Customer support: Why brands need to think beyond conversational AI
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
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
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
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
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
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
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
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.