9 months ago marked one of the biggest transitions in the way humans communicate that will become an irreversible change shortly in every industry. E-commerce is no exception, and the implications of Chat GPT and tools alike pose massive advantages for businesses that can leverage AI effectively.
Like anything new, there has to be the trial and error stage where businesses figure out how the tool fits into their processes. The first issue that immediately stands out with Chatbots is the generic and repetitive responses. If your site implements a Chatbot to manage customer support, how are you going to want the experience to be? For most business owners, the answer is a simple “Unlike anything they’ve ever seen before” which is great but we should also add “And can’t get anywhere else”.
What Happened With Chatbots in eCommerce at The Start (The Epic Fail)
In the early days of eCommerce Chatbots, rather than expediting processes, they ended up causing delays. Chatbots would struggle to locate information, resulting in sluggish responses that left customers feeling more frustrated than if they had waited for a human representative to assist them.
Even when Chatbots managed to provide fast responses, they frequently failed to address the specific questions customers posed. For instance, if a customer inquired about the precise location of their package, the chatbot might respond with a generic message like "Your package is in transit".
In their initial stages, Chatbots were limited in their ability to handle anything beyond basic requests. While they could handle queries like "How do I start a return?" they were incapable of handling more complex requests like "I'd like to check the status of an ongoing return."
The biggest downfall of these early-day Chatbots was their struggles to retain previously gathered information. If a customer was transferred to a human representative, that representative often had none of the information the chatbot had already collected.
Even today, Chatbots are not universally trusted. Under the Bot Disclosure Act implemented in California in July 2019, retailers are required to inform consumers when Chatbots are in use, with non-compliance resulting in fines of up to $2,500 per violation.
What’s Changed?
We can’t come off talking about this Chatbot dystopia without telling you about the strides the technology has made in recent years. So with that said, here’s a look at what’s been going on:
From a technological perspective, this is what’s gotten better:
Natural Language Processing (NLP): NLP lets Chatbots understand and interpret human language, which makes interactions feel more natural and meaningful.
Machine Learning (ML): ML algorithms let Chatbots remember and learn from past interactions, which over time makes them more efficient. This is essential for personalization and handling any issues brought to light by customers.
Chatbot Architectures: The design and development of Chatbots have evolved to include components like user interfaces, NLP engines, and ML algorithms, which make the Chatbots more powerful and enhance their responsiveness.
Rule-Based vs. AI-Based Chatbots: Rule-based Chatbots use predefined rules to respond to queries, and AI-based Chatbots leverage NLP and ML to understand and respond to user queries. Match those up against each other, and AI Chatbots are the clear winner.
Best Practices: Developers now follow best practices in chatbot design, focusing on clear purposes, and the user experience, and prioritizing ongoing testing and refinement.
With this part covered, let’s shift to what these technological advancements have translated into:
Human-Like Chatbots: Chatbots have become more human-like, thanks to the advancements in Natural Language Processing and machine learning algorithms. This makes interactions with Chatbots more relatable and user-friendly. Recall that in the past, Chatbots often provided generic and robotic responses.
Deep Customer Insights: Modern Chatbots are designed to use deep customer insights to inform their responses. This is a fancy way of saying; they can analyze user data and give those personalized/relevant responses that companies want their users to have.
Voice Bots: Voice bots (Siri, Alexa, etc.) have obviously become a massive deal since they also give a more natural and intuitive interface for users. Think about booking appointments, ordering food, or making reservations using voice commands. This was a massive improvement over text-only Chatbots.
Improved Customer Satisfaction: Chatbots are now designed to create a sense of connection between the customer and the company instead of simply being a means to automate support services. They provide quick, personalized experiences that improve customer satisfaction and loyalty. In the past, as we know, Chatbots often left customers feeling disconnected and dissatisfied.
What a Successful Chatbot Implementation Looks Like
A few good examples of companies leveraging Chatbots effectively include Rawbank, Starbucks, and Lyft. To break down what each of these companies is doing as straightforwardly as possible, we’ll say that effective Chatbots can be recognized under three pillars:
How it understands language
How it personalizes the experience
How it continues to get better
With Rawbank for example, it has over 50 different use cases which is what makes it so well regarded. With this amount of ground covered, it’d mean that there isn’t a whole lot users could throw at the system that it wouldn’t be able to handle. This brings me to the next point; how it personalizes.
We’ll use Starbucks for this one just because it’s super simple. The chatbot can access a customer's order history, it lets them customize things, it gives recommendations, and it’s a barista in your pocket. This is a system that’s going to set the standard for any local coffee shops now and guess what? The companies who leverage it better than others, will get more customers and retain them longer.
Lastly, Lyft. They recognize that their market is heavily controlled by Uber - which puts a lot of pressure on them when it comes to the customer experience. At first glance, you can see that the Chatbot interface for Lyft closely resembles an iMessage chat which is certainly user-friendly, but how does it stand out? Well, Lyft beat Uber to market. Which has given them time to get some mileage on their Chatbot and optimize the user experience.
The Takeaway
It’s no surprise that a lot of people’s knowledge and understanding of AI stops at Chat GPT. These Chatbot interfaces are setting a new standard for how people find and interact with information, which is now pouring over into the business world. Want to get behind the shift? Find out if your business is ready for AI today.
Written By Ben Brown
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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.
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