Episode 1: Why Motivation AI Matters

1
00:00:02.480 –> 00:00:16.699
[email protected]: All right. We are here with our first guest, Lisa Spira, the head of Content Intelligence at Prisato Lisa, We’re very excited to have you. Why, don’t you introduce yourself and tell us a little bit about your background.

2
00:00:16.870 –> 00:00:28.539
Lisa Spira: Thank you for having me. I’m excited to be the first guest and talk about motivation. Ai. With you. I’m. The head of Content Intelligence at Prisato, as you said,

3
00:00:28.550 –> 00:00:51.460
Lisa Spira: and Content intelligence that team my team is the language experts at Versato content. Intelligence is the intersection between creativity and data. It’s data backed content, creation and it’s identifying what is good content. So we work really closely with the data science team to train this Ai to hold its hand really,

4
00:00:51.470 –> 00:01:07.259
Lisa Spira: and interpret what it knows. And I got into this because I have always loved language, and I especially always love names. I’m an automatician which is a type of linguist with an expertise in names,

5
00:01:07.270 –> 00:01:23.790
Lisa Spira: and one of the things I always loved about names was the pattern recognition element. And so through that interest I stumbled into my first job at a different marketing technology company whose technology was really based around the origins of names.

6
00:01:23.800 –> 00:01:33.760
Lisa Spira: And that’s how I got enamored with the whole field of marketing language and Ai and technology and product building.

7
00:01:33.770 –> 00:01:55.469
Lisa Spira: And that’s how I landed at Crosado. So that’s my story of of getting here, and my team is made up of people with all sorts of unusual stories about how we’ve gotten into content intelligence. There’s no one particular background that makes someone good at this, except maybe just a true love of language.

8
00:01:55.530 –> 00:02:05.930
[email protected]: I love that, and I think it’s such a unique differentiator for the prisato experience, not only internal or other forsatoans like myself, but

9
00:02:05.940 –> 00:02:16.670
[email protected]: working with our clients to understand what motivates their consumers to interact with their brands and create happy customers for life.

10
00:02:16.840 –> 00:02:31.220
[email protected]: So I want to touch a little bit more on motivation. Ai. Can you, in your own words, define what motivation Ai means? And then who is motivation? Ai important to?

11
00:02:32.540 –> 00:02:37.889
Lisa Spira: I think, as marketers? There’s something i’m gonna start that one again.

12
00:02:37.900 –> 00:02:57.040
Lisa Spira: So there is a difference between wants and needs, and then actually taking that next step to act. And that’s where motivation Ai comes into play. We all want things. We all need things, and we all buy a lot of things. But

13
00:02:57.050 –> 00:03:09.780
Lisa Spira: how do you motivate someone to make that purchase and not just make that purchase, but by your specific product right now across this channel at this moment, in time

14
00:03:10.060 –> 00:03:19.709
Lisa Spira: it’s about action. We have to motivate action. I can want a lot of things, but i’m not necessarily going to click that ad or see that email

15
00:03:19.720 –> 00:03:46.819
Lisa Spira: and take the action. And so what motivation Ai is is an Ai that uses language to motivate consumers, to take an action, to click, to purchase, to convert, to make a connection, whatever that action is, and I think that motivation Ai is is really the important piece in doing this, and it’s really grounded in the language.

16
00:03:47.030 –> 00:03:53.920
[email protected]: I love that motivating the next best action for consumers to take the

17
00:03:53.930 –> 00:04:23.559
[email protected]: which, and I know we’ll talk about some of the challenges that marketers face. We’ll. We’ll talk about that next. But one of the things that I like about the way you define motivation. Ai. Is that you are understanding the next best action across the entire consumer journey. In fact, some of the best use cases for motivation ai or around customer service, and being able to deliver a consistent on-brand experience

18
00:04:23.570 –> 00:04:28.330
[email protected]: well beyond a first purchase or initial transaction.

19
00:04:28.500 –> 00:04:35.670
Lisa Spira: One of the most interesting things I’ve seen in the data working with this tool is how

20
00:04:35.840 –> 00:04:39.770
Lisa Spira: depending on what action you’re trying to motivate

21
00:04:39.780 –> 00:04:59.410
Lisa Spira: different language might work better. So we always work with our clients to choose what they’re measuring. What is important to them Is it clicks? Is it less calls to the call center in servicing, Is it items in the cart? Is it

22
00:04:59.420 –> 00:05:01.969
Lisa Spira: like actual emails back?

23
00:05:01.980 –> 00:05:28.240
Lisa Spira: There are lots of different things that could be. Some are very common, some are very uncommon, but we can motivate all of them, but we won’t. Use the same language to motivate all of them. The language that entices people to open an email may not be the language that motivates someone to click through that email to the landing page. And it may not be the same language that would motivate someone all the way through to putting the item in their heart

24
00:05:28.470 –> 00:05:57.679
[email protected]: one hundred percent. And actually one of the most interesting projects my team has worked on with yours was understanding that what motivates consumers actually changes over time. So we took a look at banking and credit card data as well as retail conversions, and what we found out was depending on how far the average consumer’s dollar goes.

25
00:05:57.690 –> 00:06:03.390
[email protected]: This is important, especially in a period of inflation like we’re experiencing now

26
00:06:03.400 –> 00:06:20.300
[email protected]: depending on how far the average consumer dollar goes that influences what motivates a consumer to interact with a brand. And the language that motivates consumers. To click on an ad or interact with an email

27
00:06:20.340 –> 00:06:31.790
[email protected]: is actually completely different when consumers are spending more or when they’re saving more of their paycheck. A very unique any site that I think we uncovered, together with their team.

28
00:06:31.800 –> 00:06:40.700
Lisa Spira: Oh, one hundred percent. That was one of my favorite things that we worked on, because context is really just so important to language, resonating

29
00:06:40.970 –> 00:06:55.299
[email protected]: absolutely. And I know we’re. We’re answering my next question without explicitly getting to it. But what what issues or challenges that marketer space can motivation, Ai help solve

30
00:06:56.090 –> 00:07:13.360
Lisa Spira: one of the challenges that marketers face today is a wealth of data more data than they know what to do with, and data can be scary and mountains of data typically leave you with no direction.

31
00:07:13.370 –> 00:07:33.899
Lisa Spira: You have all the pieces there in front of you, but you don’t know what to do with them, or you don’t have the talent or the direction, So I think that data is a huge challenge, and I think motivation Ai really helps solve this because it is a platform that is grounded in data

32
00:07:34.050 –> 00:08:02.519
Lisa Spira: within our platform. We do it, testing through experimental design, which is like a B testing. But essentially it’s A. Through Q. Testing with an exponent. It’s really hard to understand. It’s really complicated, but it’s a lot, and what it does is measure the minute elements within the language, and how they work, and how how they motivate what works best, and

33
00:08:02.600 –> 00:08:28.219
Lisa Spira: um and how. And then we we use all of this data to continually train our Ai so that it learns. And so I think it’s really exciting, because what we’ve done is we’ve harnessed so much data into knowledge that can really drive consumer behavior. So I think it’s motivation. Ai is really great at solving a data challenge

34
00:08:28.230 –> 00:08:37.569
[email protected]: absolutely. And I think the type of data challenge that motivation ai solves is a very interesting

35
00:08:37.780 –> 00:09:04.089
[email protected]: way to address something that is going to challenge marketers in the future with the appreciation of pixels and cookies. So I I talk to you, and I know that your team talks to marketers a lot of our clients, and one of the things that they are focused on is understanding what motivates their consumer base without necessarily needing to collect

36
00:09:04.100 –> 00:09:23.340
[email protected]: personally identifiable information, or Pii. And one of the most interesting case studies that I’ve worked on at Prisato with our clients is a cash back or miles Redemption case. Study with a Co. Branded credit card,

37
00:09:23.350 –> 00:09:42.899
[email protected]: and what’s interesting is for the same card holders, whether a consumer prefers cash back in their pocket or miles redeemed for experiences the language that motivates them to interact with the points. Redemption platform is completely different.

38
00:09:43.100 –> 00:09:45.870
[email protected]: So that’s a great example

39
00:09:46.040 –> 00:10:03.599
[email protected]: of taking demographic data and not relying on a pixel or a cookie to garner the same understanding of what motivates a consumer and create an authentic experience between the consumer and the brand.

40
00:10:03.970 –> 00:10:13.699
Lisa Spira: Absolutely, I think, as we move into a world where cookies are less reliable and less the future of

41
00:10:13.710 –> 00:10:26.970
Lisa Spira: data and marketing efforts and personalization efforts moving into the world of motivation. Ai. Where we build segments differently, and we look for language segments that’s going to be just so important.

42
00:10:26.990 –> 00:10:28.490
[email protected]: Absolutely

43
00:10:28.500 –> 00:10:42.809
[email protected]: so. I think we’ve done a great job for the listeners of shaping what the market looks like talking about motivation Ai as a category. I want to shift a little bit to give the listeners some background on.

44
00:10:42.820 –> 00:10:50.360
[email protected]: I know both you and I work there, But for the listeners, let’s start from square one. What is Prisado?

45
00:10:51.490 –> 00:11:06.210
Lisa Spira: Cursado? Is the company that created motivation? Ai Versato has a platform that motivates consumers to take action, and it does this through language. It generates messages by levering.

46
00:11:06.220 –> 00:11:15.860
Lisa Spira: It generates messages by leveraging the world’s most advanced language, knowledge, base of more than one million tag, and scored words and phrases.

47
00:11:16.220 –> 00:11:21.659
Lisa Spira: But to me persado is a playground for language innovation.

48
00:11:21.760 –> 00:11:40.470
Lisa Spira: At first we’re focused on language that motivates action, but that’s a small scope, and it’s a huge opportunity. So we’re continuously training our Ai to write in new ways to look for different types of patterns to become

49
00:11:40.480 –> 00:11:54.830
Lisa Spira: something it’s not yet doing um to me it’s a playground, and from my vantage point pers auto blends the best of human and machine to drive results for our clients, and those results are actions.

50
00:11:55.160 –> 00:12:19.209
[email protected]: I love that a playground for language innovation. I do want to touch on something you mentioned earlier about experimental design. You know one of the unique things that we get to work on with clients is not so much understanding the optimum message to send. That’s important. But I think ruling out messages that to the

51
00:12:19.220 –> 00:12:24.300
[email protected]: tribal knowledge, to the creative teams that our clients might seem like a good fit.

52
00:12:24.310 –> 00:12:53.840
[email protected]: But Um, back backed up by the linguistics and the natural language process you’ve been talking about. Pricado can actually help eliminate years of sub-optimal decision making and creative. Um! In fact, our clients have reported that they learn things about their audience. Fifty! That’s five zero times faster than if they had done traditional A. B testing. So talk about a great playground,

53
00:12:53.850 –> 00:12:57.449
innovating language and understanding what motivates your customers.

54
00:12:57.570 –> 00:13:09.749
Lisa Spira: Yeah, absolutely. I think that the personato knowledge base has something like the equivalent of six hundred and forty five years worth of testing inside of it.

55
00:13:09.890 –> 00:13:27.870
Lisa Spira: It’s some large number like that, but it’s Ah, it’s really a mind-boggling stat. But that is how our customers learn so much faster, and one of the other things that they can do is they can test hypotheses with us. We can, of course,

56
00:13:27.940 –> 00:13:44.880
Lisa Spira: you know, let motivation Ai suggest how we’re going to test, based on all of the data. But humans can have a hand in that and say, This is what I think works. This is what I’ve learned from other testing. Or this is what the creative team is really excited about,

57
00:13:44.890 –> 00:13:50.949
Lisa Spira: and then we can test, and we can put math behind it, and then we can know which is always really cool,

58
00:13:50.960 –> 00:14:00.419
[email protected]: absolutely. And I think that’s my favorite part of the job is working with our diverse yet very experienced customer, base

59
00:14:00.430 –> 00:14:18.380
[email protected]: to understand consumer behavior, not only within their industry, but across industries; and Prisado’s got a great list of roster of clients that sit across all parts of financial services. So banks, credit cards, wealth, management.

60
00:14:18.390 –> 00:14:25.109
[email protected]: But Prisato also does a lot of work within travel and hospitality within retail.

61
00:14:25.120 –> 00:14:38.449
[email protected]: So those are the main verticals. So we have a Prisado has others that that are covered. But more generally what type of company typically benefits from working with Prisano.

62
00:14:40.000 –> 00:14:46.489
Lisa Spira: The companies that benefit the most are the companies who are willing to experiment.

63
00:14:46.500 –> 00:14:55.409
Lisa Spira: You have to have a testing mindset to truly take advantage of everything that motivation Ai has to offer.

64
00:14:55.590 –> 00:15:08.549
Lisa Spira: I think a great fit is a company who wants creative language in marketing? Who wants data behind that, who is tired of chasing the whims of human copywriting

65
00:15:08.560 –> 00:15:25.229
Lisa Spira: and tired of the risk of intuition and the hunches. So a company that is excited about testing and excited about data is really a great fit, and a company that wants to learn and is willing to change.

66
00:15:25.430 –> 00:15:54.419
[email protected]: I love that avoiding the risk of intuition. It’s really interesting to me. Some of the insights that Rosado’s customer uncovers when they’re live on the platform. Rosado actually deconstructs every message into various components to understand is the formatting, driving an increase in engagement? Are the emotions that are of both of capturing the audience’s attention?

67
00:15:54.450 –> 00:16:14.270
[email protected]: Are the positioning or calls to action or message elements that need to be looked at when considering the optimum creative. One of my favorite projects, we work on with clients is understanding where placement and card art

68
00:16:14.420 –> 00:16:25.169
[email protected]: should go during an acquisition campaign for a credit card, and one of Prisano’s clients actually found out that eliminating a human model

69
00:16:25.180 –> 00:16:50.730
[email protected]: from the image of a credit card campaign, and only showing card art combined with the optimum language to capture consumer attention. That creative only showing the card art was twice as effective at driving clicks and ultimate conversions. So prisano customers have a wealth of knowledge that they sit on when they work with motivation. Ai,

70
00:16:50.880 –> 00:17:14.239
Lisa Spira: The formatting step is really cool, because the platform can test into all of those things, although we primarily optimize language, and that’s usually where most of the power is, and also not every channel has a visual, although more and more they do. But we can also tap into those visual elements, and those can be big things like you’re talking about like the card art,

71
00:17:14.250 –> 00:17:31.620
Lisa Spira: but they can even be smaller things like whether you use bold or you use capital letters or what the spacing is, how big or small the button is, what color. And you can test into all of those things through the platform, which is really powerful.

72
00:17:31.930 –> 00:17:47.700
[email protected]: It is very powerful, and that goes back to a point we were talking about earlier about, and I love the way that you phrased it, avoiding the risk of intuition. I think, in my experience, working with prisato’s, customers, that eliminating

73
00:17:47.710 –> 00:18:01.410
[email protected]: messages that the human intuition might say, will work. But science and natural language processing and linguistics say won’t. It’s a great way to artfully combine the human and the machine,

74
00:18:02.500 –> 00:18:21.499
[email protected]: so maybe taking a step back a bit from marketing from motivation. Ai: in your experience, working with leaders across industries, What do you typically see leaders investing in who are getting it right with digital transformation?

75
00:18:22.790 –> 00:18:37.540
Lisa Spira: I think that they’re investing in Ai today, I think now is a turning point where we’re all seeing that Ai doesn’t have to be scary, and that it can have powerful results.

76
00:18:37.550 –> 00:18:45.889
Lisa Spira: But more than that, that, it can augment your humans. It won’t necessarily, or I mean it won’t. I firmly believe

77
00:18:45.900 –> 00:19:02.180
Lisa Spira: that Ai Isn’t going to take away our job. It’s going to change our jobs. It’s going to create new jobs that work with Ai in new ways. It will eliminate some jobs and create a whole host of others. So I think that today

78
00:19:02.190 –> 00:19:15.789
Lisa Spira: ah, leaders across brands are really embracing that Ai can be a powerful source of good in their company, and it can be transformational for their business.

79
00:19:15.800 –> 00:19:17.479
Lisa Spira: You know. I think, that

80
00:19:17.760 –> 00:19:22.559
Lisa Spira: um recently, with the with Dolly to the

81
00:19:22.680 –> 00:19:41.240
Lisa Spira: people all over have been able to see Ai generating content, and that’s quite a bit different from what versatile does. But I think it has been eye-opening to so many people that when you can see it, what it is an Ai working

82
00:19:41.250 –> 00:19:50.570
Lisa Spira: that you know, you can be more willing to embrace language Ai, as a different sorts or a motivation. Ai

83
00:19:50.610 –> 00:20:13.729
[email protected]: Dalitu is a great example for the listeners. Dolly Two is actually Ai that is submitting ah to competitions, and is in some cases eating human submitted pieces. And there’s a language example as well with Gpt. Three that has submitted Ai author,

84
00:20:13.740 –> 00:20:22.819
[email protected]: poems and short stories that have actually won rises competitive against humans. And I agree with you. At least I think it’s a great point.

85
00:20:22.830 –> 00:20:38.460
[email protected]: The The vision is for machined and humans to complement each other to understand holistically what is motivating a consumer, to choose a brand and choose to stay loyal to it over time.

86
00:20:38.680 –> 00:20:57.370
[email protected]: Um! So let’s focus a little bit more on the work that you do at Prisato. I know that you speak to Prisato’s clients regularly, and you’ve got a great perspective of the work that they’re doing, but talk to me a little bit about what content content intelligence does at Prisano

87
00:20:58.490 –> 00:21:08.840
Lisa Spira: We do a little bit of everything. We sort of sit in the middle of data, science, product and operations.

88
00:21:08.850 –> 00:21:24.789
Lisa Spira: And so we’re at the nucleus of creation of content at Versato. So what do we do? We write words, we teach an Ai to write words, we train it, We give it a thumbs up, comes down on how it’s doing.

89
00:21:24.800 –> 00:21:39.040
Lisa Spira: We We analyze a lot of data coming out of different Ai systems that are trained to do very specific things. We create insights, We really just because an Ai is learning.

90
00:21:39.050 –> 00:21:57.810
Lisa Spira: Well, the humans want to learn, too. What is it? No. So we create ways of interpreting and understanding the language data that we have visualizing that and tagging content. It’s a lot. We also hold its hands. Sometimes we have

91
00:21:57.820 –> 00:22:15.639
Lisa Spira: maybe a use case or a channel that the Ai has never seen before, so we’ll help it along. Step in if it hasn’t seen enough data to do something yet where it’s partner. And yeah, so

92
00:22:15.660 –> 00:22:23.980
Lisa Spira: content. Intelligence is really just at the nucleus of all things, language, creation at this, at the central point of our product.

93
00:22:23.990 –> 00:22:36.010
[email protected]: That’s awesome. And I love how you personify the Ai, too, because that’s where I really do believe that brands and marketers get motivation. Ai right?

94
00:22:36.020 –> 00:22:47.780
[email protected]: Ah, really embrace the idea of taking. Ah! You know the creative spirit of an in-house team or an agency team where that’s the operating model

95
00:22:47.790 –> 00:23:06.490
[email protected]: and supplementing that expertise and knowledge with math experimental design, and with linguistics

96
00:23:06.500 –> 00:23:11.110
[email protected]: any that You’ve left off the list that you want to talk for listeners about.

97
00:23:11.870 –> 00:23:37.090
Lisa Spira: I have two that I would love to share. I think they’re pretty interesting insights. So the first is around the idea of reinvention. And Susan Lee from my team did this research across six years of email Facebook and push notification campaigns that come through versatile from two thousand and fifteen to two thousand and twenty one. And

98
00:23:37.100 –> 00:23:40.079
Lisa Spira: that was a really weird way to say that i’m going to start it again.

99
00:23:40.210 –> 00:23:49.089
Lisa Spira: So one thing, so one that I would love to share is an insight into reinvention.

100
00:23:49.100 –> 00:24:07.890
Lisa Spira: Susan Lee, from my team, worked on this project where she was looking at six years of email Facebook and push notification campaigns between two thousand and fifteen and two thousand and twenty one. And she was looking at the idea of New Year New Year.

101
00:24:07.900 –> 00:24:15.289
Lisa Spira: That phrase, and that idea of variations on that phrase is really big with marketing every New Year,

102
00:24:15.300 –> 00:24:29.050
Lisa Spira: especially in retail. But not only you see it across industries, and it was the least inspiring concept in retail when tested against other language,

103
00:24:29.160 –> 00:24:45.259
Lisa Spira: it just. We we proved over those years that time and time again it doesn’t work, and what we were able to do is find the alternatives that tapped into different motivations that are not reinvention, that work better

104
00:24:45.540 –> 00:25:01.940
Lisa Spira: at New Year. So, for example, individuality New Year celebrating the same, you or this year, permission to just do you um or freedom. Do what you love in twenty twenty three. No resolutions, no problem

105
00:25:01.950 –> 00:25:06.199
Lisa Spira: hype this year will be awesome. We can sense it.

106
00:25:06.210 –> 00:25:23.029
Lisa Spira: So there’s so many ways that you can speak to the New Year if you want to bring that into your marketing, and the one that so many people with intuition just go to immediately, and we all think about is New Year new. You but don’t do that.

107
00:25:23.610 –> 00:25:41.400
[email protected]: How interesting! I know You’ve got a second one. But What a great example of probably a campaign that many creative teams would not address year over year, and assume that it would work simply because it’s a New Year. I love that example. What’s your second one?

108
00:25:41.410 –> 00:25:55.920
Lisa Spira: My second one is about research into the language of deadlines, and this research was done by Sheena vera on my team, and she did work sort of a case study around the language of tax season,

109
00:25:55.940 –> 00:26:01.080
Lisa Spira: and what we found is that it’s not it’s

110
00:26:01.330 –> 00:26:12.260
Lisa Spira: deadline-driven language, such as urgency, or like alert language that works the best to motivate actions when deadlines are looming

111
00:26:12.270 –> 00:26:37.169
Lisa Spira: as tax season approaches. For example, more gentle language that nudges the customer, think encouragement is more effective. Um. The language should ensure that the process will be painless, and encourage customers to read the message to get further information. So we don’t want to go all asap now, like, you know. Sort of

112
00:26:37.180 –> 00:26:54.510
Lisa Spira: um, really intense or deadline driven, and we want to go a lot more softer. So we have language like you’re still on track for filing your taxes on time. There’s still time to file for your refund. It’s easy to file. We recommend starting here,

113
00:26:54.520 –> 00:27:09.289
Lisa Spira: Um. And that this gentler language proved to be a lot more effective, and as the deadline approached, and, in fact, counterintuitively, as the deadline gets closer, More dental language works better.

114
00:27:09.360 –> 00:27:28.170
[email protected]: That makes a lot of sense to me. I could see how, on the surface marketers might think you need to ratchet up the urgency the closer. A non-negotiable deadline approaches. But taxpayers are aware they know they need to get their taxes in so maybe one less

115
00:27:28.180 –> 00:27:39.280
[email protected]: um nagging force, one less annoyance, a little bit gentler language can make a big difference in whether or not consumers respond well to the marketing that they’re seeing,

116
00:27:39.400 –> 00:27:59.210
Lisa Spira: and I think your reaction right. There is one of the things that I love about motivation. Ai. Because when you stop and you think about it, you think? Oh, yeah, I totally see why that makes sense, even though it is against everyone’s initial intuition. The intuition being deadline

117
00:27:59.220 –> 00:28:05.769
Lisa Spira: dates. So yeah, that’s and we see that time and time again with motivation. Ai:

118
00:28:05.820 –> 00:28:16.900
[email protected]: Absolutely. So in your experience, working with prisato’s, customers talking to marketers out around the industry.

119
00:28:17.330 –> 00:28:40.029
[email protected]: I know we’ve talked a lot about, you know, marketing and data teams working together. There’s a lot of data that flows through, you know. Motivation Ai platform like Prisato. What are two or three things that data and marketing teams can do to work closely together and get started with motivation. Ai.

120
00:28:41.960 –> 00:28:54.680
Lisa Spira: The first thing to do is to embrace that something that seems diametrically opposed can really belong together. In the case of Chris auto. That’s math and words.

121
00:28:54.690 –> 00:29:08.870
Lisa Spira: Um, here. We are trying to optimize words and measuring them with math. And I think there are a lot of people who grow up saying, i’m a math person, or i’m a words person or

122
00:29:08.880 –> 00:29:17.480
Lisa Spira: i’m none of those, and put themselves in some other bucket, and just decide that it is opposed. And so my

123
00:29:17.490 –> 00:29:40.989
Lisa Spira: first piece of advice is to embrace the other one, whatever the other one is in your organization, and build connections, and figure out how those different skill sets or datasets can come together and enhance value. I think that’s one of the things that Prisado does really well, and I mean to to punctuate that

124
00:29:41.000 –> 00:29:54.989
Lisa Spira: I report to the Cto. Who is a data scientist, and my background is linguistics, but it works well for us to keep those ties closely together.

125
00:29:55.230 –> 00:30:19.669
[email protected]: I think it does, And what ah, what better example of putting our money where our mouth is showing two diametrically opposed disciplines, data, science and linguistics working together internally at Prisato, and that’s been my experience working with Prisato’s customers as well. I think, many times the best insights that pricato’s services with our customers are

126
00:30:19.680 –> 00:30:39.400
[email protected]: moments where the customer sits back and says, You know I had a bunch that this was a trend that we were seeing, but we couldn’t necessarily prove it with math and vice versa. The customers are sitting on a lot of data and just want to understand. How can we communicate phrase or set of emotions

127
00:30:39.410 –> 00:30:42.560
[email protected]: that motivates our consumers to interact with us.

128
00:30:42.610 –> 00:30:59.850
[email protected]: Well, Lisa, thank you very much. I have one final question, but I really appreciate your time and being our first guest. We’ve talked a lot about motivation. Ai. We’ve introduced versato as a motivation. Ai platform.

129
00:30:59.960 –> 00:31:04.119
[email protected]: What’s next for Prisado? And what’s next? For your team

130
00:31:04.900 –> 00:31:19.670
Lisa Spira: we will continue to explore new types of motivations within language, and translating what we find into new verticals, new channels, new use cases beyond marketing

131
00:31:19.680 –> 00:31:37.289
Lisa Spira: um, an expansion of the machine and human understanding which will lead us to better insights if we can continue to build that relationship between humans and machines and continuing to make motivation Ai really dynamic

132
00:31:37.300 –> 00:31:47.910
Lisa Spira: in real time, taking its decision engine to the moment where the customer is. I think there is a really exciting future in a lot of

133
00:31:47.920 –> 00:32:00.609
Lisa Spira: different directions within motivating language. So i’m really excited about many of the next phases of research and testing and building that we have happening at Versato.

134
00:32:00.620 –> 00:32:19.900
[email protected]: Awesome. I I love that. And you know I like bringing the motivation ai engine to each moment of customer interaction, no matter if it’s attracting new customers, retaining them as loyal customers, or delivering that ever important great customer service.

135
00:32:19.990 –> 00:32:30.890
[email protected]: Well, Lisa, thank you very much for being our first guest. This was Lisa Spyra, the head of Content Intelligence at Prisato. Ah, thank you very much, Lisa.

136
00:32:30.900 –> 00:32:33.290
Lisa Spira: Thank you for having me. It’s been a pleasure,

137
00:32:33.300 –> 00:32:34.440
[email protected]: of course,