4 Ways Behavioral Analytics Can Improve Your Business in 2024

Until a decade ago, experts’ experience, education, and instinct were the best tools to understand their customers’ preferences and behaviors. However, all of these could be biased and lead companies to ineffective directions. Today, businesses can make data-driven decisions thanks to behavioral analytics that interpret big data. In this research, we introduce in detail the concept of behavioral analytics and the areas where it is used.

• Sales teams want to find answers about who is buying a particular product and why.
• Marketers want to understand how their campaigns influence customer behavior.
• Financial institution professionals want to know who is making transactions to prevent fraud.
• Game/mobile app makers want to understand user experience to improve overall features of the game/platform for the next versions.

What is behavioral analytics?

Behavioral analytics is a subset of data analytics. It provides insights into people’s actions by tracking and interpreting people’s browsing history, clicks, mouse action, time spent on the platform, and purchase data, usually on websites and in custom mobile applications. Behavioral analytics is used in e-commerce, fraud detection, gaming, social media, etc.

The performance of behavioral analytics is directly related to the amount of data stored (see Figure 1). It is therefore related to big data and the rapid proliferation of smart devices such as phones, watches, homes, etc. As the actions of individuals can be tracked better thanks to such technologies, companies can better understand their customers and carry out more efficient, tailored marketing and sales activities.

Figure 1: Volume of data.

1. Develop personalization and customized Offers

Thanks to behavioral analysis, it is possible to understand consumers’ preferences and develop customized offers that meet their needs. For example, companies can customize their e-commerce platforms to recommend certain products to certain people. As we know Amazon is quite successful at doing this. According to McKinsey, 35% of Amazon.com’s revenue is generated by its recommendation engine. 

Other large companies such as Youtube, Netflix, and Spotify are also able to ensure customer satisfaction thanks to their recommendation engines.

The below video provides information about how Netflix implements recommendation engines to provide convenient movie/series experience for their users.

2. Measure success of marketing campaigns

Thanks to behavioral analytics, companies can use detailed behavioral data to determine the success of their campaigns. For example, a cosmetics company would not rely solely on sales figures to understand the success of its new product. It can also rely on consumer responses on online platforms, such as: 

• Number of clicks. 
• Click through rate (CTR).
• Bouncing rate.
• Pages per visit.
• Time spent on the page. 
• Number of links shared.

Figure 2 provides information regarding the Amazon.com’s marketing KPIs that is taken by similarweb. Companies can track their success by using the same KPIs.

Figure 2: Amazon.com’s potential marketing KPIs

3.Improve fraud detection

Fraud costs about $600 billion, or 0.8% of global GDP. That makes it a significant burden on businesses and consumers. Businesses can use behavioral analytics to improve the fraud detection capabilities of their AI/ML models. Thanks to behavioral analytics, ML models can identify individually unique factors such as: 

• Mouse movements 
• Typing speed and habits 
• Login history, etc. 

to confirm the security of a transaction. Such improvement reduces the percentage of successful fraudulent actions.

The following video of AWS further explains how behavioral analytics can be used for fraud prevention.

4. Facilitate better A/B testing

A/B testing is ideal for testing a hypothesis you have developed based on other user data or insights. For instance, if a company cannot decide on the color of the font of a custom mobile app platform, it can test the impact of different colors on customer engagement with an A/B test. 

By using behavioral analytics, companies can understand how customer usage behavior changes when a parameter is changed. Do customers spend more time using the app? Do they shop more? etc. It’s worth noting that such testing and analytics are also valuable for game developers, as they can identify the “boring” parts for gamers that should be improved.

Figure 3: Example of use of behavioral analytics on A/B testing

For more information on behavioral nudging, you can check our article Internet of Behaviors.

You can also check our list of analytics platforms.

To find out more about behavioral analytics please do not hesitate to reach us: