With the recent rise of big data and behavioural insights tools, data is becoming more accessible to professionals than ever before. Behavioural analytics plays a significant part in the rise of big data, which is becoming much more common and affordable. Standard web analytics reports provide ample information about past performance: page views, click-throughs, and time on site, etc. While this information is critical to report about the areas and metrics you should watch, behavioural analytics can guide you on what to do next.
Behavioural analytics examines consumer attitudes and emotional tendencies, and leverages it to spur a behavioural change and drive consumers towards products and services that feed these attitudes and tendencies. Behavioural analytics looks beyond data alone to search for patterns and groupings of individual actions that can turn information into a representation of user behaviour and lead to actionable change. This can help catch fraud before it happens, increase sales, decrease costs, analyse revenue trends, improve customer experience and satisfaction, and help build better products.
Behavioural analytics looks for patterns in things like keyword searches, navigation paths and clicks. This can show the best way to communicate with segments of users, and deliver personalised experiences for those segments. By doing these two things, the possibility of a conversion increases.
Implementing behavioural analytics
The real challenge is getting the company to think behavioural. By using different predefined widgets, a typical user can easily answer behavioural business questions such as user retention, churn analysis, funnel analysis, recency, and break these down by several segments.
This leads us to the conclusion that behavioural analytics focuses on what the users do and not who they are. Demographics and traditional analytics are very important and are the foundations of behavioural analytics. But to truly get differentiating insights, you have to focus more on the user’s behaviour and less on who they are.
This is true for all verticals whether its e-commerce, media or gaming. Behavioural analytics is vital to really understanding your users and creating a baseline for predictions that will help to generate revenue.
Relationship with predictive analytics
Predictive models define the relationship between a user’s behaviour and one or more known variables. They can be gender, age, time of purchase, item purchased, location, a sequence of actions online, etc. The model’s objective is to predict the likelihood that a similar subject in a different sample will exhibit the same performance or behaviour. Advancements in computing speed have enabled individual agent modeling systems to effectively simulate human behaviour or reactions to given scenarios.
Behaviour is an inseparable key component of predictive analytics. It is used to identify trends, patterns and relationships. Only after the identification process is complete, can the predictive model be developed. The goal of data mining processes is to extract information from a data set and transform it into an understandable structure for future use – which in our case is prediction.
Understanding customer interactions is not enough to drive business value and improve customer experience. Today’s analytics solutions should lead to actionable insights and simplify complex data structures into useful business outcomes.
Online businesses wanting to get the most out of big data should learn to capitalise on behavioural analytics. After all, getting a better sense of why customers do what they do will benefit every industry – not just e-commerce or gaming. With its impressive predictive capabilities, every online business should make behavioural analytics a part of their toolkit for unlocking customer insight.