CX AI Features

// // overview modern consumers expect seamless, efficient digital experience s, and every interaction contributes to overall customer satisfaction to meet these experiences, organizations need tools that deliver deep, actionable insights into customer conversations powered by nice's cx ai models – and backed by decades of contact center research and billions of real world interactions – elevateai provides immediate analysis of voice and chat data at scale using pre built behavioral models what's new with elevateai v1 11 , all declared interactions – declare an audio interaction , declare a chat interaction , and declare a transcript – are now automatically processed through the full range of cx ai services this release expands coverage across all communication types and ensures consistent, end to end customer experience analysis key capabilities elevateai's pre built cx ai models make it easy to integrate intelligence into applications and workflows, unlocking faster time to value (ttv) automated interaction analysis at enterprise scale predictive and prescriptive insights powered by behavioral models purpose built models for immediate deployment and measurable impact // // voice activity for any audio interaction, voice activity segments returned with get cx ai include start and stop times (in milliseconds) for both voice and silence periods this enables immediate insight into non interaction time – such as hold time or extended silence – when participants are not actively engaging in addition, elevateai provides speaker diarization for cx ai results using both the cx and echo transcription models each voice segment is automatically associated with participantone or participanttwo , giving you visibility into total talk time per speaker cross talk detection (when both participants speak simultaneously) initiator identification (who spoke first during cross talk) note the cx model supports diarization for both mono (single channel) and stereo (dual channel) audio the echo model currently supports diarization for stereo (dual channel) only sample cx ai response { "allparticipants " { "voiceactivitysegments" \[ { "starttimeoffset" 0, "endtimeoffset" 4920, "result" "voice" }, { "starttimeoffset" 4920, "endtimeoffset" 7560, "result" "silence" } ] } } // // enlighten ai bundles nice enlighten scores are generated for each behavioral model and provided both as an overall score and as quartile based scores (segmented by interaction stage) this allows users to track behavioral shifts over the course of a conversation and pinpoint when key behaviors occur behaviors for customer satisfaction // // acknowledge loyalty the acknowledge loyalty score imeasures how effectively an agent acknowledges a caller's long term relationship with the organization and expresses appreciation for their loyalty higher scores indicate the agent consistently recognizes customer tenure in a natural, conversational way the model evaluates the entire conversation , using language from both the agent and the customer for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // active listening the active listening score reflects how well an agent listens and responds throughout a conversation higher scores indicate that the agent is engaged, responsive, and avoids asking the customer to repeat information unnecessarily the model evaluates language from both agent and customer to assess listening behavior for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // be empathetic the be empathetic score reflects how well an agent acknowledges the caller's issues and demonstrates understanding of their impact or hardship higher scores indicate stronger predicted proficiency in showing empathy during conversations the model evaluates both agent and customer language to assess empathetic behavior for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // build rapport the build rapport score measures how effectively an agent creates a personal connection with the caller by engaging in friendly, general conversation outside the main issue higher scores indicate strong proficiency in building trust and comfort through small talk or positive interaction the model analyzes both agent and customer input to assess rapport building behavior for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // demonstrate ownership the demonstrate ownership score measures how well an agent shows understanding of the caller's issue and a commitment to resolving it higher scores indicate strong proficiency in providing reassurance and taking responsibility the model evaluates both sides of the conversation to assess ownership behaviors for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // effective questioning the effective questioning score reflects how well an agent asks thoughtful, purposeful questions to better understand the caller's experience, concerns, or needs higher scores indicate stronger proficiency in uncovering issues and opportunities through meaningful questions the model analyzes agent and caller exchanges across the full conversation for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // inappropriate action the inappropriate action score reflects how well an agent avoids unprofessional or offensive behaviors – such as refusing a reasonable transfer request, using inappropriate language, or showing disrespect higher scores indicate stronger proficiency in consistently maintaining professional conduct the model analyzes both agent and customer language to evaluate this behavior for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // promote self service the promote self service score measures how effectively an agent encourages customers to use available self service options – such as an ivr system, website, or mobile app – when appropriate higher scores indicate strong proficiency in guiding customers toward tools that can streamline future interactions the model evaluates language from both agent and customer to assess this behavior for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // sentiment the sentiment score predicts the likelihood that a customer would give a positive post interaction rating in a survey the score is derived from both spoken language ( what was said) and acoustic cues ( how it was said), including pitch, tone, laughter detection, and cross talk the model analyzes the full conversation across both agent and customer to determine customer sentiment for the most accurate results, use the allparticipants score, which reflects the interaction as a whole // // set expectations the set expectations score measures how effectively an agent communicates next steps , outlines required actions , and ensures the customer understands what to expect after the interaction higher scores indicate strong proficiency in providing clear, proactive guidance that builds confidence and reduces uncertainty the model evaluates language from both agent and customer to assess this behavior for the most accurate results, use the allparticipants score, which reflects the interaction as a whole vulnerable customer behaviors a vulnerable customer is defined by the uk financial conduct authority (fca) as "someone who, due to their personal circumstances, is especially susceptible to harm, particularly when a firm is not acting with appropriate levels of care " these customers may require additional support or actions from organizations to ensure they receive fair, equitable, and appropriate service built by nice, elevateai's vulnerable customer models help identify potential signals of vulnerability in real time interactions // // vulnerable customer the vulnerable customer score predicts the likelihood that a caller may fall into one or more of the five subcategories commonly referenced in contact centers health, life events, resilience, capability, and age related factors higher scores suggest customer language or behaviors that may signal potential vulnerability the model evaluates both agent and customer language across the interaction for the most accurate results, use the allparticipants score, which reflects the conversation as a whole // // vulnerable capability the vulnerable capability score predicts the likelihood that a customer has limited knowledge, confidence, or skills in managing financial matters this may also reflect challenges in areas such as financial literacy or digital skils higher scores indicate the customer may require extra support to navigate the interaction effectively the model analyzes both agent and customer input to identify signals of limited capability for the most accurate results, use the allparticipants score, which reflects the conversation as a whole // // vulnerable health the vulnerable health score predicts the likelihood that a customer is experiencing physical health conditions or illnesses that impact their ability to manage daily activities or interactions higher scores suggest the customer may require additional assistance due to health related challenges the model evaluates both agent and customer language throughout the interaction for the most accurate results, use the allparticipants score, which reflects the conversation as a whole // // vulnerable life event the vulnerable life event score predicts the likelihood that a customer is undergoing significant life changes or hardships , such as bereavement, job loss, or relationship breakdown higher scores indicate the customer may need additional care and support during the interaction the model analyzes language from both agent and customer to identify potential signals for the most accurate results, use the allparticipants score, which reflects the conversation as a whole // // vulnerable mental health the vulnerable mental health score predicts the likelihood that a customer is experiencing mental health challenges that may affect their ability to manage daily activities or decision making higher scores suggest the customer may require additional support or accomodations the model evaluates both agent and customer language to identify relevant indicators for the most accurate results, use the allparticipants score, which reflects the conversation as a whole // // vulnerable resilience the vulnerable resilience score predicts the likelihood that a customer has a reduced ability to cope with financial or emotional stress higher scores indicate the customer may need extra support due to difficulty managing stress or pressure the model evaluates both agent and customer language to detect signals of reduced resilience for the most accurate results, use the allparticipants score, which reflects the conversation as a whole additional models in currently in beta include financial distress high risk transactions for the most up to date details on new models and features, visit the elevateai release notes need more help? contact the elevateai support team