La industria del call center tiene una capacidad abrumadora para generar información de valor, pero actualmente solo exprime el 1% de su potencial. Ahí entran en juego Speech Analytics y su evolución: la inteligencia conversacional.
Y es que la falta de visibilidad sobre métricas clave del negocio y la incapacidad para analizar estos datos de forma masiva son limitantes, tanto para las empresas del sector como para las grandes corporate que contratan sus servicios.
Digamos que si estás usando tecnología de Speech Analytics y no inicias proyectos que introduzcan la inteligencia artificial en el call center, te estarás quedando atrás.
En este artículo ponemos blanco sobre negro las diferencias entre las soluciones estándar de Speech Analytics y las herramientas de IA conversacional, como Upbe. Hablaremos de:
- Las limitaciones de Speech Analytics vs IA conversacional
- Qué se puede hacer con una herramienta de IA conversacional
- Cómo darle contexto a toda esa información
- Qué conclusiones puedes sacar con los datos de SA o IA conversacional
Analizando llamadas con Speech Analytics
Las conversaciones en las que los clientes cuentan sus incidencias y expresan sus emociones son un auténtico diamante en bruto por explotar.
El problema es que Speech Analytics es una tecnología incapaz de procesar toda esta información de forma masiva y escalable para convertirla en conocimiento.
El procedimiento para entender esa información con un software de Speech Analytics consiste en realizar escuchas manuales, que cubren menos del 1% de las llamadas, y que por lo tanto ofrecen una información no relevante estadísticamente, subjetiva y muy costosa.
De esta necesidad surge la evolución hacia la inteligencia artificial aplicada al entorno del call center, con dos grandes diferencias que la separan de la tecnología SA.
La primera: Upbe utiliza aprendizaje automático y procesamiento de lenguaje natural para identificar conceptos e ideas, no palabras previamente especificadas.
Y la segunda: dispone de un motor que evalúa y analiza el sentimiento de los interlocutores basado en más de 100 características de la señal de audio.
Más adelante te explicamos las diferencias en detalle. Pero antes, sentemos las bases para comprender la cuestión: qué es Speech Analytics y qué limitaciones tiene.
¿Qué es speech analytics?
Speech Anlytics es una tecnología que sirve para transcribir una grabación de audio a texto: una pieza fundamental para el análisis de llamadas en la industria del contact center.
Sin embargo, un software estándar de Speech Analytics tiene limitaciones para proporcionar un buen entendimiento de lo que sucede en todas esas conversaciones. En definitiva, para realizar un análisis exhaustivo de forma automatizada.
Para los profesionales responsables de equipos y negocio esto significa basar su toma de decisiones en datos no estructurados e incompletos.
No tienen la capacidad de evaluar la calidad de las llamadas, el ajuste de los agentes al discurso comercial o la vinculación que consiguen generar con el cliente. Mucho menos, de entender la empatía con la que se gestionan los problemas.
Entonces, ¿para qué sirve realmente Speech Analytics? ¿Sigue siendo una tecnología relevante?
Sí, en conversaciones muy literales. Por ejemplo, para evaluar ítems de compliance (datos personales, consentimientos, etc.) donde los términos utilizados no varían.
En el resto de situaciones, Speech Analytics es útil como base tecnológica, o como pieza de un engranaje. Es decir, cuando se completa con otras tecnologías, como el Machine Learning, incorporando así el contexto y el análisis de sentimiento a la transcripción de las llamadas.
Limitaciones de Speech Analytics
Como acabamos de ver, hay escenarios en los que un software de Speech Analytics puede resultar de gran utilidad. Pero son sus limitaciones las que han llevado al mercado a buscar soluciones que consigan superar las barreras, especialmente estas tres:
- Ausencia de contexto en las conversaciones grabadas
- Enorme esfuerzo de categorización de las llamadas
- Revisión manual (humana) por parte de auditores de calidad
1. Ausencia de contexto en las conversaciones grabadas
El contexto lo es todo en una conversación. Las palabras pueden tener diversos significados dependiendo de la situación en la que se ubiquen, pero Speech Analytics no identifica el contexto asociado a una palabra o expresión.
El caso más claro es de la homonimia. Piensa en esas palabras que se escriben o se pronuncian igual pero no significan lo mismo, como «gato» (animal o herramienta) o «pata» (pierna o ave).
Esto sucede constantemente, incluso en palabras usadas en una misma región. Pero también es frecuente que una misma palabra varíe de significado según el país, como «ahora», que en España significa «inmediatamente», mientras que en otros países de habla hispana significaría «a lo largo del día».
Speech Analytics puede reconocer palabras expresadas de forma literal. Pero es incapaz de asociar varios significados a una misma palabra y decidir cuál de ellos es el correcto en función de la situación.
Dicho de otra manera: Speech Analytics es un sistema que reconoce pero no es capaz de interpretar.
Como decíamos antes, en entornos muy concretos puede resultar de utilidad. Pero allá donde se necesite el contexto para evaluar la calidad de una llamada o el desempeño de un agente, no.
2. Enorme esfuerzo de categorización de las llamadas
Construir categorías mediante Speech Analytics requiere pensar y anticipar multitud de posibles combinaciones y escenarios de palabras que reflejen la categoría o ítem de lenguaje que se quiere identificar.
Un ejemplo muy sencillo: piensa en una posible categoría llamada «saludo».
Para poder identificar una expresión y meterla dentro de esa categoría, Speech Analytics te obliga a seleccionar todas las posibilidades en su forma literal:
- Hola, qué tal
- Hola, cómo estás
- Hola, buenas
- ¿Qué hay? Hola
- Buenos días
Y un largo etcétera.
Puede parece algo trivial pero, como ves, en cualquier conversación telefónica hay muchas formas de saludar. E igual que hay muchas formas de saludar, hay innumerables formas de despedirse, solicitar ayuda, quejarse o formular cualquier cuestión relevante para tu negocio.
Es un enfoque que dificulta la escalabilidad: es difícil de reproducir en múltiples campañas y supone que personas con experiencia dediquen una cantidad enorme de tiempo a tareas manuales de bajo valor.
En vez de analizar y proponer mejoras a los agentes o los clientes, tienen que dedicar su tiempo a labores con un ratio coste/beneficio cuestionable.
En contraste, la IA conversacional se basa en tecnologías inteligentes con capacidad de aprendizaje por lo que es capaz de interpretar múltiples expresiones y asignarlas dentro de una misma categoría (como «saludo»), de forma automática.
3. Revisión manual
Los resultados derivados de las categorías definidas deben ser revisados de forma manual. Se tiene que comprobar la incidencia de falsos positivos o negativos que pueden llevar a tomar decisiones erróneas.
Y esta labor también exige una cantidad de tiempo y esfuerzo que obliga a los profesionales cercanos a la toma de decisiones a realizar tareas de bajo valor.
En lugar de analizar y planificar acciones con un enfoque estratégico, se encuentran secuestrados revisando llamadas sin significancia estadística y puntuando a agentes. Es una gestión ineficaz.
La evolución de Speech Analytics a la IA conversacional
Vistas todas las limitaciones de Speech Anlytics, surge la cuestión de cómo solucionar los problemas del sector.
La inteligencia artificial y, en concreto, el procesamiento de lenguaje natural, supone un cambio de paradigma tecnológico para mejorar la gestión del call center.
Con un software de IA conversacional como Upbe puedes interpretar el contenido de estas conversaciones y aumentar el análisis abarcando el 100% de las llamadas.
De analizar el 1% de las llamadas (o incluso menos) a analizar el 100% hay una diferencia abismal. Suficiente para entender que, si no lo haces, te estarás quedando muy atrás.
Y es que la comunicación entre humanos es compleja. Nuestras interacciones tienen mucha ambigüedad y jerga. Están determinadas por patrones culturales y geográficos y, a menudo, hay un uso incorrecto de la gramática.
Sin embargo, Speech Analytics funciona cuando el lenguaje no está condicionado por la ambigüedad o el contexto, algo que ocurre muy pocas veces.
Y si pierdes todo ese contexto, pierdes la información: el análisis de los datos se vuelve incompleto o inexacto. Y surgen las ineficiencias.
Esta es una de las razones por las que surge una nueva categoría denominada inteligencia conversacional, que utiliza machine learning para superar estas limitaciones y erigirse como la evolución tecnológica de Speech Analytics.
Pero ¿en qué se basa la IA conversacional para mejorar la comprensión del contexto de una llamada? En dos conceptos: NLP (Natural Language Processing) y NLU (Natural Language Understanding).
NLP es una rama de la Inteligencia Artificial que ayuda a las máquinas a procesar el lenguaje humano, transcribiendo el audio y convirtiéndolo en texto.
NLU, por su parte, es una subrama de la anterior, y es la que permite la comprensión humana del significado de un texto. NLU profundiza en la compresión de las palabras ya que trata de identificar el sentido del lenguaje cuando este incorpora jerga o errores gramaticales reproducidos por personas de forma habitual.
Speech Analytics vs inteligencia conversacional
Veamos cuáles son, en definitiva, las 4 diferencias clave entre las soluciones de Inteligencia conversacional, como Upbe, y Speech Analytics.
Diferencia #1: Tecnología
Upbe se apoya en inteligencia artificial y aprendizaje continuo (machine learning) para desarrollar algoritmos con rangos de precisión altos.
Uno de los inconvenientes de Speech Analytics son los esfuerzos de categorización, que además tienen limitaciones para ser luego replicables en otros entornos. Upbe, gracias a la IA, propone otra innovación mediante lo que denominamos el Aprendizaje No Supervisado.
Este sistema identifica tópicos de expresiones que se repiten bajo determinados patrones en una tipología de llamadas.
De esta forma, es posible identificar categorías de lenguaje sugeridas por la máquina de forma automática. Es decir, la generación de este conocimiento no surge de la reflexión humana, sino de la categorización automática del software.
Es un sistema muy robusto para identificar, por ejemplo, llamadas con alta probabilidad de derivar en bajas de clientes. Lo que se le exige a la operación es que sea capaz de actuar sobre la información que hay en Upbe para que la herramienta aprenda de forma continua.
Diferencia #2: Contexto y análisis de sentimiento
Entender el contexto de una conversación y tomar decisiones en base a ello no es un sueño: con Upbe es una realidad.
Somos capaces de decirte cómo se sienten tus clientes, cómo reaccionan en relación a cómo gestiona la llamada el agente. No te pierdes nada.
Añadiendo el contexto de las llamadas, podemos mejorar el análisis conversacional y llevarlo a sus últimas consecuencias: al análisis de sentimiento.
El análisis conversacional no incluye solo elementos textuales. La comunicación paraverbal (que deriva del tono de la llamada o el énfasis que damos a determinadas cosas) es parte del mensaje, y las soluciones de Speech Analytics se pierden toda esa información.
Otra de las innovaciones que ha incorporado la inteligencia artificial es la capacidad de extraer información relevante de la señal de audio y la voz humana, ya sea del agente o el cliente.
Eso nos permite incorporar al análisis de parámetros relacionados con el tono, la frecuencia, o la amplitud, que pueden inferir el estado emocional del interlocutor de manera objetiva. De esta forma es posible identificar si un agente ha sido agresivo o rudo. O si, en cambio, ha generado un sentimiento positivo en el cliente con su discurso.
Esta combinación de NLP, NLU y Voice Analytics ofrece una solidez a la hora de interpretar la comunicación en llamadas telefónicas no vista hasta ahora. Te da una dimensión adicional para la toma de decisiones y para hacer más eficientes cualquier operación.
Es un paso que tienes que dar con tecnologías de IA Conversacional. Speech Analytics no te lo da.
Diferencia #3: Flexibilidad y autonomía en la gestión
Upbe es una plataforma self-service. Seas gestor de operaciones, director de innovación o responsable de customer experience, la herramienta está pensada para sacarle el máximo rendimiento sin necesitar conocimientos técnicos.
Esto tiene la gran ventaja de simplificar la operativa y reducir los habituales cuellos de botella: puedes gestionar directamente las campañas y realizar cambios con facilidad.
En Upbe, además, pagas por lo que usas. Sin licencias perpetuas ni compromisos. Detrás de nuestro modelo de negocio SaaS ( Software as a Service) está el compromiso de aportar valor a nuestros clientes de forma continua.
Diferencia #4: Puesta en marcha
Podemos poner en marcha un proyecto en unas 3-4 semanas. En nuestros webinars construimos una prueba de concepto en tan solo 20 minutos como muestra de lo que permite la plataforma: agilidad y rapidez para poner al servicio de la operación las mayores capacidades de análisis lo antes posible.
Además, combinando tecnología y conocimiento del sector, ofrecemos una plantilla de calidad adaptada a tus necesidades. Hay muchos elementos comunes transversales a los distintos servicios que se pueden utilizar. No tienes que empezar de cero integrando tu plantilla.
Modelando el call center del futuro con IA conversacional
En este punto ya ha quedado clara una cosa: los que no den el paso a la IA conversacional, más tarde o más temprano, van a quedarse atrás.
Hemos empezado hablando de las limitaciones de Speech Analytics. Hemos visto cómo la IA permite llevar el análisis de llamadas a otro nivel, tanto por la cantidad de llamadas analizadas como por la calidad de la información que se extrae.
Con todo esto, ya tenemos mucha tela que cortar, pero el compromiso de Upbe con la construcción de una herramienta que transforme el sector del call center no queda ahí.
Trabajamos de forma continua en mejoras de producto, como por ejemplo, el análisis conversacional en el momento exacto de la llamada. En tiempo real y disponible tanto sobre el análisis de señal de audio (Voice Analytics), como de texto (NLU).
¿Quieres saber cómo te puede ayudar la inteligencia conversacional en tu Call Center? Solicita una demo y te lo explicamos con todo detalle.
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