March 19, 2025 • 62 mins
🔥 How can AI transform customer retention and drive revenue growth? In this episode of The MongoDB Podcast, join host Shane McAllister and Samir Agarwal, Chief Product Technology Officer at Agmeta, as they dive into the future of AI-driven customer insights. Discover how Agmeta leverages MongoDB Atlas Vector Search and cutting-edge AI tools to predict churn, personalize customer experiences, and unlock hidden value in customer interactions.
🔹 Key Takeaways:
Why traditional customer surveys fail—and how AI extracts real-time insights from calls, chats, and transcripts.
The role of sentiment analysis and vector search in identifying high-risk churn candidates.
How generative AI creates actionable recommendations to retain customers and boost upsell opportunities.
Real-world demo: See AI transform unstructured data into a visual dashboard for instant decision-making.
Why MongoDB’s flexibility makes it the go-to database for AI/ML workflows.
🔹 Perfect For:
Developers and data engineers building AI-powered customer solutions.
Product leaders focused on retention, lifetime value, and reducing churn.
Startups exploring AI/ML tools (shoutout to the MongoDB Startup Program!).
👉 Subscribe for more episodes on AI, databases, and scaling tech stacks!
TAGS: #AI #CustomerInsights #MongoDB #AtlasVectorSearch #ReduceChurn #GenerativeAI #CustomerLifetimeValue, #SentimentAnalysis #StartupProgram #Agmeta
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:06):
Hello everyone and welcome to the MongoDB Podcast Live.
I'm Shane McAllister. I'm one of the leads on our
developer relations team here atMongoDB.
Today's show is entitled using AI to increase customer lifetime
value. I'm joined by Samir Agwal, Chief
Product Technology Officer with AG Meta to take us through
today's topic. So with that, let's get Samir
(00:28):
onto the show. Samir, your very welcome to
MongoDB Podcast Live. How are you?
Very well, Shane, it's great to be with you.
Samir, what I'd love to do before we get into the meat of
today's topic is learn a little bit about your career path to
date. How did you get started?
Where have you been? What companies have you worked
in? Before we get to the AG meta
(00:49):
story, tell us a little bit about your background.
Absolutely. Yeah, I'd love to.
So I'm a second time entrepreneur, have run global
engineering organizations, run global product organizations,
managed PNLS, worked at startupsand large companies.
That tells you a little bit about how old I must be.
I've done all of. Those things, listen, there's
always a history in everyone andI think.
(01:10):
Exactly. Right.
It's just statistics, Amir, about the best.
The startups are generally founded by people who've gone
through an awful lot before. We tend to think of startups by
the, you know, the young 20 somethings surviving on a diet
of coke and pizza. But I think the successful ones
are much, much more towards the other end of the age spectrum.
Absolutely. There's the I think it's a mix
(01:33):
here. Who knows, but beers that may.
I used to work for a startup, sort of an early employee at a
startup in the golden age of theInternet year of the circa 2000.
We were acquired by Nokia, spentseveral years at Nokia,
including some in Finland. Another story for another time,
when somebody moves from barrierto Finland.
(01:54):
That's it. Back when mobile phones were all
different. And all the.
Models and and back before therewas some interest there.
Except for currently, since 2007, everything's been a
rectangular piece of of glass and plastic and metal, right?
Absolutely. So I saw some of that in the
making, which is really interesting.
So lots of history there. I, I also worked at other
(02:18):
product organizations, security,but really and, and, and in AIA
company that was focused entirely on AI, which gave me
insight seats to seeing what works and does not work in AI.
But the really interesting sort of part of my journey was when
when I ran P&L centres and really sort of felt and saw
first hand what customer loss means, What happens when a
(02:40):
customer churns out and what happens when you don't know why
your customers churn out. And as I spoke to others who
were people like myself and customers, I saw that there was
a problem that was always present, didn't know why their
customers were leaving. And most times they would find
(03:01):
out much too late that the customers had left and, and by
then there was really nothing tobe done.
And I think this was something that kind of stuck in the back
of my head. And then an opportunity came
along and I said, you know what?Why don't we solve a problem
that focuses entirely on that? And that's kind of how Agmeta
came in to be. So like the best startup
stories, they're founded out of a level of personal insight, a
(03:25):
level of frustration, and a level of I think I can solve
this problem or at least make itbetter, right?
Absolutely, I've got a personal anecdote to share on that sort
of front. I've been with the same Internet
service provider and cable TV provider for either 20 plus
years and as we. All do because we we were loads
(03:47):
to move and to change. I know exactly.
Right. Yeah, exactly right.
And, and so, you know, it just continued.
And then about about a year back, my bill goes up by 35% and
I, I, I was prepared for the drill.
You call them, they tell you that your promotion has expired.
They put you on a new promotion,they charge you another 10 bucks
(04:08):
a month. I knew the drill down pat.
So you call them, and this guy was really nice.
He told me that your promotion has expired.
And I said, you know, we're following the script here, this
is going to be fine. And then he comes back and says,
but there is no other promotion.And I'm like, oh, this guy
doesn't get it. Yeah.
So. Because usually you expect the
(04:30):
fact that you've phoned up and taken the time to, you know.
Get the next. Promotion a year on or get some
savings right? He told you there was none.
There was none and I said you know what, green guy doesn't
know what he's doing, you know, let me just call back again.
So I called back another three times over the next day or two
and each time I was told that there is no other promotion and
(04:55):
I decided to leave them. After 20 plus years, I decided
to leave them. OK, OK.
And I moved on a full week later, 4 calls and a full week
later, I get a call from this ISP and they go, you know, we
noticed you've been with us for more than 20 years.
Would you mind telling us why you left?
A full week later, they still didn't know why I left.
(05:17):
And if that happens to me, you know, $3500 give or take a year
customer and they didn't know why I left.
This must happen to other customers as well.
And I think people can relate tothese kinds of conversations.
What if this could be made so simple that these guys could
have seen it on day one or day 2that this customer is going to
(05:39):
be leaving them. They could have made it right
then rather than a week later when the ship had sailed and
they call in and say, you know what, we can take you back to
the previous prices and stuff like that.
It was just too late. And that's the.
Problem you see a lot of the time do you know, like even when
you unsubscribe to an e-mail newsletter, for example, and
then you get a sorry to see you go, what was wrong?
(06:01):
It's like, well, they never asked that before and it's the
same thing. I suppose going back to your
original example, you expected to get a better offer.
You've been with them for 20 years at whatever many thousands
of dollars per year. You're a, you know, you were a
locked in lifetime customer and three calls later you went, I've
had enough of this, right? And they didn't.
(06:22):
Know. They didn't know and they didn't
know. They didn't know.
And you know, I have to say for somebody at that size, that
scale and technology savvy company that it was, if they
didn't know, what does that meanfor the rest, you know, other
organization, other companies that may not be, that may not
have the wherewithal, you know, to be able to do that.
(06:43):
So what if we could make it deadsimple for them to see which
customer is at risk of churn andalso very importantly where
there is an upsell opportunity. And that's the other part that
gets missed out because everybody is so focused on
churn. Then you forget the fact that
there is the two parts to the customer's lifetime value.
There's one about reducing churn, of course.
(07:04):
The second is about getting morefrom this customer.
And if you miss that part, that's money you left at the
table. So that's what we wanted to
address. Yeah.
And and I suppose given that this is everywhere, Samir, how
like, and this is a huge market,right?
Every company selling the service has, you know, phone
(07:26):
lines or online chat, etcetera. What was it that kind of other
than your frustration, did you see, did you see going, hold on,
I can, I can use my technical expertise, my knowledge, what
I've what I've worked with to date to maybe solve this
problem. What was the other piece of you
kind of going? I think I can I can work on
this. Yeah, I, I think the one is just
(07:51):
the sort of the technology evolution, you know, things that
seem kind of far away, things that seem like, you know, you're
going to get answers that using technology, you're going to get
answers that are not really close enough or precise enough.
They're moving to a point where you said technology makes it
easy for me to do this and and why people are trying to solve
stupid complicated problems. Here's a simple problem.
(08:14):
This, this problem doesn't go away.
You know, it never gets out of fashion.
You can replace humans with withAI based agents.
You know, AI doing the talking, you can have AI chat bots, but
on the other side you still havehumans and their customer
experience matters. If they make an investment in a
(08:35):
company, they pick up a phone. It's not easy.
I mean in today's day and age, Ipersonally don't like the idea
of picking up a phone and calling a business.
You know, I'd rather just do something on the website and and
be done with it. But if I'm.
Spending You and me both, I think yeah, look, it depends.
Obviously I'm more comfortable, you know, using a chat tool.
My wife, on the other hand, definitely picks up the phone.
(08:57):
I think she does better than me most of the time when she talks
to these companies. But, but I had like you, you
mentioned there, your ISP, my, my deal is up, you know, last
month or something in December and, and yesterday I went, OK, I
better, exactly as you said, better phone them trying or get
in touch with them, try and get a better deal.
So I did what I would do, go to the website, click the chat
(09:21):
button. And then I was told, no, you're
not through to the broadband. You're through to the mobile or
the cell phone. And I went, no, well, I clicked
the button on the broadband page.
Why am I with the mobile team? And then, and then they said,
Oh, we'll pass you on. And I got passed on to the home
broadband and I went, no, no, no, my package is business
because I need it for work, obviously, etcetera.
(09:41):
And they're going, Oh, you have to phone us.
And, you know, that was 30 minutes of crazy cat stuff, you
know, So yeah, I think and we talk about a lot of topics on
the podcast live, but this one, I think everybody who joins us
in the comments has a story to probably tell in the same space.
So I'm really looking forward tokind of, you know, the
(10:02):
comparison and and what you're solving in AG meta.
But talk to us a little bit about the the old way because,
you know, when I finish one of these calls, I usually get a
survey or, you know, a, a text or an SMS to go.
How would you rate your experience?
How, how that obviously didn't work for them or it's not uptake
or anything on that, do you mean?
(10:23):
No, it is I, I, I think at a, ata gut level, we all understand,
every business understands they need to keep their customers
happy. But I think there is a, a gap,
there is a chasm between how thevarious parts of the
organization work. And, and I think this is where
we started to sort of hone in onand, and say, you know, can we
(10:44):
make a difference here? I'll give you a typical sort of
journey. Somebody calls into a contact
centre. Contact centre metrics are very
different from the metrics of the guy who's trying to retain
the customer in the marketing organization, for instance.
OK. So somebody calls in, just like
me. I called in, the guy was very
polite, very nice, and told me he could not help me.
(11:08):
From his perspective, he resolved the problem.
You know, the call was a resolution.
There's nothing we can do for this guy.
That was the resolution he reached.
From my perspective, he was 100%lost.
Stuck to the script, he couldn'tdo anything for you and and that
was that, right? But but, but from his point of
view, this was a resolution. Nothing could be done.
(11:29):
Was the resolution. From my point of view, it was
100% lost. There was no resolution
whatsoever. My bill was still very high.
And this is the problem that I think continues to exist.
And everybody says, you know, you know, you've got to get
right customer experience. The customer experience needs to
be good. We all sort of swear by customer
experience. The challenge is how do you how
(11:50):
do you really measure customer experience, right?
And this is a big question. And typically the sort of the
shorthand version has been if you had a positive sentiment on
the call or on the chat, then this must be a good experience.
And I just think that is not thefull story.
That's part of the story. And the analogy I like to use
(12:13):
is, you know, the blind man sortof feeling an elephant for the
first time. 1 catches hold of the tail, another catches hold
of the leg, another catches holdof the trunk.
And they all have a different picture of what that elephant
looks like, you know, what that elephant must be.
And I think customer experience is somewhat that way.
We just kind of, you know, feel the different things and say
that's customer experience. But what is customer experience?
(12:35):
How do you quantify it? So what we've done is, and this
is where we've filed patents andsuch where we say, look, we will
quantify customer experience using CSAT and that's a measure
that the industry uses. But what do you how do you
compute the CSAT? The customer satisfaction score?
And this is where what? CSAT stands for customer
(12:57):
satisfaction. What's the SAT?
OK. Satisfaction C satis customer
satisfaction. But what we have said is.
Surely a customer unsatisfactionscore would be much more
appropriate for some of these? Industries.
We like to believe that every business wants good high
customer satisfaction. But what we do is we compute a
(13:22):
customer centric C set rather than a customer satisfaction
score based on it. This call was a polite call,
positive call. You know, all of those things
are component parts to the larger picture.
If I as a customer don't feel good about this call and don't
feel satisfied, it doesn't matter how good the call was, it
(13:42):
is still not a happy call for me.
And I think this is really the thing that we compute that C SAT
score and then we bring it back and marry it into other stuff.
And we can go back and say theseguys are at a risk of churn.
These guys are opportunities forupset.
And this is something that we doin a unique way.
We believe that makes a difference for the businesses.
(14:04):
Perfect. It sounds great.
And I know in a while we'll we'll see a demo of this in
action Samir, which is brilliant.
So for those tuned in and addingto the comments etcetera, we're
we're going to keep chatting at the high level for the moment,
but then we're going to start sharing the screen and actually
see how this plays out. But tell me a little bit about
obviously that C SAT score, right?
(14:24):
But with the advances that we have in, in machine learning and
AI and everything in the last couple of years, how is that
really layered in on top of thatdata that we're getting back
from these type of customer interactions?
Absolutely. I think Gen.
AI has opened doors that we didn't know existed.
(14:46):
At least for me it was, it was revelation and I've been
following it for the last sort of two to three years.
But there are things that we cando now and do it much more
easily and more precisely than we could have done before.
And I think we've, we've tried to leverage Gen.
AI to the hilt, but we've also used our own sort of secret
(15:07):
sauce, if you will. We've also used NLP machine
learning to make sure that we pick on other things.
And I'll give you an example. For instance, if I as a customer
have a contract with the provider, an ISP for instance,
and I'm, you know, committed to being with them for 24 months or
I pay through my nose, I'm unhappy.
(15:30):
But I might still be unlikely toleave because I don't relish the
idea of having to pay those large sums of money as a
transit. On the other hand, if I'm in
month 23 of that 24 month contract, it's like, you know,
I'm out of here. I've I've had it.
And, and that factors into the whole story.
So we do use machine learning along with our CSAT and, and
(15:51):
other things to come back and say, you know, this person is at
a risk of churn. This other person, not so much.
And, and that's the sort of information, but really the key,
I believe Shane, is do businesses have this information
in an easy to consume manner, ina timely manner?
And if you have that, I think there is action to be taken very
(16:14):
quickly where somebody can keep their customers.
And you know, in the example that I shared with you, if they,
if these folks had come back to me the following day and said,
look, you had a conversation or more than one conversations, we
noticed that you were not happy with this thing, here's what we
can do for you. I would absolutely have stayed
with them. And that's where we must repeat
(16:36):
1000 times each day. That's 1000 customers walking
away each day and nobody is any wiser.
So. Yeah.
And that's it. And as you touched on earlier,
not only, you know, retention but also potential upsell and
higher lifetime value and stuff.The you, you mentioned, you
know, sentiment and, and obviously, you know, sentiment
(16:57):
analysis using, you know, vectorsearch, et cetera.
To do that, we have in in Mongo DB amongst the developer
relations team, we've got a few team members, few colleagues of
mine who've built some really good sentiment analysis demos
and they're brilliant, you know,and that's the key thing I think
that Gen. AI has brought to the table is
data has existed for ages, but it's getting the meaning behind
(17:20):
the data that I think is, is beyond the ability, as you say,
to surface that to the top and turn that into actionable
insights as well too. I think is is really, really,
really powerful. But Samir, you also mentioned
when we were prepping for this call too, that you don't need
all the data. You don't need, you know, an, a
mother load of data to come in on this.
(17:42):
You can get these insights with some, some kind of very kind of
bare, not a bare minimum of, of data, but you know, really,
really kind of niche areas that you're looking for in your
solution. Right, good.
One of the big learnings for me when I was sort of experimenting
with AI and, and was part of an organization that delivered AI
(18:04):
based solutions, the challenge was less of AI and more of data.
And companies would tell us all the time our data is scattered
all over the place. It's in bad shape.
And yes, we love AI, but we're going to come to it when when
we've got our data story sorted out.
And that sounded like something that might happen 10 years
(18:25):
rather than something that wouldhappen, you know, two weeks out
or two months out. Yeah.
So from get go, what we have focused on is do not start that
conversation with saying I need all your data and then we will
create value for you. We have taken an approach where
he said, give me your basic data, give me your
conversations, your interactionswith your customers.
(18:48):
Give that to me and I can already create value for you.
At that point, I can already create value for you.
And if you can give me a little bit more data, I can create more
value and I can refine it further.
And if I can get more data stillfrom your C SAT and such, you
can see even more value, even more precise sort of results.
And and so we've taken an approach where you can bring in
(19:09):
more data piece meal. It becomes part of the overall
so the equation and it gives youbetter results.
It gives you more complete results, but you don't have to
wait for an eternity while your data gets sorted out that you'll
start to see results. So on day 2 of being
operational, you're actually seeing results.
And then you can of course make it better.
(19:30):
Excellent. So, so as we all know these
conversations if their phone call are recorded, they always
tell you that on the way in, right.
And and obviously in the chats that you might have are there's
transcripts available too. So you're basically saying to
your clients and customers that we'll work with what we already
know that you're recording before we go into the huge
(19:51):
minefield of what's left of, of all of the other data that might
require, you know, it's all in different silos, as you say,
it's all in different places. You might need to transform it
and it's very unstructured, right.
So you're you're working with what's easy for the client to
give you straight away and then enhancing that if they can give
you more in the future. That's exactly right.
(20:13):
I mean, I'll tell you, I was speaking to this is not in the
context of Eggmeta, but in a previous life, I was speaking to
a major airline here in the US and they painted such a scary
story of their data that, you know, is put a fear of God in
me. It's like I don't think I can
fly this airline ever again. So we wanted to make sure that
(20:33):
that doesn't happen here. OK, Yeah, that's a good one.
I think people can can certainlyresonate with that.
I see a lot of people joining usagain from all over the place,
from London and Charlotte and LAand Iran and Brazil.
So you're very welcome. If you've any comments or
questions for Samir, please dropthem into the chat and we'll try
(20:53):
and take care of them as well too.
Usually when make Samir, when weget to the demo portion, that's
when the comments start to come because people can see what
you're sharing and have a look and they have their own
questions. So we've spoken about the data
and we've spoken about how you approach towards that.
But tell us a little bit about how you ended up on the the
Mongo DB podcast live. So when was this?
(21:14):
When did you first start to use Mongo DB and how are you using
it in AG meta today? Yeah.
So, you know, when we started our journey, we clearly needed
some place to store the the results and the the first sort
of obvious conversation is one of the relational databases and
very shortly there. For most developers or people to
(21:35):
start right, it's like, yeah, exactly right.
These have been around for a long time.
Let's just do it that way. Exactly right.
And and very shortly thereafter we also realised that that's
going to be the wrong solution for us because the data is going
to be very unstructured. Different customers are going to
want to see different things at different times and a relational
(21:57):
database is just not the right answer for us.
And so we were looking at no SQLdatabases and we had a few
choices there. I'd clearly, I knew of Mongo DB,
I'd partnered with Mongo DB in the past.
You know, I had them in great respect.
But the important thing for us was we also knew where we are
going with this. We do need, we do need
capabilities in terms of vector database capabilities, in terms
(22:20):
of draft DB capabilities. And the choices that I had to
make were, do I take a no SQL database, then shuffle data from
that database into a vector database and then into a graph
database? And I'm going to be doing
nothing but, you know, moving data around between all of these
databases, which is not clearly the goal of.
(22:43):
Yeah, that's a lot of heavy lifting and there's a lot of, as
we call it, ETL, extract, transform and load and and keep
keep that data In Sync, keep it concurrent as well too.
So which is a lot of extra overhead and and generally extra
cost as well too. Absolutely.
And and our thought was look, ifthere is a solution that can
(23:05):
solve that problem, that's the one to go after.
And at least our research shows Mongo DB is a great solution.
That way it solves all of those problems.
Is a check box for each of thesethings and the folks are easy to
work with, which is never a bad thing.
Love to hear that. Love to hear that.
And probably timing was good. So we launched Atlas Vector
(23:27):
Search back a year and a half ago now I suppose.
So that probably coincided with when you were building out your
solutions as well, too, right Samir?
That's correct. That's exactly right.
And in in our journey, we are sort of just at the point where
we are saying, you know, we are using the vector DB enabling
that use and also looking at graph DB capabilities to see if
(23:51):
we can marry all of these and get a graph rank solution as we
take the next steps in our. Solution.
OK, OK. And you mentioned on our prep
too that you're obviously using GCP as part of this as well too.
Tell us a little bit more about that because obviously they're a
huge partner of ours in terms ofof hosting as well.
Yeah, GCP when, when we first started with an idea and not
(24:15):
much else, it was just a thoughtwith nothing else.
No company, no people, no nothing.
It was just a thought. And we started with just the
Google infrastructure really just just go back to collab and
try something out and see how that works.
And then we reached out to someone at Google startup and,
(24:36):
you know, they helped us secure a good number of credits.
And then we started to feel likethis was the right platform for
us. It did all the things that we
needed to. We also use Google's Gemini
Flash LLMS and everything just sort of kind of clicked and, and
Mongo DB works very nicely with it.
You know, it's all integrated well.
(24:56):
So for us, it just turned out tobe the right solution.
And we've stayed with GCP and we've stayed with Mongo DB and
it, you know, it just helps us not have to worry about the
infrastructure piece and having to sort of spend our time on
that. It leaves us time to go focus on
what value we can create for. Our customers excellent,
(25:16):
excellent and you were were you start a part of the Mongo DB
startup program as well too, Samir?
Very much still amp yeah, good, good and.
For those that are are not familiar with the startup
program, essentially it's a, it's a guided program.
There's a very straightforward application.
I'm part of the team that reviews a lot of those
(25:37):
applications. And essentially you're getting
Atlas credits, of course, to make it super simple to build
your umm, you know, your first iteration or your MVP as people
would call it as well too, and but also insights and, and kind
of extra Technical Support from our teams here as well too.
And then obviously if things progress, we get an opportunity
(25:57):
to do a case study, get you on the live stream, etcetera.
So there's a little bit of quid pro quo, but I think in general
it's it's in your favor to certainly join that as well too.
Rajiv gave us a question there, but I think this is what we're
going to see in the demo, which we're going to get to next.
So he's on querying as to whether it's a real time
(26:18):
solution and yeah, and how quickly the provider can know if
the customer is a high risk churn.
Yeah, this. Is this is, yeah, this is a
great question. We, we struggled with this.
I see a lot of contact centre solutions today that do real
time stuff. And real time meaning is on the
(26:39):
in the conversation you're doingstuff.
And what typically happens is they they sort of hone in on
certain keywords and then based on that they do something.
We've made a conscious choice tonot do this as a real time
millisecond based stuff. I just think it is a little
overrated because the contact centre operates on different
(27:00):
rules and a marketing organization that has
responsibility for churn and customer growth operates on
different rules. OK, using the example that that
I used before, I didn't need somebody to tell me in a
millisecond if they could solve my problem or not.
But if they had come back to me the same day or the next day and
(27:21):
said, you know what, we're goingto bring down your bill by 40%
or what have you, I would have happily stayed with them.
I didn't need an answer right that second.
I just needed somebody to take care of me.
That's what that's what we decided to do.
Instead of saying we will do this in real time, we'll solve
it. I just don't think the contact
(27:42):
centers and the the growth organisations necessarily work
in that sort of tandem at all. The case in point, I spoke to
several marketing leaders and I said how much of your contact
centre information is being used?
And they said not very much. Typically what happens, Shane,
is after every contact, every conversation, chat or otherwise,
(28:06):
you get a survey in the mail andyou know, and that's a great way
to collect. So to get information from the
horse's mouth, you really want to know what the customer felt
like. The challenge with that is the
response rates on those surveys are three to 5%, which means
that the you're happily waiting for a response when your
customer walks away and you've got incomplete information
(28:28):
there. In our case, we replace the
survey entirely because we can generate that customer centric
information using AI and that makes a difference.
OK, I see, I see. That makes it.
Yeah, because I don't answer those surveys most of the time.
I'm yet to find one that says wedo.
(28:50):
Yeah. No.
And I suppose look, the ones I might answer are the ones that
are only one or two questions. If they go 3-4 or five
questions, I got you know what, I'm not doing this.
I'm just going to, I'm just going to drop.
Well, look, I think you framed the problem really well so far.
You framed the insights as to why you went about this.
You framed the fact that the technology has caught up to get
(29:13):
those insights and you know you can leverage GCP and Mongo and
vector search, etcetera as well too.
I think it's probably a decent time to transition across to
let's see this in action, Samir.Let's show us, show us what you
have in the demo so hopefully people can understand it a
little bit more. This is something that a
customer would never see. You know, the, the agents, the
AI agents would process calls asthey come in through REST API
(29:37):
and such. So I'm doing this in a browser.
It's kind of a force thing. Just makes the demo easier.
And that's what I'm so I'm goingto pick up an audio file.
I'm taking a customer, telco, customer service billing issue.
And we'll process that and let you listen to this thing.
And then we'll also sit and see where we thank.
You for calling Sprite. My name is Rhea.
(29:57):
How can I help you today? Hi, my name is Francis and I
have a question about my bill. Oh, hi, Francis.
I'll be glad to answer any questions that you have about
your bill. May I have the account number or
the phone number of the account?OK, the account number is 12345.
(30:19):
Great. Thank you.
So may I know what your questionis?
Oh, it's like this. Last month my bill was $25.00,
but for this month it shows thatI need to pay $35.
What's going on? Oh, I see.
I understand. Sometimes looking at your bill
can be quite confusing. So let me check your account and
(30:41):
let's go through this together, OK?
By the way, do you have a copy of your bill right now?
Yes. That's great.
Let's go over your bill togetherand see the difference Upon
checking your account, yes, you're right, the bill last
month was $25. However, this billing period
there is an additional charge of$10.00 for a data usage that
(31:06):
totals to three gigs. OK, I really thought that my
plan included data. Well, no it does not, but I will
be happy to add a data to your plan.
For only $5 more you get 10 gigsof data and it's more value for
your money. Considering the current
(31:28):
situation. How does that sound to you?
OK, that sounds good. That's good.
So I'll add the data plan to your account right away.
And since you are a valued customer, I will also take care
of the $10 charge for you. And that's great.
So let me just process the reversal real quick.
(31:49):
OK. And it's done.
So let me just recap everything we have talked about the $10
charge. I have added a data plan of 10
gigs to your account. So that's going to be $5 more in
addition to the $25.00 that you are already paying every month.
(32:09):
And the $10 charge for this billing period was already
reversed. It's strictly appreciated.
Thank you. You're welcome.
And just to let you know, you can also check your data usage
by texting data to 777. So have I addressed all your
concerns today? Yes, you have.
(32:29):
Well, it's been my pleasure to assist you, Francis.
Once again, my name is Reya. Thank you for calling Sprite and
hope you have a great day. Do the same.
Bye, bye, bye. Bye, very good.
And here if you notice, can you read the screen or Shane if you
can give me some direction. Yeah, I I think it's big enough
for for most of our viewers, I'dsay.
(32:51):
Anyway, if anyone has trouble, we might just drop it into the
comments, but certainly clear tome that that's that's even
better. That's that's perfect.
And one other thing, Samir, do you see the little alert from
Stream yard, the platform we're on?
You can hide that. Do you see the stop sharing
button down below? Yes.
And then the hide beside that. Absolutely lovely.
(33:11):
Yes, thank you. Great.
In this particular instance, we created or computed AC SAT
customer satisfaction score of five and there but right below
that you see the reason and thisis a customer talking to you.
So you know you would have received a survey where you
would have answered the questionand and provided your comments.
This is what this is in a way. So I gave a 5 because my issue
(33:36):
was completely resolved. The agent Tria was incredibly
helpful, went above and beyond to assist me.
Not only did she explain the unexpected charges on my bill
clearly, but she also practically offered a solution
that was better value for my money.
Waived the unexpected charges. She did everything that you
would expect from an agent. You know, this guy clearly was
unhappy about the $10 charge on the bill and she addressed that.
(33:59):
She put him on another plan. She took care of things and This
is why it was a fight. Now had just as a discussion
point. If she had only sort of put him
on another plan but not waived the charges, there's a good
chance this would still be a positive call, but just not
nearly as positive. And that might have been a CSAT
score of a four where you are happy with the outcome, but not
(34:23):
super thrilled. And then this particular
instance, this guy is just really, really happy that, you
know, the charge is gone. She's you put him on a different
plan that is better value and soon and so on.
And so this is really what we capture here as a customer.
Is there a resolution from a customer's point of view?
Absolutely there is. Yes.
Agent's effort was great. There were no unanswered
(34:45):
questions. There was no follow up promised
here. So we do capture things like
those that usually get left out,right?
Because you know, an agent tellsme I'm going to call you
tomorrow afternoon. I patiently sit by my phone.
Nobody calls me. I'm not a happy guy and this is
the sort of thing that we would also capture.
Then there is somebody here for the entire call, which is a 3
(35:07):
minute call. You know, we've got all of that
here. That's fine.
The sentiment was positive. We also captured the reason and
the intent of the call and that's going to be helpful in
terms of understanding where thebusiness is seeing the most
traffic. So we don't keep that
information. This is kind of a summary in
terms of a number of different things.
But the really interesting part that I wanted to bring your
(35:29):
attention to is the upsell track.
So one of the things we are tracking here and this is all
customizable in our platform, wesaid did the agent try to
upsell? And the answer here is yes,
there was no mention of anybody buying anything or selling
anything. You're not once did the agent
say I'm trying to sell you something or do you want to buy
(35:51):
another plan? But she just quietly said, you
know, can I put you on another plan that's going to solve your
problems, get you more credits, whatever.
And there it is. So new monthly plan that was
part then what were the issues? Why did the customer call?
And we use all of this information in what we can then
show at an aggregate level and at an individual.
(36:12):
So this is really what the call is.
We capture this information and then in after all of this, we do
do redaction as well. So if you're going to be keeping
this information, we want to make sure you're on the right
side of privacy laws. So we take care of that.
Understood. Yeah, I know that that's very
clear. You mentioned earlier and back
(36:32):
to the question, it wasn't real time and there's loads of
reasons is not real time, but how long does it take to produce
this what you're showing? So I processed this in real time
for you after I received the audio.
I, I, you know, uploaded the audio and I said let's go
process. This was a 3 minute.
You processed in about 21 seconds.
(36:53):
OK, OK, it is near real time. It's not real time.
Yes, right. So there is if.
Somebody wants to take action asa business within seconds they
can. I think what I have seen in my
own experience is these things get done in batches rather than
being done as one offs that thatat least might take on it.
(37:17):
Let me see what this looks like from a dashboard perspective.
What, how do you consume all of this information when there are
hundreds of calls or thousands of these calls happening all the
time? So what we do is a few things.
One, we show you Intel, sort of the actionable intelligence part
of it. That's the Intel here.
There's also analytics from an aggregate perspective.
And then the individual interactions, they all show up
(37:39):
here. So the first thing we show is
the organizational NPS score. This is the Net Promoter score,
which goes from -100 to a + 100.And what we do is we extract the
information out of the individual interactions and then
we compute the NPS scores based on how many times the
interaction happened over a period of time.
(38:01):
And based on all of that, for the the sample of people that
did interact, we can show what that NPS curve looks like.
And in this particular case, yousee these dots and the trend
line, it's trending upwards, which is generally a positive
sign it's. Going in the right direction,
yeah, it's. Going in the right direction,
then we also extract informationout of the customers calls and
(38:22):
customers interactions, which are the biggest issues that the
customers are facing over any given period of time.
So here we are saying these are the major issues.
I go here, this is a telecom setup and the four big issues
that they're seeing are competitive offers coming in,
network issues, billing issues, handset refresh issues.
They have got an old phone kind of thing.
(38:44):
If you look at the competitive offer, I can go down and say,
you know, there are three big things here.
People are saying I'm getting free handsets, I'm getting free
extras. This can be streaming services
and such, or I'm getting lower prices from the competitor that
you are not offering. And now you can see how many
people called, what was their experience like.
(39:05):
This is not so much saying I canlook at the individual call
details, it's just not useful. But knowing that red and green
sort of mix here, you kind of say what's going on in my past
straight for each of these particular things and I can do a
drill down into your network issues related stuff or whatever
it is now the operating. Highly visual and the and the
(39:27):
traffic lights work really well in terms of absolutely
understanding what things you'reyou're taking me back this
you're you're the same vintage as me perhaps where we remember
used to have to defrag your disks and you could see where
all the counts of storage where this is super taking me back
Samir. But.
Yeah, it works really well. It's like those stock tickers as
(39:48):
well too. But yeah, exactly right.
It it does. I'd never thought of it that
way, but it really does. The the interesting thing here
is the the chief operating officer's office is looking at
it and saying, what are my customers telling?
And this is not guessing, this is really knowing the stuff.
And this is again, the point where, you know, does real time
(40:11):
really match? And the answer to me is not so
much you really want to see whatyour customers are talking
about. And it doesn't have to be in a
millisecond level because you'renot going to change your
competitive offer situation on network issues in milliseconds.
But once you know what's going on or where the customers are
sort of yelling the loudest, youknow what you need to do.
(40:32):
So that's one part of it. And just before you advance off
that Samir, would it be the casethat there's much as much
insights to gain from the green and the positive versus the the
yellow and the red, For example,would they tend to look at
what's working well versus what's working bad or just
concentrate on the bad? No, no, you're exactly right.
This particular graphic is mostly about what the customers
(40:56):
are bringing up. But I do have another graphic
and I'll show it to you in a second on a different tab, which
tells you what's working well and what's not working OK.
So I will absolutely talk about that as well.
But in this particular instance,the the most important take away
is what are the customers talking of?
What are the customers? What's causing them to to pick
(41:18):
up the phone or to get on a chat?
And and that's the thing that sort of comes to the fore here.
Excellent and Kelson has spottedit was a lot of green.
So this is obviously not a bad company you are dealing with
here, but still work to do, right?
Still work to do, otherwise nobody would be picking up the
phone or chatting here is the churn risk part of it.
(41:42):
And what we've done here is we've taken just a tiny bit of
data information, right? I said give me the interactions
and I've taken a little bit more.
I've said look, don't tell me what your customer revenue is,
but by the customer tell me are they in bucket one or bucket 2
or bucket three or four or five.Bucket one is your least
important customers, Bucket 5 are your most customers, you
(42:04):
know the guys that pay the most or what have you.
And now I can tell you how many of these customers called today,
how many of these customers called in the past week.
So there is something going on with these guys and I think just
the five here in terms of sayingthe most important customers,
I'm only interested in looking at those here.
(42:25):
And now I'm showing you information.
This is again we've taken the all of the interactions we've
looked at the past week. We take the data from the C, the
CRM system or what have you. And then we'd run machine
learning algorithms and come back and say here was an
interaction, this was the account number, this was the
customer. When did they call?
(42:45):
What was the customer satisfaction for that particular
interaction? What were their major issues and
what's the probability of churn for these guys?
And we produce this information in its entirety here so that you
can see what's what went on. So if I click on Mary Todd here,
Mary Todd, this is the account number, a very important
(43:07):
customer in the bucket 5, our computed NPS score is a three.
On a scale of one to 10, that's a three, which means she is
absolutely a detractor. And what's the reason for that?
These are the interaction details.
Over the past week. She had called in the last week.
Major issues were network issues, excessive buffering,
(43:27):
Internet connection. Customer satisfaction score of
1. Called again in the past week,
same issues. Customer satisfaction score over
2. All again today, same issues,
Customer satisfaction score over2.
Stands to reason that she's thinking about other options
yes. And this is the sort of thing
(43:48):
that we can show very easily andsay, you know, she's a .87
probability she's going to be gone.
Now what we do is we set it up so that you can receive an Excel
with the same information at 4:00 PM every day or you know,
at three times a day or what have you.
And this can go into some sort of a system that says I'm going
(44:10):
to send out a message that says we will make it right, or you
know, you called, you were unhappy, something along those
lines. But solving the problems based
on the issues that you have here.
Then I do a similar sort of a thing with upsell where somebody
did have a good experience. Oh, OK, makes sense.
And, and I'd say they they are very likely to buy from you
(44:34):
because they had a good experience with you.
So if I click on on one of these, they called, we solved
their problem. They had a high satisfaction
score. They were AC set a customer
value three kind of middle of the road customer very happy.
This is an opportunity to see ifthey can become a more valued
customer because we can sell them more services.
(44:54):
They just had a good experience.Rather than wait, go back to
them the same day or the next day and say look you called into
the centre, we noticed that you had this problem.
We were glad we could resolve it.
And by the way, we have this another service that we are
offering 20% discount on for ourvalued customers like
yourselves. And this is an upsell
(45:15):
opportunity that usually gets lost because nobody saw.
So we solved that problem quite nicely as well.
And again excels get sent out and such in our next Rev. What
we are thinking about is automating that process with an
agentic setup where we can take this information, pull it out of
(45:36):
Mongo DB and then we can say setit up so that based on the
available promotions, based on the guidelines that have been
set by the organization, we can send out those messages
directly, bring back the resultsif somebody clicked on it or not
and and close up on it all without having to touch it.
And then only escalate things that need to be escalated.
(46:00):
So that's kind of the thought process.
That sounds ideal. Obviously you know, a lot of
people now are in that phase of agentic solutions.
This is a a prime example of howto use that.
And I love the fact that you're,you know, you're using it to
reduce the churn at one side of things, so retain that customer.
And then you're also using the same understandings to, to look
(46:21):
at towards the upsell as well too.
So it's, it's a win win. And then obviously from the
clients that you speak to Samir,it's huge proof of value to them
to be able to to show them this from the extractions of their
audio calls or their interactions, right?
Absolutely. And and I think this is what we
found interesting. They had these audio calls
(46:42):
forever and they still lost customers because a, they were
waiting on surveys or they had sort of festival journeys mapped
out AD nauseam, but not the basic information about what the
customer felt. You know, how do you put that to
use? How can you be responsive within
the timeframes that you can address something?
(47:03):
And I think this is where we arecreating that value.
I'll go to the next tab here that talks about the more sort
of cumulative analytics, if you will, that shows you several
different things. Overall, if I look at my
customer satisfaction scores over the last month, am I
generally looking green or red or yellow?
The details are almost not important, but it's kind of
(47:24):
important to see how am I doing overall?
Am I more green? Am I more red?
Where are we? Same thing by sort of dates, you
know, over the last month, am I generally doing OK?
Am I generally not doing OK based on the customer
satisfaction source? And then you can get into the
details. And this is something that
touches upon what you had said earlier, Shane.
(47:47):
When you get interactions from customers, they always have an
intent. The customers call you because
there is an intent. You know, I have a billing
problem, I have a return to be processed, I have a, a product
support issue, whatever something.
And now you can see and say, look at it very easily and say
how am I doing qualitatively even just how am I doing on
(48:08):
intent and resolution? Which of my intents are the
least number of resolutions? And here you can very easily see
that I've got product support, that's a problem, I've got
return and refund, that's a problem.
I've got a bunch of things that have issues and other things.
Like that? Yeah.
Intent and CSAT, Which intents have the lowest CSAT scores
(48:29):
versus the highest CSAT scores? Which intents have positive
versus negative versus neutral sentiments?
So you can slice and dice this in multiple different ways,
including looking at agents, youknow, which agents are doing
well, which agents are not doingso well in terms of the seasides
intense. So there's a ton of stuff here
in terms of looking at that information and being able to
(48:50):
tell where things are going right, where things are not
going right. And then we're being able to
take some action around. And then final piece here is
looking at the data for the calls of the interactions
themselves, where I can say lookhere was the call that we made.
This is the details of that call.
It shows you every bit of information here, yes, absent
(49:12):
tried, you know, so on and so forth.
We can look at the details here,the summary, the C sat reason,
other things. We have the transcript, we have
the redacted transcript. All of this data is basically
available to the customer to take action and then also go do
things of their own that they wanted to do if they should want
to access this data and do something with it.
(49:34):
And that's really what we are doing right now.
It's the nutshell piece and the details piece that you're using
the LLMS to generate and create and store, right?
Absolutely. We are using LLMS there.
We also use some of our proprietary information in terms
of the customer centric C sat. This is where we've filed a
patent for because we think mosttimes the LLMS will come back
(49:59):
with a vanilla answer that doesn't reflect the customer
centric. And now we've we've tried this
out with quite literally thousands of interactions and
our hit ratio in terms of getting the customer centric
view of C sat is fairly. So essentially using the.
(50:19):
Standard LLMS, The Gemini LLMS, are we using a fine-tuned
version of them or anything somewhere?
No, we we are not using fine tuning.
We do use LLMS and we also are extremely careful in our use of
LLMS because they do tend to help in it.
We've got to be careful because we cannot provide customers
(50:41):
wrong answers. So we've tried to build
guardrails around it. We've tried to use the features
available within LLMS to make sure that creativity is minimum
and we stick as close to it as possible.
So that's what we are doing. Understood.
While you're still on the dashboard screen from Agneta,
Rajiv had another question. You captured data point that
(51:04):
gives some sense of timing between the last interaction and
when they churn out. Is there, is there a notion of
that? Maybe there was and I wasn't
paying attention as you went through it, but is there
something there? Yeah.
Absolutely. So there is a, there is a time
received time here, OK, that that absolutely has every
interaction captured by time andyou should be able to filter it,
(51:26):
you should be able to get any ofthat information.
We we do provide filters for that sort of thing as well.
OK, absolutely. Given Rajiv's question, he's
probably thinking, going back toyour original, I suppose you
know the issue that you had, youphoned three times, you didn't
get a resolution, they didn't follow up within the day, so you
churned. Whereas if you have the timing
(51:48):
of all the interactions, you could say, hey, if we don't get
back to this customer within 24 hours, 36 hours, they're going
to go. So let's try and make sure that
we have a no. Absolutely correct.
OK. So in this example that I have
on the screen with with Mary Todd, again, we've we're showing
you her interactions in the pastweek and a week as sort of that
(52:11):
unit of time beyond which I think it just became still, you
know, I cannot say she called four months back.
So this must be relevant. You know, it is not anymore.
And the upsell opportunity also walks away three months later.
So this is all talking about howcan you be current within a day
or two days, you know, somethingthat's a short window of
(52:32):
opportunity where you are looking at it and saying in the
past week, this is what happened.
She or he called these many times and were we able to solve
their problems? What kind of an NPS score was
generated out of this thing? And and is this customer worth
keeping? I mean, let's be honest, right?
There are the the customers thatspend a ton of money and you do
(52:52):
want to keep them. And then there are those that
fall a million times but don't spend any money.
And maybe it's good returns, I don't know.
Yeah, yeah, they have those as well too.
Most definitely, yeah. But this?
Has been a great demo. Is there other areas we haven't
touched on yet? Samir, OR.
I wanted to show this just easy to do.
And then as I said, we are working on some of the things
(53:14):
that automate the full cycle where the information sort of
travels, the the agents pick up the information, send it across,
process it, come back, and then you only get to see what worked
and what did not work and then ahuman can do something with it.
Perfect, perfect. Well, look, I, I think as, as
(53:35):
demos go, I think that's been highly illustrative of, of
everything that we discussed at the beginning for anybody.
And I, we're not again, we have a huge audience from all over
the place as well too. For anyone with any questions,
please drop them in the chat. We'd love to take care of them
if we can. And I suppose the key thing then
is if anybody works in this industry or works in this space,
(53:58):
how do they get in touch with you and, or how do they try this
out or, or how does it work for AG Meta when you're looking at
new customers? Yeah, we've tried to make it
simple instead of deep integrations to be built and you
know, a trial that's very difficult proof of value.
What we've said is look, sign anNDA with us.
(54:20):
You know your stuff is going to be secure with us.
We'll process 100 of your files,audio files or chat files,
whatever you give us, we'll process that.
We'll stand up a dashboard just like what you just saw here, and
you will start to see if there'svalue here.
If it's not solving any problemsfor you, if you already have
solutions that do all of those things, wonderful, You don't
(54:42):
need us. If on the other hand, you see
value that you didn't see so far, then it's all worth doing
and it's a very simple integration.
All of our stuff is REST API enabled, which means it's super
easy with Mongo DB. What we've also done, Shane, is
we've taken an approach where weare not super possessive about
data. We bring in people's audio
(55:03):
files, bring bring in their chatfiles, process them, and then we
delete those source files. So we don't keep any of that and
I'm saying you can use my Mongo DB instance and that's fine, or
we can also use your own Mongo DB instance.
Brilliant and. Keep the data with yourselves.
We'll just keep the basic billing data.
Everything else stays in your sort of autonomous system
(55:23):
boundaries, and that way life becomes a lot easier in terms of
doing stuff. Yeah, that's ideal.
I suppose. Look, obviously we hear the
stories all the time, customer data leaks, etcetera, etcetera
and people are cautious and rightly so.
And, and look, obviously within Mongo and particularly with
Atlas, we take care of most of everything, but there's always a
(55:43):
case of, well, my data has come in, say if they are already on
Atlas going to you and then backand, and that, so that makes a
ton of sense. I, I love that idea.
That's a nice way to go. And they, I love that approach
to a demo. Give us some of your data, not
lots, but some, and we'd be ableto prove our point, which is
really nice way to go about it as well too.
(56:04):
So they just go to AG meta and and just kind of get in touch
there, Samir or what's, what's the process?
Simple, you see the URL up here.At least I hope you do.
Go there, there is a get in touch with us or request a demo
or or something similar. Just click on it, leave your
contact information, we get in touch with you and in two weeks
(56:28):
you have all the information youever wanted and see if this is a
good match. Excellent.
Well, listen, that's a that's a really good way to round out the
AG meta story. Thank you, Samir, for for all of
your time and effort into that. I wanted to just double back on,
you know, the founding of this and and kind of scratching that
(56:48):
itch and seeing that things could be better.
Any words of advice for We've got a varied audience who joined
these live streams all the time on the podcast live.
Any words of advice for those looking to break out, to do
something on their own to createthat start up from you?
Somebody experienced in this space?
I I think there there is a proverb look before you leap.
(57:12):
I think that has done a great deal of disservice to humanity.
I think it's better to leap before you look because more
times than not we walk ourselvesout of opportunities.
I think we tell ourselves, we convince ourselves that this is
too difficult. It's not going to happen and
there will be difficult times. That's a given.
It's a roller coaster ride for sure, but take that step and
(57:38):
you'll find out. The other thing is just the
commitment. It is, people think of start-ups
as sort of straight lines that do really well, but it's it's
actually an up and down like this that goes on.
But it is a fulfilling journey. It is an interesting journey.
You have only one life to live, might as well try it.
(57:59):
So I love it. And I suppose going back to what
I was saying at the introductionwas, you know, obviously I was
trying to say that the successful startup founders tend
to be on the older side of things, but the, the younger
startup founders exactly that they've leaped, they don't know
what they don't know, so they just get on and do it.
So I think that's, that's incredible, Uh, piece of advice.
(58:20):
And I, I had my own startup for about 13 years, probably a bit
too long to be called still a startup, but, uh, you know, uh,
but I agree wholeheartedly, you know, just jump straight into
it. And if you've and it's to
scratch, and particularly if it's an area that you're keen
in, it's yeah, it's hard work. Don't get me wrong.
I would imagine. And as I said to you before we
(58:42):
came on air, it was a holiday inthe US yesterday, but you were
still emailing me. So this is the way, this is the
way it works. Any final thoughts or words for
our audience before we go? And I'm just watching the the
chat that comes in and we're just getting some nice
compliments. And I appreciate that.
And anybody who all the viewers who joined in.
(59:03):
So it's been great to have you on board and and do join in for
future shows as well too. It's really nice that we can
share these type of stories. And you know, I think for me,
Samir, you really, you know, youshared the problem very
straightforward, very eloquently, but it's a problem
we generally understand, but we don't see the nuances within it.
(59:23):
And I thought you did that really, really well.
I love the fact that you've jumped, you know, through kind
of the problem and then actuallyhow the more recent technology
of LMS and using GCP with their Gemini models, et cetera has
helped. And then you, you gave us some
nice thumbs up for MongoDB and Atlas and particularly Atlas
vector search and storing the vectors of your data alongside
(59:47):
them in, in the same document essentially.
So there's no round trip, there's no extraction, there's
no transforming and loading datahere, there and everywhere.
So you did my job for me there in the middle of our
conversation as well too, a bit of the pitch, so I appreciate.
I'm a fanboy so you know there we go with Mongo DB.
Perfect. Perfect.
(01:00:08):
Yeah. And for those that want to learn
a bit more about MongoDB, if you're if you're not so
familiar, as I said earlier, we have sentiment analysis demos,
etcetera and you can go to developer.mongodb.com to check
those out. It would be remiss of me.
Most of my colleagues create content there all the time and
go in there and search for for Gen.
(01:00:29):
AI or search for semantic searchetcetera as well too.
And then obviously the startup program that you mentioned, just
search for MongoDB startup and you'll see it there.
You'll see what we have on offer.
Super simple application form aswell too.
Samir, it's been a pleasure to have you on board.
I hope you enjoyed yourself while it's here.
(01:00:49):
It's been a great guest. And I have said this a number of
times and I'll say it again, thehad a boss who said the reward
for good work is more work. So Samir, as AG Meta progresses,
I want to get you back on the show to show us what you've
done, particularly when you get to those agentic applications
that you say AI is going to go off and do some of the heavy
(01:01:10):
lifting for you. I'd love to see that in in
action because I think that's the next step.
We've we have our chat bots, etcetera, etcetera.
But getting the tools to do something to take the what I
would call the mundane or that the drudgery out of out of the
normal work and allow people to do the high value stuff is for
agentic AI solutions are all that.
And I very much look forward to that as well too.
(01:01:32):
Perfect Shane, it's been a pleasure, really enjoyed it.
No, the pleasure's been all mine, Samir.
So listen from me in pretty coldish West of Ireland and
Limerick to you and Samir in SanFrancisco.
Still cold, but not quite as cold as the rest of parts of the
US. It's been a pleasure to have you
on board. For all our viewers who joined
(01:01:52):
in and left questions and those fabulous comments towards the
end, thank you so much. Keep an eye on Mongo DB's,
YouTube and LinkedIn for future shows.
Generally, most Tuesdays you've got to listen to me and A and a
informative guests such as Samir.
So from me, Shane McAllister. Thank you so much Samir.
It's been a pleasure to have you.
Do take care and have a good rest of your day.
(01:02:13):
Thank you. Take care folks.
Hello everyone and welcome to the MongoDB Podcast Live.
I'm Shane McAllister. I'm one of the leads on our
developer relations team here atMongoDB.
Today's show is entitled using AI to increase customer lifetime
value. I'm joined by Samir Agwal, Chief
Product Technology Officer with AG Meta to take us through
today's topic. So with that, let's get Samir
(00:28):
onto the show. Samir, your very welcome to
MongoDB Podcast Live. How are you?
Very well, Shane, it's great to be with you.
Samir, what I'd love to do before we get into the meat of
today's topic is learn a little bit about your career path to
date. How did you get started?
Where have you been? What companies have you worked
in? Before we get to the AG meta
(00:49):
story, tell us a little bit about your background.
Absolutely. Yeah, I'd love to.
So I'm a second time entrepreneur, have run global
engineering organizations, run global product organizations,
managed PNLS, worked at startupsand large companies.
That tells you a little bit about how old I must be.
I've done all of. Those things, listen, there's
always a history in everyone andI think.
(01:10):
Exactly. Right.
It's just statistics, Amir, about the best.
The startups are generally founded by people who've gone
through an awful lot before. We tend to think of startups by
the, you know, the young 20 somethings surviving on a diet
of coke and pizza. But I think the successful ones
are much, much more towards the other end of the age spectrum.
Absolutely. There's the I think it's a mix
(01:33):
here. Who knows, but beers that may.
I used to work for a startup, sort of an early employee at a
startup in the golden age of theInternet year of the circa 2000.
We were acquired by Nokia, spentseveral years at Nokia,
including some in Finland. Another story for another time,
when somebody moves from barrierto Finland.
(01:54):
That's it. Back when mobile phones were all
different. And all the.
Models and and back before therewas some interest there.
Except for currently, since 2007, everything's been a
rectangular piece of of glass and plastic and metal, right?
Absolutely. So I saw some of that in the
making, which is really interesting.
So lots of history there. I, I also worked at other
(02:18):
product organizations, security,but really and, and, and in AIA
company that was focused entirely on AI, which gave me
insight seats to seeing what works and does not work in AI.
But the really interesting sort of part of my journey was when
when I ran P&L centres and really sort of felt and saw
first hand what customer loss means, What happens when a
(02:40):
customer churns out and what happens when you don't know why
your customers churn out. And as I spoke to others who
were people like myself and customers, I saw that there was
a problem that was always present, didn't know why their
customers were leaving. And most times they would find
(03:01):
out much too late that the customers had left and, and by
then there was really nothing tobe done.
And I think this was something that kind of stuck in the back
of my head. And then an opportunity came
along and I said, you know what?Why don't we solve a problem
that focuses entirely on that? And that's kind of how Agmeta
came in to be. So like the best startup
stories, they're founded out of a level of personal insight, a
(03:25):
level of frustration, and a level of I think I can solve
this problem or at least make itbetter, right?
Absolutely, I've got a personal anecdote to share on that sort
of front. I've been with the same Internet
service provider and cable TV provider for either 20 plus
years and as we. All do because we we were loads
(03:47):
to move and to change. I know exactly.
Right. Yeah, exactly right.
And, and so, you know, it just continued.
And then about about a year back, my bill goes up by 35% and
I, I, I was prepared for the drill.
You call them, they tell you that your promotion has expired.
They put you on a new promotion,they charge you another 10 bucks
(04:08):
a month. I knew the drill down pat.
So you call them, and this guy was really nice.
He told me that your promotion has expired.
And I said, you know, we're following the script here, this
is going to be fine. And then he comes back and says,
but there is no other promotion.And I'm like, oh, this guy
doesn't get it. Yeah.
So. Because usually you expect the
(04:30):
fact that you've phoned up and taken the time to, you know.
Get the next. Promotion a year on or get some
savings right? He told you there was none.
There was none and I said you know what, green guy doesn't
know what he's doing, you know, let me just call back again.
So I called back another three times over the next day or two
and each time I was told that there is no other promotion and
(04:55):
I decided to leave them. After 20 plus years, I decided
to leave them. OK, OK.
And I moved on a full week later, 4 calls and a full week
later, I get a call from this ISP and they go, you know, we
noticed you've been with us for more than 20 years.
Would you mind telling us why you left?
A full week later, they still didn't know why I left.
(05:17):
And if that happens to me, you know, $3500 give or take a year
customer and they didn't know why I left.
This must happen to other customers as well.
And I think people can relate tothese kinds of conversations.
What if this could be made so simple that these guys could
have seen it on day one or day 2that this customer is going to
(05:39):
be leaving them. They could have made it right
then rather than a week later when the ship had sailed and
they call in and say, you know what, we can take you back to
the previous prices and stuff like that.
It was just too late. And that's the.
Problem you see a lot of the time do you know, like even when
you unsubscribe to an e-mail newsletter, for example, and
then you get a sorry to see you go, what was wrong?
(06:01):
It's like, well, they never asked that before and it's the
same thing. I suppose going back to your
original example, you expected to get a better offer.
You've been with them for 20 years at whatever many thousands
of dollars per year. You're a, you know, you were a
locked in lifetime customer and three calls later you went, I've
had enough of this, right? And they didn't.
(06:22):
Know. They didn't know and they didn't
know. They didn't know.
And you know, I have to say for somebody at that size, that
scale and technology savvy company that it was, if they
didn't know, what does that meanfor the rest, you know, other
organization, other companies that may not be, that may not
have the wherewithal, you know, to be able to do that.
(06:43):
So what if we could make it deadsimple for them to see which
customer is at risk of churn andalso very importantly where
there is an upsell opportunity. And that's the other part that
gets missed out because everybody is so focused on
churn. Then you forget the fact that
there is the two parts to the customer's lifetime value.
There's one about reducing churn, of course.
(07:04):
The second is about getting morefrom this customer.
And if you miss that part, that's money you left at the
table. So that's what we wanted to
address. Yeah.
And and I suppose given that this is everywhere, Samir, how
like, and this is a huge market,right?
Every company selling the service has, you know, phone
(07:26):
lines or online chat, etcetera. What was it that kind of other
than your frustration, did you see, did you see going, hold on,
I can, I can use my technical expertise, my knowledge, what
I've what I've worked with to date to maybe solve this
problem. What was the other piece of you
kind of going? I think I can I can work on
this. Yeah, I, I think the one is just
(07:51):
the sort of the technology evolution, you know, things that
seem kind of far away, things that seem like, you know, you're
going to get answers that using technology, you're going to get
answers that are not really close enough or precise enough.
They're moving to a point where you said technology makes it
easy for me to do this and and why people are trying to solve
stupid complicated problems. Here's a simple problem.
(08:14):
This, this problem doesn't go away.
You know, it never gets out of fashion.
You can replace humans with withAI based agents.
You know, AI doing the talking, you can have AI chat bots, but
on the other side you still havehumans and their customer
experience matters. If they make an investment in a
(08:35):
company, they pick up a phone. It's not easy.
I mean in today's day and age, Ipersonally don't like the idea
of picking up a phone and calling a business.
You know, I'd rather just do something on the website and and
be done with it. But if I'm.
Spending You and me both, I think yeah, look, it depends.
Obviously I'm more comfortable, you know, using a chat tool.
My wife, on the other hand, definitely picks up the phone.
(08:57):
I think she does better than me most of the time when she talks
to these companies. But, but I had like you, you
mentioned there, your ISP, my, my deal is up, you know, last
month or something in December and, and yesterday I went, OK, I
better, exactly as you said, better phone them trying or get
in touch with them, try and get a better deal.
So I did what I would do, go to the website, click the chat
(09:21):
button. And then I was told, no, you're
not through to the broadband. You're through to the mobile or
the cell phone. And I went, no, well, I clicked
the button on the broadband page.
Why am I with the mobile team? And then, and then they said,
Oh, we'll pass you on. And I got passed on to the home
broadband and I went, no, no, no, my package is business
because I need it for work, obviously, etcetera.
(09:41):
And they're going, Oh, you have to phone us.
And, you know, that was 30 minutes of crazy cat stuff, you
know, So yeah, I think and we talk about a lot of topics on
the podcast live, but this one, I think everybody who joins us
in the comments has a story to probably tell in the same space.
So I'm really looking forward tokind of, you know, the
(10:02):
comparison and and what you're solving in AG meta.
But talk to us a little bit about the the old way because,
you know, when I finish one of these calls, I usually get a
survey or, you know, a, a text or an SMS to go.
How would you rate your experience?
How, how that obviously didn't work for them or it's not uptake
or anything on that, do you mean?
(10:23):
No, it is I, I, I think at a, ata gut level, we all understand,
every business understands they need to keep their customers
happy. But I think there is a, a gap,
there is a chasm between how thevarious parts of the
organization work. And, and I think this is where
we started to sort of hone in onand, and say, you know, can we
(10:44):
make a difference here? I'll give you a typical sort of
journey. Somebody calls into a contact
centre. Contact centre metrics are very
different from the metrics of the guy who's trying to retain
the customer in the marketing organization, for instance.
OK. So somebody calls in, just like
me. I called in, the guy was very
polite, very nice, and told me he could not help me.
(11:08):
From his perspective, he resolved the problem.
You know, the call was a resolution.
There's nothing we can do for this guy.
That was the resolution he reached.
From my perspective, he was 100%lost.
Stuck to the script, he couldn'tdo anything for you and and that
was that, right? But but, but from his point of
view, this was a resolution. Nothing could be done.
(11:29):
Was the resolution. From my point of view, it was
100% lost. There was no resolution
whatsoever. My bill was still very high.
And this is the problem that I think continues to exist.
And everybody says, you know, you know, you've got to get
right customer experience. The customer experience needs to
be good. We all sort of swear by customer
experience. The challenge is how do you how
(11:50):
do you really measure customer experience, right?
And this is a big question. And typically the sort of the
shorthand version has been if you had a positive sentiment on
the call or on the chat, then this must be a good experience.
And I just think that is not thefull story.
That's part of the story. And the analogy I like to use
(12:13):
is, you know, the blind man sortof feeling an elephant for the
first time. 1 catches hold of the tail, another catches hold
of the leg, another catches holdof the trunk.
And they all have a different picture of what that elephant
looks like, you know, what that elephant must be.
And I think customer experience is somewhat that way.
We just kind of, you know, feel the different things and say
that's customer experience. But what is customer experience?
(12:35):
How do you quantify it? So what we've done is, and this
is where we've filed patents andsuch where we say, look, we will
quantify customer experience using CSAT and that's a measure
that the industry uses. But what do you how do you
compute the CSAT? The customer satisfaction score?
And this is where what? CSAT stands for customer
(12:57):
satisfaction. What's the SAT?
OK. Satisfaction C satis customer
satisfaction. But what we have said is.
Surely a customer unsatisfactionscore would be much more
appropriate for some of these? Industries.
We like to believe that every business wants good high
customer satisfaction. But what we do is we compute a
(13:22):
customer centric C set rather than a customer satisfaction
score based on it. This call was a polite call,
positive call. You know, all of those things
are component parts to the larger picture.
If I as a customer don't feel good about this call and don't
feel satisfied, it doesn't matter how good the call was, it
(13:42):
is still not a happy call for me.
And I think this is really the thing that we compute that C SAT
score and then we bring it back and marry it into other stuff.
And we can go back and say theseguys are at a risk of churn.
These guys are opportunities forupset.
And this is something that we doin a unique way.
We believe that makes a difference for the businesses.
(14:04):
Perfect. It sounds great.
And I know in a while we'll we'll see a demo of this in
action Samir, which is brilliant.
So for those tuned in and addingto the comments etcetera, we're
we're going to keep chatting at the high level for the moment,
but then we're going to start sharing the screen and actually
see how this plays out. But tell me a little bit about
obviously that C SAT score, right?
(14:24):
But with the advances that we have in, in machine learning and
AI and everything in the last couple of years, how is that
really layered in on top of thatdata that we're getting back
from these type of customer interactions?
Absolutely. I think Gen.
AI has opened doors that we didn't know existed.
(14:46):
At least for me it was, it was revelation and I've been
following it for the last sort of two to three years.
But there are things that we cando now and do it much more
easily and more precisely than we could have done before.
And I think we've, we've tried to leverage Gen.
AI to the hilt, but we've also used our own sort of secret
(15:07):
sauce, if you will. We've also used NLP machine
learning to make sure that we pick on other things.
And I'll give you an example. For instance, if I as a customer
have a contract with the provider, an ISP for instance,
and I'm, you know, committed to being with them for 24 months or
I pay through my nose, I'm unhappy.
(15:30):
But I might still be unlikely toleave because I don't relish the
idea of having to pay those large sums of money as a
transit. On the other hand, if I'm in
month 23 of that 24 month contract, it's like, you know,
I'm out of here. I've I've had it.
And, and that factors into the whole story.
So we do use machine learning along with our CSAT and, and
(15:51):
other things to come back and say, you know, this person is at
a risk of churn. This other person, not so much.
And, and that's the sort of information, but really the key,
I believe Shane, is do businesses have this information
in an easy to consume manner, ina timely manner?
And if you have that, I think there is action to be taken very
(16:14):
quickly where somebody can keep their customers.
And you know, in the example that I shared with you, if they,
if these folks had come back to me the following day and said,
look, you had a conversation or more than one conversations, we
noticed that you were not happy with this thing, here's what we
can do for you. I would absolutely have stayed
with them. And that's where we must repeat
(16:36):
1000 times each day. That's 1000 customers walking
away each day and nobody is any wiser.
So. Yeah.
And that's it. And as you touched on earlier,
not only, you know, retention but also potential upsell and
higher lifetime value and stuff.The you, you mentioned, you
know, sentiment and, and obviously, you know, sentiment
(16:57):
analysis using, you know, vectorsearch, et cetera.
To do that, we have in in Mongo DB amongst the developer
relations team, we've got a few team members, few colleagues of
mine who've built some really good sentiment analysis demos
and they're brilliant, you know,and that's the key thing I think
that Gen. AI has brought to the table is
data has existed for ages, but it's getting the meaning behind
(17:20):
the data that I think is, is beyond the ability, as you say,
to surface that to the top and turn that into actionable
insights as well too. I think is is really, really,
really powerful. But Samir, you also mentioned
when we were prepping for this call too, that you don't need
all the data. You don't need, you know, an, a
mother load of data to come in on this.
(17:42):
You can get these insights with some, some kind of very kind of
bare, not a bare minimum of, of data, but you know, really,
really kind of niche areas that you're looking for in your
solution. Right, good.
One of the big learnings for me when I was sort of experimenting
with AI and, and was part of an organization that delivered AI
(18:04):
based solutions, the challenge was less of AI and more of data.
And companies would tell us all the time our data is scattered
all over the place. It's in bad shape.
And yes, we love AI, but we're going to come to it when when
we've got our data story sorted out.
And that sounded like something that might happen 10 years
(18:25):
rather than something that wouldhappen, you know, two weeks out
or two months out. Yeah.
So from get go, what we have focused on is do not start that
conversation with saying I need all your data and then we will
create value for you. We have taken an approach where
he said, give me your basic data, give me your
conversations, your interactionswith your customers.
(18:48):
Give that to me and I can already create value for you.
At that point, I can already create value for you.
And if you can give me a little bit more data, I can create more
value and I can refine it further.
And if I can get more data stillfrom your C SAT and such, you
can see even more value, even more precise sort of results.
And and so we've taken an approach where you can bring in
(19:09):
more data piece meal. It becomes part of the overall
so the equation and it gives youbetter results.
It gives you more complete results, but you don't have to
wait for an eternity while your data gets sorted out that you'll
start to see results. So on day 2 of being
operational, you're actually seeing results.
And then you can of course make it better.
(19:30):
Excellent. So, so as we all know these
conversations if their phone call are recorded, they always
tell you that on the way in, right.
And and obviously in the chats that you might have are there's
transcripts available too. So you're basically saying to
your clients and customers that we'll work with what we already
know that you're recording before we go into the huge
(19:51):
minefield of what's left of, of all of the other data that might
require, you know, it's all in different silos, as you say,
it's all in different places. You might need to transform it
and it's very unstructured, right.
So you're you're working with what's easy for the client to
give you straight away and then enhancing that if they can give
you more in the future. That's exactly right.
(20:13):
I mean, I'll tell you, I was speaking to this is not in the
context of Eggmeta, but in a previous life, I was speaking to
a major airline here in the US and they painted such a scary
story of their data that, you know, is put a fear of God in
me. It's like I don't think I can
fly this airline ever again. So we wanted to make sure that
(20:33):
that doesn't happen here. OK, Yeah, that's a good one.
I think people can can certainlyresonate with that.
I see a lot of people joining usagain from all over the place,
from London and Charlotte and LAand Iran and Brazil.
So you're very welcome. If you've any comments or
questions for Samir, please dropthem into the chat and we'll try
(20:53):
and take care of them as well too.
Usually when make Samir, when weget to the demo portion, that's
when the comments start to come because people can see what
you're sharing and have a look and they have their own
questions. So we've spoken about the data
and we've spoken about how you approach towards that.
But tell us a little bit about how you ended up on the the
Mongo DB podcast live. So when was this?
(21:14):
When did you first start to use Mongo DB and how are you using
it in AG meta today? Yeah.
So, you know, when we started our journey, we clearly needed
some place to store the the results and the the first sort
of obvious conversation is one of the relational databases and
very shortly there. For most developers or people to
(21:35):
start right, it's like, yeah, exactly right.
These have been around for a long time.
Let's just do it that way. Exactly right.
And and very shortly thereafter we also realised that that's
going to be the wrong solution for us because the data is going
to be very unstructured. Different customers are going to
want to see different things at different times and a relational
(21:57):
database is just not the right answer for us.
And so we were looking at no SQLdatabases and we had a few
choices there. I'd clearly, I knew of Mongo DB,
I'd partnered with Mongo DB in the past.
You know, I had them in great respect.
But the important thing for us was we also knew where we are
going with this. We do need, we do need
capabilities in terms of vector database capabilities, in terms
(22:20):
of draft DB capabilities. And the choices that I had to
make were, do I take a no SQL database, then shuffle data from
that database into a vector database and then into a graph
database? And I'm going to be doing
nothing but, you know, moving data around between all of these
databases, which is not clearly the goal of.
(22:43):
Yeah, that's a lot of heavy lifting and there's a lot of, as
we call it, ETL, extract, transform and load and and keep
keep that data In Sync, keep it concurrent as well too.
So which is a lot of extra overhead and and generally extra
cost as well too. Absolutely.
And and our thought was look, ifthere is a solution that can
(23:05):
solve that problem, that's the one to go after.
And at least our research shows Mongo DB is a great solution.
That way it solves all of those problems.
Is a check box for each of thesethings and the folks are easy to
work with, which is never a bad thing.
Love to hear that. Love to hear that.
And probably timing was good. So we launched Atlas Vector
(23:27):
Search back a year and a half ago now I suppose.
So that probably coincided with when you were building out your
solutions as well, too, right Samir?
That's correct. That's exactly right.
And in in our journey, we are sort of just at the point where
we are saying, you know, we are using the vector DB enabling
that use and also looking at graph DB capabilities to see if
(23:51):
we can marry all of these and get a graph rank solution as we
take the next steps in our. Solution.
OK, OK. And you mentioned on our prep
too that you're obviously using GCP as part of this as well too.
Tell us a little bit more about that because obviously they're a
huge partner of ours in terms ofof hosting as well.
Yeah, GCP when, when we first started with an idea and not
(24:15):
much else, it was just a thoughtwith nothing else.
No company, no people, no nothing.
It was just a thought. And we started with just the
Google infrastructure really just just go back to collab and
try something out and see how that works.
And then we reached out to someone at Google startup and,
(24:36):
you know, they helped us secure a good number of credits.
And then we started to feel likethis was the right platform for
us. It did all the things that we
needed to. We also use Google's Gemini
Flash LLMS and everything just sort of kind of clicked and, and
Mongo DB works very nicely with it.
You know, it's all integrated well.
(24:56):
So for us, it just turned out tobe the right solution.
And we've stayed with GCP and we've stayed with Mongo DB and
it, you know, it just helps us not have to worry about the
infrastructure piece and having to sort of spend our time on
that. It leaves us time to go focus on
what value we can create for. Our customers excellent,
(25:16):
excellent and you were were you start a part of the Mongo DB
startup program as well too, Samir?
Very much still amp yeah, good, good and.
For those that are are not familiar with the startup
program, essentially it's a, it's a guided program.
There's a very straightforward application.
I'm part of the team that reviews a lot of those
(25:37):
applications. And essentially you're getting
Atlas credits, of course, to make it super simple to build
your umm, you know, your first iteration or your MVP as people
would call it as well too, and but also insights and, and kind
of extra Technical Support from our teams here as well too.
And then obviously if things progress, we get an opportunity
(25:57):
to do a case study, get you on the live stream, etcetera.
So there's a little bit of quid pro quo, but I think in general
it's it's in your favor to certainly join that as well too.
Rajiv gave us a question there, but I think this is what we're
going to see in the demo, which we're going to get to next.
So he's on querying as to whether it's a real time
(26:18):
solution and yeah, and how quickly the provider can know if
the customer is a high risk churn.
Yeah, this. Is this is, yeah, this is a
great question. We, we struggled with this.
I see a lot of contact centre solutions today that do real
time stuff. And real time meaning is on the
(26:39):
in the conversation you're doingstuff.
And what typically happens is they they sort of hone in on
certain keywords and then based on that they do something.
We've made a conscious choice tonot do this as a real time
millisecond based stuff. I just think it is a little
overrated because the contact centre operates on different
(27:00):
rules and a marketing organization that has
responsibility for churn and customer growth operates on
different rules. OK, using the example that that
I used before, I didn't need somebody to tell me in a
millisecond if they could solve my problem or not.
But if they had come back to me the same day or the next day and
(27:21):
said, you know what, we're goingto bring down your bill by 40%
or what have you, I would have happily stayed with them.
I didn't need an answer right that second.
I just needed somebody to take care of me.
That's what that's what we decided to do.
Instead of saying we will do this in real time, we'll solve
it. I just don't think the contact
(27:42):
centers and the the growth organisations necessarily work
in that sort of tandem at all. The case in point, I spoke to
several marketing leaders and I said how much of your contact
centre information is being used?
And they said not very much. Typically what happens, Shane,
is after every contact, every conversation, chat or otherwise,
(28:06):
you get a survey in the mail andyou know, and that's a great way
to collect. So to get information from the
horse's mouth, you really want to know what the customer felt
like. The challenge with that is the
response rates on those surveys are three to 5%, which means
that the you're happily waiting for a response when your
customer walks away and you've got incomplete information
(28:28):
there. In our case, we replace the
survey entirely because we can generate that customer centric
information using AI and that makes a difference.
OK, I see, I see. That makes it.
Yeah, because I don't answer those surveys most of the time.
I'm yet to find one that says wedo.
(28:50):
Yeah. No.
And I suppose look, the ones I might answer are the ones that
are only one or two questions. If they go 3-4 or five
questions, I got you know what, I'm not doing this.
I'm just going to, I'm just going to drop.
Well, look, I think you framed the problem really well so far.
You framed the insights as to why you went about this.
You framed the fact that the technology has caught up to get
(29:13):
those insights and you know you can leverage GCP and Mongo and
vector search, etcetera as well too.
I think it's probably a decent time to transition across to
let's see this in action, Samir.Let's show us, show us what you
have in the demo so hopefully people can understand it a
little bit more. This is something that a
customer would never see. You know, the, the agents, the
AI agents would process calls asthey come in through REST API
(29:37):
and such. So I'm doing this in a browser.
It's kind of a force thing. Just makes the demo easier.
And that's what I'm so I'm goingto pick up an audio file.
I'm taking a customer, telco, customer service billing issue.
And we'll process that and let you listen to this thing.
And then we'll also sit and see where we thank.
You for calling Sprite. My name is Rhea.
(29:57):
How can I help you today? Hi, my name is Francis and I
have a question about my bill. Oh, hi, Francis.
I'll be glad to answer any questions that you have about
your bill. May I have the account number or
the phone number of the account?OK, the account number is 12345.
(30:19):
Great. Thank you.
So may I know what your questionis?
Oh, it's like this. Last month my bill was $25.00,
but for this month it shows thatI need to pay $35.
What's going on? Oh, I see.
I understand. Sometimes looking at your bill
can be quite confusing. So let me check your account and
(30:41):
let's go through this together, OK?
By the way, do you have a copy of your bill right now?
Yes. That's great.
Let's go over your bill togetherand see the difference Upon
checking your account, yes, you're right, the bill last
month was $25. However, this billing period
there is an additional charge of$10.00 for a data usage that
(31:06):
totals to three gigs. OK, I really thought that my
plan included data. Well, no it does not, but I will
be happy to add a data to your plan.
For only $5 more you get 10 gigsof data and it's more value for
your money. Considering the current
(31:28):
situation. How does that sound to you?
OK, that sounds good. That's good.
So I'll add the data plan to your account right away.
And since you are a valued customer, I will also take care
of the $10 charge for you. And that's great.
So let me just process the reversal real quick.
(31:49):
OK. And it's done.
So let me just recap everything we have talked about the $10
charge. I have added a data plan of 10
gigs to your account. So that's going to be $5 more in
addition to the $25.00 that you are already paying every month.
(32:09):
And the $10 charge for this billing period was already
reversed. It's strictly appreciated.
Thank you. You're welcome.
And just to let you know, you can also check your data usage
by texting data to 777. So have I addressed all your
concerns today? Yes, you have.
(32:29):
Well, it's been my pleasure to assist you, Francis.
Once again, my name is Reya. Thank you for calling Sprite and
hope you have a great day. Do the same.
Bye, bye, bye. Bye, very good.
And here if you notice, can you read the screen or Shane if you
can give me some direction. Yeah, I I think it's big enough
for for most of our viewers, I'dsay.
(32:51):
Anyway, if anyone has trouble, we might just drop it into the
comments, but certainly clear tome that that's that's even
better. That's that's perfect.
And one other thing, Samir, do you see the little alert from
Stream yard, the platform we're on?
You can hide that. Do you see the stop sharing
button down below? Yes.
And then the hide beside that. Absolutely lovely.
(33:11):
Yes, thank you. Great.
In this particular instance, we created or computed AC SAT
customer satisfaction score of five and there but right below
that you see the reason and thisis a customer talking to you.
So you know you would have received a survey where you
would have answered the questionand and provided your comments.
This is what this is in a way. So I gave a 5 because my issue
(33:36):
was completely resolved. The agent Tria was incredibly
helpful, went above and beyond to assist me.
Not only did she explain the unexpected charges on my bill
clearly, but she also practically offered a solution
that was better value for my money.
Waived the unexpected charges. She did everything that you
would expect from an agent. You know, this guy clearly was
unhappy about the $10 charge on the bill and she addressed that.
(33:59):
She put him on another plan. She took care of things and This
is why it was a fight. Now had just as a discussion
point. If she had only sort of put him
on another plan but not waived the charges, there's a good
chance this would still be a positive call, but just not
nearly as positive. And that might have been a CSAT
score of a four where you are happy with the outcome, but not
(34:23):
super thrilled. And then this particular
instance, this guy is just really, really happy that, you
know, the charge is gone. She's you put him on a different
plan that is better value and soon and so on.
And so this is really what we capture here as a customer.
Is there a resolution from a customer's point of view?
Absolutely there is. Yes.
Agent's effort was great. There were no unanswered
(34:45):
questions. There was no follow up promised
here. So we do capture things like
those that usually get left out,right?
Because you know, an agent tellsme I'm going to call you
tomorrow afternoon. I patiently sit by my phone.
Nobody calls me. I'm not a happy guy and this is
the sort of thing that we would also capture.
Then there is somebody here for the entire call, which is a 3
(35:07):
minute call. You know, we've got all of that
here. That's fine.
The sentiment was positive. We also captured the reason and
the intent of the call and that's going to be helpful in
terms of understanding where thebusiness is seeing the most
traffic. So we don't keep that
information. This is kind of a summary in
terms of a number of different things.
But the really interesting part that I wanted to bring your
(35:29):
attention to is the upsell track.
So one of the things we are tracking here and this is all
customizable in our platform, wesaid did the agent try to
upsell? And the answer here is yes,
there was no mention of anybody buying anything or selling
anything. You're not once did the agent
say I'm trying to sell you something or do you want to buy
(35:51):
another plan? But she just quietly said, you
know, can I put you on another plan that's going to solve your
problems, get you more credits, whatever.
And there it is. So new monthly plan that was
part then what were the issues? Why did the customer call?
And we use all of this information in what we can then
show at an aggregate level and at an individual.
(36:12):
So this is really what the call is.
We capture this information and then in after all of this, we do
do redaction as well. So if you're going to be keeping
this information, we want to make sure you're on the right
side of privacy laws. So we take care of that.
Understood. Yeah, I know that that's very
clear. You mentioned earlier and back
(36:32):
to the question, it wasn't real time and there's loads of
reasons is not real time, but how long does it take to produce
this what you're showing? So I processed this in real time
for you after I received the audio.
I, I, you know, uploaded the audio and I said let's go
process. This was a 3 minute.
You processed in about 21 seconds.
(36:53):
OK, OK, it is near real time. It's not real time.
Yes, right. So there is if.
Somebody wants to take action asa business within seconds they
can. I think what I have seen in my
own experience is these things get done in batches rather than
being done as one offs that thatat least might take on it.
(37:17):
Let me see what this looks like from a dashboard perspective.
What, how do you consume all of this information when there are
hundreds of calls or thousands of these calls happening all the
time? So what we do is a few things.
One, we show you Intel, sort of the actionable intelligence part
of it. That's the Intel here.
There's also analytics from an aggregate perspective.
And then the individual interactions, they all show up
(37:39):
here. So the first thing we show is
the organizational NPS score. This is the Net Promoter score,
which goes from -100 to a + 100.And what we do is we extract the
information out of the individual interactions and then
we compute the NPS scores based on how many times the
interaction happened over a period of time.
(38:01):
And based on all of that, for the the sample of people that
did interact, we can show what that NPS curve looks like.
And in this particular case, yousee these dots and the trend
line, it's trending upwards, which is generally a positive
sign it's. Going in the right direction,
yeah, it's. Going in the right direction,
then we also extract informationout of the customers calls and
(38:22):
customers interactions, which are the biggest issues that the
customers are facing over any given period of time.
So here we are saying these are the major issues.
I go here, this is a telecom setup and the four big issues
that they're seeing are competitive offers coming in,
network issues, billing issues, handset refresh issues.
They have got an old phone kind of thing.
(38:44):
If you look at the competitive offer, I can go down and say,
you know, there are three big things here.
People are saying I'm getting free handsets, I'm getting free
extras. This can be streaming services
and such, or I'm getting lower prices from the competitor that
you are not offering. And now you can see how many
people called, what was their experience like.
(39:05):
This is not so much saying I canlook at the individual call
details, it's just not useful. But knowing that red and green
sort of mix here, you kind of say what's going on in my past
straight for each of these particular things and I can do a
drill down into your network issues related stuff or whatever
it is now the operating. Highly visual and the and the
(39:27):
traffic lights work really well in terms of absolutely
understanding what things you'reyou're taking me back this
you're you're the same vintage as me perhaps where we remember
used to have to defrag your disks and you could see where
all the counts of storage where this is super taking me back
Samir. But.
Yeah, it works really well. It's like those stock tickers as
(39:48):
well too. But yeah, exactly right.
It it does. I'd never thought of it that
way, but it really does. The the interesting thing here
is the the chief operating officer's office is looking at
it and saying, what are my customers telling?
And this is not guessing, this is really knowing the stuff.
And this is again, the point where, you know, does real time
(40:11):
really match? And the answer to me is not so
much you really want to see whatyour customers are talking
about. And it doesn't have to be in a
millisecond level because you'renot going to change your
competitive offer situation on network issues in milliseconds.
But once you know what's going on or where the customers are
sort of yelling the loudest, youknow what you need to do.
(40:32):
So that's one part of it. And just before you advance off
that Samir, would it be the casethat there's much as much
insights to gain from the green and the positive versus the the
yellow and the red, For example,would they tend to look at
what's working well versus what's working bad or just
concentrate on the bad? No, no, you're exactly right.
This particular graphic is mostly about what the customers
(40:56):
are bringing up. But I do have another graphic
and I'll show it to you in a second on a different tab, which
tells you what's working well and what's not working OK.
So I will absolutely talk about that as well.
But in this particular instance,the the most important take away
is what are the customers talking of?
What are the customers? What's causing them to to pick
(41:18):
up the phone or to get on a chat?
And and that's the thing that sort of comes to the fore here.
Excellent and Kelson has spottedit was a lot of green.
So this is obviously not a bad company you are dealing with
here, but still work to do, right?
Still work to do, otherwise nobody would be picking up the
phone or chatting here is the churn risk part of it.
(41:42):
And what we've done here is we've taken just a tiny bit of
data information, right? I said give me the interactions
and I've taken a little bit more.
I've said look, don't tell me what your customer revenue is,
but by the customer tell me are they in bucket one or bucket 2
or bucket three or four or five.Bucket one is your least
important customers, Bucket 5 are your most customers, you
(42:04):
know the guys that pay the most or what have you.
And now I can tell you how many of these customers called today,
how many of these customers called in the past week.
So there is something going on with these guys and I think just
the five here in terms of sayingthe most important customers,
I'm only interested in looking at those here.
(42:25):
And now I'm showing you information.
This is again we've taken the all of the interactions we've
looked at the past week. We take the data from the C, the
CRM system or what have you. And then we'd run machine
learning algorithms and come back and say here was an
interaction, this was the account number, this was the
customer. When did they call?
(42:45):
What was the customer satisfaction for that particular
interaction? What were their major issues and
what's the probability of churn for these guys?
And we produce this information in its entirety here so that you
can see what's what went on. So if I click on Mary Todd here,
Mary Todd, this is the account number, a very important
(43:07):
customer in the bucket 5, our computed NPS score is a three.
On a scale of one to 10, that's a three, which means she is
absolutely a detractor. And what's the reason for that?
These are the interaction details.
Over the past week. She had called in the last week.
Major issues were network issues, excessive buffering,
(43:27):
Internet connection. Customer satisfaction score of
1. Called again in the past week,
same issues. Customer satisfaction score over
2. All again today, same issues,
Customer satisfaction score over2.
Stands to reason that she's thinking about other options
yes. And this is the sort of thing
(43:48):
that we can show very easily andsay, you know, she's a .87
probability she's going to be gone.
Now what we do is we set it up so that you can receive an Excel
with the same information at 4:00 PM every day or you know,
at three times a day or what have you.
And this can go into some sort of a system that says I'm going
(44:10):
to send out a message that says we will make it right, or you
know, you called, you were unhappy, something along those
lines. But solving the problems based
on the issues that you have here.
Then I do a similar sort of a thing with upsell where somebody
did have a good experience. Oh, OK, makes sense.
And, and I'd say they they are very likely to buy from you
(44:34):
because they had a good experience with you.
So if I click on on one of these, they called, we solved
their problem. They had a high satisfaction
score. They were AC set a customer
value three kind of middle of the road customer very happy.
This is an opportunity to see ifthey can become a more valued
customer because we can sell them more services.
(44:54):
They just had a good experience.Rather than wait, go back to
them the same day or the next day and say look you called into
the centre, we noticed that you had this problem.
We were glad we could resolve it.
And by the way, we have this another service that we are
offering 20% discount on for ourvalued customers like
yourselves. And this is an upsell
(45:15):
opportunity that usually gets lost because nobody saw.
So we solved that problem quite nicely as well.
And again excels get sent out and such in our next Rev. What
we are thinking about is automating that process with an
agentic setup where we can take this information, pull it out of
(45:36):
Mongo DB and then we can say setit up so that based on the
available promotions, based on the guidelines that have been
set by the organization, we can send out those messages
directly, bring back the resultsif somebody clicked on it or not
and and close up on it all without having to touch it.
And then only escalate things that need to be escalated.
(46:00):
So that's kind of the thought process.
That sounds ideal. Obviously you know, a lot of
people now are in that phase of agentic solutions.
This is a a prime example of howto use that.
And I love the fact that you're,you know, you're using it to
reduce the churn at one side of things, so retain that customer.
And then you're also using the same understandings to, to look
(46:21):
at towards the upsell as well too.
So it's, it's a win win. And then obviously from the
clients that you speak to Samir,it's huge proof of value to them
to be able to to show them this from the extractions of their
audio calls or their interactions, right?
Absolutely. And and I think this is what we
found interesting. They had these audio calls
(46:42):
forever and they still lost customers because a, they were
waiting on surveys or they had sort of festival journeys mapped
out AD nauseam, but not the basic information about what the
customer felt. You know, how do you put that to
use? How can you be responsive within
the timeframes that you can address something?
(47:03):
And I think this is where we arecreating that value.
I'll go to the next tab here that talks about the more sort
of cumulative analytics, if you will, that shows you several
different things. Overall, if I look at my
customer satisfaction scores over the last month, am I
generally looking green or red or yellow?
The details are almost not important, but it's kind of
(47:24):
important to see how am I doing overall?
Am I more green? Am I more red?
Where are we? Same thing by sort of dates, you
know, over the last month, am I generally doing OK?
Am I generally not doing OK based on the customer
satisfaction source? And then you can get into the
details. And this is something that
touches upon what you had said earlier, Shane.
(47:47):
When you get interactions from customers, they always have an
intent. The customers call you because
there is an intent. You know, I have a billing
problem, I have a return to be processed, I have a, a product
support issue, whatever something.
And now you can see and say, look at it very easily and say
how am I doing qualitatively even just how am I doing on
(48:08):
intent and resolution? Which of my intents are the
least number of resolutions? And here you can very easily see
that I've got product support, that's a problem, I've got
return and refund, that's a problem.
I've got a bunch of things that have issues and other things.
Like that? Yeah.
Intent and CSAT, Which intents have the lowest CSAT scores
(48:29):
versus the highest CSAT scores? Which intents have positive
versus negative versus neutral sentiments?
So you can slice and dice this in multiple different ways,
including looking at agents, youknow, which agents are doing
well, which agents are not doingso well in terms of the seasides
intense. So there's a ton of stuff here
in terms of looking at that information and being able to
(48:50):
tell where things are going right, where things are not
going right. And then we're being able to
take some action around. And then final piece here is
looking at the data for the calls of the interactions
themselves, where I can say lookhere was the call that we made.
This is the details of that call.
It shows you every bit of information here, yes, absent
(49:12):
tried, you know, so on and so forth.
We can look at the details here,the summary, the C sat reason,
other things. We have the transcript, we have
the redacted transcript. All of this data is basically
available to the customer to take action and then also go do
things of their own that they wanted to do if they should want
to access this data and do something with it.
(49:34):
And that's really what we are doing right now.
It's the nutshell piece and the details piece that you're using
the LLMS to generate and create and store, right?
Absolutely. We are using LLMS there.
We also use some of our proprietary information in terms
of the customer centric C sat. This is where we've filed a
patent for because we think mosttimes the LLMS will come back
(49:59):
with a vanilla answer that doesn't reflect the customer
centric. And now we've we've tried this
out with quite literally thousands of interactions and
our hit ratio in terms of getting the customer centric
view of C sat is fairly. So essentially using the.
(50:19):
Standard LLMS, The Gemini LLMS, are we using a fine-tuned
version of them or anything somewhere?
No, we we are not using fine tuning.
We do use LLMS and we also are extremely careful in our use of
LLMS because they do tend to help in it.
We've got to be careful because we cannot provide customers
(50:41):
wrong answers. So we've tried to build
guardrails around it. We've tried to use the features
available within LLMS to make sure that creativity is minimum
and we stick as close to it as possible.
So that's what we are doing. Understood.
While you're still on the dashboard screen from Agneta,
Rajiv had another question. You captured data point that
(51:04):
gives some sense of timing between the last interaction and
when they churn out. Is there, is there a notion of
that? Maybe there was and I wasn't
paying attention as you went through it, but is there
something there? Yeah.
Absolutely. So there is a, there is a time
received time here, OK, that that absolutely has every
interaction captured by time andyou should be able to filter it,
(51:26):
you should be able to get any ofthat information.
We we do provide filters for that sort of thing as well.
OK, absolutely. Given Rajiv's question, he's
probably thinking, going back toyour original, I suppose you
know the issue that you had, youphoned three times, you didn't
get a resolution, they didn't follow up within the day, so you
churned. Whereas if you have the timing
(51:48):
of all the interactions, you could say, hey, if we don't get
back to this customer within 24 hours, 36 hours, they're going
to go. So let's try and make sure that
we have a no. Absolutely correct.
OK. So in this example that I have
on the screen with with Mary Todd, again, we've we're showing
you her interactions in the pastweek and a week as sort of that
(52:11):
unit of time beyond which I think it just became still, you
know, I cannot say she called four months back.
So this must be relevant. You know, it is not anymore.
And the upsell opportunity also walks away three months later.
So this is all talking about howcan you be current within a day
or two days, you know, somethingthat's a short window of
(52:32):
opportunity where you are looking at it and saying in the
past week, this is what happened.
She or he called these many times and were we able to solve
their problems? What kind of an NPS score was
generated out of this thing? And and is this customer worth
keeping? I mean, let's be honest, right?
There are the the customers thatspend a ton of money and you do
(52:52):
want to keep them. And then there are those that
fall a million times but don't spend any money.
And maybe it's good returns, I don't know.
Yeah, yeah, they have those as well too.
Most definitely, yeah. But this?
Has been a great demo. Is there other areas we haven't
touched on yet? Samir, OR.
I wanted to show this just easy to do.
And then as I said, we are working on some of the things
(53:14):
that automate the full cycle where the information sort of
travels, the the agents pick up the information, send it across,
process it, come back, and then you only get to see what worked
and what did not work and then ahuman can do something with it.
Perfect, perfect. Well, look, I, I think as, as
(53:35):
demos go, I think that's been highly illustrative of, of
everything that we discussed at the beginning for anybody.
And I, we're not again, we have a huge audience from all over
the place as well too. For anyone with any questions,
please drop them in the chat. We'd love to take care of them
if we can. And I suppose the key thing then
is if anybody works in this industry or works in this space,
(53:58):
how do they get in touch with you and, or how do they try this
out or, or how does it work for AG Meta when you're looking at
new customers? Yeah, we've tried to make it
simple instead of deep integrations to be built and you
know, a trial that's very difficult proof of value.
What we've said is look, sign anNDA with us.
(54:20):
You know your stuff is going to be secure with us.
We'll process 100 of your files,audio files or chat files,
whatever you give us, we'll process that.
We'll stand up a dashboard just like what you just saw here, and
you will start to see if there'svalue here.
If it's not solving any problemsfor you, if you already have
solutions that do all of those things, wonderful, You don't
(54:42):
need us. If on the other hand, you see
value that you didn't see so far, then it's all worth doing
and it's a very simple integration.
All of our stuff is REST API enabled, which means it's super
easy with Mongo DB. What we've also done, Shane, is
we've taken an approach where weare not super possessive about
data. We bring in people's audio
(55:03):
files, bring bring in their chatfiles, process them, and then we
delete those source files. So we don't keep any of that and
I'm saying you can use my Mongo DB instance and that's fine, or
we can also use your own Mongo DB instance.
Brilliant and. Keep the data with yourselves.
We'll just keep the basic billing data.
Everything else stays in your sort of autonomous system
(55:23):
boundaries, and that way life becomes a lot easier in terms of
doing stuff. Yeah, that's ideal.
I suppose. Look, obviously we hear the
stories all the time, customer data leaks, etcetera, etcetera
and people are cautious and rightly so.
And, and look, obviously within Mongo and particularly with
Atlas, we take care of most of everything, but there's always a
(55:43):
case of, well, my data has come in, say if they are already on
Atlas going to you and then backand, and that, so that makes a
ton of sense. I, I love that idea.
That's a nice way to go. And they, I love that approach
to a demo. Give us some of your data, not
lots, but some, and we'd be ableto prove our point, which is
really nice way to go about it as well too.
(56:04):
So they just go to AG meta and and just kind of get in touch
there, Samir or what's, what's the process?
Simple, you see the URL up here.At least I hope you do.
Go there, there is a get in touch with us or request a demo
or or something similar. Just click on it, leave your
contact information, we get in touch with you and in two weeks
(56:28):
you have all the information youever wanted and see if this is a
good match. Excellent.
Well, listen, that's a that's a really good way to round out the
AG meta story. Thank you, Samir, for for all of
your time and effort into that. I wanted to just double back on,
you know, the founding of this and and kind of scratching that
(56:48):
itch and seeing that things could be better.
Any words of advice for We've got a varied audience who joined
these live streams all the time on the podcast live.
Any words of advice for those looking to break out, to do
something on their own to createthat start up from you?
Somebody experienced in this space?
I I think there there is a proverb look before you leap.
(57:12):
I think that has done a great deal of disservice to humanity.
I think it's better to leap before you look because more
times than not we walk ourselvesout of opportunities.
I think we tell ourselves, we convince ourselves that this is
too difficult. It's not going to happen and
there will be difficult times. That's a given.
It's a roller coaster ride for sure, but take that step and
(57:38):
you'll find out. The other thing is just the
commitment. It is, people think of start-ups
as sort of straight lines that do really well, but it's it's
actually an up and down like this that goes on.
But it is a fulfilling journey. It is an interesting journey.
You have only one life to live, might as well try it.
(57:59):
So I love it. And I suppose going back to what
I was saying at the introductionwas, you know, obviously I was
trying to say that the successful startup founders tend
to be on the older side of things, but the, the younger
startup founders exactly that they've leaped, they don't know
what they don't know, so they just get on and do it.
So I think that's, that's incredible, Uh, piece of advice.
(58:20):
And I, I had my own startup for about 13 years, probably a bit
too long to be called still a startup, but, uh, you know, uh,
but I agree wholeheartedly, you know, just jump straight into
it. And if you've and it's to
scratch, and particularly if it's an area that you're keen
in, it's yeah, it's hard work. Don't get me wrong.
I would imagine. And as I said to you before we
(58:42):
came on air, it was a holiday inthe US yesterday, but you were
still emailing me. So this is the way, this is the
way it works. Any final thoughts or words for
our audience before we go? And I'm just watching the the
chat that comes in and we're just getting some nice
compliments. And I appreciate that.
And anybody who all the viewers who joined in.
(59:03):
So it's been great to have you on board and and do join in for
future shows as well too. It's really nice that we can
share these type of stories. And you know, I think for me,
Samir, you really, you know, youshared the problem very
straightforward, very eloquently, but it's a problem
we generally understand, but we don't see the nuances within it.
(59:23):
And I thought you did that really, really well.
I love the fact that you've jumped, you know, through kind
of the problem and then actuallyhow the more recent technology
of LMS and using GCP with their Gemini models, et cetera has
helped. And then you, you gave us some
nice thumbs up for MongoDB and Atlas and particularly Atlas
vector search and storing the vectors of your data alongside
(59:47):
them in, in the same document essentially.
So there's no round trip, there's no extraction, there's
no transforming and loading datahere, there and everywhere.
So you did my job for me there in the middle of our
conversation as well too, a bit of the pitch, so I appreciate.
I'm a fanboy so you know there we go with Mongo DB.
Perfect. Perfect.
(01:00:08):
Yeah. And for those that want to learn
a bit more about MongoDB, if you're if you're not so
familiar, as I said earlier, we have sentiment analysis demos,
etcetera and you can go to developer.mongodb.com to check
those out. It would be remiss of me.
Most of my colleagues create content there all the time and
go in there and search for for Gen.
(01:00:29):
AI or search for semantic searchetcetera as well too.
And then obviously the startup program that you mentioned, just
search for MongoDB startup and you'll see it there.
You'll see what we have on offer.
Super simple application form aswell too.
Samir, it's been a pleasure to have you on board.
I hope you enjoyed yourself while it's here.
(01:00:49):
It's been a great guest. And I have said this a number of
times and I'll say it again, thehad a boss who said the reward
for good work is more work. So Samir, as AG Meta progresses,
I want to get you back on the show to show us what you've
done, particularly when you get to those agentic applications
that you say AI is going to go off and do some of the heavy
(01:01:10):
lifting for you. I'd love to see that in in
action because I think that's the next step.
We've we have our chat bots, etcetera, etcetera.
But getting the tools to do something to take the what I
would call the mundane or that the drudgery out of out of the
normal work and allow people to do the high value stuff is for
agentic AI solutions are all that.
And I very much look forward to that as well too.
(01:01:32):
Perfect Shane, it's been a pleasure, really enjoyed it.
No, the pleasure's been all mine, Samir.
So listen from me in pretty coldish West of Ireland and
Limerick to you and Samir in SanFrancisco.
Still cold, but not quite as cold as the rest of parts of the
US. It's been a pleasure to have you
on board. For all our viewers who joined
(01:01:52):
in and left questions and those fabulous comments towards the
end, thank you so much. Keep an eye on Mongo DB's,
YouTube and LinkedIn for future shows.
Generally, most Tuesdays you've got to listen to me and A and a
informative guests such as Samir.
So from me, Shane McAllister. Thank you so much Samir.
It's been a pleasure to have you.
Do take care and have a good rest of your day.
(01:02:13):
Thank you. Take care folks.
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