Behavioural Analysis

Quick definition: Behavioural analysis is a data analysis practise that identifies how users interact with a brand property like a mobile app or website and how aspects of that property drive customer engagement.

Key takeaways:

• Behavioural analysis looks at what customers do - and how they do it - to help marketers understand why they do it.
• Marketers can collect behavioural data from websites, mobile apps, call centres, digital help desks and billing systems.
• When an organisation conducts behavioural analysis, decision makers understand target audiences better and can create more appealing products, services and experiences.
• Looking forward, behavioural analytics systems will be able to merge online and off-line behaviours to help analysts understand how they influence each other.

The following information was provided during an interview with Travis Sabin, principal product manager for Adobe Analytics.

What is behavioural analysis?
How do you conduct a behavioural analysis?
Why is behavioural analysis important?
How do businesses use behavioural analysis?
How has behavioural analysis evolved?

What is behavioural analysis?

Behavioural analysis is the study of learning and behaviour. This discipline is focused on discovering and interpreting the principles behind what people do and why they do these things.

Though machine learning, behavioural analysis can be used to better understand customers and address them individually, in scale.

How do you conduct behavioural analysis?

Your exact approach to customer behaviour analysis will depend on your digital analytics platform, but the process typically looks like this:

1. Segmenting audiences. Categorise customers by the characteristics that are most valuable to your business. You may consider demographics like age, profession and location, as well as what types of media the customers use, how active they are online and other relevant habits that you’d like to explore.
2. Determining motivation. Expand the persona profiles you’re creating for each customer segment by examining those customers’ motivations and values. Determine why that type of customer chooses your business. Defining customer needs will help you increase customer satisfaction when you meet those needs.
3. Collecting quantitative data. While the first two steps focus on qualitative data, marketers need both qualitative and quantitative data for complete data analysis. Using your analytics platform, you can track and tag data based on the events and properties that you find most relevant and interesting. This would include numbers behind metrics like page views and click-through rates.
4. Comparing and analysing. If you bring together the quantitative and qualitative metrics, you can map customer journeys and identify what’s working and what you need to fix. Examine which personas purchase certain products and services, which personas become loyal customers and which personas don’t follow through with desired outcomes. You can also identify trends across personas to identify major obstacles that should be addressed first.
5. Making changes. With new insights into customer behaviour patterns, marketers can make informed decisions and changes that will improve customer experiences.

A tool like Adobe Analytics allows you to gather and analyse data from anywhere in the customer journey.

Marketers can also take advantage of any predictive analytics capabilities an analytics tool might have, which use artificial intelligence (AI) and machine learning to highlight hidden opportunities.

Why is behavioural analysis important?

When an organisation conducts behavioural analysis, decision makers understand target audiences better and can create more appealing products, services and experiences. Based on their analysis, businesses can optimise their efforts to meet key performance indicators (KPIs).

There are many benefits to behavioural analysis marketing besides understanding target audiences better:

• Improved content personalisation. Both B2C and B2B companies need to deliver relevant content to their customers. But you can’t deliver tailored experiences without understanding your customers’ preferences. Customer behaviour patterns identified using behavioural data can help marketers deliver experiences that increase customer satisfaction.
• Better content optimisation. Results from a behavioural analysis marketing can help marketers improve campaigns, target the most valuable customer segmentations and reach potential customers on the channels they frequent most. Optimised content also increases your website traffic and brand reputation.
• Increased customer retention. Marketers can retain more of their brand’s loyal customers by continually reducing friction points identified through behavioural analysis. Analysing customer behaviour can help you to determine where customers get lost, where they struggle to find what they’re looking for and what key products and pages they’re using most, so you can give those assets frequent updates and attention.

How do businesses use behavioural analysis

Businesses use behavioural analysis in many ways, but these few are the most common:

• Funnel analysis. Analysing the marketing and sales funnel helps marketers understand the steps required to reach a desired outcome, like a customer purchasing a product, registering for an event or subscribing to a service. Behavioural data can help you to understand how many people are moving through the process, which steps are losing potential customers and which steps seem to cause friction.
• Metric tracking. Another approach to customer behaviour analysis involves tracking interactions with specific pages or products and collecting data like page views, clicks and conversion rates.
• Retention analysis. Retention analysis helps marketers understand how to turn new customers into loyal customers. You can see how effective your current strategy is at bringing them back for more and make changes to decrease customer churn.

How has behavioural analysis evolved?

Long before social media and ecommerce even existed, businesses wanted to know why their customers behaved the way they did, why they bought what they bought and, even more importantly, why they didn’t complete a desired action. The digital age has made decoding customer behaviour much more achievable.

The start of collecting behavioural data came from using simple page view counters. This evolved into more sophisticated web page tracking.

Now, marketers can even track customer behaviour across multiple brands and platforms — from desktop to mobile websites and apps.

Looking forward, behavioural analytics systems will be able to merge online and off-line behaviours to help analysts understand how they influence each other.

AI and machine learning will continue to make the behavioural analysis process much faster, delivering customer insights in real-time and creating in-depth reports in minutes.

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