User behaviour data is a method for collecting, processing, and interpreting various types of user data. It helps us understand how people interact with a website or service. This method not only allows you to determine who these users are but also to track what they do, why they do it, and predict what they’ll do next.

Today, having the right user behaviour data at your disposal can make a difference for any business. Companies need this data to create suitable experiences for every one of their customers, and customers are quickly getting used to new circumstances. Customer experience (CX) quality has become one of the most important discerning factors between brands. It’s also become one of the main reasons people trust certain brands, come back to them, and ultimately convert.

That’s why it’s important to understand how behaviour data works and how it can benefit your conversions and business. So, let’s check out what information you can extract from this data and a few tips on using it properly.

4 types of data

Many kinds of data should be analysed to get a comprehensive picture of your customers. Roughly speaking, we can divide all these into four basic types of raw data you’re supposed to obtain.

  • Who your users are in terms of personal information, demographics or interests
  • Where these users come from, both geographically and in the online sense
  • Which actions they perform, what triggers them to perform these actions and how these actions are grouped and ordered
  • How each of your pages, sections and even parts of your pages perform, both globally and in specific circumstances
  • Once you have all this, you can start making sense of your collected data. Or rather, not you but super-advanced algorithms able to analyse colossal amounts of data and give meaningful interpretations.

Naturally, the quality of the data you get depends on the quality of the software you use.

Connecting the data points

By matching, combining, and integrating these 4 types of data, you can derive a wealth of information that will help you increase your conversion rates. User behaviour data can highlight areas of your website and your service that need improvement.

Numerous possible problems and barriers can hold your conversion rates back. These may be general issues such as below-par content, poor design, slow page loading, or targeting the wrong keywords. The issues could, however, be more specific and sometimes come down to a single page or even a single design element done wrong. In both cases, user behaviour data can help you identify problems by using different methods. These methods include:

  • Heat maps
  • Session replays
  • Funnel analysis
  • Exit reports
  • Clickstream data
  • Form analytics
  • Scroll behaviour analysis
  • All kinds of A/B testing

What’s more, by utilising this data, you can go beyond just understanding overall trends in user behaviour and detecting specific technical problems. Namely, user behaviour data can help you predict an individual’s future behaviour based on their specific past behaviour.

This allows companies to personalise each customer’s experience in ways that weren’t imaginable until recently. Personalisation has become a standard that must be met. Research by Epsilon indicates that 80% of consumers are more likely to buy from brands that offer personalised experiences.

Having the right data is the first step to fixing problems that hinder your conversion rates and improving customer experience.

Fixing technical issues

Sometimes, poor conversion rates are due to simple technical issues and don’t require complex interpretation. Unusually high bounce rates at certain pages can suggest bugs and errors you weren’t aware of. If these bounce rates apply to most of your pages, many technical and non-technical problems may be causing them. One of the most common ones is page loading speed.

In other words, it may be too slow if you don’t seem to have any particular technical problems with your website. Unsurprisingly, users don’t plan to spend their days staring at loading bars nowadays. A 1-second delay in page load can cause a 7% drop in conversions.

On the other hand, speeding up your website can have the opposite effect. Numerous studies suggest this. For instance, Walmart increased conversions by 2% for every second of load time improvement, Mozilla’s conversions were boosted by 15.4% after speeding up uploading by 2.2 seconds, and similar results were noted by giants like Amazon, Microsoft, and Yahoo.

Viewing user behaviour data can also help you discover the cause of slow page loading. If bounce rates are high only on specific pages, test those pages. If they fail the speed test, you’ll know which exact pages need to be fixed or better optimised.

Fixing UX issues

User experience (UX) is often a huge factor in establishing healthy conversion rates. UX issues are often just technical issues that badly affect the visitors’ experience. But sometimes, your website may technically be perfectly fine and yet completely hopeless and unusable, which will inevitably plunge conversion rates.

This may be due to various causes, such as a confusing layout, counterintuitive navigation, an overabundance of unnecessary design elements, or specific details, such as the colour of the Click to Action (CTA) button or the number of fields in opt-in forms.

In any event, well-processed behaviour data can help you find out.

You can use heat maps or full session replays to identify where and why people struggle to use your website. Try using more advanced metrics, such as rage clicks, bird’s-nest, or dwell time. These will show where the bursts of clicks and taps that suggest frustration were detected, or where exactly on your website users tend to spend the most time seemingly inactive, probably trying to figure out what they should do next. Pages that provoke this user behaviour need improvement in the UX department.

A great illustration of how data can improve UX details is the nameOn case study, a company that sells personalised gifts. They noticed a discrepancy between the add-to-cart page and the checkout page. Up to 31.7% of those who added products to their carts never started the checkout process.

They successfully used heatmaps to pinpoint the problem. After a round of testing, they determined that some CTA buttons distracted and confused users. Heatmaps also point to the exact parts of the page that get the most attention. Now, they could eliminate the unnecessary CTA buttons and place the “continue to checkout” one in the most suitable place. This led to a substantial increase in conversions and a 11.4% revenue boost.

Personalised recommendations

Modern technology has influenced customer experience in many ways; product recommendations are everyday examples.

It’s obvious to consumers that algorithms that recommend videos, songs, products, or TV shows sometimes know what we want better than we do. It’s also obvious for companies. Big corporations like Amazon and Netflix generate huge revenues driven by the efficiency of their algorithms.

These recommendations account for one-third of Amazon’s sales and 75 per cent of Netflix viewers’ activity. Thanks to machine learning and analytics that can predict consumers’ interests, needs, and upcoming decisions, they open up amazing upsell and cross-sell opportunities.

For this algorithm to be useful, companies need all the user behaviour data they can get, and they need it in real-time. Many different kinds of data can be extremely useful for recommendation software, such as users’ purchase history, browsing history, items they looked at or liked, items already in their cart, and items purchased by others with similar purchase history.

These recommendations can be sent to users even when they leave the site. Sending trigger-based special offers and recommendations via email can boost your conversions. They must be relevant, though; otherwise, they’ll only increase your unsubscribe rates.

For instance, what seems to be working well is automatically reminding users of items they originally had in the cart but decided not to buy. Around 5% of people who receive this kind of follow-up email will return to purchase the product. Of course, if you add a discount or offer free shipping, you’ll probably see this percentage increase substantially.

Personalised content

As was already pointed out, the impact of personalised experiences on conversions and sales is huge. Consumers are starting to recognise when they’re offered generic experiences and easily lose patience if they encounter a website that isn’t built to suit their needs.

A significant aspect of personalised experience is personalised content. Once a user lands on your website, the content they encounter first is very important. For instance, you shouldn’t show the same content to first-time visitors and returning customers.

New customers should see more general info about your brand to become familiar with it, while regular visitors may find such content annoying. Furthermore, new visitors should be especially encouraged to sign up for your mailing list, while urging those already signed up to sign up is a waste of time, resources, and patience.

Also, the content should be customised based on referral traffic. If a user ends up on your website by clicking a product offer they saw on social networks or another website, they shouldn’t have to land on your homepage and look for what they need on their own. With relevant user behaviour data, you can determine how a particular user arrived on your website and direct them to a relevant section or a specific product page.

A good example of how this works is Tokeo, a Polish local service that connects businesses and individuals with expert advisors across different fields. Initially, they had all visitors land on the same page, regardless of the specific type of expert they needed. Afterwards, they made 11+ of these specific landing pages, all of which converted better than the original. These pages performed 40-700% better only because they featured a particular kind of expert the user was looking for.

Adapting your messages

Another aspect of personalisation tactics that can be based on user behaviour data is adapting the marketing messages you send.

This applies to messages that you send to particular users. For example, the follow-up email mentioned should be tailored to fit the demographics and interests of the person you’re addressing. User behaviour data can tell you a lot about what provokes them to act or buy, and you should use this to your advantage.

The same goes for your retargeting campaigns and other online ads. With the right user behaviour data, you can choose between different designs and messages that will fit a specific user’s profile. Retargeted visitors are 70 per cent more likely to convert on a retailer’s website, and you should keep that in mind.

Secondly, you can also use this data when creating broader marketing strategies. You’ll learn a lot about your customers, which may prompt you to reconsider aspects of your overall marketing strategy and brand messages. You’ll have a better idea about what drives your customers, what attracts them, and what triggers them.

These data will help you target the right audience with the right messages, affecting some of your key metrics. Naturally, there will be proportionally fewer people who end up at your website by pure chance, possibly completely indifferent to your brand. Thus, attracting people interested in your company and products to your website will surely improve your conversion rates in the long run.

Conclusion

Accurate user behaviour data is the present and the future of doing business virtually anywhere in the world. This shouldn’t come as a surprise—knowing your customers has always been especially beneficial across industries; now it’s easier than ever to collect valuable customer information.

Finally, the algorithms that try to figure out behavioural patterns are only getting more powerful and subtle, making them increasingly useful for companies of all kinds. Very soon, possessing relevant user behaviour data won’t be a handy additional business acquisition; it will become necessary.