5 Steps to Maximize Your Chatbot’s Potential

Chatbots have a huge potential to transform how companies engage with their customers. But scaling a chatbot up to production readiness can often be challenging. It’s important to consider any conversational AI implementation as part of a wider strategy that drives the business forward, instead of a point solution that fixes an individual pain point.

With Conversational AI revolutionizing business processes, what key points can ensure that your chatbot is implemented successfully and to maximize its potential?

With companies investing in digital transformation strategies, Conversational AI has become a prominent feature in many enterprises’ plans for innovation and investment. The clear benefits that come from improving customer engagement and driving sales revenue, while making the most of new voice interfaces and omnichannel capabilities, has led to the discussion no longer being “why should a company implement chatbots?”, but “how do they start doing it?”.

The current Covid-19 pandemic has only accelerated the need to develop conversational AI platforms that can guarantee 24/7 services now that employees and customers have restricted mobility. However, choosing the best platform isn’t easy. There are only a few on the market that meet the strict demands of enterprise use and that can provide the knowledge and features to provide a competitive advantage to enterprises.

Here we will highlight five points to bear in mind when deploying chatbots that will ensure you get the most of your conversational AI platform.

Make them Conversational:

A common cause of frustration when talking to chatbots is that users discover that some chatbots don’t understand what is being said. Conversations can follow linear paths, and if something falls slightly out of context, or synonyms are used instead of a precise word, the chatbot gets confused and the conversation stalls.

Conversational AI platforms allow users to speak naturally to a chatbot and be understoodas if they were talking to a human agent, without interruptions or misunderstandings.

It is also necessary to be on-brand while being conversational. The tone of voice and personality of a chatbot needs to be in line with corporate identity.

Don’t Experiment on your Customers:

Machine Learning has been a major player in the rise of artificial intelligence, particularly in situations where there is a vast amount of data that can be mined and analyzed and used in conversational applications. Yet the need for large quantities of data can also be one of the handicaps of machine learning.

Machines need to learn how humans speak. However, machine learning algorithms can only discover the many ways a human can phrase a question, typos included, by processing numerous examples. And there is a lot of diversity in language that takes time to be learned.

Enterprises that need to collect this conversational data without a conversational system make the mistake of developing apps that must be trained by their customers.

In this case, the interface will only be able to recognize questions asked in a specific way, with any other inputs being an anomaly. The customer will then receive a default “safety-net” answer or will be transferred to a human agent. Also, answers that are not proactively flagged as incorrect will become part of the machine-learning process and will only frustrate customers even more.

The best conversational AI platforms have their own language resources ready to use. Having been built in multiple languages and from billions of conversations, conversational AI platforms can respond to questions asked in manifold ways and even out of context in a personalized manner.

Platforms like Teneo, take a hybrid approach, combining linguistic and machine learning at a native level. This way, chatbots can make smart and complex interpretations, while maintaining consistency and a correct personality. All this while saving time and resources that would be spent having the customer train the platform.

Treat Data Privacy as an insurance policy

First-person conversational data is extremely valuable to a business. However, it is imperative to look after the data and privacy of your users. This data cannot be compromised in any way because, if done so, companies will lose their customers.

Depending on the specific data each enterprise deals with and how sensitive it is, they must be able to anonymize or pseudonymize conversational data with placeholders that can still be used for analytics purposes but without revealing customer identities or details. If it is required to know identifiable data, for example in a transaction, then data encryption can be used before sending the information over the internet and into a company’s system.

Before building a conversational AI application, an enterprise must ensure that it can use the data without compromising customer privacy.

Have a clear business case

Conversational AI applications shouldn’t be temporary pet projects that are built and then left forgotten as an almost ornamental asset. Chatbots should be integral elements that optimize ROI and are vital to future strategies.

If you can’t articulate the business case or the value that you want to achieve from the conversational AI application, then it is important to collaborate in defining the business value that you expect to achieve or to reconsider starting the project. 

Establishing a business case helps teams deploy the best technology and focus on the right objective when entering unchartered territory. By having a clear idea of the features needed to achieve these goals, it is easier to implement them while adhering to budgetary or time restrictions.

It is therefore essential to stay practical and focused on these objectives and to avoid features that may seem glossy but that do little to achieve established KPIs and may end up being detrimental.

Have flexibility through a platform

When venturing into a new channel you may not have a full idea of all the required features right from the beginning. It is prudent to think big, but to start small, and the best way to carry out these procedures step-by-step is by choosing a scalable platformthat will give you options moving forward.

It is better to have a strong idea of the objectives that you want to achieve with this technology and to start with a smaller project that lets you see and measure the success of this deployment and choose how to proceed to the next phase. In order to capitalize on this initial investment and advance to the next phase, however, you will require a scalable platform.

Imagine that an application has been built in one language and that it now needs to be deployed in several other languages. With a flexible and scalable platform, you can take the investment you’ve already made in dialogue flows and integration, including all the on-brand requirements that have been set, such as tone and personality, and deploy it in different languages, without having to build new solutions for each one.

The same applies when introducing new user intents. Think of an intentas something the user wants to achieve. Some enterprise focused platforms can deal with hundreds of intents, but they are limited all the same and eventually, enterprises have to build multiple solutions to cope.

Managing these intents exclusively on a machine learning basis is difficult. The process takes time to understand the new data that is introduced, and this affects the precision and response of the application.

Similarly, using Machine Learning without a platform with language rules and conditioningcan be less reliable when there are flows with precise conversational traits. Small minutiae such as the variance between “You cancelled my flight, can I get a refund?” or “I cancelled my flight, can I get a refund?” can make a big difference.  This is another reason why Teneo takes a hybrid approach using both linguistic and machine learning techniques.

Ensuring that your conversational platform is flexible also means allowing the easy deployment of personalized data and vocabulary into conversational flows, and dialogue components within multiple conversations and across different channels. That way an application being used on a website, for example, can then be used on another channel like Facebook Messenger or Alexa.

Conclusion:

Deciding to go-live is just the first step. Having a clear idea of the business case and knowing what guidelines to follow when deploying this technology can result in a complete and engaging customer journey that will be highly beneficial to your enterprise and integrate the business into a modern digital landscape.

By making the most of these platforms, engagement increases, and the direct feedback from what people are saying to your brand expands knowledge about customer traits and desires so that you can continuously improve the system and the overall value of the solution.

Do you want to see real-life examples of conversational AI in action, and discover how it is revolutionizing business processes? See first-hand experiences with Conversational AI platforms in the Inspired Minds webinar:

AI in Action - Business Use Case Examples of AI Adoption with Artificial Solutions