Hello, cloud gurus and welcome to AWS service spotlight. In this series,
we put the spotlight on specific AWS services,
walking through detailed technical demos to explore their features, benefits,
and costs. I'm Kelby Enevold.
And today we're going to talk about a service that isn't exactly new,
but has some new exciting features.
The service I'm referring to is Amazon connect,
which has a feature called contact lens that we'll dive into today.
Amazon connect provides us with an omni-channel contact center and by
omni-channel I simply mean customers that call in can choose between using voice
or chat to get the assistance that they need.
Any conversation context is maintained across both channels, chat and voice.
Meaning customers can easily swap back and forth between these available options
without restarting their conversation from scratch.
It has integrations to quite a few other services, such as RDS dynamo, DB,
Lambda, Kinesis, Lex,
and even more connect also has support for a feature named contact lens,
which provides us with sentiment analysis, transcriptions,
and the ability to redact sensitive information from recordings and
transcriptions.
Amazon connect is a service that really revolutionizes traditional context
center experiences for both the agents and the customer.
I call it a contact center because it's no longer just your traditional voice
calls now with support, for voice chat, chat, bots,
sentiment analysis,
and so much more connect is incredibly easy to configure and it's completely
cloud-based.
So you're not stuck integrating a bunch of call management systems into your on
premises environment. Having the Lex integration is huge.
You can build a Lex chat bot to fit your needs, build custom skills,
and then use those skills to interact with your customers.
The entire call flow is customizable without any coding required.
And that allows your team to easily make changes to existing workflows on demand
without requiring a significant amount of rework. Let's talk a bit about costs.
Connect is free tier eligible for the first 12 months with some usage
limitations. Connect pricing is broken down in a few ways.
First you're billed for your voice usage, roughly 1.8 cents per minute.
There's also a charge of 2.5 cents per voice ID as well as four tenths of a cent
per chat message and 4 cents per task.
If you use contact lens for your calls,
it adds an additional 1.5 cents per minute for the first 5 million
minutes that are analyzed. Now,
I hope we've covered a lot of random sounding costs,
but each of these usage charges are inconsequential.
The overall pricing is still relatively cheap.
Let's go over a pricing example for a 10 minute call to a US-based direct inward
dial number in the Northern Virginia region.
It would cost us roughly 39 cents with contact lens contributing about
15 cents of those costs.
Now let's discuss some of connects benefits as well. First we have the,
to rapidly deploy changes to our contact center.
We can make changes to the contact flow on demand, save our changes,
and then immediately publish those changes to our live environment.
Without interruption connect is also incredibly scalable.
We can add hundreds or thousands of new agents to our workforce quickly and then
use skill-based routing to ensure that agents are routed contacts that matched
their configured skillset. The connect instance itself is managed by AWS.
So we're not stuck worrying about the underlying resource utilization.
We only have to worry about our service limits,
which can be increased with a service quota increase.
Let's go ahead and hop over to the demo so I can showcase some of these features
and then really drive home just how easy it is to use this service and configure
your own contact center.
Okay. Cloud gurus,
I've navigated to the Amazon connect service and created my Amazon connect
instance. The instance creation process is actually relatively simple.
You provide an access URL,
which is just the URL that you're going to use to authenticate to your instance,
through the creation process,
we can create an administrator user define our inbound or outbound call
settings,
and then finally define a storage bucket for all of our connect related logs.
After you've created your connect instance,
and you can actually navigate to the access URL and authenticate,
you'll be redirected to the Amazon connect dashboard.
Our initial landing page gives us a clear idea of what we can configure for our
new contact center. We have the ability to set our hours of operations,
create queues, create prompts, create new contact flows,
create new routing profiles. And lastly, create new users.
The thing to know here is Amazon connect comes pre-packaged with a bunch of
contact flows right out of the box.
So you're immediately able to start testing functionality without much
configuration.
What I've done here is I've created a contact flow called John's hotline
and they contact flow is basically the flow that a customer will experience when
they dial in. And so here we have different categories of contact blocks.
And one of those is set the recording and analytics behavior.
This block is how you enable the contact lens feature along with call
recording. So here, if I expand that block where I've already dragged out,
we have the call recording feature and here you can see I've enabled it for both
the agent and the customer.
So you can see that I'm able to enable it for just the agent or just the
customer, or I can completely disable call recording down here farther.
I have the contact lens, speech analytics feature.
And so this is the focus of today's service spotlight. So here,
I've already configured this. I'm not going to click save.
And after I've done the initial set recording and analytics behavior,
the call will just progress through the rest of this flow.
And we'll be able to review the call,
recording the transcripts and the sentiment for these calls.
Okay. And here I have the rest of this contact here.
We actually have a get customer input block where we're actually collecting
input from the customer using DTMF or dual tone, multi frequency.
DTMF is just when you're on the phone and you push one or two or three,
you're just signifying an intent. And that's what this is relying on.
So in this case, if a customer presses one, they get routed to John.
If they press two, they get to hear a joke, but then they're routed to John.
If they press three, they get to speak to any other agent other than John.
And if they press zero, they're replayed those options again.
And so here,
I'm also using a distribute by percentage Block to distribute incoming callers
to the various joke blocks here. And that's really it.
And so I'll go ahead and take a look at what our interactions looked
like. So we've already performed some test calls.
So I'm just going to navigate to the contact search category here on the left
hand side, under metrics and quality.
And here we have all the various calls. You can see there's several columns.
The contact ID is listed, which channel it came in on the timestamps,
the phone number that was dialed, which queue serviced it,
the agent that serviced it, we can play the transcript,
download it or delete it. And we have the customer phone number.
I'm going to click into this call right here.
This is one of our examples of sentiment analysis. And at a quick glance,
we already have a basic contact summary with a bunch of metadata associated with
this contact. And over on the right hand side,
we also have a graph that indicates the customer sentiment trend along with
two bar graphs,
to break down the customer sentiment throughout the entire call and the total
talk time breakdown as in, uh,
the dead time where nobody was talking the time where the customer is talking
and the time where the agent was talking. So here, quickly at a glance,
we can already get an idea of what the sentiment was throughout the conversation
for more detailed analysis.
All we have to do is just scroll down and we have access to the transcript right
here, and we have access to the call recording here.
We can see the sentiment breakdown throughout the entire phone
call. And so if I click play here, while this auto scroll selected,
you're going to begin to hear, um,
the agent in one ear and the customer in the other ear.
And it'll auto scroll here along with the transcript.
And so what you want to pay attention to here is that it redacts the names as
well as things like addresses. So let's go ahead and see here.
What kind of conversation this was? Hi, this is [redacted] helpful hotline.
We're here to help you with stuff that needs helping with.
Hi [redacted]. My name is [redacted]
I was having a pretty rough day and a friend of mine told me that I could reach
out to this number and complain a little bit. Am I in the right place?
It sure is. We're here to let you get things off your chest.
Well, I just had a really bad day. I, uh,
just did my taxes and discovered I owe thousands of dollars to the IRS.
Uh, do you think you could send somebody over to my address to help?
My address is [redacted] here in town?
Well, unfortunately we don't do tax assistance, but that does sound really hard.
Money problems are always difficult.
And if I could complain about something else, I honestly,
wasn't very impressed with your joke selection.
It could really use some improvement.
Can I speak to your manager so I can provide some feedback? Well,
unfortunately you can't because this is John's helpful Hotline,
and I'm [redacted] so it's just me, but, uh,
I will work on those jokes for you. Well, thank you
[redacted]. Thank you for hearing, hearing me complain for a little bit. I appreciate your time today. Sure. You have a nice day. Bye. Bye. And so what's really interesting here is you'll notice it only missed
John's name a couple of times throughout this entire conversation. So the redaction worked pretty well, but this is already evidence that it's not foolproof, it's not going to redact all of the sensitive information a hundred percent of the time.
So we have a demonstration of that immediately.
I do find it very neat that the audio redaction feature worked as well as it
did, because if we scroll up here on the transcript,
I had clearly given an address and my name several times,
and those continued to be redacted.
And you can see that every time where there is a bracket symbol and PII, uh,
listed in that transcript.
So we also have one other call here and we won't actually listen to the entire
audio of that call, but let's go ahead and just see if at a glance,
we get an idea of what this contact was like.
Okay. And immediately we can see at a glance,
this looks to be a more positive interaction. We have 60% positive,
40% neutral and 0% negative. So just immediately at a glance,
as a manager,
I could hop in here and immediately tell if this was a positive or negative call
from the customer's perspective. That is. And so here, I'll scroll down.
And on the right-hand side, we have the agent sentiment.
Left-hand side over here, we have the customer sentiment.
And as I continue to scroll down this transcript,
you can see that there's again, redacted information.
And we can see that the customer is complimenting John's hotline,
and there's mostly positive and neutral sentiment throughout this entire
conversation.
And so I would say this feature works extremely well.
I had a lot of fun testing this feature.
So what is the verdict on Amazon connect? Let's go over some pros and cons.
First. It's incredibly easy to configure your users, your contact flows,
or even allocate new or additional phone number. The ease of use here is huge.
It means you're not stuck. Scratching your head,
wondering what custom work you'll need to do to make this fit your business's
needs. You just allocate a phone number, import or create your users,
build your desired contact flow, and then start taking calls and chats.
If you need additional phone numbers, agents, contact flows,
or even queues that take advantage of the skill based routing.
It's all really easy to configure.
Connect also has support for integrations to Amazon Lex,
meaning we can build advanced chat bots that interact with our customers.
These chatbots can be built to do just about anything.
Allow self-service for customers,
confirm basic information before passing off to an agent,
or you can even drive the entire experience through that Lex chat bot.
Using Lambda integrations,
We can build advanced functionality for our chat bots,
which can be made to do just about anything.
We also looked at the newest feature contact lens,
which provides us with the ability to generate transcripts from our contacts,
redact sensitive information,
such as credit cards or phone numbers from those transcripts and perform
sentiment analysis on those conversations. This is a huge feature.
Being able to transcribe incoming calls and perform sentiment analysis can help
us gain some insight into what drove the customer's positive or negative
sentiment throughout the conversation.
Contact lens really allows us to make informed decisions when making changes to
our contact flow, which has a direct impact on our customer experience.
Let's quickly discuss the cons of Amazon connect. At this time,
Amazon connect instances can't be created via cloud formation.
It means you're stuck allocating the connections since manually and then
deploying cloud formation templates around that pre-created resource. Granted,
this isn't a huge con,
but it can get pretty frustrating if your organization leverages cloud formation
on a regular basis. Lastly,
when redacting sensitive information from transcripts,
there are no guarantees that all sensitive information will be removed.
The redaction itself is powered by machine learning.
So it's recommended to verify sensitive information has actually been redacted.
I've listed this as a con because while I understand that this is just a side
effect of being powered by machine learning,
it's important to make this call out.
Imagine a scenario where someone truly expects the sensitive info to be
redacted. A hundred percent of the time. Time goes on an annual audit occurs.
And then we find out that there was still sensitive information in our
transcripts.
It's just something that you want to be aware of if you're considering this
feature for your contact center. So let's wrap this up.
Should you invest you evaluate or revisit?
My recommendation is to invest.
If you're using older more traditional call center software,
give Amazon connect to try. It's really easy to use.
It's easy to configure and you can start testing your configuration in no time
at all.
Being able to build and customize chatbots that can interact directly with the
customer when they call or chat in is huge.
It allows us to develop more personalized experiences that can cater to our
customer's needs. We can deploy chatbots that facilitate self-service options,
allowing customers to do things like book their own appointments,
or reschedule upcoming appointments all without interacting with one of our
agents directly. That's it for this service spotlight cloud gurus.
Thank you for watching.