MARTIN OMANDER: Hello.
My name is Martin Omander, and I welcome you
to Google Root Access, the show featuring actionable startup
advice.
My guest today is Mani Doraisamy,
founder of the startup Guesswork.
Welcome on the show, Mani.
MANI DORAISAMY: Thank you, Martin.
MARTIN OMANDER: So tell me a bit about your startup, Guesswork.
What do you do?
MANI DORAISAMY: We are a machine learning startup
that helps to qualify leads and increase customer conversion.
We do that by building predictive intelligence in CRM.
MARTIN OMANDER: Predictive intelligence in CRM.
OK.
How come you decided to focus on that?
MANI DORAISAMY: We found this problem
that the leads from internet marketing
don't convert into customers very well.
Take this example of real estate agents.
They get like 100 leads every day.
But out of the 100 leads, 90 of them
are from internet, from Trulia, Zillow, and AdWords.
Only 10 of them are from the traditional marketing channels.
But the problem is, out of the 90 leads, only five of them
convert into real customers.
But on the other hand, if you take the ten leads
from the traditional channels, the same five gets converted.
So business owners, most of the time,
think that internet marketing doesn't work very well for them
and start ignoring those leads.
We found that you could use machine learning
and find out which are the ones that
are serious inquiries and casual inquiries,
and we could surface them on CRM so that the small business
owners can focus on the serious ones rather
than the casual ones.
MARTIN OMANDER: I see.
So you are taking those 90 and finding the gold in them,
finding the five there.
MANI DORAISAMY: Exactly.
Yes, that's the one that increased, doubles the sales.
MARTIN OMANDER: Doubles them, right.
You don't want to discard the 90,
but you want to find the five.
So strikes me as a little bit of trying to find
the needle in the haystack.
How do you find those good leads in there?
MANI DORAISAMY: So when a new user comes into the CRM,
we find out the tone of the inquiry,
and we find out the customer's social profile
and their interests to figure out
whether there is a match between the product that you sell
and the customer's interest.
But once the customer or the lead starts engaging with you,
then you also have a lot more information about the customer.
Like, for example, what newsletter
clicks, what other newsletter clicks,
and what's your subsequent sales inquiries, and so on.
So that gives us a lot more information
about how likely he's going to convert into a customer
and when he's going to convert into a customer.
And we accomplish this by a combination
of the machine learning algorithm and a rule engine.
So for machine learning algorithm,
we obviously use Google Prediction API.
And the rule engine is something that we have build on our own,
and that's our [? IP ?].
MARTIN OMANDER: I see.
So tell me a bit more about that.
I imagine when a new customer comes onboard,
there isn't a lot of training data
that the machine learning algorithm can chew on.
MANI DORAISAMY: Exactly.
MARTIN OMANDER: How do you deal with that situation?
MANI DORAISAMY: We call it a cold start
problem in the machine learning world.
I think that's one significant problem
that all startups and small and medium companies face.
That's one of the reasons why machine learning has
been the forte of big companies like Google.
Because Google has a lot of data that it has collected
over time-- be it in search or be it in Google Maps,
it's able to predict how long it will
take to reach a particular place,
because there is so much of training data for the machine
learning algorithm to predict it.
So what we have done in our case is
we use a combination of rule engine
so that we can predict without the training data.
Take, for example, the traditional CRM case.
So if a customer is replying to your emails consistently,
then you know the probability of conversion is more.
You don't have to use machine learning to actually do
the text analytics to find out what is the tone
or what is the interest.
So during using the initial stages,
we use the rule engine to determine the accuracy.
And once we start getting training data that's
good enough to do the prediction,
then it seamlessly switches to the machine learning algorithm.
So it's a kind of handoff that rule engine does
to the machine learning algorithm,
and all this complexity is hidden to the consumers.
MARTIN OMANDER: So you provide a sort
of on ramp for the machine learning algorithm,
but the customers don't see anything.
They just see good data coming out of it.
MANI DORAISAMY: Exactly.
And I think it's not just a technology problem.
Some of these algorithms work from day one,
because we focus on a particular vertical.
We also collect data about the social profile
and the interests, and that gives us
the accuracy on the machine learning algorithm
from day one for certain algorithms.
And that also helps in getting the algorithm working
from day one.
MARTIN OMANDER: Yeah, OK.
So then you have the deterministic rules engine
working together with the probabilistic machine learning.
MANI DORAISAMY: Exactly.
MARTIN OMANDER: So we have here two types
of machine intelligence, if we put it that way.
Is there any place for human intuition in this equation?
MANI DORAISAMY: Yeah.
I think human intuition is something
that we have been relying on so long.
For example, if you look at a sales person,
he looks at the tone of the inquiry,
and he looks at how many times he
has called in the last week, and then his profile,
and then makes that human judgment.
You can't completely ignore it.
So what we do is, whenever we find that the machine learning
algorithms confidence score is not that high--
it's like at 50-50-- then we ask the humans
to classify whether it is a serious inquiry or not.
And that becomes a feedback loop into the machine
learning algorithm.
So it's this combination of the rule engine
that gives you the deterministic algorithm that we have been
building for so long and the machine
learning algorithm that is coming up to the same speed
as the deterministic algorithm, and then
bringing in that human intuition, which
is the triangle of the algorithms
that we think will be the future of how machine learning will
evolve.
Because machine learning is not going
to be this puristic, single play algorithm.
It has to be combined with the combination
of deterministic, probabilistic, and human intuition
to be successful.
MARTIN OMANDER: And you weave all of those into your product
and make good data come out of it.
MANI DORAISAMY: It's not just Guesswork.
We think that's how machine learning will evolve
in the future as well, because I think
that makes practical sense rather than just being
puristic about the data signs.
MARTIN OMANDER: Right.
So let's get down to brass tacks, then.
You've talked about machine learning and rules
engine on a fairly high level.
What products or platforms are you actually using
to actually make this happen?
MANI DORAISAMY: So obviously, we use Google Prediction API
for the machine learning algorithm.
In fact, from a machine learning standpoint,
Google Prediction API is the best
in the classification algorithm.
And as far as the app itself, the one that customers
use to configure machine learning algorithms
is concerned, that runs on Google App Engine.
And we run the rule engine on top
of Google App Engine and BigData.
So for cases where you have to transform customers'
data into the way the machine learning requires,
we use vQuery.
So it does that ETL job to transform
customers' data into machine learning data.
So these are the three products that we use internally.
MARTIN OMANDER: Great.
Well, thank you very much for coming on the show, Mani,
and sharing your experiences with us.
MANI DORAISAMY: Thank, Martin.
It's my pleasure.
MARTIN OMANDER: Thank you for joining us.
See you next time.
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