Meeting Title: CTA PowerBI Quick Sync Date: 2026-04-09 Meeting participants: Chi Quinn, Amber Lin
WEBVTT
1 00:00:37.810 ⇒ 00:00:39.100 Chi Quinn: Hello!
2 00:00:39.260 ⇒ 00:00:42.059 Amber Lin: Hi! Nice to talk to you again.
3 00:00:42.240 ⇒ 00:00:45.169 Chi Quinn: Yes, likewise. How are you?
4 00:00:45.170 ⇒ 00:00:47.960 Amber Lin: I’m good. Are you working from home today?
5 00:00:47.960 ⇒ 00:00:49.150 Chi Quinn: I am…
6 00:00:49.150 ⇒ 00:00:50.690 Amber Lin: Me too.
7 00:00:50.690 ⇒ 00:00:53.060 Chi Quinn: Nice.
8 00:00:54.290 ⇒ 00:00:58.249 Amber Lin: Do you guys work in office ever, or is it just working from home?
9 00:00:58.560 ⇒ 00:01:06.700 Chi Quinn: So it’s hybrid. On Mondays, Tuesdays, and Wednesdays, we’re in the office, and then Thursdays and Fridays, we’re working from home.
10 00:01:06.880 ⇒ 00:01:07.570 Amber Lin: Hmm.
11 00:01:07.690 ⇒ 00:01:11.000 Amber Lin: Cool. Okay. Exciting.
12 00:01:11.000 ⇒ 00:01:12.279 Chi Quinn: Yeah, yeah, that’s nice.
13 00:01:13.000 ⇒ 00:01:25.170 Chi Quinn: Especially being new, I like being in the office, just so I can actually see faces and interact with people. But I do also like, you know, the working from home perks of.
14 00:01:25.170 ⇒ 00:01:25.939 Amber Lin: I know that for you.
15 00:01:25.940 ⇒ 00:01:31.300 Chi Quinn: I just need, like, a quiet space to do some work, so…
16 00:01:31.300 ⇒ 00:01:43.290 Amber Lin: Yeah. And save some… like, technically, you get off work earlier because you… you don’t have the commute time, so you’re off work, like, 30 minutes earlier than your usual time.
17 00:01:43.480 ⇒ 00:01:45.889 Chi Quinn: Yeah, exactly, exactly.
18 00:01:46.130 ⇒ 00:02:02.799 Amber Lin: Cool! I just wanted to see the Power BI reports. I don’t know if you can add me or not, because I know there’s seat limits, so I just wanted to see what’s in there, maybe download or screenshot a few, so when I… when I do the semantic
19 00:02:02.850 ⇒ 00:02:09.560 Amber Lin: When I do Cortex, I can know, oh, this is what they’re used to seeing, so I can try to see
20 00:02:09.820 ⇒ 00:02:12.779 Amber Lin: Get it to answer the same questions that way.
21 00:02:13.290 ⇒ 00:02:20.610 Chi Quinn: Yeah, absolutely. So I’ll have to ask, because I think the license… I don’t know how many licenses we have out there.
22 00:02:20.610 ⇒ 00:02:21.270 Amber Lin: Yeah.
23 00:02:21.270 ⇒ 00:02:22.820 Chi Quinn: I’m happy…
24 00:02:22.820 ⇒ 00:02:26.150 Amber Lin: Maybe it was just a download, like a… Like a screenshot.
25 00:02:26.150 ⇒ 00:02:26.650 Chi Quinn: shot.
26 00:02:26.770 ⇒ 00:02:35.880 Amber Lin: a screenshot, or if you export as PDF, or whatever, for the main reports you guys look at, like, that’s enough for me.
27 00:02:35.880 ⇒ 00:02:36.930 Chi Quinn: Okay.
28 00:02:36.930 ⇒ 00:03:01.899 Chi Quinn: Yeah, so what I can do right now, like, I can show you, and then later I can try to at least send the screenshot versions of the reports. Yeah. I could also ask the team if there might be… if they already have something in somewhere where I can just simply share it to you, so I will do that. But in the meantime, yeah, from my understanding, so with the Power BI
29 00:03:01.900 ⇒ 00:03:07.569 Chi Quinn: reports. There are a lot, but it seems like right now the focus is just based on the CES.
30 00:03:08.380 ⇒ 00:03:23.060 Chi Quinn: So, I will look into, or at least show you some of these reports, and some of these are, of course, they’re outdated, but what I’ve seen for most of the reports that people consume… let me see if I can make this a little bit bigger…
31 00:03:24.040 ⇒ 00:03:27.329 Chi Quinn: So, okay, this one is…
32 00:03:27.330 ⇒ 00:03:28.030 Amber Lin: Damn.
33 00:03:28.030 ⇒ 00:03:33.009 Chi Quinn: product categories of interest. So it’s really interesting because you have
34 00:03:33.030 ⇒ 00:03:44.100 Chi Quinn: like, two types of reports. So you’ll have one that has all the graphs, the charts and, the graphs and stuff, and it’s based on a certain topic.
35 00:03:44.100 ⇒ 00:03:54.110 Chi Quinn: In this case, this is the product categories of interest. So this is when, from my understanding, when someone registers for CES,
36 00:03:54.190 ⇒ 00:04:03.140 Chi Quinn: they might have an option to check, like, which topics are you interested in? So, that could be, in this case,
37 00:04:03.490 ⇒ 00:04:12.979 Chi Quinn: They have an option of… there’s, like, various options, and of course, there’s artificial intelligence, in this case, education tech.
38 00:04:13.120 ⇒ 00:04:17.040 Chi Quinn: Energy, power, fitness, food tech, so there’s…
39 00:04:17.700 ⇒ 00:04:27.939 Chi Quinn: various, topics that they can choose from, and they can choose more than one. Now, I think for certain registration types.
40 00:04:27.940 ⇒ 00:04:40.430 Chi Quinn: they can go up to 10, and I apologize, I don’t know that right away, like I said, I’m still learning all of this. But for, certain registration types, I know, I think for media, they can go…
41 00:04:40.790 ⇒ 00:04:49.519 Chi Quinn: I think they have unlimited, like, there’s no limit for them, but for, like, industry attendees, I will have to double check, but I think they can select up to 10.
42 00:04:49.710 ⇒ 00:04:51.860 Chi Quinn: Yes.
43 00:04:51.860 ⇒ 00:05:07.210 Amber Lin: Okay. If they didn’t select anything, would it affect the count of, say, like, registration numbers? Would it re… like, are they still counted in attendees if they didn’t select any product categories of interest?
44 00:05:07.210 ⇒ 00:05:23.640 Chi Quinn: That… that… my… I would think so, because at the end of the day, we still want to get the full count, regardless of if someone selects one or not. Now, that’s a good question, because now I have a question on that one, and that’s…
45 00:05:24.080 ⇒ 00:05:30.809 Chi Quinn: If… if it’s an op… if it’s optional, and I’ll get back to you on that one.
46 00:05:30.990 ⇒ 00:05:34.250 Chi Quinn: Because…
47 00:05:34.430 ⇒ 00:05:56.619 Chi Quinn: from my understanding, we still want to count attendees regardless if they selected a product category of interest or not, because we just want to say, like, hey, this person was still interested, but they… even though they didn’t give us the specifications of what areas they were interested in, they still attended. So, I think they would still count, but I will double check on that.
48 00:05:57.130 ⇒ 00:06:02.379 Amber Lin: Cool, okay. Can we scroll down a little bit?
49 00:06:03.100 ⇒ 00:06:11.059 Amber Lin: Is this the main report, or was the… oh, is there more on the left-hand sidebar? Okay.
50 00:06:11.060 ⇒ 00:06:12.829 Chi Quinn: Yeah, so these are… so…
51 00:06:12.830 ⇒ 00:06:37.059 Chi Quinn: I don’t know if you’re familiar with Power BI, you can create an app, and this is more so for the end users, so they can just go directly to the reports that they’re interested in. And in this case, they created… so this was from the previous, data team, but they created an app that was dedicated for, C… anything CES-related.
52 00:06:37.130 ⇒ 00:06:44.830 Chi Quinn: There is CES, there’s another app called CES Conferences, and that’s more so… I think it kind of…
53 00:06:44.830 ⇒ 00:07:03.570 Chi Quinn: expands beyond the attendee information. From what I’m looking at right now, this looks like it’s based on just attendee counts, more so. But for this particular report, the product categories of interest, usually when someone selects
54 00:07:03.570 ⇒ 00:07:12.700 Chi Quinn: The product cat… the interest that they want to see, like, oh, we want to see how many of those who selected artificial intelligence
55 00:07:12.910 ⇒ 00:07:31.920 Chi Quinn: what’s their… what is the breakdown of this? And what… and then this is also by region as well. So when you look at the report, it’s pretty straightforward. It has the breakdown of the job titles. So in this case, this is old data, but this is from 2025.
56 00:07:31.920 ⇒ 00:07:37.449 Chi Quinn: So, I guess in this example, who… those who selected home entertainment.
57 00:07:37.450 ⇒ 00:07:42.539 Chi Quinn: And… can’t see the other parts of it, but for home entertainment,
58 00:07:42.960 ⇒ 00:07:51.989 Chi Quinn: 10,000, so that was a total of 10,201 attendees who selected that product, and then.
59 00:07:51.990 ⇒ 00:07:52.540 Amber Lin: Because…
60 00:07:52.540 ⇒ 00:07:56.939 Chi Quinn: Further break down, like, who’s the industry attendee versus the media, those are.
61 00:07:56.940 ⇒ 00:07:57.410 Amber Lin: Oh, yeah.
62 00:07:57.410 ⇒ 00:08:05.569 Chi Quinn: registration types. Also, the breakdown by geographical locations. So, at the regional level and at the country level.
63 00:08:05.900 ⇒ 00:08:18.370 Chi Quinn: For the job titles, we have the blue… we have this highlighted, the blue’s highlighted, because they want to see those who are senior level attendees, which is.
64 00:08:18.370 ⇒ 00:08:18.860 Amber Lin: Yeah.
65 00:08:18.860 ⇒ 00:08:24.570 Chi Quinn: Interesting, because this old version, they include board member.
66 00:08:24.860 ⇒ 00:08:43.489 Chi Quinn: as a senior level attendee, but from, when I spoke with some of the folks in marketing, some of the folks with CES, even for the executive, they’re only looking at those who are in C-level, vice presidents, director, or president level, so they have.
67 00:08:43.990 ⇒ 00:08:45.550 Chi Quinn: gluten board member.
68 00:08:45.690 ⇒ 00:08:59.749 Amber Lin: Okay. Can we scroll through the reports? The meetings recorded, so I can just… I can look at the recording, so you don’t have to take too many screenshots, so I just want to kind of scroll through as many reports as we can right now. Absolutely.
69 00:08:59.750 ⇒ 00:09:00.270 Chi Quinn: Yeah.
70 00:09:00.270 ⇒ 00:09:03.509 Amber Lin: And you don’t have to, like, screenshot page by page.
71 00:09:03.510 ⇒ 00:09:28.479 Chi Quinn: Awesome. Well, okay, so that’s the product categories of interest. So, the total attendance by country, like I said, there’s two different types, it seems like there’s two different types of reports. You have those that have, like, the bar charts, little visuals, but then for some, it’s just a simple table, and that table would have, in this case, total attendance by country, so
72 00:09:28.480 ⇒ 00:09:31.160 Chi Quinn: the countries and the…
73 00:09:31.280 ⇒ 00:09:49.990 Chi Quinn: the counts of the attendees, and then the percentage of the total attendance. So, in this case, you know, for the U.S, this is the count of the… and I say attendee… yes, attendees. You can specify which type of attendees you want to look at.
74 00:09:49.990 ⇒ 00:09:59.240 Amber Lin: So this is not, like, registration, because I know attendance and registration’s different, but this is, like, registration?
75 00:09:59.530 ⇒ 00:10:11.050 Chi Quinn: This is attendance, so these are… well, at least the definition that I was told. These are those who have not only registered, but they picked… they picked up the badge. They went.
76 00:10:11.600 ⇒ 00:10:12.810 Chi Quinn: So they attended.
77 00:10:12.990 ⇒ 00:10:16.970 Amber Lin: Okay, but we’re just filtering by, like, when they register, what type they are.
78 00:10:17.350 ⇒ 00:10:22.669 Chi Quinn: This is by attendance. Like, they picked up their badge, so these are.
79 00:10:22.670 ⇒ 00:10:26.230 Amber Lin: Oh, oh, yeah, I’m lawyer looking at the registration type under.
80 00:10:26.230 ⇒ 00:10:26.800 Chi Quinn: I know.
81 00:10:26.800 ⇒ 00:10:28.130 Amber Lin: boxes right there.
82 00:10:28.130 ⇒ 00:10:40.289 Chi Quinn: Yeah, it varies. It’s funny, because I saw that, too. It’s like, oh, but they’re saying registration type, like, what type, what did you select when you registered? So, and those are the three options you have.
83 00:10:40.290 ⇒ 00:10:46.259 Amber Lin: Cool. Okay. Let’s… I know we’re a little bit short on time, so I would love to go through
84 00:10:46.650 ⇒ 00:10:50.420 Amber Lin: of rest, because I know, I think there’s more than…
85 00:10:50.420 ⇒ 00:10:50.890 Chi Quinn: Yeah.
86 00:10:51.020 ⇒ 00:10:55.689 Amber Lin: Yeah, I just need, like, a quick glance, so if you can click through, like, the rest…
87 00:10:55.690 ⇒ 00:10:56.670 Chi Quinn: Absolutely.
88 00:10:56.670 ⇒ 00:10:57.360 Amber Lin: That’ll work.
89 00:10:57.960 ⇒ 00:10:59.730 Chi Quinn: So yes, oop.
90 00:10:59.840 ⇒ 00:11:06.989 Chi Quinn: Wow, I didn’t mean to go all the way down, but that was… this is the Fortune 500 attendees here. Okay, that’s too big, but…
91 00:11:07.590 ⇒ 00:11:11.180 Chi Quinn: My goodness, sorry, it’s like this really tiny screen.
92 00:11:11.560 ⇒ 00:11:17.110 Chi Quinn: Okay, there we go. So this is simple. Fortune 500 attendance. You have the metric.
93 00:11:17.230 ⇒ 00:11:20.959 Chi Quinn: And then the, information about the companies.
94 00:11:22.310 ⇒ 00:11:24.199 Chi Quinn: Cool. This… so…
95 00:11:24.200 ⇒ 00:11:28.010 Amber Lin: It feels very similar to the previous one, but just, like…
96 00:11:28.770 ⇒ 00:11:31.849 Amber Lin: Different breakdown. Okay, cool.
97 00:11:32.290 ⇒ 00:11:41.450 Amber Lin: I kind of see the main, like, slicers, people are using here. Okay, great, I understand that.
98 00:11:41.700 ⇒ 00:11:47.180 Amber Lin: And then… Job function… okay.
99 00:11:47.590 ⇒ 00:11:51.310 Amber Lin: Event profile…
100 00:11:52.970 ⇒ 00:11:59.150 Chi Quinn: So it’s going to look similar as the product categories of interest, but this is more so…
101 00:11:59.380 ⇒ 00:12:18.320 Chi Quinn: For those who attended, like, where did they attend specifically? So, during the show, we have scanners, and so here, you can start to filter by the event type. Was it a session, an event? Was this a, tour?
102 00:12:19.470 ⇒ 00:12:23.410 Chi Quinn: a VIP, so this is based on, I guess, just
103 00:12:23.570 ⇒ 00:12:34.219 Chi Quinn: Areas where people had scanners, where you could scan someone in, and so after that, people want to see, like, well, how many people attended my session?
104 00:12:34.370 ⇒ 00:12:35.510 Amber Lin: Mmm, okay.
105 00:12:35.510 ⇒ 00:12:36.150 Chi Quinn: Yeah.
106 00:12:36.520 ⇒ 00:12:41.809 Chi Quinn: And it’s pretty much formatted the same way as it was for the product categories of interest with the…
107 00:12:41.810 ⇒ 00:12:45.669 Amber Lin: I see, I see. Just a different slicer. Yes. Cool.
108 00:12:47.930 ⇒ 00:12:49.339 Chi Quinn: Okay, so that’s that.
109 00:12:49.480 ⇒ 00:12:58.259 Chi Quinn: Final decision maker profile. So, again, when someone registers, they usually let
110 00:12:58.410 ⇒ 00:13:09.400 Chi Quinn: They have a section that dedicates to, like, what is your decision-making skills? Like, are you someone who makes the final decisions? Are you a significant influence?
111 00:13:09.510 ⇒ 00:13:12.990 Chi Quinn: And so, usually.
112 00:13:13.170 ⇒ 00:13:26.629 Chi Quinn: at CTA, they want to examine those who selected final decision makers, because those… they typically tend to be, like, the senior level attending folks. At times, yeah.
113 00:13:26.900 ⇒ 00:13:32.579 Amber Lin: What’s attendance status? Oh, so that’s just attended versus registered.
114 00:13:32.580 ⇒ 00:13:33.260 Chi Quinn: Correct.
115 00:13:33.260 ⇒ 00:13:34.410 Amber Lin: Gotcha, okay.
116 00:13:34.820 ⇒ 00:13:43.660 Amber Lin: Okay, the… on the left… oh, conference and tour purchases, customer base…
117 00:13:47.510 ⇒ 00:13:48.440 Amber Lin: Okay.
118 00:13:48.780 ⇒ 00:13:57.880 Amber Lin: Cool. I can come back to these in my recording. Weekly registration to… Hmm…
119 00:13:57.880 ⇒ 00:14:08.150 Chi Quinn: As you can see, this is pretty old. I think this is something, very old, but it’s just a basic, you know, weekly registration, like, how many people have registered?
120 00:14:08.240 ⇒ 00:14:10.059 Amber Lin: Mmm. Okay, I was trying to.
121 00:14:10.060 ⇒ 00:14:11.810 Chi Quinn: 17 weeks out, basically.
122 00:14:12.560 ⇒ 00:14:14.450 Chi Quinn: And it breaks down by.
123 00:14:14.450 ⇒ 00:14:18.209 Amber Lin: By the event tie. Okay, sounds good.
124 00:14:18.490 ⇒ 00:14:19.480 Amber Lin: Okay. Okay.
125 00:14:20.400 ⇒ 00:14:25.050 Amber Lin: Media… outlets… Very clear.
126 00:14:25.050 ⇒ 00:14:25.750 Chi Quinn: forward.
127 00:14:25.750 ⇒ 00:14:31.590 Amber Lin: Cool. Is this all the reports, or is there gonna be… like…
128 00:14:32.440 ⇒ 00:14:33.020 Chi Quinn: It…
129 00:14:33.370 ⇒ 00:14:53.679 Chi Quinn: So, what we’re doing right now, we’re going to recreate these reports in Stream… in the Streamlit app, but what we’re doing is we’re trying to consolidate some of these reports, because really, some of them, it seems like it was based on one question, and a report was created from that one question.
130 00:14:54.590 ⇒ 00:15:03.710 Chi Quinn: But what we’re trying to do now is just, we’re trying to simplify, the reporting process, basically, so instead of having
131 00:15:03.780 ⇒ 00:15:06.460 Chi Quinn: 200 and some odd reports.
132 00:15:06.460 ⇒ 00:15:26.949 Chi Quinn: We’re gonna start off with, I think right now, it’s just something like 12. So, not all of the reports that are in Power BI will be, migrated over to Stream… streamlit ad in Snowflake right away. It might be something later down the road, but for now, we just want to put the high-level stuff, so, like, attendance information. Okay.
133 00:15:26.950 ⇒ 00:15:29.200 Chi Quinn: So… Cool. Yeah.
134 00:15:29.200 ⇒ 00:15:36.410 Amber Lin: Is this everything about CES? Can you click the go back button on the bottom? I think…
135 00:15:36.660 ⇒ 00:15:38.020 Amber Lin: Bottom left.
136 00:15:38.190 ⇒ 00:15:38.520 Chi Quinn: Yeah.
137 00:15:38.520 ⇒ 00:15:40.179 Amber Lin: I think there was…
138 00:15:40.500 ⇒ 00:15:42.290 Chi Quinn: CES conferences.
139 00:15:42.290 ⇒ 00:15:47.019 Amber Lin: What’s the conference’s program and programs? Like, what are these?
140 00:15:47.190 ⇒ 00:16:12.129 Chi Quinn: So, it looks like for the conferences, it’s basically looking at, in this case, the sessions. So, during the conference, like, we want to see how many people attended a particular session. In this example here, we have… this is from 2025, but this is the conference attendance, and so what the end user would do is they would select the specific session.
141 00:16:12.130 ⇒ 00:16:13.810 Chi Quinn: So in this case.
142 00:16:13.810 ⇒ 00:16:14.480 Amber Lin: Hmm.
143 00:16:14.620 ⇒ 00:16:24.710 Chi Quinn: it might be like, oh, the AARP. And in this case, we would see, so the only metric is how many people attended that, session.
144 00:16:24.710 ⇒ 00:16:26.060 Amber Lin: Gotcha. Okay.
145 00:16:27.470 ⇒ 00:16:31.240 Amber Lin: And then, conferences profile?
146 00:16:32.720 ⇒ 00:16:40.860 Chi Quinn: Yes, so conference’s profile is basically… oh, this is interesting. This is my first time seeing this, so there’s a breakdown.
147 00:16:42.180 ⇒ 00:16:51.119 Chi Quinn: You learn something every day, huh? So let me actually go here. So this is what I’ve seen originally, but the conference’s buyer profile. So…
148 00:16:51.120 ⇒ 00:17:09.919 Chi Quinn: Obviously, for the show, people have, companies or people have an option to purchase, like, some packages for a tour, or if that’s my understanding, yes. So this is, like, the tour of… I think the two different types. You can have, like, there’s a deluxe package and a track package.
149 00:17:09.920 ⇒ 00:17:16.770 Chi Quinn: And so it’s just, again, it’s analyzing for those who have purchased these, who are they, basically?
150 00:17:18.250 ⇒ 00:17:27.130 Chi Quinn: Yeah, so for, for those… oh, so it defaults to all, but let’s just say if you want to see the deluxe, let me see… track… track code…
151 00:17:27.270 ⇒ 00:17:35.800 Chi Quinn: Track title. So, it’s really not that much data, but it just… from what it looks like, it’s basically, we want to examine those who have purchased
152 00:17:35.800 ⇒ 00:17:49.720 Chi Quinn: are, conference passes. Who are they? Again, the, it’s the same, same information, same data, basically, so you have the, you know, geography, the job title.
153 00:17:49.800 ⇒ 00:17:54.719 Chi Quinn: Category… product category, interest, primary business.
154 00:17:56.250 ⇒ 00:18:03.949 Amber Lin: Let’s see, how would, like, for example, maybe if we look at attendee profile, how would that be different than
155 00:18:04.130 ⇒ 00:18:10.779 Amber Lin: Like, the… just the CAS event breakdown ones that we just saw.
156 00:18:10.970 ⇒ 00:18:11.520 Amber Lin: I just…
157 00:18:11.520 ⇒ 00:18:11.910 Chi Quinn: So…
158 00:18:11.910 ⇒ 00:18:13.370 Amber Lin: It’s so similar.
159 00:18:13.370 ⇒ 00:18:29.940 Chi Quinn: It looks very similar if you… what I just showed you with the conferences, the one that just had the aggregated total of who attended this session. This is more so, hey, let’s look at this… the specific session of those 129
160 00:18:29.940 ⇒ 00:18:34.380 Chi Quinn: total attendees, and let’s look at the breakdown. So, let me see if I can go to that.
161 00:18:34.380 ⇒ 00:18:35.279 Amber Lin: Okay.
162 00:18:35.280 ⇒ 00:18:41.090 Chi Quinn: for example. Nathan was like… There was 3 titles.
163 00:18:42.170 ⇒ 00:18:44.890 Amber Lin: Oh, gosh, okay, I understand now.
164 00:18:45.810 ⇒ 00:19:02.069 Chi Quinn: Yeah, cool. And then you can see the breakdown. Same format. Geography, job title, buying power, primary business, customer base. Were there any, attendees who were part of a Fortune 500 company? Were they in attendance?
165 00:19:02.210 ⇒ 00:19:05.889 Chi Quinn: And then the top tier media outlets in attendance.
166 00:19:06.190 ⇒ 00:19:11.519 Amber Lin: Cool. Any… can we look at the speaker match?
167 00:19:12.200 ⇒ 00:19:13.060 Chi Quinn: Yes.
168 00:19:15.680 ⇒ 00:19:17.330 Chi Quinn: It’s archived.
169 00:19:17.330 ⇒ 00:19:18.700 Amber Lin: So…
170 00:19:18.700 ⇒ 00:19:33.249 Chi Quinn: Well, basically, if I recall, I think I have to, like, download it, but it was just a tabular, basically… so I guess this is kind of more of administrative, because there may be a case where
171 00:19:33.490 ⇒ 00:19:34.030 Chi Quinn: One…
172 00:19:34.030 ⇒ 00:19:34.520 Amber Lin: Hmm.
173 00:19:34.520 ⇒ 00:19:43.569 Chi Quinn: person who is assigned as a speaker, somehow in the data… in the back end, they’re… they need to connect, and so that’s… and usually for
174 00:19:43.570 ⇒ 00:19:55.510 Chi Quinn: From what I can see from here is that they’re just trying to make sure that the speaker that’s listed in the session, or that’s scheduled to be there, that they are… that it’s recorded, and so they just want to make sure that.
175 00:19:55.510 ⇒ 00:19:55.970 Amber Lin: at the speed.
176 00:19:55.970 ⇒ 00:19:58.489 Chi Quinn: speaker information is, matches.
177 00:19:58.490 ⇒ 00:20:04.389 Amber Lin: Okay, I… I think with the time… sorry, we’re already…
178 00:20:04.390 ⇒ 00:20:05.000 Chi Quinn: I know.
179 00:20:05.000 ⇒ 00:20:15.919 Amber Lin: A little bit over, but I do want to just lastly understand what the programs are, so if we can go back again, like, there’s a CES programs…
180 00:20:16.180 ⇒ 00:20:19.870 Amber Lin: I’m not 100% sure where… what these are.
181 00:20:19.870 ⇒ 00:20:39.809 Chi Quinn: Yeah, so this is… what it looks like is more for the special programs, and at CTA, we do have a dedicated group for the delegations, and that is, from my understanding, these are more of people who are from international, like, who are not in the U.S, but we try to
182 00:20:39.810 ⇒ 00:20:48.670 Chi Quinn: get people… from the international parts to encourage folks from their home countries to come to CES, and so…
183 00:20:48.670 ⇒ 00:20:49.180 Amber Lin: Nope.
184 00:20:49.180 ⇒ 00:20:59.100 Chi Quinn: It looks like we reach out to… so there might be one person who’s a delegate for a particular country, and then they invite… they’ll invite folks, or encourage folks
185 00:20:59.290 ⇒ 00:21:01.530 Chi Quinn: To register for the show, and.
186 00:21:01.530 ⇒ 00:21:02.060 Amber Lin: And this is.
187 00:21:02.060 ⇒ 00:21:19.169 Chi Quinn: where we want to see, like, okay, well, did these people come to the show? And so, this is where we’ve seen, for example, so, like, for Afghanistan, and what particular company that looks like that they’ve represented, within, in this case, from Afghanistan.
188 00:21:19.240 ⇒ 00:21:26.170 Chi Quinn: When was the last year delegation in attendance? The first year, and then other information, like.
189 00:21:26.170 ⇒ 00:21:26.780 Amber Lin: Hmm.
190 00:21:26.780 ⇒ 00:21:43.689 Chi Quinn: the leader… so it looks like here, like, did they register? Did they attend? And we could see, for some cases that are populated, that some have not registered, or some have attended. So we would see, I guess, we’re looking at the… the most recent,
191 00:21:44.410 ⇒ 00:21:53.479 Chi Quinn: status. So, as you can see, like, obviously they did register, but then they eventually attended. So that’s from my understanding. I wish I could… I…
192 00:21:53.590 ⇒ 00:22:02.120 Chi Quinn: from what I’ve collected so far, but I will elaborate on that if I find something even, like, more information about that, and I’ll let.
193 00:22:02.120 ⇒ 00:22:14.389 Amber Lin: Yeah, sounds good. This is probably not super urgent right now. I’m gonna do… I think for my sake, I’m gonna do the attendees part first, and then if I have time, I’m gonna do a little bit of conferences.
194 00:22:14.390 ⇒ 00:22:23.849 Amber Lin: And then, like, after that, we’ll probably talk again about the other stuff, but it’ll be maybe, like, a week, two weeks, or more on down the line.
195 00:22:24.300 ⇒ 00:22:37.939 Chi Quinn: That sounds good, and honestly, yeah, attendees would definitely be the best to start, because I know, if I’m understanding, or at least we’re still trying to figure out some things in terms of what the conference passes and stuff.
196 00:22:38.190 ⇒ 00:22:38.590 Amber Lin: Yeah.
197 00:22:38.590 ⇒ 00:22:42.960 Chi Quinn: But I know the attendee stuff, it’s definitely a good place to go from there.
198 00:22:42.960 ⇒ 00:22:55.359 Amber Lin: Okay. All right, thank you, this is really helpful. I also have the recording, so no need to do screenshots, but if you, like, run into something that’s a clarification, feel free to just shoot me a Slack message.
199 00:22:55.540 ⇒ 00:22:56.500 Chi Quinn: Alright, sounds.
200 00:22:56.500 ⇒ 00:23:03.790 Amber Lin: Alright, yeah, nice talking to you. Enjoy the rest of your day. I think your day is almost over, because we’re in a different time zone.
201 00:23:03.790 ⇒ 00:23:05.420 Chi Quinn: Yes, yes.
202 00:23:05.420 ⇒ 00:23:07.080 Amber Lin: So I appreciate it.
203 00:23:07.920 ⇒ 00:23:09.590 Chi Quinn: All right, thank you, Amber.
204 00:23:09.590 ⇒ 00:23:10.880 Amber Lin: See you next week. Bye!
205 00:23:10.910 ⇒ 00:23:12.040 Chi Quinn: Bye.