[MUSIC PLAYING]
MANI DORAISAMY: My name is Mani Doraisamy.
I'm the founder of Guesswork, a machine learning startup.
We help CRM companies to build predictive intelligence
in their product.
There's a lot of buzz around machine learning and big data
nowadays.
I want to talk about how it impacts ordinary uses.
If you think about successful products,
they have three elements working together very well-- software,
infrastructure which runs that software,
and then the users who make it useful.
If you think about CRM, for example, Salesforce
disrupted the CRM market by combining
two of these elements, the software and the infrastructure
portion of it.
And thus, SAS was born.
Software as a service was born.
We think the next big CRM will be
the one that combines user intelligence along
with software as a service.
Think about how sales people interact with software today.
That kind of intelligence will be built inside the software.
This is something that every business software
will have in the next decade.
This predictive intelligence that machine learning offers
will augment human intelligence and make
software more intelligent.
If you think about CRM, CRM records every interaction
about customers.
But even with a wealth of data, it still
doesn't understand what customers want.
But on the other hand, think about what
Google does in Search and AdWords.
It understands customer intention
with a few keywords they type in search box.
And that makes it the largest business in the world.
Now it's time that CRM learns from what Google does, and then
does the same kind of customer intention
prediction inside CRM.
With the launch of Google Prediction API,
Google has opened up that secret sauce
as to how it predicts customer intention to the developers.
At Guesswork, we have taken Google Prediction API,
and we have simplified it just for one use case,
for one vertical-- to predict customer intention
for CRM type of applications.
Also, we have built a rule engine
on top of Prediction API.
Because most of these predictions about customer
intention are kind of a combination of both rules
as well as machine learning.
So we have built that rule engine to improve accuracy
on top of Google Prediction API.
So I'm going to show you a demo of how you can use Guesswork
to predict customer intention and also personalize
customer interaction.
So let's take an example where a customer sends you
an inquiry or a feedback.
And then we want to understand what that inquiry is all about
and automatically respond to those inquiries.
So by doing that, you can make the sales people
more productive.
But at the same time, by understanding what customers
are asking, you'll be able to understand
what the customer intention is.
So you will understand what kind of products he likes,
or if he's most likely to stay with your company,
or is he going to churn out.
So that's the demo that we're going to show.
So here is our dashboard.
So I'm going to click on New Project.
So once I click on New Project, you
can select one of these three templates
that you already have.
So let's name this project as Autobot.
And let's select the auto-response engine template.
And once you click Next, you can see the design screen.
Here, you get the customer's feedback as the input.
And we're going to predict three information.
So for example, let's say a customer gives
a feedback saying, the food was good.
Now, in that case, we predict that the category
of this feedback is food.
And the property is quality.
And the type of the feedback is praise.
So based on these three predictions,
we're going to say, what should be
the feedback that the customer representative or the sales
person should do?
So let's click on Next.
So this gives you a screen where you
can upload the training data.
In this case, you're going to leave it
to the default training data.
So Guesswork automatically comes up
with a sample set of training data to populate the project.
So once I click on Publish in the fourth screen,
Guesswork automatically creates the machine
learning project underneath.
So it uses Google Prediction API and then creates
the classification algorithm that's needed for it,
and also creates the rule engine that's
needed to automatically respond to these enquiries or feedback.
So now that this is published, I can go and click on Test.
So let's see how that works.
So I'm going to click on Run.
So now you can see the try it out screen.
Here, I can give a feedback saying, food was good.
And when we click on test, you can see that it automatically
predicts that the category is food, property is quality,
and type is praise.
And it automatically comes up with a response saying,
we're glad that you enjoyed the food.
Hope we get an opportunity to serve you again.
So that's the demo.
So what you saw was the ability to automatically respond
to the inquiry and improve the productivity of the sales
staff or the customer service representative.
But at the same time, by understanding
what the customers are speaking about,
which product is he talking about,
and whether he is praising the product
or complaining about the product,
you know exactly what the customers
want so that sales people can upsell or cross-sell
new products to the customers from the CRM.
I want to conclude with two predictions
about the future of predictive intelligence.
Now, RelateIQ acquisition by Salesforce
has caused huge interest in machine learning in CRM space.
We think other business software verticals will follow suit,
be it health care, HR, law, or any other software verticals.
The second one we think will happen
is that as machine learning infrastructures
like Google Prediction API flourish,
vertical services like Guesswork will emerge.
Because there is a lot of need to fulfill machine learning
needs in business specific verticals.
So we think that will happen in future.
Thanks for listening.
This is Mani Doraisamy from Guesswork.