About Exagens
Exagens is a behavioral banking software from Exagens that increases engagement and results. It combines behavioral economics, psycholinguistics, and human psychology so users can better connect with their clients. The software allows banks to use insights into consumer behavior to tailor their products and messaging, fostering stronger relationships with customers. Exagens provides tools for data analysis and personalized communication, improving the overall banking use. Key capabilities: behavioral insights customer engagement tools personalized messaging data analysis reporting tools Best for: banks and financial institutions that need to improve customer interactions and drive engagement.
Exagens by Exagens is a cutting-edge machine learning platform specifically designed to enhance customer engagement in the financial services industry. Its core purpose is to drive behavioral engagement through intelligent virtual assistants that operate within digital banking environments. Leveraging behavioral science, real-time analytics, and machine learning algorithms, Exagens helps financial institutions improve customer conversion, retention, and satisfaction by offering personalized, contextual experiences at scale. Key features include real-time behavioral predictions, dynamic conversational interfaces, tailored nudges, and seamless integration with online banking systems. The user interface of Exagens is clean and well-structured, focusing on usability for financial institutions rather than traditional developers or data scientists. It provides an admin dashboard that allows product managers and marketing teams to monitor performance, configure engagement rules, and view real-time analytics. The interface emphasizes ease of navigation, with sections organized into “Engagement Journeys,” “Nudges,” “Customer Insights,” and “Performance Metrics.” The conversational flow builder is intuitive, offering a visual, drag-and-drop interface to design interactions and customize messaging based on user behavior. This low-code approach empowers non-technical staff to fine-tune interactions without deep machine learning expertise.
Pros & Cons
- Delivers highly individualized financial guidance.
- Focuses on nudging users to take concrete financial actions.
- Aims to improve both customer financial well-being and institutional growth.
- Leverages AI, machine learning, and data analytics for effectiveness.
- Covers digital sales, adoption, and overall customer satisfaction.
- Integrating such a system might be complex and require significant IT resources.
- Reliance on detailed user data could raise privacy concerns for some customers.
- If behavioral models are flawed, they might lead to suboptimal recommendations.
- Quantifying the direct impact of behavioral nudges can be difficult.
Features
Key features
Combines principles of human behavior and decision-making to create more effective financial interactions.
Utilizes artificial intelligence, data analytics, and machine learning to deliver individualized and proactive financial guidance.
Provides tailored tips, insights, and advice to help users manage their finances smarter, adapting to their specific needs.
Focuses on turning passive content consumers into active participants, catalyzing them to take beneficial financial actions rather than just consuming information.
Aims to boost conversion rates, influence customer behavior, cross-sell/up-sell products, and ensure full usage of digital tools.
Supports the financial well-being of individuals while simultaneously driving growth for financial institutions through increased deposits, lending, and loyalty.
Additional features
Communicates in a way that resonates emotionally with users.
Focuses on selling products and services through digital channels.
Improves the rate at which users complete desired actions (e.g., signing up for a product).
Shapes customer actions in beneficial ways.
Promotes additional or higher-value products to existing customers.
Helps customers form regular positive financial behaviors.
Encourages and ensures full usage of digital financial tools.
Actively encourages customers to use online and mobile banking features.
Fosters stronger relationships through meaningful exchanges.
Gently encourages users to take desired financial steps.
Enhances customer involvement with digital platforms.
Improves overall contentment of customers.
Boosts the use of digital and retail banking channels.
Provides deep understanding of customer financial behavior.
Enhances the overall journey and interaction customers have with the institution.
Facilitates dynamic, interactive exchanges with users.
Delivers advice that is practical and customized, prompting action.
Motivates users to actively engage rather than just passively consume information.
Pricing
Countries & Languages
Available in
Interface languages
Billing currencies
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About Exagens
Exagens is a behavioral banking software from Exagens that increases engagement and results. It combines behavioral economics, psycholinguistics, and human psychology so users can better connect with their clients. The software allows banks to use insights into consumer behavior to tailor their products and messaging, fostering stronger relationships with customers. Exagens provides tools for data analysis and personalized communication, improving the overall banking use. Key capabilities: behavioral insights customer engagement tools personalized messaging data analysis reporting tools Best for: banks and financial institutions that need to improve customer interactions and drive engagement.
Exagens Details
Exagens's In-App Market Place
Does Exagens have an in-app market place?
Yes
How many Mini-Apps in the marketplace?
1
Mini Apps
N/A
Pricing Options
Accepted Payment Currencies
USD ($), EUR (€), GBP (£)
Pros & Cons
- Delivers highly individualized financial guidance.
- Focuses on nudging users to take concrete financial actions.
- Aims to improve both customer financial well-being and institutional growth.
- Leverages AI, machine learning, and data analytics for effectiveness.
- Covers digital sales, adoption, and overall customer satisfaction.
- Integrating such a system might be complex and require significant IT resources.
- Reliance on detailed user data could raise privacy concerns for some customers.
- If behavioral models are flawed, they might lead to suboptimal recommendations.
- Quantifying the direct impact of behavioral nudges can be difficult.
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