Meeting Title: Matter More Case Study Planning Date: 2025-07-01 Meeting participants: Hannah Wang, Amber Lin
WEBVTT
1 00:02:27.470 ⇒ 00:02:28.940 Amber Lin: Hi! There!
2 00:02:29.530 ⇒ 00:02:30.510 Hannah Wang: Hello!
3 00:02:31.430 ⇒ 00:02:32.560 Amber Lin: How are you?
4 00:02:33.330 ⇒ 00:02:34.600 Hannah Wang: Good! How are you?
5 00:02:35.574 ⇒ 00:02:38.119 Amber Lin: I woke up from a nap.
6 00:02:38.120 ⇒ 00:02:40.565 Hannah Wang: Oh, I understand!
7 00:02:44.526 ⇒ 00:02:52.790 Amber Lin: Oh, I did not ask them. If we can do case studies. I can do that.
8 00:02:55.761 ⇒ 00:03:04.340 Amber Lin: I was thinking that we could use this time to write some of the
9 00:03:04.620 ⇒ 00:03:12.490 Amber Lin: at least start some of the case studies. I know Robert was talking about matter more, and
10 00:03:13.050 ⇒ 00:03:22.010 Amber Lin: how he’s talked to someone at Lv. Lvmh. And it might be similar or helpful if we
11 00:03:22.570 ⇒ 00:03:28.290 Amber Lin: use the Baltimore case study because the people he’s talking to is from Hr.
12 00:03:31.110 ⇒ 00:03:35.530 Amber Lin: Like, even if we don’t get them to use it like
13 00:03:36.540 ⇒ 00:03:40.389 Amber Lin: anonymous, we can still do it like anonymously.
14 00:03:40.390 ⇒ 00:03:40.980 Hannah Wang: Yeah.
15 00:03:40.980 ⇒ 00:03:45.540 Amber Lin: Even if we don’t get their permission to username.
16 00:03:48.300 ⇒ 00:03:55.580 Hannah Wang: Oh, okay, yeah, that that sounds good. Could I have more context on what matter? More is like, what type of client it is
17 00:03:55.580 ⇒ 00:04:00.820 Hannah Wang: sure. Sure. Yeah, do you have like a list of questions, or
18 00:04:01.866 ⇒ 00:04:03.850 Hannah Wang: that we should go through?
19 00:04:06.100 ⇒ 00:04:12.699 Hannah Wang: Yeah, for the case studies in notion. Let me try to find it. There’s like,
20 00:04:24.910 ⇒ 00:04:27.130 Hannah Wang: this is locked. Okay?
21 00:04:28.930 ⇒ 00:04:29.800 Hannah Wang: Is.
22 00:04:49.220 ⇒ 00:04:54.960 Hannah Wang: I guess these are the questions. Let me.
23 00:04:56.050 ⇒ 00:04:59.130 Hannah Wang: Copy it and send it.
24 00:04:59.680 ⇒ 00:05:03.690 Hannah Wang: Chat. Okay, yeah. So I just
25 00:05:04.770 ⇒ 00:05:16.629 Hannah Wang: what I do is I like, just ask whoever’s knows about the client and the case study. I just ask them to use AI to fill this out, or, Yeah, fill it out themselves. And then I like.
26 00:05:16.630 ⇒ 00:05:17.070 Amber Lin: Hmm.
27 00:05:17.070 ⇒ 00:05:20.280 Hannah Wang: Go go from here and templatize it, basically to fit.
28 00:05:20.910 ⇒ 00:05:28.309 Hannah Wang: That we have for our case. Study? So yeah, these are the questions that I think would be helpful to
29 00:05:28.520 ⇒ 00:05:35.010 Hannah Wang: have someone. I guess, Robert. Answer, or if there’s like a.
30 00:05:35.530 ⇒ 00:05:39.210 Amber Lin: I think I can answer it for mine. I
31 00:05:39.310 ⇒ 00:05:45.009 Amber Lin: I don’t for the other questions you will have to answer.
32 00:05:45.955 ⇒ 00:05:49.420 Amber Lin: Okay, sorry. I think, Robert, let me
33 00:05:50.000 ⇒ 00:05:58.760 Amber Lin: let me send you a screenshot of what Robert was asking about in terms of matter more.
34 00:06:02.220 ⇒ 00:06:09.400 Hannah Wang: Hmm, hmm, I get a quick view of our work
35 00:06:15.360 ⇒ 00:06:16.760 Hannah Wang: employee print.
36 00:06:17.220 ⇒ 00:06:20.950 Hannah Wang: So you’re in charge of matter more right? Or your pm, okay.
37 00:06:20.950 ⇒ 00:06:21.620 Amber Lin: Yes.
38 00:06:22.990 ⇒ 00:06:24.650 Hannah Wang: What’s lvmh.
39 00:06:25.798 ⇒ 00:06:29.211 Amber Lin: It’s lv, it’s probably Louis Vuitton.
40 00:06:29.780 ⇒ 00:06:30.410 Hannah Wang: Cool.
41 00:06:30.810 ⇒ 00:06:34.369 Amber Lin: I don’t know how Ravi got there, but very impressive.
42 00:06:34.370 ⇒ 00:06:37.200 Hannah Wang: Yeah, what? The heck? Okay.
43 00:06:37.720 ⇒ 00:06:38.640 Amber Lin: Yeah.
44 00:06:39.164 ⇒ 00:06:46.610 Hannah Wang: Blah, blah. And more, we do talk through.
45 00:06:54.210 ⇒ 00:06:58.230 Hannah Wang: Okay. So that last message is what he talked about with.
46 00:06:58.955 ⇒ 00:07:01.130 Amber Lin: With them, yeah.
47 00:07:07.170 ⇒ 00:07:08.240 Hannah Wang: I see.
48 00:07:12.230 ⇒ 00:07:20.649 Hannah Wang: Hmm, okay, because what normally happens with case studies is that
49 00:07:21.080 ⇒ 00:07:31.310 Hannah Wang: someone or whoever is like pming it, basically. I get to that page.
50 00:07:33.220 ⇒ 00:07:40.829 Amber Lin: Hmm, okay. Can you make a copy for matter more specifically? And I can add it in.
51 00:07:41.550 ⇒ 00:07:46.930 Hannah Wang: Yeah, let me try to get to that database.
52 00:07:55.260 ⇒ 00:07:58.930 Hannah Wang: Sorry. I’m just trying to remember what we did. Hmm.
53 00:07:58.930 ⇒ 00:07:59.760 Amber Lin: Oh, good!
54 00:08:02.500 ⇒ 00:08:03.220 Hannah Wang: This.
55 00:08:12.470 ⇒ 00:08:33.419 Hannah Wang: Well, anyway, for, like a lot of the AI ones that we created, basically, Uton just sent me like a loom video of Mustafa, or Casey, or Miguel, like walking through the demo, and just like talking through it. And I took the transcription, and then I like fed it into chat with like this? These questions, asking.
56 00:08:33.429 ⇒ 00:08:33.909 Amber Lin: Hmm.
57 00:08:33.909 ⇒ 00:08:37.739 Hannah Wang: Help answer it. So I don’t know if there’s like a.
58 00:08:37.740 ⇒ 00:08:41.790 Amber Lin: Yeah, I fed it through, I think, at least for the
59 00:08:42.340 ⇒ 00:08:49.079 Amber Lin: for the 1st 2 sections. I just read the answer, that’s pretty good. So I’m gonna.
60 00:08:49.260 ⇒ 00:08:49.750 Hannah Wang: Okay.
61 00:08:49.750 ⇒ 00:08:55.830 Amber Lin: I’m just gonna paste it at the bottom of the notion page. You can paste it elsewhere.
62 00:08:57.900 ⇒ 00:09:02.610 Amber Lin: Rather more case study.
63 00:09:05.980 ⇒ 00:09:07.170 Hannah Wang: Which page are you.
64 00:09:07.432 ⇒ 00:09:09.530 Amber Lin: The one that you just shared with me.
65 00:09:09.860 ⇒ 00:09:11.200 Amber Lin: Well, this is chat.
66 00:09:12.450 ⇒ 00:09:13.360 Hannah Wang: Okay.
67 00:09:13.360 ⇒ 00:09:15.080 Amber Lin: We’ll see using.
68 00:09:18.340 ⇒ 00:09:20.940 Hannah Wang: Is there a client, Hub, for matter more.
69 00:09:21.550 ⇒ 00:09:22.440 Amber Lin: Yes.
70 00:09:22.440 ⇒ 00:09:23.290 Hannah Wang: Okay.
71 00:09:24.694 ⇒ 00:09:28.005 Amber Lin: Wouldn’t it? Wouldn’t you put it in your
72 00:09:29.110 ⇒ 00:09:34.359 Amber Lin: Sorry in the marketing database?
73 00:09:35.880 ⇒ 00:09:41.320 Amber Lin: I don’t know whatever. Yeah, whatever organization works best for you.
74 00:09:41.500 ⇒ 00:09:47.430 Hannah Wang: Yeah, I mean, I think Utam had already created like this. dB, for case studies. So.
75 00:09:47.430 ⇒ 00:09:47.770 Amber Lin: I just.
76 00:09:47.770 ⇒ 00:09:49.370 Hannah Wang: Pop everything in here, no.
77 00:09:49.370 ⇒ 00:09:50.610 Amber Lin: Okay. Great.
78 00:09:58.660 ⇒ 00:10:06.510 Hannah Wang: I’m just trying to think of how we can or incorporate those 3 questions that Robert sent into.
79 00:10:07.530 ⇒ 00:10:11.640 Amber Lin: Yeah, I think those I think what he sent is more.
80 00:10:30.660 ⇒ 00:10:31.930 Amber Lin: let’s see.
81 00:10:32.710 ⇒ 00:10:40.140 Hannah Wang: Because that’s like the last sentence that he has. He’s just basically, that’s what Lv is.
82 00:10:40.490 ⇒ 00:10:41.230 Amber Lin: I see.
83 00:10:41.230 ⇒ 00:10:42.450 Hannah Wang: Focusing on right? It’s like.
84 00:10:43.410 ⇒ 00:10:48.779 Hannah Wang: They’re more concerned about like those engagement programs like employee growth retention.
85 00:10:53.950 ⇒ 00:10:55.959 Hannah Wang: yeah, I’m worried.
86 00:10:55.960 ⇒ 00:11:04.849 Amber Lin: Yeah, I think if you look at Section 2, under matter more, does that answer those questions? Because that is more strategic framing.
87 00:11:04.850 ⇒ 00:11:05.720 Hannah Wang: Right.
88 00:11:05.720 ⇒ 00:11:12.360 Amber Lin: Yeah. Can you check that out? I’m checking the other questions. If they’re if they’re accurate.
89 00:11:13.030 ⇒ 00:11:22.770 Hannah Wang: If anything like, I can just make like a draft of it, and then you can send it to Robert and see what he thinks, and then, if he thinks it needs rework, then we can do that together.
90 00:13:22.830 ⇒ 00:13:25.509 Amber Lin: Do we need a personal learning lesson.
91 00:13:30.150 ⇒ 00:13:31.210 Hannah Wang: No.
92 00:13:32.570 ⇒ 00:13:38.349 Amber Lin: I see oh.
93 00:15:04.280 ⇒ 00:15:06.250 Amber Lin: sorry. Did I miss anything.
94 00:15:07.240 ⇒ 00:15:08.020 Hannah Wang: No.
95 00:15:08.020 ⇒ 00:15:11.880 Amber Lin: Okay, should we use this?
96 00:15:22.380 ⇒ 00:15:25.199 Hannah Wang: Are you just running it through the client? Hub right now.
97 00:15:25.960 ⇒ 00:15:30.100 Amber Lin: No, the client Hub is not as great yet.
98 00:15:31.030 ⇒ 00:15:32.070 Amber Lin: Yeah.
99 00:15:32.430 ⇒ 00:15:33.880 Hannah Wang: What are you using to get.
100 00:15:33.880 ⇒ 00:15:41.479 Amber Lin: I’m I’m using my chat gpt because I’ve talked with it so much about this client. So it has like
101 00:15:42.570 ⇒ 00:15:43.556 Amber Lin: it has.
102 00:15:44.350 ⇒ 00:15:46.109 Amber Lin: It knows what to do.
103 00:15:46.110 ⇒ 00:15:46.960 Hannah Wang: Okay.
104 00:16:24.230 ⇒ 00:16:38.760 Amber Lin: I mean, I can fill. I can fill this out. I think that’s a pretty good outcome for our meeting today, and then I’ll send this to the promotional promotion channel. Ping, Robert, tell him we’ve wrote this together.
105 00:16:39.030 ⇒ 00:16:40.000 Amber Lin: How’s that?
106 00:16:40.530 ⇒ 00:16:44.070 Hannah Wang: Yeah, I see that I feel like he created this
107 00:16:44.928 ⇒ 00:16:53.750 Hannah Wang: prompt like a brain forge case. I feel like there’s so many like prompts. For like.
108 00:16:53.750 ⇒ 00:16:54.380 Amber Lin: Sales.
109 00:16:54.380 ⇒ 00:16:54.890 Hannah Wang: While ago.
110 00:16:55.740 ⇒ 00:17:07.279 Hannah Wang: And we created this just now. So I don’t really know which one to run through like feed. Whatever output we have through to get like the case study that we want. But.
111 00:17:07.280 ⇒ 00:17:08.490 Amber Lin: Hmm.
112 00:17:09.740 ⇒ 00:17:12.389 Hannah Wang: Like the structure that I have.
113 00:17:12.579 ⇒ 00:17:15.843 Hannah Wang: Like, I, I feel like you’ve seen it. It’s just
114 00:17:16.829 ⇒ 00:17:21.520 Hannah Wang: It’s the context, the challenge solution results and impact. And that’s how we’ve been.
115 00:17:22.020 ⇒ 00:17:23.280 Hannah Wang: Bring all of your case, studies.
116 00:17:23.280 ⇒ 00:17:23.790 Amber Lin: Hmm.
117 00:17:23.790 ⇒ 00:17:28.479 Hannah Wang: I don’t know if, like Robert wants to change that or anything. But.
118 00:17:28.480 ⇒ 00:17:33.889 Amber Lin: Can you copy and paste the prompt or send me the link to the prompt somewhere?
119 00:17:34.620 ⇒ 00:17:36.900 Hannah Wang: You want these prompts.
120 00:17:37.901 ⇒ 00:17:41.619 Amber Lin: The the ones that you used to write case studies.
121 00:17:41.620 ⇒ 00:17:43.960 Hannah Wang: Oh, yeah, I didn’t have a
122 00:17:44.670 ⇒ 00:17:56.650 Hannah Wang: I didn’t create a prompt for it. I just kinda like chatted with the AI and I just told it like, Oh, give me context, challenge solution results impact. So I don’t have like a prompt for it. But
123 00:17:57.596 ⇒ 00:18:01.130 Hannah Wang: maybe in use
124 00:18:01.640 ⇒ 00:18:11.039 Hannah Wang: this one. It’s like a I think Ryan created it, based on what I kind of shared with him and asked him to help me write copy for
125 00:18:11.579 ⇒ 00:18:20.369 Hannah Wang: so this is the structure that we use like context challenge, blah, blah, so maybe you can like feed the whatever.
126 00:18:20.370 ⇒ 00:18:20.750 Amber Lin: There it is!
127 00:18:20.750 ⇒ 00:18:26.069 Hannah Wang: I’ll put it here like, give it this outline and then feed it. This all these sections.
128 00:18:26.070 ⇒ 00:18:26.470 Amber Lin: And.
129 00:18:26.470 ⇒ 00:18:28.089 Hannah Wang: And tell it to like. Give you.
130 00:18:28.860 ⇒ 00:18:30.399 Amber Lin: Okay. Okay.
131 00:18:30.510 ⇒ 00:18:34.979 Hannah Wang: And then you can send that to Robert, and like ping me in it, or tag me in it
132 00:18:37.100 ⇒ 00:18:38.230 Hannah Wang: once you’re done with it.
133 00:18:38.230 ⇒ 00:18:38.820 Amber Lin: Okay.
134 00:18:39.851 ⇒ 00:18:42.919 Amber Lin: Did you send that to me?
135 00:18:42.920 ⇒ 00:18:47.420 Hannah Wang: Yeah. Oh, in chat in the zoom chat, I think.
136 00:18:48.310 ⇒ 00:18:50.409 Amber Lin: Okay, let me go. Grab that.
137 00:18:50.410 ⇒ 00:18:51.520 Hannah Wang: Last, link.
138 00:19:01.940 ⇒ 00:19:02.790 Amber Lin: Okay.
139 00:19:03.240 ⇒ 00:19:08.190 Amber Lin: I think the only thing lacking would be like the
140 00:19:11.850 ⇒ 00:19:17.460 Amber Lin: like you are. Oh, like the challenge. I think I think we’ll go find the.
141 00:19:18.380 ⇒ 00:19:20.710 Amber Lin: Resolution. Challenge. Okay.
142 00:19:20.820 ⇒ 00:19:21.800 Amber Lin: Great.
143 00:19:22.930 ⇒ 00:19:36.590 Hannah Wang: Like. I don’t know if this is the best process like Oh, have it. Give us this first, st and then feed those results into the second link that I shared with you, or, if we can just like, consolidate all of it.
144 00:19:36.590 ⇒ 00:19:48.539 Amber Lin: I think we can consolidate both of it because the context is similar. Right? And then I think the context challenge solution results. It’s a. It’s like it’s a
145 00:19:48.710 ⇒ 00:19:50.330 Amber Lin: it’s a pretty good one.
146 00:19:51.840 ⇒ 00:19:58.770 Hannah Wang: Yeah, I guess this one was more so like if we asked like
147 00:19:59.250 ⇒ 00:20:07.100 Hannah Wang: the client, I guess like sent it to the client and told them to answer these questions. Then I guess we would like send this.
148 00:20:07.100 ⇒ 00:20:07.680 Amber Lin: Hmm.
149 00:20:07.680 ⇒ 00:20:12.370 Hannah Wang: But what we’ve been doing so far is just internally like answering.
150 00:20:12.960 ⇒ 00:20:21.130 Hannah Wang: Questions using AI, so yeah, I don’t know.
151 00:20:22.130 ⇒ 00:20:25.519 Amber Lin: It’s it’s pretty good. Wait. Let me copy it over.
152 00:20:26.940 ⇒ 00:20:30.020 Amber Lin: And add that under.
153 00:20:38.270 ⇒ 00:20:49.557 Amber Lin: Well, technically, we’re working for a startup that works. So are you on the document with me? So in the same case, study that you sent me. So if you scroll down
154 00:20:50.150 ⇒ 00:20:59.029 Amber Lin: we are working for a startup that works for the Fortune 500 enterprise. I don’t know how you want to word that
155 00:20:59.210 ⇒ 00:21:02.429 Amber Lin: if it’s anonymized I don’t. I don’t know if it’s matter.
156 00:21:03.080 ⇒ 00:21:05.209 Hannah Wang: I feel like this is pretty anonymized.
157 00:21:09.290 ⇒ 00:21:12.830 Hannah Wang: We can always like play around with the title later.
158 00:21:27.680 ⇒ 00:21:34.080 Amber Lin: Solution. Integrated bigquery data models, real time dashboards.
159 00:21:53.990 ⇒ 00:21:54.920 Amber Lin: Wow!
160 00:21:57.710 ⇒ 00:22:01.049 Amber Lin: Well, there is no from pilot to roll out
161 00:22:14.860 ⇒ 00:22:16.730 Amber Lin: framework. Now, power
162 00:22:28.890 ⇒ 00:22:30.810 Amber Lin: doesn’t exist.
163 00:22:32.804 ⇒ 00:22:37.199 Amber Lin: Curious that this is not AI.
164 00:22:37.770 ⇒ 00:22:39.420 Hannah Wang: Yeah, okay.
165 00:22:39.420 ⇒ 00:22:46.250 Amber Lin: Okay, let me make a few change the call to action.
166 00:22:55.890 ⇒ 00:22:57.320 Amber Lin: Okay, great.
167 00:22:58.930 ⇒ 00:23:07.816 Hannah Wang: Before you continue. Can you copy over everything, just paste it into the last link that I sent in zoom? I created.
168 00:23:08.220 ⇒ 00:23:10.447 Amber Lin: I’ll do that.
169 00:23:37.760 ⇒ 00:23:39.290 Amber Lin: I’m gonna move.
170 00:24:07.920 ⇒ 00:24:10.339 Amber Lin: I think this is good.
171 00:24:23.640 ⇒ 00:24:30.540 Amber Lin: And then, as a bonus, write a short message.
172 00:24:34.000 ⇒ 00:24:35.060 Amber Lin: oh.
173 00:26:07.650 ⇒ 00:26:17.480 Amber Lin: okay, I’m so at the top, I’ve added.
174 00:26:18.170 ⇒ 00:26:24.140 Amber Lin: So if you go into the Madam case study, I’ll share my screen super fast.
175 00:26:24.680 ⇒ 00:26:29.009 Amber Lin: So I’ve added brief description.
176 00:26:29.720 ⇒ 00:26:32.999 Amber Lin: A short email on how we might be able to help.
177 00:26:33.200 ⇒ 00:26:36.629 Amber Lin: Lvmh, this is these are the notes
178 00:26:36.810 ⇒ 00:26:43.209 Amber Lin: based on the sections. This is the project. Why not want to change the I want to change the title
179 00:26:43.953 ⇒ 00:26:48.070 Amber Lin: and then I think I read through these. These are pretty good.
180 00:26:48.230 ⇒ 00:26:49.280 Hannah Wang: Okay. Cool.
181 00:27:06.430 ⇒ 00:27:07.740 Amber Lin: all right.
182 00:27:08.130 ⇒ 00:27:10.720 Amber Lin: Some title options for you.
183 00:27:16.660 ⇒ 00:27:17.590 Amber Lin: great!
184 00:27:18.140 ⇒ 00:27:25.259 Amber Lin: I think that’s great. I think we’ve knocked out a case study. Let’s just get Robert to see if how he feels about this.
185 00:27:25.610 ⇒ 00:27:28.429 Amber Lin: and then I think we can get it out soon.
186 00:27:29.360 ⇒ 00:27:35.660 Hannah Wang: Okay, yeah, I I’ll make a ticket for this in our board. Just so we can track.
187 00:27:36.320 ⇒ 00:27:40.969 Hannah Wang: In the case. Study and then.
188 00:27:41.340 ⇒ 00:27:44.190 Hannah Wang: yeah, ping me or tag me in the
189 00:27:44.490 ⇒ 00:27:48.730 Hannah Wang: thread. So I know if it’s good to go, and then we’ll start the design.
190 00:27:49.450 ⇒ 00:27:53.749 Amber Lin: Okay, do we use the promotion channel?
191 00:27:54.420 ⇒ 00:27:56.180 Hannah Wang: Or do you use.
192 00:27:56.180 ⇒ 00:27:57.190 Amber Lin: To mark any channel.
193 00:27:57.190 ⇒ 00:27:58.490 Hannah Wang: Start getting.
194 00:27:58.490 ⇒ 00:27:58.870 Amber Lin: Okay.
195 00:27:58.870 ⇒ 00:28:01.120 Hannah Wang: Usually, I think, for case studies.
196 00:28:10.480 ⇒ 00:28:13.029 Amber Lin: Does does Robert check this channel.
197 00:28:14.326 ⇒ 00:28:15.220 Hannah Wang: He does.
198 00:28:15.220 ⇒ 00:28:15.940 Amber Lin: Okay.
199 00:28:16.690 ⇒ 00:28:17.730 Amber Lin: Oh.
200 00:29:00.530 ⇒ 00:29:01.350 Amber Lin: okay.
201 00:29:01.900 ⇒ 00:29:04.519 Amber Lin: Great productive meeting.
202 00:29:04.520 ⇒ 00:29:04.890 Hannah Wang: Nice.
203 00:29:04.890 ⇒ 00:29:06.449 Amber Lin: I’m glad we booked this.
204 00:29:06.970 ⇒ 00:29:07.600 Hannah Wang: Ha! Ha!
205 00:29:08.630 ⇒ 00:29:09.790 Amber Lin: Yeah, so.
206 00:29:10.500 ⇒ 00:29:11.346 Hannah Wang: Okey, dokey.
207 00:29:11.770 ⇒ 00:29:12.490 Amber Lin: Okay.
208 00:29:13.030 ⇒ 00:29:13.950 Hannah Wang: All right.
209 00:29:13.950 ⇒ 00:29:14.770 Amber Lin: Hi.
210 00:29:14.770 ⇒ 00:29:16.350 Hannah Wang: Back, bye, bye.