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The Future of Retail Customer Service

The Future of Retail Customer Service

/ Technology, Artificial Intelligence
The Future of Retail Customer Service

Why - and how - modern AI chatbots can get contact centers there.

As seen by slower mall traffic and stacked boxes on doorsteps, online shopping remains on the rise and shows no signs of slowing down.

The share of all retail sales that are expected to take place online is projected to increase to 24% by 2026 (Forbes Advisor). With close to a quarter of the overall retail experience taking place in the virtual space instead of the physical, that means that the need for chatbots will increase accordingly.

While the necessity of human assistance will outlast any technology, the artificial intelligence (AI) behind the chatbots has made huge strides, reports The New York Times, alleviating some of the workload for customer service representatives and providing 24/7 customer assistance.

This evolution of chatbots taking on menial customer assistance tasks will allow customer experience (CX) teams to focus on higher-level tasks that will help their customer bases feel more connected to the brand as a whole.

To reach this perfect balance and improve the overall CX, chatbots need to be modernized. Ideally, chatbots should run with minimal human interference and make a positive impression on the customers they serve. And do this while providing engaging conversation and a personalized experience: just like a human representative would provide.

Retailers can accomplish this through a transition from legacy chatbot methods to a more modern AI-driven approach to chatbots, which should go right along with the ongoing digital transformation being planned and implemented by organizations everywhere.

Engaging Conversations

Basic chatbots have long put the onus on the customer or user to figure out how to initiate the conversation, keep it going, and direct the chatbot to where they need to go to receive assistance. This has led to countless frustrating interactions where the customer inevitably begs to speak to a real person to try and solve their issue or answer their question.

These outcomes have the twofold impact of inundating human customer service representatives with what should have been easily solved requests. By the time a representative gets to a customer, that customer is already frustrated, making assistance an issue of solving the initial request as well as smoothing over the issue of chatbot-related frustration.

By gathering context, AI-based chatbots can more fluidly participate in conversations.

To prevent upset customers and conversations going in circles, the first step to the ideal chatbot is engaging conversation. Chatbots should be concise and quick to respond and should give customers an easy route to share the specific issues they want to discuss. Once those issues have been identified, the chatbots can then tailor specific responses to match the customers’ needs.

This approach helps keep customers on the line until their issues are resolved. The best path to engaging conversation is to transition from traditional rule-based chatbots to AI chatbots, as demonstrated by the meteoric rise of ChatGPT.

Rule-based chatbots follow pre-programmed response pathways, triggered by if/then coding. This results in a straightforward but limited set of interactions.

AI-based chatbots, on the other hand, get around the pre-programmed responses by being able to do what rule-based chatbots can’t; namely, learn from current and past interactions.

By gathering context, AI-based chatbots can more fluidly participate in conversations. Even if the customers on the other ends do not provide the rigid set of pathways required by rule-based chatbots to ultimately get to the desired outcomes.

Of course, AI chatbots aren’t going to come “out of the box” being totally prepared to serve your exact customer base. But with machine learning and natural language processing, they learn quickly and preserve that learning for all future interactions.

Personalization is Key

Another recognizable difference between AI chatbots and rule-based chatbots is learned personalization.

With a wealth of product options, CX is what sets a business apart. If chatbot interactions are a dime a dozen - and 74% of customers are saying they actually prefer to use chatbots (IBL News) - then interactions with those chatbots will be what separates CX leaders from the rest.

Personalization is an efficient and effective way to provide a customer with a memorable experience. Developing rapport is a skill human customer service representatives are trained on, and that same sense of rapport is crucial to developing a positive virtual CX as well.

As organizations shift their CX strategies and chatbots take on more customer-facing interactions, those chatbots are what customers will come to see as more than just the “faces” of the customer service departments. Instead, they will be viewed as representations of the companies. With that much dependency on chatbot interaction, investments in technology must match the desired outcome.

Increased chatbot interaction is beneficial for a few key reasons. Rather than working with a different representative each time, the customer is always interfacing with the “same” bot, and with strengthened machine learning, that “same” bot will “remember” those past interactions.

Moreover, allowing data and historical interactions to lead all of these conversations strengthens that feeling of familiarity, which will drive brand trust and loyalty. Think of this as similar to a human customer service representative referencing a customer profile to familiarize themselves with the customer on the other end of the line; it’ll now be a chatbot doing all of that legwork.

Know Your Audience

Another benefit of transitioning from simple rule-based chatbots to an AI-powered system is the ability to serve customers with different answers or options if the initial interaction isn’t sufficient.

If a customer is presented with the same generic answer several times that doesn’t fix their problem, they will likely get frustrated and leave the chat without a resolution: a problem that is common with rule-based chatbot interactions.

AI chatbots get around the if/then logic that rule-based chatbots follow. They aren’t restricted by generic answers that need certain phrases from the customer to be triggered.

As phrases or questions show up in patterns, AI has the ability to constantly scan for commonalities among chats across the company to ensure that chatbots are programmed to answer questions effectively. For example, if a coffee machine company is consistently seeing the phrase “help with de-scaling my machine” in initial chat requests, they can make that an option right off the bat.

You’ve probably seen this in FAQ-like sections on chatbots: the “common issues you may have” prompts. Just like a phone menu where you can press “3” to get straight to the department you need, a chatbot can function the same way. Ease of use is a historically important feature of any technology, and this is an excellent example of that in play.

Chatbots: The Way of the Future

While the customer service issues solved by a smart, modern chatbot are endless, there’s one lingering question in the minds of customer service centers everywhere: “what about the humans?”

As technology evolves, there will always be questions about the people whose jobs it could potentially replace. In the case of customer service representatives, chatbots aren’t meant as replacements, but rather as a tool to lighten the immense administrative burden they face.

This issue – and the need for solutions - came to the forefront after the rise of eCommerce and other online interactions during the COVID-19 pandemic. There were the headlines (like in The Washington Post) sparking concerns about record phone wait times, people being stuck on hold (Vox) without ever speaking to representatives, and support centers being overwhelmed with calls (CEoutlook).

But the documentation each representative had to fill out remained immense, consuming invaluable time. A situation that still exists today.

...improved, modern AI chatbots are a clear step towards a simpler future.

With chatbots available as the first line of defense, organizations can free up their customer service representatives’ schedules to handle the more complicated issues.

Chatbots and AI can also fix the frustrating issues of customers being on hold for too long - or having their calls reach the representatives in the wrong departments - because of the improved learning capabilities of the chatbots responsible for things like directory menus.

This doesn’t leave customer service representatives with ample time to twiddle their thumbs, either. With less time spent jumping from call to call addressing similar questions and issues, they have more time for training, learning, and growing into (and beyond) their support roles.

Additionally, chatbots free up manpower for the all-important CX, which is about more than just base-level interactions between customers and representatives and requires thoughtful strategies. This could be honing in on proper branding, focusing on customer retention, or simply having time to really dig through and address longstanding customer issues.

Any way you look at it, improved, modern AI chatbots are a clear step towards a simpler future. They have the ability to field questions that don’t need to be elevated to human interaction and to learn from each interaction building a stronger foundation for future interactions. And, most critically, they have the overall strengthened ability to personalize each conversation to truly engage with the customer.

Modern chatbots, then, will lead to a modern customer service, which ultimately results in happy customers and well-supported employees.

Fara Haron

Fara Haron

Fara Haron is the Teleperformance Regional CEO, NAISAUKI (North America, Ireland, Southeast Asia, United Kingdom, Kenya & India) and EVP Global Clients.

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