DevRev Demo
Summary
TLDRThe video script showcases a demo of the support capability on the DevRev platform, emphasizing the importance of customer support for retention and satisfaction. It highlights features like multi-channel support, SLA targets, AI-powered deflection with 'DevRev Turing', and seamless ticket escalation. The script also demonstrates the efficiency of using AI for summarizing conversations and clustering tickets, improving visibility and prioritization in customer support.
Takeaways
- 📊 The script is a demo of the support capability within the DevRev platform, emphasizing the importance of customer support for retention and satisfaction.
- 🔍 The support platform integrates multiple communication channels, allowing for a unified inbox view that consolidates emails, chats, and other forms of customer inquiries.
- 🤖 An AI capability called DevRev Turing is utilized for intelligent deflection, providing automated responses and recommendations to customer queries based on context and external information.
- 🔑 The platform highlights the significance of Service Level Agreements (SLAs) in customer support, with different targets depending on the customer's tier and the nature of the problem.
- 🔗 The demo showcases the ability to link tickets and issues, allowing for a streamlined workflow from customer inquiry to problem resolution.
- 🔄 The script demonstrates the use of a chat widget, called the plug widget, which can be embedded in applications to provide in-app support.
- 👥 The platform enables easy switching between user personas, such as from a support engineer to an end user, to understand and manage the support process from different perspectives.
- 📈 The script illustrates the use of AI for grouping similar tickets, reducing noise and providing clear visibility into common customer issues and priorities.
- 🛠️ The platform includes a feature to escalate customer inquiries into tickets, which can then be linked to ongoing work items or issues that developers are addressing.
- 🗣️ The demo includes a 'huddle' feature for quick communication and collaboration between support engineers and developers working on related issues.
- 📝 The script concludes with the ability to automatically summarize conversations and tickets using AI, providing efficiency and clarity in the support process.
Q & A
What is the critical aspect of the support capability discussed in the script?
-The critical aspect of the support capability discussed in the script is its importance in customer retention and satisfaction, which in turn influences customer loyalty, product purchases, and promotion.
What are the different mediums through which customers can seek support according to the script?
-Customers can seek support through various mediums including email, phone, chat, and potentially others, highlighting the omnichannel approach to customer support.
What is an SLA Target in the context of the script?
-An SLA (Service Level Agreement) Target is a predefined service level that the support team aims to meet, which can vary depending on the customer's tier of support and the nature of their issue.
How does the script describe the integration of AI in the support platform?
-The script describes the integration of AI, specifically 'devrev Turing', which uses context about the user's query, user details, and external information to provide intelligent recommendations and automate responses.
What is the purpose of the plug widget mentioned in the script?
-The plug widget is a chat widget that can be deployed in applications, making it easy for users to initiate support conversations within the app they are using.
How does the script illustrate the process of escalating a customer issue to a ticket?
-The script illustrates the process by showing a support engineer reviewing a customer's issue, recognizing it as something outside their control, and then escalating it to a ticket for further action by creating a new ticket and linking it to the customer's inquiry.
What is the significance of linking tickets to issues in the script?
-Linking tickets to issues allows for better tracking and management of customer problems. It also ensures that updates on the issue's status are automatically propagated to the related tickets and customer conversations.
How does the script demonstrate the use of AI for ticket clustering?
-The script demonstrates the use of AI to automatically group tickets based on similarity, reducing noise and providing clear visibility into common problems and priorities among customers.
What feature of the platform allows for automatic summarization of customer conversations?
-The platform features an AI-powered 'summarize' command that leverages 'devrev Turing' to automatically generate a synopsis of the conversation history and resolution.
What is the script's stance on the importance of visibility and clarity in the support platform?
-The script emphasizes the importance of visibility and clarity as key factors in making the lives of support engineers and customers easier, by providing automated updates, intelligent ticket clustering, and automatic summarization.
What is the 'hangout' functionality mentioned in the script and why might it be considered hidden?
-The 'hangout' functionality appears to be a feature within the devrev app that allows for quick, in-app communication with team members. It might be considered hidden because it's not yet publicly disclosed or is in a testing phase.
Outlines
📘 Support Capability Demo Overview
The speaker introduces a demo of the support capability within the devrev platform, emphasizing the importance of customer support for retention and satisfaction. The platform integrates multiple communication channels, such as email, chat, and phone, allowing for a unified support experience. The demo showcases an inbox view with ongoing conversations and highlights the platform's ability to meet SLA (Service Level Agreement) targets based on customer tier and issue severity. The speaker also switches perspectives to demonstrate the end-user experience, including the use of a chat widget for initiating support requests.
🔄 AI-Powered Ticket Escalation and Issue Tracking
The script continues with a support engineer's perspective, escalating a customer's issue into a ticket and linking it to a similar existing issue. The platform's AI, named devrev Turing, is utilized to automatically deflect common queries and provide recommendations based on user context and external information. The speaker then discusses the process of identifying and prioritizing widespread issues by searching and linking tickets to ongoing development work, illustrating a collaborative approach to resolving customer problems.
🤖 Streamlining Support Workflows with AI and Huddles
The speaker demonstrates the use of the devrev app for initiating a 'huddle'—a quick meeting with engineers—to prioritize and address a customer's issue. The platform's integration allows for seamless communication and collaboration without the need for external tools. The AI capability is highlighted again for its role in updating ticket statuses and linking issues to tickets automatically, providing a clear view of the customer's journey from inquiry to resolution.
📊 Leveraging AI for Ticket Clustering and Summarization
The final paragraph focuses on the use of AI for ticket management, including automatic clustering of tickets based on similarity to identify common issues and prioritize them effectively. The speaker also introduces the 'summarize' command, which uses devrev Turing to generate a synopsis of a ticket's history and resolution, enhancing efficiency for support engineers. The demo concludes with a summary of the platform's capabilities and a tease for future features.
Mindmap
Keywords
💡Support Capability
💡Customer Retention
💡Persona
💡Inbox View
💡SLA (Service Level Agreement)
💡Plug Widget
💡DevRev Turing
💡Ticket
💡Huddle
💡CICD (Continuous Integration/Continuous Deployment)
💡Automation
💡Visibility
💡Clustering
Highlights
The importance of customer support in ensuring customer satisfaction and retention.
Support capabilities are not limited to email, phone, or chat, but span across multiple mediums.
The platform's ability to provide support where customers are, such as in-app chat or email.
The significance of Service Level Agreements (SLAs) in customer support and their tier-based variations.
Demonstration of the support engineer's perspective, handling customer requests and triaging issues.
Introduction of the AI capability called 'devrev Turing' for context-based support recommendations.
The system's automatic deflection using AI to provide immediate responses to customer queries.
How the platform leverages AI to offload the burden from users and make support more efficient.
The end-user perspective with the plug widget for in-app chat support.
The ability to escalate customer inquiries into tickets within the support system.
Linking similar work items and tickets to streamline issue tracking and management.
The use of 'huddle' feature to quickly collaborate with engineers working on related issues.
Automatic status updates and notifications propagated by the system, reducing manual effort.
Visibility into customer accounts, active conversations, and associated tickets for better customer management.
The introduction of AI-based ticket clustering to reduce noise and highlight common issues.
Automatic summarization of conversations and tickets using AI for internal clarity and customer communication.
The overall efficiency and visibility provided by the platform's AI capabilities for support engineers.
A sneak peek at upcoming features and the potential impact on customer support workflows.
Transcripts
foreign
is walking through a quick demo of the
support capability which is part of the
devrev platform
now obviously support is a very very
critical thing nowadays when we start
thinking about uh customer retention how
to ensure that customers are happy right
the happier the customer is the more
inclined they are to One support your
product purchase your product as well as
promote your product and so I think
you're seeing a lot of reinvigorated
focus on the support front
um so
right now for this demo I'm going to
start it out as the Persona of a support
engineer right so I'm on the battlefield
handling customer requests you know
triaging things what be in so this is my
inbox View
so if I zoom in here you can see a few
things one I can see that I have four
conversations taking place uh but more
importantly these are coming from a
multitude of sources and this is an
important thing you know support is not
only email or phone or you know chat
it's really across all of those various
mediums and that's one of the nice
things with this platform is
irrespective of whether I you know send
an email to get support I could you know
in that same thread
get support via chat right and so you
know it kind of comes back to one of the
scenes which is bring your support to
where your customers actually are if
they're in your app bring them chat
support if they're in their inbox bring
them email based support and that
plugability and portability is very
important
one of the other things to note here is
for this customer conversation we have
an SLA Target now SLA is obviously when
it comes to support or very very
important just because these are you
know things that we need to meet
contractually and so depending on the
tier of support who the customer is as
well as the problem that they're facing
you could have various or different SLA
targets here
so that's a brief overview of the inbox
one of the things I'm going to do now is
actually shift context or personas and
show you what it looks like
to be an end user
so here we have our Maple software
quote-unquote company which is a demo
company that provides SAS services for
you know things like billing payment
Services Etc
now one of the keys to highlight here is
in this lower left hand corner
I can see our plug widget
the plug widget is essentially a chat
widget which you can deploy in your
application
whether it's a mobile app a web app
would be it so very plugable and
portable
so here you can see that I have a few
previous conversations as Elon Tusk and
to demonstrate how to initialize another
conversation I'm going to go ahead and
show that to you now
so I'm going to go ahead and send a
message I'm going to say
so one of the first things to highlight
here is obviously I sent my message to
the to the system to the support inbox
but before a user or support engineer
actually responded the system
automatically deflected that now if you
look at traditionally traditionally you
know deflection would be you know rule
based and look for keywords and be very
very rigid
however one of the things we built into
the platform is an AI capability called
devrev Turing
so for this functionality it's actually
using context about the user's query
context and detail about the user as
well as potentially you know external
information like logs and things that
the the application is generating and
it's feeding that into an AI service
very similar to what people see with
chat GPT and then based upon that
context providing a recommendation so in
this scenario I sent a message about a
payment
dispute
and it actually gave me a reference to a
knowledge based article as well as a
link including some summary information
now in certain scenarios this may solve
that problem and that's awesome you know
because me as a user or a support
engineer I didn't have to do anything
and that's one of the key themes of the
system is you know offload the burden or
onus from the user and push it onto the
system leverage an intelligent system to
make your life as a customer as well as
user much more efficient
and so this is one example now just for
the sake of this demo I'm going to say
that this did not solve my problem in
some cases it actually will
and I'm going to
input my stack trays that I'm seeing in
this scenario
so here I went ahead and set that Strack
Trace
now what we're going to do is we're
going to switch back to the support
Engineers persona
now in my inbox I can see that
conversation that was
started by quote unquote Elon Tusk so
now let's go ahead and click on that
conversation
now as a support engineer you know I've
may or may not have seen this stack
Trace before but this looks like
something that is outside of my control
so I'm actually going to escalate this
up into a ticket
so I go here to link tickets and then
I'm going to go ahead and create a new
ticket
I'll input the title
and one of the keys here is you can see
that there are some similar work items
and so you know the system has that
intelligence to see things that are very
similar and may actually make more sense
to link as compared to creating a new
item in this scenario we're just going
to go ahead and create a new ticket
and because this has to do with our pay
service I'm going to select our pay
feature to create this ticket under
so here what you can see is I have all
the history in regards to the initial
customer inquiry and then I escalated
that up into a ticket
now I want to see if this is actually a
common
problem or if anyone else is having the
problem so what I'm going to do here is
I'm actually going to
search
so here we can see that there are a few
tickets associated with this as well as
an issue now issues are things that
developers are actually working on
so if there's a bug or a defect or you
know a new functionality they would be
working on these issues no different
than you would in a traditional
engineering Work Management platform so
I'm going to go ahead and click on this
and here
something right off the bat looks very
very familiar so if I look at this
description
for this particular bug or issue I can
see that this stack Trace
is the exact same stack Trace that my
customer seen
now if I go up here I can see that you
know it looks like it's a P3 so it
hasn't been prioritized
and it's currently in backlog now I saw
my customer had this problem and I saw
that there were a few other uh tickets
so I'm going to go ahead and talk to
this engineer and see what's actually
going on here because this may be a more
widespread than we think
so here I'm actually going to go ahead
and start a huddle and then what we're
going to do is we're actually going to
invite a few people here so in this
scenario looks like there are a few
users which are actually actively
involved in working on this so I'm going
to go ahead and invite these users here
hey what's going on in India
hey Steve
hey how's it going man
um so I'm gonna go ahead and share my
screen out here
um it looks like there's an issue
assigned to you that my customer is
actually facing the same problem so let
me go ahead and share this with you
it looks like they're getting a a stack
Trace
um for the payment API uh where there's
a you know it's throwing a 404 due to a
nil pointer reference
um is this something that you're
familiar with
yeah I actually saw this in one other uh
stack days for some other customer so
I'm pretty sure this is happening across
the board
yeah no definitely definitely
interesting so it looks like right now
it's just in backlog
um and a P3 is this something that you
could help out with and and hop on and
start handling
yeah this definitely looks like a bigger
problem in the blast radius is pretty
big so I'll start working on it I know
it's a P3 right now I'll bump up the
priority I'll let the team know that I'm
prioritizing this over the others and
I'll start working on it
perfect no thanks again yeah I got to
make sure this customer's happy
um so appreciate you hopping on this and
and starting to work on this uh uh on a
minute swim I may get in trouble for
showing this little hangout
functionality or feature just because
it's supposed to be hidden right now but
uh this is pretty awesome we didn't go
to slack did we where are we we're in
the devra v app right no yeah we are in
order yep
nice not good stuff man all right well
yeah thanks again in India and uh yeah
let me know if you have any questions on
from the customer obviously you can see
the associated uh conversation history
and logs but yeah let me know if you
need anything from me man and definitely
appreciate it
thanks for helping our customers Steve
appreciate it yeah okay that that's
that's my job
thank you
so there you can see all natively within
the devrov app I didn't have to switch
contacts I didn't have to go to slack I
didn't have to you know dial someone I
could literally just start a quick
huddle there with the engineer working
on the issue and
get some feedback from him to help
prioritize this issue
and so because this is related I'm
actually going to go ahead and Link this
issue
to this ticket so that's issue five six
one it's cool that looks good
now what are the keys to highlight here
is when I look at this View
I can see the initial customer inquiry
right and in that
I can see that the devrev bot is
actually automatically updating status
for me uh when tickets are linked when
issues are starting development
and that's a very important thing you
know I didn't have to send these status
updates the system is actually
propagating and sending those updates to
me automatically
so here I can see the customer inquiry
I can see my escalation which I'm
working on
I can see the linkage to the issue and I
can actually you know see that in India
has started working
on this problem and as he you know
progresses through that cicd flow the
status updates will propagate to my
ticket as well as to that conversation
without me having to do anything and
that visibility I mean we can't harp on
that enough that visibility that
automatic State transitioning as well as
notifications having the system do that
makes my life as a support engineer so
much easier now one of the things that
has kind of been visible here but you
know hasn't really been pointed out is
if I look at this I can see the
associated customer
so now I'm going to go ahead and view
this customer account
so in here I can see we have a customer
account for Umbrella Corporation
obviously a lot of people seem to be
fans of raccoon city or Resident Evil
but underneath here I have visibility in
regards to you know obviously some
internal conversation and discussion
here
but also you know currently active
conversations who the users are as well
as what tickets
they currently have with us
and so here let's go ahead and take a
look at all these tickets
so in this scenario we can see that this
customer in particular has about you
know 12 tickets
but this is just one of my customers so
let's go ahead and take a look at
tickets for all customers
here you can see there's a lot of
tickets right there's a lot of stuff
going on I may have 5 000 tickets and so
one of the key things that we introduced
was leveraging that same artificial
intelligence that we leveraged for
deflection as well as a few other things
we added the ability to group Things
based upon similarity
so now you can see instead of having you
know a list of 40 distinct tickets
the system automatically clusters those
into common themes so here we can see
very frequently I'm seeing a lot of
payment and API issues I can see there's
a payment dispute 404 error
you know there's some network problems I
need some slack payment Integrations
and so you know rather than you know
having all that noise I can see what the
actual real problems and priorities and
common problems that my customers are
facing and this is a very very important
thing it's all about cutting down noise
and providing visibility if we can
provide as a system visibility and
Clarity it makes everyone's lives easier
the ability to leverage devrev Turing to
automatically cluster your tickets into
chunks or common items which you can
then use to prioritize adequately
now I'm going to hop back over to my
ticket
and one of the things is you know I want
to to summarize this kind of ticket
experience right I may be obligated to
send this to my user or customers but
just for internal Clarity you know I
want to make sure that we have a summary
of the events that actually took place
here
and so that's where you know leveraging
that same
turning AI offload I can actually
automatically summarize the conversation
and so one of the things that we added
was this summarize command and
essentially what this does is this
leverages the exact same devrev touring
artificial intelligence that we
leveraged and highlighted throughout
multiple pieces of this demo but it will
look at the actual history the series of
events as well as provide a synopsis on
what actually happened and what the
resolution was so in this scenario it
actually you know shows that the user
was having a problem with the payment
dispute
um and then based upon the log message
the customer actually sent me it
extracted the exact method and object
that was having the problem right and
you know obviously there's a blurb that
it was traced back to you know various
components of the system obviously we
saw that uh pay feature was having some
problems that in India was working on
so by leveraging these artificial
intelligence offloads not only can we
provide much better visibility
but we can also provide extreme
efficiency right so in that deflection
scenario I may not have actually had to
do anything as a support engineer and
that makes me much more productive
you know rather than having to go
through my case history here or my
conversation history the system actually
automatically summarized that for me and
again a very very powerful thing about
this platform is it can do all that
stuff for you
so you know just a very short demo for
you today but I hope you enjoyed it we
obviously have a lot of very cool stuff
hopefully I don't get in trouble for
showing that hangout thing
but a lot of cool stuff in the pipeline
so definitely stay tuned and we'll show
you some sweet stuff soon
foreign
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