What is Predictive Behavioral Analytics?
The concept of Predictive Behavioral Analytics is the most recent development in the world of Business Analytics. The data experts use techniques like data mining, data visualization, neural networking, and algorithm clustering to understand the user’s behavioral trends and patterns. Alongside, this massive data is captured from applications related to games, websites, social media, purchase history, marketing preferences, etc. Such event-driven consumer data helps to establish a strong grounding for predicting the future outcomes of human behavior. The idea of Predictive Behavioral Analytics allows organizations to develop accurate insights into the customers’ responses to products and services. Thus, organizations are able to take calculative decisions and prioritize customer preferences.
How can Predictive Behavioral Analytics be useful?
Inarguably, Big Data has been of great importance in various fields as it helps to determine the optimal strategies to streamline operational processes and developments.
|Understanding Customer Dynamics||Predictive Behavioral Analytics allows organizations to track customer interactions and dynamics. An understanding of dynamics like processes, equipment, and people can help to effectively analyze the trend. The rich insight into the customer dynamics is impactful in gaining a detailed and accurate picture of market-related movements.|
|Develop Customer Behavioral Insights||A complete understanding of customer behavior helps marketers to plan the right and customized offers for all sets of customers. Customer Profiling is the basic necessity for building basic transactional insights and understanding the correlation of multiple factors in the customer value curve.|
|Manage Workforce||The use of Predictive Behavioral Analytics is not limited to customers rather it stretches to the internal workforce too. Behavioral analytics try to break the organization’s network and develop the ability to receive the notification related to employee activity. Using behavioral analytics organizations can develop employee-related protocols and streamline the organizational efforts.|
|Insider Threat Detection||Providing the safeguard to the organization from insider threats is the need of an hour. These threats usually arise as a result of data vulnerability. Thus, organizations make use of Predictive Behavioral Analytics to identify these threats and employees’ responses to these threats. By predicting the threats the organization is able to govern the behavior and performance of the employees.|
|Maintain Organizational Efficiency||Several business houses have benefited from the use of Predictive Behavioral Analytics in managing their crucial, especially the ones involving human interactions. By utilizing the forecasting feature of data analytics in predicting behaviors of the team, the administration is able to reduce the time and resource wastage which further helps to achieve the organizational objectives more effectively.|
Metrics used for Predictive Behavioral Analytics
Predictive Behavioral Analytics is all about monitoring the changes and tracking user behavior. In order to derive useful insights from these analytics, the organization uses several metrics or Key Performance Indicators (KPIs) which are mentioned below.
|Time to Value for Distinct Users||This time period refers to the time taken by users to realize and experience the value of the product. Reaching the activation milestones for users indicates high customer satisfaction, customer lifetime value, and conversion rate.|
|Depth of Adoption||The product adoption stage is said to occur when users move from trial to the payment stage. The depth of adoption measures the frequency of product usage and defines customer loyalty across the various target audience. The proper analysis of the adoption rate helps to identify the gaps and take measures for improving the adoption strategy.|
|Documentation used||The analytical features of applications like Help Center and Knowledge Base support organizations in understanding user behavior. These are termed Application documents and provide the data to optimize the organizational workflow and resource utilization.|
|Premium Conversion Rate||Considering the conversion phase of consumers from freemium to premium helps to identify the revenue stream for the organization. An ideal rate conversion rate lies between 2 to 5% and helps to identify which segment of users generates the most revenue and helps the organization in the achievement of its targets.|
Examples of Industries using Predictive Behavioral Analytics
Several industries have welcome Predictive Behavioral Analytics into their ecosystem with open hands. Some of the key industries that have incorporated the features of user-focused data analytics in their space are:
Financial Services – Locating ATMs and Branches
Financial Services organizations use Behavioral Analytics to identify the users’’ behavioral patterns which are generally suspicious and anomalous. By identifying such patterns companies are able to strengthen their anti-fraud capabilities and link demographic data to the right traffic /customer pattern.
Retailers – Entertainment Industry
Retailers track the customer movement across various channels and find out when, where, and how frequently they make transactions. It may include their responses to email campaigns, television ads, social media campaigns, etc.
E-Commerce – Ease of Shopping
The industry experts use the test scenarios to track and understand the intention behind every click. These actions involve customers’ decision to abandon the shopping cart, leave the funnel without completing the checkout, and more such things.
Communication – a 360-degree view
Predictive Behavioral Analytics helps organizations to get enriched access to customer information. This includes understanding the patterns related to external sources and network usage which further helps to gain a better view of subscribers’ behavior. One of the European Telco experienced 40% cost savings by using the 360-degree view of the customers’ response.
Software used for Predictive Behavioral Analytics
The commonly used tools in the field of Predictive Behavioral Analytics are as follows.
|Hotjar||It is the most comprehensive User Behavior Analytics that uses heatmaps, feedback, surveys, and recordings to optimize the visitors to websites. This tool integrates with other popular business tools such as Hubspot, Slack, Omniconvert, Optimizely, etc.|
|Mixpanel||This analytical tool is useful for developing in-depth reports about customer journeys and behavior. Some of the critical features of Mixpanel are A/B testing, conversion tracking, audience targeting, and data visualization. Major business platforms integrated into the tool are AWS, Convert, AB Tasty, etc.|
|CrazyEgg||It is the best tool to support detailed heatmaps for user interactions and is built on features like project management, a centralized dashboard, and marketing analytics. Some of the third-party platforms such as Shopify, Aweber, Wix, and WordPress are well-integrated with this Behavioral Analytics tool.|
|Mouseflow||This behavioral analytics tool focuses on highlighting the area that demands urgent attention. It helps organizations contextualize the behavioral data, improve the lead generation process, and launch feedback campaigns. Some of the business tool that integrates with Mouse flow, Hubspot, Google Analytics, WordPress, etc.|
|Pendo||It is a user-focused product management tool that helps organizations to retain customers, and ensure smooth onboarding, and product optimization. This tool can also create and distribute reports to generate detailed insight into customer sentiments. Some of the third-party apps such as Hubspot, Slack, Salesforce, and Jira are integrated with the Pendo tool.|
Understanding user behavior is the major focus of every organization as this acts as the strong base for growth and expansion. As a result, the idea of Predictive Behavioral Analytics took a great leap ahead amidst this urgency for understanding human behavior. The use of Behavioral Analytics in workforce management, resource mapping, and policy deployment still has a long way to go with its potent features.