ClickFox Launches Customer Behavior Intelligence Suite for Self-Service Channels

ClickFox announced the availability of a Customer Behavior Intelligence solution suite designed to model, measure and improve self-service applications.  This comprehensive solution allows businesses and government organizations to translate complex customer interactions across a variety of self-service applications including, interactive voice response (IVR) systems (DTMF), speech recognition, Web applications, customer relationship management (CRM), interactive kiosks and mobile devices—into fact-based decisions that optimize single-channel and cross-channel business performance.  Using ClickFox's Customer Behavior Intelligence suite, organizations are able to analyze, understand and optimize their customer interactions within their self-service applications in an effort to reduce operational costs, improve customer satisfaction and enhance the overall interactive customer experience.    "Customer service organizations have invested millions of dollars in customer self-service channels like interactive voice response (IVR) and Web sites to serve their customers at a lower transaction cost," said Art Schoeller, senior analyst at The Yankee Group.  "Our research indicates that one of the top spending priorities for enterprises is to optimize and extend self service capabilities so as to further reduce costs and improve customer service. At the same time a significant percentage of companies do not understand how their customers are interacting with these systems, where they fail and where they succeed.   ClickFox's Customer Behavior Intelligence suite is based on VIBE™ (Visualized Interactive Behavioral Engine), ClickFox's patent-pending technology in the area of user behavior modeling and optimization. ClickFox's founding team includes several faculty members and researchers from Georgia Tech who have applied their advanced academic research into software modeling solutions that enable companies to visualize and map user interactions across a variety of interactive systems.   The ClickFox Customer Behavior Intelligence suite is a decision support platform that includes system modeling, flow and behavior visualization, root cause analysis and reporting and an advanced recommendations engine.  The suite can be applied to a variety of self-service applications.  The Customer Behavior Intelligence suite consists of the following:  

·         ClickFox for IVR/Speech

·         ClickFox for Web

·         ClickFox for Cross-Channel

  ClickFox's Customer Behavior Intelligence suite provides a comprehensive array of modeling, analysis and optimization features and functions for optimizing self-service applications:  

• Behavior and Application Visualization - Allows organizations to visually see traffic, task and transaction flows for any step or task in self-service applications.  ClickFox provides an interactive visual system capability for both business and technical analysts.  It enables companies to find problems areas and get essential information on user conversion rates through analyzing user tasks or interaction sequences.  This helps organizations pin-point drop-off points at any point in the customer transaction process and follow the "failures" so that business-critical scenarios can be optimized.
• Cross-Channel Modeling and Analysis - Using ClickFox's business modeling capability, the entire customer self-service experience can be optimized.  Customer behavior can be viewed and analyzed across multiple systems, including IVR, speech, Web and CRM systems.  Organizations are now able to see the entire experience in one system view - allowing organizations to understand and improve customer experience across channels.  ClickFox analyzes aggregate customer behavior and can link behavior to individual utterances in speech applications or to recorded phone calls to identify and fix the highest impact customer service issues.
• Automated Optimization Engine - ClickFox's optimization engine utilizes artificial intelligence to enable organizations to identify immediately those application areas that are misaligned with user expectations and behaviors.  This engine compares application usage with system structure and mathematically generates robust recommendations for improvement based upon customer behavior. Recommendations deliver specific actions to help streamline call flows or restructure interactive flows.