Chatbots: Implement to scale, not to fail

Chatbots are software applications that use artificial intelligence & natural language processing to understand human needs and guides them to their desired outcome with as little work for the end user as possible.

However, the chatbot hype has been so high in recent years that many companies have gone ahead and implemented chatbots without thinking twice. As a result, they have invested a lot without realizing the benefit of the same or even some point of time decided to decommission the chatbots.

Recently we have done extensive market research on 20+ chatbot solution providers to understand what is the reasons chatbot projects don’t deliver the desired outcome. We are now engaging with our customers to guide them with the right approach to scale in their chatbot implementation plan.

This article addresses the five things you must know before you start your chatbot journey

Your scaling strategy

Yes, chatbots are easy to pilot. You can start with quick FAQ chatbot for your IT support team or you may implement chatbot just address one marketing campaign. But unless you know how to scale with more and more use cases across functions within your enterprise, your chatbot pilot success story will be forgotten soon.

While delivering the pilot keep your dialogues on with different parts of your company on, to understand the future use cases. Evaluate all channels for internal, external and targeted communication for your customers, prospects, partners and employees. You will realize that chatbots can be useful in many more ways than you have thought.  

Chatbot does not always mean natural language understanding(NLU)!

It’s sounds crazy but is it true. During our evaluation we reviewed chatbot companies. While many of them talk about machine learning and AI, reality may be that the engine behind the chatbot is a simple rule-based engine.Instead of natural language understanding or processing many chatbot product vendors build on keyword/text search-based engine.

Never hesitate to ask your product vendor questions about the core technology behind the chatbot. It is also important to understand how the chatbot is learning. You may suddenly realize that some chatbots are not smart enough as they are not designed learn themselves.

Intent is key. Train your chatbot it with your business specific intent.

A successful interaction with your user (customers, partner and employees) needs to very clear understanding of your day today operations, the culture and the business language you use. Intent is critical for success of your chatbots. Hence, chatbot training is a resource intensive task. Linguistic analysis provides different solutions that speed up training and, most importantly, solve some structural issues with bot development.

Pay extra attention to integration and process automation requirements while creating a chatbot to solve specific business problem.

Most of the chatbots can deal with APIs. However,  many companies have critical business systems in house which may not support APIs. In that scenario making your chatbot perform specific task may be very difficult.

Paying attention how your chatbot should integrate to your ticketing system or your finance system is critical for business success. Your integration strategy will enable you to serve your chatbot users (customers, partners or employees)  live. If not designed well you may not be able to provide seamless experience for your users.

In either scenario users should be aware what to expect from their conversation with your chatbot.

Always measure the chatbot performance

Measuring your chatbots performance is important. If you have not been able to redirect some part of your human intensive interactions to your chatbots, it eventually did not serve the purpose. A fully bundled analytical solution for your chatbot makes your life easy and provides a simpler way to measure the success of your chatbot project.

Many companies ignore the need of analytics in pilot phase and find it extremely difficult to scale in future due to lack of metrics that supports their future business case.