Analyst Webinar
Unlock the transformative power of AI for contact centers
Analyst Webinar
Unlock the transformative power of AI for contact centers
Deliver 24/7 customer support with bots for CX sustainability
[cutoff co_thick="2px"][webinarschedule]Offering 24/7 customer support is more important than ever during these uncertain times. Fortunately, artificial intelligence (AI) is poised to transform contact centers and improve customer experience — without compromising operational efficiency. It can empower leading brands to make customers happier and increase customer lifetime value.
Yet, a 2020 IDC research shows that less than 10% of customer interactions occur via virtual agents. And only one-third of the organizations surveyed were prepared to enable a remote contact center workforce when COVID-19 stay-at-home mandates were issued.
Join IDC, Google Cloud and Genesys panelists as they discuss how AI-powered chatbots and voicebots can achieve customer experience sustainability. You’ll also learn:
Top three actions to improve the customer experience
Primary business drivers and measured benefits of using AI for customer service
How contact center AI can increase customer satisfaction, empower human agents and augment business insights
[cutoff co_thick="2px"]1
00:00:05,040 --> 00:00:08,530
Good morning, evening, and afternoon everyone. This is Josh Reed
2
00:00:08,530 --> 00:00:11,010
from the digital events team here at Genesys, and let me be
3
00:00:11,010 --> 00:00:14,400
the first to welcome you all to today's webcast, Unlock
4
00:00:14,400 --> 00:00:18,810
the Transformative Power of AI for Contact Centers. As we
5
00:00:18,810 --> 00:00:20,510
always do, we're going to start off with a couple
6
00:00:20,510 --> 00:00:23,690
of brief housekeeping items. First off, if you experience any
7
00:00:23,690 --> 00:00:27,040
problems with viewing or listening to today's webcast, refresh your
8
00:00:27,040 --> 00:00:28,730
browser and make sure that it's up to date to
9
00:00:28,730 --> 00:00:33,090
support HTML5, as it usually fixes any console issues. Also,
10
00:00:33,090 --> 00:00:35,480
if you're having trouble seeing any of the windows, either
11
00:00:35,480 --> 00:00:38,070
the slide window or the webcam window, you can enlarge
12
00:00:38,070 --> 00:00:41,240
that window by dragging one of the corners and enlarging
13
00:00:41,240 --> 00:00:45,380
them in real time. And please note that this is an interactive
14
00:00:45,380 --> 00:00:48,650
experience between you and our three panelists today. Feel free
15
00:00:48,650 --> 00:00:51,180
to throw questions into the Q&A window, and we'll answer as
16
00:00:51,180 --> 00:00:53,830
many as we can at the end of our presentation
17
00:00:53,830 --> 00:00:57,920
today. However, sometimes as it does, if time gets away
18
00:00:57,920 --> 00:01:00,120
from us and we aren't able to read your question
19
00:01:00,120 --> 00:01:03,630
aloud during our live Q& A, will actually follow up
20
00:01:03,630 --> 00:01:05,990
with you via email within the next few business days.
21
00:01:07,280 --> 00:01:09,750
And also note, if you have to jump early or
22
00:01:09,820 --> 00:01:11,870
for any reason you feel like you're running out of
23
00:01:11,870 --> 00:01:14,870
time, don't worry. We actually are recording this. You'll receive
24
00:01:14,870 --> 00:01:18,410
an on- demand recording link via ON24 within the next few
25
00:01:18,410 --> 00:01:22,240
business days. So just be on the lookout for that. And also,
26
00:01:22,240 --> 00:01:26,050
we have a resource list here below the Q&A window. You
27
00:01:26,050 --> 00:01:28,950
can actually access those resources at any time during the
28
00:01:28,950 --> 00:01:30,740
webcast. It will open up in a new tab in
29
00:01:30,740 --> 00:01:32,500
your browser, and they won't take you away from the
30
00:01:32,500 --> 00:01:36,640
webcast. But these resources expand on today's topic of AI
31
00:01:36,640 --> 00:01:40,700
in Contact Centers. Also, we encourage you to participate in
32
00:01:40,700 --> 00:01:43,930
our brief survey. That's the last icon on the left of
33
00:01:43,930 --> 00:01:46,230
your widget bar. We'd love to collect your feedback on
34
00:01:46,230 --> 00:01:49,280
today's presentation so that we can tailor these webcasts to
35
00:01:49,280 --> 00:01:53,240
what you want to hear in the future. And as I
36
00:01:53,240 --> 00:01:56,470
always say, short and sweet. Today, we have three excellent
37
00:01:56,470 --> 00:02:00,430
presenters excited to discuss how AI- powered chatbots and voicebots can
38
00:02:00,430 --> 00:02:04,150
achieve customer experience sustainability. With that being said, I'm actually
39
00:02:04,150 --> 00:02:06,400
going to hand things off to our moderator today, Chris
40
00:02:06,400 --> 00:02:09,910
Connolly. Chris, the floor is yours. Thanks, Josh. And good
41
00:02:09,910 --> 00:02:13,390
morning, good afternoon, good evening, wherever you are in the world. We've got a pretty exciting agenda that we're
42
00:02:15,110 --> 00:02:17,020
going to share with you today. But first, I want
43
00:02:17,020 --> 00:02:22,090
to introduce some of our speakers. You might have met them before. If not, you're going to hear from them today. First
44
00:02:22,090 --> 00:02:27,870
is, Antony Passemard. He's the Head of Applied Conversational AI at
45
00:02:28,200 --> 00:02:33,440
Google, looking after Contact Center AI. He's presented with us before, and we're
46
00:02:33,540 --> 00:02:36,830
very lucky to have him back today to talk about some the innovations and
47
00:02:36,830 --> 00:02:45,510
evolutions of this space. Including that is, Ritu Jyoti, who's the Vice President of Artificial Research
48
00:02:45,570 --> 00:02:49,580
at IDC. Welcome, both of you. And myself, Chris Connolly,
49
00:02:49,930 --> 00:02:54,060
Vice President of Product Marketing here at Genesys. If we're looking at
50
00:02:54,060 --> 00:02:58,060
our agenda, we're going to look at some Insights from IDC, what the
51
00:02:58,060 --> 00:03:00,820
world looks like today, and really looking at how we
52
00:03:00,840 --> 00:03:05,500
unlock the power of artificial intelligence, and apply that in in
53
00:03:05,500 --> 00:03:10,280
Contact Center specifically. We're also going to have a panel discussion and please feel
54
00:03:10,280 --> 00:03:12,430
free to ask us questions now, or if you've got something
55
00:03:12,430 --> 00:03:15,170
that's on your mind, put under the Q& A window, because
56
00:03:15,170 --> 00:03:17,440
we're going to get to it very, very soon. We
57
00:03:17,440 --> 00:03:20,510
also want to talk about some of the things that you can do right away
58
00:03:20,510 --> 00:03:24,530
to improve your customer experience using artificial intelligence. And we're going to
59
00:03:24,530 --> 00:03:27,390
recap on some of the key takeaways from the research, and some of the
60
00:03:27,960 --> 00:03:31,610
experience from Genesys and Google in implementing this technology in the
61
00:03:31,610 --> 00:03:34,090
real world. And then lastly, we want to hear from
62
00:03:34,090 --> 00:03:37,610
you. So, if there are any questions, please, as Josh
63
00:03:37,610 --> 00:03:52,600
said, put them in the Q&A window, we will get to them live. So with that, thanks for kicking off. And I'm going to head over to Ritu Jyoti from IDC. Welcome, Ritu. Thank you, Chris. Hello, everyone. Pleasure to be
64
00:03:52,600 --> 00:03:57,650
a part of this webinar today. Before we delve into some cool
65
00:03:57,720 --> 00:04:00,840
insights that we got from a joint study we did
66
00:04:00,840 --> 00:04:04,950
together earlier this year in May 2020, let me first level
67
00:04:04,950 --> 00:04:08,640
set the stage here. We all know today that customers no
68
00:04:08,640 --> 00:04:13,240
longer base their loyalty on price or product. Instead, they
69
00:04:13,240 --> 00:04:16,520
stay loyal with companies due to the experience they receive. Customer
70
00:04:17,620 --> 00:04:22,360
experience has fast become a top priority for businesses, and
71
00:04:22,360 --> 00:04:26,530
often 24 by seven customer support is more important than ever,
72
00:04:27,040 --> 00:04:31,250
especially during these uncertain times. As per our research across
73
00:04:31,240 --> 00:04:35,300
most industries, brands of all sizes have started to push
74
00:04:35,330 --> 00:04:39,950
heavily towards increased automation in their customer service, as well as
75
00:04:39,950 --> 00:04:47,810
employee self- service, sales, marketing, human resources, IT help desk. You might wonder, "What has really changed?"
76
00:04:48,680 --> 00:04:52,460
Most of these brands are now looking for conversational AI as one
77
00:04:52,460 --> 00:04:57,880
of the key drivers for that automation. And as conversational AI allows
78
00:04:57,880 --> 00:05:02,020
brands to use natural language processing and machine learning- based
79
00:05:02,060 --> 00:05:06,560
tools, to support both their customers and the agents who support these
80
00:05:06,910 --> 00:05:14,700
customers. And the conversational AI chatbots and voicebots, they're more sophisticated
81
00:05:14,800 --> 00:05:20,480
these days. They incorporate bias and explainability, and exploit natural language for general
82
00:05:20,850 --> 00:05:24,860
question and answer capability. What we did earlier this year
83
00:05:24,860 --> 00:05:28,230
is that we run a joint study, and the study was
84
00:05:28,230 --> 00:05:32,960
focused on understanding what is the value of Contact Center AI. And for
85
00:05:33,260 --> 00:05:36,670
the suggested study, we wanted to validate the benefits of
86
00:05:36,670 --> 00:05:40,800
Contact Center AI in enterprises, what's the business ROI, What
87
00:05:40,800 --> 00:05:47,920
is the improved customer experience metric? How does it help the customer service agent
88
00:05:47,920 --> 00:05:51,410
efficiency? And before I get into the stack, let me just
89
00:05:51,410 --> 00:05:54,180
quickly walk you through some of the demographics of this study.
90
00:05:55,220 --> 00:05:59,850
We run a global study, it constitutes about 407 organizations worldwide. 50% were from
91
00:05:59,850 --> 00:06:06,020
U. S. and Canada, 25% they're from France and UK, and the
92
00:06:06,020 --> 00:06:10,610
remaining 26% were from Australia, India, Philippines, and Japan. We had
93
00:06:10,610 --> 00:06:13,070
a good mix of the industry. We had folks from
94
00:06:13,070 --> 00:06:18,120
financial institutions, insurance, telecommunications, and the rest of the industry.
95
00:06:18,160 --> 00:06:23,300
We made sure that these were polling or surveying, so folks
96
00:06:23,300 --> 00:06:26,620
who had the decision of quality. We had a good
97
00:06:26,620 --> 00:06:31,150
balance of companies of different sizes, as well as different
98
00:06:31,170 --> 00:06:34,080
types of customer handling formats. The people who were having
99
00:06:34,080 --> 00:06:38,160
internal Contact Center, or have an external Contact Center, as well as some
100
00:06:38,480 --> 00:06:41,350
internal customer service functions. And they had a broad range
101
00:06:41,350 --> 00:06:46,410
of respondents from different levels of customer service agents, starting
102
00:06:46,410 --> 00:06:56,320
from as small as 20 customer service agents. With that, let me, before
103
00:06:56,320 --> 00:07:00,150
level- set, that what we're seeing in terms of what
104
00:07:00,150 --> 00:07:03,910
is the three AI- driven competence that are transforming the Contact Center
105
00:07:04,770 --> 00:07:08,170
today? There are three competence. The first is the virtual agent, the
106
00:07:08,170 --> 00:07:14,790
second is the Agent Assist, and the third is the Insight. Virtual agent is basically a platform for creating voicebot
107
00:07:14,830 --> 00:07:20,410
and chatbot to automate customer interactions with voice or text,
108
00:07:20,860 --> 00:07:24,730
and the conversation to a live agent when the bot
109
00:07:24,730 --> 00:07:28,140
is unable to help a customer. So that's the first one. The
110
00:07:28,140 --> 00:07:31,380
second one is that an Agent Assist. It is a
111
00:07:31,380 --> 00:07:35,930
platform that integrates into the agent desktop, which uses AI to
112
00:07:35,930 --> 00:07:40,270
augment agent interactions with customers in the real time, and
113
00:07:40,270 --> 00:07:45,300
provide tone- by- tone guidance not to of relevant knowledge
114
00:07:45,300 --> 00:07:51,360
bases. It really, really help the agent become more efficient, it's augmentation of AI capability.
115
00:07:51,950 --> 00:07:55,250
And the third one is Insight. It's basically a module
116
00:07:56,110 --> 00:07:59,850
which uses natural language processing to identify the call center. "
117
00:08:00,460 --> 00:08:03,860
Why did the customer call? What were the call drivers? What
118
00:08:03,860 --> 00:08:07,890
was the sentiment?" And this helps the Contact Center managers learn about
119
00:08:07,890 --> 00:08:16,430
customer interaction, and improve call outcomes. How has CAST 2020 changed customer service?
120
00:08:16,790 --> 00:08:19,030
We all know what's going on in the industry today.
121
00:08:19,060 --> 00:08:25,450
And when we asked them what were the top difficulties that they were facing adjusting to
122
00:08:25,450 --> 00:08:30,220
the new stay at home mandate, not as surprise, but 39% of the
123
00:08:30,220 --> 00:08:34,080
respondents, the demographics that I just shared with you, they
124
00:08:34,080 --> 00:08:37,370
shared that they had higher than usual call volumes, and
125
00:08:37,370 --> 00:08:41,400
we all know the reasons why. But in addition to
126
00:08:41,400 --> 00:08:44,980
that, the situation that really compounded the problem was that there
127
00:08:45,740 --> 00:08:49,820
was fewer agents available. 43% of them reported that they
128
00:08:49,820 --> 00:08:53,790
had a few number of agents available to this part of this
129
00:08:53,790 --> 00:08:57,680
higher than usual call volumes. Partly some of them were
130
00:08:58,080 --> 00:09:03,840
reporting sick, or they were not able to respond to the work because of the changing dynamics, as well as, because of a
131
00:09:04,100 --> 00:09:13,200
more than usual call volumes. They did not even prepare to that level of number of ratio of the agents needed to support that kind of call volume. This is specifically interesting. With
132
00:09:13,200 --> 00:09:24,560
that, I'm going to. Yeah, Chris. these are really interesting stats.
133
00:09:24,630 --> 00:09:27,690
2020 has been an interesting year for all of us.
134
00:09:28,330 --> 00:09:31,090
So, if we look at those stats that Ritu just presented, we're
135
00:09:31,090 --> 00:09:36,430
curious about you, what are you seeing in your world, whether it's in your
136
00:09:36,710 --> 00:09:39,870
Contact Center, if you operate one or in your clients,
137
00:09:39,870 --> 00:09:42,730
if you're a partner of ours or you're helping operate
138
00:09:42,730 --> 00:09:46,490
a Contact Center? And are you seeing the increase in
139
00:09:46,490 --> 00:09:50,690
chat and call volumes, or are you seeing a decline? I'll give
140
00:09:50,690 --> 00:09:52,610
that a few more seconds to get a few more
141
00:09:52,610 --> 00:09:57,630
responses. But I'm particularly curious to see what results we
142
00:09:57,630 --> 00:10:02,220
get off the back ends here. I'll give that one
143
00:10:02,220 --> 00:10:07,200
or two more seconds. All right. Well, Ritu you couldn't
144
00:10:07,330 --> 00:10:11,210
be more right. Your research confirms exactly what our audience
145
00:10:11,210 --> 00:10:17,180
is seeing as well, with I guess unsurprisingly 81% are saying, " Have you
146
00:10:17,180 --> 00:10:20,500
seen an increase in call and chat volumes in 2020?" And
147
00:10:20,500 --> 00:10:25,160
that presents unique business challenges. How do you handle that
148
00:10:25,160 --> 00:10:29,790
increase in call volume, or interaction volume, or chat volume,
149
00:10:30,730 --> 00:10:40,840
just with the same resources that you have at the moment? So Ritu, leaving on a little bit here in terms of being prepared for that stay at home mandate
150
00:10:40,840 --> 00:10:43,840
that a lot of us have seen, what have you seen in the research
151
00:10:43,880 --> 00:10:50,010
in financial services? Yeah, I think the most important point to level set here is that
152
00:10:50,050 --> 00:10:53,590
the dynamics are changing dramatically. The survey we conducted was
153
00:10:53,590 --> 00:10:58,250
in the month of May 2020, and three months has changed quite a
154
00:10:58,250 --> 00:11:00,780
lot of the situation, but we caught this into right
155
00:11:00,780 --> 00:11:04,030
in the middle of COVID situation. And when the stay at
156
00:11:04,110 --> 00:11:07,290
home mandates are down, not a surprise to us, but
157
00:11:07,810 --> 00:11:13,370
I'm very well prepared with only one respondent. And I'm not
158
00:11:13,370 --> 00:11:15,230
surprised to see the other part of the results that
159
00:11:15,230 --> 00:11:18,270
I share this in the slide is that, the financial services in
160
00:11:18,270 --> 00:11:21,170
U. S. organizations were the best prepared. So you might
161
00:11:21,540 --> 00:11:24,930
sit and think, " What led to this?" And IDC as
162
00:11:24,930 --> 00:11:27,810
a research firm, we spend a lot of time advising our end
163
00:11:27,810 --> 00:11:32,250
users on what they really need to do. The correlation
164
00:11:32,250 --> 00:11:36,320
here is that the organizations who were born digitally transformed,
165
00:11:36,820 --> 00:11:40,790
they're higher stages of maturity, of digital transformation, they were
166
00:11:40,790 --> 00:11:45,290
definitely well prepared. And you can see that there was a direct correlation between
167
00:11:45,360 --> 00:11:48,990
that, and even the financial organization, they have been embarking
168
00:11:48,990 --> 00:11:52,180
on their journey much longer and earlier. And that's why
169
00:11:52,180 --> 00:11:56,000
you could see that correlation. But now I will share in one of
170
00:11:56,540 --> 00:12:00,580
the upcoming slides here, that what the organizations are doing to
171
00:12:00,580 --> 00:12:04,260
be better prepared next time. And that's more important. I would
172
00:12:04,260 --> 00:12:06,970
like to focus on the forward looking approach and I'll
173
00:12:07,030 --> 00:12:10,050
walk you through and back in a couple of slides.
174
00:12:11,030 --> 00:12:18,790
Awesome. Well- With that if I'm going to... Yeah. Sorry, please go ahead. The next one
175
00:12:18,790 --> 00:12:23,080
is basically, we had asked them that the process of customer
176
00:12:23,110 --> 00:12:27,270
interactions that is happening through this newer channels. And again,
177
00:12:27,270 --> 00:12:29,420
I would like to reiterate on the point here that,
178
00:12:30,070 --> 00:12:33,920
this is the average that I'm presenting here. So, there
179
00:12:33,920 --> 00:12:37,200
are a couple of organizations who might be better prepared
180
00:12:37,200 --> 00:12:40,690
than the other, and the volume and the my team, but this is the mean,
181
00:12:40,690 --> 00:12:44,770
and the median value. Good sharing here. So, there could
182
00:12:44,770 --> 00:12:47,730
be a possibility that the U. S. organizations, if I presented
183
00:12:47,730 --> 00:12:51,080
the data cut just so that it could have been a little bit different. But
184
00:12:51,080 --> 00:12:53,530
not surprisingly, if I look at it from the mean and median
185
00:12:53,650 --> 00:12:58,700
perspective, it's very small percentage. And again, factor it that this is
186
00:12:58,700 --> 00:13:03,350
May, 2020 was happening to virtual agents. This number, I suspect
187
00:13:03,350 --> 00:13:05,580
if I'd understand it today, it would be a little
188
00:13:05,580 --> 00:13:10,130
bit higher than this, but it's a small percentage. Yeah,
189
00:13:10,230 --> 00:13:13,920
exactly. And the agents, the voicebots and chatbots percentage will
190
00:13:14,340 --> 00:13:18,990
also dramatically change. Most of them they're originally doing idea. But before
191
00:13:18,990 --> 00:13:21,060
I move on to the next slide, another very interesting
192
00:13:21,060 --> 00:13:23,980
stat that I want to share here is that we
193
00:13:23,980 --> 00:13:26,130
spend a lot of time talking to the end users
194
00:13:26,350 --> 00:13:29,250
and don't worry, there's still research as well, that what
195
00:13:29,250 --> 00:13:33,070
is really coming up and shaping up in addition to just
196
00:13:33,070 --> 00:13:38,220
the voicebots and chatbots is the computer vision effect to it. The computer vision
197
00:13:38,220 --> 00:13:42,360
has become more mature with image recognition achieving significant improvements.
198
00:13:42,470 --> 00:13:46,260
Thanks to deep learning techniques, computer vision, and CRM is
199
00:13:46,260 --> 00:13:49,910
very early stage. And it's far from being widely adopted.
200
00:13:49,910 --> 00:13:52,200
But if I look back, sit back and think that the rate
201
00:13:52,280 --> 00:13:55,860
at which things are changing, it will be relevant across the
202
00:13:55,860 --> 00:14:00,260
entire customer journey. It will be a through force multiplier for
203
00:14:00,260 --> 00:14:04,120
adding more essential insights for customer upsells and cross sells.
204
00:14:04,120 --> 00:14:06,550
So, that is something that we all need to look at and watch,
205
00:14:06,810 --> 00:14:22,780
and that, that's going to shape up big time in the near future. to that next slide is, there's an obvious question. We've seen voice agents and chat agents
206
00:14:22,780 --> 00:14:27,090
in this case really low at only 8%. And I guess Antony
207
00:14:27,820 --> 00:14:30,450
I'll put this question to you. What do you see?
208
00:14:30,970 --> 00:14:32,350
What do you think this is so low at this
209
00:14:32,350 --> 00:14:38,260
point in time? Well, I can't really blame customers on a low
210
00:14:38,260 --> 00:14:40,900
adoption of virtual agents for voice and chat. If you
211
00:14:40,900 --> 00:14:43,420
look historically, you look back two or three years ago,
212
00:14:44,260 --> 00:14:46,760
either you have an IVR that's forces you down a
213
00:14:46,760 --> 00:14:50,270
tree, or the chats are really just about routing you
214
00:14:50,720 --> 00:14:56,090
to the right agent. I can't say that the bots on
215
00:14:56,090 --> 00:15:02,500
voice or chat were really good to be mild in
216
00:15:02,560 --> 00:15:07,880
my assessment. But that is cheapest thing. The technology that
217
00:15:07,880 --> 00:15:12,400
enables us to deliver very high quality bots, which means
218
00:15:12,400 --> 00:15:15,400
they understand where you're saying, they can drive the conversation,
219
00:15:15,400 --> 00:15:19,700
they can answer questions, do backend fulfillment, actually deliver value
220
00:15:19,700 --> 00:15:23,610
to a customer. This is more new, in fact, in
221
00:15:23,610 --> 00:15:27,190
the last two years I would say, and really taking off.
222
00:15:27,190 --> 00:15:30,010
So on that, 8% makes sense what we're seeing across
223
00:15:30,010 --> 00:15:33,990
customers. Some don't use those channels at all. The ones
224
00:15:33,990 --> 00:15:38,190
who do use them are around 15 ish, 20% of
225
00:15:38,280 --> 00:15:41,510
no volume. So, that 8% average doesn't surprise me. It's very
226
00:15:41,950 --> 00:15:44,470
in line with our customers. And that low adoption, if
227
00:15:44,820 --> 00:15:47,580
you look a year from now, it's going to be completely different
228
00:15:47,580 --> 00:15:51,670
graph. Yeah, I totally agree. It might be low today,
229
00:15:51,670 --> 00:15:54,620
but this is where the growth factor is. And we're
230
00:15:54,620 --> 00:16:00,660
expecting to see the same customer base in Genesys. We constantly inundated with inquiries
231
00:16:00,660 --> 00:16:05,990
on, " How do we apply the virtualization technology?" Ritu, I guess, again,
232
00:16:05,990 --> 00:16:08,410
coming back to current times and the research that you've
233
00:16:08,410 --> 00:16:19,590
seen, how will the Contact Centers adapt in to the modern world and the things that are going on today? not an easy thing, but I always
234
00:16:20,130 --> 00:16:23,230
love to say this, " There's no choice." Customers have to do
235
00:16:23,230 --> 00:16:28,010
this in order to be innovative, providing, improving on the
236
00:16:28,010 --> 00:16:31,830
customer experience, but at the same time, not compromising on
237
00:16:31,830 --> 00:16:34,950
the operational efficiency. And it's a great balance that customers need
238
00:16:34,950 --> 00:16:38,510
to do. So, it all starts with me. This is universal
239
00:16:38,780 --> 00:16:44,590
across all AI initiatives, but it's very, very prevalent here. Culture, that's
240
00:16:44,750 --> 00:16:50,550
transforming the culture, the customer insights is democratized. Every employee
241
00:16:50,550 --> 00:16:55,060
becomes a change agent for the customer. I cannot emphasize
242
00:16:55,060 --> 00:16:58,390
this more because in the case of AI sometimes, people are
243
00:16:58,450 --> 00:17:02,290
a little bit because it's also another... It's just getting the
244
00:17:02,290 --> 00:17:05,070
comfort level, trusting it. And there are lots of advancements
245
00:17:05,070 --> 00:17:09,080
happening in that area. But once you have that culture, you're
246
00:17:09,250 --> 00:17:12,650
willing to understand where the customer needs are. This is
247
00:17:12,650 --> 00:17:14,650
really going to be day and night for the customer
248
00:17:14,650 --> 00:17:17,430
experience. And at the same time, you will be playing
249
00:17:17,430 --> 00:17:20,790
a very active role in building, working with the tools,
250
00:17:20,790 --> 00:17:24,490
and technologies, and the supplies, and the offerings to meet
251
00:17:24,820 --> 00:17:28,820
and improve your AI systems as well. So that's one.
252
00:17:29,170 --> 00:17:32,200
The second is that, gone are the days when people
253
00:17:32,200 --> 00:17:37,110
are doing metrics of measurement in isolation. Here people are
254
00:17:37,110 --> 00:17:40,990
trying to bring them together. There are AI, KPIs, and
255
00:17:41,030 --> 00:17:45,750
AI metrics have globally aligned with how they're measuring their customer
256
00:17:45,940 --> 00:17:50,700
experience metrics. So, they're both evolving so that there's huge amount of
257
00:17:50,800 --> 00:17:54,810
emphasis on improving the experience, and improving the measurements and
258
00:17:54,810 --> 00:17:58,810
aligning them together. And the third and the last thing is
259
00:17:58,810 --> 00:18:03,770
that, there's a huge amount of emphasis on human efficiency.
260
00:18:04,030 --> 00:18:06,730
And there was a lot of debate going on for some time, that
261
00:18:08,270 --> 00:18:11,370
AI is going to take over the human jobs and there
262
00:18:11,370 --> 00:18:14,790
was no experience with this in the foreseeable future. We
263
00:18:14,790 --> 00:18:18,250
see that it's making us much more efficient. It is
264
00:18:18,610 --> 00:18:21,090
helping us to be more empathetic. It is giving us
265
00:18:21,090 --> 00:18:24,550
real time guidance. So these are the three important things. Of course there are
266
00:18:25,010 --> 00:18:28,690
other parts of it, but these are that we are
267
00:18:28,690 --> 00:18:31,800
seeing in customers, how they're doing what they're doing to
268
00:18:31,800 --> 00:18:39,880
adapt to the newer customer Contact Center scenarios today. Awesome. And- point,
269
00:18:39,890 --> 00:18:46,270
Ritu. Just one point here, Ritu. The survey earlier with
270
00:18:46,270 --> 00:18:49,340
80 plus percent of people saying they see their volume
271
00:18:49,340 --> 00:18:51,730
going up, I think that's where really AI can help.
272
00:18:51,730 --> 00:18:55,180
Is to avoid having to increase your Contact Center resources
273
00:18:55,180 --> 00:18:59,220
by 80%, to answer that need. That's really where we're seeing
274
00:18:59,220 --> 00:19:02,310
actually a lot of usage of AI for Contact Centers,
275
00:19:02,630 --> 00:19:05,990
is to kind of tamper that growth of volume and
276
00:19:05,990 --> 00:19:09,180
make sure you have operational efficiency across your Contact Center
277
00:19:09,180 --> 00:19:12,020
without adding more people. It's not about a reduction of
278
00:19:12,020 --> 00:19:16,540
people. That's not what we've seen. Exactly. It's not about replacing
279
00:19:17,990 --> 00:19:22,410
human beings. It's making them more efficient, so they address the changing
280
00:19:22,410 --> 00:19:26,380
dynamics of the industry. Because the volumes will definitely increase, but
281
00:19:26,970 --> 00:19:31,290
we do not, and cannot adding more and more people to meet their demands.
282
00:19:34,550 --> 00:19:38,030
Exactly. And so, our audience can maybe discern what other
283
00:19:38,030 --> 00:19:41,710
customers are doing in the station. And that's a great example. It's
284
00:19:41,710 --> 00:19:44,610
not about replacing people, and maybe that's one of the
285
00:19:44,610 --> 00:19:48,780
big business drivers, just handling more. Are you seeing... And
286
00:19:48,780 --> 00:19:50,740
I guess this question is for both of you. Are you seeing
287
00:19:51,750 --> 00:19:56,040
other business drivers in the application of artificial intelligence that
288
00:19:56,040 --> 00:20:00,910
are not correlated, that are maybe not volume related, but other benefits of using
289
00:20:01,190 --> 00:20:08,470
applied, and machine learning, and AI to deliver a new experience? Yeah, absolutely.
290
00:20:08,470 --> 00:20:16,950
I think- Yeah, go ahead, Ritu. business driver, what we're seeing is,
291
00:20:17,800 --> 00:20:21,860
now the customer will come and first think about operational efficiency, cost reduction are
292
00:20:22,610 --> 00:20:24,740
the first thing they have in mind. But the reality is,
293
00:20:24,740 --> 00:20:28,490
when you start doing a Contact Center and put Contact
294
00:20:28,490 --> 00:20:31,670
Center AI in your Contact Center, you're really transforming your
295
00:20:31,670 --> 00:20:35,290
customer experience across all channels. And the business drivers to
296
00:20:35,290 --> 00:20:38,330
provide a coherent experience when you're on chat, when you're
297
00:20:38,330 --> 00:20:40,300
on voice, when you're on your web, or your mobile
298
00:20:40,300 --> 00:20:44,730
app, where the engine behind it, the AI behind is
299
00:20:44,730 --> 00:20:48,300
able to manage across those channels, switch channels, understand what was
300
00:20:48,300 --> 00:20:51,440
said, the context, the past history, et cetera, and provide
301
00:20:51,440 --> 00:20:56,050
you a unique voice for the company to that customer
302
00:20:56,050 --> 00:20:59,710
or that user, if talk about organizations. I think that
303
00:21:00,010 --> 00:21:02,890
the business driver is really having that coherence across the
304
00:21:02,890 --> 00:21:07,620
board, and having a very wonderful experience, no matter the
305
00:21:07,620 --> 00:21:11,000
channel, no matter the choice that the end user or customer is
306
00:21:11,000 --> 00:21:15,730
making, they get that experience across all channels in a
307
00:21:15,730 --> 00:21:19,720
very unified way. I think that's a big business driver.
308
00:21:21,300 --> 00:21:25,730
Agree, agree. And it is also reflected in our study
309
00:21:25,810 --> 00:21:28,220
here, which I'm just going to share the chart, with some
310
00:21:28,260 --> 00:21:33,730
of the facts on there. But for very first experience we're all talking about it, but
311
00:21:33,730 --> 00:21:39,930
because of usage of these as the, it's not just
312
00:21:39,930 --> 00:21:42,880
the consistent experience, which is very, very critical, but it's
313
00:21:42,990 --> 00:21:50,690
also passive into issue. Because enriching the human agent with
314
00:21:50,730 --> 00:21:56,210
the Insight in real time, it provides that timely response and a
315
00:21:56,520 --> 00:21:58,770
more intelligent response for lack of a better word here,
316
00:21:59,940 --> 00:22:07,400
is really, really being consummated with operational efficiency, consistency, and
317
00:22:07,400 --> 00:22:13,130
experience. But also getting the fastest response in the fastest time,
318
00:22:13,430 --> 00:22:17,960
in the more intelligent way, this is what AI is
319
00:22:17,960 --> 00:22:21,620
known for. It can do volumes and volumes of data,
320
00:22:21,810 --> 00:22:25,050
to pattern recognition, get Insights, and this advances in natural
321
00:22:25,050 --> 00:22:28,860
language processing can stop through tons and tons of data, and
322
00:22:28,860 --> 00:22:32,170
get you the right Insight at the right time. That's
323
00:22:32,170 --> 00:22:37,670
another very huge advantage. So, it's overall including bringing a good ... I'm
324
00:22:38,590 --> 00:22:42,970
IDC research. We also say that there's a huge correlation between
325
00:22:42,970 --> 00:22:46,470
customer experience and employee experience. And by usage of AI
326
00:22:46,650 --> 00:22:50,860
technology, you're not just making the customer happy, but you're
327
00:22:50,860 --> 00:22:55,750
also making the employee happier, employee more efficient. And that has
328
00:22:55,750 --> 00:22:59,310
a direct correlation on the customer experience. When many times we
329
00:22:59,310 --> 00:23:01,860
all come to work and if we're unhappy more, we're
330
00:23:01,860 --> 00:23:06,960
excited to serve our customers better. So there's a direct correlation there, which
331
00:23:07,400 --> 00:23:10,780
the advancements in AI technology is helping, and that's why the
332
00:23:10,780 --> 00:23:15,900
human agent efficiency, and human needs, and empowerment, and augmentation is so,
333
00:23:15,900 --> 00:23:20,110
so fundamental to the successful adoption of this. And people
334
00:23:20,110 --> 00:23:23,820
can't go to the other parts of the chart, but I also
335
00:23:23,820 --> 00:23:30,820
emphasize that empathy... Because you can understand the... Remember in one of
336
00:23:31,220 --> 00:23:34,830
the slides before, I was talking about where you could actually understand the
337
00:23:34,830 --> 00:23:38,870
sentiment. You can understand the rational of why people are
338
00:23:39,140 --> 00:23:44,130
calling you, and you could react in that particular situation in a much more empathetic
339
00:23:44,260 --> 00:23:48,850
way, because then you have that kind of intelligence real time. It's way
340
00:23:49,520 --> 00:23:54,690
more to the agents. This is very, very much aligned
341
00:23:54,690 --> 00:23:57,420
to what we are trying to think from a bigger picture
342
00:23:57,420 --> 00:24:03,220
than the survey insights that also align to that. Before
343
00:24:03,580 --> 00:24:05,730
I move on to the next slide, I just wanted
344
00:24:05,730 --> 00:24:09,640
to say that I think Tracy you asked this question, "What are the customers doing?" Of course their primary work, is one of
345
00:24:09,640 --> 00:24:14,330
the top reactions, but they'd also feed up a little
346
00:24:14,330 --> 00:24:18,460
bit of more question on the survey that because of
347
00:24:18,460 --> 00:24:21,390
this crisis... This is not a one time, this could happen again.
348
00:24:21,390 --> 00:24:24,490
How do you make yourself more resilient organization? So, what
349
00:24:24,490 --> 00:24:29,380
are the new IT investments that this experience has taught the customers and
350
00:24:29,380 --> 00:24:32,080
what they're doing it? The three things that you see
351
00:24:34,960 --> 00:24:38,790
here is that, in the past, sometimes people sit and decide whether they want to be on the public cloud
352
00:24:39,280 --> 00:24:43,690
scenario. This has really accelerated that, and people are seriously
353
00:24:45,080 --> 00:24:50,270
looking into it. The second is that the maturity that Antony also
354
00:24:50,270 --> 00:24:52,810
mentioned, and I also mentioned, and Chris, you mentioned at
355
00:24:52,810 --> 00:24:55,970
the start of the presentation is that the conversational AI
356
00:24:55,970 --> 00:24:59,700
technologies have improved so much in leaps and bounds in
357
00:24:59,700 --> 00:25:02,190
the last 12 to 18 months. And because of that,
358
00:25:02,510 --> 00:25:05,160
there is a sophistication in the response. It's not just, "
359
00:25:05,160 --> 00:25:09,580
Rule says yes or no." There's a significant amount of depth
360
00:25:09,580 --> 00:25:13,610
in the answering of the questions and answers. So people are getting
361
00:25:13,610 --> 00:25:16,820
more comfortable with the chatbot and also an omni experience.
362
00:25:17,280 --> 00:25:20,810
Whether they're working for a live chat, or for this chat, or
363
00:25:20,810 --> 00:25:24,220
through an IVR, everything they want that sense of experience. So, there's
364
00:25:24,220 --> 00:25:29,130
much more willingness to embrace it. And that is not
365
00:25:29,130 --> 00:25:32,980
the least, there are other factors, but digitization. We have been
366
00:25:32,980 --> 00:25:36,530
saying this for a long time that accelerated. Two years
367
00:25:36,530 --> 00:25:39,140
of digital transformation has happened in two months and it
368
00:25:39,240 --> 00:25:44,610
has accelerated the digital transformation. Increased digital digitization in the
369
00:25:44,610 --> 00:25:52,810
customer environment is really the investments that people are going to make. IDC has predicted
370
00:25:52,810 --> 00:25:57,070
that while we are in grim times, the investments in AI
371
00:25:57,070 --> 00:26:00,670
and the investments embracing of AI technology to make them
372
00:26:00,930 --> 00:26:07,400
more resilient, and this in the next new normal is something that AI
373
00:26:07,440 --> 00:26:11,660
will play a very significant part in that. Yeah, absolutely.
374
00:26:11,900 --> 00:26:14,980
And I want to maybe echo something that you said there, about
375
00:26:15,260 --> 00:26:20,020
providing empathetic customer experiences. And now more than ever, I
376
00:26:20,020 --> 00:26:22,480
feel like we need to have empathy for our fellow
377
00:26:22,720 --> 00:26:27,680
human. We're all going through something we know this is a generational thing. Something
378
00:26:27,680 --> 00:26:32,240
we haven't seen in many generations. So, what we're experiencing
379
00:26:32,240 --> 00:26:38,280
now is different, but crisis creates opportunity. And we're also
380
00:26:38,280 --> 00:26:41,870
seeing that this is actually being a bit of a
381
00:26:41,870 --> 00:26:44,730
non dialogue, as you said, Ritu, to some of the
382
00:26:45,000 --> 00:26:50,130
applications of AI. I want to talk a little bit about how we're seeing this
383
00:26:50,130 --> 00:26:55,220
technology start to be applied in three major use cases. I'm going to echo
384
00:26:55,220 --> 00:26:59,500
back to something which you said earlier with our voicebots, chatbots, and Agent
385
00:26:59,580 --> 00:27:02,150
Assist. And let me just take you through very quickly on
386
00:27:02,150 --> 00:27:04,930
what those are, and how they might be applied and
387
00:27:04,930 --> 00:27:08,550
why you might apply them in your organization. So the
388
00:27:08,550 --> 00:27:11,560
first if I could really summarize a lot of that
389
00:27:11,560 --> 00:27:15,810
upfront processing, is voicebots. And we're at a point now
390
00:27:15,810 --> 00:27:20,580
with the technology with natural language understanding the speaker recognition, a
391
00:27:20,580 --> 00:27:26,240
lot of that powered by Google to really understand phrases
392
00:27:26,810 --> 00:27:29,690
better than we ever had before, and at least a
393
00:27:30,080 --> 00:27:34,170
customer intents, so that we can have a much more data experience. And if
394
00:27:34,170 --> 00:27:36,170
you've been around the industry for any length of time
395
00:27:36,670 --> 00:27:40,610
like myself, that in the two thousands, it was a single
396
00:27:40,610 --> 00:27:43,450
word that was being said, and it couldn't detect that
397
00:27:43,680 --> 00:27:46,920
piece of recognition. And then we had to build these complicated
398
00:27:46,920 --> 00:27:51,890
speech, IVRs to deliver an experience. And really today what we're
399
00:27:51,890 --> 00:27:56,060
seeing is your IVR is a voicebot. And that's the
400
00:27:56,060 --> 00:28:00,480
evolution of the Contact Center technology, artificial or machine learning
401
00:28:00,480 --> 00:28:04,900
being applied in our space. Which brings the question, " Why would you do
402
00:28:04,900 --> 00:28:09,190
that? What is better than my IVR today?" And it really
403
00:28:09,190 --> 00:28:14,860
is that natural language processing that allows your customers to
404
00:28:15,100 --> 00:28:18,910
speak in plain speak, and to get to an outcome
405
00:28:18,910 --> 00:28:23,680
much more quickly, which ultimately improves their experience. But it's
406
00:28:23,680 --> 00:28:27,760
not just that upfront or self service experience that benefits
407
00:28:27,760 --> 00:28:31,560
from a voicebot, it also means that by the time that
408
00:28:31,790 --> 00:28:35,030
customer gets to an agent, the agent has a better
409
00:28:35,030 --> 00:28:38,950
understanding of what's going on as well. Because we're using
410
00:28:39,150 --> 00:28:43,630
CCAI in this case to intent, fill in the slots,
411
00:28:43,630 --> 00:28:47,930
and really have a more complete picture of that interaction. And again,
412
00:28:48,380 --> 00:28:52,420
overall, it rolls into that improved customer experience. We're going to go into
413
00:28:52,420 --> 00:28:56,100
a little bit more about the Genesys and Google partnership in just a
414
00:28:56,100 --> 00:28:59,700
few seconds. The other major use case that we see
415
00:28:59,700 --> 00:29:03,020
is chatbots. And I want to really say from the outset
416
00:29:03,530 --> 00:29:06,830
chatbots doesn't mean web chat. Chatbots can be applied on
417
00:29:06,850 --> 00:29:10,110
any textual medium that Genesys AI is able to route
418
00:29:10,160 --> 00:29:15,340
using the very same technology, natural language processing that you see in voice,
419
00:29:15,610 --> 00:29:19,820
as you see in text, and applying that to WhatsApp, WeChat, Apple
420
00:29:19,820 --> 00:29:23,450
Business Chat, of course Web Messaging as well when I
421
00:29:23,450 --> 00:29:28,260
synchronize messaging. All of that same technology, because Genesys can
422
00:29:28,260 --> 00:29:31,860
route that, and we've connected that with Google Contact Center AI.
423
00:29:32,140 --> 00:29:34,610
You can take advantage of the machine learning that's being
424
00:29:35,340 --> 00:29:46,110
applied there. Very similar experiences that we're seeing in the voicebot world, that in the chatbot world, that maybe with some richer experiences, depending on
425
00:29:46,110 --> 00:29:48,560
the medium that they're operating in. And what I mean
426
00:29:48,560 --> 00:29:52,660
by that is, with asynchronous media or Web Messaging, you've
427
00:29:52,660 --> 00:29:57,780
got the opportunity to actually insert, and images, and mix
428
00:29:57,780 --> 00:30:00,940
that with text. And the bot can actually do some pretty sophisticated
429
00:30:02,130 --> 00:30:07,040
things in that, including the complete self- service interactions, but
430
00:30:07,040 --> 00:30:10,540
be on your orchestrated platform that brings in the right
431
00:30:10,540 --> 00:30:14,660
technology at the right time, and when it does get to
432
00:30:14,850 --> 00:30:18,580
an agent that entire conversation is seen, and you can
433
00:30:18,580 --> 00:30:22,330
actually act on it in real time. And then lastly, and
434
00:30:22,330 --> 00:30:24,800
this is probably the one that I think is the
435
00:30:24,800 --> 00:30:28,510
most leading edge, but one that we probably expect to
436
00:30:28,510 --> 00:30:34,230
see more and more adoption as we go forward, which is Agent
437
00:30:34,230 --> 00:30:38,190
Assist. An Agent Assist, is what it says. It's providing
438
00:30:38,560 --> 00:30:43,600
artificial intelligence augmentation with the agent in the Contact Center to provide
439
00:30:43,600 --> 00:30:48,090
a better experience. And there's been, again, those leaps and bounds of
440
00:30:48,340 --> 00:30:53,140
how we integrate with this technology from an agent experience,
441
00:30:53,140 --> 00:30:56,350
from a desktop experience, from a voice experience, so that
442
00:30:56,420 --> 00:30:59,710
we're providing insights to the agent and surfing through them
443
00:30:59,920 --> 00:31:04,050
with knowledge in real time, and really having a very
444
00:31:04,410 --> 00:31:10,490
augmented conversation. I want to pause on that one because, Antony, this is absolutely in your domain
445
00:31:10,490 --> 00:31:17,240
as something that you talk about every day. So, how is the technology at
446
00:31:17,240 --> 00:31:20,690
Google, that we've talked about in voicebots and chatbots being applied
447
00:31:20,690 --> 00:31:26,930
in Agent Assist? An Agent Assist as you said, is
448
00:31:26,930 --> 00:31:29,270
a little newer in it's technology, but it's really a
449
00:31:29,270 --> 00:31:33,590
breakthrough that's been enabled by the advent of voice and speech
450
00:31:33,590 --> 00:31:39,230
to text in particular, speech to text technologies. Google has a
451
00:31:39,230 --> 00:31:44,090
long history of trying to understand speech either through understanding
452
00:31:44,090 --> 00:31:46,700
YouTube videos, to be able to search them and remove
453
00:31:46,700 --> 00:31:50,810
hate speech in YouTube videos. Either through a Google Home
454
00:31:50,810 --> 00:31:53,990
Assistant, and understand all the requests from users, that's millions
455
00:31:53,990 --> 00:31:59,770
and millions of requests every year. And also through transcription
456
00:31:59,770 --> 00:32:03,110
of voice messages on phone. You have Google voice and
457
00:32:03,110 --> 00:32:07,430
you can do your voicemail transcription. So all that technology...
458
00:32:07,430 --> 00:32:10,310
And voice search, obviously. So, Google has put a lot
459
00:32:10,310 --> 00:32:13,740
of effort in R& D into understanding voice, and thanks
460
00:32:13,740 --> 00:32:17,900
to that, we can now propose real time, voice transcription
461
00:32:18,280 --> 00:32:21,650
and understanding of a conversation between an agent and a
462
00:32:21,650 --> 00:32:26,810
user, and really provide in real time, the right suggestions,
463
00:32:26,810 --> 00:32:30,710
the right responses, the right document, the right flows that
464
00:32:30,770 --> 00:32:34,130
the agent needs to do their job faster. And that's
465
00:32:34,130 --> 00:32:37,240
really changing the game for agents. Ritu was talking about
466
00:32:37,240 --> 00:32:39,860
agent satisfaction, we're seeing that go up. We're seeing average
467
00:32:39,860 --> 00:32:43,140
handling time go down, we're seeing call summary going down
468
00:32:43,280 --> 00:32:46,070
in terms of how much time they spend typing after
469
00:32:46,070 --> 00:32:49,480
the call, the call disposition, we see first call resolution
470
00:32:49,620 --> 00:32:53,560
improving. On and on, the Agent Assist is really now the
471
00:32:55,120 --> 00:33:01,080
little coach or assistive technology for human agents in the Contact
472
00:33:01,080 --> 00:33:07,580
Center, enabling new experiences for the end user. Absolutely. So I
473
00:33:07,580 --> 00:33:10,410
want to come to another poll question in interest of
474
00:33:10,410 --> 00:33:15,740
time. From your perspective, what are you looking to implement? Any
475
00:33:15,740 --> 00:33:19,880
of these technologies, voicebots or chatbots specifically in the next three
476
00:33:19,880 --> 00:33:25,980
to six months, or maybe six to 12 months? Is that a
477
00:33:25,980 --> 00:33:28,750
new thing? Is it something that you want to act on now? Is it something that
478
00:33:29,350 --> 00:33:32,240
is more future- looking for you? I'm guessing the fact
479
00:33:32,240 --> 00:33:33,940
that you've turned up to this webinar today that means
480
00:33:34,860 --> 00:33:38,220
all interest to you, but we want to get a bit of a gauge to
481
00:33:38,220 --> 00:33:42,800
see if that deals with our research on how people
482
00:33:42,800 --> 00:33:45,620
are starting to adopt this technology. I'll give that a
483
00:33:45,620 --> 00:33:50,160
few more seconds. Eyeballs on screen, and then I'll go
484
00:33:50,160 --> 00:33:52,650
to the poll results. Do we want to take a guess
485
00:33:52,650 --> 00:33:58,620
anyone on where people are going to land? All right. Yes. Three
486
00:33:58,620 --> 00:34:02,170
to six months, in six to 12? Okay, I think
487
00:34:02,170 --> 00:34:07,880
the call out here is actually the people that said no, that are in the minority. So, the majority-
488
00:34:08,470 --> 00:34:11,020
Maybe because they already have something. I don't know. Maybe,
489
00:34:11,100 --> 00:34:14,920
no. Because we saw 8% of people already have it. So maybe the nos are people
490
00:34:14,920 --> 00:34:18,720
who have something in place. now we should have put that option in. We should
491
00:34:18,720 --> 00:34:20,760
have said" I've already got it." That no would have been on 1%.
492
00:34:24,870 --> 00:34:28,300
That's wonderful. I did see a question that came through
493
00:34:28,300 --> 00:34:32,850
as we're going, here from Rick. And Rick, I hope
494
00:34:32,850 --> 00:34:36,780
I'm going to touch on some of your question, and the question is, "
495
00:34:36,780 --> 00:34:42,360
How does Google and Genesys differentiate and add some capability
496
00:34:42,360 --> 00:34:46,150
and value?" I want to talk about that, but also when we get
497
00:34:46,150 --> 00:34:49,550
into Q&A, I'm going to come back to your question and answer it in a bit more detail as well.
498
00:34:50,320 --> 00:34:52,870
When we think about Google and Genesys, these are two industry
499
00:34:52,870 --> 00:34:57,350
leaders. Genesys being the only channel contacted a platform that really allows you
500
00:34:57,350 --> 00:35:02,160
to do journey orchestration, bringing multiple technologies from different vendors
501
00:35:02,380 --> 00:35:05,760
and really orchestrate all of those together in a cohesive
502
00:35:05,820 --> 00:35:09,650
manner, to deliver the very best experience that is out
503
00:35:09,650 --> 00:35:13,240
there. But we can't do it alone. And when we've looked across
504
00:35:13,910 --> 00:35:18,280
the marketplace, hope Google has done the same, it's really providing
505
00:35:18,280 --> 00:35:22,790
the best technology to our customers that integrates really, really neatly
506
00:35:23,010 --> 00:35:28,280
to deliver artificial intelligence capability inside the platform that maybe
507
00:35:28,280 --> 00:35:34,280
you have today, or the platform that you think of moving to in the future. What that means is that
508
00:35:34,710 --> 00:35:38,750
if you look at artificial intelligence technology in isolation, you
509
00:35:38,750 --> 00:35:41,070
can do a lot. You can use APIs, you can
510
00:35:41,070 --> 00:35:44,320
build what you need to build, and that might take
511
00:35:44,320 --> 00:35:49,930
time, but there's a depth of capability within Genesys in our platform, in the
512
00:35:49,930 --> 00:35:54,480
cloud, but also in our multicloud platform for everyone else,
513
00:35:54,760 --> 00:35:58,540
that allows you to plug in these different technology components,
514
00:35:58,540 --> 00:36:03,200
and take advantage of all of the routing of the orchestration services, all of
515
00:36:03,700 --> 00:36:08,610
the reporting, the analytics, the entering conversation view, the widgets,
516
00:36:08,610 --> 00:36:13,220
the desktop. That complete Contact Center package that you have,
517
00:36:13,460 --> 00:36:18,750
strapped on with artificial intelligence technology as well. So Antony,
518
00:36:19,040 --> 00:36:22,540
I know this is a long running partnership for us, at
519
00:36:22,540 --> 00:36:25,630
least from 2018. Do you want to add any commentary
520
00:36:25,630 --> 00:36:31,670
around how Google Cloud and Genesys put together? This is
521
00:36:31,670 --> 00:36:33,820
a very exciting partnership we've been at. I think you were
522
00:36:33,820 --> 00:36:36,770
one of the very, very first partners that we talked
523
00:36:36,770 --> 00:36:40,360
to, and we made that partnership. The goal here for
524
00:36:40,360 --> 00:36:45,230
us was really to also leverage, not only the innovation
525
00:36:45,230 --> 00:36:48,480
that Genesys is bringing to the market, but also leverage
526
00:36:49,150 --> 00:36:51,960
a lot of customers that have Contact Centers with Genesys
527
00:36:51,960 --> 00:36:55,220
in place and wanted to bring more AI, more capabilities.
528
00:36:55,250 --> 00:36:59,160
And we're seeing the opportunity here to avoid a rip
529
00:36:59,160 --> 00:37:04,240
and replace, of your implementation, and really upgrade whatever you
530
00:37:04,240 --> 00:37:07,830
had from Genesys. Genesys upgrading a lot of their own
531
00:37:07,830 --> 00:37:11,210
capabilities into their customer experience, but I think the Agent
532
00:37:11,210 --> 00:37:15,960
Assist, the virtual agent, and those capabilities are really things
533
00:37:15,960 --> 00:37:20,730
that we feel strongly about our capabilities, and having that
534
00:37:20,730 --> 00:37:25,770
as part of the Genesys platform is really powerful. I feel
535
00:37:25,770 --> 00:37:30,410
so super exciting to have this in place. I'm going
536
00:37:30,410 --> 00:37:33,960
to come to another question that's being asked in real time here
537
00:37:33,960 --> 00:37:36,940
about some of the joint customer use cases that we
538
00:37:36,940 --> 00:37:41,120
see from Google and Genesys. And I know, just by coincidence,
539
00:37:41,120 --> 00:37:45,030
that is the very next slide that Antony is going to talk to, but I'll give
540
00:37:45,030 --> 00:37:47,650
you an anecdotal example of a customer that's gone live
541
00:37:47,650 --> 00:37:53,220
with a Google Contacts Center AI and Genesys recently, where they reduced 26
542
00:37:53,390 --> 00:38:00,010
speech IVR applications down to one. And if you ever
543
00:38:00,010 --> 00:38:03,620
had to operate these or build these, the significance of
544
00:38:03,720 --> 00:38:06,760
every single one of those applications. We're able to use Dialogflow
545
00:38:06,760 --> 00:38:12,060
in this instance with Genesys new gauge. This customer, which was a telecommunications customer,
546
00:38:13,980 --> 00:38:19,440
was able to greatly reduce their managed applications down to
547
00:38:19,620 --> 00:38:24,680
essentially one application that's orchestrated by Genesys. And the way
548
00:38:24,680 --> 00:38:28,640
they did that, this is using some of the technologies that are on this slide, which
549
00:38:28,690 --> 00:38:31,250
Antony, I might hand over to you to talk about in a bit more detail. I
550
00:38:31,250 --> 00:38:36,680
know I will add something on that telecom customer. It was
551
00:38:36,680 --> 00:38:39,930
not in English, which is also one of the power
552
00:38:39,930 --> 00:38:43,700
the Google platform, is we support quite a few languages for
553
00:38:43,940 --> 00:38:46,950
CCAI, and this is a very big success story that
554
00:38:46,950 --> 00:38:51,840
was not only for English speakers. I'll just maybe do
555
00:38:51,840 --> 00:38:59,000
that. So, yes, as Chris mentioned, we have three main
556
00:38:59,000 --> 00:39:02,650
products as part of our CCAI portfolio. To build virtual
557
00:39:02,650 --> 00:39:05,510
agents, there's is a product called Dialogflow, we'll talk a little
558
00:39:05,510 --> 00:39:10,770
bit more about that. Our Agent Assist capability named Agent
559
00:39:10,770 --> 00:39:14,460
Assist, very creative, will be there to help your agents
560
00:39:14,750 --> 00:39:18,330
both on chat and voice channels. And our newborn Insights
561
00:39:18,490 --> 00:39:23,300
Platform just launched now factually yesterday, and will go GA
562
00:39:23,300 --> 00:39:28,940
pretty quickly. After that there's looking all of the data that's
563
00:39:28,940 --> 00:39:32,340
coming in, in your Contact Center, on chat and voice
564
00:39:32,340 --> 00:39:36,850
channel and using all that data to help you derive
565
00:39:36,950 --> 00:39:43,960
insights, trends, search, help QA managers and training, review calls
566
00:39:44,510 --> 00:39:47,250
and really drive insight sentiment, et cetera, is all in
567
00:39:47,250 --> 00:39:50,020
the Insights Platform. So, those are the three products that you
568
00:39:50,020 --> 00:39:53,560
will see as part of the portfolio called TCI at
569
00:39:53,560 --> 00:40:03,460
Google, and they're available on the Genesys platform. is putting the elephant on
570
00:40:03,460 --> 00:40:06,860
the table, so to speak. Which is my way of
571
00:40:07,110 --> 00:40:10,470
saying, address the elephant in the room. Have you tried
572
00:40:10,470 --> 00:40:14,020
natural language processing with bots? And if so, which are
573
00:40:14,020 --> 00:40:22,790
the ones that you've tried? Because there are multiple choices out there that you can work with, and maybe the answer is no, you're not there yet, and you're maybe
574
00:40:22,790 --> 00:40:26,930
evaluating, or you've experimented, or maybe you already live. So
575
00:40:28,080 --> 00:40:30,190
I'll give that a few seconds. The options here are
576
00:40:30,190 --> 00:40:36,120
Amazon Lex, Google Dialogflow, Microsoft LUIS, IBM's Watson, something else?
577
00:40:36,120 --> 00:40:40,760
Maybe homegrown, or a third party, or you haven't tried
578
00:40:40,760 --> 00:40:47,650
any of them. I know this is probably a multiple choice answer, but let's see where people land. And we've got to put in an even
579
00:40:47,650 --> 00:40:58,560
split. Oh, that's. I try which is amazing. But also
580
00:40:58,560 --> 00:41:01,710
the call out here for me is the majority, and
581
00:41:01,830 --> 00:41:06,700
the majority haven't tried natural language understanding yet. And really,
582
00:41:06,880 --> 00:41:09,640
I would encourage you to maybe look at it. This is what we're talking
583
00:41:09,640 --> 00:41:13,810
about. It's the fundamental technology behind all of these cases that
584
00:41:13,810 --> 00:41:19,850
we're talking about today. It is a good time to
585
00:41:19,850 --> 00:41:24,550
try because this slide actually is relevant too. You probably haven't
586
00:41:24,550 --> 00:41:28,420
tried because you've had very bad experience calling Contact Centers,
587
00:41:29,610 --> 00:41:33,690
and you really think those things don't work. In terms of NLU,
588
00:41:36,070 --> 00:41:37,980
Google is really... It's quite a lot of paper. And actually
589
00:41:38,050 --> 00:41:41,700
Google even open sourced a very large transformer called BERT,
590
00:41:42,240 --> 00:41:45,640
which is transforming the NLU space right now. And Google
591
00:41:45,640 --> 00:41:48,500
has its own version of BERT, which uses a lot
592
00:41:48,500 --> 00:41:55,130
more data. But that's where NLU has drastically changed in,
593
00:41:55,240 --> 00:42:01,070
let's say, 12 to 18 months. Drastically change, entities traction, intent detection,
594
00:42:01,470 --> 00:42:04,240
the courtesy of intent detection and matching is much, much
595
00:42:04,240 --> 00:42:06,440
higher than what you would get in two or three
596
00:42:06,440 --> 00:42:08,570
years ago. So I think it's a good time to
597
00:42:08,570 --> 00:42:12,630
test it out. Obviously Google, as I mentioned earlier, is
598
00:42:14,000 --> 00:42:19,420
understanding speech. And NLU for Google, as a whole Google
599
00:42:19,420 --> 00:42:23,210
search even, is a core technology that is needed for
600
00:42:23,210 --> 00:42:28,440
Google to function. So the investment we're making there is
601
00:42:28,510 --> 00:42:31,230
obviously massive because it's not just investment that's made for
602
00:42:31,230 --> 00:42:33,770
CCAI, it's investment that's made for Google as a whole.
603
00:42:34,200 --> 00:42:37,170
Obviously we're very, very careful about what data we use.
604
00:42:37,180 --> 00:42:40,560
We use public data, Google Assistant, YouTube, Voicemail, et cetera.
605
00:42:40,880 --> 00:42:44,590
We're not using any other cloud customer's data, because that would completely
606
00:42:44,590 --> 00:42:50,650
breach our privacy and confidentiality. But the customers do benefit
607
00:42:50,650 --> 00:42:53,180
from a lot of that research and investment that Google
608
00:42:53,180 --> 00:42:57,750
is doing in the space. Antony, I know people are
609
00:42:57,750 --> 00:43:00,000
really curious about some of these things. So, I want
610
00:43:00,000 --> 00:43:02,320
to flash some of the stats and ask you to talk
611
00:43:02,320 --> 00:43:06,020
through them on conversational AI, which is what you lead
612
00:43:06,020 --> 00:43:12,230
the people. Those are some of the numbers that are a
613
00:43:12,250 --> 00:43:15,060
reflection, a bit of that investment that we're making, and
614
00:43:15,060 --> 00:43:19,810
they turn into vast adoption from customers. Thousands and thousands
615
00:43:19,810 --> 00:43:24,610
of customers are using a Dialogflow out of price customer. Dialogflow has
616
00:43:24,610 --> 00:43:27,750
reached a big milestone last year. I think it was last,
617
00:43:27,750 --> 00:43:30,940
next, it was around eight or 900, 000 developers on
618
00:43:30,940 --> 00:43:34,160
the platform. We actually reached 1. 4 million developers on the platform
619
00:43:34,160 --> 00:43:36,360
the last two months. And it's still growing very, very
620
00:43:36,360 --> 00:43:40,780
fast. So Dialogflow has a massive public community that really
621
00:43:41,110 --> 00:43:46,320
helps people with creating bots. We support 32 languages for
622
00:43:46,320 --> 00:43:49,580
voice and a lot, lot more. I think it's 80 or something
623
00:43:49,580 --> 00:43:54,270
in chat. We have full integration with Genesys platform, which
624
00:43:54,270 --> 00:43:57,120
is the partnership here, which is awesome, but things like...
625
00:43:57,380 --> 00:44:00,620
Our bots can handle 20,000 intent for example, in a
626
00:44:00,620 --> 00:44:04,540
single agent. In a single agent. That means one entry
627
00:44:04,540 --> 00:44:08,190
point, and you can do 20, 000 in intent detection
628
00:44:08,190 --> 00:44:11,150
and route people throughout the entire company. You don't have
629
00:44:11,150 --> 00:44:13,610
to ask people to press one for service, and two
630
00:44:13,610 --> 00:44:16,300
for support. This is all part of the conversation get
631
00:44:16,300 --> 00:44:19,650
down and it all use case in switch between each of them.
632
00:44:19,840 --> 00:44:21,640
You call for billing first, and then you want to
633
00:44:21,640 --> 00:44:25,640
change plan. All that is done in a single agent,
634
00:44:26,250 --> 00:44:30,230
thanks to that kind of capabilities. WaveNet is interesting. WaveNet is the
635
00:44:30,230 --> 00:44:32,850
ability to create... It's text to speech. It's the reverse.
636
00:44:33,050 --> 00:44:34,720
You're speaking to the bot, the bot has to speak
637
00:44:34,720 --> 00:44:37,950
back to you. And Google has released and developed, and
638
00:44:37,950 --> 00:44:42,360
actually released the WaveNet technology, to create voices that are
639
00:44:42,420 --> 00:44:48,370
very human sounding. And what we've realized is the more
640
00:44:48,370 --> 00:44:51,710
human sounding, the more engaging those voices are, the more
641
00:44:51,710 --> 00:44:55,680
the customers or users of the platform are actually engaged. So, getting
642
00:44:55,680 --> 00:44:58,700
to a point where those voices are very, very human,
643
00:44:59,560 --> 00:45:05,050
will help adoption of voice bots in the field. And
644
00:45:05,250 --> 00:45:07,750
without ever deceiving customers, you never want to make them
645
00:45:07,750 --> 00:45:09,910
think they're talking to a human when they're talking to
646
00:45:09,910 --> 00:45:13,040
a bot. But having that engaging voice is very, very
647
00:45:13,040 --> 00:45:15,440
important to deploying a bot, and that's available today. There's
648
00:45:17,740 --> 00:45:21,700
150 or 60, I can't remember exactly, WaveNet voices available
649
00:45:21,700 --> 00:45:27,950
to you on the Google platforms in many, many languages. It's funny. I'm
650
00:45:28,490 --> 00:45:31,390
going to pick up on something about WaveNet, which again, for
651
00:45:31,390 --> 00:45:35,310
a Contact Center audience, this is quite transformative. If you think of
652
00:45:36,890 --> 00:45:40,250
the thousands of different prompts, they recorded as part of
653
00:45:40,250 --> 00:45:50,640
your flows today, and having to take that to a recording artist or somebody in the Contact Center, just to record that quick message. Imagine what
654
00:45:50,640 --> 00:45:55,140
that means for you when you can start to type
655
00:45:55,140 --> 00:45:57,510
them and allow the system to use brand then use... Sorry, to use a
656
00:45:58,960 --> 00:46:02,420
voice that is on brand for you, and choosing one of
657
00:46:02,420 --> 00:46:06,530
those waves in their voices. That's a huge operational efficiency that is just built
658
00:46:06,530 --> 00:46:11,240
into the platform. And that brings me to the peanut
659
00:46:11,240 --> 00:46:14,310
butter and jelly, the better to get a slide. And
660
00:46:14,310 --> 00:46:19,560
the reason for that is, when we think about all of the things that go into operating and managing a
661
00:46:19,630 --> 00:46:25,340
Contact Center operation, Genesys has user experience. Unlike where we
662
00:46:25,340 --> 00:46:29,250
put a bot in the front, and have that as isolated experience,
663
00:46:29,490 --> 00:46:33,440
really what we advocate at Genesys is let your customer experience platform
664
00:46:33,530 --> 00:46:37,950
orchestrate that entire end- to- end customer conversation. Genesys has
665
00:46:37,950 --> 00:46:41,350
some artificial intelligence technology on its own with something we call Predictive
666
00:46:41,350 --> 00:46:44,810
Web Engagement, that allows you to engage customers based on
667
00:46:44,810 --> 00:46:47,230
all of the data that we know and drive them to an
668
00:46:47,230 --> 00:46:50,500
experience that is very best for them. Sometimes that's a
669
00:46:50,500 --> 00:46:56,440
human. Sometimes that's a piece of content. Other times that's a bot. And using that upfront decision
670
00:46:56,440 --> 00:46:59,660
logic in an orchestration, we can really optimize that end
671
00:47:00,370 --> 00:47:03,520
to end customer experience and bring in the technology at
672
00:47:03,520 --> 00:47:06,730
the right time. I'm going to come back to Ritu
673
00:47:07,060 --> 00:47:08,220
here for a second, because we've got a couple of
674
00:47:08,650 --> 00:47:11,210
key takeaways and then we want to get into your
675
00:47:11,770 --> 00:47:25,840
audience Q& A. So, Ritu, why don't you just recap for us some of the research? all dimensions. That's fantastic.
676
00:47:26,220 --> 00:47:30,810
And so, if I actually walk you through, in the
677
00:47:30,880 --> 00:47:34,830
study that we just talked about, the 407 different organizations worldwide,
678
00:47:35,630 --> 00:47:38,390
and across the different industry, there are folks who are
679
00:47:38,390 --> 00:47:41,910
early adopters of this technology. So, kudos to them and we
680
00:47:41,910 --> 00:47:44,910
ask them, " What are your metrics and what are the
681
00:47:44,980 --> 00:47:48,240
rate of improvements that you're actually seeing today, and also what
682
00:47:48,520 --> 00:47:50,610
you envision it to be in the next three years?"
683
00:47:50,960 --> 00:47:53,880
So, very heartening to see, and I saw one question
684
00:47:53,880 --> 00:47:56,900
in the chat here that someone was asking that, " What is
685
00:47:56,900 --> 00:48:01,470
the ROI of this investment? Is it just the employee, human
686
00:48:01,470 --> 00:48:04,310
agent productivity?" And if you think the earlier adopters, of
687
00:48:04,310 --> 00:48:07,590
course they have seen improvement in efficiency and that is
688
00:48:07,590 --> 00:48:10,650
one of the metrics, but the top seat, is not just
689
00:48:10,650 --> 00:48:14,480
the efficiency of the human agents. It's actually better customer
690
00:48:14,480 --> 00:48:20,100
experience. They might be measuring it either through NPS, or that they're measuring it through customer lifetime
691
00:48:20,190 --> 00:48:24,390
value. There are different measurements. The customers might be doing it, but
692
00:48:24,930 --> 00:48:30,580
that's just number one. They feel that they actually can reduce the
693
00:48:30,580 --> 00:48:33,960
average handling time, from someone who's actually giving you a
694
00:48:33,960 --> 00:48:37,940
call, to making it more sufficient than us, either just
695
00:48:38,020 --> 00:48:42,770
being answered by the voicebot, or the chatbot, textbot. All
696
00:48:42,770 --> 00:48:45,540
tapping is on, in a reasonable amount of time, at
697
00:48:45,540 --> 00:48:49,440
the right time, with the right level of messaging to the human agent, to
698
00:48:49,440 --> 00:48:51,850
be answered in the most diligent and the most efficient way.
699
00:48:52,440 --> 00:48:55,770
So the end- to- end handling time is greatly improved, only
700
00:48:56,250 --> 00:49:00,020
24% of them say. And improved call deflection. In certain
701
00:49:00,120 --> 00:49:03,540
situations, if you see that the bot is not equipped to answer that, then
702
00:49:04,230 --> 00:49:07,070
it can be rightly moved on to a different means
703
00:49:07,070 --> 00:49:12,450
of communication. Those are the three most important rate of improvements the customers
704
00:49:13,080 --> 00:49:17,900
are reporting. Of course improvement in efficiency, and all of that is
705
00:49:17,900 --> 00:49:22,570
extremely important and improvement in operational efficiency. But I hope that answers
706
00:49:23,000 --> 00:49:25,860
the question that I saw in terms of the ROI, but
707
00:49:26,290 --> 00:49:29,540
this is something that we'll be constantly watching and running
708
00:49:29,800 --> 00:49:34,380
more extensive amounts of research over the next couple of months, and over the course of
709
00:49:34,380 --> 00:49:36,570
a year. And we'd be happy to report in more
710
00:49:37,100 --> 00:49:40,280
further interesting insights. So with that, I'll pass it on
711
00:49:40,280 --> 00:49:56,100
to you, Chris. Chris? the use cases. The answering might be coming off mute there.
712
00:49:56,440 --> 00:49:59,920
But as he's coming out- Sorry. There we go. I
713
00:49:59,920 --> 00:50:07,360
lost my track. I lost my trail and I saw here the
714
00:50:07,360 --> 00:50:13,030
window was answering questions. The way you want to implement that
715
00:50:13,030 --> 00:50:16,170
is, and we've seen that mistake happen in many customers, is they're
716
00:50:16,170 --> 00:50:20,590
trying to think about all that customer experience transformation and start
717
00:50:20,820 --> 00:50:24,450
trying to want to change an entire BU. They take
718
00:50:24,970 --> 00:50:27,440
a floor organization in their company and try to do
719
00:50:27,440 --> 00:50:30,040
a full change of that. And that's usually not the
720
00:50:30,040 --> 00:50:32,060
right way to do it. It's good to have that
721
00:50:32,060 --> 00:50:34,470
as the silver lining of all your transformation, but you
722
00:50:34,470 --> 00:50:36,690
can start with simple things. And we have seen those
723
00:50:36,690 --> 00:50:40,380
simple things being deployed. We launched about three months ago
724
00:50:40,380 --> 00:50:44,440
in April, I think, a Rapid Response VA because of
725
00:50:44,440 --> 00:50:47,420
the situation. We had massive demand for Rapid Response VA.
726
00:50:47,550 --> 00:50:49,580
We launched that and you could deploy a bot in two
727
00:50:49,580 --> 00:50:53,840
weeks for simple use cases, no return processing in retail,
728
00:50:53,840 --> 00:50:57,920
hours of operation in healthcare, balance and query in financial
729
00:50:57,920 --> 00:51:00,460
services. Those are simple use cases you can launch very, very
730
00:51:00,470 --> 00:51:04,560
quickly and get going with your customers, and tamper a
731
00:51:04,560 --> 00:51:07,480
lot of that load that did increase actually with the
732
00:51:07,480 --> 00:51:10,560
current situation through the use of bot. And then you
733
00:51:10,560 --> 00:51:13,240
expand from there adding more use cases, as you go
734
00:51:13,840 --> 00:51:18,840
to fit into your broader customer experience transformation journey. But
735
00:51:18,840 --> 00:51:23,880
start small. You can't start fast, that's the idea. Wonderful. And
736
00:51:24,210 --> 00:51:28,300
lastly, before we get to Q&A, from a Genesys perspective,
737
00:51:28,300 --> 00:51:31,490
from a Genesys vision, we want to provide the most flexible AI
738
00:51:31,490 --> 00:51:35,080
powered CX solution for the digital age. And really if
739
00:51:35,080 --> 00:51:37,640
I break that apart, as you see, that's whether you
740
00:51:37,640 --> 00:51:41,140
deploy on prem, or in the cloud, depending on the business
741
00:51:41,140 --> 00:51:44,470
outcomes that you want to optimize for breaking those silos across
742
00:51:45,760 --> 00:51:48,760
your organization, whether it's sales, marketing or service, and really
743
00:51:48,760 --> 00:51:53,580
being able to orchestrate those experiences together. So now we
744
00:51:53,580 --> 00:51:56,580
get into questions. And so I'm going to start with the question, that I'm going to answer myself. It comes from Susan, and it's about call deflection. "What is call deflection?" Call deflection at the end of
745
00:52:07,360 --> 00:52:11,810
the day means taking a customer that might otherwise consume a
746
00:52:11,810 --> 00:52:15,410
high cost resource like a human talking to an agent,
747
00:52:15,710 --> 00:52:17,950
to something that they could maybe self- service. And you're
748
00:52:17,950 --> 00:52:21,990
deflecting them from one experience to something that you will satisfy them
749
00:52:22,260 --> 00:52:26,340
well. And increasingly we're seeing that as a virtual agent
750
00:52:26,420 --> 00:52:31,830
experience, where, for a customer, they want to get an answer quickly, virtual
751
00:52:31,830 --> 00:52:34,900
agents can provide accurate, timely advice. You don't have to
752
00:52:34,900 --> 00:52:37,680
wait in queue for five minutes to get to someone that's going to tell
753
00:52:37,920 --> 00:52:46,010
you exactly the same thing. That's called deflection. I'm going
754
00:52:46,010 --> 00:52:50,970
to go to another question here, and this is going to be, Antony, it's for you- Yes. which is, " Is there
755
00:52:50,970 --> 00:52:55,170
a white paper on Google Contacts Center AI for virtual
756
00:52:55,170 --> 00:53:01,130
agents on how to deploy it?" There isn't a white
757
00:53:01,130 --> 00:53:07,300
paper, no. That said, we do have quite a few
758
00:53:07,980 --> 00:53:11,000
SI partners that are not certified, but trained on CCAI and
759
00:53:12,030 --> 00:53:15,220
can deploy that very, very well. I can name a
760
00:53:15,220 --> 00:53:20,220
few, Quantiphi, SpringML,, et cetera, are all very, very good at
761
00:53:20,220 --> 00:53:23,430
size, that have done many deployments and are targeting many.
762
00:53:25,480 --> 00:53:29,080
And can really help you through that journey creating the
763
00:53:29,080 --> 00:53:32,750
bots and deploying them, and also helping you with the agency's experience.
764
00:53:33,650 --> 00:53:36,820
Absolutely. And I will mention there is a video out
765
00:53:36,820 --> 00:53:40,080
there that you can check out which Genesys Group created
766
00:53:40,080 --> 00:53:45,310
together, which articulates some of the experiences that you can
767
00:53:45,310 --> 00:53:48,590
achieve using this technology. Ruti, next question is for you.
768
00:53:49,270 --> 00:53:52,410
It comes from Mary Ellen and it says, " Ruti, mentioned
769
00:53:52,410 --> 00:53:56,780
the democratization of data and changing culture." What tools do
770
00:53:56,780 --> 00:54:02,500
you see customers using to achieve the democratization of data, getting
771
00:54:02,500 --> 00:54:07,300
data out to the right people at the right time?" Oh, that's
772
00:54:07,300 --> 00:54:11,820
a great question. The very interesting thing today is there's
773
00:54:11,820 --> 00:54:16,390
no... It's not a few figures of the structured data
774
00:54:16,390 --> 00:54:19,840
now. There's a lot of unstructured data that is being used today.
775
00:54:20,030 --> 00:54:24,010
There's semi structured data. So, there's a cost of new set
776
00:54:24,010 --> 00:54:26,650
of breed of tools and technologies coming into place in
777
00:54:26,650 --> 00:54:31,860
terms of consolidating one common data platform. But that's just
778
00:54:31,860 --> 00:54:36,050
the starting this, specifically Facebook, because how do you transform
779
00:54:36,050 --> 00:54:39,470
the data, how you make sure the right set of
780
00:54:39,470 --> 00:54:43,780
metadata is available around them. It's a little bit fragmented market
781
00:54:43,780 --> 00:54:47,930
right now. There's something one separate for unstructured data, something for
782
00:54:47,930 --> 00:54:51,810
structured data, but there are a couple of tools and technologies, including
783
00:54:51,810 --> 00:54:55,180
from Google and the other incumbents that we talked, about
784
00:54:55,240 --> 00:54:58,100
that they can reach out to me separately to get
785
00:54:58,100 --> 00:55:01,010
the specific names, but there are things coming into for
786
00:55:01,010 --> 00:55:05,530
consolidation of different datasets, having a common metadata catalog. And
787
00:55:05,530 --> 00:55:11,170
also there are some advancements happening in transformational technology to improve
788
00:55:11,290 --> 00:55:14,320
the using Insights. AI is being used to even assess
789
00:55:14,320 --> 00:55:17,560
the quality of the data, to eliminate the biases in the
790
00:55:17,560 --> 00:55:21,920
data, to do the whole automation data pipeline. So there are
791
00:55:21,960 --> 00:55:24,300
a whole bunch of tools, but essentially it's a little
792
00:55:24,450 --> 00:55:26,640
bit fragmented right now if I have to say, but
793
00:55:26,640 --> 00:55:30,240
it's changing very rapidly. All right. So we have more
794
00:55:30,240 --> 00:55:33,410
questions that I think we're going to be able to answer in five minutes. So, we're going to get to do rapid
795
00:55:33,410 --> 00:55:38,290
fire. Antony, next question is for you, which is, " where are we seeing the
796
00:55:38,290 --> 00:55:46,820
ROI on chatbots versus increased staffing?" There's a few areas
797
00:55:46,820 --> 00:55:48,500
where you could get that. So the first one is
798
00:55:50,800 --> 00:55:55,480
when the chatbot reaches the agent, usually there's a conversational chatbot
799
00:55:55,480 --> 00:55:59,420
that did a lot of the data gathering, trying to
800
00:55:59,420 --> 00:56:01,500
solve the answer for. So, when the agent gets it,
801
00:56:02,010 --> 00:56:04,270
a lot of that information is already gathered, passed to
802
00:56:04,270 --> 00:56:06,840
the human agent, and then the call is obviously shorter,
803
00:56:06,840 --> 00:56:09,060
because you don't have to redo all that data gathering.
804
00:56:09,580 --> 00:56:12,990
There's a gain there. Second gain is a smart reply,
805
00:56:12,990 --> 00:56:16,410
which is something that we have in our technology, which actually
806
00:56:16,410 --> 00:56:23,480
suggests answers directly, and the human agent can click that
807
00:56:23,480 --> 00:56:26,740
answer, and there's these choices, there's usually two or three answers.
808
00:56:26,740 --> 00:56:28,550
They can click it, just edit it if they want,
809
00:56:28,550 --> 00:56:31,970
or just send it. So faster typing, more accurate responses,
810
00:56:32,130 --> 00:56:34,750
less typos. If you look at chat agents, they're always
811
00:56:35,560 --> 00:56:39,560
switching back to fix a typo, that doesn't happen. And
812
00:56:39,560 --> 00:56:44,700
then also the documents suggestion, knowledge suggestion help the human
813
00:56:44,700 --> 00:56:47,250
agent not have to search and put somebody on post
814
00:56:47,250 --> 00:56:50,500
search, either on Google or in their own internal systems
815
00:56:50,500 --> 00:56:54,340
to find a document. Those documents are automatically suggested through
816
00:56:54,340 --> 00:56:56,800
the Agent Assist. So that goes faster. And at the
817
00:56:56,800 --> 00:56:59,560
end of the call, the call dispositions also shorten because
818
00:56:59,560 --> 00:57:02,020
all that conversation, the intents that were detected in the
819
00:57:02,020 --> 00:57:05,130
conversational, are put in a little summary that can be put
820
00:57:05,130 --> 00:57:08,100
on the case. So you're gaining efficiency in every step
821
00:57:08,100 --> 00:57:12,430
of the way there. Wonderful. I'll take the next question
822
00:57:12,430 --> 00:57:16,250
from Christine, which is, " Where can I find more information about
823
00:57:16,250 --> 00:57:19,460
Agent Assist, how it works, what does the implementation look
824
00:57:19,780 --> 00:57:24,970
like, what can you do with it? And how it's being used by other customers currently?" Genesys. com.
825
00:57:25,500 --> 00:57:28,500
So we would love to talk to you about that as an implementation partner with
826
00:57:28,500 --> 00:57:34,390
Google, about how real world customers are using it, the benefits,
827
00:57:34,740 --> 00:57:38,140
the timelines, the whole lot. So very happy to engage
828
00:57:38,140 --> 00:57:41,100
with you on that. There is additional information on google.
829
00:57:41,320 --> 00:57:44,830
com if you looked at Contact Centre AI, which takes you
830
00:57:44,830 --> 00:57:50,140
through the Contact Center AI Agent Assist capabilities as well. My
831
00:57:50,140 --> 00:57:54,730
next question, Antony, I'm going to come back to you
832
00:57:54,730 --> 00:57:58,420
on this one. This is from Darrius which is, " What level
833
00:57:58,420 --> 00:58:02,880
of effort does it take to tune the AI for multi-
834
00:58:02,880 --> 00:58:07,990
tone responses, for it to be conversational?" Just for the
835
00:58:07,990 --> 00:58:11,360
audience benefit, the multi- tone response is really when you're
836
00:58:11,360 --> 00:58:15,130
going back and forth in the conversation, so it feels
837
00:58:15,130 --> 00:58:19,640
natural. But Antony what's your answer there? There's a few
838
00:58:19,640 --> 00:58:22,130
things in that question. First thing I would say the
839
00:58:22,190 --> 00:58:27,150
model itself, the understanding model does not need to be
840
00:58:27,150 --> 00:58:30,640
retrained, or trained, or do anything special. But one we provide out of
841
00:58:30,720 --> 00:58:32,810
the box for you is actually very powerful and can
842
00:58:32,860 --> 00:58:35,520
adapt. So how you're going to tune it, is based
843
00:58:36,730 --> 00:58:39,290
via courtesy of the training phrases and the appropriate training
844
00:58:39,290 --> 00:58:42,960
phrases to tell our NLU what they need to search for
845
00:58:43,240 --> 00:58:47,860
in those sentences, that's on a one tone basis. Multi-
846
00:58:47,860 --> 00:58:53,410
tone Dialogflow supports many, many bot returns, conversation, and can
847
00:58:53,410 --> 00:58:55,890
do all the back end calls, have different paths based
848
00:58:55,890 --> 00:58:59,190
on the responses from those web hooks that you're doing
849
00:58:59,190 --> 00:59:05,060
the back end. It's not technically complicated, obviously you need
850
00:59:05,060 --> 00:59:07,540
to adapt to conversation. So there is a level of effort,
851
00:59:07,830 --> 00:59:09,430
as I said, you can start in a couple of
852
00:59:09,430 --> 00:59:11,610
weeks and get one or two bugs going and then
853
00:59:11,850 --> 00:59:17,200
add more as you go. I would say overall, it's
854
00:59:17,200 --> 00:59:19,980
going to be a journey because you probably have hundreds of
855
00:59:19,980 --> 00:59:23,570
use cases, but you can get started pretty quickly, and
856
00:59:23,650 --> 00:59:28,570
you do not have to retrain the model. This is
857
00:59:28,570 --> 00:59:34,280
done automatically by Dialogflow, even in a multi- tone environment.
858
00:59:34,280 --> 00:59:39,330
Awesome. It's hard to answer a short statement on this one. It's really
859
00:59:39,330 --> 00:59:42,900
encouraging to see that we have so many questions. Just because we didn't answer
860
00:59:42,910 --> 00:59:44,610
them right now, it doesn't mean that we're not going to. I'm going to
861
00:59:44,610 --> 00:59:50,100
answer one last question and hand back to Josh, our moderator here.
862
00:59:50,290 --> 00:59:55,810
So the question is from HCL, "Is there a specific version of Genesys that is
863
00:59:55,810 --> 00:59:59,070
required for CCAI to work?" Guess what the good news
864
00:59:59,200 --> 01:00:05,720
is, we've integrated Google CCAI all on our various capabilities
865
01:00:05,970 --> 01:00:11,260
across Genesys, PureConnect, Engage and Genesys Cloud. So no matter
866
01:00:11,260 --> 01:00:13,700
what platform you're on, we have something for you. We
867
01:00:13,700 --> 01:00:18,510
can look at your specific requirements and give you a path
868
01:00:18,510 --> 01:00:21,640
forward. So with that, Josh, I'm going to hand it
869
01:00:21,640 --> 01:00:25,940
back to you to take us through what's next. Awesome.
870
01:00:26,260 --> 01:00:29,650
Thank you very much, Chris. Like you mentioned, unfortunately, that
871
01:00:29,650 --> 01:00:31,410
is the end of our Q& A, but don't freak,
872
01:00:31,820 --> 01:00:33,870
even though we didn't answer it live, we will follow
873
01:00:33,870 --> 01:00:36,790
up with you via email within the next few business days. So just be
874
01:00:36,890 --> 01:00:40,990
on the lookout for our prompt responses to you. So
875
01:00:41,080 --> 01:00:44,270
with all that being said, to wrap up, first, don't
876
01:00:44,270 --> 01:00:46,740
forget to take advantage of the additional resources within the
877
01:00:46,740 --> 01:00:50,560
resource list. Specifically the Contact Center AI success kit, the
878
01:00:50,560 --> 01:00:54,270
IDC spotlight and the Genesys and Google Cloud partnership page
879
01:00:54,270 --> 01:00:57,080
on genesys. com. Be sure to click those before today's
880
01:00:57,080 --> 01:00:59,310
session ends, and they'll open up in a new tab
881
01:00:59,310 --> 01:01:01,720
in your browser and give you an additional information on
882
01:01:01,720 --> 01:01:06,360
today's topic. Also after today's session concludes, you'll have the
883
01:01:06,360 --> 01:01:08,610
opportunity to fill out that brief survey that I mentioned
884
01:01:08,610 --> 01:01:12,470
before. We'd love and appreciate your feedback. And lastly, be
885
01:01:12,570 --> 01:01:14,470
sure to check out our new podcast Tech Talks in Twenty, where
886
01:01:15,080 --> 01:01:17,900
we sit down with Genesys experts to discuss the topics
887
01:01:17,900 --> 01:01:20,270
that you want to hear, in about 20 minutes. You
888
01:01:20,270 --> 01:01:22,580
can listen on our website as well as anywhere you
889
01:01:22,680 --> 01:01:27,010
get your podcasts, iTunes, Google Play, Spotify, and Stitcher. So
890
01:01:27,010 --> 01:01:29,700
with all that being said on behalf of Chris, Ritu and
891
01:01:29,700 --> 01:01:32,630
Antony, as well as the entire Genesys team, we thank
892
01:01:32,630 --> 01:01:36,280
you for joining today's webcast titled, Unlock the transformative power
893
01:01:36,280 --> 01:01:39,670
of AI for Contact Centers. Until next time, have a
894
01:01:39,670 --> 01:01:42,540
good one, everyone. Thank you.[mktoform cta_header="WATCH NOW" cta_button="Watch Now" cms_hold="RG" cid_id="7011T000001t8gXQAQ"]
Meet the Speakers
Antony Passemard
Guest Speaker
Applied Conversational AI and CCAI Lead
Google Cloud
Ritu Jyoti
Guest Speaker
Program Vice President, Artificial Intelligence
IDC
Chris Connolly
Vice President, Product Marketing
Genesys