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Customer support: Why brands need to think beyond conversational AI


Presented By: CrmXchange

Contributed Article by Mike Platek - Head of Customer Success, Mavenoid  

With inflation skyrocketing and recessions looming, businesses are looking to tighten budgets and reduce overhead costs. In light of this, support and service organisations are typically asked to become more efficient with fewer resources. As a result, companies of all industries and sizes are looking to automate more of their customer support functions. This has led to the rise of conversational AI and chatbots. However, is this at the detriment to the overall customer experience?

The problem? Applying the wrong tech— like question-and-answer chatbots that can only follow simple logic trees— can end up frustrating customers and often doesn’t actually resolve their issue. More often than not, these chatbots typically end up escalating the issue to human support teams anyway, with the chance of linking to a general knowledge base article in between. As a result, they are not saving brands any time or money in the process. The issue may have been deflected away from a real person on the support team, but at what cost? Sure, this deflection may have a short-term savings impact, but the inferred long-term costs are surely greater.

In today’s corporate landscape, brand loyalty isn’t guaranteed—it’s earned. And if you don’t provide exceptional product support, people will turn to competitors without hesitation. As such, organisations need to think beyond conversational AI to engage in less ‘chit-chat’, and more problem solving. Additionally, the expectations when escalating an issue to a human have changed - making your customers repeat their entire query as if they have not already engaged with a chatbot causes frustration, wastes time and leads to more abandoned cases. 

 1. Focus on resolution, not deflection 

Deflection rate is the percentage of support requests that are addressed by self-service tools that would otherwise be serviced by agents. Put simply, it refers to the number of tickets your team avoids having to deal with as a result of automation. If 20 out of your 100 tickets are intercepted by a chatbot and avoid being sent to your support team, the deflection rate would be 20%. However, this ignores the customer experience side of tech support and completely neglects if a customer's issue was actually resolved.

In an ideal world, these tickets are deflected because the customer is happy. In reality, it’s more likely that they were neglected because of the tickets timing-out, the chatbot failing to provide adequate information, or the user simply becoming frustrated and ending the session.

For automation to be successful, they need to focus on resolution, not deflection. This can be achieved with a diagnostic approach where the self-service bot leverages machine learning to create a looped troubleshooting experience. It's similar to how a doctor would help a patient. A product assistant will ask questions to the customer to understand the issue symptoms based on observations. Once enough information is determined, it will suggest the most appropriate solution. The result? The query is actually solved and the customer is left happy.

2. Leverage AI to tailor and improve support  

According to Gartner, a mere 9% of support journeys are resolved by a chatbot alone. This is because conversational AI alone can only process information with definitive information, with no room for interpretation. 

But AI has so much more to give in optimising the customer journey. One way is by pre-training AI powered virtual assistants on specific issues that are expected to appear. For instance, brands can feed their AI information on what to do if its headphones are buzzing. So when a customer comes for help, the bot can recognise and diagnose this specific issue with accuracy so customers receive quality self-service support. Additionally, large pools of readily-available data that describe hardware related issues can be better leveraged to pre-train models so that customers find relevant solutions immediately.

With automated tech support, brands can also ensure 24/7 responsiveness. Whereas human agents require lunch breaks, coffee runs, and shuteye to function properly, AI-powered software never needs to recharge meaning customers can get help around the clock. AI doesn’t quite replace the human-touch that a real agent can provide, but it can diagnose and solve the issues that plague your customers, especially when the same types of questions are repeated 50 to 80% of the time! 

AI-powered solutions often result in faster resolutions. Troubleshooting is a major time hole for tech support teams. An automated support flow puts hours back in everybody’s day, allowing staff to move past repetitive tasks and focus on bigger challenges. 

Most importantly, brands can adopt AI solutions that are constantly improving – rather than static chatbots that only think inside the box. As technology advances, so should quality and accuracy. This way, a support solution gets smarter with each new service request.

3. Implement a hybrid support approach  

Today, customers expect instant, around-the-clock resolution via any channel. The best way to provide that is by implementing a hybrid support model that leverages the best of AI and humans—meeting customers where they are to provide the right level of support, at the right time.

By leveraging smart technology and people, businesses can reap the many benefits of both. AI solutions offer speed, scalability, and cost savings. Meanwhile, human agents are experts at establishing emotional connections and providing personalised service. Put them together into one hybrid support model, and the result is a comprehensive strategy that brings the customer experience full circle.

At its core, the hybrid support model recognizes that customer support is not one-size-fits-all. Instead of limiting tech support to one or two channels, it offers flexibility and customization through tools like self-service assistants, AI feedback loops, and live video support.

Bringing together the best of both worlds, hybrid support helps companies provide phenomenal customer service across multiple channels. Companies can’t sit back and offer the same old tech support as always. As competitors in the customer experience era, they must actively adapt to evolving preferences —meeting people across a variety of support channels.

The bottom line  

When it comes to providing complex hardware and product support, conversational AI alone simply won't cut it. They lack intelligence and true troubleshooting skills. And instead of actually resolving customer problems, they hit walls, only serving to frustrate customers. 

But companies don’t have to settle for offering subpar support. Instead of opting for generic automation technology, brands need to pick advanced AI solutions and a hybrid approach that can be tailored to meet specific product support needs. The right tool will be intelligent, purpose-built and designed to continually improve over time. This is how to really pave a path to superior customer service.