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https://doi.org/10.37815/rte.v34n3.958

Artículos originales

 

Implementación de un chatbot con NLP para recibir pedidos en una plataforma de delivery

Implement= ation of a chatbot with NLP to receive orders in a delivery platform

&nbs= p;

Miguel Langarano Guerrero1 <= /span>https://orcid.org/0000-0= 002-4186-5612,

Franklin Montal= uisa Yugla1  https://orcid.org/0000-0= 002-5588-5016, Milton Navas Moya1 https://orcid.org/0000-0= 001-5862-9475

 =

1Universidad de las Fuerzas Armadas, Latacunga, Ecuador

melangarano@espe.edu.ec, fjmontaluis@espe.edu.ec, mpnavas@espe.edu.ec

 

Enviado:         2022/07/03

Aceptado:       2022/09/16

Publicado:      2022/11/30                         

Resumen

El uso de chatbots = cada vez es más recurrente en una variedad de negocios, debido a la escalabilida= d al momento de atender clientes y generar procesos de compra automáticos. En es= te proyecto, se desarrolla un sistema de chatbot, llamado Chatty, que utiliza NLP para recibir pe= didos de clientes a través de una aplicación de mensajería instantánea para la plataforma de delivery Snap eats. La implementación del chatbot logró un incremen= to en la cantidad de pedidos en un porcentaje significativo durante el periodo de tiempo en que fue medido, además de recibir calificaciones positivas por pa= rte de los usuarios respecto a la facilidad de uso. Utilizar NLP en un chatbot para la comunicación con clientes es poco com= ún debido a la complejidad y al poco control sobre la conversación; sin embarg= o, al aplicar un flujo de datos definido, dicha complejidad se reduce ya que se direcciona al usuario sin la necesidad de utilizar formularios predetermina= dos, creando una interacción más fluida. El motor de NLP utilizado para este proyecto es GPT-3, que es un modelo generador de lenguaje muy potente creado por la empresa OpenAI.

 

= Pa= labras clave: = Chatbot, comunicación, conversación, GPT-3, NLP.<= /o:p>

 

Abstract

Sumario: Introducción, Materiales y Métodos, Resultados, Discusión de resultados y Conclusiones.

 <= /o:p>

Como citar:<= /span> Langarano, M., Montaluisa, F. &= ; Navas, M. (2022). Implementación de un chatbot co= n NLP para recibir pedidos en una plataforma de deliver= y. Revista Tecnológica - Espol, 34(3), 157-170. http://www.rte.espol.e= du.ec/index.php/tecnologica/article/view/958


The use of chatbots in a variety of businesses is becoming more and more recurrent due to the scalability of customer service and the generation of automatic purchase processes. This project develops a chatbot system called Chatty that uses NLP to receive customer orders through an instant messaging application for the Snap eats delivery platform. The implementation of the chatbot achieved a significant percentage increase in= the number of orders during the period in which it was measured. In addition to receiving positive ratings from users regarding ease of use. Using NLP in a chatbot for communication with customers is unusual due to the complexity a= nd lack of control over the conversation; however, by applying a defined data flow, such complexity is reduced since the user is addressed without the ne= ed to use predetermined forms, creating a more fluid interaction. The NLP engi= ne used for this project is GPT-3, which is a very powerful language generator model created by the company OpenAI.

 

Keywords: Chatbot, communication, conversation, GPT-3, NLP.=

 

Introducción

 La modernidad exige a las empresas una innovación cada vez may= or. Es el caso de la omnicanalidad en la atención a sus clientes, procesos ágil= es y seguros, reducción de costos, automatización, entre otros varios requisitos para poder tener organizaciones sanas y, sobre todo, competitivas. No solo contra la competencia local sino contra los gigantes de la industria tecnol= ógica, por lo que implementar nuevas herramientas para ser eficiente, puede hacer = la diferencia entre crecer o dejarse vencer.

 

En Ecuador, las plataformas de delivery internacionales como Rappi, Uber eats, Pedidos Ya, se han centrado en atender a sus clientes por sus propios medios electrónicos, como aplicaciones móviles o páginas Web. Así mismo ha nacido competencia local que ha tenido una estrategia parecida para atender a sus clientes como Super Easy, Picker, Snap eats, entre otras, con acceso a los medios para poder desarrollar la tecnología necesaria y brindar este servicio. Pero, por otro lado, existe una competencia local, que no ha logrado desarrollar tecnología propia para atender a sus clientes por medios electrónicos, y ha optado por utilizar aplicaciones de mensajería como Whatsapp, para ingresar al mercado, con un nicho que prefiere una comunicación más fluida, muchas veces con dispositivos móviles, en los que tienen que decidi= r si descargar una aplicación de delivery o una de mensajería. La decisión es obvia debido a la mayor utilidad que se le puede= dar a una aplicación de mensajería.

 

También existe la necesidad directa de los comercios, de comunicarse= con sus clientes para vender sus productos. Esta comunicación muchas veces se realiza a través de Whatsapp, debido a la facil= idad y fluidez que existe en la transmisión del mensaje. Claro que, esta facilidad= es para el cliente, ya que para los comercios y empresas esto se vuelve una ca= rga operativa más, lo cual da una ventaja adicional al uso de sistemas automáti= cos de respuesta e interacción con los clientes.

 

Para la plataforma Snap eats es una oportunidad muy grande poder ofrecer atención a sus usuarios, a través de canales de mensajería, por la fluidez de la compra y comodidad del usuario = para utilizar una aplicación que ya conoce y utiliza a diario. Es por todo lo planteado anteriormente que se formula la siguiente pregunta: ¿Cómo se puede ofrecer el servicio de delivery a usuarios de la plataforma Snap eats, de forma ágil y fácil, a = través de Whatsapp?

 

En la actualidad, existe una creciente demanda de formas de mantener= la lealtad de los clientes de una empresa, aumentar las ventas, tener canales = de atención abiertos 24/7, entre otras necesidades empresariales. Es así que se estima que los asistentes virtuales o chatbots alcancen un tamaño de mercado de 9.17 mil mi= llones de dólares para 2025. Esto se ve dramáticamente precipitado por la crisis d= e la Covid-19 que ha obligado a muchas personas a utilizar sistemas informáticos para realizar tareas que antes no eran necesarias como el teletrabajo o ped= ir comida a domicilio (Bloomberg, 2020).

 

La omnicanalidad, que es la comunicación de las empresas con sus clientes a través de múltiples canales (Pizzolo, 2015), se ha vuelto imperativa en la experiencia de compra de los clientes = de cualquier producto o servicio, tanto físico o digital. Las empresas buscan = que sus clientes tengan comunicación omnicanal, lo = cual es intensivo en talento humano, por lo que la integración de chatbots es muy importante para brindar un servicio de atención al cliente eficiente, especialmente con los usuarios millennials, que son la generación que más consume productos y servicios a través de medios electrónicos (Bloomberg, 2019).

 

En este mismo contexto, en Ecuador, 13 de cada 100 personas realizar= on su primera compra en línea durante la pandemia de la Covid-19, usando como medio de comunicación con los vendedores la aplicación móvil Whatsapp. El 60% de las personas indicaron que esta decisión, la tomaron por el alto riesgo de contagio que presenta salir en m= edio de una pandemia, mientras que un 44% lo hizo por una adaptación al cambio. = Esto deja notar que las ventas, pero sobre todo la comunicación por medios electrónicos, es cada día mucho más necesaria, incluso= en países como Ecuador, donde la inserción tecnológica aún no es mayoritaria e= n la población (Diario Expreso, 2020).

 

El uso de Whatsapp como medio de comunic= ación entre vendedores y compradores también es un tema de relevancia en este trabajo, ya que es el medio por el que la plataforma Snap eats decidió abrir un nuevo canal de atención a sus clientes. Y dado que, “el 49= % de las compras electrónicas se realizaron a través de Wha= tsapp durante el confinamiento” (Diario Expreso, 2020), es muy importante, no solo mantener un nuevo canal de atención, sino automatizar el proceso de atención para reducir costos y tiempos de servicio.

 

Las dinámicas laborales están cambiando mucho gracias a la tecnologí= a y en este rubro, los chatbots son herramientas qu= e pueden utilizar desde grandes bancos y corporaciones, hasta negocios pequeños y medianos; con el fin de atender a sus clientes de forma más eficiente, ayud= ando a ahorrar dinero en su operación. Es así que se estima que para el año 2022, esta tecnología de agentes conversacionales, pue= de beneficiar a los negocios para ahorrar más de 8 mil millones de dólares anualmente; además de permitir escalar sus operaciones de atención a sus clientes de una forma nunca antes vista (Fortune, 2017).

 

Es importante mencionar que Snap eats es= una plataforma de delivery que funciona en varias ciudades de Ecuador y todos sus pedidos los recibe a través de su propia aplicación móvil. Por lo tanto, es necesario para la plataforma, abarcar el segmento de usuarios que solicitan sus pedidos a través de Whatsapp, pero es muy importante también, que este nuevo segmento de usuarios, pueda ser atendido de forma automática y sin intervención humana para no aumentar los costos operativos de forma dramática.

 

Esta investigación tiene por objetivo implementar un chatbot para recibir pedidos de clientes dentro de la plataforma Snap eats y poder constatar que el ch= atbot, como canal comercial, es un factor importante de crecimiento dentro de las ventas de la plataforma.

 

En este artículo se pueden encontrar las herramientas, métodos, arquitectura y tecnología que se utilizó para crear el chatbot, así como su lugar dentro del modelo de negocio. También se pueden encontrar= los resultados de la implementación del chatbot dur= ante dos meses de funcionamiento y cómo evolucionó el número de órdenes en la plataforma, antes y después del chatbot, y su comparación con otros años, así como la reacción y opinión de los usuarios = de la plataforma respecto a la interacción con el chatbot= para crear pedidos. Por último, se encuentran las conclusiones del funcionamiento del chatbot y una comparación con otras implementaciones de chatbots, en entornos similares.=

 

Materiales y Métodos

Se planteó desarrollar Chatty, un sistem= a de recepción de pedidos para delivery que permita = a los usuarios de la plataforma Snap eats realizar sus requerimientos de forma automática dentro de la aplicación de mensajería Whatsapp. Las órdenes se reciben a través de un chatbot que da instrucciones al usuario de los pasos a seguir para ejecutar su compra e integra toda la selección del usuario al r= esto del flujo normal de servicio de la plataforma. Este ch= atbot tiene como soporte el NLP y GPT-3.

 

Chatty integró un flujo de compra más a la plataforma Snap eats, mediante otro canal de at= ención a sus usuarios. Esto permitió aumentar las transacciones en la plataforma, = ya que está apuntando a un segmento de posibles clientes que actualmente no utilizan la aplicación Snap eats para realizar = sus pedidos a domicilio.

 

Se determinaron todos los procesos de lógica de negocio que se necesitaron para la integración de este nuevo canal de pedidos, a la plataf= orma de delivery ya existente. Luego se realizó toda= la documentación necesaria para el desarrollo como requerimientos, casos de us= o, diagrama de clases y modelo de datos.

 

Herramientas y tecnologías

Procesamien= to del Lenguaje Natural (PNL)

Procesamiento del lenguaje natural (NLP, por sus siglas en inglés) es una subdivisión del lenguaje natural, la cual implementa semántica controla= da y es traducible en lo que se denomina Lenguaje Controlado Generalizado (Zadeh, 2003).

 

El procesamiento del lenguaje natural es un prerrequisito y condición para la traducción de máquina. En términos del procesamiento del lenguaje natural, la traducción de máquina neural, no solo tiene más generalidad, también refleja el potencial de la big data y el big data thinkin= g (Zong, 2018).

 

GPT-3<= /o:p>

GPT-3 es un nuevo modelo de lenguaje con 175 miles de millones de parámetros y 96 capas entrenadas con 499 miles de millones de tokens de contenido web, haciéndolo por mucho, el modelo de lenguaje más grande construido hasta la fecha (Dale, 2021).

 

Arquitectura cliente-servidor

El sistema cliente-servidor se puede definir como una arquitectura de software formada tanto por el cliente como por el servidor, donde los usuar= ios siempre envían solicitudes mientras el servidor responde a los requerimient= os enviados. Cliente-servidor proporciona una comunicación entre procesos porq= ue implica el intercambio de datos tanto del cliente como del servidor por lo = que cada uno de ellos realiza diferentes funciones (Shakir= at, 2014).

 

API

Las API permiten que sus productos y servicios se comuniquen con otr= os, sin necesidad de saber cómo están implementados. Esto simplifica el desarro= llo de las aplicaciones y permite ahorrar tiempo y dinero (Re= dHat, 2020).

 

Mediante API, es la manera en que el sistema se comunicará con servi= cios externos para obtener datos como: los comercios disponibles, los productos disponibles, los horarios de los establecimientos, precio del transporte, e= ntre otra información necesaria antes de ejecutar una orden exitosa.<= /span>

 

Nodejs

Node.js, es un software que permite ejecutar programas creados en el lenguaje de programación JavaScript sin necesidad de un navegador. Se basa = en la implementación del motor V8 de Google. V8 y Node se crearon en C y C ++ principalmente, centrándose en el rendimiento y el b= ajo consumo de memoria. Node tiene como objetivo ad= mitir procesos de servidor de larga duración (Tilkov, 2010).

 

NodeJS ayudará a ejecutar el programa dentro del contenedor Docker. Es importante señalar que se necesita realizar configuraciones de entorno previas, para que NodeJS pueda ejecutar de manera correcta, todas las librerías necesarias para el funcionamiento del chatbot y la interfaz de comunicación con Whatsapp Web.

 

Firebase

Firebase es un servicio de la empresa Google, que ofrece varias herramientas de backend, con el objetivo de que los desarrolladores se concentren en el producto que están creando. Esto incluye análisis, autenticación, bases de datos, configuració= n, almacenamiento de archivos, mensajería push. Los servicios están alojados en la nube y escalan de forma automática, hasta ci= erto punto, con precios muy accesibles (Firebase, 20= 20).

 

Este servicio en la nube proveerá principalmente de dos funciones al= chatbot, un lugar de almacenamiento de datos, así com= o de funciones de efecto secundario, que se ejecutan a través de eventos preconfigurados. Esto con el objetivo que el chatbot pueda responder también, a eventos dentro de la base de datos o a llamadas = de servicios externos.

 

Bases de da= tos NoSQL

Las bases de datos NoSQL no siguen los principios RDBMS (Relational Database Manag= ement System) las cuales buscan alternativas a las bases de= datos relacionales con un contexto en donde prima la velocidad, manejo de un gran volumen de datos y la posibilidad de tener un sistema distribuido (Castro, 2012).

 

Desarrollo del sistema

El desarrollo del sistema se centró en el backe= nd ya que la interfaz de usuario que se utilizó, es= una aplicación de terceros, la cuál es la aplicación de mensajería Whatsapp.

 

El sistema fue desarrollado con Javascript, sobre NodeJS y como parte fundamental, la libre= ría de testing Puppeteer. = Esta librería permite interactuar con la aplicación Whatsap= p Web, de forma automática, y poner en marcha conversaciones con usuarios que requieran el servicio de delivery.

 

Además, todo el proyecto tiene un ambiente de ejecución en Docker, p= or lo que su despliegue en diferentes contextos es rápido y no requiere de may= or configuración.

 

Adicionalmente se generó un modelo de datos capaz de mantener conversaciones en una secuencia lógica con los usuarios, que puedan ser revisados posteriormente, y que la misma instancia de agente conversacional, pueda tener concurrencia en conversaciones sin confundir los pedidos de var= ios clientes. Este aspecto es necesario dentro del chatbot= ya que existirán muchos usuarios interactuando con el = chatbot para realizar sus pedidos, pero todos deben ser atendidos dentro de la misma cuenta de Whatsapp, por lo que solo es posible mantener una instancia del chatbot, pero con va= rios procesos de conversaciones.

 

Es importante saber, que para realizar un pedido a través del chatbot, tiene que haber comunicación constante entre= este y la plataforma Snap eats, para que se pueda ob= tener información sobre comercios, productos, horarios de atención, disponibilida= d, tiempos de atención, entre otras variables importantes que el usuario deber= ía conocer.

 

En la parte del modelo de NLP, se creó una API para poder tener comunicación con el servicio de GPT-3, que brindará toda la potencia para el procesamiento de las conversaciones.

 

Por último, se debe integrar, una vez el pedido del usuario haya sido completado, geolocalizado, pagado (en el caso de tarjetas de crédito y débi= to) y confirmado, con la plataforma Snap eats, a tr= avés la misma API que utilizan los pedidos realizados desde la aplicación móvil.=

 

Fig= ura 12Tabla 1<= /span>).

 

Tabla 1=

Cantidad de pedidos de abril-mayo de l= os años 2020, 2021 y 2022 (valores)

PEDIDOS/AÑO

2020

2021

2022

ABRIL

2869

3221

4534

MAYO

2646

2933

5233

 

 

 

 

Cantidad de pedidos

Figura 3<= /span>

Cantidad de pedidos de 6 meses en la plataforma Snap eats

 

Lo que se obse= rvar en la Figura 4= es la cantidad de pedidos realizados a través d= e la plataforma Snap eats durante 6 meses. Estos 6 m= eses incluyen abril y mayo, que son los dos meses en que se implementó el chatbot para recepción de pedidos.<= /span>

 

Se puede obser= var que el número de pedidos sube notablemente en estos dos últimos meses, sien= do incluso mayor dicha cantidad que en diciembre considerado un mes de altas ventas. Sin embargo, se pone en contraste la cantidad de pedidos de los mis= mos meses contra años anteriores.

 

Figura 4=

Cantidad de pedidos de abril-mayo de l= os años 2020, 2021 y 2022 (barras)

 

En la <= /span>Figura 5= se observa qu= e la cantidad de pedidos en los meses donde se implementó el chatbot fue significativamente superior a la de anteriores años, ya que no correspo= nde al crecimiento porcentual mes-año que es de 12.23% en promedio, sino que da= un valor del 40.76%, el cual es significativamente mayor.

 

Por último, se analizó la cantidad de pedidos de abril y mayo sumados, que se realizaron a través de los medios preexistentes en la plataforma contra los pedidos ejecutados a través del chatbot y se obtuvieron= los siguientes resultados:

 

Figura 5=

Cantidad de pedidos de abril-mayo 2022= por medio

 

Cómo se puede observar, el 32.8% de pedidos en los meses de abril y mayo de 2022, se realizaron a través del chatbot, siendo un porc= entaje significativo, dado que era un medio de recepción de pedidos que previament= e no existía. Esto indica que de 9767 pedidos, 3204 f= ueron realizados con el chatbot; sin embargo, esto no necesariamente significa que todos ellos sean usuarios nuevos de la platafo= rma, pero la cantidad de pedidos si aumentó sustancialmente.

 

Calificación de usabilidad

La calificació= n de los usuarios es otro criterio de validación que se tuvo en este proyecto. E= stos resultados fueron obtenidos a través de una encuesta realizada a cada usuar= io de forma automática, cada vez que un cliente creaba un pedido con éxito. Es= ta encuesta se realizó en 3204 pedidos que fueron hechos a través del chatbot durante los meses abril y mayo de 2022.<= /o:p>

 

Se planteó una= sola pregunta con respuesta cerrada de si o no y fue la siguiente: ¿Usted sintió= que su pedido fue creado de forma satisfactoria a través del chat?

 

Las respuestas afirmativas se consideraron como usuarios contentos, mientras que las negat= ivas como usuarios descontentos.

 

Un porcentaje mayoritario de usuarios del chatbot respondió la encuesta, y de igual forma, la mayoría de los usuarios que la contestaron estuvieron contentos con el desempeño, usabilidad y facilidad de uso del chatbot.

 

 

 

 

 

Figura 6<= /span>

Calificaciones de usuarios del chatbot

 

Cantidad de errores

En el caso de = los errores de código, estos fueron recogidos a través de manejo de excepciones= en situaciones que el chatbot no haya podido respo= nder a un comentario o pregunta que un usuario haya realizado, o se haya comunicado una opción que no existe, en la selección de los productos para un pedido.<= o:p>

 

Para los error= es de pedidos, cada repartidor tuvo como tarea validar con el cliente, a través de una aplicación móvil, marcando si el pedido fue correcto o no.

 

Figura 7<= /span>

Cantidad de errores<= /span>

 

Se puede obser= var que dentro de los 3204 pedidos realizados a través del chatbot, hubo una cantidad poco significativa de errores de tan solo un 0.5%. Por ot= ro lado, hubo una alta incidencia de inexactitudes de código, que no significa= que el pedido no se haya realizado con éxito, sino que, en algún momento de la interacción con el cliente, se presentó una imposibilidad de responder de f= orma adecuada al requerimiento solicitado. La gran mayoría de pedidos se realiza= ron sin error alguno, pero a pesar de esto, los errores de código pueden llevar= a un descontento de los clientes y las equivocaciones de pedidos, por más pequeñas que sean, pueden ocasionar pérdidas a la plataforma si no existier= a un filtro humano para su validación.

 

Discusión de resultados

Chatbot de ventas en empresa = Eximport Distribuidores del Perú S.A.C<= /p>

El caso de la empresa Eximport es un ejemplo de la implementa= ción exitosa de un chatbot para ventas, como registr= o se realizó una tesis de pregrado sobre dicha implementación.

 

En su conclusi= ón se observa que se acepta la hipótesis de que la implementación del chatbot influencia positivamente la fidelización de l= os clientes (Guerrero, 2018). Se demuestra a través de un estudio estadístico = en el que se toma una muestra de usuarios del chatbot, los cuales fueron encuestados, obteniendo así resultados que indican un beneficio al utilizar esta herramienta tecnológica.

 

Contrastando l= os resultados de esta investigación con el presente proyecto, se puede decir q= ue los chatbots tienen un impacto positivo sobre l= os clientes de una empresa, a pesar de los retos que puedan presentarse en la implementación.

 

Chatbot para toma de órdenes = en restaurantes

Este estudio e= xploró las percepciones y comportamientos de clientes en restaurantes que utilizar= on chatbots para generar órdenes de compra, basándose en= la teoría de presencia social y poder comparar tres métodos para crear una órden (Leung, 2020).=

 

Las conclusion= es demuestran que las órdenes generadas por teléfono e int= ernet, causan mayor satisfacción a los clientes frente a un c= hatbot. Sin embargo, también se pudo observar que los chatbots= tenían mucho mejor desempeño frente a los clientes en restaurantes con opci= ones de selección sencillas y fáciles de representar en una conversación escrita= .

 

Para contrasta= r con los resultados obtenidos en el presente proyecto, se puede decir que los resultados de ambas investigaciones concuerdan en el tipo de uso en el que tiene mejor desempeño un chatbot. En el caso de= Snap eats, se ofrecen restaurantes con opciones sencillas = de seleccionar y muy rápidas de comunicar por chat, dejando sin espacio a equivocaciones humanas en la toma de pedidos que deben ser rápidos y con po= cas opciones.

 

Conclusiones

La implementación de un chatbot para la recepción de pedidos de la plataforma de delivery Snap eats, a base de los resultados obtenidos, = fue exitosa a pesar de existir mucho espacio para mejorar.

 

Se pudo determinar que la cantidad de pedidos tuvo un aumento promed= io del 40.76% en abril y mayo del 2022, respecto a esos mismos meses de años anteriores. El aumento del número de órdenes se debió a la implementación d= el chatbot ya que el 32.8% de los requerimientos realiza= dos, fueron efectuados por este medio. Es decir, que sin la implementación del <= span class=3DSpellE>chatbot como canal de ventas, no se registrarían 3204 pedidos adicionales en este periodo de tiempo.

 

La mayoría de usuarios del chatbot se mostraron positivos a su implementación y uso con un 58.2% de usuarios satisfechos en la interacción para realizar un pedido. Esto demuestra que la implementación permanente de Chatty sería bien recibida por los usuarios de la plataforma Snap eats, además de beneficioso respecto al aumento de los pedidos.=

 

Por último, los errores que se detectaron en un ambiente de producci= ón, a pesar de ser importantes, no afectaron negativamente la opinión de los usuarios del chatbot, ya que en su mayoría reci= bieron respuestas acertadas por parte del chatbot. Se considera que son mucho más graves los errores de pedidos que se dieron, a pesar de ser muy poco representativos a nivel porcentual, son errores que no solo pueden afectar negativamente a la opinión de los usuarios en caso de recibir un pedido incorrecto, también puede implicar cargos económicos en contra de la plataforma para solventar los errores en los pedidos de los clientes. Por ello, se recomienda mantener siempre una validación humana pa= ra verificar que el requerimiento esté correcto, ya que siempre existe la posibilidad de que el chatbot realice de forma incorrecta los pedidos de los clientes, aunque esta incidencia sea muy baja= .

 

Limitaciones del estudio

Este estudio se encuentra limitado principalmente por el área geográ= fica y las condiciones que esta impone, ya que la ciudad de Esmeraldas en que fue aplicado el chatbot, es considerada de tamaño m= ediano con aproximadamente 190.000 habitantes, de los cuáles se cuenta con una bas= e de clientes recurrentes de alrededor de 2000 y una baja competitividad en uso = de tecnología. Esto indica que los resultados podrían variar en ciudades más g= randes con una mayor cantidad de habitantes o con mayor competencia en herramientas tecnológicas.

 

Futuras investigaciones

Las futuras investigaciones deben incluir a ciudades y entornos diversos, con más habitantes y otros competidores comerciales que ofrezcan servicios similares. También, debe explorarse la opción de utilizar chatbots sin NLP, y que simplemente sigan conversacio= nes predefinidas y poco flexibles.

 

Una gran innovación sería investigar sobre la respuesta de clientes, frente a un asistente virtual telefónico que utiliza reconocimiento de voz y NLP para mantener una conversación hablada y fluida con los clientes= .

<= o:p> 

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6

Miguel Langarano,<= span style=3D'mso-spacerun:yes'>  Franklin Montaluisa, Milton Navas

5

Implementación de un chatbot con NLP para recibir pedidos en una plata= forma de delivery

 

E= scuela Superior Politécnica del Litoral, ESPOL

<= /p>

 

R= evista Tecnológica Espol – RTE Vol. 34, N° 3 (Noviembre, 2022) / e-ISSN 1390-= 3659

<= /p>

 

R= evista Tecnológica Espol – RTE Vol. 34, N° 3 (Noviembre, 2022) / e-ISSN 1390-= 3659

<= /p>

 

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