Chatbots are quickly becoming an integral part of an organization's customer experience strategy. Why? The answer is simple. Consumers love simple self-service and businesses love efficiency. But far too many businesses stop short of implementing chatbots because they fear the onboarding process. They believe that in order for a chatbot implementation to work correctly, it will need mountains of data and the staff will have to devote a ton of resources to training the technology on how best to respond to customers. As with many things, bigger does not always mean better when it comes to AI-fueled chatbot solutions. So let’s dig into that a bit deeper.
Mountains of Data Does Not Equal AI Success
It may have been true in the past—especially when we look at traditional machine learning models, but the truth is in today’s landscape, it’s all about prioritizing quality over quantity. Models that require thousands of machine learning examples to get up and running often waste precious time and impact already scarce resources. More importantly, they don’t provide businesses with a real competitive advantage—at least not in a timely manner, if at all—due to an extended onboarding process. A better way to approach the “data problem” is to start with quality data and work with a solution that can learn over time as it ingests more and more information. This helps the AI strategy remain agile, spur innovation and future-proof a company’s ability to solve a new problem or adjust to changing landscapes.
One Shot Learning: Better, Faster, Smarter
Much of the shift away from the mountain of data strategy is because of one-shot learning technologies. Unlike traditional machine learning which uses thousands of examples to learn, one-shot learning is able to learn from one or very few examples and then can predict an answer based on those training examples. Rather than algorithms learning from a very limited set of data, and therefore having a limited scope for analysis, a one-shot learning approach can take the information at hand and provide actionable recommendations to fill in gaps. For chatbots, this approach helps account for the numerous types of phrases and vernacular that can be used to ask a single question without having to input every possible version. More importantly, the technology makes sure there is human control over the experience design, ensuring the recommendations made by an algorithm are appropriate and do not generate unwanted outcomes. Supervision is embedded into how the technology learns in real-time, optimizing the time and resources required from human supervision to balance control and effort.
For example, if you are training an algorithm to identify and sort pictures of cats and dogs with the parameters that a cat is a four-legged animal smaller than a dog, it will learn by incorrectly placing a smaller dog, like a pug, into the cat category. After it’s corrected, the algorithm will adjust to not assume that all small four legged animals are cats and improve its categorization accordingly. The original learning process that figures out how to categorize, give weight to features and appropriately catalog information is faster and needs less information than others. Most importantly, though, the technology has the agility and capacity to glean new insight from every transaction. As the system learns, it evolves, empowering it to consistently align with the needs of users without prompt. Furthermore, once new learning recommendations are confirmed by a human, they are immediately available for use, unlike other technologies that require lengthy model buildup and refresh.
These same principles can apply to customer service communications. A chatbot using one-shot learning can learn to respond to customer queries based on the defined intents a business wants to allow customers to solve through self-service. For example, a business can identify its international shipping policies and the chatbot will immediately be able to answer questions like, “can I make a purchase if I live in Ireland?” A smart chatbot digests the information, recognizes that Ireland is a country where the customer lives, determines whether this location is within the company’s shipping limits and responds accordingly.
On the other hand, if a customer asks "can I buy if I live in a dorm?" the system will recognize that it cannot place “dorm” in its existing determinations and would be able to identify a new intent or need and adjust accordingly. This kind of agility provides the framework for chatbots that contribute to long-term success. The single biggest upside of one-shot learning is its ability to start quickly and evolve and adapt based on real-life scenarios as they occur, rather than spending months building theoretical models. It also empowers brands to consistently be on target when it comes to understanding the needs and demands of customers -- without the need to constantly rework the parameters or strategy.
No Gimmicks - Just Immediate Results
Learning from examples as it goes, one-shot learning allows businesses to derive immediate conclusions from very few data points. This is key because, like with any new technology investment, a quick return on investment helps show business value and justification for the project. Chatbots are an investment and should be treated as such. They have the potential to drive revenue, improve experiences and cut costs—but only if they are implemented efficiently.
Out-of-the-box agility and one-shot learning allow businesses to immediately implement chatbots without compromising quality. Investing in the right chatbot solution can result in both immediate and long-term ROI. The more people who use the chatbot, the better it gets at understanding customer requests and, ultimately, the more customer insights it provides a business. These valuable insights can then be used to improve customer service and experience—directly impacting customer retention and conversions.
It all comes down to this. Businesses cannot afford to stand on the precipice of innovation and not take the leap. AI is leading the charge into the future of customer engagement. The champions of this digital disruption will be those who can clear the data hurdle and start delivering their customers this experience before their competitors do.
Yaniv Reznik, Chief Product Officer and SVP of Customer Success at Nanorep
Image Credit: Montri Nipitvittaya / Shutterstock