Discover how CX organizations are adapting and evolving with the use of generative AI. Learn about the impact of AI on customer interactions, the roles and responsibilities within support organizations, and the importance of bot builders and bot managers. Explore the benefits of conversational design and data analytics in enhancing customer experiences. Find out how success metrics such as first response time and first contact resolution are being prioritized. Stay ahead of the curve with generative AI in CX organizations.
The video discusses the impact of AI on customer interactions and support organizations. AI allows organizations to gain more insight into customer data, prioritize issues, and take action. The video also explains the roles and responsibilities within a support organization before and after deploying AI. It highlights the importance of bot builders and bot managers, as well as the premium placed on conversational design and data analytics. The video concludes by discussing success metrics and the focus on first response time and first contact resolution.
when we put AI in place really we begin
to morph the way that we're interacting
with our customers we gain a lot more
insight into the data than we
necessarily had before an example would
be in the support organization where
tickets are coming in and there could be
a wide variety of channels that we're
tackling here traditional channels like
voice or email it could also be newer
channels where we're actually embedding
chat within mobile devices or within
websites or social channels and as a
result of that you have a lot more data
and a lot more different types of
questions and interaction flows than you
would normally have in a traditional
what this allows for is for that
organization to be able to take
advantage of that data prioritize where
the biggest impact items are and where
they're coming from and then take action
in deploying the AI to reconcile those
issues the Paradigm that you want to put
in place from a support organization CX
organization standpoint is to understand
what are all the reasons that your
customers or users are contacting you
and then where can you get the biggest
impact from deploying Ai and where is it
synergistic and where may it not be in
which case you actually want to leverage
your legacy channels where that might
involve a support agent
ex organization prior to deploying AI
you see a lot of traditional roles roles
that are focused on Services
implementing customers onboarding we
generally see a support organization
that's dealing with inbound requests and
queries that are coming in from the
customer base and we often also see an
organization that's responsible for the
long-term impact value that comes from
that customer relationship it begins
within an organization by pulling
individuals directly out of the front
lines who are dealing with customers day
in day out and they have to direct
exposure they know what the interactions
are like and so they then are interested
in being able to use the AI technology
to begin to automate some of those
interactions and so that generally
begins with a bot Builder who is
responsible for being able to design
what the conversation and and
interaction flows look like and put them
into a tool like Ada to be able to
when you look at generative and the way
that the industry is going and the Ada
is innovating that takes a lot of the
pressure out of the build part and it
really begins to morph it into the more
analytical part of things so it's not
simply about uh setting up and
generating initial questions and
responses like an FAQ it's about
um what are causing those interactions
and what is a different type of flow or
interaction framework that could
mitigate these types of concerns and
questions that customers have then from
a resourcing and tooling standpoint and
skill set standpoint you're beginning to
bridge out of the bot Builder into a
true bot manager and then that bot
manager begins to leverage other areas
of of skills or individuals for instance
there's a a premium that's placed on the
conversational design that's going on
there's a premium that's placed on the
data analytics because in many cases you
want to integrate and pull in data
sources to personalized interactions
there's a premium on the data science
aspect which is looking at when we Force
rank all of those types of interactions
how do we ensure that we're getting the
the biggest return on our investment for
deploying AI so it really does begin to
morph and it's less about the
fundamental building and more about the
you think about success metrics like net
promoter score or a fixation on customer
satisfaction and in often our clients
will be focused specifically on agent
handle times this morphs definitely when
you put AI in place and it's not that
those old measures necessarily go away
but you have a different lens that
you're applying I focus heavily on First
Response time and first Contact
resolution what that essentially means
is when someone reaches out regardless
of the channel in a truly omni-channel
environment it doesn't matter whether
they're contacting Us in voice chat
emailing us social channels ultimately
the goal is to be able to understand
what it is that they're contacting us
about and reconciling it in the shortest
amount of time where we have confidence
that actually we've resolved their issue
so they're not having to either go back
through another channel to get their
question answered or to re-contact or
get frustrated and have to get routed to
an agent with things that really AI
should be able to handle
With the implementation of AI, customer support has undergone significant changes. One major advantage is gaining deeper insights into customer data, thanks to the wide variety of channels through which customers now interact with businesses. These channels include traditional channels like voice and email, as well as newer ones like chat embedded in mobile devices, websites, and social media platforms.
This influx of data enables support organizations to prioritize their actions by identifying the most impactful issues and their sources. AI can then be deployed to address these issues efficiently. However, it is essential to assess whether AI is synergistic in these situations, or whether leveraging legacy channels, such as support agents, would be more effective.
Prior to the integration of AI, support organizations typically had traditional roles focused on customer services, onboarding, and handling inbound requests and queries. These roles have evolved with the introduction of AI technology.
Organizations now pull individuals directly from the front lines, where they interact with customers daily. These individuals, familiar with customer interactions, become interested in leveraging AI to automate certain interactions. The process often begins with a bot builder, responsible for designing conversation and interaction flows using tools like Ada to automate them.
As the industry progresses towards generative AI, the role of the bot manager becomes crucial. A premium is placed on conversational design, data analytics, and data science, ensuring personalized interactions and maximizing the return on investment for deploying AI. The focus shifts from fundamental building to analytics and optimization.
Success metrics in customer support have traditionally included net promoter score, customer satisfaction, and agent handle times. However, with the introduction of AI, different metrics gain prominence.
First response time and first contact resolution become critical in an omni-channel environment, regardless of the channel used to contact the support team. The goal is to understand the customer's issue quickly and efficiently reconcile it, ensuring their problem is resolved without the need for further contact or frustration.
The integration of AI in customer support has revolutionized the way businesses interact with their customers. Through AI-powered automation and analytics, businesses can provide faster, more personalized support. This not only enhances customer satisfaction but also improves internal efficiency and resource allocation.
By leveraging AI technology effectively, support organizations can streamline their operations, enhance the customer experience, and allocate resources more efficiently. However, it is crucial to strike a balance between AI automation and the human touch, ensuring a seamless customer support experience.
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