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Conversational AI: Definition and Advantages

Pia Heßler4/25/2023

Why you should invest in Conversational AI

Table of contents

What is Conversational AI?
What are the benefits of Conversational AI?
Energy
, by arranging an appointment or referring the person to another department.
Evolves automatically
Emarsys Demo

Where it is feasible and sensible, machines relieve employees of simple and monotonous tasks. An important tool for this is Conversational AI: AI expert Prof. Dr. Gentsch confirms, that companies whose business processes are based on it can systematically unlock sales potentials and benefit from improved customer service.

For future-oriented companies, the question is therefore not whether they want to invest in Conversational AI, but whether they can afford not to. In this article, you'll learn what's behind the technology, how you can use it, and which tools can help you.

What is Conversational AI?

Conversational AI stands for Conversational Artificial Intelligence and is an artificial intelligence (AI) that automatically leads conversations with one or more people. The human-like dialogue system is a kind of fusion between AI and communication. Conversational AI is now used for all AI-based technologies, both for oral (e.g., Alexa) and written (e.g., Chatbots) conversations.

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What are the benefits of Conversational AI?

More and more companies rely on Conversational AI-based assistants in their processes. As they take away monotonous tasks from employees, all areas of the company benefit from it.

Conversational AI can

relieve employees of repetitive questions and tasks
,improve user experience or candidate experience
and help with pre-qualification in sales, marketing, and HR, improve customer service and internal first-level support (e.g., with a 24/7 service, quick response to messages, no telephone queues, and long research work on the web) improve
,advise customers
on products and create individual offers for them,take over
live communication during an event,minimize
internal communication requirements (e.g., by intercepting sick leave notifications),speed up
complaint management,minimize
discussion potential for sensitive issues (sentiment analyses identify emotions and can thus hit the right tone),
communicate with multiple people at the same time (e.g., on the homepage, in the FAQ section, via messenger services like WhatsApp, and on social media platforms),relieve
employees in case of staff shortages by taking over the „day-to-day business“,
especially support growing companies, because for them it makes no difference whether they get the same question 100 or 10,000 times a day,
automatically evolve and thus keep maintenance effort to a minimum,

collect data that serves as foundation for various business decisions.

These
industries
are already frequently using Conversational AI:
E-commerce
Finance
Industry
Tourism
Education

Energy

Publishing

Health Care

Conversational AI vs. Chatbots: What's the difference?

Both technologies are communication assistants and have their parallels. Sometimes they are even used synonymously, even though they differ considerably:

Rule-based Chatbots A rule-based chatbot is not based on AI. It works with defined rules. Free text entries are mostly not possible - and if they are, the chatbot understands comparatively little of them. The interaction takes place automatically, but the possible answers are limited. You can imagine it as a pre-made question-answer-catalog. The rule-based chatbot is suitable e.g. as an assistant in the FAQ section of your website. It informs your website visitors about your opening hours and the status of their order.Intelligent Chatbots

An intelligent

Chatbot

is based on AI. That means it learns from conversations. Strictly speaking, Conversational AI is the technology that is inside the intelligent chatbot. It can handle free text and understand contexts (learn).

Conversational AIConversational AI is more than a question-answer game. The AI can automatically conduct conversations with one or more people. Besides the written chat function, it can also communicate orally.

Technical BackgroundSource: https://nuacem.com/top-5-conversational-ai-trends-and-their-impact-on-enterprises

In Conversational AI, users typically speak to their conversation partners. Thanks to Natural Language Processing (NLP) and Machine Learning, the Chatbot can convert the information into text - it transcribes. NLP is often combined either with Machine Learning or Deep Learning to recognize words in context and understand whole sentences. The chatbots infer intentions and respond in the best case just as humans would. The contact can be made via a website, an app, or a smart home device.

The AI finds out the

User Intent and processes the information. From this point on, the data is managed and processed just like by “normal“ chatbots. To reply, you can program a code yourself or access a platform.
Basically you have three options:Search internal databases: If you want to know, for example, how many production employees are currently present.Search external databases: For most questions you will have to access the databases of your systems (like your ERP system
or CRM

) via interfaces.

The Chatbot finishes the dialogue

, by arranging an appointment or referring the person to another department.

If the answer is not displayed, but read out, we speak of "Text-to-Speech". Here, images and other media can also improve the user experience or candidate experience.

What users don't directly realize: In the background, machine learning algorithms analyze every dialogue in order to better respond to concerns in future conversations. This process is called Machine Learning.

Examples of Conversational AI

That means: Customer service alone is good, customer service with the support of a chatbot is better, and customer service with the support of a conversational bot (Conversational AI) is best. We'll look at this in detail using three concrete examples:

Example 1: The company with a customer service team

The customer of an online shop wants to order again after a long time and have her packages delivered to her work in the future, but her account is now deactivated. The electrical company has five employees in the customer service department. When she picks up the phone, all lines are busy. She ends up on hold, googles parallel for FAQ - unsuccessfully - and lays down annoyed after 30 seconds. She would have needed immediate help to proceed with the installation. Next time she'll purchase from the competition.

Example 2: The company offers a chatbot to its customers as the first point of contact

In this scenario, a chatbot is part of the described customer service team. It's supposed to fend off simple questions to relieve the team.

Chatbot: „Hello, how can I help you?“

Customer: „I want to change my delivery address, but my account is deactivated.“

The chatbot recognizes her issue from the first part of the sentence and sends her a link to the instructions for changing her delivery address. The problem described in the second part of the message is ignored by the chatbot. Now she dials the customer service number to fix the problem more quickly. On the phone, the account is quickly activated again, but that cost the service team unnecessary time.

Example 3: The company offers a conversational bot to its customers as the first point of contact

In this scenario, a conversational bot is part of the described customer service team.

Conversational Bot: „Hello, how can I help you?“

Customer: „I want to change my delivery address, but my account is deactivated.“
The conversational bot understands both concerns and sends the customer both instructions for activating her account and instructions for changing her delivery address.
The most important differences between the rule-based chatbot and the conversational bot:
Remembers personal preferences
Interacts cross-channel and ensures uniform communication (Omnichannel)
Understands the context

Processes audio inputs and outputsInteracts human-like instead of rule-based

Evolves automatically

Source: https://www.hyro.ai/post/whats-the-difference-between-chatbots-and-conversational-aiWith which tools can you integrate Conversational AI into your processes?

There are several tools with which you can implement Conversational AI in your company. One option is the

SAP Commerce Cloud. It deals with the entire buyer journey - from Awareness to Loyalty, every area of your funnel is covered. Many companies decide in favor of this combination for the digitization of their processes: Emarsys, Zoovu and SAP Commerce Cloud.

EmarsysEmarsys helps marketers to create and publish relevant content on a large scale. Predict is a self-learning function for product recommendation included. The function Send Time Optimization ensures that customers receive the right messages at the right time. With Emarsys the creation and scaling of the Omni-Channel Engagement becomes possible for each enterprise.

Another helpful function for your content marketing could soon follow: The AI-based chatbot ChatGPT is on everyone's lips. It helps many content marketers with idea finding and the creation of relevant content. The CEO of SAP Emarsys Joanna Milliken talked recently about her thought of integrating

ChatGPT into the Emarsys platform.Zoovu

The conversation assistant
Zoovu
has it all:
a 24/7 shopping advice that understands the context,
an automatic generation of relevant and up-to-date product content,

a performance analysis with which you can find out the needs of your target group,

the possibility of integrating sales partners and Actions can be scaled across channels and regardless of location. With the so-called No-Code platform, you can create and use conversational assistants without technical prior knowledge in order to permanently optimize the customer experience of your customers.

By the way, you can book a free

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, If you like Emarsys, Zoovu and the SAP Commerce Cloud, you can use them together and thus perfect the omnichannel experience of your customers.