Make personalized customer experience a reality with
Genesys AI

1
00:00:15,330 –> 00:00:17,630
Thank you all for joining. I’m excited to be here

2
00:00:17,630 –> 00:00:21,550
today to talk about Genesys AI. My name is Elcenora

3
00:00:21,550 –> 00:00:24,600
Martinez, and I’m the VP of product management for AI

4
00:00:24,600 –> 00:00:28,800
here at Genesys. I’m Dan Arra. I am the VP

5
00:00:28,800 –> 00:00:34,240
of sales for AI at Genesys. So let me walk

6
00:00:34,240 –> 00:00:37,220
you through what we’ll be talking about today, and at

7
00:00:37,220 –> 00:00:39,910
any point in time if you have any questions, please

8
00:00:39,910 –> 00:00:41,730
feel free to ask them in the chat. We are

9
00:00:41,730 –> 00:00:43,500
going to have folks that are going to be monitoring

10
00:00:43,500 –> 00:00:46,190
live and able to ask your questions, and if you

11
00:00:46,190 –> 00:00:49,640
do miss anything, this whole recording will be available on

12
00:00:49,640 –> 00:00:53,260
Genesys. com in a couple of days. So today we’re

13
00:00:53,260 –> 00:00:55,610
going to be talking about a couple of trends that

14
00:00:55,610 –> 00:00:59,370
you really can’t afford to ignore and how those dovetail

15
00:00:59,370 –> 00:01:02,450
really nicely into what we believe is a roadmap for

16
00:01:02,450 –> 00:01:06,690
success. We’re going to talk about why Genesys AI and

17
00:01:06,690 –> 00:01:09,800
introduce you to this notion of experience as a service,

18
00:01:09,800 –> 00:01:12,730
which is a best way to supersize the experience that

19
00:01:12,740 –> 00:01:15,900
you’re providing your customers today. We’re going to go deep

20
00:01:15,900 –> 00:01:18,950
into our predictive engagement product, which is one of our

21
00:01:18,950 –> 00:01:22,270
lead offerings on the AI portfolio. Then we’re going to

22
00:01:22,270 –> 00:01:25,870
talk about where to go for more information or if

23
00:01:25,870 –> 00:01:31,340
you would like some additional demonstrations. So first and foremost,

24
00:01:31,430 –> 00:01:33,350
AI is here to stay. I don’t think I have

25
00:01:33,350 –> 00:01:37,540
to tell anyone that. IDC estimates that this is a

26
00:01:37,540 –> 00:01:40,900
market that’s going to grow to $ 97 billion by

27
00:01:40,910 –> 00:01:45,280
2023, and that over 50% of interactions are going to

28
00:01:45,280 –> 00:01:48,750
be augmented or enriched by AI. But what we want

29
00:01:48,750 –> 00:01:51,910
to do is inspire any chief marketing officers or chief

30
00:01:51,910 –> 00:01:55,440
experience officers to understand how AI can help you in

31
00:01:55,440 –> 00:01:57,730
the contact center, to make sure that you have the

32
00:01:57,730 –> 00:02:01,010
ability to lobby for some share of market where your

33
00:02:01,010 –> 00:02:05,090
company wants to spend their AI dollars. The reason that

34
00:02:05,090 –> 00:02:09,290
is, is because you want to be able to differentiate

35
00:02:09,290 –> 00:02:12,550
the experience that you give your customers, and that requires

36
00:02:12,550 –> 00:02:16,080
being able to personalize every single one of those interactions.

37
00:02:16,610 –> 00:02:20,430
Now, the need for personalization is going to stretch beyond

38
00:02:20,430 –> 00:02:24,550
what is available on a customer data platform today. CDPs

39
00:02:24,550 –> 00:02:29,060
have the ability to store information about customers, but if

40
00:02:29,060 –> 00:02:32,330
you really want to take that next level on personalization,

41
00:02:32,610 –> 00:02:36,670
it’s going to require stitching, enriching, and augmenting much of

42
00:02:36,670 –> 00:02:41,410
that data to be able to personalize the interactions. If

43
00:02:41,410 –> 00:02:45,240
we think about the fact that 50% of these interactions

44
00:02:45,260 –> 00:02:49,140
will be augmented by AI, then we need to go

45
00:02:49,140 –> 00:02:52,390
beyond just a single interaction in a single channel to

46
00:02:52,390 –> 00:02:56,240
really be able to understand the context across many channels

47
00:02:56,410 –> 00:03:00,440
over the lifetime of a customer’s relationship with a brand.

48
00:03:00,650 –> 00:03:03,410
So being able to harmonize and understand all of that

49
00:03:03,410 –> 00:03:08,130
context is going to be really important. No AI investment

50
00:03:08,130 –> 00:03:11,150
would be complete without the right level of analytics and

51
00:03:11,150 –> 00:03:13,650
reporting. So we want to make sure that you have

52
00:03:13,650 –> 00:03:17,880
the ability to really understand how your AI investment is

53
00:03:17,880 –> 00:03:23,040
performing, the optimization and really be able to fine tune

54
00:03:23,040 –> 00:03:28,420
this investment. Now, Forrester estimates in a survey last year

55
00:03:28,420 –> 00:03:34,280
that 53% of companies already have implemented or are implementing

56
00:03:34,280 –> 00:03:38,200
some form of artificial intelligence, and this is great because

57
00:03:38,200 –> 00:03:41,570
what we see is that we’ve passed critical mass. But

58
00:03:41,570 –> 00:03:45,260
it also means that 47% of companies have not yet

59
00:03:45,260 –> 00:03:48,930
deployed artificial intelligence. Now, there’s a couple of barriers to

60
00:03:48,930 –> 00:03:54,400
adoption and that can range from being able to achieve

61
00:03:54,400 –> 00:03:58,680
it at scale, to security, to adoption, and even some

62
00:03:58,680 –> 00:04:01,700
of the conversational intelligence. But what I want to leave

63
00:04:01,700 –> 00:04:05,570
you with today is how Genesys is helping you overcome

64
00:04:05,570 –> 00:04:07,790
some of those challenges. So I’m going to go through

65
00:04:07,790 –> 00:04:12,240
these one by one. We have made a significant investment

66
00:04:12,240 –> 00:04:15,780
in a data science team that is very focused on

67
00:04:15,780 –> 00:04:19,290
AI ethics and algorithmic integrity, and to be able to

68
00:04:19,340 –> 00:04:22,560
deliver some of this AI at scale. So we have

69
00:04:22,560 –> 00:04:26,530
the ability to optimize, orchestrate and really automate many of

70
00:04:26,530 –> 00:04:30,200
these processes. For those of you that are Genesys customers

71
00:04:30,280 –> 00:04:34,030
today, that our Genesys Cloud, our Genesys Multicloud CX and our PureCloud

72
00:04:34,030 –> 00:04:39,700
platforms are all 100% secured to the core. We’re also

73
00:04:39,700 –> 00:04:43,030
driving adoption because the way we think of our AI

74
00:04:43,030 –> 00:04:46,640
platform is everything is event driven, and what this means

75
00:04:46,640 –> 00:04:49,890
for you is it gives you greater flexibility to be

76
00:04:49,890 –> 00:04:54,210
able to think about web events, conversation event, maybe even

77
00:04:54,210 –> 00:04:58,010
custom events, and so your starting point can different ways

78
00:04:58,010 –> 00:05:02,700
it can really drive that adoption within the company. We’re

79
00:05:02,700 –> 00:05:07,160
also very focused on usability. We’ve made great investments in

80
00:05:07,160 –> 00:05:11,190
a design thinking functionality, so that delivering products that are

81
00:05:11,190 –> 00:05:13,970
user centered are very much to the core of how

82
00:05:13,970 –> 00:05:16,900
we think of our roadmap. So we think about ways

83
00:05:16,900 –> 00:05:19,960
to onboard, to make sure that you can see successes

84
00:05:19,990 –> 00:05:23,010
early on in ways that it doesn’t require an army

85
00:05:23,010 –> 00:05:27,940
of data scientist. Then finally, we’re really focused on bringing

86
00:05:27,940 –> 00:05:31,200
you a higher level of conversational intelligence through all of

87
00:05:31,200 –> 00:05:34,030
the bot interactions in ways that it feels like you’re

88
00:05:34,030 –> 00:05:39,260
conversing with a human agent. So why Genesys AI, and

89
00:05:39,260 –> 00:05:42,130
more specifically why predictive engagement, which I said is one

90
00:05:42,130 –> 00:05:44,560
of the lead offerings in our AI suite of offerings?

91
00:05:46,040 –> 00:05:50,820
Simply put, the average consumer receives up to 10,000 brand

92
00:05:50,820 –> 00:05:54,820
messages every day, and what that means for you as

93
00:05:54,820 –> 00:05:58,210
a brand is you have to rely on information that

94
00:05:58,210 –> 00:06:02,260
exists within your own customer data platform, enrich that with

95
00:06:02,260 –> 00:06:06,500
CRM information or some other custom applications to really reach

96
00:06:06,870 –> 00:06:12,560
that target customer. But there’s definitely a better way. If

97
00:06:12,560 –> 00:06:15,700
you think about your customer at the center of how

98
00:06:15,700 –> 00:06:18,650
you think about these experiences, we want to introduce you

99
00:06:18,660 –> 00:06:21,550
to the notion of experience as a service as a

100
00:06:21,550 –> 00:06:26,460
better way of delivering exceptional experiences, and what this entails

101
00:06:26,490 –> 00:06:29,600
is to really think about all of these interactions we’ve

102
00:06:29,600 –> 00:06:32,530
talked about over the course of a lifetime of a

103
00:06:32,530 –> 00:06:35,430
customer and a brand, and all of the data that

104
00:06:35,430 –> 00:06:37,970
is collected both on the customer side as well as

105
00:06:37,970 –> 00:06:40,760
all of the information that’s collected from all of the

106
00:06:40,780 –> 00:06:45,530
employee and agent interactions, and enrich that in augment it

107
00:06:45,610 –> 00:06:48,730
with artificial intelligence, so that you have a higher level

108
00:06:48,730 –> 00:06:52,470
of conversational intelligence and so that you can really optimize.

109
00:06:52,770 –> 00:06:55,180
So what that does is it gives you the ability

110
00:06:55,180 –> 00:06:58,850
to really provide experience as a service to every single

111
00:06:58,850 –> 00:07:01,860
one of these customers. Now, this is a big idea,

112
00:07:02,130 –> 00:07:04,170
and so what I want to do is I want

113
00:07:04,170 –> 00:07:07,410
to talk about what some of these imperatives for experience

114
00:07:07,410 –> 00:07:10,880
as a service are. As I go through each one

115
00:07:10,880 –> 00:07:13,100
of these, what I want to challenge you is to

116
00:07:13,100 –> 00:07:17,360
think about how well each one of your companies is

117
00:07:17,360 –> 00:07:21,450
doing on delivering some of these imperatives. So as a

118
00:07:21,450 –> 00:07:24,830
customer, I want you to show me that me before

119
00:07:24,830 –> 00:07:27,370
you’ve ever met me, and that means I’ve been a

120
00:07:27,370 –> 00:07:30,360
customer for a long time. I haven’t necessarily had to

121
00:07:30,380 –> 00:07:33,370
interact with your brand, but when I do, you already

122
00:07:33,370 –> 00:07:35,380
know who I am and how long I have been

123
00:07:35,380 –> 00:07:39,380
your customer. In some cases, I want you to help

124
00:07:39,380 –> 00:07:43,010
me before I even know there’s a problem. Let’s say

125
00:07:43,010 –> 00:07:45,760
my bill has increased, my telephone bill has increased by

126
00:07:45,780 –> 00:07:49,550
25%. Wouldn’t it be nice if I got notified in

127
00:07:49,550 –> 00:07:51,690
advance? Maybe I want to look at a different data

128
00:07:51,690 –> 00:07:54,220
plan. Maybe I want to look at different options. So

129
00:07:54,600 –> 00:07:56,570
reach out to me before I even know there’s a

130
00:07:56,570 –> 00:08:01,210
problem. Know the road I traveled to get here, and

131
00:08:01,210 –> 00:08:03,370
this can be one of two things. It can be

132
00:08:03,370 –> 00:08:07,610
understanding that your conversation started over the phone and ended

133
00:08:07,610 –> 00:08:10,780
with an agent, and throughout that entire experience, you’ve retained

134
00:08:10,780 –> 00:08:14,260
the context, or it can also be again over the

135
00:08:14,260 –> 00:08:19,240
course of a customer’s lifetime with a brand, understanding what

136
00:08:19,240 –> 00:08:23,590
they’ve been through. This one is so important to experience

137
00:08:23,590 –> 00:08:26,110
as a service, present me with the answers I want

138
00:08:26,110 –> 00:08:29,180
and the ones I didn’t know I needed. Often time,

139
00:08:29,180 –> 00:08:32,930
our agent assist tools allow agents to provide customers with

140
00:08:32,930 –> 00:08:36,070
the right information in realtime, but we feel that it’s

141
00:08:36,070 –> 00:08:39,300
important to take that a step beyond, and that means

142
00:08:39,450 –> 00:08:42,870
really look at what the customer may want, what are

143
00:08:42,870 –> 00:08:45,860
some of the other products and services that they’re using and

144
00:08:45,860 –> 00:08:48,470
how you can really help them in ways that they

145
00:08:48,470 –> 00:08:50,490
didn’t even know they needed, but it will be a

146
00:08:50,490 –> 00:08:54,780
better experience for them. Don’t ask me to repeat myself,

147
00:08:54,780 –> 00:08:57,990
I think every single person has had some form of

148
00:08:57,990 –> 00:09:01,080
experience with this. We want to make sure that we

149
00:09:01,080 –> 00:09:03,960
explain things to an agent once and that context is

150
00:09:04,970 –> 00:09:10,120
available throughout the entire interaction. Empathize with my situation, and

151
00:09:10,120 –> 00:09:12,270
what that really means is that in that moment of

152
00:09:12,270 –> 00:09:14,750
truth, we want the agents to be able to respond

153
00:09:14,750 –> 00:09:18,210
with the appropriate emotion. So there are certain hearing aids,

154
00:09:18,210 –> 00:09:20,100
as we call them that have been built into the

155
00:09:20,100 –> 00:09:25,270
entire platform to help the agent respond appropriately. Fix it

156
00:09:25,270 –> 00:09:28,700
and stay with me until it’s fixed, and oftentimes, check

157
00:09:28,700 –> 00:09:30,390
in with me to make sure that it all worked

158
00:09:30,390 –> 00:09:34,020
out, and this goes above and beyond just a survey.

159
00:09:34,270 –> 00:09:37,220
This goes with having the right level of empathy, where

160
00:09:37,220 –> 00:09:40,570
you are reaching out to these critical customers and make

161
00:09:40,570 –> 00:09:43,170
sure that we’ve been able to resolve their problem or

162
00:09:43,170 –> 00:09:46,950
their challenge or their concern. Then above all, keep my

163
00:09:46,950 –> 00:09:50,380
data safe. So what this does is it brings the

164
00:09:50,380 –> 00:09:53,960
concept of experience as a service to life in ways

165
00:09:53,960 –> 00:09:56,790
that you can really understand how to deliver that exceptional

166
00:09:56,790 –> 00:10:00,700
service. So before I turn it over to Dan, let

167
00:10:00,700 –> 00:10:02,780
me spend a couple of minutes recapping some of the

168
00:10:02,780 –> 00:10:07,950
things that we’ve talked about. An event- driven orchestration platform

169
00:10:08,240 –> 00:10:11,340
that allows you to be proactive. Reach out to your

170
00:10:11,340 –> 00:10:14,320
customers in advance. Engage with them in that moment of

171
00:10:14,320 –> 00:10:18,200
truth. When there’s a break in the workflow, or when

172
00:10:18,590 –> 00:10:23,020
there’s a possibility that they might churn. Personalize every interaction,

173
00:10:23,040 –> 00:10:27,490
show that them, strengthen the relationship, and above all be

174
00:10:27,490 –> 00:10:30,780
able to retain history and the context throughout all of

175
00:10:30,800 –> 00:10:34,680
the interactions because empathy builds trust and trust builds loyalty.

176
00:10:35,410 –> 00:10:37,360
So let me turn it over to Dan to run

177
00:10:37,360 –> 00:10:41,210
through a demonstration of predictive engagement. Thank you very much,

178
00:10:41,390 –> 00:10:45,730
Elcenora. I’m going to provide a demonstration of Genesys predictive

179
00:10:45,730 –> 00:10:49,260
engagement, and before I jump into the demonstration, I’m going

180
00:10:49,390 –> 00:10:52,980
to go through a single slide to connect the dots

181
00:10:53,080 –> 00:10:57,050
a bit between predictive engagement and experience as a service.

182
00:10:57,260 –> 00:11:00,950
I’m going to highlight some of the elements that Elcenora

183
00:11:00,950 –> 00:11:05,160
touched on prior to doing the demonstration. So you’ve heard

184
00:11:05,540 –> 00:11:09,170
quite a bit about the show me that me, and

185
00:11:09,360 –> 00:11:11,850
the way that we do that with predictive engagement is

186
00:11:12,090 –> 00:11:15,870
we observe what is happening in all of the interactions

187
00:11:16,110 –> 00:11:19,440
between the customer and your business, what’s happening on your

188
00:11:19,440 –> 00:11:23,410
website, what’s happening in the contact center, and we analyze

189
00:11:23,410 –> 00:11:27,210
that information in realtime for the purpose of segmenting those

190
00:11:27,210 –> 00:11:32,390
customers into different groups and personalizing the experience and driving

191
00:11:32,440 –> 00:11:37,350
realtime interactions that demonstrate that we’re showing the customer that

192
00:11:37,350 –> 00:11:43,050
we know them. At its core, empathetic communication is about

193
00:11:43,530 –> 00:11:46,720
listening, is about letting people know that you really understand

194
00:11:46,720 –> 00:11:49,860
who they are. We’re answering the questions, are you really

195
00:11:49,860 –> 00:11:52,760
listening to me? Do you care about me enough to

196
00:11:52,920 –> 00:11:55,990
remember what I’ve told you before? That’s what we’re doing

197
00:11:55,990 –> 00:11:59,140
here as we go from left to right. We’re leveraging

198
00:11:59,170 –> 00:12:03,630
all of those interactions and observations, analyzing and then predicting

199
00:12:03,730 –> 00:12:06,790
an outcome, predicting how to engage, that could be with

200
00:12:07,810 –> 00:12:12,340
a bot, and it could be with some information, more

201
00:12:12,340 –> 00:12:15,960
context that makes the agent smarter, more effective. Maybe we’re

202
00:12:15,960 –> 00:12:20,290
using AI to predict the right agent to route that

203
00:12:20,670 –> 00:12:24,920
interaction to. What we’re doing here is we’re driving people

204
00:12:24,920 –> 00:12:29,840
toward outcomes, outcomes that are your business’s desired outcomes, as

205
00:12:29,840 –> 00:12:33,060
well as the customer’s desired outcomes. I may be a

206
00:12:33,060 –> 00:12:36,850
customer visiting a website, interested in making a purchase, or

207
00:12:36,850 –> 00:12:39,870
I may be looking to get support for a product.

208
00:12:40,250 –> 00:12:44,150
Those are my outcomes as a end customer. The business

209
00:12:44,150 –> 00:12:48,160
wants to deliver the right information at the right time efficiently.

210
00:12:48,630 –> 00:12:52,680
So everybody’s outcome is being considered here in an effort

211
00:12:52,680 –> 00:12:57,710
to connect the dots between the analysis, the outcome prediction,

212
00:12:57,710 –> 00:13:02,130
the first touch interaction and so on. As I go

213
00:13:02,130 –> 00:13:04,680
into the demonstration here in a moment, I want you

214
00:13:04,680 –> 00:13:08,220
to think about the real life experience of walking into

215
00:13:08,220 –> 00:13:11,740
a retail store, and somebody walking up to you saying,

216
00:13:11,930 –> 00:13:14,110
a clerk at the store, ” Can I help you? Can

217
00:13:14,110 –> 00:13:18,170
I help you now? How about now?” An experienced a

218
00:13:18,170 –> 00:13:21,770
store clerk, salesperson or support person is going to observe

219
00:13:21,770 –> 00:13:23,740
what you’re doing, what are you putting in your cart,

220
00:13:23,740 –> 00:13:27,210
where are you stopping in the store, which aisles and

221
00:13:27,210 –> 00:13:30,690
so on, and they’re going to take that into context intelligently

222
00:13:30,840 –> 00:13:34,970
about how, when, with whom to engage to personalize that

223
00:13:34,970 –> 00:13:38,770
experience. Okay, now I’m going to switch and share my

224
00:13:38,770 –> 00:13:45,560
browser and show you the demonstration. All right. So I

225
00:13:45,560 –> 00:13:48,510
am going to show you a visitor on the left,

226
00:13:48,710 –> 00:13:52,100
visiting a website, the GSOL website, and on the right,

227
00:13:52,100 –> 00:13:54,120
I’m going to give you a peek under the hood

228
00:13:54,160 –> 00:13:57,180
of the predictive engagement platform. I’m going to show you

229
00:13:57,180 –> 00:14:01,220
what the system can see, how we’re analyzing and predicting,

230
00:14:01,400 –> 00:14:05,660
when and with whom to engage. Then after I do

231
00:14:05,660 –> 00:14:09,350
that, I’m going to show you an agent experience. So

232
00:14:09,350 –> 00:14:12,740
keep in mind, initially, I’m showing you what is happening

233
00:14:12,740 –> 00:14:16,250
under the hood of the platform. What we’re doing here

234
00:14:16,250 –> 00:14:17,940
is looking at all of the people who are on

235
00:14:17,940 –> 00:14:20,660
the site, some are known and some are unknown, and

236
00:14:20,660 –> 00:14:24,410
we’re observing their interactions. This is also looking at what’s

237
00:14:24,410 –> 00:14:29,910
happening in the contact center and connecting through our event-

238
00:14:29,910 –> 00:14:34,050
driven orchestration to other systems where we’re leveraging CRM data,

239
00:14:34,050 –> 00:14:38,110
marketing automation data, and even other systems that have web-

240
00:14:38,330 –> 00:14:42,160
based APIs. So I’m going to highlight this one individual

241
00:14:42,490 –> 00:14:45,520
that happens to be me, and you’ll notice something here,

242
00:14:45,520 –> 00:14:48,730
as the visitor on the left starts to navigate around

243
00:14:48,980 –> 00:14:53,360
and look at different pages, we’re going that behavior being

244
00:14:53,360 –> 00:14:56,930
observed and analyzed, and we’re even making predictions about this

245
00:14:57,050 –> 00:15:00,310
unknown visitor, now unknown. We can see what part of

246
00:15:00,310 –> 00:15:04,240
the world they’re located, some technographic information about the devices

247
00:15:04,240 –> 00:15:08,140
that they’re using, and we can see historical information here

248
00:15:08,380 –> 00:15:11,410
as well, if there were previous visits. So again, I’m

249
00:15:11,410 –> 00:15:14,040
unknown at the moment. But now I start to put

250
00:15:14,220 –> 00:15:17,470
something in my shopping cart. I add these batteries to

251
00:15:17,470 –> 00:15:20,580
the shopping cart, and you’ll see here that now that

252
00:15:20,580 –> 00:15:23,420
I’ve added something to my shopping cart, the system can

253
00:15:23,420 –> 00:15:26,600
see what is in the cart, what’s the value, which

254
00:15:26,600 –> 00:15:29,910
product category. We can see that were identified as a

255
00:15:29,910 –> 00:15:33,700
particular segment, and there are other events that occurred like

256
00:15:33,700 –> 00:15:38,070
our cart value changed from zero to $ 216. So

257
00:15:38,120 –> 00:15:41,300
before the visitor on the left proceeds to check out,

258
00:15:41,720 –> 00:15:43,750
they may want to log in because they have some

259
00:15:43,750 –> 00:15:48,050
stored payment information in the system, and you’ll see a

260
00:15:48,050 –> 00:15:51,780
few things happening. Now that we’ve logged in, we know

261
00:15:51,780 –> 00:15:55,210
the identity of that visitor, and we can see that

262
00:15:55,260 –> 00:15:59,840
maybe they’re eligible for other communication options. Now that we’re

263
00:15:59,840 –> 00:16:03,080
logged in, maybe our purchase history or the value of

264
00:16:03,080 –> 00:16:06,340
our shopping cart gives us a few more options to

265
00:16:06,340 –> 00:16:10,630
communicate. That just appeared after I logged in. When I

266
00:16:10,630 –> 00:16:13,900
proceed back to check out, I go back here and

267
00:16:13,900 –> 00:16:17,190
you’ll see that the prediction of checking out is higher,

268
00:16:17,310 –> 00:16:20,390
and just to highlight here, we’re making two predictions that

269
00:16:20,390 –> 00:16:24,790
make sense in our configured for the GSOL customer. Your

270
00:16:24,790 –> 00:16:28,040
business and any other business would have different configured outcomes,

271
00:16:28,560 –> 00:16:31,800
making a purchase or being identified as a lead, those

272
00:16:31,800 –> 00:16:35,170
are sales and marketing outcomes. But you could have care

273
00:16:35,170 –> 00:16:38,350
outcomes, like make an inbound call for support or open

274
00:16:38,350 –> 00:16:42,570
a trouble ticket. So here, the visitor in their purchase

275
00:16:42,570 –> 00:16:49,040
motion doesn’t know what the discount code is, and before

276
00:16:49,040 –> 00:16:52,010
they check out, they start searching for it. Maybe they

277
00:16:52,010 –> 00:16:56,800
go over here and they search for discount codes, and

278
00:16:56,800 –> 00:17:00,050
we’re going to pick up that search information and what

279
00:17:00,050 –> 00:17:02,760
just happened here on the left is we triggered, the

280
00:17:02,760 –> 00:17:08,600
platform triggered an automated chat offer that is leveraging all

281
00:17:08,600 –> 00:17:11,220
of the context, all of the analysis and predictions that

282
00:17:11,220 –> 00:17:14,410
we’re observing under the hood in the platform on the

283
00:17:14,410 –> 00:17:20,370
right. As the visitor accepts this chat, they’re going to

284
00:17:20,370 –> 00:17:25,170
initially be prompted with an intelligent chat bot who knows

285
00:17:25,170 –> 00:17:28,670
this is Dan, and that he’s likely to need help

286
00:17:28,670 –> 00:17:32,850
with his purchase today. So first, we’re leveraging the intelligence

287
00:17:33,020 –> 00:17:36,560
bots, and here we may want to say, ” Yes, I’ve

288
00:17:36,560 –> 00:17:45,800
got a question. Wondering about,” oops, type, ” about shipping availability.”

289
00:17:49,340 –> 00:17:52,390
We are going to get a response back telling us

290
00:17:52,430 –> 00:17:55,060
a bit about that topic, and then, ” Yes, I’ve got

291
00:17:55,060 –> 00:17:57,810
questions about my discount code and I want to talk

292
00:17:57,810 –> 00:18:05,950
to an agent please.” So when that initial step connecting

293
00:18:05,950 –> 00:18:08,270
us to an agent occurs, we’re doing a few different

294
00:18:08,270 –> 00:18:13,900
things here. We’re going to answer this interaction, and now

295
00:18:14,070 –> 00:18:18,440
I’m going to show you the agent view, okay? Again,

296
00:18:18,740 –> 00:18:20,810
what we were looking at was what was happening under

297
00:18:20,810 –> 00:18:24,670
the hood. But now as an agent, I have access

298
00:18:24,900 –> 00:18:29,210
to what was in the shopping cart, maybe previous visits,

299
00:18:30,360 –> 00:18:33,800
the likelihood, hey, this visitor got very likely to making

300
00:18:33,800 –> 00:18:37,330
a purchase and then abandoned, and then we could continue

301
00:18:37,330 –> 00:18:39,810
to watch while we’re thinking about what we’re going to

302
00:18:39,810 –> 00:18:43,410
say, watch what that customer is doing. We’re going to

303
00:18:43,410 –> 00:18:47,760
leverage these segments and the behaviors, and as I hover

304
00:18:47,760 –> 00:18:50,060
over as an agent some of these elements on the

305
00:18:50,060 –> 00:18:53,890
right, I decide I want to respond with a canned

306
00:18:53,920 –> 00:19:00,540
response about discount codes. So I search for my available

307
00:19:00,540 –> 00:19:04,940
canned responses. I find the appropriate one here and now

308
00:19:05,220 –> 00:19:08,640
I can guide that customer to the right information so

309
00:19:08,640 –> 00:19:13,300
that they have the context and information that they need

310
00:19:13,560 –> 00:19:18,520
to improve and streamline the process. Now they know that

311
00:19:18,520 –> 00:19:21,420
the discount code could be entered here. They have that

312
00:19:21,420 –> 00:19:25,890
discount code available to them. I apply it and proceed

313
00:19:25,890 –> 00:19:29,810
to check out. So when I check out, you’ll see

314
00:19:29,810 –> 00:19:32,800
here that the outcome is achieved. We see the payment

315
00:19:32,800 –> 00:19:35,360
was made, what was the size of the order. This

316
00:19:35,360 –> 00:19:40,970
goes from 99% to 100% and we’re ready to wrap

317
00:19:40,970 –> 00:19:43,140
up. We ask the customer, ” Do you need anything else at

318
00:19:43,140 –> 00:19:47,770
the moment?” Maybe they say, ” Well, yes, I actually had

319
00:19:47,770 –> 00:19:51,940
some questions about your panels.” So the agent has an

320
00:19:51,940 –> 00:19:56,940
opportunity to take advantage of that additional conversation, create a

321
00:19:56,940 –> 00:20:01,590
new lead in the CRM with our integration here to

322
00:20:01,590 –> 00:20:05,010
extend the reach beyond what’s just happening in predictive engagement.

323
00:20:06,330 –> 00:20:08,490
We could create a lead here, but now I’m going

324
00:20:08,490 –> 00:20:11,870
to show you a separate example where we do that

325
00:20:11,870 –> 00:20:15,270
in an automated fashion. That’s going to end up creating

326
00:20:15,270 –> 00:20:19,130
a lead and adding an individual to a campaign all

327
00:20:19,130 –> 00:20:23,560
under the hood automatically. So we wrap up this interaction

328
00:20:24,370 –> 00:20:27,560
and we now know exactly which wrap- up code to

329
00:20:27,560 –> 00:20:31,490
use because we saw that order placed, and now we’re

330
00:20:31,490 –> 00:20:35,900
done here with this particular interaction and we’re waiting for

331
00:20:35,900 –> 00:20:42,970
another. So let’s say this visitor later that night or

332
00:20:42,970 –> 00:20:46,140
the next day decides they’re going to go back onto

333
00:20:46,140 –> 00:20:50,440
the site and put some items in their shopping cart.

334
00:20:51,950 –> 00:20:55,090
In this second case, so you have to fast forward

335
00:20:55,090 –> 00:20:58,780
a little bit and this is a later that that

336
00:20:58,960 –> 00:21:02,920
night. They put some items in their shopping cart, and

337
00:21:02,920 –> 00:21:06,170
we’re going to, again, observe what that visitor is doing

338
00:21:06,170 –> 00:21:09,320
so we can see under the hood what’s happening. This

339
00:21:09,320 –> 00:21:12,480
visitor decided they would like, Dan over here would like

340
00:21:13,210 –> 00:21:17,120
10 of these items and he proceeds to check out.

341
00:21:18,330 –> 00:21:21,460
So now he’s got a pretty high cart value and

342
00:21:21,460 –> 00:21:25,640
he’s eligible for another discount, and this is a solar

343
00:21:25,640 –> 00:21:28,370
panel so it’s a different discount code. He doesn’t have

344
00:21:28,370 –> 00:21:31,560
it available to him at the moment, and he starts

345
00:21:31,560 –> 00:21:36,520
to again search around for it. He abandons this shopping

346
00:21:36,520 –> 00:21:42,220
cart, and here, what we’re doing is taking advantage of

347
00:21:42,260 –> 00:21:46,270
all that information and creating a new lead in our

348
00:21:46,270 –> 00:21:52,530
CRM system that is going to inform the enterprise account

349
00:21:52,530 –> 00:21:56,330
rep who is working on deals larger than $ 2,

350
00:21:56,330 –> 00:22:00,130
000 to follow up with this customer the next day

351
00:22:00,130 –> 00:22:02,820
or first thing in the morning. Now, if I take

352
00:22:02,820 –> 00:22:05,380
a look here in our CRM system, and we want

353
00:22:05,380 –> 00:22:10,140
to observe what happened under the hood, we’re going to

354
00:22:10,140 –> 00:22:13,740
look up that lead and we’re going to find this

355
00:22:13,740 –> 00:22:17,460
lead, Dan was created. You can look at those details.

356
00:22:18,280 –> 00:22:21,810
In here we can see we’re leveraging the journey, the

357
00:22:21,810 –> 00:22:25,350
intelligence of predictive engagement can see that the shopping cart

358
00:22:25,350 –> 00:22:29,620
value was $ 2, 100. We can see what the

359
00:22:29,620 –> 00:22:31,770
items were that were in the cart, and then we

360
00:22:31,770 –> 00:22:38,280
also created a new member to our GSOL campaign. It

361
00:22:38,280 –> 00:22:43,190
is going to inform the enterprise account rep that he

362
00:22:43,190 –> 00:22:46,620
needs to follow up, either initiate a call or send

363
00:22:46,620 –> 00:22:50,300
an email to follow up with Dan the next day.

364
00:22:52,940 –> 00:22:58,060
So that concludes my demo. Thank you, Dan. That was

365
00:22:58,060 –> 00:23:01,160
great. Thank you for joining us for this edition of

366
00:23:01,170 –> 00:23:03,410
LinkedIn Live and what I hope is the first of

367
00:23:03,410 –> 00:23:05,850
many that do a deep dive into the Genesys AI

368
00:23:06,150 –> 00:23:09,590
portfolio. Thank you all for joining us. If any of

369
00:23:09,590 –> 00:23:13,180
you would like more information, there is a self guided

370
00:23:13,180 –> 00:23:17,400
tour available at Genesys. com, along with a hyperlink at

371
00:23:17,400 –> 00:23:19,600
the bottom that allows you to reach out in case

372
00:23:19,600 –> 00:23:22,580
you want to contact any of our account executives, your

373
00:23:22,580 –> 00:23:26,470
Genesys support team, or generally any more information on anything

374
00:23:26,470 –> 00:23:29,760
that you saw today. So again, thank you for your

375
00:23:29,760 –> 00:23:33,270
time. We appreciate your partnership and we look forward to

376
00:23:33,270 –> 00:23:35,670
seeing you at another edition of LinkedIn Live soon.