Imagine the ability to accurately distinguish whether or not a genuine account holder is trying to access financial services — or a criminal. With behavioral analytics, this is not only entirely possible, it’s just the tip of the iceberg.
Right now, behavioral analytics is having a moment. Industries like financial services, gaming, ecommerce, and healthcare are adopting it at a remarkable rate for a broad variety of use cases. Globally, the mark for user and entity behavior analytics is expected to hit $4.2 billion by 2026, up from $1.2 billion this year.
The digital age has cracked open a chasm of online activity that’s creating significant new opportunities for personalized services and products and convenient interactions. But it has also created a number of new risk trends. Behavioral analytics can provide critical insights into actual user actions, habits, interactions, and usage on digital channels to help organizations effectively respond to these dynamics.
Emerging Behavioral Analytics Use Cases
Organizations are leveraging behavioral analytics techniques and technologies for diverse use cases. By analyzing patterns in user behaviors in specific circumstances, businesses can gain valuable insights for competitive differentiation or solve unique challenges.
Expanding on the earlier example of a use case in financial services, one of the biggest applications of behavioral analytics is for risk reduction. A bank can use behavioral analytics and machine learning to combat cybercriminals who use highly complex methods to infiltrate their organization, usually by using stolen credentials and new technologies to mimic the actions of authentic customers. Behavioral analytics can help banks understand and establish a baseline for normal customer behaviors, such as mouse movements and typing speed. This enables them to recognize behavioral anomalies that might characterize an attack being executed by a criminal in real-time and prevent it from happening.
Other popular use cases include:
- Personalize customer experiences: To find opportunities to improve the end-to-end customer journey based on an understanding of user intents and motivations.
- Product development: To better understand how customers are using your products and then continuously adapt and enhance those products based on what your customers really want from them.
- Optimize gamer retention: To assist video game developers in better maintaining user attention, identify cheaters to improve community experiences, modify game features, boost user purchases, convert players into paying customers, and enhance marketing campaigns.
Behavioral Analytics Technologies and Techniques
Behavioral analytics is an offshoot of business analytics that uses a mixture of data, technologies, and techniques that are focused on driving particular business outcomes or mitigating risks. This includes activities such as predictive modeling, prescriptive action, and segmentation.
Unlike business analytics, which analyzes historical data utilizing statistical techniques and technologies, behavioral analytics blends event monitoring and user segmentation technologies for a more precise inference.
In behavioral analytics, data is collected and analyzed from user interactions on digital channels, like a website, wearable device, voice-enabled device, or mobile app, during their digital experience. This data can provide a very accurate forecast of user intent and future behaviors, and can be combined with other data, like the user’s past transactional data, to obtain even deeper insights.
There are plenty of behavioral analytics tools and software available that are tailored for different use cases and business goals, whether it’s improving customer retention or detecting risks. In general, organizations seek tools that can identify and eliminate unnecessary customer friction, target specific behaviors that lead to better customer lifetime value, inform marketing campaigns, target potential customer churn, and optimize the customer journey.
Many eCommerce, product analytics, fraud prevention, digital experience, and content management platforms incorporate behavioral analytics capabilities or provide integrations. Centralized behavioral data and capabilities are also a feature in some artificial intelligence and machine learning platforms, which help organizations use automation and predictive capabilities to augment behavioral analytics use cases.
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Final Thoughts
Customer and user habits are constantly changing, and the digital world is expanding. Behavioral analytics will continue to be an important factor in helping organizations stay on top of trends, market shifts, and changing customer demands.
Be sure to check out Simplilearn for educational information and insights about behavioral analytics. However, if you wish to strengthen your analytical skills, and make a mark in the field of business analytics, you must enroll in Simplilearn’s UMN Post Graduate Program in Business Analytics course right away.
