1
00:00:04,800 –> 00:00:06,930
Good morning, evening and afternoon
2
00:00:06,930 –> 00:00:08,490
everyone. My name is Josh
3
00:00:08,490 –> 00:00:09,300
Reed and I’m from the
4
00:00:09,300 –> 00:00:10,460
digital events team here at
5
00:00:10,460 –> 00:00:11,310
Genesys, and I’ll be the
6
00:00:11,310 –> 00:00:13,540
moderator for today’s webcast. And
7
00:00:13,540 –> 00:00:14,390
let me be the first
8
00:00:14,390 –> 00:00:15,500
to welcome you and say
9
00:00:15,500 –> 00:00:16,540
thank you for joining to
10
00:00:16,540 –> 00:00:18,720
this webcast Mega Trends Shaping
11
00:00:18,720 –> 00:00:21,920
Customer Service in 2020. Before
12
00:00:21,920 –> 00:00:23,390
we get started, as usual,
13
00:00:23,390 –> 00:00:24,620
we have a few housekeeping
14
00:00:24,620 –> 00:00:25,720
items to go through before
15
00:00:25,720 –> 00:00:27,100
we get started. First off,
16
00:00:27,280 –> 00:00:29,100
if you experience any issues
17
00:00:29,180 –> 00:00:30,490
viewing or listening to today’s
18
00:00:30,490 –> 00:00:32,510
presentation, refresh your browser and
19
00:00:32,510 –> 00:00:33,210
make sure that it’s up
20
00:00:33,210 –> 00:00:34,540
to date to support HTML
21
00:00:34,540 –> 00:00:36,010
5 as this usually fixes
22
00:00:36,010 –> 00:00:37,170
any console issues you may
23
00:00:37,170 –> 00:00:39,360
experience. Also it my help
24
00:00:39,360 –> 00:00:40,470
to switch over to Chrome
25
00:00:40,470 –> 00:00:41,680
or Firefox as well are
26
00:00:41,960 –> 00:00:42,810
as these are the best
27
00:00:42,810 –> 00:00:44,410
browsers to support the webcast
28
00:00:44,410 –> 00:00:46,290
platform. And if you’re having
29
00:00:46,290 –> 00:00:47,770
trouble seeing the slides or
30
00:00:47,770 –> 00:00:49,470
the webcams today, you’re welcome
31
00:00:49,470 –> 00:00:51,040
to enlarge those by dragging
32
00:00:51,040 –> 00:00:51,950
the bottom right corner of
33
00:00:51,970 –> 00:00:55,070
each window. Also, note this
34
00:00:55,070 –> 00:00:55,890
is designed to be an
35
00:00:55,890 –> 00:00:57,450
interactive experience between you and
36
00:00:57,450 –> 00:00:58,900
our two presenters today. So
37
00:00:58,900 –> 00:00:59,850
at any time during the
38
00:00:59,850 –> 00:01:01,370
webcast, feel free to throw
39
00:01:01,370 –> 00:01:02,680
questions into the Q& A
40
00:01:02,680 –> 00:01:03,330
window in the middle of
41
00:01:03,330 –> 00:01:04,540
your screen and we’ll answer
42
00:01:04,540 –> 00:01:05,420
as many as we can
43
00:01:05,420 –> 00:01:06,180
with the time that we
44
00:01:06,180 –> 00:01:07,120
have at the end of
45
00:01:07,120 –> 00:01:09,330
the presentation. However, don’t fret
46
00:01:09,330 –> 00:01:10,470
if we do run out
47
00:01:10,470 –> 00:01:11,190
of time and we don’t
48
00:01:11,190 –> 00:01:12,750
answer your question aloud, we
49
00:01:12,750 –> 00:01:13,630
will follow up with you
50
00:01:13,630 –> 00:01:14,930
via email within the next
51
00:01:14,930 –> 00:01:17,390
few business days. And to
52
00:01:17,390 –> 00:01:18,870
note that if something happens
53
00:01:18,870 –> 00:01:19,860
during the webinar and you
54
00:01:19,860 –> 00:01:21,540
miss something, don’t worry, you
55
00:01:21,540 –> 00:01:22,860
will receive the on demand
56
00:01:22,860 –> 00:01:24,870
recording via email from ON24
57
00:01:24,870 –> 00:01:25,990
within the next few business.
58
00:01:27,840 –> 00:01:29,130
And also at any time
59
00:01:29,130 –> 00:01:30,250
during the webcast, feel free
60
00:01:30,250 –> 00:01:31,440
to check out the resources
61
00:01:31,440 –> 00:01:32,980
below the Q& A window. Clicking
62
00:01:32,980 –> 00:01:33,950
won’t take you away, so
63
00:01:33,950 –> 00:01:34,870
don’t worry about that. It’ll
64
00:01:34,870 –> 00:01:35,580
open up in a new
65
00:01:35,580 –> 00:01:36,920
tab in your browser and
66
00:01:36,920 –> 00:01:38,370
that’ll help expand on today’s
67
00:01:38,370 –> 00:01:41,500
topic of mega trends. See,
68
00:01:41,620 –> 00:01:42,570
told you short and sweet.
69
00:01:43,500 –> 00:01:45,370
So today we have two
70
00:01:45,370 –> 00:01:47,400
excellent presenters, excited to give you a
71
00:01:47,400 –> 00:01:48,850
glimpse of the future and
72
00:01:48,850 –> 00:01:49,990
discuss mega trends that are
73
00:01:49,990 –> 00:01:52,010
redefining contact center protocols in
74
00:01:52,010 –> 00:01:53,810
2020. So with that being
75
00:01:53,810 –> 00:01:54,640
said, I’m going to hand
76
00:01:54,640 –> 00:01:55,550
things off to one of
77
00:01:55,550 –> 00:01:56,740
our speakers of the hour,
78
00:01:56,880 –> 00:01:58,890
Joe Ciuffo product marketing director
79
00:01:58,890 –> 00:02:00,460
here at Genesys. Joe, the
80
00:02:00,460 –> 00:02:02,500
floor is yours. Thanks Josh.
81
00:02:02,500 –> 00:02:03,590
And hi everyone. Thank you
82
00:02:03,590 –> 00:02:04,540
so much for joining us
83
00:02:04,540 –> 00:02:06,730
today. As mentioned, and for
84
00:02:06,730 –> 00:02:07,570
the first time my name
85
00:02:07,570 –> 00:02:08,370
is spelled right. But I’m
86
00:02:08,370 –> 00:02:10,760
Joe Ciuffo and I’m a product marketing director
87
00:02:10,760 –> 00:02:12,040
here at Genesys. So my
88
00:02:12,040 –> 00:02:14,420
current position is I focus
89
00:02:14,420 –> 00:02:15,920
on artificial intelligence, which right
90
00:02:15,920 –> 00:02:17,630
now surfaces up through chat
91
00:02:17,630 –> 00:02:19,300
bots, using bots on a
92
00:02:19,300 –> 00:02:21,650
voice channel and using predictive
93
00:02:21,650 –> 00:02:23,810
technologies to identify when to
94
00:02:23,810 –> 00:02:25,550
engage with customers and really
95
00:02:25,530 –> 00:02:26,950
find the right time. But
96
00:02:26,950 –> 00:02:27,640
I always like to point
97
00:02:27,640 –> 00:02:29,410
out that I started here as a support
98
00:02:29,540 –> 00:02:30,980
engineer. So near and dear
99
00:02:30,980 –> 00:02:32,420
to my heart is knowing how to
100
00:02:32,420 –> 00:02:33,480
help people and knowing how
101
00:02:33,480 –> 00:02:35,210
to use these technologies to
102
00:02:35,210 –> 00:02:36,480
actually help the agents that
103
00:02:36,480 –> 00:02:37,430
are using it day in
104
00:02:37,430 –> 00:02:38,010
and day out. So with
105
00:02:39,080 –> 00:02:40,330
that, I’ll stop blabbing for
106
00:02:40,330 –> 00:02:41,590
a moment. And Kate, would
107
00:02:41,590 –> 00:02:42,600
you mind introducing yourself to
108
00:02:42,600 –> 00:02:44,310
everyone as well? No, no
109
00:02:44,640 –> 00:02:46,680
problem. Hi there. I’m Kate
110
00:02:46,830 –> 00:02:48,440
Leggett, I’m a VP and
111
00:02:48,440 –> 00:02:52,030
principal analyst here at Forrester Research
112
00:02:52,230 –> 00:02:55,210
and I focus on all
113
00:02:55,210 –> 00:02:58,070
things customer service and customer
114
00:02:58,070 –> 00:03:00,420
engagement. And thank you for
115
00:03:00,440 –> 00:03:01,700
taking an hour out of your
116
00:03:01,700 –> 00:03:02,940
busy days to listen to Joe
117
00:03:03,460 –> 00:03:04,970
and I talk about contact
118
00:03:04,970 –> 00:03:07,240
center trends. So Joe, back
119
00:03:07,240 –> 00:03:09,140
to you. Awesome. Well I
120
00:03:09,140 –> 00:03:09,940
think we’re in a good
121
00:03:09,940 –> 00:03:10,940
place here. Why don’t we
122
00:03:11,240 –> 00:03:12,370
go ahead and get started.
123
00:03:17,380 –> 00:03:18,030
So I guess I got
124
00:03:18,030 –> 00:03:19,060
to move the slide right?
125
00:03:19,790 –> 00:03:24,010
There you go. So my
126
00:03:24,010 –> 00:03:27,030
first prediction is that agents
127
00:03:27,260 –> 00:03:30,330
aren’t essential to scale anymore.
128
00:03:31,020 –> 00:03:32,510
And let me tell you
129
00:03:32,510 –> 00:03:34,480
what I mean by that and
130
00:03:34,480 –> 00:03:35,250
what I want to do
131
00:03:35,250 –> 00:03:36,290
is just take a step
132
00:03:36,370 –> 00:03:40,510
backwards and think about customer
133
00:03:40,510 –> 00:03:43,030
engagement and actually think about
134
00:03:43,030 –> 00:03:45,390
all the wonderful experiences that
135
00:03:45,390 –> 00:03:46,530
surround us in our daily
136
00:03:46,530 –> 00:03:47,860
lives. I mean, I think
137
00:03:47,860 –> 00:03:50,140
about ride sharing apps like
138
00:03:50,150 –> 00:03:52,310
Lyft and Uber that take all
139
00:03:52,310 –> 00:03:54,230
anxiety out of me getting
140
00:03:54,230 –> 00:03:56,020
to my destination because I
141
00:03:56,020 –> 00:03:58,960
have a full disclosure of
142
00:03:59,250 –> 00:04:01,290
information that I need about
143
00:04:01,290 –> 00:04:02,490
my ride to make me
144
00:04:02,490 –> 00:04:04,910
feel comfortable in that experience.
145
00:04:05,540 –> 00:04:07,480
I think about services like
146
00:04:07,580 –> 00:04:11,530
Amazon, like Netflix. I always
147
00:04:11,530 –> 00:04:13,260
joke that those two services
148
00:04:13,260 –> 00:04:14,240
know more about me than
149
00:04:14,240 –> 00:04:16,450
my husband does because if
150
00:04:16,450 –> 00:04:19,300
they’re able to recommend products,
151
00:04:20,130 –> 00:04:24,440
movies, content based on my
152
00:04:24,440 –> 00:04:26,920
particular history, what I’ve done,
153
00:04:26,920 –> 00:04:29,080
that intimate knowledge of where
154
00:04:29,080 –> 00:04:30,210
I’ve been, what I’ve done,
155
00:04:30,410 –> 00:04:32,510
how I’ve rated products or
156
00:04:32,510 –> 00:04:35,300
content. And so what we say
157
00:04:35,300 –> 00:04:38,940
today is that we’re surrounded
158
00:04:38,940 –> 00:04:42,330
by these differentiated experiences and
159
00:04:42,330 –> 00:04:43,770
these experiences have done a
160
00:04:43,770 –> 00:04:45,740
good shop at up leveling
161
00:04:45,820 –> 00:04:48,940
our expectations for engagement with
162
00:04:49,230 –> 00:04:50,750
any company that we do business
163
00:04:51,250 –> 00:04:52,570
with, both in our lives
164
00:04:52,640 –> 00:04:55,210
as consumers or in our
165
00:04:55,210 –> 00:04:57,380
business lives. And at Forrester
166
00:04:57,380 –> 00:04:58,050
we say we’re in the
167
00:04:58,050 –> 00:04:59,610
age of the customer where
168
00:04:59,610 –> 00:05:02,550
you, the consumer, the B2B
169
00:05:02,940 –> 00:05:06,560
customer, you control the conversation
170
00:05:06,680 –> 00:05:09,000
with any company that you
171
00:05:09,000 –> 00:05:10,880
do business with. And your
172
00:05:10,880 –> 00:05:13,600
expectations are heightened because of
173
00:05:13,600 –> 00:05:16,050
all the wonderful consumer experiences
174
00:05:16,050 –> 00:05:18,670
that surround us. And it’s
175
00:05:18,670 –> 00:05:19,500
like when I see on
176
00:05:19,500 –> 00:05:21,970
the screen here, you expect
177
00:05:22,360 –> 00:05:24,590
that any information that you
178
00:05:24,590 –> 00:05:27,330
need is available on any
179
00:05:27,330 –> 00:05:29,460
device at a person’s moment
180
00:05:29,520 –> 00:05:32,210
of need. And so what
181
00:05:32,210 –> 00:05:36,130
has happened is again, our
182
00:05:36,130 –> 00:05:42,850
expectations are heightened because of
183
00:05:43,330 –> 00:05:46,230
these wonderful consumer experiences. And
184
00:05:46,230 –> 00:05:47,910
then the way that we
185
00:05:48,000 –> 00:05:51,930
interact with companies has also
186
00:05:51,930 –> 00:05:53,810
changed. So let’s look at
187
00:05:53,810 –> 00:05:55,840
some data here. This is data
188
00:05:56,680 –> 00:05:59,180
from last year. It’s from
189
00:05:59,260 –> 00:06:01,390
probably the best trove of
190
00:06:01,390 –> 00:06:03,660
contact center data. It comes
191
00:06:03,660 –> 00:06:06,840
from dimension data’s a benchmarking
192
00:06:06,840 –> 00:06:08,510
report where they go out
193
00:06:08,510 –> 00:06:10,300
and they survey thousands and
194
00:06:10,300 –> 00:06:12,280
thousands of contact center decision-
195
00:06:12,280 –> 00:06:14,600
makers around the world. And
196
00:06:14,600 –> 00:06:16,270
what they are telling us,
197
00:06:16,270 –> 00:06:17,180
and it’s very close to
198
00:06:17,180 –> 00:06:19,080
one Forrester sees as well,
199
00:06:19,500 –> 00:06:22,690
is that customers, again, either
200
00:06:22,690 –> 00:06:25,860
consumers or B2B customers want
201
00:06:25,860 –> 00:06:27,900
very little friction when they
202
00:06:27,900 –> 00:06:31,440
interact with companies, they tend
203
00:06:31,440 –> 00:06:33,640
to choose self- service as
204
00:06:33,640 –> 00:06:35,760
a first point of contact
205
00:06:36,140 –> 00:06:39,420
as they interact with brands.
206
00:06:40,490 –> 00:06:42,920
And if they’re not able
207
00:06:42,920 –> 00:06:45,190
to find what they’re looking
208
00:06:45,190 –> 00:06:48,360
for via self service, they’re moving
209
00:06:48,360 –> 00:06:53,970
to digital engagement modalities, chat
210
00:06:54,280 –> 00:06:58,020
messaging for example, because it
211
00:06:58,090 –> 00:07:00,110
values their time. A data point
212
00:07:00,110 –> 00:07:03,530
from Forrester says that 73%
213
00:07:04,030 –> 00:07:06,800
of customers say that valuing
214
00:07:06,800 –> 00:07:08,480
their time is the most
215
00:07:08,480 –> 00:07:11,730
important thing that companies can
216
00:07:11,730 –> 00:07:13,630
do to provide great customer
217
00:07:13,630 –> 00:07:16,370
service. So again, just looking
218
00:07:16,370 –> 00:07:17,410
at the data here, you’d
219
00:07:17,410 –> 00:07:19,120
look at that top bar
220
00:07:19,440 –> 00:07:21,480
and it says 88% of
221
00:07:21,480 –> 00:07:24,090
contact center decision makers project
222
00:07:24,090 –> 00:07:26,070
that their self service volumes
223
00:07:26,070 –> 00:07:27,320
will increase. They’re calling it
224
00:07:27,320 –> 00:07:29,760
robotic automation, but these are
225
00:07:29,760 –> 00:07:31,960
self- service volumes are increasing
226
00:07:31,960 –> 00:07:35,730
this year. 77% say that
227
00:07:35,730 –> 00:07:38,910
digital agent assistant service volumes
228
00:07:38,910 –> 00:07:42,470
will increase. And the third
229
00:07:42,470 –> 00:07:45,470
bar says, and my old
230
00:07:45,470 –> 00:07:46,390
eyes can’t see it, I
231
00:07:46,390 –> 00:07:50,210
think it says 66% of
232
00:07:51,080 –> 00:07:53,060
contact center decision makers say
233
00:07:53,060 –> 00:07:57,090
that their overall interaction volumes
234
00:07:57,130 –> 00:07:59,260
will increase. This is because
235
00:07:59,260 –> 00:08:00,880
you make it easier to
236
00:08:00,880 –> 00:08:03,620
engage with the companies. And so your
237
00:08:04,010 –> 00:08:07,320
customers will engage more with
238
00:08:07,320 –> 00:08:08,470
companies. And so what this
239
00:08:08,470 –> 00:08:09,530
means is that companies are
240
00:08:09,530 –> 00:08:13,750
being flooded by this mass
241
00:08:13,920 –> 00:08:17,970
of digital engagement from their
242
00:08:17,970 –> 00:08:20,160
customers. The better, the easier
243
00:08:20,160 –> 00:08:21,780
they make it for customers
244
00:08:21,780 –> 00:08:23,820
to engage, again, more customers
245
00:08:23,930 –> 00:08:25,260
will engage with you. And
246
00:08:25,510 –> 00:08:27,820
you want this because better
247
00:08:27,820 –> 00:08:31,740
engagement strengthens customer relations. But
248
00:08:31,740 –> 00:08:34,330
what happens is you can’t
249
00:08:34,330 –> 00:08:36,820
keep up with these engagement
250
00:08:36,820 –> 00:08:39,680
volumes without turning to AI
251
00:08:39,680 –> 00:08:42,010
and automation. And that’s where
252
00:08:42,110 –> 00:08:43,730
the prediction of agents are
253
00:08:43,730 –> 00:08:45,810
no longer essential to scale
254
00:08:45,810 –> 00:08:49,710
comes from because companies are
255
00:08:49,710 –> 00:08:53,890
infusing AI and automation everywhere
256
00:08:53,890 –> 00:08:57,910
in their operations to keep
257
00:08:57,910 –> 00:09:01,310
up with these ballooning engagement
258
00:09:01,310 –> 00:09:03,750
falling from their customers and
259
00:09:03,750 –> 00:09:05,880
to deliver the quality of
260
00:09:05,880 –> 00:09:11,180
service that customers expect. So what
261
00:09:11,460 –> 00:09:12,200
you’re looking at on the
262
00:09:12,200 –> 00:09:15,480
screen here is what we think
263
00:09:15,480 –> 00:09:18,000
of as being value chain
264
00:09:18,560 –> 00:09:21,230
for AI for customer service,
265
00:09:21,560 –> 00:09:23,500
where AI and automation, again,
266
00:09:23,550 –> 00:09:26,860
it encompasses a wealth of
267
00:09:26,860 –> 00:09:32,210
different technologies that basically add
268
00:09:33,000 –> 00:09:36,770
intelligence to your operations and
269
00:09:37,520 –> 00:09:39,680
offload agents comes from doing
270
00:09:39,680 –> 00:09:42,540
rope repetitive work. So at
271
00:09:42,540 –> 00:09:46,350
the low end of this
272
00:09:46,350 –> 00:09:48,330
value chain, you’re looking at
273
00:09:48,330 –> 00:09:50,090
AI and automation being able
274
00:09:50,090 –> 00:09:53,410
to increase efficiency on technology
275
00:09:53,540 –> 00:09:56,510
like RPA or automatic case
276
00:09:56,510 –> 00:09:59,250
classification or automatic routing to
277
00:09:59,250 –> 00:10:01,460
be able to offload all
278
00:10:01,460 –> 00:10:04,100
the reproducible or lower value
279
00:10:04,100 –> 00:10:06,900
tasks from agents. Moving up
280
00:10:06,900 –> 00:10:08,680
the value curve, you’ve got
281
00:10:08,900 –> 00:10:10,550
AI and automation that can
282
00:10:10,550 –> 00:10:13,360
help reduce friction. For example,
283
00:10:13,360 –> 00:10:15,330
monitoring the sentiment of an
284
00:10:15,330 –> 00:10:19,650
interaction and escalating automatically if
285
00:10:19,650 –> 00:10:22,320
a customer seems to distressed.
286
00:10:22,770 –> 00:10:24,220
Moving up the value chain
287
00:10:24,220 –> 00:10:27,440
you’ve got enhanced customer empowerment.
288
00:10:27,490 –> 00:10:29,850
This is all about chat
289
00:10:29,850 –> 00:10:31,580
bots and self- service and
290
00:10:31,980 –> 00:10:34,560
self- service processes. Again, we
291
00:10:34,560 –> 00:10:35,610
know that customers want to
292
00:10:35,650 –> 00:10:36,990
self serve as first point
293
00:10:36,990 –> 00:10:40,830
of contact and these technologies
294
00:10:41,090 –> 00:10:44,240
empower great self service. And
295
00:10:44,240 –> 00:10:46,740
then four and five on
296
00:10:46,740 –> 00:10:48,450
this value chain are about
297
00:10:48,760 –> 00:10:52,150
proactive and even preemptive service
298
00:10:52,380 –> 00:10:53,760
where for example, you’re looking
299
00:10:53,760 –> 00:10:56,740
at the customer’s journey on
300
00:10:56,820 –> 00:10:58,960
a web property independent on
301
00:10:58,960 –> 00:11:01,730
the customer’s behavior, you’re proactively
302
00:11:01,730 –> 00:11:03,360
engaging with the customer to
303
00:11:03,360 –> 00:11:04,640
be able to start a
304
00:11:04,640 –> 00:11:08,860
conversation or offer content or
305
00:11:08,860 –> 00:11:10,570
give them an offer or
306
00:11:10,570 –> 00:11:13,460
preemptive services, again, all about
307
00:11:13,520 –> 00:11:16,360
connected devices and being able
308
00:11:16,360 –> 00:11:21,210
to to preemptively intervene upon
309
00:11:21,210 –> 00:11:22,900
signs of distress. So what
310
00:11:22,940 –> 00:11:25,280
it means is that companies
311
00:11:25,280 –> 00:11:29,020
are infusing AI and automation
312
00:11:29,020 –> 00:11:30,610
just about everywhere in the
313
00:11:30,610 –> 00:11:32,840
customer service operations and what
314
00:11:32,840 –> 00:11:35,740
it does is it allows
315
00:11:36,110 –> 00:11:38,380
content centers to scale without
316
00:11:38,380 –> 00:11:42,270
necessarily having add agent headcount.
317
00:11:43,540 –> 00:11:44,790
So that was a lot
318
00:11:45,450 –> 00:11:50,370
shareable but. I love that.
319
00:11:50,370 –> 00:11:52,130
Actually a few points that
320
00:11:52,130 –> 00:11:53,160
I had written down about
321
00:11:53,270 –> 00:11:55,090
you said where really that
322
00:11:55,090 –> 00:11:56,820
idea, that ease of access
323
00:11:56,820 –> 00:11:58,440
to the information is sometimes
324
00:11:58,500 –> 00:11:59,830
just as important as the
325
00:11:59,830 –> 00:12:01,900
information you’re getting itself. And
326
00:12:01,900 –> 00:12:02,550
I love that you brought
327
00:12:02,550 –> 00:12:04,990
up transportation because some side
328
00:12:04,990 –> 00:12:05,670
notes here, I live in
329
00:12:05,670 –> 00:12:07,390
San Francisco. When I think
330
00:12:07,390 –> 00:12:09,170
about this ease of access
331
00:12:09,170 –> 00:12:11,100
to information and Uber, right?
332
00:12:11,430 –> 00:12:13,440
Transportation in San Francisco, I
333
00:12:13,440 –> 00:12:15,150
can take the subway or
334
00:12:15,150 –> 00:12:15,930
I can take an Uber
335
00:12:15,930 –> 00:12:16,840
and the subway might be
336
00:12:16,840 –> 00:12:18,650
going the exact same direction,
337
00:12:19,120 –> 00:12:20,260
but man, for that last
338
00:12:20,260 –> 00:12:22,010
mile, it’s definitely a different
339
00:12:22,010 –> 00:12:23,800
experience. So I am more
340
00:12:23,800 –> 00:12:24,500
likely to pay a little
341
00:12:24,620 –> 00:12:25,840
bit more for the experience
342
00:12:25,840 –> 00:12:26,830
that gets me where I
343
00:12:26,830 –> 00:12:27,810
need to go in a
344
00:12:27,810 –> 00:12:29,970
quicker way. And we’re seeing
345
00:12:29,970 –> 00:12:31,590
this with customers as well.
346
00:12:31,930 –> 00:12:33,360
In fact, even though I
347
00:12:33,360 –> 00:12:34,780
hate this term millennial because
348
00:12:34,780 –> 00:12:35,520
it puts me in a
349
00:12:35,520 –> 00:12:37,660
bucket, we are seeing research
350
00:12:37,660 –> 00:12:39,950
that shows that millennial users
351
00:12:39,950 –> 00:12:41,420
of banking apps are much
352
00:12:41,420 –> 00:12:43,320
more likely to switch when
353
00:12:43,320 –> 00:12:44,460
they look at better mobile
354
00:12:44,510 –> 00:12:47,030
or digital capabilities. So you’re
355
00:12:47,030 –> 00:12:48,360
probably wondering why I’m talking
356
00:12:48,360 –> 00:12:50,770
about digital capabilities and my
357
00:12:50,770 –> 00:12:52,450
first prediction is actually about
358
00:12:52,450 –> 00:12:54,450
voice. I want to talk about our
359
00:12:54,450 –> 00:12:56,030
first prediction here and that voice isn’t
360
00:12:56,030 –> 00:12:58,080
dead, but it’s absolutely different.
361
00:12:58,660 –> 00:12:59,730
What we’ve seen as we
362
00:12:59,730 –> 00:13:01,310
talk with customers and we
363
00:13:01,310 –> 00:13:03,200
look at the research, is
364
00:13:03,200 –> 00:13:05,790
that consumers rate immediate responses
365
00:13:06,070 –> 00:13:07,550
as super important. In fact,
366
00:13:07,640 –> 00:13:09,940
HubSpot report noted that 90% of
367
00:13:09,940 –> 00:13:11,980
consumers put it in important
368
00:13:11,980 –> 00:13:13,580
or very important when it
369
00:13:13,580 –> 00:13:14,940
comes to the immediacy of
370
00:13:14,940 –> 00:13:16,820
the response they get. And
371
00:13:16,820 –> 00:13:17,780
we’re also looking at other
372
00:13:17,780 –> 00:13:19,680
research that’s showing that these
373
00:13:19,680 –> 00:13:22,350
self service requests or initiations
374
00:13:22,350 –> 00:13:23,820
are coming over a voice
375
00:13:24,060 –> 00:13:26,190
channel. So when you think of voice, you think
376
00:13:26,190 –> 00:13:27,430
of a phone, but that’s
377
00:13:27,430 –> 00:13:28,400
not the first place where
378
00:13:28,400 –> 00:13:29,270
it starts or even where
379
00:13:29,270 –> 00:13:30,810
it ends. I always like
380
00:13:30,810 –> 00:13:31,610
to tell the story. When
381
00:13:31,610 –> 00:13:32,250
my wife and I went
382
00:13:32,250 –> 00:13:33,410
to Ireland last year, we
383
00:13:33,410 –> 00:13:34,900
were really excited and she
384
00:13:34,900 –> 00:13:35,870
gave me two things to
385
00:13:35,870 –> 00:13:36,930
make sure that I did. It
386
00:13:36,930 –> 00:13:38,490
was make sure our data
387
00:13:38,490 –> 00:13:39,130
would work when we were
388
00:13:39,130 –> 00:13:40,130
in Ireland because I have
389
00:13:40,130 –> 00:13:41,150
no idea how to use
390
00:13:41,150 –> 00:13:42,990
a map apparently. And the
391
00:13:42,990 –> 00:13:44,430
second one was to make
392
00:13:44,430 –> 00:13:45,520
sure I set a travel alert
393
00:13:45,560 –> 00:13:46,760
on her credit card so
394
00:13:46,760 –> 00:13:47,720
we didn’t have any issues.
395
00:13:48,340 –> 00:13:49,360
I remembered to set the
396
00:13:49,360 –> 00:13:50,420
travel alert when I was
397
00:13:50,420 –> 00:13:51,720
in Dublin and my card
398
00:13:51,720 –> 00:13:52,680
got declined at the first
399
00:13:52,680 –> 00:13:54,370
restaurant. And I bring that
400
00:13:54,370 –> 00:13:55,950
up because it was something
401
00:13:55,950 –> 00:13:57,040
we fixed, we phoned in
402
00:13:57,040 –> 00:13:58,140
and fixed it, but it
403
00:13:58,140 –> 00:13:59,100
could have been very different.
404
00:13:59,270 –> 00:14:00,370
What if while I was
405
00:14:00,370 –> 00:14:02,370
packing, I just asked my
406
00:14:02,370 –> 00:14:04,130
Amazon Alexa or my Google
407
00:14:04,130 –> 00:14:05,050
home or some of those
408
00:14:05,050 –> 00:14:06,890
devices to do this for
409
00:14:06,890 –> 00:14:08,440
me. And you start to
410
00:14:08,440 –> 00:14:10,210
think about voice being less
411
00:14:10,210 –> 00:14:11,410
of a channel and more
412
00:14:11,410 –> 00:14:12,920
of an interface. When we
413
00:14:12,920 –> 00:14:14,470
look at these home devices,
414
00:14:14,790 –> 00:14:16,140
they are becoming the towns
415
00:14:16,140 –> 00:14:18,260
square where it may not
416
00:14:18,260 –> 00:14:19,550
be controlled by a business
417
00:14:19,550 –> 00:14:21,280
or even something that you
418
00:14:21,280 –> 00:14:23,830
have direct ownership of but
419
00:14:23,830 –> 00:14:25,560
it’s absolutely where communication is
420
00:14:25,560 –> 00:14:27,200
happening and it’s absolutely where
421
00:14:27,200 –> 00:14:29,460
that real conversation with customers
422
00:14:29,460 –> 00:14:31,270
are occurring. So it’s the
423
00:14:31,270 –> 00:14:33,640
notion of letting someone set
424
00:14:33,640 –> 00:14:34,930
the music they’d like to
425
00:14:34,930 –> 00:14:36,560
listen to and also navigating
426
00:14:36,560 –> 00:14:38,220
through your interface all from
427
00:14:38,220 –> 00:14:39,350
the same place in the
428
00:14:39,350 –> 00:14:41,420
moment of need and alleviating
429
00:14:41,750 –> 00:14:44,310
those user experience issues. Just
430
00:14:44,310 –> 00:14:45,200
to note on that, I
431
00:14:45,200 –> 00:14:46,030
still to this day have
432
00:14:46,030 –> 00:14:47,510
no idea where sending the
433
00:14:47,510 –> 00:14:48,610
travel alert would occur in
434
00:14:48,610 –> 00:14:50,280
my banking app. It’s something
435
00:14:50,280 –> 00:14:51,020
I would call in for
436
00:14:51,020 –> 00:14:51,900
or have to dig around
437
00:14:51,900 –> 00:14:53,200
for, but not something that
438
00:14:53,200 –> 00:14:55,980
I would proactively research. So
439
00:14:55,980 –> 00:14:57,700
to tie that in, it
440
00:14:57,700 –> 00:14:58,830
comes down to why should
441
00:14:58,830 –> 00:15:01,170
we care? Well, this is the first time,
442
00:15:01,170 –> 00:15:02,360
especially looking in this next
443
00:15:02,360 –> 00:15:04,090
year, that that technology is
444
00:15:04,090 –> 00:15:05,820
good enough to start leveraging
445
00:15:05,820 –> 00:15:07,740
on the voice channel. As
446
00:15:07,740 –> 00:15:08,980
you think about calling in
447
00:15:08,980 –> 00:15:10,230
on a number now, we
448
00:15:10,230 –> 00:15:11,660
can extend this voice bot
449
00:15:11,660 –> 00:15:13,820
technology. It has the ability
450
00:15:13,820 –> 00:15:15,180
to pick up on nuances
451
00:15:15,180 –> 00:15:17,150
like alphanumeric where if you’re
452
00:15:17,150 –> 00:15:18,210
an airline and you’re giving
453
00:15:18,210 –> 00:15:19,630
your confirmation code, which is
454
00:15:19,630 –> 00:15:22,760
numbers, digits and letters, this
455
00:15:22,760 –> 00:15:23,610
used to be hard to
456
00:15:23,610 –> 00:15:25,060
understand from a natural language
457
00:15:25,060 –> 00:15:26,790
processing. We don’t have these
458
00:15:26,790 –> 00:15:28,450
problems anymore so that we can
459
00:15:28,450 –> 00:15:30,100
change the experience to be
460
00:15:30,100 –> 00:15:31,920
more about the conversation and
461
00:15:31,920 –> 00:15:32,650
where we need to take
462
00:15:32,650 –> 00:15:34,140
you next because we understand
463
00:15:34,140 –> 00:15:35,740
that instead of a maze
464
00:15:35,740 –> 00:15:36,770
of menus that you have
465
00:15:36,770 –> 00:15:38,640
to listen to and hopefully
466
00:15:38,640 –> 00:15:40,890
select the right one. Now
467
00:15:40,960 –> 00:15:41,790
I did cheat a little
468
00:15:41,790 –> 00:15:42,500
bit here. I have a
469
00:15:42,500 –> 00:15:43,820
1A as well on our
470
00:15:43,820 –> 00:15:45,570
predictions and it’s because I
471
00:15:45,570 –> 00:15:46,320
don’t want you to think that
472
00:15:46,550 –> 00:15:47,470
we’re just keying in on
473
00:15:47,470 –> 00:15:50,140
voice. Commerce is messaging. We’re
474
00:15:50,140 –> 00:15:51,450
seeing a lot of research
475
00:15:51,540 –> 00:15:52,610
hint at that as well
476
00:15:52,610 –> 00:15:54,460
from our end and for
477
00:15:54,460 –> 00:15:55,330
you out there in the
478
00:15:55,340 –> 00:15:57,220
field and in the audience,
479
00:15:57,460 –> 00:15:58,780
have you ever kept multiple
480
00:15:58,780 –> 00:16:00,420
tabs open on your browser
481
00:16:00,830 –> 00:16:02,180
because you wanted to purchase something,
482
00:16:02,180 –> 00:16:03,450
but you just had another
483
00:16:03,450 –> 00:16:04,790
question around it, right? Think
484
00:16:05,020 –> 00:16:06,530
about shoes that you wanted to buy.
485
00:16:06,880 –> 00:16:08,010
Do those shoes run small,
486
00:16:08,010 –> 00:16:09,060
do they run large? You’ve
487
00:16:09,060 –> 00:16:09,900
got to look that up.
488
00:16:10,410 –> 00:16:11,420
Think about a flight you
489
00:16:11,420 –> 00:16:12,870
might have for work, is
490
00:16:12,880 –> 00:16:13,810
there a chance if it’s
491
00:16:13,810 –> 00:16:14,600
a long flight that you
492
00:16:14,600 –> 00:16:15,620
can get the upgrade even
493
00:16:15,620 –> 00:16:16,840
though that’s on the wishlist,
494
00:16:16,840 –> 00:16:18,390
not always happening. It’s worth
495
00:16:18,390 –> 00:16:19,600
checking and it stops you
496
00:16:19,600 –> 00:16:21,400
from purchasing it. And what
497
00:16:21,400 –> 00:16:23,300
about something else, maybe even
498
00:16:23,300 –> 00:16:24,580
more in the weeds. You’re
499
00:16:24,580 –> 00:16:25,290
trying to get a car
500
00:16:25,290 –> 00:16:26,540
insurance policy. You’ve just moved
501
00:16:26,540 –> 00:16:27,840
to a new state, you’ve
502
00:16:27,840 –> 00:16:28,930
already got home insurance with
503
00:16:28,930 –> 00:16:30,340
that company, do you get
504
00:16:30,340 –> 00:16:31,590
a discount? Would it stop
505
00:16:31,590 –> 00:16:32,880
you from just self- serving
506
00:16:32,880 –> 00:16:33,800
and filling that form out
507
00:16:33,800 –> 00:16:36,300
on your own? Messaging is
508
00:16:36,300 –> 00:16:37,970
asynchronous by nature and Kate
509
00:16:37,970 –> 00:16:39,650
brought this up earlier. I
510
00:16:39,650 –> 00:16:41,330
could start chatting with my
511
00:16:41,330 –> 00:16:42,720
company on the train on
512
00:16:42,720 –> 00:16:43,870
the way to work asking
513
00:16:43,870 –> 00:16:45,570
them about this insurance policy.
514
00:16:46,150 –> 00:16:46,730
But once I’m in the
515
00:16:46,730 –> 00:16:48,240
office, I might switch to
516
00:16:48,240 –> 00:16:49,580
my laptop and switching to
517
00:16:49,580 –> 00:16:50,910
my laptop means I’m picking
518
00:16:50,910 –> 00:16:52,520
up on that same conversation
519
00:16:52,540 –> 00:16:54,400
across a messaging channel but
520
00:16:54,400 –> 00:16:55,330
I’m doing it in a different
521
00:16:55,330 –> 00:16:56,650
way and I’m doing it
522
00:16:56,650 –> 00:16:58,830
at a different time. Great
523
00:16:58,830 –> 00:17:00,290
thing about messaging is we
524
00:17:00,290 –> 00:17:02,430
see this burst capability, so
525
00:17:02,430 –> 00:17:03,800
asynchronous, meaning we pick up
526
00:17:03,800 –> 00:17:05,210
when we’re ready and then
527
00:17:05,210 –> 00:17:06,340
we can really jump in
528
00:17:06,340 –> 00:17:07,670
and start that conversation when we
529
00:17:07,670 –> 00:17:09,470
have the free time. And
530
00:17:09,470 –> 00:17:10,180
this is what it comes
531
00:17:10,180 –> 00:17:11,840
down to, is giving someone
532
00:17:11,840 –> 00:17:13,170
a tangible reference of when
533
00:17:13,170 –> 00:17:14,410
things are working, when they’re
534
00:17:14,410 –> 00:17:15,680
going in the right direction,
535
00:17:16,120 –> 00:17:17,110
and then having a place
536
00:17:17,110 –> 00:17:17,960
that they can come back
537
00:17:18,190 –> 00:17:19,160
and pick up. And that’s
538
00:17:19,160 –> 00:17:21,220
what messaging is about. So
539
00:17:21,590 –> 00:17:22,770
I think it all ends
540
00:17:22,770 –> 00:17:24,390
with the fact that messaging
541
00:17:24,390 –> 00:17:26,140
apps tie into payment applications.
542
00:17:26,320 –> 00:17:27,210
This is the first time
543
00:17:27,210 –> 00:17:28,310
we have these channels that
544
00:17:28,310 –> 00:17:30,010
bring everything together and don’t
545
00:17:30,010 –> 00:17:31,500
call it a disjointed experience.
546
00:17:31,950 –> 00:17:32,960
Kate I know I have babbled
547
00:17:32,960 –> 00:17:34,060
for a while over back
548
00:17:34,060 –> 00:17:34,280
to you. What do you think about that?
549
00:17:38,380 –> 00:17:40,610
agents are no longer essential to
550
00:17:40,610 –> 00:17:42,620
scale. I talked about digital
551
00:17:42,620 –> 00:17:43,870
engagement in the house self-service
552
00:17:44,390 –> 00:17:46,510
and digital engagement is rising.
553
00:17:46,690 –> 00:17:48,700
You talked about voice self
554
00:17:48,840 –> 00:17:51,380
service, you talked about conversational
555
00:17:51,380 –> 00:17:54,440
commerce, which is powered in
556
00:17:54,500 –> 00:17:58,150
part by bots, by automation.
557
00:17:58,530 –> 00:18:00,910
It all makes sense. This
558
00:18:00,910 –> 00:18:02,210
is the way the world
559
00:18:02,210 –> 00:18:04,250
is going. So what happens
560
00:18:05,240 –> 00:18:09,710
to your agents. Where do
561
00:18:09,710 –> 00:18:13,320
they fall in the spectrum
562
00:18:13,420 –> 00:18:16,250
of importance if again, so
563
00:18:16,250 –> 00:18:21,220
much engagement is going to
564
00:18:21,250 –> 00:18:23,970
self- service, to digital channels,
565
00:18:23,970 –> 00:18:26,450
voice channels that are automated?
566
00:18:27,440 –> 00:18:29,040
So the next trend is
567
00:18:29,040 –> 00:18:32,680
about agents and the technologies
568
00:18:32,680 –> 00:18:35,010
that agents need to be
569
00:18:35,010 –> 00:18:38,820
able to effectively support their
570
00:18:38,820 –> 00:18:41,470
customers. So to be able
571
00:18:41,470 –> 00:18:43,040
to talk about this, let’s
572
00:18:43,040 –> 00:18:44,280
look at this data again,
573
00:18:44,440 –> 00:18:46,300
the dimension data that I
574
00:18:46,300 –> 00:18:48,380
had brought up before. So
575
00:18:48,380 –> 00:18:50,700
the bottom set of data is
576
00:18:50,700 –> 00:18:54,010
really interesting. It’s all about
577
00:18:54,520 –> 00:18:56,050
phone volumes and it’s not
578
00:18:56,440 –> 00:18:58,350
voice self- service, this is
579
00:18:58,350 –> 00:19:01,110
live agents on the phone
580
00:19:01,200 –> 00:19:03,910
answering customer calls. And what
581
00:19:03,910 –> 00:19:06,480
we find is that 64%
582
00:19:06,480 –> 00:19:08,490
of contact center decision makers
583
00:19:08,720 –> 00:19:13,200
believe that voice volumes will
584
00:19:13,200 –> 00:19:17,350
drop. And this is understandable
585
00:19:17,350 –> 00:19:19,680
because we’re moving to a
586
00:19:19,680 –> 00:19:23,920
digital first self- service first
587
00:19:23,920 –> 00:19:26,530
world. But what’s actually getting
588
00:19:26,530 –> 00:19:29,480
into the contact center? It’s
589
00:19:29,480 –> 00:19:31,600
the harder calls, the calls
590
00:19:31,730 –> 00:19:33,730
that weren’t able to be
591
00:19:33,730 –> 00:19:37,700
answered via self surface, where a
592
00:19:37,860 –> 00:19:40,010
customer has already gone to
593
00:19:40,010 –> 00:19:41,400
your website, to your mobile
594
00:19:41,400 –> 00:19:43,790
site, looked for information, perhaps
595
00:19:43,790 –> 00:19:46,240
even chatted with an agent,
596
00:19:46,300 –> 00:19:47,900
isn’t able to really get
597
00:19:47,900 –> 00:19:49,630
the answer. So they’re picking
598
00:19:49,630 –> 00:19:50,860
up the phone and they’re
599
00:19:50,860 –> 00:19:53,440
calling a contact center. So
600
00:19:53,830 –> 00:19:56,300
voice calls, the actual volume
601
00:19:56,300 –> 00:19:58,470
is dropping. Again, because self
602
00:19:58,470 –> 00:20:00,280
service is picking off a
603
00:20:00,280 –> 00:20:01,900
lot of the easy inquiries.
604
00:20:02,140 –> 00:20:05,410
But the length of calls
605
00:20:05,470 –> 00:20:08,220
is actually getting longer again,
606
00:20:08,220 –> 00:20:09,260
because you’re getting the more
607
00:20:09,260 –> 00:20:11,660
complicated calls. It could be
608
00:20:11,660 –> 00:20:13,470
the exceptions, it could be
609
00:20:13,470 –> 00:20:18,470
the calls where there’s multiple
610
00:20:18,470 –> 00:20:22,090
questions within a call. So
611
00:20:22,090 –> 00:20:23,140
your agents are getting the
612
00:20:23,140 –> 00:20:25,860
harder calls. Something else is
613
00:20:25,860 –> 00:20:31,120
happening as well, your customers
614
00:20:32,030 –> 00:20:34,190
are frustrated as they get
615
00:20:34,190 –> 00:20:36,830
to the agent. So the
616
00:20:36,830 –> 00:20:39,090
agent doesn’t necessarily only need
617
00:20:39,090 –> 00:20:41,420
to deal with the harder
618
00:20:41,420 –> 00:20:43,850
call, but they may be
619
00:20:43,910 –> 00:20:45,530
having to deal with the customer
620
00:20:45,530 –> 00:20:47,530
who’s frustrated because their time
621
00:20:47,530 –> 00:20:49,020
has been wasted by going
622
00:20:49,020 –> 00:20:50,240
to self- service and not
623
00:20:50,240 –> 00:20:51,470
finding what they’re looking for.
624
00:20:51,820 –> 00:20:53,680
Or are they maybe anxious. They
625
00:20:53,680 –> 00:20:55,360
have a medication that’s not
626
00:20:55,360 –> 00:20:58,570
covered by their policy and
627
00:20:58,570 –> 00:21:00,350
it’s a medication that’s prescribed
628
00:21:00,350 –> 00:21:01,860
that they really need. Or
629
00:21:01,860 –> 00:21:04,050
they’re angry because on their
630
00:21:04,050 –> 00:21:05,750
bills there’s a surcharge that
631
00:21:05,750 –> 00:21:07,730
they don’t understand. And so
632
00:21:07,730 –> 00:21:09,300
they’re in a combative mood.
633
00:21:09,500 –> 00:21:11,500
So the agent actually has a
634
00:21:11,500 –> 00:21:13,460
tough time. They’re getting this
635
00:21:13,460 –> 00:21:17,910
escalated call which is a
636
00:21:18,120 –> 00:21:20,610
harder call because the work
637
00:21:20,610 –> 00:21:22,330
is more complex and they’re
638
00:21:22,330 –> 00:21:24,130
having to understand the emotional
639
00:21:24,130 –> 00:21:26,020
state of the customer and
640
00:21:26,020 –> 00:21:28,480
react to that emotional state,
641
00:21:28,830 –> 00:21:32,300
turn the conversation around and
642
00:21:32,300 –> 00:21:34,350
do the right thing for
643
00:21:34,350 –> 00:21:36,760
the customer. So where does
644
00:21:36,860 –> 00:21:38,820
this all tie into the
645
00:21:38,820 –> 00:21:43,410
prediction about the agent desktop
646
00:21:44,170 –> 00:21:46,450
evolve? And this is because
647
00:21:46,450 –> 00:21:48,700
your agents today have to
648
00:21:48,700 –> 00:21:50,860
be supported by a much
649
00:21:50,860 –> 00:21:53,490
greater range of technologies to
650
00:21:53,490 –> 00:21:55,450
be able to serve the
651
00:21:55,450 –> 00:21:58,550
customer and provide the quality
652
00:21:58,550 –> 00:22:00,660
of service that they expect.
653
00:22:01,170 –> 00:22:03,500
So what we find is
654
00:22:03,500 –> 00:22:04,520
if you look at most
655
00:22:04,520 –> 00:22:07,550
agent desktops, they have a customer
656
00:22:07,550 –> 00:22:10,130
service solution that they’re doing
657
00:22:10,130 –> 00:22:11,530
their work in. And their
658
00:22:11,530 –> 00:22:13,920
customer service solution does things like
659
00:22:14,470 –> 00:22:16,410
help you identify the customer,
660
00:22:16,410 –> 00:22:18,210
pull up the customer history,
661
00:22:18,910 –> 00:22:21,460
being able to capture the
662
00:22:21,460 –> 00:22:24,350
inquiry details, workflow the inquiry.
663
00:22:25,340 –> 00:22:26,670
It’s got components of case
664
00:22:26,670 –> 00:22:29,390
management. You may also be
665
00:22:29,390 –> 00:22:31,050
able to pop up some
666
00:22:31,580 –> 00:22:33,770
associated knowledge from the knowledge
667
00:22:33,770 –> 00:22:41,530
base. And this customer service
668
00:22:41,530 –> 00:22:43,770
solution is also able to
669
00:22:43,770 –> 00:22:45,760
work omnichannel inquiries. So not
670
00:22:45,760 –> 00:22:47,390
only phone calls but digital
671
00:22:47,390 –> 00:22:49,530
inquiries as well. But what we
672
00:22:49,530 –> 00:22:52,420
also find is that many
673
00:22:52,420 –> 00:22:54,420
contact centers are layering on
674
00:22:54,480 –> 00:22:57,910
additional technologies to make agents
675
00:22:57,910 –> 00:23:01,070
more efficient, more effective, and
676
00:23:01,070 –> 00:23:03,860
to be able to prescribe
677
00:23:03,860 –> 00:23:05,470
the right set of actions
678
00:23:05,470 –> 00:23:07,940
for the agent. So on
679
00:23:07,940 –> 00:23:10,430
desktops, in terms of efficiency
680
00:23:10,430 –> 00:23:12,600
tools, we see many companies
681
00:23:12,600 –> 00:23:15,130
adopting things like RPA or
682
00:23:15,130 –> 00:23:19,030
process guidance that handhold agents
683
00:23:19,030 –> 00:23:24,890
through predefined processes around effectiveness
684
00:23:24,890 –> 00:23:26,740
tool to make agents more
685
00:23:26,740 –> 00:23:30,420
effective. We see, for example
686
00:23:30,420 –> 00:23:32,450
cognitive search solutions that are
687
00:23:32,450 –> 00:23:36,530
layered on top of silos
688
00:23:36,530 –> 00:23:39,380
of data like bug databases
689
00:23:39,380 –> 00:23:42,040
or content repositories to be
690
00:23:42,040 –> 00:23:43,380
able to pull up the
691
00:23:43,380 –> 00:23:45,710
right content or the right
692
00:23:45,960 –> 00:23:48,640
related data based on the
693
00:23:48,640 –> 00:23:51,380
customer’s inquiry. We also see
694
00:23:51,380 –> 00:23:53,250
tools like agent facing chat
695
00:23:53,250 –> 00:23:56,430
bots to help the agent
696
00:23:56,640 –> 00:23:58,540
surface the right data, the
697
00:23:58,540 –> 00:24:00,680
right information that they need
698
00:24:00,940 –> 00:24:02,720
depending on the intent that’s
699
00:24:02,720 –> 00:24:05,490
captured from the customer. We
700
00:24:05,490 –> 00:24:08,710
also see collaboration tools where
701
00:24:08,710 –> 00:24:10,760
agents can collaborate with other
702
00:24:10,760 –> 00:24:11,940
agents to work on the
703
00:24:11,940 –> 00:24:14,360
harder work. And we also
704
00:24:14,360 –> 00:24:16,030
see in terms of prescriptive
705
00:24:16,030 –> 00:24:17,020
tool, a lot of the
706
00:24:17,020 –> 00:24:21,360
AI or intelligence fueled solutions
707
00:24:21,360 –> 00:24:23,150
to be able to push
708
00:24:23,150 –> 00:24:25,660
the next best action to
709
00:24:25,660 –> 00:24:26,930
the agent. What’s the next
710
00:24:26,930 –> 00:24:29,150
best conversation the agent needs
711
00:24:29,150 –> 00:24:30,570
to have or the right
712
00:24:30,640 –> 00:24:32,500
offer to be able to
713
00:24:32,960 –> 00:24:34,980
present to the agent that
714
00:24:34,980 –> 00:24:38,690
has the highest rate of
715
00:24:38,690 –> 00:24:40,620
being accepted. So what we
716
00:24:40,620 –> 00:24:43,060
find is again, the agents
717
00:24:43,130 –> 00:24:44,680
are working on the harder work
718
00:24:45,040 –> 00:24:49,010
and they are helped along
719
00:24:49,240 –> 00:24:52,420
by this set of tooling
720
00:24:52,640 –> 00:24:54,580
that tends to be assembled
721
00:24:55,260 –> 00:24:56,780
by starting off with a customer
722
00:24:56,780 –> 00:24:58,610
service solution and then layering
723
00:24:58,610 –> 00:25:00,770
on the technologies that are
724
00:25:00,770 –> 00:25:02,100
needed to be able to
725
00:25:02,100 –> 00:25:11,650
adequately support the agent. So
726
00:25:11,750 –> 00:25:14,460
Joe, does that resonate? Yeah. And
727
00:25:15,640 –> 00:25:16,460
it almost looks like I
728
00:25:16,460 –> 00:25:17,310
stole your homework a little
729
00:25:17,570 –> 00:25:18,690
bit, but I love what
730
00:25:18,690 –> 00:25:19,830
you just brought up because
731
00:25:19,830 –> 00:25:21,010
it ties right into our
732
00:25:21,010 –> 00:25:23,640
second prediction that employees become
733
00:25:23,640 –> 00:25:25,590
a brand differentiator that are
734
00:25:25,590 –> 00:25:27,790
augmented by AI. And Kate,
735
00:25:27,790 –> 00:25:28,440
if there’s one thing I think that
736
00:25:28,950 –> 00:25:29,910
you hit on really nicely
737
00:25:29,950 –> 00:25:31,550
that I got from that was as
738
00:25:31,550 –> 00:25:33,420
AI is becoming increasingly more
739
00:25:33,420 –> 00:25:36,460
consistent and capable, we’re finding
740
00:25:36,460 –> 00:25:37,850
that in the contact center,
741
00:25:37,850 –> 00:25:39,160
the agent’s work is going
742
00:25:39,160 –> 00:25:40,050
to become not just more
743
00:25:40,050 –> 00:25:41,980
difficult, but empathetic as well.
744
00:25:42,610 –> 00:25:43,880
So it’s important that we
745
00:25:43,880 –> 00:25:46,110
understand that really AI will
746
00:25:46,110 –> 00:25:47,530
enable agents to make better
747
00:25:47,720 –> 00:25:49,680
decisions and focus on empathy
748
00:25:49,860 –> 00:25:52,350
within those customer interactions. And
749
00:25:52,350 –> 00:25:53,900
we’re seeing that now through
750
00:25:53,900 –> 00:25:55,450
just- in- time interfaces that
751
00:25:55,450 –> 00:25:57,540
are surfacing both information and
752
00:25:57,540 –> 00:25:59,220
even applications as they’re needed
753
00:25:59,220 –> 00:26:01,220
in real time. What I’ve
754
00:26:01,320 –> 00:26:02,360
gotten away from this, and
755
00:26:02,360 –> 00:26:03,110
even as I was a
756
00:26:03,110 –> 00:26:05,220
support engineer, is that complicated
757
00:26:05,220 –> 00:26:07,350
interfaces and integrations and other
758
00:26:07,350 –> 00:26:09,250
systems, those should no longer
759
00:26:09,250 –> 00:26:10,410
be the obligation of the
760
00:26:10,410 –> 00:26:12,290
agent, rather the bandwidth to
761
00:26:12,290 –> 00:26:14,070
pay attention to this interaction.
762
00:26:14,650 –> 00:26:16,430
So as we’re tying that
763
00:26:16,430 –> 00:26:18,130
up for our prediction around
764
00:26:18,130 –> 00:26:20,100
employees becoming a brand differentiator,
765
00:26:20,620 –> 00:26:21,670
we think about that the
766
00:26:21,670 –> 00:26:23,420
rising interactions are happening across
767
00:26:23,470 –> 00:26:25,510
channels, but what’s going to
768
00:26:25,580 –> 00:26:26,960
the agents are more difficult.
769
00:26:27,130 –> 00:26:28,110
So the tools that they
770
00:26:28,110 –> 00:26:30,210
need are companion tools that
771
00:26:30,210 –> 00:26:31,860
are infused in those applications,
772
00:26:31,860 –> 00:26:33,760
not beside them, and AI
773
00:26:33,760 –> 00:26:35,370
is helping agents make these
774
00:26:35,370 –> 00:26:36,890
great judgment calls while they’re
775
00:26:36,890 –> 00:26:38,970
on an interaction not replacing
776
00:26:38,970 –> 00:26:40,900
agents. So if you remember
777
00:26:40,900 –> 00:26:41,670
a few years ago, Apple
778
00:26:41,670 –> 00:26:42,960
music was really popular for
779
00:26:42,970 –> 00:26:45,570
having real DJs curating playlists.
780
00:26:46,210 –> 00:26:47,910
It was about humans being
781
00:26:47,910 –> 00:26:49,220
important and part of that new
782
00:26:49,220 –> 00:26:50,500
offering they had around Apple
783
00:26:50,500 –> 00:26:52,000
music. I think we’re seeing
784
00:26:52,000 –> 00:26:53,290
that pendulum swing come back
785
00:26:53,290 –> 00:26:55,640
again to humans being crucial
786
00:26:55,690 –> 00:26:57,710
to the experience. And just
787
00:26:57,710 –> 00:26:59,180
a quick story there before
788
00:26:59,180 –> 00:27:00,700
I babble like I always
789
00:27:00,700 –> 00:27:02,210
do. When we look at
790
00:27:02,210 –> 00:27:04,050
large retailers, imagine you’re a
791
00:27:04,050 –> 00:27:05,670
parent moving your son or
792
00:27:05,670 –> 00:27:07,530
daughter into college. What if
793
00:27:07,530 –> 00:27:08,090
you went to one of
794
00:27:08,090 –> 00:27:09,240
those large retailers and bought
795
00:27:09,240 –> 00:27:10,130
all the items you need
796
00:27:10,130 –> 00:27:11,410
for a dorm, right? The
797
00:27:11,720 –> 00:27:13,530
air conditioning unit, maybe a
798
00:27:13,530 –> 00:27:15,100
mini fridge, maybe some food,
799
00:27:15,540 –> 00:27:17,440
all these different items. If
800
00:27:17,490 –> 00:27:18,570
the roommate already had them,
801
00:27:18,570 –> 00:27:19,520
you might want to return
802
00:27:19,520 –> 00:27:20,690
them. But think about how
803
00:27:20,690 –> 00:27:22,070
many return policies that is.
804
00:27:22,070 –> 00:27:23,160
And we’ve done the research
805
00:27:23,160 –> 00:27:24,740
and seen on average, these
806
00:27:24,740 –> 00:27:26,020
large retailers have upwards of
807
00:27:26,040 –> 00:27:28,500
19 different return policies. So
808
00:27:28,500 –> 00:27:29,420
if you call in just
809
00:27:29,420 –> 00:27:30,180
to figure out and what
810
00:27:30,180 –> 00:27:31,250
you can actually bring back
811
00:27:31,250 –> 00:27:33,010
and what’s a lost cause,
812
00:27:33,370 –> 00:27:34,240
that’s a lot for the
813
00:27:34,240 –> 00:27:35,660
agent to dig through. That
814
00:27:35,660 –> 00:27:36,690
means they’re going on hold.
815
00:27:36,690 –> 00:27:37,500
That means there’s a lot
816
00:27:37,540 –> 00:27:38,870
of ums and uhs as they try
817
00:27:38,870 –> 00:27:39,740
to figure it out on
818
00:27:39,740 –> 00:27:41,930
their end. Using these AI
819
00:27:41,930 –> 00:27:43,960
assisted technologies mean I can
820
00:27:43,960 –> 00:27:45,650
pull up the closest location
821
00:27:45,650 –> 00:27:46,650
to you based around your
822
00:27:46,650 –> 00:27:47,650
call and what you said,
823
00:27:47,650 –> 00:27:49,610
where you’re located, what college
824
00:27:49,950 –> 00:27:50,770
and then I can let
825
00:27:50,770 –> 00:27:53,040
the AI identify the nuances
826
00:27:53,040 –> 00:27:54,220
of what items are you
827
00:27:54,220 –> 00:27:56,260
returning and what’s the gotchas
828
00:27:56,260 –> 00:27:57,500
there that are important in
829
00:27:57,500 –> 00:27:59,470
that return process. This means
830
00:27:59,470 –> 00:28:00,480
I’m focused on you, the
831
00:28:00,480 –> 00:28:02,840
person calling in, the son
832
00:28:02,840 –> 00:28:03,850
or daughter you’ve just moved
833
00:28:03,850 –> 00:28:04,880
in and the situation you
834
00:28:04,880 –> 00:28:06,100
have at hand, not on
835
00:28:06,100 –> 00:28:08,280
these individual line items. So
836
00:28:08,280 –> 00:28:09,570
Kate, I’ll hand it back
837
00:28:09,570 –> 00:28:10,530
to you here for your
838
00:28:10,530 –> 00:28:11,820
final point and any questions
839
00:28:11,820 –> 00:28:12,790
or comments you have on
840
00:28:12,790 –> 00:28:14,970
this one too? Yeah. The
841
00:28:14,970 –> 00:28:16,020
one thing that I forgot
842
00:28:16,020 –> 00:28:17,150
to say is, and you
843
00:28:17,150 –> 00:28:18,640
said it really well, is
844
00:28:18,760 –> 00:28:21,280
agents have to be supported
845
00:28:21,280 –> 00:28:22,890
by these companion tools or
846
00:28:22,890 –> 00:28:24,660
desktop technologies to be able
847
00:28:24,660 –> 00:28:26,220
to focus on the conversation
848
00:28:26,220 –> 00:28:28,100
at hand. And there’s also
849
00:28:28,240 –> 00:28:30,610
technologies that are helping make
850
00:28:30,610 –> 00:28:32,500
agents more empathetic. For example,
851
00:28:32,500 –> 00:28:34,850
behavioral routing, being able to
852
00:28:34,850 –> 00:28:37,220
understand the conversation style of
853
00:28:37,220 –> 00:28:38,420
the customer and routed to
854
00:28:38,790 –> 00:28:40,140
the agent that’s got the same
855
00:28:40,260 –> 00:28:43,370
conversational style. Or for example,
856
00:28:43,440 –> 00:28:45,370
popping up on the agent’s
857
00:28:45,370 –> 00:28:49,530
screen for example, indicators of
858
00:28:52,030 –> 00:28:55,280
the customer’s emotion. Are they
859
00:28:55,360 –> 00:28:57,540
anxious or are they angry?
860
00:28:57,800 –> 00:28:59,090
And again, these are tools, they’re
861
00:28:59,500 –> 00:29:02,010
companion tools to not only
862
00:29:02,010 –> 00:29:03,820
help the agent work on the
863
00:29:03,820 –> 00:29:05,840
harder work, but as well
864
00:29:05,840 –> 00:29:09,500
emotionally connect with the customer. Because
865
00:29:09,500 –> 00:29:11,070
if you get these interactions,
866
00:29:11,070 –> 00:29:12,880
these live agent interactions right
867
00:29:12,880 –> 00:29:14,680
it actually has a disproportionate
868
00:29:14,850 –> 00:29:18,140
effect on customer satisfaction and
869
00:29:18,140 –> 00:29:21,010
they’re all for overall retention
870
00:29:21,550 –> 00:29:23,480
and loyalty to the brand.
871
00:29:23,480 –> 00:29:25,410
So again, these companion tools
872
00:29:25,410 –> 00:29:26,970
are really important to make
873
00:29:26,970 –> 00:29:27,970
sure that the agents are
874
00:29:28,060 –> 00:29:30,070
fully supported and that they’re
875
00:29:30,070 –> 00:29:32,640
able to concentrate on the
876
00:29:32,640 –> 00:29:34,790
conversation of the customer. So
877
00:29:38,400 –> 00:29:40,220
that goes to our next
878
00:29:40,220 –> 00:29:44,360
trend where as you infuse
879
00:29:44,710 –> 00:29:47,610
all of these companion tools, all
880
00:29:47,610 –> 00:29:49,710
this automation, all this AI
881
00:29:49,710 –> 00:29:52,140
into your contact center, the
882
00:29:52,140 –> 00:29:54,790
way that you staff your
883
00:29:54,790 –> 00:29:57,560
contact center has to change.
884
00:29:57,970 –> 00:30:00,310
And this is really interesting.
885
00:30:00,350 –> 00:30:02,470
Think about it this way.
886
00:30:04,040 –> 00:30:06,180
You now have great self
887
00:30:06,180 –> 00:30:10,210
service technology, self service process,
888
00:30:10,580 –> 00:30:12,970
knowledge management, FAQs on your
889
00:30:12,970 –> 00:30:16,300
websites, chat bots that are
890
00:30:16,300 –> 00:30:18,920
able to help answer the
891
00:30:18,920 –> 00:30:23,210
simple, the reproducible questions that your
892
00:30:23,350 –> 00:30:25,900
customers have. So ultimately what
893
00:30:25,900 –> 00:30:27,830
happens to your generalists, what
894
00:30:27,830 –> 00:30:28,990
happens to your tier one
895
00:30:28,990 –> 00:30:31,440
agents? And what many companies
896
00:30:31,440 –> 00:30:33,640
find is that these roles
897
00:30:34,310 –> 00:30:37,620
aren’t needed as much as
898
00:30:37,620 –> 00:30:39,100
they were a couple of
899
00:30:39,100 –> 00:30:41,320
years ago. So jobs are
900
00:30:41,320 –> 00:30:45,940
changing where companies need fewer
901
00:30:46,160 –> 00:30:48,220
of the lower tiered agents
902
00:30:48,440 –> 00:30:49,710
and they may take these
903
00:30:49,710 –> 00:30:52,790
agents and retrain them or
904
00:30:52,790 –> 00:30:55,070
repurpose them into new roles.
905
00:30:55,300 –> 00:30:57,030
What about having a tier
906
00:30:57,030 –> 00:30:58,720
one agent now be the
907
00:30:58,720 –> 00:31:01,750
bot supervisor who is supervising
908
00:31:01,750 –> 00:31:04,830
the bot who’s answering all
909
00:31:04,830 –> 00:31:06,930
the routine questions that the
910
00:31:07,500 –> 00:31:09,560
agent used to answer? The
911
00:31:09,560 –> 00:31:11,640
agent can take over when
912
00:31:11,640 –> 00:31:13,760
the automation fails or the
913
00:31:13,810 –> 00:31:17,700
agent can recommend new automations
914
00:31:17,800 –> 00:31:20,450
dependent on the customers’ incoming
915
00:31:20,450 –> 00:31:22,870
requests. But again, this bot
916
00:31:22,870 –> 00:31:25,670
supervisor or bot manager is
917
00:31:25,670 –> 00:31:27,430
a new role that is
918
00:31:27,430 –> 00:31:29,220
opening up in the contact center
919
00:31:29,220 –> 00:31:31,210
that’s perfect for a tier
920
00:31:31,210 –> 00:31:33,210
one agent and it’s a
921
00:31:33,210 –> 00:31:34,590
role that didn’t exist a couple of
922
00:31:34,910 –> 00:31:36,970
years ago. So what we
923
00:31:36,970 –> 00:31:37,980
also see is that some
924
00:31:37,980 –> 00:31:40,390
jobs are going to become
925
00:31:40,640 –> 00:31:42,660
a lot more important. For
926
00:31:42,660 –> 00:31:46,250
example, think about the roles
927
00:31:46,310 –> 00:31:49,310
that script or create the
928
00:31:49,310 –> 00:31:52,480
content that it fills your FAQs or your
929
00:31:52,480 –> 00:31:54,430
knowledge bases, here on the
930
00:31:54,430 –> 00:31:55,630
screen I call them knowledge
931
00:31:55,630 –> 00:31:58,440
workers. Or think about the
932
00:31:58,590 –> 00:32:00,730
tier three, tier four agents.
933
00:32:02,630 –> 00:32:03,630
The harder work is now
934
00:32:03,630 –> 00:32:05,060
getting to the contact center
935
00:32:05,060 –> 00:32:07,210
agent. And so your agents
936
00:32:07,210 –> 00:32:08,670
have to be retrained, they
937
00:32:08,670 –> 00:32:09,830
have to be up scaled
938
00:32:09,830 –> 00:32:10,870
or perhaps you need a
939
00:32:10,870 –> 00:32:13,130
whole new profile of agents
940
00:32:13,730 –> 00:32:14,900
to work on the really
941
00:32:14,900 –> 00:32:17,950
complex work. We call these
942
00:32:18,000 –> 00:32:19,780
folks super agents. Not only
943
00:32:19,780 –> 00:32:22,760
are they technically competent, they
944
00:32:22,760 –> 00:32:25,070
have all the skills to
945
00:32:25,070 –> 00:32:26,900
be able to answer the
946
00:32:26,900 –> 00:32:29,360
harder questions, but they also
947
00:32:29,360 –> 00:32:33,570
have great emotional intelligence to
948
00:32:33,570 –> 00:32:36,100
be able to relate to
949
00:32:36,100 –> 00:32:38,800
the customer in their anxious
950
00:32:38,800 –> 00:32:41,670
or angry or frustrated state.
951
00:32:42,100 –> 00:32:42,680
And then you’re going to
952
00:32:42,680 –> 00:32:43,680
have a whole new set
953
00:32:43,710 –> 00:32:44,960
of jobs that didn’t exist
954
00:32:44,960 –> 00:32:47,140
in the contact center. All
955
00:32:47,140 –> 00:32:49,800
the data science roles to
956
00:32:49,800 –> 00:32:50,770
be able to create the
957
00:32:50,780 –> 00:32:52,680
automations, to be able to
958
00:32:53,060 –> 00:32:55,650
create and manage and optimize
959
00:32:55,650 –> 00:32:58,560
the machine learning models. And
960
00:32:58,560 –> 00:33:01,010
then conversational designers. These are
961
00:33:01,010 –> 00:33:05,430
actually business analysts or they
962
00:33:05,430 –> 00:33:09,050
could even be former agents
963
00:33:09,300 –> 00:33:11,230
that are responsible for scripted
964
00:33:11,520 –> 00:33:13,720
bot conversations. So when we
965
00:33:13,720 –> 00:33:16,570
find is that the more
966
00:33:16,570 –> 00:33:18,980
you automate within your contact
967
00:33:18,980 –> 00:33:20,130
center, the more you add
968
00:33:20,130 –> 00:33:23,670
AI, your jobs will slowly
969
00:33:23,670 –> 00:33:25,080
change over time. And let
970
00:33:25,080 –> 00:33:26,530
me tell you two stories.
971
00:33:27,360 –> 00:33:28,480
First of all, there’s the
972
00:33:29,420 –> 00:33:33,850
tax service that we probably
973
00:33:33,850 –> 00:33:38,300
all use. They don’t hire
974
00:33:38,300 –> 00:33:39,860
agents anymore. They hire two
975
00:33:39,860 –> 00:33:42,150
different roles. The first role
976
00:33:42,150 –> 00:33:44,940
is a software engineer. Somebody
977
00:33:44,940 –> 00:33:46,610
who can trouble shoot their
978
00:33:46,610 –> 00:33:49,410
tax software. The second role
979
00:33:49,410 –> 00:33:50,810
that they hire for is
980
00:33:50,810 –> 00:33:53,040
a tax accountant, somebody who
981
00:33:53,040 –> 00:33:56,240
is able to answer the
982
00:33:56,240 –> 00:33:59,140
harder tax questions that customers
983
00:33:59,140 –> 00:34:02,450
have. So again, they’ve seen
984
00:34:03,110 –> 00:34:05,160
their jobs change over time.
985
00:34:05,460 –> 00:34:07,570
Pier 1 Imports is really
986
00:34:07,570 –> 00:34:10,780
interesting example. So Pier 1
987
00:34:10,780 –> 00:34:16,480
sells modern furniture over the
988
00:34:16,480 –> 00:34:20,370
web. They don’t hire agents
989
00:34:20,370 –> 00:34:23,170
anymore, they hire folks with
990
00:34:24,230 –> 00:34:26,630
design degrees or folks who
991
00:34:26,630 –> 00:34:27,980
have a passion for home
992
00:34:27,980 –> 00:34:29,540
decorating because the questions that
993
00:34:29,540 –> 00:34:30,930
they get aren’t about the
994
00:34:30,930 –> 00:34:32,860
dimensions of table or chair
995
00:34:32,860 –> 00:34:36,040
for example. But questions like,
996
00:34:36,270 –> 00:34:38,060
I have yellow walls and
997
00:34:38,060 –> 00:34:39,450
I have a green carpet.
998
00:34:39,480 –> 00:34:41,250
Would the orange couch look
999
00:34:41,250 –> 00:34:42,640
better, would the green couch
1000
00:34:42,640 –> 00:34:44,030
look better? So it’s more
1001
00:34:44,030 –> 00:34:47,500
consultancy and advice and they
1002
00:34:47,500 –> 00:34:50,470
find that there’s only a
1003
00:34:50,470 –> 00:34:52,220
select number of folks that
1004
00:34:52,220 –> 00:34:54,780
have a real passion for
1005
00:34:54,780 –> 00:34:56,720
home decorating or design and
1006
00:34:56,720 –> 00:34:58,510
they go after those roles.
1007
00:34:58,510 –> 00:34:59,870
What they’ve also found is
1008
00:34:59,870 –> 00:35:02,000
that they can’t source those
1009
00:35:02,000 –> 00:35:05,170
roles within a small geographic
1010
00:35:05,170 –> 00:35:07,070
area to be able to
1011
00:35:07,070 –> 00:35:08,540
staff their contact center. And
1012
00:35:08,540 –> 00:35:10,170
so they actually have had
1013
00:35:10,170 –> 00:35:12,280
to move to a remote
1014
00:35:12,760 –> 00:35:14,300
work at home model for
1015
00:35:14,300 –> 00:35:17,810
their contact center. The other
1016
00:35:17,810 –> 00:35:19,460
big change that’s going to
1017
00:35:19,460 –> 00:35:22,110
happen is as the harder work
1018
00:35:22,360 –> 00:35:23,760
gets into your contact center,
1019
00:35:25,710 –> 00:35:27,300
the way that you measure
1020
00:35:27,360 –> 00:35:30,010
outcomes has to change. You
1021
00:35:30,010 –> 00:35:31,850
may not want to hold
1022
00:35:31,920 –> 00:35:34,110
your agents’ feet to the
1023
00:35:34,110 –> 00:35:37,230
fire anymore and monitor their
1024
00:35:37,230 –> 00:35:39,410
handle times and their speed
1025
00:35:39,410 –> 00:35:41,330
of answer and all the
1026
00:35:41,330 –> 00:35:43,210
other productivity measures that we
1027
00:35:43,210 –> 00:35:45,350
use in the contact center. You may
1028
00:35:45,350 –> 00:35:47,670
want to be more focused on
1029
00:35:47,730 –> 00:35:49,890
outcomes. How good was the
1030
00:35:49,890 –> 00:35:53,970
interaction, customer satisfaction, quality of
1031
00:35:53,970 –> 00:35:56,440
service metrics that then can
1032
00:35:56,440 –> 00:36:00,100
be tied to customer retention
1033
00:36:00,850 –> 00:36:04,150
and customer lifetime value and
1034
00:36:04,150 –> 00:36:09,040
ultimately company revenue. Shopify for
1035
00:36:09,040 –> 00:36:12,320
example, in one of their
1036
00:36:12,320 –> 00:36:14,300
contact centers they have over 500
1037
00:36:14,300 –> 00:36:17,530
agents and they have moved
1038
00:36:17,580 –> 00:36:19,780
to a quality of service
1039
00:36:19,780 –> 00:36:22,300
metric. They still measure handle
1040
00:36:22,300 –> 00:36:25,150
times mainly to be able
1041
00:36:25,150 –> 00:36:28,610
to appropriately staff their contact
1042
00:36:28,610 –> 00:36:32,070
center, but their agents aren’t
1043
00:36:32,430 –> 00:36:37,060
emphasized and penalized on handle
1044
00:36:37,060 –> 00:36:39,740
time or of speed of answers. Again,
1045
00:36:39,780 –> 00:36:42,110
the only measure of success
1046
00:36:42,550 –> 00:36:45,070
and measure of agent success is
1047
00:36:45,070 –> 00:36:48,600
the quality of service. So
1048
00:36:48,600 –> 00:36:50,000
again, as you add AI
1049
00:36:50,000 –> 00:36:51,040
and automation, you’ve got to
1050
00:36:51,040 –> 00:36:54,410
rethink not only the jobs
1051
00:36:54,920 –> 00:36:57,880
but measures of success metrics
1052
00:36:58,170 –> 00:36:59,690
and as well as your
1053
00:36:59,690 –> 00:37:04,940
workforce staffing policies. So Joe,
1054
00:37:05,670 –> 00:37:07,560
what do you think? I
1055
00:37:07,560 –> 00:37:08,900
love how you brought about
1056
00:37:08,970 –> 00:37:09,850
all of the changes that
1057
00:37:09,850 –> 00:37:10,830
are happening. I think this
1058
00:37:10,830 –> 00:37:12,840
is a really big thing
1059
00:37:12,840 –> 00:37:14,370
and we talk a lot
1060
00:37:14,370 –> 00:37:16,100
about experiences today, right? I
1061
00:37:16,100 –> 00:37:18,070
think we look at experience
1062
00:37:18,070 –> 00:37:19,460
as the platform being our third
1063
00:37:19,460 –> 00:37:20,460
one, and that is about
1064
00:37:20,460 –> 00:37:22,010
as umbrella as umbrella statements
1065
00:37:22,010 –> 00:37:22,940
can get. I want to
1066
00:37:22,940 –> 00:37:24,990
give some detail here around
1067
00:37:24,990 –> 00:37:25,770
what we mean when we
1068
00:37:25,770 –> 00:37:27,390
say experience of the platform
1069
00:37:27,390 –> 00:37:29,430
and why that’s important. So
1070
00:37:29,430 –> 00:37:30,910
many companies are going for
1071
00:37:31,290 –> 00:37:33,600
personalized at scale, right? Making
1072
00:37:33,600 –> 00:37:35,060
sure that every customer gets
1073
00:37:35,060 –> 00:37:36,440
the interaction they’re looking for.
1074
00:37:36,800 –> 00:37:37,610
There’s a few that do
1075
00:37:37,610 –> 00:37:39,190
this really well. When you look
1076
00:37:39,190 –> 00:37:40,480
at Netflix, you don’t want
1077
00:37:40,480 –> 00:37:42,550
to browse 20000 movies, that’s
1078
00:37:42,630 –> 00:37:43,750
probably not why you’re paying
1079
00:37:43,750 –> 00:37:45,310
for it. What do you want to do is
1080
00:37:45,310 –> 00:37:46,360
watch a comedy on a
1081
00:37:46,480 –> 00:37:47,560
Thursday night and you only
1082
00:37:47,560 –> 00:37:48,110
have an hour and a
1083
00:37:48,110 –> 00:37:49,800
half. And when you look
1084
00:37:49,800 –> 00:37:50,930
at other servers there’s like
1085
00:37:50,930 –> 00:37:52,310
Lynda which is now LinkedIn
1086
00:37:52,370 –> 00:37:54,990
Learning. I don’t want to just take an
1087
00:37:54,990 –> 00:37:56,970
Adobe premiere pro 101 course
1088
00:37:57,080 –> 00:37:57,520
to learn how I’ll be
1089
00:37:58,350 –> 00:38:00,220
using this software and I want to be
1090
00:38:00,220 –> 00:38:02,190
a film producer. So it’s on
1091
00:38:02,190 –> 00:38:04,220
these companies to curate the
1092
00:38:04,710 –> 00:38:06,620
just wild amounts of content
1093
00:38:06,850 –> 00:38:08,500
they have and make it
1094
00:38:08,500 –> 00:38:10,210
personalized to the person using
1095
00:38:10,210 –> 00:38:11,880
it. This is the year
1096
00:38:11,880 –> 00:38:13,270
that we have that capability
1097
00:38:13,550 –> 00:38:14,670
and this is the year that I think
1098
00:38:14,670 –> 00:38:15,810
we started to see that being
1099
00:38:15,810 –> 00:38:17,670
necessary in the contact centers.
1100
00:38:18,620 –> 00:38:20,050
Today we talked about new
1101
00:38:20,050 –> 00:38:21,840
channels opening up these homes
1102
00:38:21,840 –> 00:38:23,940
or self service agents being
1103
00:38:25,260 –> 00:38:26,830
nudged in certain ways because of
1104
00:38:27,060 –> 00:38:28,350
AI and AI getting this
1105
00:38:28,350 –> 00:38:29,500
new insight. Well something that
1106
00:38:29,500 –> 00:38:31,530
was actually brought up in
1107
00:38:31,530 –> 00:38:32,750
a recent webinar with Ian
1108
00:38:32,750 –> 00:38:34,630
Jacobs towards the notion that
1109
00:38:34,680 –> 00:38:36,920
data science doesn’t always know
1110
00:38:36,920 –> 00:38:38,500
contact center and contact center may
1111
00:38:38,790 –> 00:38:40,050
not always know data science.
1112
00:38:40,560 –> 00:38:42,420
So having a platform that
1113
00:38:42,420 –> 00:38:44,090
is unified in that its
1114
00:38:44,090 –> 00:38:46,140
ability to understand why are
1115
00:38:46,140 –> 00:38:47,590
we engaging with that customer
1116
00:38:47,590 –> 00:38:48,830
at this moment of truth
1117
00:38:48,830 –> 00:38:51,070
here and are we personalizing
1118
00:38:51,070 –> 00:38:53,320
this current interaction, the realtime
1119
00:38:53,320 –> 00:38:54,550
data we have about them
1120
00:38:55,060 –> 00:38:56,680
and historical context that we’re
1121
00:38:56,680 –> 00:38:58,360
pulling in from integrations around
1122
00:38:58,360 –> 00:39:00,820
them. Lastly, what about that
1123
00:39:00,820 –> 00:39:02,900
context? That context is so
1124
00:39:02,900 –> 00:39:05,020
important so that every conversation
1125
00:39:05,370 –> 00:39:06,500
feels like that customer is
1126
00:39:06,500 –> 00:39:08,420
reaching out to some conversation of
1127
00:39:08,420 –> 00:39:10,150
the company, not just another
1128
00:39:10,150 –> 00:39:11,460
agent that is only talking to them
1129
00:39:11,460 –> 00:39:13,300
right now, but an ongoing
1130
00:39:13,300 –> 00:39:14,970
conversation that not only feeds
1131
00:39:14,970 –> 00:39:17,010
into what’s happening between this
1132
00:39:17,010 –> 00:39:19,280
customer and agent relationship but
1133
00:39:19,280 –> 00:39:20,910
also what type of training
1134
00:39:20,910 –> 00:39:22,520
are we providing. We talked
1135
00:39:22,520 –> 00:39:23,350
a lot about that on
1136
00:39:23,350 –> 00:39:25,780
the WEM side around if
1137
00:39:25,780 –> 00:39:27,400
we’re training our agents, the
1138
00:39:27,400 –> 00:39:28,410
culture we’re building for them
1139
00:39:28,410 –> 00:39:30,130
should be personalized to what they
1140
00:39:30,280 –> 00:39:31,800
need to excel on their
1141
00:39:31,800 –> 00:39:32,980
own as well. I think
1142
00:39:33,120 –> 00:39:34,130
that’s really important here is
1143
00:39:34,130 –> 00:39:35,980
that as personalization comes into
1144
00:39:35,980 –> 00:39:37,370
the tools provided to everyone
1145
00:39:37,380 –> 00:39:38,680
in the company, not just
1146
00:39:38,680 –> 00:39:40,200
the interactions that we have here. There’s lot
1147
00:39:40,200 –> 00:39:42,800
we can learn. So I
1148
00:39:42,800 –> 00:39:44,260
have babbled, but what I want
1149
00:39:44,300 –> 00:39:46,750
to talk about is experience of platform being
1150
00:39:46,750 –> 00:39:48,500
important as having a commonality
1151
00:39:48,500 –> 00:39:49,760
to do this in unison
1152
00:39:50,060 –> 00:39:51,130
across all the things we
1153
00:39:51,130 –> 00:39:52,900
talked about today. And with
1154
00:39:52,900 –> 00:39:54,450
that I want to end
1155
00:39:54,450 –> 00:39:55,400
on our, what it means
1156
00:39:55,400 –> 00:39:56,540
slides before we open up
1157
00:39:56,540 –> 00:39:57,130
to that Q& A. So
1158
00:39:58,250 –> 00:39:58,850
Kate to kind of bring
1159
00:39:58,850 –> 00:40:00,090
it back to you here,
1160
00:40:00,350 –> 00:40:02,170
is there any of these five points
1161
00:40:02,170 –> 00:40:03,640
that you wanted to highlight as
1162
00:40:03,640 –> 00:40:04,880
we end today, before the
1163
00:40:04,880 –> 00:40:08,280
Q& A? I think it
1164
00:40:08,670 –> 00:40:10,010
all starts with the customer,
1165
00:40:10,390 –> 00:40:14,500
understanding your customer, whether you’re
1166
00:40:14,500 –> 00:40:16,170
a consumer brand or you’re a B2B
1167
00:40:17,140 –> 00:40:19,750
brand, understand the customer and
1168
00:40:19,750 –> 00:40:22,830
understand the value of supporting
1169
00:40:22,830 –> 00:40:24,470
your customer in the way
1170
00:40:24,470 –> 00:40:25,670
that they want to be
1171
00:40:25,670 –> 00:40:28,280
supported because better customer experiences
1172
00:40:28,510 –> 00:40:30,740
will ultimately translate into a
1173
00:40:30,740 –> 00:40:32,890
more loyal customer base that
1174
00:40:32,890 –> 00:40:35,630
will then translate into increased
1175
00:40:35,630 –> 00:40:38,380
customer retention and ultimately revenue.
1176
00:40:38,840 –> 00:40:40,470
And so understanding your customer,
1177
00:40:40,470 –> 00:40:41,880
you also have to understand
1178
00:40:41,880 –> 00:40:42,740
that they want their time
1179
00:40:42,740 –> 00:40:44,380
to be valued and that
1180
00:40:44,380 –> 00:40:45,920
they want to self serve
1181
00:40:46,150 –> 00:40:47,030
as a first point of
1182
00:40:47,030 –> 00:40:48,840
contact with the company and
1183
00:40:48,840 –> 00:40:50,390
that they are moving to
1184
00:40:50,390 –> 00:40:53,210
digital interactions. Whether it’s voice
1185
00:40:53,210 –> 00:40:56,140
self service, whether it’s asynchronous
1186
00:40:56,140 –> 00:40:58,580
messaging or whether it’s synchronous
1187
00:40:58,580 –> 00:41:00,280
chat, but you really have
1188
00:41:00,280 –> 00:41:03,680
to understand your customers, the
1189
00:41:03,680 –> 00:41:04,850
way they want to interact
1190
00:41:04,850 –> 00:41:06,100
with you and support you’re
1191
00:41:06,100 –> 00:41:08,490
customers and the modalities that they
1192
00:41:08,490 –> 00:41:10,450
want to use. As you
1193
00:41:10,450 –> 00:41:11,460
do that, you’re going to
1194
00:41:11,460 –> 00:41:12,930
find that your customers want
1195
00:41:13,260 –> 00:41:14,230
to engage with you more
1196
00:41:14,230 –> 00:41:15,410
and more. It’s a two
1197
00:41:15,410 –> 00:41:17,220
way relationship but you can’t
1198
00:41:17,220 –> 00:41:19,430
keep up with the ballooning
1199
00:41:19,430 –> 00:41:21,520
volumes of interactions. So you’ve got to
1200
00:41:21,520 –> 00:41:23,800
turn to AI and automation
1201
00:41:23,800 –> 00:41:25,280
to be able to automate
1202
00:41:25,330 –> 00:41:29,020
as much of the interaction
1203
00:41:29,080 –> 00:41:30,900
or the engagement as possible
1204
00:41:31,220 –> 00:41:34,310
and then leave the value
1205
00:41:34,310 –> 00:41:37,030
added interactions to humans. So
1206
00:41:37,030 –> 00:41:38,660
it’s AI and automation, like
1207
00:41:38,660 –> 00:41:41,240
Joe said, working together with
1208
00:41:42,040 –> 00:41:44,540
your agents. As you add
1209
00:41:44,540 –> 00:41:46,470
AI and automation to your
1210
00:41:46,470 –> 00:41:50,870
operations, realize that the work
1211
00:41:51,030 –> 00:41:52,960
that your line agents do,
1212
00:41:53,000 –> 00:41:54,560
whether they’re digital agents or
1213
00:41:54,560 –> 00:41:55,770
whether they’re phone agents is
1214
00:41:56,150 –> 00:41:57,090
going to change, it’s going
1215
00:41:57,090 –> 00:41:59,430
to get harder. So your
1216
00:41:59,430 –> 00:42:00,650
interactions are going to get
1217
00:42:00,640 –> 00:42:03,090
longer, the work is going
1218
00:42:03,090 –> 00:42:05,160
to get harder. And so
1219
00:42:05,160 –> 00:42:08,030
you need to train to
1220
00:42:08,030 –> 00:42:09,670
up level your agents. You
1221
00:42:09,670 –> 00:42:11,540
need to staff them differently,
1222
00:42:11,540 –> 00:42:13,180
you need to measure them
1223
00:42:13,180 –> 00:42:15,750
differently. You need to think
1224
00:42:15,750 –> 00:42:18,240
about career pathing them. You need
1225
00:42:18,240 –> 00:42:20,170
to make your agents comfortable
1226
00:42:20,230 –> 00:42:22,630
with AI and automation and
1227
00:42:22,630 –> 00:42:24,270
explain the value of these
1228
00:42:24,270 –> 00:42:25,850
technologies to agents and then
1229
00:42:25,850 –> 00:42:30,830
career path them into roles
1230
00:42:30,830 –> 00:42:32,780
where they have a greater
1231
00:42:32,780 –> 00:42:35,840
impact to the end customer.
1232
00:42:36,140 –> 00:42:37,210
If you do that well, you’re going to
1233
00:42:37,480 –> 00:42:39,030
find out that your agents want to
1234
00:42:39,030 –> 00:42:42,340
stay with you longer. Your
1235
00:42:42,340 –> 00:42:44,240
contact center’s actually becoming a
1236
00:42:44,240 –> 00:42:45,590
more attractive place to work
1237
00:42:45,590 –> 00:42:51,060
in. And again, look at
1238
00:42:51,060 –> 00:42:52,260
the measures of success. I
1239
00:42:52,260 –> 00:42:53,700
guess that’s my bullet five
1240
00:42:54,390 –> 00:42:56,490
and think back to being
1241
00:42:56,490 –> 00:42:58,410
customer centric, think about customer
1242
00:42:58,410 –> 00:43:01,210
centric measures of success. And Joe what
1243
00:43:01,210 –> 00:43:01,940
else? What did I miss?
1244
00:43:03,000 –> 00:43:04,070
I know everyone has heard
1245
00:43:04,070 –> 00:43:05,220
enough from me today, but
1246
00:43:05,220 –> 00:43:05,870
if I think I can
1247
00:43:05,870 –> 00:43:07,170
end it with one sentiment,
1248
00:43:07,410 –> 00:43:08,440
it all comes down to
1249
00:43:08,440 –> 00:43:10,140
what you said, it’s trust.
1250
00:43:10,580 –> 00:43:11,390
Even before we get to
1251
00:43:11,390 –> 00:43:12,320
the data we’d like to
1252
00:43:12,320 –> 00:43:14,050
use to build machine learning
1253
00:43:14,050 –> 00:43:15,400
models to help our agents,
1254
00:43:15,400 –> 00:43:16,420
it just comes down to
1255
00:43:16,420 –> 00:43:17,460
do we have that trust
1256
00:43:17,460 –> 00:43:19,080
with the customer? And that’s
1257
00:43:19,080 –> 00:43:20,680
the seed. I think it’s
1258
00:43:20,680 –> 00:43:22,900
so important that you construct
1259
00:43:22,900 –> 00:43:25,340
these interactions and these experiences
1260
00:43:25,340 –> 00:43:26,290
that are built around the
1261
00:43:26,290 –> 00:43:28,090
notion of is this something
1262
00:43:28,090 –> 00:43:29,270
that’s good for the customer?
1263
00:43:29,610 –> 00:43:30,390
And then you’ll have the
1264
00:43:30,400 –> 00:43:31,810
data to make those insights.
1265
00:43:31,970 –> 00:43:32,580
And then if you take
1266
00:43:32,580 –> 00:43:33,500
care of that data and use
1267
00:43:33,500 –> 00:43:35,090
it effectively, you have those
1268
00:43:35,090 –> 00:43:36,470
insights to train your agents
1269
00:43:36,470 –> 00:43:37,300
and help them on those
1270
00:43:37,300 –> 00:43:39,230
interactions. But it all starts
1271
00:43:39,230 –> 00:43:40,510
with the notion that you
1272
00:43:40,510 –> 00:43:41,780
have to have that trust
1273
00:43:42,050 –> 00:43:44,410
to get that ability. And
1274
00:43:44,410 –> 00:43:45,960
with that, I think we
1275
00:43:45,960 –> 00:43:46,700
can open it up to a
1276
00:43:46,700 –> 00:43:48,110
few questions here today too.
1277
00:43:48,170 –> 00:43:49,310
Thanks so much to everyone
1278
00:43:49,310 –> 00:43:50,250
and again for sticking with
1279
00:43:50,250 –> 00:43:55,080
us here. Thanks Joe. So
1280
00:43:55,160 –> 00:43:56,970
to remind everybody, if you
1281
00:43:56,970 –> 00:43:57,900
want to participate in the
1282
00:43:57,900 –> 00:43:58,760
quick Q& A that we’re
1283
00:43:58,760 –> 00:43:59,910
going to have time for,
1284
00:44:00,900 –> 00:44:01,590
go ahead and throw those
1285
00:44:01,590 –> 00:44:03,130
questions into the Q& A window
1286
00:44:03,130 –> 00:44:03,980
in the top center of
1287
00:44:03,980 –> 00:44:05,870
your screen. And although we
1288
00:44:05,870 –> 00:44:06,900
are almost at a time,
1289
00:44:06,910 –> 00:44:08,660
we’ll have enough time for
1290
00:44:08,660 –> 00:44:09,790
about one question that we
1291
00:44:09,790 –> 00:44:11,050
have so far. But don’t
1292
00:44:11,050 –> 00:44:12,730
fret, throw your questions in
1293
00:44:12,730 –> 00:44:13,780
there and we’ll follow up
1294
00:44:13,780 –> 00:44:15,290
with you via email within
1295
00:44:15,290 –> 00:44:16,480
the next few business days.
1296
00:44:17,580 –> 00:44:18,730
So we did have one
1297
00:44:18,730 –> 00:44:21,290
question regarding demographics Kate, do
1298
00:44:21,860 –> 00:44:22,730
or do you have any
1299
00:44:22,730 –> 00:44:25,270
information of these trends that you discussed
1300
00:44:25,810 –> 00:44:27,970
today or are the same
1301
00:44:27,970 –> 00:44:29,680
across all age groups? Or
1302
00:44:29,680 –> 00:44:30,440
can you go into a
1303
00:44:30,440 –> 00:44:33,490
little bit about the demographics? Yeah,
1304
00:44:33,490 –> 00:44:34,960
they’re basically the same across
1305
00:44:34,960 –> 00:44:38,380
all age groups except the…
1306
00:44:41,570 –> 00:44:44,410
what’s the demographic of a 75
1307
00:44:44,410 –> 00:44:46,570
year old plus? I forget.
1308
00:44:46,570 –> 00:44:48,120
It’s not the golden generation.
1309
00:44:48,120 –> 00:44:53,470
Is it the silent generation? So
1310
00:44:53,500 –> 00:44:57,750
baby boomers, gen Xs, millennials,
1311
00:45:02,420 –> 00:45:06,440
gen Zs, all show that
1312
00:45:06,440 –> 00:45:10,050
they are… because self service has
1313
00:45:10,050 –> 00:45:12,010
gone so good, they are
1314
00:45:12,010 –> 00:45:13,350
self- serving as a first
1315
00:45:13,350 –> 00:45:14,520
point of contact. Of course
1316
00:45:14,520 –> 00:45:17,230
the younger generations self serve
1317
00:45:17,230 –> 00:45:19,350
at a rate that’s higher
1318
00:45:19,990 –> 00:45:21,310
and more frequent than the
1319
00:45:21,310 –> 00:45:24,320
older generations. But all demographics
1320
00:45:24,320 –> 00:45:26,110
self serve as a first point of contact
1321
00:45:26,110 –> 00:45:28,110
and all demographics have turned
1322
00:45:28,110 –> 00:45:29,610
to digital engagement to be
1323
00:45:29,610 –> 00:45:32,290
able to reduce friction with
1324
00:45:32,290 –> 00:45:34,410
the exception of the, I
1325
00:45:34,410 –> 00:45:35,410
think it’s the 70 or
1326
00:45:35,410 –> 00:45:37,830
75 plus age group that
1327
00:45:37,830 –> 00:45:40,030
is still very phone centric.
1328
00:45:40,320 –> 00:45:42,650
There are some geographic differences,
1329
00:45:42,690 –> 00:45:48,830
there are some slight demographic
1330
00:45:48,830 –> 00:45:51,070
differences. But the trends that
1331
00:45:51,070 –> 00:45:52,910
we have articulated on this
1332
00:45:52,910 –> 00:45:59,070
webinar are fairly common, again,
1333
00:45:59,070 –> 00:46:02,390
across all demographics. So the
1334
00:46:02,390 –> 00:46:03,420
data that I showed was
1335
00:46:03,420 –> 00:46:04,710
from dimension data. If you
1336
00:46:04,710 –> 00:46:06,280
go to their site and
1337
00:46:06,280 –> 00:46:09,100
you can actually segment it
1338
00:46:09,160 –> 00:46:10,780
and drill into it by
1339
00:46:10,780 –> 00:46:13,590
geography and by demographic and
1340
00:46:14,890 –> 00:46:17,410
again, you’ll see there are
1341
00:46:17,410 –> 00:46:19,300
regional differences, there are demographic
1342
00:46:19,300 –> 00:46:23,200
differences, but the overarching statements
1343
00:46:23,200 –> 00:46:24,710
that we made are accurate
1344
00:46:25,470 –> 00:46:26,510
and are reflected in the
1345
00:46:26,510 –> 00:46:29,680
data. Joe, anything you want
1346
00:46:29,680 –> 00:46:31,640
to add? I think that
1347
00:46:31,640 –> 00:46:32,670
was a great way to
1348
00:46:32,670 –> 00:46:34,150
end it. I know there’s
1349
00:46:34,150 –> 00:46:35,480
more questions in there and
1350
00:46:35,480 –> 00:46:36,880
we can absolutely follow up
1351
00:46:36,880 –> 00:46:38,410
on those, but definitely some
1352
00:46:38,410 –> 00:46:39,950
deeper dives into the nuances
1353
00:46:39,950 –> 00:46:41,300
of agent assist or even
1354
00:46:41,600 –> 00:46:43,210
how business users can have
1355
00:46:43,210 –> 00:46:44,300
a big effect on bot
1356
00:46:44,300 –> 00:46:45,650
building and not need a
1357
00:46:45,650 –> 00:46:47,010
data scientist and everything. But
1358
00:46:47,540 –> 00:46:48,340
we will make sure to
1359
00:46:48,340 –> 00:46:49,180
follow up on that as
1360
00:46:49,180 –> 00:46:53,700
well. And to that, we
1361
00:46:53,700 –> 00:46:54,720
will go ahead and start
1362
00:46:54,720 –> 00:46:56,650
to close out today. So
1363
00:46:56,650 –> 00:46:57,390
all of the data that we
1364
00:46:57,390 –> 00:46:59,100
talked about, all of these trends
1365
00:46:59,100 –> 00:47:00,920
that we discussed within the
1366
00:47:00,920 –> 00:47:02,580
resource list below the Q& A
1367
00:47:02,580 –> 00:47:03,370
window, we do have the
1368
00:47:03,370 –> 00:47:04,810
full report so be sure
1369
00:47:04,810 –> 00:47:06,130
to click and download that
1370
00:47:06,350 –> 00:47:07,650
today. And also be sure
1371
00:47:07,650 –> 00:47:09,260
to check out our upcoming
1372
00:47:09,260 –> 00:47:10,810
webinars and you can click
1373
00:47:10,810 –> 00:47:11,770
the links to that page
1374
00:47:11,770 –> 00:47:13,760
as well. Also as a
1375
00:47:13,760 –> 00:47:16,610
friendly reminder, when you click
1376
00:47:16,610 –> 00:47:17,480
on those, they’ll open up
1377
00:47:17,480 –> 00:47:18,190
in a new tab. Be
1378
00:47:18,190 –> 00:47:19,090
sure to click them before
1379
00:47:19,090 –> 00:47:21,230
today’s session closes out or
1380
00:47:21,230 –> 00:47:22,130
you will receive an on
1381
00:47:22,130 –> 00:47:23,340
demand recording within the next
1382
00:47:23,340 –> 00:47:24,820
few business days. So just
1383
00:47:24,820 –> 00:47:26,080
be on the lookout. And
1384
00:47:26,080 –> 00:47:27,200
with that, on behalf of
1385
00:47:27,200 –> 00:47:28,590
Joe, Kate and the entire
1386
00:47:28,590 –> 00:47:30,060
Genesys team, we thank you
1387
00:47:30,060 –> 00:47:31,790
again for joining today’s webcast,
1388
00:47:31,870 –> 00:47:33,780
Mega Trends Shaping Customer Service
1389
00:47:33,780 –> 00:47:35,850
in 2020. Until next time,
1390
00:47:35,980 –> 00:47:37,010
have a good one everyone.
1391
00:47:39,810 –> 00:47:39,830
Bye bye.