{"id":11,"date":"2020-08-17T07:52:14","date_gmt":"2020-08-17T07:52:14","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/08\/17\/how-to-build-your-own-transactional-chatbot\/"},"modified":"2020-08-17T07:52:14","modified_gmt":"2020-08-17T07:52:14","slug":"how-to-build-your-own-transactional-chatbot","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/08\/17\/how-to-build-your-own-transactional-chatbot\/","title":{"rendered":"How to Build your Own Transactional Chatbot"},"content":{"rendered":"
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Chatbots Magazine<\/a><\/span> found out 67% of US millennials said they are likely to purchase products and services from brands using a chatbot. According to HubSpot<\/a>, \u201c47%<\/span> of shoppers are open to purchasing items through a bot\u201d.<\/p>\n

In this context, is critical for brands to seriously consider implementing a transactional chatbot on their website if they haven\u2019t done so. This is also a good reason to clearly define what transactional chatbots are, how they work, and what they\u2019re used for.<\/p>\n

What is a transactional chatbot?<\/b><\/h2>\n
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\u201cTransactional bots allow customers to make a transaction within the context of a conversation.\u201d
<\/i>(Source: Chatbot Magazine)<\/p>\n<\/blockquote>\n

A transactional chatbot acts as an agent on behalf of humans and interacts with external systems in order to accomplish a specific action.<\/p>\n

As such, a transactional chatbot is different from the most common bots, also called informative or conversational chatbots, in the sense that its goal is to automate a transaction and to simplify the user experience by providing a quick, convenient channel for one specific purpose. It is optimized to execute a limited amount of specialized processes that replace the need to talk to an expert or use more complicated interfaces such as mobile apps or websites.<\/p>\n

As a consequence of connecting to external systems, answers provided by transactional chatbots are dynamic, meaning that they can vary depending on the data contained in those external platforms. On the other hand, informational bots provide static answers: ask the same question and you will get the same answer over and over again.<\/p>\n

How does a transactional chatbot work?<\/b><\/h2>\n

Being designed to only perform a few tasks doesn\u2019t mean that a transactional chatbot is a basic and limited bot. On the contrary, it can be quite intelligent and able to understand natural language thanks to the right technology.<\/p>\n

Symbolic AI and Natural Language Processing<\/a><\/strong> is what makes the whole difference between a basic bot and a transactional chatbot developed by Inbenta.<\/p>\n

Symbolic AI<\/a><\/strong> is a branch of Artificial Intelligence that implements Natural Language Processing using patented algorithms and Inbenta Lexicon, a proprietary database that describes a human language and the world in general, using symbols and their semantic relationships.<\/p>\n

Combining our technology with our Lexicon enables Inbenta chatbots<\/a><\/strong> to understand the users\u2019 questions and to select and provide the proper answer between several possible responses.<\/p>\n

Having that accumulated lexical and semantic knowledge already built-in also means that transactional chatbots built on the Inbenta platform can go live within a few days, where our competitors often require months to prepare due to their brute-force machine learning algorithms. Our customers obtain optimal results with minimal or even no training data sets or utterances.<\/p>\n

Transactional chatbot use cases<\/b><\/h2>\n

So now you know what transactional chatbots are and how they work, let\u2019s see what they can do.<\/p>\n

Transactional chatbots can be implemented in various sectors such as banking, insurance or e-commerce. In the financial sector<\/a><\/strong> for example, they can automate simple tasks that would otherwise be performed by a bank operator over the phone, such as verifying your identity, blocking your stolen credit card, giving you the working hours of nearby branches or confirming an outgoing transfer.<\/p>\n

An insurance company<\/a><\/strong> can use a transactional chatbot in order to provide a quote to potential customers or download an insurance certificate to its customers. Most elaborate transactional chatbots can even go further and convert prospective customers without leaving the chatbot platform. If the quote meets the user\u2019s budget and requirements, he can then directly sign up by providing the requested information to the bot, which will then send him the contract and documentation. This use case can also be applied to energy companies or mobile phone providers<\/a><\/strong>.<\/p>\n

In the e-commerce sector<\/a><\/strong>, a transactional chatbot can help the user filter products to find what he\u2019s looking for, and eventually make a purchase. It can also be used to modify or cancel an order if needed.<\/p>\n

To learn more about how to build a transactional chatbot, watch our webinar and our CEO Jordi Torras do a live demo of how our technology works.<\/strong><\/p>\n

\"Transactional

https:\/\/www.inbenta.com\/en\/blog\/transactional-chatbot\/<\/p>\n","protected":false},"author":1,"featured_media":12,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/11"}],"collection":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/comments?post=11"}],"version-history":[{"count":0,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/posts\/11\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media\/12"}],"wp:attachment":[{"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/media?parent=11"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/categories?post=11"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/salarydistribution.com\/machine-learning\/wp-json\/wp\/v2\/tags?post=11"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}