What Is The Behavioural Analytics Process?

In the last two decades, the rapid expansion of software and IT has radically affected the environment we live in and how we interact with it. Engineers have figured out how to save data on how users interact with software, right down to how the user moves their mouse. These acquired data would not be very useful on its own. However, advances in analytics capabilities, particularly in artificial intelligence, have allowed huge volumes of user data to be mined for insights in recent years. The process of analyzing vast amounts of user data in this way is known as behavioral analytics.
 

Introduction to Behavioural Analytics Process

 

Behavioral analytics is a business analytics concept that provides insights into customer behavior on your website, e-commerce platform, mobile app, chat, email, connected product/Internet of Things (IoT), and other digital channels. Every time a user interacts with your digital channels, they provide important signals about their needs and desires, including their willingness to buy—information that can be used to inform your customer profiles.

 

The goal of behavioral analytics, a type of digital analytics, is to anticipate consumers' demands by identifying where they are in the customer journey, what information or contact they require next, and what barriers are in the way. While there are numerous data and analytics available to accomplish this goal, behavioral data is distinct in that it is concrete, user-generated data that can drive very precise forecasts of intent. Furthermore, combining cross-channel behavioral analytics with other types of consumer data, such as past purchases and demographics, yields richer insights that can be used to build even more tailored experiences.

 

That is why behavioral analytics is critical for business growth, assisting in attracting new customers—known and unknown—as well as retaining existing customers based on actual interactions and usage. The foundation of behavioral analytics is hard data. It makes advantage of the massive amounts of raw data that users generate while using social media, gaming applications, marketing, retail sites, or applications. This data is collected and processed before being utilized to make specific decisions, such as how to forecast future trends or economic activities, such as ad placement.

 

Also Read | Introduction to Internet of Behaviour (IoB)

 

Who needs Behavioral Analytics?

 

The beauty of behavioral analytics is that once your team begins utilizing it to help inform your customer profiles, everyone in your organization—at any level—can benefit from its insights. While these statistics can be used by anybody in your organization, specific roles gain the most from them:

 

1. Marketers:

 

Marketers may employ behavioral analytics to create cohort data that can assist them in getting the most out of campaigns, optimizing customer acquisitions, and increasing retention and conversions. When behavioral data is combined with transactional and demographic information, it can be used to construct more detailed, multidimensional consumer profiles. Insights and predictions about your clients can then be used to create more relevant, customized interactions.

 

2. Teams of Information Technology:

 

One of the primary reasons firms collect and analyze data is to gain insight into consumer habits. However, behavioral analytics can also be used to better analyze employee behavior. Given the increased threat of cyber attacks, IT teams should use behavior analytics to improve organizational data security. Login, endpoint, and network event behavioral tracking can alert IT staff to inconsistent or suspect activities. Find malware infestations before they cause harm and implement security measures swiftly to thwart a potential security breach.

 

3. Sales:

 

Behavioral analytics is where marketers and sales teams come together to create a winning plan. A marketing team that uses behavioral data to produce successful campaigns assists the sales team in demonstrating a meaningful return on investment (ROI) from such campaigns while also generating a larger, more qualified funnel. Following users' browsing behaviors and reactions, for example, shows chances to upsell and cross-sell products to clients who are most likely to respond to those offers, resulting in more sales and at a greater volume.

 

4. Customer Service Teams:

 

Customer experience is a major motivator of loyalty in the digital age, and this translates directly to revenue. According to research, firms that exploit consumer behavioral data outperform peers by 85% in terms of sales growth and more than 25% in terms of gross margin. Looking at the entire customer experience through the lens of facts rather than assumptions is the quickest way to improve your customer experience strategy. Just ask the Ritz-Carlton, Singapore Airlines, or Uber. 

 

5. Analysts of data:

 

Data analysts assist in deciphering the entire customer experience by comparing user intent to reality using signals collected from behavioral analytics. The data can also be used to distinguish between consumers who are prone to churn and those who are more likely to remain loyal clients. Data analysts can perform user analytics on complex data, translating it into usable insights. Marketers may then utilize those insights to make data-driven decisions about optimizing workflows, allowing teams to focus on activities that add the most value.

 

6. Teams for Digital Experience:

 

The metrics surrounding how visitors interact with specific components of your website or app are the focus of digital experience analytics. Digital experience analytics is intended for behavioral deep dives rather than simply counting admissions to your web pages. Understanding how consumers engage with each button, tab, and image will help you achieve your overall objectives.

 

Also Read | Customer Behavioral Analytics - An Overview

 

How Does Behavioural Analytics Work? 

 

You must first invest in a behavioral analytics solution, such as Amplitude Is Digital Analytics Platform, before you can begin extracting insights from your behavioral data. Your analytics platform should capture user behaviors from your product as well as a data warehouse or CDP, and it should interface with additional tools such as social and email message systems. The behavioral data can then be visualized using a dashboard or reporting system.

 

After gathering behavioral data, you must establish the particular business goals and objectives you wish to attain. These objectives are specific to your organization and product, but retention, conversion, and engagement are almost universal across industries. It will be considerably more difficult if you do not have precise short- and long-term business goals.

 

Ultimately, your primary goal should be to learn as much as possible about your customers: what they want, what they don't want, and what keeps them engaged. To get answers, utilize a platform like Amplitude to investigate the habits and preferences of a group of people. These groups are referred to as behavioral cohorts.

 

When specific groups' behaviors are examined, user patterns and tendencies emerge. Cohorts can be segmented based on certain behaviors to uncover possibilities to optimize the overall customer journey. Behavioral analytics is a digital product and a branch of business analytics that focuses on delivering specific business objectives or managing risks by combining collected data, behavioral analytics tools, and marketing automation systems.

 

Benefits of Behavioural Analytics

 

The following are some of the primary advantages of business analytics: 

 

1. Identifying and mitigating cyber dangers: 

 

The main advantage of BA is that it allows enterprises to discover a broader spectrum of cyber risks, such as insider threats, advanced persistent attacks, sensitive data leakages, and so on. User activity and behavior analytics can be used across the whole enterprise, including networks, PCs, servers, routers, endpoints, and applications. It detects even the most sophisticated threats, such as advanced persistent threats and zero-day exploits. As a result, firms may handle hazards in real time, far before they escalate into major cybercrime. 

 

2. Enhance fraud detection:

 

Fraud costs approximately $600 billion every year, or 0.8% of global GDP. As a result, it imposes a tremendous cost on businesses and consumers. Businesses can employ behavioral analytics to improve their AI/ML models' fraud detection skills. Because of behavioral analytics, ML models may identify factors that are unique to each individual, such as: Mouse motions , Typing habits and speed Login history, for example, can be used to confirm the security of a transaction. This type of advancement lowers the percentage of successful fraudulent actions.

 

3. Advanced persistent threats (APTs) detection:

 

In enterprises, behavioral analytics can be extremely useful in detecting advanced persistent threats (APTs). Because of their specific methods of entering systems and maintaining persistence, APTs pose a serious challenge to established security solutions today. APTs seek to get prolonged access to an organization's server, making detection more difficult. Behavioral analytics, on the other hand, can assist in detecting the presence of APTs by monitoring any odd activity that deviates from conventional patterns and behaviors. 

 

4. Improve your A/B testing:

 

A/B testing is perfect for putting to the test a hypothesis you've made based on previous user data or insights. For example, if a company can't decide on the color of the font on a custom mobile app platform, it can use an A/B test to see how different colors affect user engagement. 

 

Companies can use behavioral analytics to understand how client usage behavior changes when a parameter is altered. Do customers devote more time to the app? Do they go shopping more? etc. It's worth noting that such testing and analytics are also beneficial to game developers because they can uncover "boring" areas of the game that might be enhanced.

 

5. Create personalized and customized offers:

 

It is feasible to learn consumers' tastes and produce personalized offerings that match their demands thanks to behavioral analysis. Companies, for example, can tailor their e-commerce systems to recommend specific things to specific consumers. As we all know, Amazon is quite good at this. According to McKinsey, Amazon.com's recommendation engine generates 35% of its revenue. 

 

Currently, many companies employ behavior analytics to analyze trends, patterns, and anomalous behaviors and make data-driven decisions. BA is used in cybersecurity to detect malicious activity of individuals, systems, apps, networks, and many other interconnected components of an organization.

 

Depending on the area of concentration, there are various forms of behavioral analytics. The most frequent and successful form for cybersecurity is user behavior analytics. Organizations now gain numerous benefits from behavior analytics, including detecting a wide range of cyber risks, detecting advanced cyber threats such as APIT, expediting security incident response, and assisting with compliance challenges.