Meeting Title: Insomnia Cookies Automation Scoping Date: 2025-08-06 Meeting participants: Sam Roberts, Mustafa Raja, Amber Lin


WEBVTT

1 00:01:00.930 00:01:02.830 Sam Roberts: Oh, hey! Let’s see who joins up.

2 00:01:03.910 00:01:05.220 Mustafa Raja: Hey, Sam, how are you?

3 00:01:05.880 00:01:06.860 Sam Roberts: Doing all right.

4 00:01:11.580 00:01:12.590 Amber Lin: Hi! Both.

5 00:01:13.330 00:01:14.399 Mustafa Raja: Hey! How are you?

6 00:01:14.560 00:01:20.090 Amber Lin: I’m good hopping around from many meetings, but I will.

7 00:01:20.090 00:01:26.319 Amber Lin: This one’s my favorite, but I don’t have to talk with clients or argue with them.

8 00:01:28.182 00:01:36.010 Amber Lin: That’s it’s nice to have an internal meeting anyways. Let me brief you on what this is for.

9 00:01:36.290 00:01:47.130 Amber Lin: So this is for our new client insomnia cookies there, I mean the same. You’re in the Us. You have probably had their cookies before, so they’re like a.

10 00:01:47.130 00:01:47.730 Sam Roberts: Yes.

11 00:01:47.730 00:02:01.560 Amber Lin: Cookie, but for Mustafa is just that they have physical products, and then they need to they have reports and stuff that they need to automate. So let me share my screen and find

12 00:02:01.690 00:02:06.219 Amber Lin: that report. It’s not this one.

13 00:02:08.100 00:02:09.259 Amber Lin: Project.

14 00:02:09.669 00:02:16.380 Amber Lin: Nope, oh, dear, okay, yeah.

15 00:02:17.090 00:02:22.839 Amber Lin: Oh, insomnia cookies. Oh, gosh!

16 00:02:23.460 00:02:28.210 Amber Lin: I okay, let me find that and share that with you all.

17 00:02:32.630 00:02:33.779 Amber Lin: And so

18 00:02:37.540 00:02:51.549 Amber Lin: so I’ll share the link in our chat. So essentially, they have a spreadsheet that their head of marketing fills in every morning.

19 00:02:51.730 00:02:52.870 Amber Lin: So

20 00:02:53.130 00:03:03.300 Amber Lin: she takes data from different sources and then sums it up and puts it in this spreadsheet. They want us to help

21 00:03:03.480 00:03:15.760 Amber Lin: own that. So we have 2 workflows. We are manually filling it in every day, which I assigned to Giselle to help, and I want help on

22 00:03:16.140 00:03:21.059 Amber Lin: how to automate taking that data and putting it in the spreadsheet.

23 00:03:21.600 00:03:29.260 Amber Lin: So I’ll show you the different sources. Let me grab that

24 00:03:30.300 00:03:54.229 Amber Lin: so right now, what we are, the current, most important, most current one is to automate from this dashboard called braise to the spreadsheet, and I’ll share access to all of them. Once we’re done with this meeting, let me just grab where it is.

25 00:03:55.656 00:03:57.170 Amber Lin: Not that.

26 00:03:59.420 00:04:00.660 Amber Lin: Oh, okay.

27 00:04:02.360 00:04:05.699 Amber Lin: So here’s another link. I’ll share my screen and

28 00:04:05.940 00:04:10.499 Amber Lin: show you a brief overview of the different platforms.

29 00:04:11.610 00:04:21.210 Amber Lin: So here are we have a few platforms we need to

30 00:04:21.730 00:04:36.809 Amber Lin: automate. So this spreadsheet is their daily impact scorecard. And we want to take data and put it into this spreadsheet. So it’s pulling from a few different sources. One of them is

31 00:04:37.160 00:04:45.850 Amber Lin: another spreadsheet, I think. And then one of them is Google, Meta, the ads.

32 00:04:45.960 00:04:51.150 Amber Lin: And then there’s 1 called Brace, which is their dashboard,

33 00:04:53.250 00:05:00.420 Amber Lin: and then there’s different promotional offers for food delivery apps. So we have say, Uber doordash

34 00:05:01.495 00:05:03.880 Amber Lin: etcetera, I think.

35 00:05:05.683 00:05:16.539 Amber Lin: Yeah. So right now we are focusing on brace, which looks like, where is it?

36 00:05:20.700 00:05:21.245 Amber Lin: Okay.

37 00:05:22.590 00:05:34.560 Amber Lin: So this is the dashboard, and we need to go to. Where is the campaigns? So we’ll take

38 00:05:34.950 00:05:44.620 Amber Lin: the campaign info information for each day and then put it into the spreadsheet.

39 00:05:45.090 00:05:53.270 Amber Lin: So I think we need an intermediate spreadsheet. Currently we’re manually copying. Oh, gosh!

40 00:05:54.110 00:05:56.060 Amber Lin: I’m logged out again.

41 00:05:57.230 00:05:58.630 Amber Lin: Sorry!

42 00:05:58.800 00:05:59.380 Sam Roberts: Really.

43 00:06:04.050 00:06:09.779 Amber Lin: So we’re manually copying each say campaigns, details. So for

44 00:06:10.170 00:06:36.479 Amber Lin: android push, Ios and email, so we’re copying it here. And then we’re making a summary aggregate, and we’re putting it in this box every day. So I wanted to ask you guys, how can we automate this? It sounds like something that will scrape the website for, or maybe get an Api what do you guys think.

45 00:06:46.160 00:06:47.460 Sam Roberts: You ever thought there, or.

46 00:06:48.370 00:06:57.679 Mustafa Raja: Yeah, yeah. So so do we already have this this data that we need to consolidate in a spreadsheet. Also in a spreadsheet. No.

47 00:06:58.970 00:06:59.380 Amber Lin: No.

48 00:06:59.380 00:07:00.380 Mustafa Raja: So, if it’s already.

49 00:07:00.380 00:07:09.400 Amber Lin: Filling it in every day. So it’s just it’s numbers on a dashboard. I can. I can try and find it

50 00:07:10.440 00:07:12.439 Mustafa Raja: Bring us a dashboard right.

51 00:07:13.260 00:07:23.830 Amber Lin: Yeah, Braze is a dashboard. They have one entry every day, so I’m I’m trying to navigate to that but.

52 00:07:25.380 00:07:28.560 Mustafa Raja: Let me show you what please.

53 00:07:29.280 00:07:32.767 Sam Roberts: I took a quick look at their docs to see if there was an Api

54 00:07:33.200 00:07:33.760 Mustafa Raja: Yeah.

55 00:07:33.760 00:07:38.240 Sam Roberts: And it looks like there is. It’s got a ton of different stuff. And so I wanna make sure we know exactly what.

56 00:07:40.780 00:07:42.800 Sam Roberts: You know we can exactly where.

57 00:07:43.160 00:07:47.220 Mustafa Raja: Yeah, if you have an Api that lets us get this data, then it’s easy. Peasy.

58 00:07:47.510 00:07:51.709 Sam Roberts: Thank you. It looks like this is that I just sent the link to what I was looking at for their endpoint.

59 00:07:54.610 00:07:58.229 Sam Roberts: But there’s a you know.

60 00:07:58.230 00:07:59.456 Mustafa Raja: Yeah campaigns?

61 00:08:01.940 00:08:04.800 Sam Roberts: Campaign detail list, data, series, yeah.

62 00:08:05.310 00:08:08.559 Mustafa Raja: Can we also take a look at the dashboard, too?

63 00:08:08.560 00:08:12.790 Amber Lin: Yeah, yeah, totally. I just figured out where to find it. So I’ll share my screen again. Now.

64 00:08:12.790 00:08:13.410 Mustafa Raja: Cool, so.

65 00:08:13.410 00:08:26.619 Amber Lin: So, and I’ll share all the one pass credentials. After this meeting. So right here I found I navigated to messaging, and then I went to campaigns. And then so every day there’s

66 00:08:27.080 00:08:34.516 Amber Lin: so it will say the date here that okay, this is for yesterday we used this email push

67 00:08:35.030 00:08:39.020 Amber Lin: for I think Ios and one for Android. And then

68 00:08:39.520 00:08:50.629 Amber Lin: we added those manually into the spreadsheet and say, if we click into email, it will show

69 00:08:50.840 00:09:03.440 Amber Lin: it will show us so the messages sent conversion, rate, total revenue, expect estimated audience, unique open rate. And that is

70 00:09:03.660 00:09:11.910 Amber Lin: what we put in here. So they have date when it’s deployed which platform it is total audience

71 00:09:12.180 00:09:24.560 Amber Lin: cost to send, which is a calculation. I think they only do for email and then open rate, conversion, rate, revenue and content. So we want to pull those data and put them in

72 00:09:24.980 00:09:25.810 Amber Lin: each.

73 00:09:26.050 00:09:31.029 Amber Lin: Each entry would be a row and thinking about how we can automate that.

74 00:09:32.800 00:09:38.080 Mustafa Raja: They have a campaign detailed endpoint. I’m wondering if it if it

75 00:09:38.978 00:09:40.651 Mustafa Raja: would give us the

76 00:09:41.260 00:09:45.420 Mustafa Raja: data that’s being displayed in the oh.

77 00:09:45.850 00:09:50.250 Mustafa Raja: dashboard! Maybe we should do spike on this.

78 00:09:50.380 00:09:52.600 Mustafa Raja: See if we can get all the data.

79 00:09:52.730 00:09:54.030 Amber Lin: What do you think, Sam?

80 00:09:54.850 00:09:58.419 Sam Roberts: Yeah, I think that’s a good call. I mean I if their Api seems pretty well documented and pretty.

81 00:09:58.420 00:09:59.010 Mustafa Raja: Yeah.

82 00:09:59.010 00:10:04.787 Sam Roberts: Thorough. So I imagine whatever’s on that dashboard is probably using the Api so we could probably get it all from there.

83 00:10:05.050 00:10:05.800 Mustafa Raja: Yeah.

84 00:10:06.620 00:10:10.230 Amber Lin: So let me. Oh, well.

85 00:10:16.163 00:10:29.340 Amber Lin: for Amanda for braids. So I’m going to make this spike to check Brace

86 00:10:29.690 00:10:35.069 Amber Lin: Api to identify if it yeah, there’s

87 00:10:37.610 00:10:44.630 Amber Lin: fields needed. I’ll put a screenshot of what it looks like.

88 00:10:45.310 00:10:45.790 Mustafa Raja: Yeah.

89 00:10:45.790 00:10:48.149 Amber Lin: In this campaign.

90 00:10:50.410 00:10:56.980 Amber Lin: Also put a screenshot what it looks like before we went into the campaign.

91 00:10:58.229 00:11:04.220 Amber Lin: If you want, it’ll be great if you can link it here as well, so I’ll assign it to you.

92 00:11:04.390 00:11:05.430 Mustafa Raja: Yeah.

93 00:11:06.340 00:11:14.500 Amber Lin: And then we’ll say to do, and what?

94 00:11:15.360 00:11:18.159 Amber Lin: Let me copy the fields they want.

95 00:11:18.700 00:11:21.939 Mustafa Raja: Okay. Can you also add me to the insomnia team?

96 00:11:22.230 00:11:25.850 Amber Lin: Oh, yeah, totally and.

97 00:11:25.850 00:11:27.000 Mustafa Raja: And the clockify 2.

98 00:11:28.170 00:11:28.990 Amber Lin: Okay.

99 00:11:32.740 00:11:36.760 Mustafa Raja: I think that 2 other tickets are already been assigned to me.

100 00:11:37.440 00:11:38.150 Amber Lin: Oh!

101 00:11:38.670 00:11:40.720 Mustafa Raja: In insomnia, cookies.

102 00:11:41.180 00:11:42.329 Amber Lin: Oh, I see.

103 00:11:42.710 00:11:44.757 Amber Lin: Yeah, I think that’s

104 00:11:46.380 00:12:00.800 Amber Lin: apart from raise. That’s something that we want to discuss now as well. So for what’s the pipeline gonna look like for the diff other different fields? How are we gonna do the automation for those? Because we know that we have this

105 00:12:01.080 00:12:30.730 Amber Lin: raise Api. But we have all these different sources that will also come in. Do you think we will need to create another? Say, source of truth? For us. Maybe that’s a spreadsheet. Maybe we pull it into a data warehouse, and then we pull data from there, and then into a spreadsheet for the clients. So I think that’s what Robert means by defining the pipeline structure, because that’s something that we need to decide on.

106 00:12:31.350 00:12:32.030 Mustafa Raja: Okay.

107 00:12:32.860 00:12:37.939 Amber Lin: Yeah, let me pull this up.

108 00:12:39.800 00:12:47.420 Amber Lin: I guess, as a 1st initial thought, what do you guys think should be the overall structure.

109 00:12:47.800 00:12:53.089 Amber Lin: So I know there’s ingestion. And then there’s like where we put the sources.

110 00:12:53.260 00:12:57.189 Amber Lin: Or is that something I should grab utham and ask him about.

111 00:12:59.996 00:13:13.149 Mustafa Raja: For me. I’ll if it’s only the fields that were in the dashboard, I’ll just grab them if I can. Via the Api. I’ll just grab them and put put it in the spreadsheet.

112 00:13:13.320 00:13:14.450 Amber Lin: That we have.

113 00:13:16.109 00:13:23.300 Mustafa Raja: If you want to. More mature pipeline or something. Maybe Sam or utum can

114 00:13:23.650 00:13:25.710 Mustafa Raja: bring in some context to that.

115 00:13:25.940 00:13:27.609 Amber Lin: Hmm, Sam, what do you think.

116 00:13:28.520 00:13:33.992 Sam Roberts: Yeah, I would say so. Let me just clarify a couple of things real quick. So where they’re pulling

117 00:13:34.410 00:13:42.039 Sam Roberts: actually, I have. I don’t have it. Can you probably screenshot it somewhere? The the braves campaigns.

118 00:13:42.500 00:13:43.230 Amber Lin: Yes.

119 00:13:43.450 00:13:48.690 Sam Roberts: Those are. So those are new campaigns every day, or we’re pulling the same data

120 00:13:51.830 00:13:54.239 Sam Roberts: from and from the campaign as it’s changing over time.

121 00:13:55.800 00:14:00.230 Amber Lin: Changing over time. I think what they want is actually a daily snapshot.

122 00:14:00.480 00:14:04.590 Sam Roberts: That’s why. So yeah, that’s why I was making sure it wasn’t so like they will be different.

123 00:14:04.590 00:14:06.240 Amber Lin: For every day, so.

124 00:14:06.240 00:14:06.760 Sam Roberts: That’s what I was.

125 00:14:06.760 00:14:07.310 Amber Lin: Let him.

126 00:14:07.310 00:14:11.479 Sam Roberts: Okay, that’s what I was trying to double check. So yeah, I mean.

127 00:14:13.250 00:14:15.530 Sam Roberts: I would say, if that’s it, like

128 00:14:17.720 00:14:20.070 Sam Roberts: getting into that spreadsheet is probably enough.

129 00:14:22.770 00:14:28.380 Sam Roberts: I don’t necessarily know enough about these other sources.

130 00:14:30.038 00:14:34.149 Sam Roberts: And are where like and where they’re going. Necessarily, if it really is just like

131 00:14:37.720 00:14:42.060 Sam Roberts: getting it into that spreadsheet, I think I think Mustafa has the right idea, for now.

132 00:14:42.560 00:14:42.950 Amber Lin: Yeah.

133 00:14:42.950 00:14:43.450 Sam Roberts: Oh!

134 00:14:43.450 00:14:53.730 Amber Lin: Ultimately what our task is to help them automate this daily invest scorecard. So ultimately all of them would come here. So we have.

135 00:14:54.010 00:15:02.360 Amber Lin: I think these are our main sources of Google, Meta ads, and that raise the different food delivery apps.

136 00:15:03.120 00:15:09.950 Amber Lin: I I guess our decision here is, do we need a data warehouse like, do we need a like

137 00:15:10.330 00:15:18.449 Amber Lin: pipeline to ingest these, or are we just going to put everything? Or or are we just going to use spreadsheets.

138 00:15:19.020 00:15:21.840 Amber Lin: Yes, that’s the decision we want. We want to make.

139 00:15:22.110 00:15:22.940 Sam Roberts: Right?

140 00:15:27.740 00:15:29.730 Sam Roberts: trying to think like, I mean.

141 00:15:30.400 00:15:36.680 Sam Roberts: will there be other uses in the future for this data? Or is it really just to get them. That that spreadsheet. You know what I mean like, what is the.

142 00:15:37.280 00:15:37.610 Amber Lin: Hmm.

143 00:15:38.390 00:15:39.719 Sam Roberts: What kind of long term?

144 00:15:41.240 00:15:42.660 Sam Roberts: You know, if it if it is.

145 00:15:42.820 00:15:48.380 Sam Roberts: they really just want like something to automate it, to get into that that spreadsheet, and that’s how they then run with it.

146 00:15:49.650 00:15:54.419 Sam Roberts: I would say, I don’t. I don’t think we’ll need kind of an intermediary storage

147 00:15:55.020 00:15:56.390 Sam Roberts: to warehouse it, or anything.

148 00:15:57.060 00:15:57.610 Amber Lin: Hmm.

149 00:15:59.230 00:16:00.130 Sam Roberts: But

150 00:16:03.220 00:16:11.900 Sam Roberts: if there’s more that might want to happen with it later, I think it probably would make sense to store it, because otherwise then we’ll start pulling all the historical data from that spreadsheet again.

151 00:16:12.480 00:16:16.360 Amber Lin: I see, I see that’s a really good point. I know that

152 00:16:16.880 00:16:27.360 Amber Lin: currently this is the only task that you want us to do. However, Robert does want to upsell them, so I’m not sure if if we want to work with them in the future, we might have to

153 00:16:27.810 00:16:56.620 Amber Lin: do the data warehousing anyways. But I don’t think we need to decide in this meeting. I just want to note all the different concerns and reasonings for like supporting different decisions. And I’ll like I’ll present that to Utam. And then Robert, and then they can decide because it does change. I I do think it takes a lot more time if we need to do data warehousing right? Or is it, or does it not change that much in terms of time?

154 00:17:01.315 00:17:09.294 Sam Roberts: I it definitely, I think, would add some and stuff might be able to speak to that a little bit more, having done it because I haven’t done. This is sort of I’ve I’ve been doing mostly internal stuff at this point. But

155 00:17:10.599 00:17:11.610 Sam Roberts: I my.

156 00:17:11.619 00:17:12.549 Mustafa Raja: Other thought.

157 00:17:12.780 00:17:13.349 Sam Roberts: Go ahead!

158 00:17:13.770 00:17:17.006 Mustafa Raja: The warehousing thing would be a 1st for me, too.

159 00:17:17.339 00:17:27.689 Sam Roberts: Okay, okay, cool. The. My only other thought is, it could possibly depend on the spike, and like what is accessible from the braze. Api.

160 00:17:27.869 00:17:28.699 Amber Lin: Hmm.

161 00:17:28.890 00:17:34.420 Sam Roberts: You know what I mean if we’re going to be having to pull data in a kind of

162 00:17:35.850 00:17:42.369 Sam Roberts: snapshot every day like they’re doing, and there’s no good way to get historical data from their Api.

163 00:17:43.550 00:17:49.857 Sam Roberts: Then another source of truth that we’re like, you know, doing every day make might make sense.

164 00:17:52.260 00:17:55.959 Sam Roberts: I mean, it’s kind of what they’re doing now. So it makes me think like it’s. It’s not

165 00:17:56.380 00:18:00.580 Sam Roberts: easy to get that data out of braise unless you do it every day, but I don’t know.

166 00:18:00.850 00:18:03.900 Amber Lin: I see. I don’t think they ever try to.

167 00:18:03.900 00:18:10.571 Sam Roberts: Yeah. And I mean, if especially if they haven’t looked at the I don’t know. I just, you know, without digging into the endpoints a little bit more.

168 00:18:11.570 00:18:19.379 Sam Roberts: I I just I don’t know. And so hopefully, we’re starting to get a better idea of like if all that data is in braise. I wouldn’t worry about the warehouse at this point.

169 00:18:20.180 00:18:20.710 Mustafa Raja: Yeah.

170 00:18:21.170 00:18:26.639 Sam Roberts: If if it’s not embrace, then maybe we worry about it. But if they’re just looking for that

171 00:18:26.840 00:18:28.830 Sam Roberts: spreadsheet for now and then, yeah.

172 00:18:28.830 00:18:31.139 Sam Roberts: later. Pull from that spreadsheet and build a warehouse

173 00:18:31.840 00:18:37.289 Sam Roberts: historically, and then moving forward. I wouldn’t. I wouldn’t wanna over overdo it at this point.

174 00:18:37.290 00:18:47.080 Amber Lin: I I see especially currently the the 1st engagement is actually quite short, so that maybe our concern is how fast we can do things, so I’ll I’ll make sure.

175 00:18:47.080 00:18:54.119 Sam Roberts: That’s a really good point, too. Yeah, yeah, if it’s if it if it’s you know, I don’t say quick and dirty, but quick and robust.

176 00:18:54.380 00:18:55.199 Sam Roberts: Just get in touch.

177 00:18:55.200 00:18:59.010 Sam Roberts: Spreadsheet. That’s probably the best way to get it done for them.

178 00:19:00.630 00:19:04.170 Sam Roberts: Because then the spreadsheet becomes the source of truth that we can lean on afterwards.

179 00:19:08.250 00:19:14.890 Amber Lin: Okay, I will, I think, as part of this decision I will

180 00:19:15.040 00:19:24.690 Amber Lin: get. I don’t know if we have access to the food delivery apps yet. But I do think we have access to the Google and Meta ads. I’ll

181 00:19:24.830 00:19:39.790 Amber Lin: check if we can get access to that to you, Mustafa and maybe, if you can also to check on the documentation for Google and Meta as it should be a lot more mature in.

182 00:19:39.790 00:19:40.180 Mustafa Raja: Okay.

183 00:19:40.180 00:19:51.119 Amber Lin: On that end of the documentation, but I just don’t know which one for you to look at. Would you know, would you know which one to look at? Or should I ask Robert to clarify.

184 00:19:52.070 00:19:54.660 Mustafa Raja: May. Maybe maybe you need some clarification on that.

185 00:19:54.950 00:19:55.640 Amber Lin: Okay.

186 00:19:57.542 00:20:06.019 Mustafa Raja: And so for the current current spike the task right now is only to see if we can pull data from Brace to the spreadsheet right?

187 00:20:06.460 00:20:25.220 Amber Lin: Yeah. And then, once you do the spike, it’ll be great if you can give an estimate of oh, this is, gonna take these steps. It! It might take this many hours. Here are some access I need, or here are some blockers that I might encounter, so just so that Robert can tell the client. Hey? We can do this by when.

188 00:20:26.100 00:20:26.730 Mustafa Raja: Okay.

189 00:20:26.940 00:20:27.245 Amber Lin: Yeah.

190 00:20:27.550 00:20:28.599 Sam Roberts: Yeah, I mean, like, I said.

191 00:20:29.040 00:20:41.790 Sam Roberts: add to the spike a little bit, just as you’re understanding the Api see if it has any like, is it? You know, we have to take a you know, hit the Api once a day to get this data like they’re doing, basically, or is there some way to like.

192 00:20:41.910 00:20:47.800 Sam Roberts: you know, filter it by historical, you know, it might be it just accumulates, and we just have to hit it every day.

193 00:20:49.707 00:21:08.060 Amber Lin: I think I just, I’m looking at this Robert’s documentation for the Google and Meta ads. Seems like Google ads are coming from. Someone built a looker report that just has just that one line. So maybe we don’t have to directly connect to

194 00:21:08.440 00:21:31.029 Amber Lin: Google, we can connect to this or like we can connect directly to Google ads. And then I think Meta Ads are coming from this sheet that’s maintained by an outside agency. So again, we can either take it from that, or we can directly connect and help them eliminate the outside agency.

195 00:21:31.030 00:21:36.560 Sam Roberts: Yeah, I mean, it might also depend, if they’re working with an agency that’s running the stuff.

196 00:21:37.560 00:21:39.390 Sam Roberts: They may not want to stop working with them.

197 00:21:39.390 00:21:40.070 Amber Lin: True that.

198 00:21:40.070 00:21:42.249 Sam Roberts: But if it is something that we can take off their plate

199 00:21:42.900 00:21:44.469 Sam Roberts: that might you know what I mean. That’s a whole.

200 00:21:44.470 00:21:46.889 Amber Lin: Yeah, we’ll get them more if we felt safe.

201 00:21:46.890 00:21:47.680 Sam Roberts: Yeah, exactly.

202 00:21:47.680 00:21:48.030 Amber Lin: Don’t worry.

203 00:21:48.030 00:21:56.949 Sam Roberts: Exactly. So. Yeah, that’s a that’s a different conversation. But I would say, you know, whatever they’re relying on now in terms of us automating. It is probably the way to go.

204 00:21:57.120 00:21:57.490 Amber Lin: Okay.

205 00:21:57.490 00:22:01.170 Sam Roberts: But in the upsell I think it is probably like a

206 00:22:01.430 00:22:05.580 Sam Roberts: yeah. You don’t need a little spreadsheet for for this kind of thing.

207 00:22:05.580 00:22:10.380 Amber Lin: Yeah. And then, okay, I’ll update that in the project plan or the

208 00:22:10.530 00:22:14.581 Amber Lin: or the automation plan, I think, for the food delivery apps.

209 00:22:15.220 00:22:21.920 Amber Lin: right now, there’s no Api access to those ad platforms. It sounds like

210 00:22:25.600 00:22:36.850 Amber Lin: we can probably still export the data. So that’s something we can automate. But then, the trans, we’ll need to do special transformations, because it seems like for each of them. We want different things.

211 00:22:38.105 00:22:39.080 Amber Lin: So

212 00:22:39.380 00:23:00.880 Amber Lin: I’ll see if I can get access to that for you, Mustafa, and then maybe we can click around to see what we actually need. Maybe we need to scrape the site. I am not sure. But just so, just for your context, I think let’s focus on the braze part, for now I will see what I can do to make this more clear.

213 00:23:01.520 00:23:02.130 Mustafa Raja: Okay.

214 00:23:02.520 00:23:03.220 Amber Lin: Okay.

215 00:23:03.350 00:23:10.940 Amber Lin: yeah. Thank you both. Very helpful inputs. I will raise the discussion to Robert and let him decide.

216 00:23:11.440 00:23:15.619 Mustafa Raja: Okay, let’s add a deadline to the spike.

217 00:23:15.620 00:23:15.935 Amber Lin: Oh,

218 00:23:18.240 00:23:18.670 Amber Lin: To you.

219 00:23:18.670 00:23:19.020 Mustafa Raja: I know.

220 00:23:19.730 00:23:23.649 Amber Lin: Time today? Or are you busy with other items.

221 00:23:25.290 00:23:28.130 Mustafa Raja: I might be able to take it today.

222 00:23:28.430 00:23:29.559 Amber Lin: Okay. I’ll see.

223 00:23:29.560 00:23:29.950 Mustafa Raja: Show, the.

224 00:23:29.950 00:23:41.420 Amber Lin: Early tomorrow. I think a spike is. I’ll let you figure out. It sounds like it’s a 2 h like a 1 point to 2 point item.

225 00:23:41.690 00:23:42.650 Mustafa Raja: Yeah.

226 00:23:42.650 00:23:43.490 Amber Lin: Yeah. Okay.

227 00:23:44.200 00:23:44.810 Amber Lin: Alright.

228 00:23:44.810 00:23:45.600 Sam Roberts: I wouldn’t even oh.

229 00:23:45.990 00:23:55.100 Sam Roberts: I would. I would just like fill in the on the, on the ticket, like what you’d find and how it works, and then we can address it there. I wouldn’t necessarily put a whole document together like we have been.

230 00:23:55.620 00:23:56.300 Mustafa Raja: Okay.

231 00:23:56.600 00:23:58.129 Amber Lin: Okay, I’ll I’ll.

232 00:23:58.130 00:24:06.230 Mustafa Raja: And if I can get credentials for for this brace not necessarily, though. But yes.

233 00:24:06.230 00:24:12.501 Amber Lin: Let me do that let me add you to this channel, I think to start with.

234 00:24:14.180 00:24:17.250 Amber Lin: I will need Rico to add you to the

235 00:24:17.830 00:24:26.290 Amber Lin: to the one pass hub. But here are the 2 links that you will need for now, and

236 00:24:27.370 00:24:34.490 Amber Lin: one spreads, and then here are the.

237 00:24:37.040 00:24:49.770 Amber Lin: Here’s the document, the sop on how we are filling it in manually right now, which will give you some insight on what fields and what what the links that we use to

238 00:24:50.377 00:24:56.779 Amber Lin: where is it? The spreadsheet link number one, spreadsheet link number 2, embrace link right here, so you will.

239 00:24:56.780 00:24:57.290 Mustafa Raja: Okay.

240 00:24:57.290 00:24:59.350 Amber Lin: So it’s here.

241 00:24:59.720 00:25:07.090 Amber Lin: Oh, okay, and I will add you to linear and clockify.

242 00:25:07.590 00:25:08.899 Mustafa Raja: Okay, thank you so much.

243 00:25:09.150 00:25:09.870 Amber Lin: Of course.

244 00:25:10.260 00:25:13.190 Amber Lin: Thank you. And Sam. I’ll add you here, too.

245 00:25:13.510 00:25:14.240 Sam Roberts: Alright, cool.

246 00:25:14.640 00:25:16.330 Amber Lin: All right. Thanks for the meeting.

247 00:25:16.570 00:25:17.260 Mustafa Raja: Thank you.

248 00:25:17.530 00:25:18.470 Amber Lin: Hi. Michael.