, low trust, too many fields in the form or hidden delivery costs usually matter more than minor visual tweaks. First remove the barriers blocking progress to the next step, not the elements that only “could be better” aesthetically.

In continuous optimisation, caution in interpreting results is equally important. A short-term rise in clicks does not have to translate into increased sales, and an improvement at one stage can worsen lead quality or the average basket value. That is why it is worth looking more broadly: at final conversion, revenue, enquiry quality, drop-offs at subsequent steps and differences between channels.

A good operational practice is maintaining a backlog of changes with a clearly defined priority, owner and implementation status. Then analytics, marketing, UX, sales and the technical team work from the same list of problems, rather than scattered notes from different meetings. Optimisation works best when it combines data, implementation and later verification of the effect.

Finally, it is worth keeping in mind that not every change can be sensibly tested in exactly the same way. With high traffic, you can compare variants and measure results more precisely, whereas at a smaller scale it is often more sensible to introduce obvious improvements and observe the trend over a longer horizon. What remains key is that the whole process is continuous, structured and based on actual user behaviour, not on the team’s assumptions.