Beyond SupportLogic Dashboards: Integrating SupportLogic Insights Into External Systems
Dashboards are just the beginning. This session will push you to go beyond surface-level insights, showing how integrating SupportLogic insights into external systems can amplify your support operations. We’ll challenge you to rethink how you use data to drive business outcomes.
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Folks, please give another warm welcome for Nepal.
He's going to do a second session for us on integrating your insights into
external systems.
This is one of the really cool parts about support logic is that we now
integrate with
Snowflake data cloud and different systems that allow you to pull all these
insights
out and do so much with them and he's going to get really into it.
So put your hands together.
[applause]
Thank you, Ryan.
Yes, so today, or this session, we're going to be talking about how we can
extract all
the stuff that support logic has captured into other external systems.
I talked about all the custom fields and all the sentiment that we extract, but
we don't
need to look at it all inside support logic.
There's a lot of avenues and a lot of things that we can extract from this.
And with me today, I have someone from NTT Data, Sundar S, who's going to help
me explain
some of the things that they're using support logic for to help bring their
customer experience
focus into their business.
That will be towards the end and we'll let him share his story on that.
So today's agenda for this session is we're going to talk about support logic
being assist
of intelligence.
This is just a reminder of the things that we're capturing, the sentiments and
the different
types of signals that we're capturing from the unstructured portions of your
case data,
the types of insights extracted, so not just the signals, but also the content
and context
around it, the different avenues of where you can send these signals to, right,
that
is already available inside support logic.
So utilizing support logic and sending these out through the core-sex
functionality, and
then also using external systems, right?
We have other stuff outside of support logic that can really help you grab
those metrics
and put it into any system that you can utilize today.
And then at the very end, with the help of Sundar, he's going to help us help
you guys
kind of brainstorm how you would use support logic metrics in some of the newer
ways to
uncover support logic data.
So with that dashboard, it's very important today because they help you bring
and drive
business decisions based off of the insights.
And true value from that is coming from the sentiments that are captured in the
unstructured
portions of your case data.
And as we have been mentioning all days, support logic focuses on the customer
experience.
We believe customers, your customers, customers are the ones that are
interacting and building
that relationship with your product.
And if you have happy customers, there are the ones that are going to continue
using
your products going forward.
So these are just some of the different types of insights that we extract using
support
logic from your system of record, right?
And this is any of your CRM stuff, Salesforce, Zendesk, anything that's
available that you
use to capture case data, we can integrate it into support logic, and we can
extract
not just the scores but the context and also QA metrics from these things.
We can use these things like the sentiment score to really detect how your
customers
are feeling, right?
Are they feeling frustrated?
Are they upset?
Are they negatively experienced in the support that they're getting?
We have things like the attention score, which talks about any critical or
urgency that might
be behind the messages coming from your customer.
Sometimes your customer may have a production issue, and we may label that as
an urgency
signal because the customer can't access their production system.
We also have things that can roll up into the health score, so taking those two
scores,
the sentiment and attention, and also all of the case data that we've had for a
particular
account and roll it up into an account health score.
In addition to that, we also have the elevate portion where we can start
measuring how your
agents are handling their support cases.
Are they speaking to your customers correctly?
Are they following up on the things that they're recommending?
We can follow an auto QA, how your agents are reacting, and working with your
customers.
The first two, the sentiment and attention score, these are on every single
case that
comes in, and as cases get updated with new messages that come in or maybe time
has passed,
these things will influence the two scores that you see.
The sentiment score, again, is how your customers are feeling, so they may
express negative sentiment,
they may express frustration, confusion, things like that.
We can help identify the type of issue your customers are experiencing, and
that will
be rolled up into the sentiment score.
We also have other signals like urgency or criticality, or other things that we
can detect
like perhaps the customer just wants a follow-up request, or just maybe a call
request.
We can detect those types of messages and roll them up into the attention score
You can think of it as the attention score as support logic telling you that
you need
to pay more attention because of the signals that we've been extracting for
those two.
These two are color-coded, so they are there to provide visual content without
actually
having to look at the numbers, but if you didn't need to know more details, you
can
just look at the numbers or hover over it and you'll get a trend line of how
the score
has been faring over the life of the case.
Those two scores and all the case data will then roll up to the account health
score.
This is the current version of the account health score.
We did show you a preview of what we will be building it out to, which is the
account hub
summary, which will incorporate this that you see.
This is just a more condensed version of what we're going to be releasing later
, what we
showed you earlier, but basically is just essentially the account health score
weighted
with the most recent cases in the last 90 days.
It will give you a trend line of what that looks like and also all of the
contributing
factors that make up that health score.
If there are any escalations, any new recent or active escalations, are there
any engineering
issues so we can tie in your juror cases and highlight any of those cases that
are
tied to those engineering tickets.
We can also look at case activity, so we will know if the account has an
increase in case
volume.
We will show you which of those cases and their sentiment scores for it, as
well as a
case sentiment.
We can show you the sentiment for the customers going going up or down and we
can coach your
agents to respond accordingly.
This is the QA metrics portion of it.
This is part of our Elevate SX, where we measure how your agents are talking to
your
customers, how they are responding to your customers.
We take in score cards or what we call a rubric that you may already have
implemented.
We can configure our AutoQA to look for these things using AI and ML and also N
LP.
What we do is we create these behaviors and skills and we measure to see if the
agent
has been doing these types of skills or behaviors.
Have they introduced themselves?
Have they identified the problem?
Have they reiterated and follow up with a scheduled call?
We can check for these things within it and add a metric value to that.
Now we can see in this particular case how the agent is fairing when they are
talking
to their customers.
We also have one more metric if you can kind of see down here.
It's called the CES metric, which is the customer effort score.
What that does is it defines how much effort the customer is putting into the
case, either
by responding, either by the signals that we were detecting, again, that's
negative signals
or urgency signals or sentiment signals.
We can now detect and this typically leads into CSAT surveys how much effort
the customer
is doing.
What we see is if the customer is putting a lot of effort into this, meaning
the CES score
is going up, that usually typically reflects as a negative CSAT because that's
putting
more effort on the customer.
It's requiring the customer to do more work and through the signals that we
detect they
do not like things when we ask them to do more work than necessary.
So with that, we have the CES score, we have the QA score, we have the other
two scores
to really give you that full 360 degree view of your customer and agents inside
your support
organization.
So the other types of signals that we also have is the likely to escalate
signal.
So in addition to us providing you scores, every single case will go through
our predictive
escalation model.
And what that does is any time the case has been updated, meaning the score has
changed,
maybe there's been a new response, maybe there just hasn't been a response
after X amount
of days or X amount of time, we will tag a case that's likely to escalate if we
believe
that nothing is going to be done if you don't take any action in the next 72
hours.
And we're not just going to tag that case, we're going to give you all the
insights on
why we believe this case is going to escalate.
And that's shown here on the right hand side for all the different key insights
on why
we think this case is going to escalate.
And this is available for every single case that comes in and if we decide it's
going
to escalate or not.
Right?
So this is something that you can really take advantage of and get in front of
the customer
before they even decide that they need to escalate.
These are just a couple of hour how we extract sentiments from the
conversations within support
logic.
On the right side is just an example of one of the communication methods your
customers
may be responding to your CRM, whether it's through email, whether it's through
the portal,
whether it's through chat, even voice transcripts, anything like that, we can
ingest it and we
can extract sentiments from it.
And on the left hand side is how it looks when we extract those sentiments.
We give you the sentiment label at the very top and then the actual message
coming from
the customer to help show you what we've labeled as these types of sentiments.
Again, this can come from chat transcripts, voice transcripts, email, portal,
web chat,
anything like that.
The next way is just going to talk about how support logic can currently
extract signals
outside of the CoreSX platform.
This is just different integrations that you support logic as a way to bring
signals outside
of support logic.
The first thing that we're going to talk about is the control plane.
This is basically support logic being the center analysis tool to bring in data
for analysis
and also push data out to other tools like MS Teams or Slack or any of your
favorite BI
tools to provide the sentiments and metrics from your support organization.
We have things like the CRM widgets that we can integrate into your CRM using i
Frames,
right?
And we mention iFrames because that's what makes us agnostic to any CRM.
We do currently support Salesforce with those widgets, but if you have anything
like ZenDesk
or ServiceNow, we can provide you with iFrames and also assist you with
creating native apps
that utilizes those iFrames inside your CRM.
Okay?
BI dashboards, so being able to extract these sentiments like the turn-risk
signal, the likely
to escalate signals externally through alerts as well as events-driven API,
right?
So you can have your third-party application be set up to receive alerts coming
from support
logic and then you can have them trigger any type of workflow or any process
within your
third-party application.
Okay?
So you can kind of think of this as the support logic being the main driver on
the left-hand
side is the CRM system.
We can connect to other systems as well.
CRM is just one of them.
I think in the last slide I showed being able to connect to engineering systems
as well so
we can incorporate that as also.
And then on the right side is us being able to push support logic metrics to
any of these
available external systems.
One is Slack and MS Teams.
We have integrations to gain site.
We can push things to Tableau.
Again through the widgets we can push things through the CRM so that you can
have and live
inside your CRM UI as well or anywhere else within the organization, right?
Through email, through events-driven API through your third-party application
that accepts API
calls.
And what's great about these things as we extract and send these or push these
signals
out, you can also take action on them.
So we don't expect you to receive these signals and then be brought back to
support logic.
Utilizing Slack and MS Teams, you can actually respond to likely to escalate
cases, cases
that we tagged as likely to escalate.
If we detected a sentiment, you can actually acknowledge sentiments from within
your Slack
messaging so you don't actually have to leave whatever it is you're doing to
take care of
the case at hand or to get proper insights from it.
We can do other things like we'll notify you when cases have been reassigned
and you can
respond to that specifically by clicking directly into your CRM UI or you can
be brought back
into the support logic UI.
So we have the customizations to be able to do any of those workflow processes
that might
be pertinent to you guys.
This was just going into more specific ones.
I know I talked a little bit about the events API.
I'll show you an example of what and how we can integrate with that.
These are other external systems that we have available that are available to
you today.
What is the support logic data cloud?
That's us being able to provide you with the raw metrics that we've detected
inside support
logic.
If you wanted to do analysis on maybe the total number of sentiments or maybe
even some
reporting on usage, you would utilize the support logic data cloud to grab all
that information.
The benefit about that is that we host the support logic data cloud and all you
need
to do is query into our database.
There's no cost to you to host it that is something that we would do for you.
Universal write back.
So being able to write back, this is a little bit different from the iFrames
that we also
have here.
This is sending data back directly into your CRM system.
With the iFrames and the widgets and the native apps, that's typically read
only, being able
to look at the insights coming from support logic.
What we've seen customers ask for is being able to build reports inside Sales
force using
support logic metrics as well.
And so to be able to do that, we've incorporated the universal write back
framework and it's
has a fancy name just so that it's also CRM agnostic, us being able to work
with any
of the CRMs that are out there.
And then also the native gain site integration, which I'll go ahead and show
you towards the
end, with that we are able to send alerts to gain sites, for example, specific
signals
like churn risk and likely to escalate.
And what that will do inside gain site is trigger actions, call to actions or
populate
things on the timeline when support logic has detected this.
So we can give your users inside gain sites your CSMs that full 360 degree
picture of
your customer that includes how they're doing inside your support organization.
So this is just an example of the events API.
I think you've probably all seen this before where we have all of our alerts
here on the
right hand side, all the custom fields that are available.
But the main thing I wanted to show you is the ability to put in your events
API URL so
you can post any of these alerts to your third party API application.
And what will this enable you to do is you can create a scoreboard of this,
right?
You can have a scoreboard for your team to show you which signals have been
coming in.
You can create workflows that are automatically triggered through the events
API.
For example, if there's a specific customer that's a high value customer and
they have
a likely to escalate case, maybe you have a trigger that gets sent to your
third party
API that triggers another workflow that alerts the CSMs or the AEs or anything
like that.
So now you have automation around when I think we saw two signals in the last
session, renewal
and expansion.
You can create events API that stir up and create automation, sorry, visibility
into when
your customers are wanting expansions and things like that, opportunities like
that.
And so we can focus on sending these alerts through the events API.
This is an example to highlight exactly where the support logic data cloud sits
, right?
This is a typical architecture that we have for most of our customers.
And our support logic data cloud is basically a copy of the analytical database
And so when you want that raw metric data, you are actually going to be getting
the raw
metric data from our support logic analytics system.
So if you want to do analysis, like understanding all the sentiments that come
in for a particular
customer or any other new idealistic, or not idealistic, a new way to kind of
analyze metrics,
feel free to go ahead and do that with support logic data cloud, share it with
the community
and can find new ways to create analysis, create new metrics, create new goals
and KPI
metrics for all of us to really achieve within our community.
This is an example of the right-back framework.
So we have two CRMs here.
We have one from Salesforce, one from Zendesk.
And we're inputting the scores into this.
And what that will allow you to do is your customers will create dashboards or
reports
for support managers.
You can even use this to create filters and use this to prioritize cases using
your existing
CRM.
For example, if you didn't want to install the widgets or the native app, but
you wanted
to use right-back framework, you have another way to prioritize your cases
using support
logic scores.
And then from here, you can also create dashboards and reports inside your
existing CRM systems.
So some customers don't want to move to support logic.
They still want to do all their dashboard reporting.
And maybe they've curated it over time, and they just don't want to change it
yet.
We can push data into your CRM system so you can continue utilizing that.
So this is the framework.
So again, this portion is just the read or only portion that we make available
to your
CRM system.
We both have--or I'm showing you now Salesforce and Zendesk.
Again, these are iFrame endpoints that we will work with you so you can create
a native
app inside your CRM system, and then we hook up those iFrame endpoints.
You will get all of those things not only that you see today here, but all the
stuff
that you saw in the earlier sessions from the main room.
This stuff includes things like case details.
So you get all that core SX support logic metrics like the scores, any of the
sentiments
that were detected within the case.
We can also show you a highlights of events that have happened within the
timeline of
a particular case.
And then as you can see here, we can start incorporating some of the other
features and
functionalities like generational AI to provide case summarization.
We can also help with responsesist or if you have resolve SX, we can
incorporate resolve
SX, the answer engine, into this as well.
Okay.
Again, it's not tied to a specific CRM.
We have this available for all CRMs that we support tonight.
And this one is the native gain site integration.
So this is a picture inside a gain site instance where we are sending signals
coming from support
logic to the gain site timeline.
This is maybe a gain site user just opening up gain site and seeing all these
signals that
came from support logic, specifically likely to escalate and turn risk signals.
These are things that we believe CSM should be aware of if these things come
into our
support organization.
Right.
Yep.
So with that, I'm going to invite Sundar here.
He's from NCT Global and he's really, they are really taking advantage of
utilizing support
logic to fulfill their vision that they have around the customer experience.
Thank you, Sundar.
Yeah.
Thanks, Nubul.
Hi, guys.
I'm Asunder.
I've been with entity for nearly nine years now.
So nine years and counting.
But we've been working with support logic as a partner for nearly a year now.
So we got on board on support logic a year ago.
When we touch on like why we started using support logic, it comes down to the
conversations
we are having with our customers.
Right.
Like, so when we talk to our customers, it's no more a product, a feature based
conversation.
It's a more of an outcome conversation.
How is entity helping entities customer deliver outcomes?
I mean, that is the conversation we are having.
In that context, if you look at entities client base, we have like about 7,000
clients.
Right.
So there's two million assets we are handling, 600, 60 to 100,000 tickets that
we are handling
a year.
In all this noise, it's so easy for us to lose track of how the customer
experience really
is.
So in that sense, our focus is to understand and articulate how a support
delivery experience
can add value to a client.
So that is the whole thinking behind why we started integrating with support
logic.
Right.
And across the 7,000 clients, we generate tremendous volumes of data.
Like there's a lot of data in the different channels, the interactions, the om
ni-channel,
whatever.
There's a lot of data sitting in our environment.
Our focus across all of this data is to make it reliable.
And when we talk about reliability, we are talking about it in the context of
what Dilip
calls us, the Dilip is our executive for technology solutions.
You might have heard him earlier in the day.
When you talk to Dilip, Dilip says data needs to be reliable in terms of for
our CEO.
When he says he owe its customers, employees and our organization.
So in that context, whenever we think of a new case, those are the three person
as we
are effectively thinking.
But so far, our operators, it's a fairly, we are aligned to more or less the
story you
would have heard like all through the day.
Right.
Like so we have data that is data is being converted into insights through the
support
logic too.
And the insights have been great.
Like we have been able to create some really valuable insights in terms of
escalation,
management and all of that.
But what we are also doing is bringing the data back into our systems.
In that sense, our operators work predominantly within a service now
environment and our agent
experience portal which is something we are building as well.
Having an operator switch between one and the other becomes challenging.
And there is time that we lose and there are problems that doesn't get resolved
So we are bringing the part of the work that we have been doing with support
logic, NAPOL
is to ensure that any insights data that we build there makes it way back into
the place
where the operators are working and making decisions on.
So that is more around the ease of access side of things.
But then we started asking us of the question.
How do we as a company explain this work that we are doing from an operational
standpoint
in an entity customer's context?
So how do we explain that value to a customer?
Or how do we create a value proposition for the customer?
So in that context, what we are doing is we are also creating a client facing
service
experience dashboard.
Like in that service experience dashboard, we are also embedding support logic
metrics.
I know when a lot of support organizations look at it, they look at it and go
only.
These are internal metrics like customers might think of it in any which way
possible.
But we take a different approach there.
So when we look at the metrics, what we think about is those metrics are going
to start a
conversation with the customer.
Those metrics are going to make the customer ask us more questions and that
will make us
more accountable.
We are looking at it more as a feedback mechanism as well in our world.
Say for example, we have a product called STI, a customer predominantly
consumes that
product from a portal interface that we call as a services portal.
In the portal, we have the service experience dashboard, which is support logic
plus other
insights and metrics that we have built within NTT.
When a customer looks at it, he knows exactly what is the value of the support
he is getting.
Historically, it has always been brushed under the radar.
So customer would get it, but until something fails, he wouldn't really know
the value of
it.
Now we are changing the conversation and flipping it around.
So effectively, we are re-imagining the way these personas work and effectively
adding
another persona to support logic.
And that is kind of how we are approaching this.
In that sense, it's not without its challenges.
There is certainly a lot of challenges in that scenario.
One being the data set of things.
A lot of work has gone behind us to make sure that any data that we are showing
to a customer
and I keep repeating to Nepal over and over again that any data that we show to
the customer
needs to be safe to consume.
So we can't really have discrepancies.
We can't really show the wrong data, but also because there is AI in this,
there is also
the perception that we need to manage with our customers, which is a major
thing that
we are focusing on in that scenario.
The second biggest challenge that we have seen is how do you get our, like when
I say
our entities internal resources on-boarded and speaking that language to a
customer.
So historically, they have not really spoken this to a customer.
They talk to a customer.
They are talking about their experience three months ago.
Now we are asking them to go and have a conversation about how we are solving
their problem and
that real-time actions much earlier on.
So that change of perception is something that we are trying to also change
internally
and that is a process.
Again like all this is how we see our support organization evolving in the
future, but also
how we are translating it from a customer context perspective.
And certainly like with support, working with support logic and seeing some of
the features
and functionalities that are coming up, I'm sure we've been having some
interesting
conversations earlier in the day trying to see how more insights can be brought
back into
the entity context and how more value can be promoted.
But looking forward to working with support logic more in the context.
Thank you.
Thank you, Sano.
[Applause]
Yeah, so that is just an example of like new ways to brainstorm how you can
utilize support
logic metric.
All right, support logic is really just in a new space and I've had a lot of
questions
like how do you even measure some of the things that support logic is showing
and exhibiting.
And we just don't know, right?
So we just find and find new ways that we can build and be transparent or
trying to drive
these insights so that we can really build new metrics to measure on, right?
How do we change the perceptive?
We know that support is changing and we need to create new metrics around that
as well.
So I encourage everyone, how do you use support logic metrics externally?
What would you do?
Something to what entity is doing, right?
Would you be transparent with your customers?
Is that something that you are interested in doing as well?
Share it with the XX community live.
And we can continue providing support that way.
So yeah, thank you very much.
Any questions?
Happy to answer them.
[BLANK_AUDIO]