Receptiviti

Receptiviti gives AI technologies emotional intelligence by revealing users' psychology, personality, decision-making style and emotions in real-time. Receptiviti enables bot makers and AI technologists to empower their platforms with emotional intelligence so they can discriminate between their users’ feelings, emotions and thinking styles, and use these insights to guide actions, communication styles and build stronger relationships and user dependencies.

When it comes to Marketing and Engagement, users will be able to segment and cluster Twitter followers, Facebook communities, customers and entire audiences by psychology, personality and decision-making styles. Engage them as individuals or in groups based on the way they think and how they make decisions.

Receptiviti delivers customer psychology insights and specific recommendations to sales and call center reps so they can best communicate with each customer based on their unique psychology and decision-making style. Receptiviti also enables its users to quantify rapport with customers, team members and accounts – all in real-time.

Receptiviti provides financial services companies with trainable psychology-based predictive models that evaluate customer risk in real-time. It identifies the markers that improve the company’s ability to predict high-risk and low-risk customers whose behaviors impact bottom line profitability.

Receptiviti also lets its users evaluate candidates without psychometric surveys. Measure employee engagement and understand the quality of relationships among employees and teams.

Receptiviti also helps the company understand how psychology impacts employee turnover, accidents, fraud, customer retention and customer satisfaction. Receptiviti helps sellers understand shoppers’ in-store and online, enabling them to make psychology-specific offers, recommendations and provide tailored shopping experiences.

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More Information on Predictive Analysis Process

Predictive Analytics Process Flow

For more information of predictive analytics process, please review the overview of each components in the predictive analytics process: data collection (data mining), data analysis, statistical analysis, predictive modeling and predictive model deployment.