Amazon Q generative SQL is now available in Amazon Redshift Query Editor for US East (Ohio) and Asia Pacific (Seoul) regions. This feature enhances SQL query authoring in the web-based Query Editor for Amazon Redshift, enabling you to write SQL queries using natural language and receive intelligent SQL code recommendations. Amazon Q generative SQL makes Amazon Redshift database querying more accessible and efficient for users, regardless of their SQL expertise.
Using generative AI, Amazon Q generative SQL analyzes user intent, SQL query patterns, and schema metadata to identify common query patterns within Amazon Redshift. The conversational interface allows users to submit SQL queries in natural language while maintaining their existing data permissions. For example, when asking «Find total revenue by region,» the system automatically suggests appropriate SQL code by joining relevant Amazon Redshift tables, reducing development time and potential errors. Users can accept suggested queries directly or iterate with follow-up questions to refine their results.
Amazon Q generative SQL is now available in Amazon Redshift Query Editor for US East (Ohio) and Asia Pacific (Seoul) regions. This feature enhances SQL query authoring in the web-based Query Editor for Amazon Redshift, enabling you to write SQL queries using natural language and receive intelligent SQL code recommendations. Amazon Q generative SQL makes Amazon Redshift database querying more accessible and efficient for users, regardless of their SQL expertise. Using generative AI, Amazon Q generative SQL analyzes user intent, SQL query patterns, and schema metadata to identify common query patterns within Amazon Redshift. The conversational interface allows users to submit SQL queries in natural language while maintaining their existing data permissions. For example, when asking «Find total revenue by region,» the system automatically suggests appropriate SQL code by joining relevant Amazon Redshift tables, reducing development time and potential errors. Users can accept suggested queries directly or iterate with follow-up questions to refine their results. To learn more about pricing, visit the Amazon Q Developer pricing page. See the documentation to get started.
Amazon Relational Database Service (RDS) for PostgreSQL now supports the latest minor versions 17.3, 16.7, 15.11, 14.16, and 13.19. We recommend that you upgrade to the latest minor versions to fix known security vulnerabilities in prior versions of PostgreSQL, and to benefit from the bug fixes added by the PostgreSQL community. This release also includes updates for PostgreSQL extensions such as pg_active 2.1.4, pg_cron 1.6.5, pg_partman 5.2.4, and others.
You can use automatic minor version upgrades to automatically upgrade your databases to more recent minor versions during scheduled maintenance windows. You can also use Amazon RDS Blue/Green deployments for RDS for PostgreSQL using physical replication for your minor version upgrades. Learn more about upgrading your database instances, including automatic minor version upgrades and Blue/Green Deployments in the Amazon RDS User Guide.
Amazon RDS for PostgreSQL makes it simple to set up, operate, and scale PostgreSQL deployments in the cloud. See Amazon RDS for PostgreSQL Pricing for pricing details and regional availability. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console.
Amazon Relational Database Service (RDS) for PostgreSQL now supports the latest minor versions 17.3, 16.7, 15.11, 14.16, and 13.19. We recommend that you upgrade to the latest minor versions to fix known security vulnerabilities in prior versions of PostgreSQL, and to benefit from the bug fixes added by the PostgreSQL community. This release also includes updates for PostgreSQL extensions such as pg_active 2.1.4, pg_cron 1.6.5, pg_partman 5.2.4, and others. You can use automatic minor version upgrades to automatically upgrade your databases to more recent minor versions during scheduled maintenance windows. You can also use Amazon RDS Blue/Green deployments for RDS for PostgreSQL using physical replication for your minor version upgrades. Learn more about upgrading your database instances, including automatic minor version upgrades and Blue/Green Deployments in the Amazon RDS User Guide. Amazon RDS for PostgreSQL makes it simple to set up, operate, and scale PostgreSQL deployments in the cloud. See Amazon RDS for PostgreSQL Pricing for pricing details and regional availability. Create or update a fully managed Amazon RDS database in the Amazon RDS Management Console.
Cómo la unificación de datos mejora las experiencias de los compradores
Por: Lindsay Berg, gerente general de marketing de la industria global.
Transformar la experiencia del cliente requiere una base sólida de datos que sean precisos, accesibles y seguros. Un patrimonio de datos sólido también ayuda a las organizaciones a prepararse para el futuro, lo que le permite aprovechar todo el potencial de las últimas innovaciones tecnológicas, como la IA, y garantizar una experiencia unificada y eficaz en todo el recorrido del cliente.
Los minoristas recopilan grandes cantidades de datos de múltiples fuentes: inventario y personal, desarrollo de productos, ventas, marketing y más. Al unificar estos datos, los minoristas pueden comprender mejor las preferencias de los clientes, anticiparse a sus necesidades y ofrecer experiencias de compra memorables que generen lealtad. Mientras tanto, las empresas de bienes de consumo (CG, por sus siglas en inglés) pueden monitorear mejor los equipos de fabricación para reducir el tiempo de inactividad, monitorear las cadenas de suministro, anticipar nuevas tendencias de productos y satisfacer mejor las necesidades de los clientes. También aumenta de manera eficaz los ingresos y equilibra los costos al proporcionar a los líderes empresariales información que impulsa una mejor toma de decisiones y gestión de recursos.
Los desafíos de los datos frenan a las organizaciones
Obtener una visión unificada de los datos conlleva varios desafíos clave. Los datos fragmentados son un desafío común en todas las industrias, tanto para los minoristas como para las empresas de CG. Los minoristas extraen datos omnicanal de varias fuentes, incluidos los sitios de comercio electrónico, las ventas en la tienda, las redes sociales, los sistemas de la cadena de suministro y las interacciones de servicio al cliente. Para las empresas de bienes de consumo, los datos provienen de la investigación y el desarrollo (I&D), el marketing, las ventas, los equipos industriales (incluidos los datos de sostenibilidad) y las herramientas de gestión de la cadena de suministro. Todos estos datos están dispersos en muchas fuentes y vienen en una variedad de formatos, lo que hace que la integración sea una tarea compleja y que requiere mucho tiempo.
¿El resultado? Información inconexa que impide a los líderes empresariales tomar decisiones oportunas e informadas.
Sin una fuente de datos unificada, los minoristas luchan por comprender las preferencias de los clientes, predecir las tendencias de compra o administrar el inventario con precisión, mientras que las empresas de CG enfrentan tiempo de inactividad de las máquinas, interrupciones de la cadena de suministro y ciclos prolongados de gestión del ciclo de vida del producto. Esta falta de cohesión dificulta el crecimiento del negocio, ya que es más difícil ofrecer ofertas personalizadas o almacenar los productos adecuados. También afecta a los márgenes de beneficio, ya que los silos de datos provocan ineficiencias y redundancias que podrían eliminarse.
Además, los datos fragmentados pueden debilitar la fidelidad de los clientes cuando la experiencia de compra se vuelve incoherente y carece de personalización. También dificulta que los empleados de atención al cliente de todos los niveles accedan, gestionen y almacenen la información con precisión, lo que plantea problemas de seguridad y cumplimiento.
En el comercio minorista, consideren una tienda de muebles como ejemplo. Un comprador navega por el sitio web, muestra interés en artículos específicos y agrega algunos a su carrito. Más tarde, visitan la tienda física, con la esperanza de ver esos artículos en persona. Sin embargo, el empleado de la tienda no tiene registro de la actividad en línea del comprador y no puede ofrecer recomendaciones personalizadas. Frustrado por la falta de conectividad entre las experiencias en línea y en la tienda, el comprador se va sin comprar, lo que afecta los ingresos y la lealtad del cliente.
En el sector de los bienes de consumo, una empresa que opera grandes fábricas puede tener dificultades para realizar un seguimiento del rendimiento en tiempo real y las necesidades de mantenimiento sin datos conectados en los equipos. Cuando una máquina se descompone, la producción se detiene, lo que provoca costosos retrasos. Al integrar datos en tiempo real en un sistema unificado, la empresa pudo anticipar mejor los problemas, programar el mantenimiento preventivo, reducir el tiempo de inactividad y mejorar la eficiencia y la rentabilidad.
Estos desafíos pueden obstaculizar de manera significativa el crecimiento de los minoristas, las empresas de CG y las de ambas categorías. Para los minoristas, la desconexión entre las experiencias en línea y en la tienda puede provocar la pérdida de oportunidades de venta, la frustración de los clientes y la disminución de la lealtad a la marca. Para las empresas de CG, la incapacidad de pronosticar con precisión la demanda, realizar un seguimiento de los datos de sostenibilidad y obtener información procesable crea ineficiencias que perjudican la rentabilidad, la reputación y la competitividad. En última instancia, la falta de una estrategia de datos unificada sofoca el crecimiento al impedir que las empresas tomen decisiones informadas, optimicen las operaciones y ofrezcan experiencias fluidas a los clientes.
Uso de datos para crear experiencias de cliente fluidas y conectadas
Los datos operativos fragmentados tienen un impacto significativo en la experiencia del cliente, y los minoristas y las empresas de CG necesitan un patrimonio de datos completo para seguir competitivos y cumplir con las crecientes expectativas.
Una plataforma unificada para datos ayuda a consolidar todos los datos relevantes en una única fuente de verdad, lo que brinda una visión de 360 grados del negocio y sus clientes. Esta sólida base de datos permite a las empresas integrar la IA y otras tecnologías avanzadas para estar mejor equipadas para desbloquear información, mejorar la personalización y optimizar el recorrido del cliente.
Una vista completa de los datos también permite a los minoristas anticiparse mejor y satisfacer las necesidades de los clientes. De vuelta al escenario de la tienda de muebles, imaginen si el historial de compras en línea del comprador estuviera disponible para el empleado de la tienda. Cuando llega el comprador, el asociado puede guiarlo sin problemas a sus artículos preferidos en la tienda e incluso ofrecer una promoción relevante.
En el escenario de CG, tener una única fuente de verdad para los datos facilitaría la predicción de las necesidades de mantenimiento de los equipos, lo que reduciría el costoso tiempo de inactividad y garantizaría que la producción se mantenga en el camino correcto para satisfacer la demanda. En ambos escenarios, reunir los datos ayuda a crear una experiencia más fluida y receptiva que impulsa la satisfacción del cliente, la eficiencia operativa y el rendimiento general del negocio.
Activación del poder de los datos en toda la organización minorista
El valor de la unificación de datos va mucho más allá de las tiendas minoristas y las fábricas. Una plataforma de datos única y unificada también simplifica el acceso y la gestión de datos en toda la organización. Ya sea que los empleados estén en ubicaciones físicas, en la sede central o que trabajen de forma remota, pueden acceder de forma segura a información relevante, lo que permite tomar mejores decisiones en todos los niveles y mejorar la eficiencia operativa.
Las ventajas de la unificación de datos se extienden más allá de las operaciones de primera línea, lo que brinda beneficios significativos tanto para el liderazgo como para los equipos de TI.
Las plataformas de datos unificadas equipan a los ejecutivos de alto nivel con información en tiempo real sobre el comportamiento de los clientes, las tendencias de compra y el movimiento del inventario. Estas herramientas permiten a los líderes:
Tomar decisiones estratégicas basadas en datos que impulsen el crecimiento de los ingresos.
Identificar los productos de alto rendimiento y las demandas de los mercados emergentes.
Identificar nuevas fuentes de ingresos, como ofertas de servicios personalizados o programas de fidelización específicos.
Asignar recursos de manera efectiva, centrándose en áreas impactantes como la expansión de líneas de productos populares o la mejora de los diseños de las tiendas en función de los datos de tráfico peatonal.
Desbloqueo de capacidades avanzadas para equipos de TI
Una base de datos consolidada para los equipos de TI abre las puertas a tecnologías innovadoras que mejoran las experiencias de los clientes. Con datos completos a su disposición, los equipos de TI pueden:
Implementar soluciones impulsadas por IA, como recomendaciones inteligentes de productos y alertas predictivas de reabastecimiento.
Desarrollar herramientas digitales sofisticadas, como servicios de conserjería basados en la web, para ofrecer asistencia personalizada en tiempo real.
Garantizar interacciones fluidas y eficientes con los clientes que fortalezcan la satisfacción y la lealtad.
Al aprovechar todo el poder de sus datos, su organización puede capacitar a todos los empleados para que tomen más decisiones basadas en datos, mejoren la eficiencia operativa y mejoren las experiencias de los clientes.
Transformen un sólido patrimonio de datos en innovación
En el panorama de compras actual, lo más probable es que tengan todos los datos necesarios para atender a sus clientes mejor que nunca. Pueden convertir esos datos en información clara y procesable con una estrategia sólida y las soluciones tecnológicas adecuadas. Una plataforma de datos unificada les permite aprovechar todo el potencial de su información, lo que les ayuda a optimizar las operaciones, mejorar las experiencias de los clientes e impulsar el crecimiento.
Para obtener más información sobre cómo los datos unificados pueden transformar su negocio, consulten nuestro libro electrónico completo. Para obtener más información sobre cómo las soluciones de Microsoft ayudan a las empresas a impulsar la eficiencia y el crecimiento, visiten Microsoft Cloud for Retail y obtengan más información sobre Microsoft para bienes de consumo.
Regístrense para una versión de prueba gratuita de Microsoft Fabric para organizar y unificar sus datos y comenzar a desbloquear su verdadero potencial.
Amazon FSx for Lustre, a service that provides high-performance, cost-effective, and scalable file storage for compute workloads, now enables you to upgrade the Lustre version of your FSx for Lustre file systems. This feature allows you to benefit from the enhancements available in newer Lustre versions on your existing file systems.
FSx for Lustre provides fully-managed file systems built on Lustre, the world’s most popular open-source high performance file system. FSx for Lustre supports multiple long-term support Lustre versions released by the Lustre community. Newer Lustre versions provide benefits such as performance enhancements, new features, and support for the latest Linux kernel versions for your client instances. Starting today, you can upgrade your file systems to newer Lustre versions within minutes using the AWS management console or the AWS CLI/SDK .
The feature is now available on all file systems at no additional cost in all AWS Regions where FSx for Lustre is available. For more information, see Amazon FSx for Lustre documentation.
Amazon FSx for Lustre, a service that provides high-performance, cost-effective, and scalable file storage for compute workloads, now enables you to upgrade the Lustre version of your FSx for Lustre file systems. This feature allows you to benefit from the enhancements available in newer Lustre versions on your existing file systems. FSx for Lustre provides fully-managed file systems built on Lustre, the world’s most popular open-source high performance file system. FSx for Lustre supports multiple long-term support Lustre versions released by the Lustre community. Newer Lustre versions provide benefits such as performance enhancements, new features, and support for the latest Linux kernel versions for your client instances. Starting today, you can upgrade your file systems to newer Lustre versions within minutes using the AWS management console or the AWS CLI/SDK . The feature is now available on all file systems at no additional cost in all AWS Regions where FSx for Lustre is available. For more information, see Amazon FSx for Lustre documentation.
AWS AppSync, a fully managed GraphQL service that helps customers build scalable APIs, announces improvements to its EvaluateCode and EvaluateMappingTemplate APIs. This update enables developers to comprehensively mock all properties of the context object during resolver and function unit testing, including identity information, stash variables, and error handling. The enhancement also introduces improved JSON input validation with clear, actionable error messages, making it easier for developers to identify and fix issues in their context setup.
These improvements simplify the setup and configuration requirements. Developers can now efficiently test functions and resolvers by accessing and validating resolver stash (ctx.stash) and error tracking (ctx.outErrors) in their test environments. The update also simplifies identity mocking by allowing developers to include only the relevant caller information in ctx.identity. The updated console experience provides better visibility into the resolver test results, helping developers troubleshoot and optimize their resolver implementations more effectively.
This enhancement is available in all AWS Regions where AWS AppSync is currently supported.
To learn more about these new features, visit the AWS AppSync documentation and explore the context object reference. You can also explore examples and best practices in the AWS AppSync Developer Guide or get started by visiting the AWS AppSync console.
AWS AppSync, a fully managed GraphQL service that helps customers build scalable APIs, announces improvements to its EvaluateCode and EvaluateMappingTemplate APIs. This update enables developers to comprehensively mock all properties of the context object during resolver and function unit testing, including identity information, stash variables, and error handling. The enhancement also introduces improved JSON input validation with clear, actionable error messages, making it easier for developers to identify and fix issues in their context setup. These improvements simplify the setup and configuration requirements. Developers can now efficiently test functions and resolvers by accessing and validating resolver stash (ctx.stash) and error tracking (ctx.outErrors) in their test environments. The update also simplifies identity mocking by allowing developers to include only the relevant caller information in ctx.identity. The updated console experience provides better visibility into the resolver test results, helping developers troubleshoot and optimize their resolver implementations more effectively. This enhancement is available in all AWS Regions where AWS AppSync is currently supported.
To learn more about these new features, visit the AWS AppSync documentation and explore the context object reference. You can also explore examples and best practices in the AWS AppSync Developer Guide or get started by visiting the AWS AppSync console.
AWS HealthScribe is a generative AI-powered service that automatically generates summarized clinical notes and transcripts from patient-clinician conversations. Documentation for behavioral health related encounters follows a goal centric format based on GIRPP (Goal, Intervention, Response, Progress, Plan) format. With this launch, AWS HealthScribe customers can directly convert a behavioral health related patient-clinician conversation to a GIRPP format note. This can potentially save clinicians hours daily in manually documenting behavioral health related encounters.
Customers using the HealthScribe StartMedicalScribeJob and StartMedicalScribeStream API can simply set note template type parameter as “GIRPP” in the ClinicalNoteGenerationSettings for both async and streaming jobs and will share the output note in GIRPP format as the conversation ends.
This feature is available in US East (N.Virginia) Region. To learn more refer to our documentation.
AWS HealthScribe is a generative AI-powered service that automatically generates summarized clinical notes and transcripts from patient-clinician conversations. Documentation for behavioral health related encounters follows a goal centric format based on GIRPP (Goal, Intervention, Response, Progress, Plan) format. With this launch, AWS HealthScribe customers can directly convert a behavioral health related patient-clinician conversation to a GIRPP format note. This can potentially save clinicians hours daily in manually documenting behavioral health related encounters. Customers using the HealthScribe StartMedicalScribeJob and StartMedicalScribeStream API can simply set note template type parameter as “GIRPP” in the ClinicalNoteGenerationSettings for both async and streaming jobs and will share the output note in GIRPP format as the conversation ends. This feature is available in US East (N.Virginia) Region. To learn more refer to our documentation.
Amazon Elastic Block Store (Amazon EBS) now displays the full snapshot size for EBS Snapshots. With this enhancement, customers can now retrieve full snapshot sizes programmatically through the DescribeSnapshots API using the new field, full-snapshot-size-in-bytes. The full snapshot size is also displayed in the EBS Snapshots console under the new ‘Full snapshot size’ column.
Since EBS Snapshots are incremental in nature, if you take multiple snapshots of a volume over time, each snapshot only stores the new or modified blocks while maintaining references to unchanged blocks from previous snapshots. The ‘full snapshot size’ field shows you the total size of all blocks that make up a snapshot, including both the blocks stored directly in that snapshot and all blocks referenced from previous snapshots. For instance, if you have a 100 GB volume with 50 GB of data, the ‘full snapshot size’ would show 50 GB regardless of whether it’s the first snapshot or a subsequent one.
The ‘full snapshot size’ field provides crucial information about your EBS snapshot storage, such as the total size of the snapshot in the archived tier or the amount of data written to the source volume at the time the snapshot was created. Please note that this is different from the incremental snapshot size, which only refers to the size of newly changed blocks stored in that specific snapshot.
This feature is now generally available in all commercial AWS regions and the AWS GovCloud (US) Regions. To get started, see the EBS Snapshots user guide and API specification.
Amazon Elastic Block Store (Amazon EBS) now displays the full snapshot size for EBS Snapshots. With this enhancement, customers can now retrieve full snapshot sizes programmatically through the DescribeSnapshots API using the new field, full-snapshot-size-in-bytes. The full snapshot size is also displayed in the EBS Snapshots console under the new ‘Full snapshot size’ column. Since EBS Snapshots are incremental in nature, if you take multiple snapshots of a volume over time, each snapshot only stores the new or modified blocks while maintaining references to unchanged blocks from previous snapshots. The ‘full snapshot size’ field shows you the total size of all blocks that make up a snapshot, including both the blocks stored directly in that snapshot and all blocks referenced from previous snapshots. For instance, if you have a 100 GB volume with 50 GB of data, the ‘full snapshot size’ would show 50 GB regardless of whether it’s the first snapshot or a subsequent one. The ‘full snapshot size’ field provides crucial information about your EBS snapshot storage, such as the total size of the snapshot in the archived tier or the amount of data written to the source volume at the time the snapshot was created. Please note that this is different from the incremental snapshot size, which only refers to the size of newly changed blocks stored in that specific snapshot. This feature is now generally available in all commercial AWS regions and the AWS GovCloud (US) Regions. To get started, see the EBS Snapshots user guide and API specification.
Today, we are excited to announce the general availability of Jasmine – new Singaporean English Neural Text-to-Speech (NTTS) female voice for Amazon Polly.
Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk and to build entirely new categories of speech-enabled products.
Jasmine is our first voice for the Singaporean variant of English. Even though Singaporean English is reported to be close to British English, there are some unique pronunciation patterns that we captured while training this voice, such as pronunciation of telephone numbers or postal codes, to make sure that Jasmine sounds like a local speaker. With this launch, we continue building a variety of voice and language options for Amazon Polly customers.
Today, we are excited to announce the general availability of Jasmine – new Singaporean English Neural Text-to-Speech (NTTS) female voice for Amazon Polly. Amazon Polly is a service that turns text into lifelike speech, allowing you to create applications that talk and to build entirely new categories of speech-enabled products. Jasmine is our first voice for the Singaporean variant of English. Even though Singaporean English is reported to be close to British English, there are some unique pronunciation patterns that we captured while training this voice, such as pronunciation of telephone numbers or postal codes, to make sure that Jasmine sounds like a local speaker. With this launch, we continue building a variety of voice and language options for Amazon Polly customers. Jasmine and all the other NTTS voices are available in AWS regions supporting Neural TTS. For more details, please read the Amazon Polly documentation and visit our pricing page.
AWS AppSync GraphQL now offers operation-level caching, a new feature that allows customers to cache entire GraphQL query operation responses. This enhancement enables developers to optimize read-heavy GraphQL APIs, delivering faster response times and improved application performance.
Operation-level caching in AWS AppSync GraphQL streamlines the caching process by storing complete query responses. This approach is particularly beneficial for complex queries or high-traffic scenarios, where it can significantly reduce latency and enhance the overall user experience. By caching at the operation level, developers can easily boost API efficiency and create more responsive applications without additional code changes.
Operation-level caching is now available in all AWS Regions where AWS AppSync is offered.
To learn more about operation-level caching in AWS AppSync GraphQL, visit the AWS AppSync documentation. You can start using this feature today by configuring caching settings in the AWS AppSync GraphQL console or through the AWS CLI.
AWS AppSync GraphQL now offers operation-level caching, a new feature that allows customers to cache entire GraphQL query operation responses. This enhancement enables developers to optimize read-heavy GraphQL APIs, delivering faster response times and improved application performance. Operation-level caching in AWS AppSync GraphQL streamlines the caching process by storing complete query responses. This approach is particularly beneficial for complex queries or high-traffic scenarios, where it can significantly reduce latency and enhance the overall user experience. By caching at the operation level, developers can easily boost API efficiency and create more responsive applications without additional code changes. Operation-level caching is now available in all AWS Regions where AWS AppSync is offered. To learn more about operation-level caching in AWS AppSync GraphQL, visit the AWS AppSync documentation. You can start using this feature today by configuring caching settings in the AWS AppSync GraphQL console or through the AWS CLI.
Amazon Connect Contact Lens now enables managers to create rules based on patterns of customer hold time and agent interaction duration, to take automated actions such as categorizing contacts, evaluating agent performance and notifying supervisors. With this launch, managers can create rules to check how well agents comply with guidelines on placing customers on hold. For example, did the agent set expectations on hold duration, before placing the customer on hold for more than 5 minutes? In addition, managers can check if the agent interaction lasted long enough to warrant assessment of complex agent behaviors such as building customer rapport, customer issue root cause analysis, etc. By excluding contacts that were too short, such as less than 30 seconds, managers can get more meaningful insights from automated contact categorization and agent performance evaluations.
This feature is available in all regions where Contact Lens performance evaluations are already available. To learn more, please visit our documentation and our webpage. For information about Contact Lens pricing, please visit our pricing page.
Amazon Connect Contact Lens now enables managers to create rules based on patterns of customer hold time and agent interaction duration, to take automated actions such as categorizing contacts, evaluating agent performance and notifying supervisors. With this launch, managers can create rules to check how well agents comply with guidelines on placing customers on hold. For example, did the agent set expectations on hold duration, before placing the customer on hold for more than 5 minutes? In addition, managers can check if the agent interaction lasted long enough to warrant assessment of complex agent behaviors such as building customer rapport, customer issue root cause analysis, etc. By excluding contacts that were too short, such as less than 30 seconds, managers can get more meaningful insights from automated contact categorization and agent performance evaluations. This feature is available in all regions where Contact Lens performance evaluations are already available. To learn more, please visit our documentation and our webpage. For information about Contact Lens pricing, please visit our pricing page.
