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 makes it possible to not just determine who these users are but also keep track of 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 why people trust certain brands, come back to them, and ultimately, why they 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 these data and a few tips on using them 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 data you get will depend on the quality of the software you use.

Connecting the data points

By matching, combining and integrating these 4 types of data, you can deduce an incredible amount 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.

Some numerous possible problems and barriers can hold your conversion rates back. These may be general issues like 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 his or her 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 needs to be fulfilled. 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 reasons and don’t demand complex interpretations. 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 one-second page loading delay 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 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 for specific pages, you should test these specific 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 different causes, such as confusing layout, counter-intuitive navigation, an overabundance of unnecessary design elements, or some very specific details, like 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 determine where and why people struggle with using your website. Try using more advanced metrics, like 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 case study of nameOn, a company that sells personalised gifts. They noticed a discrepancy between the add-to-cart page and the checkout page—as many as 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 were able to deduce that some of the 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 generated a substantial increase in conversions and a revenue boost of 11.4%.

Personalised recommendations

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

It’s obvious for consumers, as we realise that algorithms that recommend videos, songs, products, or TV shows sometimes know what we want better than we know it ourselves. It’s also obvious for companies. Big corporations like Amazon or Netflix drive huge revenues based on the efficiency of these algorithms.

These recommendations are responsible for one-third of Amazon’s sales and 75 percent of Netflix viewer activity. Thanks to machine learning and analytics capable of predicting consumers’ interests, needs, and upcoming decisions, they open amazing up-sell 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 that are already in their cart, or items purchased by others with similar interests and purchase history.

These recommendations can be sent to users even when they leave the site. Sending trigger-based special offers and recommendations by email can do wonders for 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 ends up on your website, what kind of content they’ll initially encounter is very important. For instance, you shouldn’t show first-time visitors and returning customers the same content.

New customers should see more general info about your brand to become familiar with it, while regular visitors can only get annoyed by such content. 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 customized based on referral traffic. If a user ends up at your website by clicking on a product offer they came across on social networks or another website, they shouldn’t land on your homepage and look for what they need all by themselves. With relevant user behaviour data, you can determine how a particular user has arrived at your website and land them on a relevant section or a specific product page.

A good example of how this works is the case of Tokeo, a Polish local service that connects businesses and individuals with expert advisors from different fields. Initially, they used to have all the visitors land on the same page, regardless of which specific kind of expert a visitor 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 done based on user behaviour data is adapting the marketing messages that you send out.

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 percent 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 quite a lot about your customers, which may make you reconsider some 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 your products to your website will surely do good things for 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 in any industry; it’s just that now it’s easier than ever to collect valuable customer information.

Finally, the algorithms that try to figure out behaviour patterns are only getting more powerful and subtle, which will make 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.