Intelligent virtual assistants or chatbots are currently the biggest use case for AI. And the pandemic has made them even more compelling given their use in automation and their high level of ROI.
But even in the post-pandemic world, the enterprises will continue adopting the ai chatbots or Intelligent Virtual Assistants as mentioned in the Gartner 2021 Reports. Intelligent Virtual Assistants not just automate the entire customer support cycle but also automate and accelerate the sales cycle by providing an engaging platform right from the start of the sales funnel.
“ We estimate that there are more than 1,500 conversational platform vendors worldwide, and, although we are entering a period of consolidation, the vendor landscape will continue to be too large and volatile for strategic” – Gartner
It is also seen accelerating the digitization of the entire recruitment and HR process for enterprises by taking care of all the repetitive queries. 70% of queries asked in any enterprise or to an enterprise is mostly repetitive and an Intelligent Virtual Assistant can resolve this with ease.
With the market flooded with Intelligent Virtual Assistants vendors, it can be slightly confusing for enterprises and SMB’s alike to choose the right platform for their business.
But this blog will make things easier or well clearer for you, what exactly a business should look for in an Intelligent Virtual Assistant before adopting it.
Here are the 11 features a business needs to check for in an Intelligent Virtual Assistant before adopting it. And this is per Gartner report Architecture of Conversational AI Platforms. Just an FYI Yellow Messenger Intelligent Virtual Assistants does tick all the 11 pointers and is equipped with many more features and is announced as an Emerging Intelligent Virtual Assistant platform in Gartner paper Emerging Technologies and Trends Impact Radar: 2021.
11 Features to look out for in an Intelligent Virtual Assistant in 2021
Intelligent Virtual Assistant UI needs to accommodate modality, with different user demands and considerations, the chatbot needs to alternate among chat, voice, and video. The UI should support rich messages. The easement while switching between different modes keeping in focus the user’s convenience would be a key differentiator while choosing a conversational solution.
Omnichannel Intelligent Virtual Assistant should adapt to any platform. The focus on developing once and deploying on multiple platforms keeping in mind the self-service model for businesses is a high priority for Yellow Messenger.
Another feature that is high in demand is voice support, with its allure of getting the user query resolved with a voice command given to Google Assistant, Siri, or Alexa. Intelligent Virtual Assistant should be able to handle various degrees of voice input, in different languages and be able to contextualize.
For the most part until a few years, Chatbot was mainly focussed on a chat interface with a very rigid interaction map. But now the Intelligent Virtual Assistant has evolved with the needs of the users. The Intelligent Virtual Assistant needs to provide a dialogue exchange between the user in various formats like chat, video, and voice. Multimodal capturing and rendering,i.e; capturing as well as replying in voice or chat with ease while at the same time rendering other means to add to the exchange of information is the next model that every conversational solution company is focusing on and Yellow Messenger has already started deploying multimodal chatbots for their clients. Gartner predicts in Architecture of Conversational AI Platforms that this would be the major differentiator in the coming 5 years in choosing a solution.
Natural Language Processing (NLP)
NLP or Natural Language Processing is the engine on which our Intelligent Virtual Assistant works on. It takes the input( dialogue; can in the form of voice or text) along with any other important information from the chatbot and contextualizes the entire conversation for processing it.
Because of the NLP, the chatbot can provide language support with detection of language, sentiment analysis, routing models, semantic enrichment, data mining, multimodal enrichment, and many other features that any business looks for in an AI-powered application before adoption.
Intent matching can be defined as matching the processed input to the appropriate handler or query resolution. This is done by employing ML ( Machine Language), and NLP together.
This helps the intelligent Virtual Assistant in contextualizing, intent grouping (recognition and prioritizing), handling multiple support in real-time, pattern recognition, term recognition, and a lot more.
When the Intelligent Virtual Assistant is aware of the context of the conversation, it can enhance the business and user engagement. This as discussed earlier can be done with the help of ML and NLP.
Contextual Awareness helps the intelligent Virtual Assistant to learn from a previous conversation, and reuse that information for future conversations. Apart from this, contextual awareness helps the chatbot in understanding user context, user preferences, leverage third-party data, predict user behavior as well as attributes, and provide a proactive conversation.
Handling the user query with almost 100 percent accuracy is another attribute every business looks for before adopting an AI-powered Intelligent Virtual Assistant. The various modes the query can be handled is by deploying one of the following models:
- Brokering model: Brokering different intents and aligning them with different handlers.
- Deferred handling: Pass the processed input to the custom-developed services.
- Decision tree: This is the most common and preferred way of handling requests/queries. This model consists of a dialogue tree consisting of nodes, and subnodes, where each node is matched to the intent and subnodes help in engaging the user.
The Intelligent Virtual Assistant has to get integrated with an existing system to help facilitate engagement with users. The developers have to integrate the chatbot with various API, and on different platforms to help users engage with the chatbot in the platform that they prefer.
Response Generation (NLG)
For the queries the chatbot is not trained for, the chatbot still needs to provide resolution with context, and this can be done by Natural Language Generation(NLG). NLG is based on structured data and other input.
Exceptional Handling or Agent routing, basically the ability of intelligent virtual assistants to route the query to a 3rd party like web search, agent; in case the chatbot is not able to understand the query.
Best Intelligent Virtual Assistant platform like Yellow Messenger provides the business with custom analytics and tools(such as conversational campaign platforms) to turn the analytics into action and leverage maximum ROI.
Messaging platforms are on the rise and so are automated conversational platforms. The need to combine them together and provide the best that is possible in customer support, marketing, and employee engagement is the highest priority for every business.
Yellow Messenger’s conversational platform is recognized as the leading conversational platform by Bain, Gartner, G2 to name a few publications for the above features.