According to Accenture, around 80% of CEOs want to update their customers’ interaction by using conversational AI tools.1 However, there are 2 different conversational AI solutions for the companies:
- Intelligent virtual assistant
Their distinctive features make one tool better than another for unique use cases. Therefore, for specific investment decisions, it becomes critical to which technology you pick. In this article, we comprehensively compare chatbots with intelligent virtual assistants using 6 use case scenarios to inform executives.
What is a chatbot?
A chatbot is an automation tool for client interaction that has limited natural language processing (NLP) and artificial intelligence skills. Although the name “chatbot” is frequently used as a catch-all for conversational AI tools, chatbots are typically rule-based systems that may automate repetitive tasks like responding to frequently requested inquiries.
Nevertheless, this rule-based nature can make developing a chatbot relatively easier since it does not require machine learning training.
Chatbots can be deployed on a variety of communication channels:
- Messaging apps like WhatsApp or Facebook Messenger
- Mobile applications of enterprises
- Social media apps
- Email and SMS.
Thus, companies can engage with their customers on a variety of platforms and enhance customer experience. Also, chatbots can respond to textual or auditory input.
To learn more regarding recent WhatsApp marketing trends and WhatsApp IVAs and chatbots you can download Haptik’s The State of WhatsApp Marketing 2023 report.
What is an intelligent virtual assistant?
An intelligent virtual assistant (IVA), a sophisticated kind of artificial intelligence, can automate the majority of customer interactions. Natural language understanding (NLU) and artificial emotional intelligence (AeI) are the two main ML components of virtual assistants. IVAs can therefore interpret user queries correctly even if the user is being sarcastic, making typos, or using bad language (see Figure 1).
Figure 1: Despite numerous spelling mistakes, a virtual assistant deployed on WhatsApp can understand the user.
Like chatbots, virtual assistants can be trained to respond to both texts and voice responses. Also, it is possible to deploy IVAs on Messaging apps, social media apps, custom mobile apps and company websites to provide an omnichannel experience for customers.
What is the main difference between IVAs and chatbots?
The main difference between virtual assistants and chatbots is their AI capabilities. Due to advanced NLU, IVAs can automate both complicated and repetitive tasks.
On the other hand, rule-based chatbots are associated with easier deployment. Therefore, they tend to be economic customer service automation tools.
When should companies deploy which technology?
If companies plan to:
- Automate tasks where customers can express in different ways IVAs are suitable solutions.
- Automate different tedious tasks such as answering FAQs, onboarding clients and sending notification messages, IVAs might be better options compared to chatbots
- Automating a single tedious task like onboarding customers only chatbots might be a more efficient investment option since rule-based algorithms can complete such a task and building a chatbot is easier.
Top 6 use cases comparison for IVAs and chatbots
Comparing use cases is a useful strategy for figuring out how IVAs and chatbots different from one another (see Figure 2). Executives must keep in mind, because of the time required for machine learning training, deploying an IVA is a resource-intensive task. Therefore, if both technologies can handle specific use cases, chatbots may represent a better investment for managers. To guide executives, at the end of each application we will mention which conversational AI solution tends to be more profitable.
Figure 2: Use cases compression of chatbot vs intelligent virtual assistants.
Sales Use cases
1. Answering FAQs
Sales chatbots and IVAs can both be effective solutions for automating customer service by responding to frequently asked questions. For instance, Figure 3 represents how an IVA interacts with a customer for FAQ resolution.
Although both technologies are capable of providing answers to frequently asked questions, AIMultiple suggests that investing in chatbots may be a better choice unless your business does not plan to employ conversational AI solutions for additional complex applications.
Figure 3: An IVA answers FAQs.
2. Answering complicated customer queries
Customers can request complicated things from conversational AI solutions. For instance, a client might know he needs an air conditioner but might not know whether inverter or non-inverter ACs are a suitable solution for them (see Figure 4). In such cases, conversational AI solutions should mimic a salesperson and compare the products for different scenarios.
Due to their machine learning and intent recognition capabilities, virtual assistants are capable of completing such tasks.
Figure 4: A virtual assistant helps a client discover a product.
The below video further illustrates how intelligent virtual assistants can resolve complicated customer queries.
Healthcare use cases
Nowadays, conversational AI tools with excellent NLU and NLG capabilities can serve as therapists. IVAs are a better investment option for companies that serve their patients with conversational AI since the intent recognition and ML capabilities of these tools are better.
Hospitality use cases
4. Introduction of menu
Both chatbots and virtual assistants can engage with customers and introduce them to menus (see Figure 5). If menu introduction is the only expected application of conversational AI, we recommend restaurants invest in restaurant chatbots.
Figure 5: A restaurant chatbot with the capability of introducing a menu.
5. NLP-driven dishes and beverages recommendation
Conversational AI solutions can make food and beverage recommendations to consumers based on the dialogue they carry out, just like human waiters can. IVAs are the right instruments for such a complex use case since it requires advanced intent recognition.
If you want to start your conversational commerce journey but cannot find a suitable vendor the following articles might help you:
- 50+ Chatbot Companies To Deploy Conversational AI.
- Conversational Commerce Platforms: Data-driven Benchmarking.
- WhatsApp Business Partners: Everything You Need to Know.
If you have further questions regarding intelligent virtual assistants vs chatbots you can reach us.
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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