Coming up we'll take a deeper look at the practical use of bots
to infuse artificial intelligence within your customer relationship management .
We're going to show you their evolution to intelligently
interact with natural language
and the ability to detect customer sentiment and intent.
Triage and transact customer engagement process
to make opportunistic and personalized recommendations.
And how you can quickly get bots up and running for yourself with the Azure Bot Service.
Microsoft Mechanics
I'm joined today by Gianpoalo from the Cortana Intelligence team.
Welcome.
Thank you, it's great to be here.
There's been a ton of buzz around bots and artificial intelligence,
but I know your team is really doing a lot of work to harness this technology
and apply it to more practical use cases.
Yes, in a short time we've greatly improved what bots can do.
Just to illustrate the point the typical bot experience can follow
quite the rigid and static flow
Where you need it to follow a specific path with taxonomies to get results, for example.
Here, we've go the bot asking "What can we do to help?".
And you're just answering by typing the answer to the bot that you just need A, B, or C.
Right, and traditional bots are really limited
in terms of the number of programmable interactions you might make.
You can't really deviate from that.
That's right and the good news is that
we've got huge advances with the convergence of various key technologies.
They allow us to be much more sophisticated agents
leveraging the Azure Bot Service.
Considers three different elements.
The first one is interactivity and engagement
where we were able to redefine how bots interact with your business,
with natural language understanding,
and depersonalization.
For example with Microsoft Cognitive Services and the language understanding capabilities.
The second aspect is
bots intelligently performing actions
within your business processes.
So think about connecting with your CRM data
or with Dynamics 365 to know whats the profile of the users.
And lastly is the ability for bots to be predictive.
Capitalizing on hidden insights within your data
with big data, machine learning
and for example think about Azure
Azure machine learning running a churn model
or finding upsell opportunities behind the scenes
to help with better engagements.
And of course you can also integrate these core technologies with other third party offerings.
Cool can we see a practical example of what
you can now do with bots to really harness all these technologies?
Absolutely.
In this example, I've just purchased a new car and I'm looking for a quote.
I've got different ways to interact with my insurance company.
I could search for the information
into the website
or directly call for an agent for assistance.
Or even start chatting with a human agent.
But as you mentioned, Jeremy
if all the representatives are busy
bots become even more handy.
So bots can support many different patterns or interactions with your customers.
For example, I can ask the bot simple questions like
"where are you located?"
And the interesting point is that
we've got immediate responses that it's able to identify
the intent of the question
regardless how I phrase it.
So anyone can easily have a very effective Q&A bot
thanks to the Microsoft Cognitive Services
called QnA Maker.
Right and we're going to show you QnA Maker later so you can build your own bot.
So let's type
I need car insurance.
Because this is what I'm looking for.
And what's going to happen is that the bot will understand the intent
and start the quote process.
It's asking if I'm an existing customer.
So I say yes.
And a name to authenticate.
So for this presentation I will be Alice Arson.
And of course we reduced the number of information which are required
to go in this authentication path.
But it will also ask me for a social security number.
And there are also new personalized way for bots to recognize you.
It could use my face and my voice
thank to face API and speaker recognition API.
And they could work as well to have this authentication process.
Also because it's integrated within Dynamics CRM
the bot is able to connect to business processes and retrieve customer information.
You can see that the face is now loaded with Alice
and you've got more information.
Using the CRM information, the bot is now
able to provide an upsell scenario.
And you can see there how you can take action through solutions that infuse intelligent
within business processes.
Really when you think about this this is where they bought itself,
being able to harness that intelligence
is really doing something that might take a long time or a relationship personally with your client.
This is just doing that and pulling the data directly from something like Dynamics 365
and pulling that right into the chat.
Absolutely.
And something like that takes time to establish, time that we have saved to be more effective.
Now the bot is collecting additional information relating to the car I just acquired.
So what makes up the car?
As I've got friends working in the automotive industry
I don't want to make anyone jealous, so it will be jumpy car.
A super nice amazing model.
And the model will be this one.
Which is a really long model name.
Awesome branding activity to add.
Let's just say they've acquired it last year.
So now it's asking me
something unusual and to provide a car picture.
And it seems to be a little bit unusual right now.
But it's way more interesting because uploading pictures
is critical for insurance companies especially if you've had an accident.
So I will say "yes" and then I will put the picture of my car.
But guess what?
I've got very dumb thumbs
and instead of selecting my car,
Ahh! What happened?
That happens all the time, you fat fingered it.
My little nice dog.
The interesting point here is that it's using cognitive vision API as part of Microsoft Cognitive Services,
This makes it possible to understand what's
inside an image and verify if the content is appropriate.
It will look again and again until it has a car.
Look, it looks more like a dog sitting in the grass to me.
which is pretty accurate.
So it will re-ask again.
And for the last time I will try to.
At least that dog will fit in this little car.
Absolutely.
So now, this is just a start.
Because consider in the future what we could do.
Thanks to deep learning tehcniques
you'd be able to upload a car picture and have the bot recognizing the brand and model
automatically without you putting the information.
So of course in the background now
the bot is able to pull together all the information provided and generate a quote.
$146.50 per month.
Looks like a lot more then I'm paying.
Yeah, but this number here includes input from my profile, risk assessment system,
even churn and many more.
So we can truly capitalize on insights
hidden within and beyond your unique data.
leveraging Azure machine learning.
And lastly this is the last part.
The bot is asking me what I think about this.
Let's say,
"As you mentioned, this is very expensive".
"Too much".
And what's happening is that the bot can even detect the negative sentiment of the customer's response
thanks to the cognitive services called Text Analytics API.
And what happened here with cognitive services actually look in the sentiment
And if you go silent,
let's say you're really upset and you don't want to respond to the bot.
It will even detect that and even if you're not engaging.
Absolutely.
And for example here it can bring in a human agent, Jane
to close the deal.
And the good thing is that once added, she already has all the view of what happened
by discussing with the bots.
So I'm going to play the part of Jane here and show you what I see.
So in this case I'm in CRM
and then Dynamics 365 and I can see Alice.
I can see how long she's been active,
here daughter is there and I can see this beautiful yellow jumpy car
that Gianpalo added earlier.
I also see the different interactions that that I've had with the bot as well.
And I can even see this
chat window that we were just working in.
One of the cooler things here though is that I can take all of that information
and start making suggestions for other offers
using the intelligence in the machine learning directly though Dynamics 365
and all the integration that we just saw.
Here I can see it's asking me if I want to cross sell
in the personal injury protection.
That's right and so we're leveraging here machine learning
of course to identify the likelihood of the customer to leave the service, churn.
And even making smart suggestions
regarding other products the customer may wish to purchase,
thanks to recommendation API.
So contact centers will also be able to have a complete view of all the customers requests and interactions
aggregated in order to find smart insights.
I can integrate this information with my visualization tools
and here with Power BI,
I can see how about bots are performing.
The number of chats, the number of escalations,
the chart performance with the funnel of engagement
between active, successfully ending, escalated, churn.
Of course, this is what would be the activity if there are no hand-offs between the agent and the bot,
and the machine learning churn is behind the scenes.
And the most interesting elements
are also related to the key topics which are extracting text analytics.
You can see the difference between
what you would think of a customer going in a website
and trying to understand what he has in his mind.
And extracting the information automatically
he is typing in a chat interface.
And so this is where the power of cognitive services are really
making a huge gap.
These word clouds really great for tracking Sentiment.
Absolutely.
Let's just take a look at the global activity
and I realize that in Australia something is unusual versus
the average number of interactions we're having for the last weeks.
If I just click on it, you see that everything is changing.
You've got way more escalation than expected.
Not so many business,
but lots of chats.
And you can see Contoso Insurance and expensive
are popping up as key topics.
And by cross and understanding deeper of what's happening behind the scenes
we realized that one of our competitors, Contoso Insurance
just launched a new very aggressive campaign
in which everything is fifty percent less.
And so everyone is starting to engage with us saying that we are too expensive,
let's have a deal.
Right and we've also mentioned earlier there's a number of technology that's really working
behind the scenes to get all of this to work and bring all these different experiences together.
You've just shown the reporting output of that.
But in terms of the chat experience itself.
I'm going to have my my web front-end that you'll see here on the left.
And what that's going to do is it can either directly connect to cognitive services
which are going to do things like detect speech if I'm talking to it.
Or the image recognition like we saw with your puppy in your car earlier.
As I start to engage with any of the chat and the bot itself.
What that's going to do is go through the bot connector
and at that point it's going to start to get triage.
Now it's not going to go and start chatting and sending back messages yet.
It's going to receive that.
That's going to go cognitive services.
And what that does is basically it's looking at Language Understanding Intelligence Service.
LUIS as we call it.
Taking a look at text analytics in terms of sentiment that we saw earlier
when you got angry about the price.
The language translation if you wanted to use your native French.
Which might have been a little easier in terms of writing the different questions here.
It would do that language translation dynamically on the fly.
After that what it's going to do is then take what it's learned
and either go back to the chat dialogue.
Or in this case like we saw here,
maybe even pull data and information from Dynamics 365,
information about your customer profile
and then opportunities that it can offer you up
as it formulates the response.
Then finally it formulates the response back to the user with all that collected information.
So all that stuff that we saw happened all instantly in the background.
Now I know that we saw a part of Q&A earlier,
but Microsoft just released the QnA Maker.
Is it easy to get up and running with that?
Absolutely.
So let me show you how you can quickly set up a bot service
to see questions and answers very quickly.
So in many cases you need to know that
the questions and answers already exist somewhere,
or in your internal content or external site FAQs,
URLs or documents.
So here consider that Microsoft QnA Maker
is an easy-to-use rest API and web-based service
that train AI to respond to users questions in a more natural way.
And you do not need to be an expert to train, manage and use it
for a wide range of solutions.
So it was a very simple graphical interface
that I will show you by creating a new service.
Straight forward, what is the name of the service?
Let's put "Mechanics".
And you 've got different choices.
What's the URL of your companies FAQ page?
If you already have one.
That's going to parse your FAQ page, maybe?
Exactly, on the go.
Or, no FAQ? No problem.
Let's have the document you're going to update or start it from scratch directly in the base.
Here I've got one FAQ I will show you about a very nice product called Azure Machine Learning.
With simple questions, answers.
A repetitive doc just three or four times just for the presentation.
So I'm going to pick that one.
And that's it, I'm just going to hit next
to create it automatically to have this knowledge base paired with questions and answers.
On the go, you can see now you've got five Q&A pairs
with the different questions and all the different answers
And you can change them on the go,
save, retrain and even publish.
We'll show you in a second how you can test it.
If I click here
you see that you've got the chat interface in which you can
add already answered questions.
So you remember the first question
we had the Q&A
That's quite the quiz.
What is Azure Machine Learning?
So instead of saying Azure Machine Learning,
because it's the exact same questions so I would have the same answer.
Let's try to be tricky and provide another complex question.
Tell me more about Machine learning?
Let's put a question mark.
So now what happened is that
even if it wasn't the same question,
thanks to the ability to understand what the intent of the question is
it's mapped with the already existing answer
and was able to say "okay, this is the answer".
You've got the ability to select an alternate answer if it's not the right thing.
You can even add additional questions.
The only thing you need to do after adding this element
is saving, retraining and your job is done.
Very cool.
Without any code you're able to get a Q&A bot up and running very quickly.
This is a really fast moving area, but can you tell us what's next.
Of course.
So as of today we've got more than 25 intelligent APIs and services in preview in general availability.
But more services are coming very very soon.
And in this example we showed you a text based chat.
But you could think about leveraging Bing speech API, for example.
to process speech to text
and make it even more natural to have queries and answers.
You could even think about leveraging the vision capabilities to understand
who you are with face identification.
Even your emotions can automatically adjust the flow according to how your face would react.
How would I get started If I want to start building these?
You can quickly get started discovering Microsoft cognitive services sites
and Microsoft bot framework as well, in the link below.
As well as learning more about Big Data, Advanced Analytics and Artificial Intelligence
with the Cortana intelligence suite sites.
Thanks Gianpalo.
You know this is a really fast moving space and I look forward to trying all this stuff out myself.
Keep watching out for more updates on Microsoft Mechanics.
That's all the time we have for today's show.
Thanks for watching and bye for now.
Microsoft Mechanics
www.microsoft.com/mechanics