How to make your chatbot strategy a success
To help you take your first steps with conversational automation, here are our 4 recommendations to make your chatbot strategy a success.
Written by Sam Watson on
There is no doubting the excitement around AI technologies, as they stand to revolutionise everything from manufacturing to how we manage our day-to-day lives. And yet, if there is one AI technology that has shown spectacular promise but has not been used to its full potential by businesses, it’s chatbots.
The liberal use of terms like “AI-powered”, “intelligent” and “neural network based” has distorted how businesses view chatbot uses cases, and the capabilities needed to make them effective. Expectations have been heightened to the impractical and unachievable, along with a belief that chatbots must be powered by machine learning or own the ability to understand human language. This is false, and the lack of understanding is undermining the tangible value chatbots are capable of providing when implemented correctly.
Combine this with an expanding number of chatbot software providers, continuous advancement in technology and competitors shouting about their own AI projects makes it easy to be tempted into rushing into deploying a chatbot that will only result in an uncoordinated experience and a lack of ROI.
To help you take your first steps with conversational automation, here are our 4 recommendations to make your chatbot strategy a success:
Determine your needs and error tolerance levels
The very first step for a business is to introspect and be clear on what precise problem or need they are looking to address with a chatbot. We have seen clients explore chatbots with a wide variety of motives, ranging from automating FAQs to enabling the purchasing of products. Each use case differs significantly in terms of how truly conversational and fool-proof the bot needs to be. For example, a proof-of-concept bot aimed at solving an internal problem can afford to be more rigid and somewhat less accurate than a customer-facing bot that is meant to engage with customers.
Build for failure and graceful recovery
Chatbot-based automation is still its infancy and does suffer from a negative perception from the end-users. A majority of bots built today are bound to fail in a variety of scenarios that might be valid from an end-user perspective but are hard to-anticipate edge cases from the bot builder’s perspective. So, it’s extremely crucial for bots to be equipped with the ability to both detect a failure in a conversation and seamlessly hand over the interaction to a human agent who can steer the conversation towards a successful outcome. Bot platforms that have invested in features that create a strong link between a bot and an agent are bound to see more success in this space.
Understand and curate the bot improvement process
Improving the performance of a bot is a non-trivial task and cannot fully be automated as some digital literature might indicate. Having a human in the loop to understand, curate and feed into the learning framework of the bot is vital, especially given how easy it is to impact a bot’s model with a biased data set. Gaining visibility into how customers are using the bot, what flows are more successful, bot abandon points, etc., is paramount in improving a bot’s accuracy and the underlying bot platform should ease this process.
Prepare for progressing a bot along a sophistication arc
When looking at the different chatbots that businesses are launching into the market today, they fall into one of three categories: messaging apps, information agents and virtual assistants.
Messaging apps are simplest, where customers use buttons, carousels and other rich media capabilities to navigate themselves. Information agents add on an additional layer of complexity with Natural Language Understanding (NLU), and virtual assistants take this is another level by being able to use NLU to manage multiple use-cases and provide seamless context sharing across different journeys.
Whatever bot class a business’s current needs fall under now, they are likely to underserve the users of tomorrow. There will be a need to continually improve the bot experience and the value it provides. What starts as a rule-based Messaging app for onboarding customers will progress to an information agent that automates common customer enquiries.
What’s crucial is that businesses choose an underlying bot platform that can support this transition. If not, businesses will suffer a huge redundancy in effort when building, and the advancing, their chatbot strategy.