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  1. Home
  2. Blog
  3. Enterprise Chatbots
  4. A Buyer’s Guide To Choosing The Best Chatbot Builder Platform
February 15, 2018   |    By Abhishek Shanbhag

15 Feb

A Buyers Guide To Choosing The Best Chatbot Builder Platform

A Buyer’s Guide To Choosing The Best Chatbot Builder Platform

Chatbots are fast replacing apps for some good reasons listed by Forrester’s survey  — reduce costs and labor inefficiencies, increase user experience and easy scalability and technology integrations. A chatbot in simple words is a computer program which conducts a conversation via visual, auditory or textual methods.Chatbots usually sit within instant messaging apps. It is also predicted that 80% of businesses want to deploy chatbots by 2020. The driver is ‘conversational interactions’.

Chatbots are designed to solve different functionalities in different industries.In fact, they are used in different ways, within a single enterprise: classifying the needs into B2C, B2B, and B2E.    They allow people to converse with a virtual agent and interact until a certain aspect is resolved. The adoption of chatbots, both in the consumer and enterprise worlds is on the rise. This further raises the need to build more and more relevant chatbots.

To build a chatbot that serves your purpose requires you to own three pieces: Strategy, Training, and Deployment. Chatbot strategy is the blueprint that is specific to your enterprise. Using this, you need to equip your team with the right set of know-how and skills to build and deploy a chatbot into your enterprise. Focused workshops, specific to the IT team is a good way to start implementing the chatbot strategy,  Deployment includes developing a chatbot and implementing it across the organization.

However, to build chatbots, using the right environment makes all the difference.

A BUYER’S GUIDE TO CHOOSING THE BEST CHATBOT BUILDER PLATFORM

A chatbot builder platform is a virtual environment with tools and support that is used to develop and deploy chatbots for a real enterprise. An enterprise may or may not have the capabilities and resources to own a chatbot builder platform. Before the advent of chatbot platforms, bots were difficult to build and required sophisticated toolsets.

The lacunae probe the enterprises to rely on a platform that can support the needs of an enterprise in owning customized chatbots to solve its challenges. Upon having a clear chatbot strategy, an enterprise can start using a chatbot builder platform.

Enterprise chatbot platforms also allow IT departments to have complete control and access to monitoring bots. These platforms are often powered by AI, machine learning and natural language processing.

While we have created a guide that will help you choose a chatbot builder platform, some criteria are highly relevant in choosing an enterprise chatbot builder platform.

SOCIAL MEDIA & OTHER PLUGIN INTEGRATIONS

Most chatbots are used in high-traffic social media websites. This makes social media integrations a mandate while choosing a good chatbot builder platform. Most chatbots hit the market via a social media platform. We are not just referring to social media platforms like Facebook or Twitter, enterprises are building their interface on organizational messaging platforms like Skype, Slack and so on.

Apart from building a loyalty base across social media channels, chatbots also need to be able to integrate with functional plugins. Some features like payments, search, RSS feeds and subscription can be stitched into the chatbot with the help of plugins.

SUPPORT DIFFERENT TYPES OF CHATBOTS

Can your chosen platform support creation of chatbots for different utilities? Does the bot builder platform support all B2B, B2E and B2C functionalities? Some basic functionalities based on which you can categorize a chatbot are a utility, conversational commerce, information provider, advertising and customer service chatbot. Each of these chatbots requires a different persona and hence different features. Since they also cater to different audience and contexts, the platform should allow steep customizations while developing a chatbot.

LEARNING CAPABILITIES AND KNOWLEDGE BASE

With the application of machine learning, it is important to understand how will a chatbot learn about what it needs to do. Progressively, what is the knowledge base that it will depend on to learn from and respond to the users? The knowledge base should be an encyclopedia of the context that you are working on. Workflows which enable guided conversations, make a chatbot more responsive and conversant during unique interactions. One other feature that a platform should support is Vocabulary. This needs to build with each interaction and create contexts to face complex conversations.

Typically, you will look for a platform that will allow your chatbot to build dynamically on its efficiencies. One of the most important aspects is to be able to ingest knowledge from your enterprise corpus in the form of chat logs, emails, knowledge bases, CRM data, and documents. A chatbot should be able to quickly learn the user’s conversation and provide an apt response using the library of relevant enterprise information.

FRAMEWORKS

This element is mostly confused with the number and type of languages a platform supports available in the market. Frameworks are the backbone of bot-builder platforms. They provide the primary toolsets for developers to build and define the bots’ language and dialog skills, provide automatic translation and much more. Frameworks include components like Bot Directory, SDK and connector.   Each use case to develop a chatbot is different and so does the complexity of conversation. Complex conversations need to be supported with relevant support thesaurus, taxonomies, etc. Few frameworks to name are Microsoft BOT framework, IBM Watson, Chat fuel,  MOTION.AI etc.

CONVERSATION SYSTEM

Chatbots need to perpetually build customized conversations and at times personalize them too. To allow this, a chatbot builder platform should be able to support with trigger service, message queuing and user and channel data store.

Your bot responses can be customized and defined with a scripting language like Machine Definition Language (MDL). After defining the bot responses with MDL you can render the same across all the chosen bot channels with zero changes.

NATURAL LANGUAGE PROCESSING

The adoption of chatbots highly depends on how intelligent they sound and how well they adapt the jargon. The platform should let you create a chatbot within the environment natural language processing. Human conversations are complex and not rule-based. Since sentences can be structured with emotion and expression, a chatbot should be able to understand the meaning with such degree of complexity.

A chatbot can learn from human cues only when it is in tandem with the natural language processing workflow. With more and more interactions, such chatbots get intelligent as they start learning overuse. The platform should be able to support the live chatbot with such dynamism.

BROADCASTING AND TRIGGERS

Broadcasting is the feature that lets a chatbot scale its communication across different departments. This is most relevant and useful in the case of events and conferences.

With Triggers, the bot can send personalized information and updates to users.

ROBUST MEMORY

By now you would have known that chatbots deal with non-linear structures and networks. State management is what poses the challenge in building a chatbot. Unlike websites where the web servers delete every request it gets and does not have to maintain a state, chatbots need to maintain a state. State is “all the stored information, at a given instant in time, to which the program has access”.

Since learning and context creation is involved in the creation of chatbots, so is the need to manage a state, that will remember all interactions, data, vocabulary and more.

COGNITIVE ABSTRACTION, CUX AND SECURITY

While the above parameters would help you choose a chatbot builder platform, there is some criterion that is non-compromising to your enterprise and choice of chatbot. Cognitive service abstraction, Conversational User Experience (CUX), Analytics, and Security must be evaluated to a high degree of granularity.

Cognitive service abstraction provides an abstraction layer to industry standard AI & cognitive services across text, speech, vision (image) and custom machine learning algorithms. CUX helps users reach their goal in the shortest time with maximum end-user experience.Analytics with administration allows continuous refinement of the bot’s functioning and helps your bot to learn faster from interactions. Security helps the bot be aware if a user is authorized and properly authenticated to chat with it. This ensures that the bot provides the right set of relevant services for a particular user.

BOTCORE – A CHATBOT BUILDER PLATFORM

BotCore is a highly flexible and AI-Powered enterprise bot builder platform that is designed to help several enterprises build & launch bots to meet the needs of their employees, customers and vendors.

Fortune 100 companies have already been empowered by this easy and seamless platform which uses cognitive abstraction and is powered by artificial intelligence.

If you’d like to learn more about this topic, please feel free to get in touch with one of our experts for a personalized consultation.

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Abhishek Shanbhag
Abhishek Shanbhag

Abhishek Shanbhag

Abhishek is the AI & Automation Practice Head at Acuvate and brings with him 17+ years of strong expertise across the Microsoft stack. He has consulted with clients globally to provide solutions on technologies such as Cognitive Services, Azure, RPA, SharePoint & Office 365. He has worked with clients across multiple industry domains including Retail & FMCG, Government, BFSI, Manufacturing and Telecom.