Behavioral Analysis

Behavioral Analysis

Quick definition: Behavioral analysis is a data analysis practice 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:

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

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

What is behavioral analysis?

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

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

How do you conduct behavioral analysis?

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

  1. Segmenting audiences.  Categorize 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.
  1. 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.
  1. 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.
  1. Comparing and analyzing.  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.
  1. Making changes.  With new insights into customer behavior patterns, marketers can make informed decisions and changes that will improve  customer experiences.

A tool like Adobe Analytics allows you to gather and analyze 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 behavioral analysis important?

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

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

How do businesses use behavioral analysis

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

How has behavioral 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 behavior much more achievable.

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

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

Looking forward, behavioral analytics systems will be able to merge online and offline behaviors to help analysts understand how they influence each other.

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

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