Meeting Title: Birdie Segmentation Analysis and FDA Prep Date: 2025-11-13 Meeting participants: Robert Tseng, Amber Lin
WEBVTT
1 00:06:06.340 ⇒ 00:06:07.380 Amber Lin: Hi, Robert!
2 00:06:09.790 ⇒ 00:06:10.670 Robert Tseng: Hey!
3 00:06:10.670 ⇒ 00:06:14.470 Amber Lin: Hi! Sorry, I’m a little bit late from the ADC meeting.
4 00:06:16.250 ⇒ 00:06:23.770 Robert Tseng: All good. Sorry, I’m now, like, I already moved on, and I’m, like, messaging someone, so… I guess you can pull your stuff up, but, like, I’m gonna finish this message.
5 00:06:24.100 ⇒ 00:06:24.720 Amber Lin: Okay.
6 00:06:25.040 ⇒ 00:06:25.690 Robert Tseng: Yeah.
7 00:06:26.120 ⇒ 00:06:27.589 Amber Lin: Finish it first, no worries.
8 00:08:20.850 ⇒ 00:08:22.370 Robert Tseng: Oh, God.
9 00:08:23.180 ⇒ 00:08:23.830 Amber Lin: What happened?
10 00:08:23.830 ⇒ 00:08:25.680 Robert Tseng: Oh, I’m an idiot. Okay.
11 00:08:27.550 ⇒ 00:08:29.499 Amber Lin: I assume you deleted the message.
12 00:08:43.380 ⇒ 00:08:50.020 Robert Tseng: That should not have gone to the external Remo channel. Oh my god…
13 00:08:50.770 ⇒ 00:08:56.990 Robert Tseng: Okay, it’s fine. Chances are he didn’t see anything, but I don’t know. It’s like, oh god.
14 00:08:57.220 ⇒ 00:08:58.220 Robert Tseng: Okay.
15 00:08:59.230 ⇒ 00:09:04.089 Robert Tseng: Too many channels, I just totally messed up. Okay.
16 00:09:04.090 ⇒ 00:09:05.929 Amber Lin: Okay, I didn’t see anything.
17 00:09:06.890 ⇒ 00:09:10.800 Amber Lin: So, it was within the last few…
18 00:09:11.550 ⇒ 00:09:15.069 Amber Lin: like, less 10 minutes, probably? It’s fine.
19 00:09:15.740 ⇒ 00:09:16.450 Robert Tseng: Yeah…
20 00:09:33.360 ⇒ 00:09:39.470 Robert Tseng: Okay, anyway, sorry, we can, we can, we can get to your thing. This is just, like, the constant buzz of people
21 00:09:39.580 ⇒ 00:09:41.000 Robert Tseng: Asking things.
22 00:09:41.980 ⇒ 00:09:57.880 Amber Lin: Yeah, okay. So, let me pull up what we want to discuss. So, a few things. One is the upcoming call with Matt, so that’s for Tuesday, and he… I know he covers FDA, and then he also does a bit of rewards, so just
23 00:09:57.880 ⇒ 00:10:06.949 Amber Lin: just want to see what we should bring up there, or any caution… any stuff we want to do, especially towards FDA, if we start to have a relationship with Matt.
24 00:10:06.950 ⇒ 00:10:23.479 Amber Lin: So that’s one thing. I was able to do some basket size analysis, which is very… which was very, very interesting. I’m still doing some line items analysis, that’s taking a bit longer.
25 00:10:23.530 ⇒ 00:10:33.180 Amber Lin: But that helped me see… Gave me a bit more insight on, say, the segmentation.
26 00:10:33.300 ⇒ 00:10:40.489 Amber Lin: And mostly today, I just want to walk through the segmentation with you, see if you have any feedback.
27 00:10:40.670 ⇒ 00:10:48.299 Amber Lin: And then, like, see if we can propose a very simple one for Birdie to start with, or something we can test with.
28 00:10:48.550 ⇒ 00:10:51.540 Robert Tseng: Yeah, let’s do that. Okay. Okay. Do you want to start with…
29 00:10:51.540 ⇒ 00:10:54.180 Amber Lin: Convo with math, so we get that one over with.
30 00:10:54.430 ⇒ 00:11:02.659 Robert Tseng: No, actually, let’s do the birdie one. I think the Matt one, I… I mean, it’s Tuesday, and I do have a hard stop, so I don’t feel like we have that much time.
31 00:11:02.660 ⇒ 00:11:06.280 Amber Lin: Okay, sounds good, all good. So…
32 00:11:06.380 ⇒ 00:11:07.979 Robert Tseng: Let me share screen.
33 00:11:10.370 ⇒ 00:11:12.320 Amber Lin: M… okay.
34 00:11:12.530 ⇒ 00:11:16.129 Amber Lin: Lots of tables. I’ll walk you through, like, the most important findings.
35 00:11:16.250 ⇒ 00:11:18.860 Amber Lin: So… This is…
36 00:11:19.060 ⇒ 00:11:27.930 Amber Lin: average basket size based on the number of orders they purchased. So, if someone did their second order, you can see
37 00:11:28.350 ⇒ 00:11:34.629 Amber Lin: On average, the quantity of stuff purchased goes,
38 00:11:35.560 ⇒ 00:11:37.780 Amber Lin: Goes down, especially if it’s after.
39 00:11:37.780 ⇒ 00:11:38.739 Robert Tseng: Where did you get the data?
40 00:11:39.200 ⇒ 00:11:43.590 Amber Lin: This is from our segment export, so the one where you had
41 00:11:43.840 ⇒ 00:11:50.009 Amber Lin: the… all the per user, all the campaigns they received, all the purchases they made. So this is per user.
42 00:11:50.530 ⇒ 00:11:51.340 Robert Tseng: Okay.
43 00:11:51.650 ⇒ 00:11:54.700 Amber Lin: So this is an S3. So that tells me that
44 00:11:55.050 ⇒ 00:12:04.570 Amber Lin: Like, after they become a repeat customer, they usually purchase in smaller sizes.
45 00:12:05.400 ⇒ 00:12:07.279 Amber Lin: Or it could say that…
46 00:12:07.280 ⇒ 00:12:08.220 Robert Tseng: That’s a basket size.
47 00:12:08.290 ⇒ 00:12:12.640 Amber Lin: Besticide is the quantity of items.
48 00:12:14.160 ⇒ 00:12:16.920 Amber Lin: It doesn’t tell me about the value yet.
49 00:12:17.470 ⇒ 00:12:19.370 Robert Tseng: is, like, okay.
50 00:12:19.570 ⇒ 00:12:20.539 Amber Lin: It’s like, one…
51 00:12:20.540 ⇒ 00:12:23.190 Robert Tseng: Like, one cookie, 6 cookies, 12 cookies.
52 00:12:23.210 ⇒ 00:12:25.490 Amber Lin: Yeah, or,
53 00:12:25.800 ⇒ 00:12:36.750 Amber Lin: it’s just the… I would say it’s the… even if it’s a 12-pack bundle, it just tells me… this only tells me, like, how many. If they purchase 2 12-pack bundles, it’s still…
54 00:12:36.990 ⇒ 00:12:39.140 Amber Lin: Like… It’s still…
55 00:12:39.380 ⇒ 00:12:55.270 Amber Lin: two basket items, if that makes sense. So this is not completely intuitive. This… I need to find the value to make more sense of this, because I… I’m not sure if we’re purchasing more bundles, but…
56 00:12:55.440 ⇒ 00:13:01.690 Amber Lin: like… People who repeat purchase more either are just purchasing, like.
57 00:13:01.860 ⇒ 00:13:04.160 Amber Lin: One individual cookie, or just one…
58 00:13:04.160 ⇒ 00:13:05.480 Robert Tseng: Have you looked at an order?
59 00:13:06.190 ⇒ 00:13:09.240 Amber Lin: What do you mean?
60 00:13:09.440 ⇒ 00:13:11.139 Robert Tseng: Like, have you looked at, like.
61 00:13:11.320 ⇒ 00:13:14.689 Robert Tseng: Two basket size, like, what does that order consist of?
62 00:13:15.480 ⇒ 00:13:17.079 Amber Lin: Right now.
63 00:13:17.230 ⇒ 00:13:30.049 Amber Lin: the data is at, like, the order ID level. I’m working to combine it with, like, the actual product name, so I can get a bit more sense of the price, and then the product.
64 00:13:31.190 ⇒ 00:13:33.589 Robert Tseng: The dashboards don’t let you look at orders?
65 00:13:34.390 ⇒ 00:13:35.910 Amber Lin: You mean…
66 00:13:35.910 ⇒ 00:13:41.709 Robert Tseng: Have you looked at, not just Braze, I mean, have you taken an order from Braze and just tried to…
67 00:13:42.110 ⇒ 00:13:47.360 Robert Tseng: Look for it, and… Their BI tool.
68 00:13:48.510 ⇒ 00:13:50.220 Amber Lin: I haven’t.
69 00:13:50.390 ⇒ 00:13:52.410 Robert Tseng: You mean a BI tool in Grace?
70 00:13:52.660 ⇒ 00:13:56.500 Robert Tseng: No, not Brace. They have a… they have another BI tool.
71 00:13:57.230 ⇒ 00:14:00.110 Amber Lin: Oh, in, what is it called?
72 00:14:01.080 ⇒ 00:14:03.660 Amber Lin: in hor… In this…
73 00:14:04.930 ⇒ 00:14:05.670 Robert Tseng: Yeah.
74 00:14:06.350 ⇒ 00:14:07.290 Amber Lin: Oh, okay.
75 00:14:07.530 ⇒ 00:14:11.080 Amber Lin: I can go track that, I was… I was playing around in here as well.
76 00:14:14.240 ⇒ 00:14:14.609 Robert Tseng: Let me…
77 00:14:14.610 ⇒ 00:14:27.249 Amber Lin: Let me go track that, and I think this is very interesting insight. It’s more of what we were talking about last week. I think if I go… I want to look at the line items of what they bought, and then…
78 00:14:27.360 ⇒ 00:14:32.500 Amber Lin: how much they are. So, like, right now, I only have basket size.
79 00:14:32.680 ⇒ 00:14:36.740 Amber Lin: But if I have more, I can make more conclusions.
80 00:14:36.910 ⇒ 00:14:37.620 Robert Tseng: Okay.
81 00:14:37.620 ⇒ 00:14:44.420 Amber Lin: Yeah, the other one is time from the first…
82 00:14:44.650 ⇒ 00:14:52.120 Amber Lin: purchase, so from the previous purchase. Yep. So, usually, from the first to the second, it takes about half a…
83 00:14:52.330 ⇒ 00:14:54.019 Amber Lin: A month and a half, and then…
84 00:14:54.020 ⇒ 00:14:54.410 Robert Tseng: Yep.
85 00:14:54.410 ⇒ 00:14:58.900 Amber Lin: it becomes more frequent, so I think this is… This is very interesting.
86 00:14:58.930 ⇒ 00:15:00.190 Robert Tseng: Yeah.
87 00:15:00.270 ⇒ 00:15:04.859 Amber Lin: And then, let’s say… this is from the first orders. Usually.
88 00:15:05.000 ⇒ 00:15:08.699 Amber Lin: 50% of people just make their third or…
89 00:15:08.700 ⇒ 00:15:15.269 Robert Tseng: Let’s go back up to the… to the… what you were saying. Yeah, this one right here. Great. So, you’ll see this… I mean, I would plot a curve of just the.
90 00:15:15.270 ⇒ 00:15:15.860 Amber Lin: It is.
91 00:15:15.860 ⇒ 00:15:21.629 Robert Tseng: previous days, right? And you can see, you can model out to just to prove to them
92 00:15:21.790 ⇒ 00:15:38.520 Robert Tseng: like, okay, the… it’s like a month and a half to go from the first order to the second order, and then another month to go to the second or third order. But if we can get… drive our customers to purchase… yeah, if we drive our first-time order customers to get that second purchase.
93 00:15:38.520 ⇒ 00:15:48.490 Robert Tseng: Then, like, you know, what’s the likelihood of them… do you just project out, like, the likelihood of them, ordering that third order?
94 00:15:48.780 ⇒ 00:16:07.280 Robert Tseng: I think that’s, like, one… that’s one cliff you can… you can visualize, and the other one is, like, I’m just trying to look at others, but after that, it seems like it just cuts it down every three days. But, yeah, I guess maybe after this… yeah, for the second order, and then we can just do, like, after the fourth order, I guess, is pretty… is an interesting one.
95 00:16:07.360 ⇒ 00:16:10.499 Robert Tseng: To… to call out.
96 00:16:10.680 ⇒ 00:16:19.740 Robert Tseng: Yeah, so that way, you know, we’re… I mean, it’s intuitive, people understand that, like.
97 00:16:20.310 ⇒ 00:16:32.420 Robert Tseng: Yeah, if you order… yeah, the people who are ordering more are obviously going to come back more frequently, but there may be, like, this, like, sticking point where, like, once they buy.
98 00:16:32.630 ⇒ 00:16:41.860 Robert Tseng: four orders. They’re, like, they’re very likely to come back, so… Yeah, like, I…
99 00:16:42.210 ⇒ 00:16:47.860 Robert Tseng: I think you could make… you could make a claim, even off of, like,
100 00:16:48.110 ⇒ 00:16:56.089 Robert Tseng: And you can do, like, a simple… you can do a simple comparison of, like, past month, new, first-time customers.
101 00:16:56.270 ⇒ 00:17:09.870 Robert Tseng: if we were to drive them to their second order, and they would… they would buy again in less than a month versus, like, a month and a half later, right? And you can kind of… and you can show the difference in revenue between,
102 00:17:10.030 ⇒ 00:17:21.289 Robert Tseng: getting them to come back in 28 days versus 42 days, or, like, getting them to come back within… within 18 days versus 42 days. I think that… that would be an interesting,
103 00:17:21.839 ⇒ 00:17:24.900 Robert Tseng: like… slide.
104 00:17:25.599 ⇒ 00:17:42.729 Amber Lin: Yeah, I agree. I also wanted to look at, like, perhaps retention between these. I did a cohort analysis. I do want to see, like, retention between the different orders. Yeah. So this is just how long it takes.
105 00:17:43.079 ⇒ 00:17:49.139 Amber Lin: And so… Yeah, this is Trends… I think this is Trends 5…
106 00:17:49.859 ⇒ 00:18:00.959 Amber Lin: Trends by month? Oh, here’s an interesting insight. So this column… Is the medium facet size.
107 00:18:01.750 ⇒ 00:18:10.840 Amber Lin: And if you see, this is chronological. This is right now, this is a long time ago. When this first started, either they had less bundles.
108 00:18:11.010 ⇒ 00:18:14.059 Amber Lin: But they had more, like.
109 00:18:14.060 ⇒ 00:18:18.330 Robert Tseng: Yeah, I don’t think they had 12… I don’t think they had 12 cookie boxes until…
110 00:18:18.330 ⇒ 00:18:25.460 Amber Lin: Yeah, so I wanted to know when they had that. So, probably from, like, I think the big drop.
111 00:18:25.610 ⇒ 00:18:29.109 Amber Lin: I think the slight drop, I would say, happened, like, around…
112 00:18:29.650 ⇒ 00:18:36.729 Amber Lin: Hmm, I don’t know. I’ll need to check, but somewhere here… they start at… doing more bundles.
113 00:18:36.950 ⇒ 00:18:44.260 Amber Lin: Or their average SKU had a bigger value. I do want to check, like, average order value.
114 00:18:44.420 ⇒ 00:18:48.340 Amber Lin: Do you want to check, like, products offered?
115 00:18:48.680 ⇒ 00:18:49.050 Robert Tseng: Yeah.
116 00:18:49.050 ⇒ 00:18:57.349 Amber Lin: And, like, and we can also do, like, by channel of maybe they started Uber Eats and DoorDash somewhere here?
117 00:18:57.510 ⇒ 00:19:00.899 Amber Lin: And then that changed how much people purchased.
118 00:19:01.780 ⇒ 00:19:05.370 Amber Lin: I don’t know. Do you know when they started Uber Eats and DoorDash?
119 00:19:05.370 ⇒ 00:19:06.740 Robert Tseng: In the past year.
120 00:19:06.740 ⇒ 00:19:09.420 Amber Lin: Oh, okay, okay, makes sense.
121 00:19:09.420 ⇒ 00:19:10.020 Robert Tseng: Yeah.
122 00:19:10.390 ⇒ 00:19:16.920 Amber Lin: Hmm… Okay, I did another one based on customer…
123 00:19:17.400 ⇒ 00:19:25.789 Amber Lin: lifetime value, which makes sense, like, people who are higher value have a bigger basket size, they buy more. I think pretty intuitive.
124 00:19:25.990 ⇒ 00:19:33.670 Amber Lin: Let’s see… yeah, right now I’m just trying to go through the different… different,
125 00:19:34.090 ⇒ 00:19:36.669 Amber Lin: like, product SKUs and what that means.
126 00:19:37.810 ⇒ 00:19:38.220 Robert Tseng: Okay.
127 00:19:38.220 ⇒ 00:19:49.599 Amber Lin: did a cohort analysis, I don’t know if I put it in here, but it also shows, like, when the different cohorts who joined, if there’s any shifts in behavior of
128 00:19:50.070 ⇒ 00:19:53.979 Amber Lin: What they… like, how many items they purchase.
129 00:19:56.400 ⇒ 00:19:56.720 Robert Tseng: Okay.
130 00:19:56.980 ⇒ 00:20:08.970 Amber Lin: Yeah, so I think the next step is I’m gonna do more… I’m gonna go trace a few orders, as you recommended. I’m gonna go look at the value, like, average order value, and then
131 00:20:09.340 ⇒ 00:20:12.550 Amber Lin: The line items of the orders.
132 00:20:13.020 ⇒ 00:20:13.710 Robert Tseng: Sure.
133 00:20:13.710 ⇒ 00:20:22.059 Amber Lin: Yeah. And now I’m thinking… I’m thinking about the segments I would propose, and it’s actually very, very similar to
134 00:20:22.260 ⇒ 00:20:26.049 Amber Lin: What we just saw of first order, second order.
135 00:20:26.190 ⇒ 00:20:36.370 Amber Lin: and such. I’m just… I just don’t know how detailed I want to make it, because I could have something that’s second order, or second order within, like.
136 00:20:36.740 ⇒ 00:20:43.250 Amber Lin: within… 60 days within 40 days, or just have it…
137 00:20:43.250 ⇒ 00:20:55.320 Robert Tseng: Yeah, I wouldn’t focus on, like, third, fourth, or whatever. I think the first to second seems like the biggest drop, so yeah. First, you could use second order within 30 days. For second order within 60 days. I think those are two cohorts.
138 00:20:57.820 ⇒ 00:20:59.100 Robert Tseng: And then…
139 00:20:59.680 ⇒ 00:21:09.189 Robert Tseng: Yeah, I guess we’re talking about this, like, fourth order. I think that’d be an interesting one. Kind of, yeah, fourth order, fourth order plus rewards member. I think those are good, yeah.
140 00:21:10.090 ⇒ 00:21:20.460 Robert Tseng: after that, I don’t think that we really need to create anymore, so I think… I think you have the right idea here. The at-risk, yeah, I mean, I would call it dormant.
141 00:21:21.360 ⇒ 00:21:26.819 Robert Tseng: That’s pretty just industry standard. And then laps, I would say, you know, it’s more like.
142 00:21:26.820 ⇒ 00:21:35.289 Amber Lin: they do come back, especially between their third and fourth, like, between their further orders. Sometimes they do take a long time, and then they.
143 00:21:35.290 ⇒ 00:21:35.610 Robert Tseng: Yeah.
144 00:21:35.610 ⇒ 00:21:36.580 Amber Lin: come back.
145 00:21:38.380 ⇒ 00:21:45.239 Amber Lin: So, especially, like, cookies, you just get reminded, and you start the habit again, and you lapse out, and you come back, so…
146 00:21:45.240 ⇒ 00:21:50.259 Robert Tseng: Then why don’t we just do, like, a 2-month dormant versus, like, three, like…
147 00:21:58.800 ⇒ 00:22:03.740 Robert Tseng: I wonder if 30… if 9… like, 30 to 60… 30, 60, 90…
148 00:22:04.120 ⇒ 00:22:05.859 Amber Lin: Or maybe a 180?
149 00:22:07.780 ⇒ 00:22:09.919 Robert Tseng: Because I don’t think people ever will…
150 00:22:10.000 ⇒ 00:22:15.270 Amber Lin: Like, cookies are such a low-risk purchase, it’s not like they churn forever.
151 00:22:15.460 ⇒ 00:22:19.180 Amber Lin: Someone who hasn’t bought cookies in a year might still get a cookie.
152 00:22:19.420 ⇒ 00:22:20.450 Amber Lin: Eventually.
153 00:22:21.640 ⇒ 00:22:22.670 Robert Tseng: Yeah.
154 00:22:23.600 ⇒ 00:22:30.709 Robert Tseng: Look, I… I mean, this is less… just dormant… I think, like, the…
155 00:22:31.790 ⇒ 00:22:35.160 Robert Tseng: Yeah, I really… okay, I think one month…
156 00:22:35.280 ⇒ 00:22:43.489 Robert Tseng: Or a 2-month dormant, 3-month, and then, like, more than 3 months. I don’t know what those… what those would be, but, like, yeah, something like that would be…
157 00:22:43.740 ⇒ 00:22:46.709 Robert Tseng: I don’t think 30 days inactive is too short.
158 00:22:46.710 ⇒ 00:22:47.200 Amber Lin: Okay.
159 00:22:47.200 ⇒ 00:22:52.079 Robert Tseng: I think 60, 60 days makes sense. 60, 90, 90 plus, sure, yeah.
160 00:22:54.340 ⇒ 00:22:55.120 Robert Tseng: Yeah.
161 00:22:55.730 ⇒ 00:22:57.090 Robert Tseng: I think that might make sense.
162 00:22:57.090 ⇒ 00:22:57.740 Amber Lin: Okay.
163 00:22:58.010 ⇒ 00:23:03.139 Robert Tseng: I wouldn’t worry about, like, naming them right now, but, like, I think those… those… those make sense. Yeah.
164 00:23:03.940 ⇒ 00:23:04.450 Amber Lin: Okay.
165 00:23:04.450 ⇒ 00:23:05.100 Robert Tseng: Yeah.
166 00:23:05.790 ⇒ 00:23:13.310 Amber Lin: Should we have anything that’s frequency-based of making… X orders, like.
167 00:23:14.630 ⇒ 00:23:28.099 Robert Tseng: Yeah, I mean, I think order value and, like, product, like, segment… level segmentation I want as well, but it’s not really something I feel like we… like, that to me is, like, the next… the next, like, perspective. Right now, we’re just going off of, like, order frequency.
168 00:23:28.480 ⇒ 00:23:35.440 Robert Tseng: And then, yeah, now… then we’re… then we can look at, order… like, you could do some segments off order value,
169 00:23:35.440 ⇒ 00:23:35.940 Amber Lin: Yeah.
170 00:23:35.940 ⇒ 00:23:36.490 Robert Tseng: Yeah.
171 00:23:36.490 ⇒ 00:23:42.649 Amber Lin: Because there are some people who just buy frequent, but small orders. Yes. It seems.
172 00:23:42.650 ⇒ 00:23:48.849 Robert Tseng: you stack segments, so you say second order within 30 days of, like, bigger order, like, bigger order purposes, whatever.
173 00:23:48.850 ⇒ 00:23:53.300 Amber Lin: Oh, okay, cool. So let me… let me try and model this out.
174 00:23:53.300 ⇒ 00:23:54.720 Robert Tseng: Yeah, I think this is possible.
175 00:23:54.720 ⇒ 00:23:58.780 Amber Lin: falls into each in Braze, and then I’ll… I’ll get back to you.
176 00:23:59.030 ⇒ 00:23:59.820 Robert Tseng: Okay.
177 00:23:59.820 ⇒ 00:24:00.540 Amber Lin: Okay.
178 00:24:01.010 ⇒ 00:24:05.479 Amber Lin: We have 5 minutes left, do you want to talk about what to say to Matt?
179 00:24:05.670 ⇒ 00:24:11.579 Robert Tseng: Yeah, I guess, like, Matt, his… he’s just running the FDA. Have you logged into the FDA campaigns?
180 00:24:13.050 ⇒ 00:24:16.160 Amber Lin: I remember… I think I’ve logged into one.
181 00:24:16.160 ⇒ 00:24:16.490 Robert Tseng: Okay.
182 00:24:16.490 ⇒ 00:24:17.320 Amber Lin: board them.
183 00:24:17.590 ⇒ 00:24:22.290 Robert Tseng: Well, basically, there’s just two types. There’s, like, banner ads, which are, like, just.
184 00:24:22.560 ⇒ 00:24:23.810 Amber Lin: Yeah, I remember.
185 00:24:24.190 ⇒ 00:24:29.280 Robert Tseng: Yeah, I think those are… those are the ones that perform the best, I think.
186 00:24:30.180 ⇒ 00:24:39.369 Robert Tseng: He runs, like, some limited offer banner ads, so, like, and that’s kind of why he keeps, like, messaging us and saying, like, hey, like, this wasn’t reported correctly.
187 00:24:39.700 ⇒ 00:24:50.569 Robert Tseng: So, I think I would just, like, try to understand, like, the types of campaigns that he’s running. Tell him to explain the limited time campaigns, like, and what… I don’t know the details exactly, but, like.
188 00:24:50.610 ⇒ 00:25:03.859 Robert Tseng: why, you know, apparently we’ve been measuring them, we’ve been… we haven’t been tracking them correctly, so just, like, want to get ahead of that and, like, know, you know, is he going to increase the frequency of these, like, and whatnot? Like, I think.
189 00:25:04.240 ⇒ 00:25:11.890 Robert Tseng: that’s… I think that’s… that’s important. There’s obviously fewer campaign types than email, like, it’s probably only, like, 3 or 4.
190 00:25:11.980 ⇒ 00:25:31.520 Robert Tseng: But yeah, I think you can also kind of, when he asks you, like, how do you think you can help him, you can share something similar to what you did on the, like, owned channel campaign type analysis that you did for Bernie. I think we should do a similar type of, like, analysis for him. There’s obviously less data, it’s, like, less than a year of data, it’s right, like, 9 months or something.
191 00:25:31.520 ⇒ 00:25:32.610 Amber Lin: Okay.
192 00:25:32.610 ⇒ 00:25:34.600 Robert Tseng: But… Yeah.
193 00:25:35.200 ⇒ 00:25:39.939 Robert Tseng: And then I would just also just pick his brain on, like, well, like, how does he think,
194 00:25:41.020 ⇒ 00:25:42.030 Robert Tseng: you know.
195 00:25:42.600 ⇒ 00:25:51.599 Robert Tseng: is it… like, how does he see the, like, the FDA kind of channel strategy, like, fitting into the rest, like, of the customer journey?
196 00:25:51.600 ⇒ 00:25:52.480 Amber Lin: Hmm…
197 00:25:54.290 ⇒ 00:26:05.740 Robert Tseng: I guess, like, are new customers discovering, you know, cookies, like, in the FDA apps? Like, is that… is it, like, a good top-of-funnel kind of, like, channel for them? Or is it, like.
198 00:26:05.740 ⇒ 00:26:15.389 Robert Tseng: once they’re already, like, repeat insomnia, like, buyers, and they just get lazy, and they want to stop going to the store, they know their orders, and they’d rather just, like, have somebody send it to them, like…
199 00:26:15.390 ⇒ 00:26:20.069 Robert Tseng: Yeah, what is his sense on, like, where does this channel fit in, like, kind of.
200 00:26:20.070 ⇒ 00:26:31.380 Amber Lin: Oh, that’s just such a good question. Okay, very cool. I was also planning, like, I was gonna look at line items per order, I just wanted… I wanted to grab, like, what was the most common
201 00:26:31.560 ⇒ 00:26:37.100 Amber Lin: SKU that they ordered for their first order, and see what that’s like, because I remember what you told me.
202 00:26:39.050 ⇒ 00:26:39.930 Amber Lin: Okay.
203 00:26:40.350 ⇒ 00:26:40.770 Robert Tseng: Yeah.
204 00:26:40.770 ⇒ 00:26:43.729 Amber Lin: Anything on rewards I should ask, Matt?
205 00:26:44.290 ⇒ 00:26:52.989 Robert Tseng: Well, yeah, so, I mean, I guess we should kind of poke around and punch before you go into him. Like, I still think, like.
206 00:26:53.150 ⇒ 00:26:57.259 Robert Tseng: And they did give us access to that. We were not blocked on, like, looking at rewards, but…
207 00:26:57.260 ⇒ 00:26:57.750 Amber Lin: Oh…
208 00:26:57.840 ⇒ 00:27:02.770 Robert Tseng: I feel like we should… I mean, I haven’t looked, but just figure out what…
209 00:27:03.120 ⇒ 00:27:17.129 Robert Tseng: once again, like, what offers do we make to loyalty members? Like, how many members are actually there? You have, like, a segment that was, like, 4 plus orders that are also reward members. Like, is there a point in which we, you know, are…
210 00:27:17.870 ⇒ 00:27:33.470 Robert Tseng: I guess, like, what are… what are all the ways that people kind of join, like, the rewards program? It’s usually offered at the point of sale, I’m sure, like, in-store, but are they joining… are most of them joining on their first purchase, or are they joining, like, you know, 3 or 4 purchases in, or…
211 00:27:33.490 ⇒ 00:27:51.829 Robert Tseng: Yeah, just trying to, like, understand, like, what are all the different ways that customers become rewards members. Like, I think maybe he will understand that better than we can. But we should have a good sense of, like, what offers we already make to them, and, like, what engagement looks like with the rewards program before we talk to him.
212 00:27:52.300 ⇒ 00:27:54.399 Amber Lin: Okay, okay, that’s good. Yeah.
213 00:27:54.710 ⇒ 00:28:02.440 Amber Lin: Great, that’s a lot for me to work with. That means I have more to do, which is great. I’ll go look at that, and I’ll get back to you.
214 00:28:02.800 ⇒ 00:28:07.759 Robert Tseng: Cool, yeah, I mean, everything looks good. Yeah, there were a couple good nuggets in this, yeah.
215 00:28:07.760 ⇒ 00:28:16.420 Amber Lin: It was very… it was so interesting. I, like, I… every time I thought I’d hit a ceiling, and I talked to you, I was like, oh, this is so cool, and I go do that, and that was very fun.
216 00:28:16.780 ⇒ 00:28:20.060 Robert Tseng: Okay, great. Glad you enjoy it. Alright.
217 00:28:20.060 ⇒ 00:28:22.480 Amber Lin: We’re talking to Yasami on Monday, right?
218 00:28:22.850 ⇒ 00:28:33.129 Robert Tseng: Well, I think we kind of got pulled out of those calls, so I think it’ll just be Tuesday. I mean, I want to follow up with Amrita. I’ll probably talk to her tomorrow and just see what she wants to see.
219 00:28:33.130 ⇒ 00:28:37.130 Amber Lin: Let me know so that I can prep you with some stuff to go talk to her.
220 00:28:37.340 ⇒ 00:28:55.290 Robert Tseng: Yeah, I mean, I would say, you know, you’re doing this on a weekly basis, so whatever you’ve got, try to package it up and send it to me by end of week, so at least I can tell her, hey, this is what we’ve been working on, like, we can go into more depth and have a discussion about this, and try to schedule early time with her in the week, but, it’s more ad hoc than it is recurring at this point.
221 00:28:55.420 ⇒ 00:28:57.499 Amber Lin: Okay, okay. Sounds good. Thanks.
222 00:28:57.500 ⇒ 00:28:58.449 Robert Tseng: Okay, thanks.
223 00:28:58.690 ⇒ 00:28:59.480 Amber Lin: Bye!