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How to Optimize B2B Tech Support with AI & Automation



Home > Blogs > How to Optimize B2B Tech Support with AI & Automation


HOW TO OPTIMIZE B2B TECH SUPPORT WITH AI & AUTOMATION

 * September 6, 2022


7 min read



In our recent webinar, “Cost per Case: The Economic Impact of Conversational AI
and Automation“, Dan Flagella, Founder and Head of Research at Emerj Artificial
Intelligence Research discussed the impact of conversational AI and automation
on B2B tech support with Uniphore’s Director of Product Marketing, Kim West, and
Sales Director, Peter Stevenson. Couldn’t attend the live event? We have you
covered. Read on for some of the highlights.


UNDERSTANDING THE B2B TECH INDUSTRY AND ITS SUPPORT NEEDS

The B2B tech industry has a unique audience with a highly specialized set of
needs. Unlike B2C customers, who contact customer support with typical
single-user queries, B2B clients often use products on a broader—sometimes
enterprise-wide—scale and, consequently need support on a wider range of more
complex issues.

“What’s happening in the tech sector is very different from any other place
where you’re seeing conversations between a support person and a customer in the
B2B tech space,” West explains. “It’s large organizations with multiple
employees—and they all need support.”

However, for the complex—and often highly technical—nature of its issues, the
B2B tech sector lags behind other industries in its adoption of high-tech
support solutions. The irony isn’t lost on West, who’s seen firsthand how
solutions like conversational AI and automation have transformed other “low
tech” industries. “What we’re finding when we deal with our clients in the B2B
tech support space, is that we’re finding a lot of them are not even thinking
about the idea of AI and automation being a potential solution,” she says. “The
mindset isn’t there yet.”

Overcoming that mindset requires a step back, says West. “One of the insights we
know from our experience is: let’s make sure we’re actually looking at the full
problem and understanding what’s going on. A big part of what’s going on is
that, within the tech support space, it’s not just one call or one email—it’s
multiple iterations.”


“CURING” THE COMPLEXITY WITH AI AND AUTOMATION

Multiple query iterations add up—and they add unnecessary complexity to the
already time-consuming task of tech support. West sees this as a prime
opportunity for AI and automation. By applying the technology to each support
conversation, companies can identify which issues are occurring most frequently
and can create a consistent response—whether via FAQ menu, self-service portal
or guided live assistance.

“If we start using technology that's listening to the entire conversation, it
will track all of those different emails or chat conversations that are happened
as well, so, the next time somebody calls in with a common query, we're going to
be that much quicker in resolving the issue.”

Kim West, Director of Product Marketing, Uniphore.



Stevenson agrees. He sees AI and automation as vital to identifying and
ultimately solving recurring support issues. He also acknowledges that the root
of the problem may be deeper than it initially appears.

“The challenge with identifying what’s the true issue is oftentimes a very
complex area because your technology, as a B2B tech company, sits in an overall
technology stack,” he says. He recommends that companies look closely at three
key areas:

CASE CLASSIFICATION

Are queries being properly diagnosed, classified and linked to the appropriate
resources (i.e. knowledge articles)?

TROUBLESHOOTING PROTOCOLS

Are desktop tools, knowledge base and supporting documentation (call
description, intent, case logs, etc.) being leveraged effectively?

CONSISTENCY AND QUALITY

Are previous, similar/identical cases being leveraged to create a consistent
response moving forward?


APPLYING AI AND AUTOMATION TO TECH SUPPORT OPERATIONS



The key to leveraging AI and automation effectively is to take a holistic
approach, Stevenson says. “I always think about AI and automation as it’s a
journey, not an endpoint. It’s pragmatic to think about that end-to-end view and
look at potential cases [and] impacts.”

He recommends focusing first on uses cases with the fastest rewards. “Where [are
the areas with] the biggest impact and value equation that you can get started?”
he asks. “How [does your] tech debt impact things like customer experience,
friction points or in the knowledge AI space?”

In addition to thinking end-to-end, Stevenson urges tech companies considering
AI and automation to plan for the long term. As business needs and even use
cases themselves evolve, the technology that supports them must keep pace and
allow for continuous improvement. Having an agile, unified platform and system
of intelligence makes it exponentially easier to make evolutionary adjustments
that add real value.

“Because it’s more of a journey, you need to look at solutions that protect your
investment over time,” he says. “You need to have an investment approach that
says, ‘whatever I do from a system of intelligence, I know it’s going to be
extensible to other aspects of the overall process.'”


BREAKING THE “BREAK-FIX” MINDSET

Taking a journey-wide approach is just the first step, says Stevenson. To get
the highest return on their AI and automation investments, tech companies must
abandon the “break-fix” troubleshooting mindset and consider how individual
cases can benefit the whole.

“You [need to] rethink brake-fix as a historical model, overall,” he says. “Look
for opportunities to say, ‘how can I provide intelligence in the product to be
able to fix itself, to anticipate issues, to notify the enterprise of a
potential issue and to have some sort of resolution before it becomes an
operational impact?'”

A unified AI and automation platform is the answer. In addition to identifying
and resolving recurring case issues with consistency, AI and automation, when
used efficiently, can help CX systems self-diagnose and even self-correct issues
within the platform—essentially fixing issues before they become larger
problems.

According to Stevenson, it’s high time tech companies got on board. “Most of the
world is kind of moving towards that,” he notes. “Having the product fix
itself.”


OPTIMIZING THE TECH SUPPORT EXPERIENCE FOR MAXIMUM VALUE

West agrees. She sees self-correction as part of the bigger value optimization
picture. By reducing the time and effort required to resolve complex issues,
tech companies can optimize their support operations and maximize their value.
While this includes self-service, it’s more applicable to cases requiring live
agent assistance. “How can we move more of the incidents that are coming through
to a place where instead of it being a couple of hours, it’s actually a couple
of minutes?” she asks.

Stevenson explains how tech companies can measure the success—and ultimately the
value—of their AI and automation initiatives. In addition to classic KPIs, like
resolution or turnaround times and error reduction rates, he sees one benchmark
in particular as indicative of overall success.

“One metric that describes the health of the franchise is cost per case, because
that's a reflection of how well you're driving the digital capabilities; how
well you're driving the cases that have to be handled by an agent across the
different sectors of performance.”

Peter Stevenson, Sales Director, Uniphore.



While measuring cost per case is certainly important, Stevenson cautions
companies not to confuse cost optimization with cost-cutting.

“Driving down costs is obviously, from a tech support standpoint, an important
metric overall, but it’s also that correlation [with] what does this do from a
business revenue standpoint?” he asks. “How does this technology support
experience? How does it impact a company’s commitment from a loyalty standpoint
and a company’s satisfaction level or NPS scores? How does this impact the
growth of the business?”

Tech support providers can maximize the value they receive on their AI and
automation investments—and continue to receive over time—by taking a
forward-looking, big picture approach:

 * Start with key use cases and apply the lessons learned to the broader user
   journey
 * Create a cycle of optimization that not only solves specific problems but
   also strengthens operational efficiency
 * Shorten and simplify journeys to improve end user experience and
   revenue-specific metrics (i.e. contract renewals and retention rate)
 * Refine and build upon the process to grow customer lifetime value (CLV)

Stevenson likens this to a ladder. “A lot of times I’ll find tech support
companies can handle the question about what their cost per case is, but they
don’t know the ladder, which is what does this mean in terms of service
experience overall? What does this mean in terms of the aggregate impact for a
particular enterprise account as it relates to retention, revenue, growth,
product expansion and so forth?” He asks. “That’s where I see tech support
moving away from the traditional break-fix [approach], which was always [viewed
through] a cost lens [toward asking], ‘how does that affect the business in the
continued growth and expansion?'”

COULDN’T ATTEND THE WEBINAR?

“COST PER CASE: THE ECONOMIC IMPACT OF CONVERSATIONAL AI AND AUTOMATION”

Watch it now

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