7 Key Factors to Consider Before Choosing a Chatbot Platform
7 Key Factors to Consider Before Choosing a Chatbot Platform
This article takes a look at seven key factors to consider before choosing a chatbot platform. Also look at multiple channel support and NLP and speech support.
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Artificial Intelligence and Machine Learning — The two words that are on the tip of every technophile's tongue. It is fair to say that, over the years, AI and ML have successfully transformed almost every aspect of our personal and, more importantly, our professional lives.
Research says that almost 85% of global executives believe that AI will allow their companies to obtain or sustain a competitive advantage and is going to play a much larger role in enhancing the organizational work productivity.
When it comes to the many applications of AI technologies, chatbots are the most popular and are currently making the biggest noise. Studies show that:
80% of businesses want chatbots by 2020 — Oracle
The global chatbot market is set to grow at CAGR of 37.11 during the period of 2017–2021 — Orbis Research
Chatbots are expected to cut business costs by $8 billion by 2022 — Juniper Research
Along with already popular applications in the B2C space, chatbots are revolutionizing the B2E and B2B organizational scenarios as well.
Chatbots in Enterprises
When it comes to enterprises, chatbots should be readily available and accessible across a myriad of channels and integrated with internal business systems with Customer Relationship Management (CRM) and Supply Chain Management (SCM) systems being top priority.
When coming up with a bot development strategy, enterprises have several options. A single task bot is not a feasible option for enterprises that need an automated workflow coupled with the integration of internal and external ecosystems and application of natural language processing.
Chatbot frameworks assist programmers with structures with which they can build individual chatbots. However, these frameworks are merely just a collection of a set of tools and services. The frameworks apply to a fixed set of use cases and can be used to assemble and deploy a single-task bot which, at the end of the day, lacks the end-to-end development and ongoing management capabilities.
Frameworks tend to be useful if the use case is small, however, for an enterprise where the overall requirements and scope are more demanding — this is where a chatbot platform comes into the picture.
1. Multiple Types of Chatbots Executing Multiple Tasks
This functionality is imperative for enterprises as it allows them to track and streamline multiple functions at once. Ideally, the enterprise should have to ability to deploy a chatbot that works on a single task along with creating and deploying a multi-purpose chatbot that communicates with multiple systems and completes a variety of tasks within each of them.
The chatbot development platform should offer pre-built and ready to deploy bots which address certain use cases (e.g., lead generation, customer support etc.) along with the ability to customize them to suit your business needs so as to handle multiple different workflows and processes pertaining to different customer interactions and your business offerings (e.g., a lead generation bot that also answers customer’s queries and replies with answers in a FAQ, document or website).
2. Multiple Channel Support
Enterprises should look for chatbot development platforms where the bots can be deployed to the website, mobile apps, or the channel of its choice with the user interface that is customized for each channel, be it SMS, e-mail or social media. To add on to that, the bots should have the ability to interact with corporate tools like Slack, Telegram, Skype, etc.
3. Natural Language Processing and Speech Support
Training the chatbot is yet another important consideration when it comes to the scalability of the bot. Does your chatbot development platform incorporate Natural Language Processing (NLP) training? Can the bots maintain accurate interactions and conversations using text and/or speech? A chatbot platform that provides NLP and speech support tends to provide the best results when it comes to understanding user intent and replying with relevant content post-assessment.
4. Deploying Intelligent Chatbots Through the Platform
The platform should have intelligent chatbots that understand, recollect, and continuously learn from data and information that is garnered from each customer interaction. This also includes the need to maintain the context of a customer request during interaction and using Machine Learning to develop further and perfect its natural language processing capabilities.
5. Ability to Bridge With the Platform
Does the platform have the ability to share messages between users, bots, and cross-functional systems? This would include sharing messages that are stored between users, bots, and systems whole automatically logging as well as success and failure categorization of messages. This provides a comprehensive and crystal-clear picture of the functionality of the chatbot development platform and subsequently, the bot.
6. Building the Chatbot
The platform should have an intuitive, web-based tool for designing, building and customizing the chatbot based on bot’s use-cases, tasks and the channels where it is deployed. It should also have the option to restart the process of developing the bot from scratch or reuse developed components along with testing the chatbot build throughout the development cycle.
7. Industry Experience and Domain Knowledge
Identify and engage with the right technology and platform providers that have considerable industry experience and domain knowledge.
Enterprises need to factor in and truly determine what chatbot development platform or relevant framework will augment and facilitate speed, scalability, and flexibility in order to support their customers and employees.
While there are several bot-building platforms out there offering a whole lot of features, it is imperative for enterprises to assess and identify which features will actually matter and facilitate better bottom lines.
Published at DZone with permission of Mitul Makadia . See the original article here.
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