Meeting Title: Eden and Insomnia Client Strategy Sync Date: 2025-11-14 Meeting participants: Robert Tseng, Amber Lin
WEBVTT
1 00:00:48.160 ⇒ 00:00:49.220 Amber Lin: Hi there!
2 00:00:49.480 ⇒ 00:00:50.800 Robert Tseng: Hello!
3 00:00:52.390 ⇒ 00:00:53.719 Amber Lin: How was the meeting?
4 00:00:53.720 ⇒ 00:00:57.920 Robert Tseng: We survived another week, just like…
5 00:00:59.080 ⇒ 00:01:05.260 Robert Tseng: I mean, he’s… Byron’s a nice… he’s nice as a stakeholder. Like, I feel like I had to just…
6 00:01:05.610 ⇒ 00:01:10.320 Robert Tseng: Pull something out of thin air to… Give him some updates.
7 00:01:10.320 ⇒ 00:01:13.650 Amber Lin: You did us this morning, and then you were able to tell him a lot of stuff.
8 00:01:13.650 ⇒ 00:01:20.830 Robert Tseng: Yeah, yeah, it was, like, literally just that thing that you watched me say… watched me through. I ran it through GPT and kind of just…
9 00:01:20.950 ⇒ 00:01:28.140 Robert Tseng: thought a bit more about it, and then gave him some things that he was excited to dig into, that I actually think are feasible for us to accomplish.
10 00:01:28.140 ⇒ 00:01:29.060 Amber Lin: Show me next week.
11 00:01:29.060 ⇒ 00:01:29.670 Robert Tseng: So…
12 00:01:29.850 ⇒ 00:01:30.990 Amber Lin: Yeah.
13 00:01:32.060 ⇒ 00:01:34.979 Robert Tseng: Yeah, well, I don’t know, exactly, that’s,
14 00:01:35.300 ⇒ 00:01:36.999 Robert Tseng: It’s definitely not one of the…
15 00:01:37.390 ⇒ 00:01:41.850 Robert Tseng: It’s more of a sense of relief than a sense of accomplishment.
16 00:01:42.260 ⇒ 00:01:51.759 Amber Lin: Yeah, yeah, well, you were able to do that because you know what the most likely problems are, so, like, that’s what they’re paying for, too.
17 00:01:52.500 ⇒ 00:01:53.259 Amber Lin: As you’ll tell.
18 00:01:53.260 ⇒ 00:02:03.160 Robert Tseng: I mean, I think… I mean, this is going to be related to what we’re saying, too. It’s like, yeah, we can go and, like, look at things from a million different angles, but what’s most important is, like.
19 00:02:03.460 ⇒ 00:02:08.700 Robert Tseng: Being able to think like the person we’re… we’re, like, working with, and then…
20 00:02:09.150 ⇒ 00:02:28.880 Robert Tseng: To be able to influence them, right? Like, if we can guide them to the… to taking an action, it doesn’t even have to be the right one, but an action that we can measure, we show that we’ve, like, thought through, like, options, like, that’s… that’s what we’re in the business of doing. We’re helping them make decisions, like, even the…
21 00:02:29.330 ⇒ 00:02:33.369 Robert Tseng: even the Remo call that I was on, like, that was rough, because, like, I…
22 00:02:33.720 ⇒ 00:02:38.870 Robert Tseng: I mean, I don’t know anything about building, like, software, like, I’m not really a software person.
23 00:02:39.310 ⇒ 00:02:45.309 Robert Tseng: But I think Eden kind of just trusts us to help them make tough decisions. That’s why
24 00:02:45.730 ⇒ 00:03:05.709 Robert Tseng: had us, like, kind of go into this, and yes, like, Surf is, like, kind of the technical expert, but I also feel like, you know, he’s a little bit narrow-minded, and he’s just like, oh yeah, this thing doesn’t work, he’s missing this feature, therefore you must reject everything, and you should just work with me instead. And it’s like, well…
25 00:03:05.710 ⇒ 00:03:09.900 Amber Lin: And he has his own interests, too. He wants to have the work.
26 00:03:09.900 ⇒ 00:03:25.259 Robert Tseng: Yeah, it’s a big mental jump for the Eden folks. They’re… they’re not so, like, oh, like, who cares? I mean, what does it mean to me if this feature is missing, right? As for non-technical people, that’s not how they’re gonna make their decision, so…
27 00:03:25.260 ⇒ 00:03:29.620 Amber Lin: People have invested, like, they already had money and a lot of time down the drain.
28 00:03:29.620 ⇒ 00:03:31.220 Robert Tseng: Yeah, 6 months.
29 00:03:31.220 ⇒ 00:03:33.230 Amber Lin: Nothing, it’s very hard.
30 00:03:33.500 ⇒ 00:03:39.499 Robert Tseng: Exactly. So, anyway, like, I think these are, like, the soft skill qualities that
31 00:03:39.720 ⇒ 00:03:45.529 Robert Tseng: I guess you learn when you’re in the middle of tense situations more and more, and…
32 00:03:46.440 ⇒ 00:03:55.960 Robert Tseng: Yeah, I mean, it goes a long way, I mean, probably later in your career. Early on, it’s probably just confusing, because you feel like everyone’s indecisive, but it’s kind of like…
33 00:03:56.230 ⇒ 00:03:59.670 Robert Tseng: At the end of the day, like, you’re just trying to help people make
34 00:03:59.890 ⇒ 00:04:03.260 Robert Tseng: hard decisions. So, yeah, you’d have to be… you have to…
35 00:04:04.070 ⇒ 00:04:13.329 Robert Tseng: Be able to contextualize everything that’s going on, and not be so anchored to your point of view, and
36 00:04:13.950 ⇒ 00:04:25.489 Robert Tseng: Yeah, like, I mean, obviously we need to have a recommendation, but, like, you don’t jump to the conclusion, you just have… you have to think from the perspective of the person we’re kind of consulting first, so… Yeah, anyway…
37 00:04:25.490 ⇒ 00:04:38.270 Amber Lin: a bit in default, and I know that’s what we’re talking about today. Like, I had insights, but I also felt like these can… I don’t know how much of help I am. Like, I was able to
38 00:04:38.290 ⇒ 00:04:48.199 Amber Lin: prompt her to the next step of thinking, but I feel like she would have arrived there anyways, because she’s a pretty sharp person, she works pretty fast, like…
39 00:04:48.340 ⇒ 00:04:52.390 Amber Lin: Do you feel that sometimes, if… Is…
40 00:04:52.780 ⇒ 00:05:00.240 Amber Lin: Like, what are we influencing? Are we making the decision? I don’t ever feel like we’re making the decision for them.
41 00:05:00.540 ⇒ 00:05:04.069 Amber Lin: But sometimes I don’t know how…
42 00:05:04.310 ⇒ 00:05:10.720 Amber Lin: Like, how much we should push them, or are we just giving them evidence for them to make decisions?
43 00:05:11.470 ⇒ 00:05:22.720 Robert Tseng: I think some things that we… we do push for some decision. Like, on Eden, I feel like we’re making a lot of the decisions.
44 00:05:23.130 ⇒ 00:05:27.860 Robert Tseng: I think… For the other clients, like, yeah, I mean, we’re…
45 00:05:28.230 ⇒ 00:05:33.090 Robert Tseng: they don’t necessarily want us to make the decision. We should have a point of view, but we shouldn’t.
46 00:05:33.090 ⇒ 00:05:33.440 Amber Lin: Mmm.
47 00:05:33.440 ⇒ 00:05:39.150 Robert Tseng: you know, shouldn’t push… push them to do it… to do it, like, our way. Like, they kind of still want to own…
48 00:05:39.810 ⇒ 00:05:45.290 Robert Tseng: I mean, for, like, a default, I’m sure, for README as well, like, they’re very controlling over, like.
49 00:05:45.680 ⇒ 00:05:47.570 Robert Tseng: the decisions. Like, they want to…
50 00:05:47.570 ⇒ 00:05:48.030 Amber Lin: Hmm.
51 00:05:48.030 ⇒ 00:05:53.519 Robert Tseng: Like, they just… they just want the advice, or, like, the way of framing it.
52 00:05:53.810 ⇒ 00:05:59.059 Robert Tseng: But they want to own the decision. So, I think it does kind of vary depending on the client.
53 00:05:59.940 ⇒ 00:06:07.839 Robert Tseng: But that’s also because Caitlin’s in a strategic role. She wants to be the one to make… but working with Birdie is different, because Birdie and Insomnia, she’s…
54 00:06:08.460 ⇒ 00:06:14.450 Robert Tseng: she doesn’t really do much strategy, she’s just taking orders and executing. So, in this case, like.
55 00:06:14.610 ⇒ 00:06:16.810 Robert Tseng: Yes, she’s going to have her…
56 00:06:17.280 ⇒ 00:06:30.469 Robert Tseng: like, we kind of have to… in order for her to respect, like, what we’re saying, like, we need to be speaking about things in her world, but, like, we do need to kind of push her more than I would push and Rita.
57 00:06:32.230 ⇒ 00:06:34.080 Amber Lin: That’s so interesting.
58 00:06:34.230 ⇒ 00:06:35.880 Amber Lin: Yeah, and cause…
59 00:06:36.010 ⇒ 00:06:45.549 Amber Lin: on default call, so this bridges into what we’re talking about today, because on default, I did, I think I started off…
60 00:06:46.330 ⇒ 00:06:51.300 Amber Lin: Analyzing the concentration of
61 00:06:51.580 ⇒ 00:07:00.619 Amber Lin: revenue versus their different usages. And that tells us, like, which ones correlate the most, which ones don’t actually correlate.
62 00:07:00.810 ⇒ 00:07:03.419 Amber Lin: And then based on that, we did…
63 00:07:03.610 ⇒ 00:07:12.570 Amber Lin: We found that meetings, because default is an inbound Platform… inbound automation… Platform, so…
64 00:07:12.570 ⇒ 00:07:13.190 Robert Tseng: Yep.
65 00:07:13.190 ⇒ 00:07:27.889 Amber Lin: They currently price on seats, etc, but we found that meetings correlate the most with revenue, which makes sense, because that’s what people get out of it, but they don’t price… like, there’s no pricing related around meetings.
66 00:07:28.180 ⇒ 00:07:28.730 Robert Tseng: Hmm.
67 00:07:28.930 ⇒ 00:07:38.849 Amber Lin: So that was a recommendation to, hey, you could look more into it, but when we’re talking about it, Caitlin, it’s like, can we have something that’s not directly meetings?
68 00:07:38.870 ⇒ 00:07:50.059 Amber Lin: And that led to the conversation about, because they might want to have vertical pricing in the future, and, meetings is a very specific, like, sales…
69 00:07:50.140 ⇒ 00:07:56.610 Amber Lin: outcome metric. And if they do marketing, or if they do operations.
70 00:07:56.720 ⇒ 00:08:02.799 Amber Lin: Like, they might not need meetings, because they might just use the platform for enrichment and stuff.
71 00:08:03.230 ⇒ 00:08:14.899 Amber Lin: And so when I talked with her, one of their RevOps folks did give them a vertical pricing proposal, and…
72 00:08:15.200 ⇒ 00:08:17.360 Amber Lin: This one is the…
73 00:08:17.510 ⇒ 00:08:28.950 Amber Lin: well, we didn’t have… I think we didn’t have these. There were just 3 tiers before. This was what Caitlin proposed, and most likely we’re gonna go ahead with that.
74 00:08:29.090 ⇒ 00:08:35.700 Amber Lin: And… It was this and the vertical pricing. Let me find that.
75 00:08:42.659 ⇒ 00:08:43.490 Amber Lin: Oh.
76 00:08:50.880 ⇒ 00:08:51.780 Amber Lin: Okay.
77 00:08:52.650 ⇒ 00:08:53.430 Amber Lin: What?
78 00:09:00.080 ⇒ 00:09:00.920 Amber Lin: Yeah.
79 00:09:01.110 ⇒ 00:09:03.770 Amber Lin: So, I…
80 00:09:04.030 ⇒ 00:09:20.800 Amber Lin: Did the… this is the current pricing on their website, so they don’t have a free tier. I believe everything requires sales support, and everyone gets a Slack channel, and they all get set up, so, their current pricing is…
81 00:09:21.000 ⇒ 00:09:29.180 Amber Lin: very support-heavy. There’s no pre-tier, there’s no self-serve, it’s just 3 different… Big tears.
82 00:09:29.690 ⇒ 00:09:34.189 Amber Lin: And right now, their goal is to
83 00:09:34.430 ⇒ 00:09:43.449 Amber Lin: introduce PLG pricing so that, not that they can get more money in the door, but because she wants more people.
84 00:09:43.980 ⇒ 00:09:46.519 Amber Lin: that she can convert to Enterprise.
85 00:09:46.520 ⇒ 00:09:47.740 Robert Tseng: Yeah, true.
86 00:09:47.920 ⇒ 00:09:50.580 Amber Lin: That’s their goal, which… which…
87 00:09:50.980 ⇒ 00:09:53.930 Amber Lin: Determines, like, what type of pricing they go for.
88 00:09:55.660 ⇒ 00:10:08.660 Amber Lin: And this is the vertical pricing that one of their RevOps folks proposed. Mostly, like, marketing focused, sales-focused, operation focused, and they have different things there.
89 00:10:09.420 ⇒ 00:10:18.339 Amber Lin: And then… So, surrounding that, my initial recommendation is that
90 00:10:18.520 ⇒ 00:10:23.490 Amber Lin: This is too early, because they don’t even have enough features
91 00:10:23.660 ⇒ 00:10:30.379 Amber Lin: Let alone the data on how to segment people and what to include in packages.
92 00:10:30.570 ⇒ 00:10:37.770 Amber Lin: So, like, that was, like, 2 calls ago, and then… so they want to know, how do we design the current pricing
93 00:10:38.430 ⇒ 00:10:43.350 Amber Lin: So that we can transition to a vertical pricing in the future.
94 00:10:44.230 ⇒ 00:10:50.900 Amber Lin: And so… I did my research, and I did recommendations.
95 00:10:51.010 ⇒ 00:11:04.179 Amber Lin: And the key thing is that I’ll say, keep the tiers simple now. Have feature gates so that we can collect more information on features, because before they didn’t have… they only had
96 00:11:04.430 ⇒ 00:11:10.270 Amber Lin: like this. They didn’t have any feature gates, they didn’t know… it was very, very simple.
97 00:11:10.570 ⇒ 00:11:13.450 Amber Lin: And then…
98 00:11:14.340 ⇒ 00:11:24.429 Amber Lin: like, in the middle transition phase, maybe introduce more of the PLG tiers, maybe more of the… Pro tiers?
99 00:11:24.800 ⇒ 00:11:29.320 Amber Lin: And then transition to vertical pricing.
100 00:11:29.830 ⇒ 00:11:32.500 Amber Lin: And then there’s… I think…
101 00:11:32.500 ⇒ 00:11:33.049 Robert Tseng: We go together.
102 00:11:33.050 ⇒ 00:11:37.980 Amber Lin: If their goal is to transition, then there’s data that we need to collect.
103 00:11:38.100 ⇒ 00:11:45.979 Amber Lin: And I know they’re… Caitlin just told me they’re going to transition their data model, and they’re gonna collect more
104 00:11:46.100 ⇒ 00:11:49.559 Amber Lin: event-based data, which I think is
105 00:11:49.770 ⇒ 00:11:56.359 Amber Lin: is going to be very helpful, and where we will… we, as a company, will be very helpful.
106 00:11:56.870 ⇒ 00:12:03.390 Amber Lin: That’s the overview. Let me know any questions so I can dive deeper into any of these areas.
107 00:12:04.870 ⇒ 00:12:07.350 Robert Tseng: Yeah, I guess…
108 00:12:10.440 ⇒ 00:12:14.620 Robert Tseng: Let’s see… I’m gonna look up this stock.
109 00:12:15.220 ⇒ 00:12:19.149 Amber Lin: Yeah, it’s in the default client, like, default documents.
110 00:12:33.740 ⇒ 00:12:39.909 Robert Tseng: I think pricing per feature is tough if you don’t know what the feature… like, how these features are being used.
111 00:12:39.930 ⇒ 00:12:48.440 Amber Lin: Yeah, yeah, clearly. I recommend it just to have gates, especially if they’re just having a free, and then pro, and then enterprise.
112 00:12:48.440 ⇒ 00:13:04.180 Amber Lin: I wanted them to at least have some level of pricing so that we can collect users’ clicks on locked features, or at least for the users to see, oh, the premium has this, so I would upgrade.
113 00:13:04.280 ⇒ 00:13:05.939 Robert Tseng: Yeah. No, they don’t.
114 00:13:05.940 ⇒ 00:13:07.389 Amber Lin: They don’t lock anything.
115 00:13:08.390 ⇒ 00:13:09.989 Robert Tseng: Yeah, I think that makes sense.
116 00:13:14.890 ⇒ 00:13:19.700 Amber Lin: Let me send… Dukebox EU.
117 00:13:22.730 ⇒ 00:13:33.460 Amber Lin: First one is the pricing analysis stuff that I did, and then… The second one… is the…
118 00:13:35.940 ⇒ 00:13:39.640 Amber Lin: Current pricing, vertical pricing, transition.
119 00:13:40.280 ⇒ 00:13:48.640 Amber Lin: And binge… I can… Screenshot what she has.
120 00:13:59.440 ⇒ 00:14:02.680 Amber Lin: So, based off of the meeting, she wants to…
121 00:14:03.280 ⇒ 00:14:10.920 Amber Lin: do some more A-B tests. She just added another tier, I think, in Pro to start.
122 00:14:11.260 ⇒ 00:14:17.900 Amber Lin: I guess at that point,
123 00:14:18.600 ⇒ 00:14:24.860 Amber Lin: what’s our next step? Like, we know they want to start A-B testing,
124 00:14:26.120 ⇒ 00:14:30.080 Amber Lin: And Caitlin says she wants to go find an A-B testing tool.
125 00:14:30.470 ⇒ 00:14:37.160 Amber Lin: She’s like, you’ve done so much work, I want to do it now, so I think she wants to be doing the decision-making, but…
126 00:14:37.380 ⇒ 00:14:39.540 Amber Lin: I wanna see how we can…
127 00:14:39.760 ⇒ 00:14:42.649 Amber Lin: Chime in there without her feeling, like.
128 00:14:43.210 ⇒ 00:14:47.430 Amber Lin: She’s losing the decision-making power, whatever.
129 00:14:48.360 ⇒ 00:14:49.220 Robert Tseng: Sure.
130 00:14:51.040 ⇒ 00:14:56.479 Robert Tseng: Yeah, I mean, this is my first time looking through all this. I don’t think I have any other reactions right now. I think,
131 00:14:58.600 ⇒ 00:15:08.599 Robert Tseng: figuring out what features to get and all of that is probably more undetermined, but yeah, I think, like, the tiers make sense. Free, pro, enterprise,
132 00:15:08.770 ⇒ 00:15:11.369 Robert Tseng: One… one paid pricing tier.
133 00:15:13.110 ⇒ 00:15:23.049 Robert Tseng: that ends up… they end up testing, like, because they don’t know how to set the price of that… of that… of that paid tier either. Enterprise, obviously, you don’t show a price, so…
134 00:15:23.050 ⇒ 00:15:24.220 Amber Lin: Huh.
135 00:15:24.340 ⇒ 00:15:31.109 Robert Tseng: I mean, at this early stage, like, I think the best thing to do from a pricing perspective is just to, like.
136 00:15:31.960 ⇒ 00:15:36.160 Robert Tseng: Yeah, very specific, and…
137 00:15:36.280 ⇒ 00:15:40.939 Robert Tseng: just… like, they just have to run a lot of pricing experiments, right? It’s like…
138 00:15:43.160 ⇒ 00:15:50.540 Robert Tseng: For every, like, cluster of features, like, it’s price per workflow,
139 00:15:51.080 ⇒ 00:15:56.660 Robert Tseng: Like, free has limited access, then paid gives you free plus one workflow.
140 00:15:56.890 ⇒ 00:16:09.089 Robert Tseng: and you get a price there, and then you just kind of cycle through that with different prices. So, I sent you, like, this D2C, like, pricing tool, IntelliGems, and, like, the pricing kind of,
141 00:16:09.090 ⇒ 00:16:11.300 Amber Lin: Wait, where did you send me that?
142 00:16:11.300 ⇒ 00:16:17.540 Robert Tseng: I, I mean, I think I sent it in some, some, some channel, but, yeah, I guess, like…
143 00:16:17.540 ⇒ 00:16:21.670 Amber Lin: I found it. Yeah, we can recommend that to her.
144 00:16:21.670 ⇒ 00:16:34.109 Robert Tseng: I mean, I’m not sure if that tool is the best approach, but, like, I think the approach is correct. Like, that’s… you don’t know how you’re pricing things until you actually cross that line, so…
145 00:16:34.110 ⇒ 00:16:34.640 Amber Lin: Yeah, yeah.
146 00:16:34.640 ⇒ 00:16:38.790 Robert Tseng: So, yeah, like, I think they just need to…
147 00:16:40.220 ⇒ 00:16:56.620 Robert Tseng: they’re driving a lot of traffic to their site, and there’s free users testing pricing, they… they can’t be afraid to be, be putting a price tag on there, so they… they just… they… the… we just need to get them to… to test their, like, different types of pricing very quickly.
148 00:16:56.620 ⇒ 00:16:57.290 Amber Lin: Yeah.
149 00:16:57.660 ⇒ 00:17:02.550 Amber Lin: Oh, also, last time I talked to her, she’s…
150 00:17:02.770 ⇒ 00:17:18.049 Amber Lin: Probably thinking of not having a free tier because their support costs are super high, and she says she rather has less people come in and have adequate support than them turning by not having any support.
151 00:17:18.349 ⇒ 00:17:21.519 Amber Lin: But their support cost is pretty high.
152 00:17:24.869 ⇒ 00:17:25.799 Robert Tseng: Okay.
153 00:17:28.160 ⇒ 00:17:32.739 Amber Lin: And they’re thinking about that. But that’s also something they would need to test, right?
154 00:17:34.350 ⇒ 00:17:42.759 Robert Tseng: I… I mean, if they’re trying to do PLG, PLG means that they have a free tier, so I don’t really know if that’s PLG anymore, if they don’t have a free tier. Yeah.
155 00:17:46.100 ⇒ 00:17:47.929 Amber Lin: Well, that’s… that’s what…
156 00:17:49.110 ⇒ 00:17:57.630 Amber Lin: At this crossroad, do we make the recommendation, or do we just push them towards testing and give them more evidence to think with?
157 00:17:59.890 ⇒ 00:18:06.839 Robert Tseng: I haven’t looked at their data enough to, like, really be able to make, like, any claim there. I…
158 00:18:07.530 ⇒ 00:18:11.680 Robert Tseng: Yeah, I don’t know, I don’t know off the top of my head, that’s all I got for now.
159 00:18:12.050 ⇒ 00:18:13.300 Amber Lin: That’s okay.
160 00:18:13.620 ⇒ 00:18:15.800 Amber Lin: Let’s see…
161 00:18:19.420 ⇒ 00:18:22.939 Amber Lin: All their data should be in Mother Doc.
162 00:18:23.780 ⇒ 00:18:24.630 Amber Lin: sentence…
163 00:18:28.360 ⇒ 00:18:29.930 Robert Tseng: When is this due by?
164 00:18:31.410 ⇒ 00:18:40.349 Amber Lin: I think we have a bit of time, because Caitlin says she wants to look at a bit of stuff and think over it. We just talked yesterday, so I would say, like, next Thursday?
165 00:18:40.670 ⇒ 00:18:41.330 Robert Tseng: Okay.
166 00:18:41.330 ⇒ 00:18:42.050 Amber Lin: Yeah.
167 00:18:42.210 ⇒ 00:18:47.730 Robert Tseng: All right, well then we can kind of kick that. I mean, this is good for me to think about, I’ll keep it in mind.
168 00:18:47.730 ⇒ 00:18:48.370 Amber Lin: Okay.
169 00:18:48.530 ⇒ 00:18:53.269 Amber Lin: I wanted to make sure that we sent something to Insomnia today, so… Oh, yeah, totally. Yeah.
170 00:18:53.550 ⇒ 00:18:54.769 Amber Lin: I mean, I have a…
171 00:18:54.770 ⇒ 00:19:03.199 Robert Tseng: I have a few minutes, so, like, I don’t know, can we… like, I just… I left some comments there or whatever, but… Yeah. It’s just a lot to look through, and I.
172 00:19:03.200 ⇒ 00:19:05.130 Amber Lin: Oh, I know. Sorry about that.
173 00:19:05.130 ⇒ 00:19:23.290 Robert Tseng: to me, like, the takeaway is you’re telling me, promote pumpkin spice at… send pumpkin spice cookie emails from 3 a.m. to 7am. And I’m like, well, that doesn’t make any sense. Like, that to me is, like, what I’m getting out of the message, like, oh, I see, okay. So… or a pumpkin chocolate chunk.
174 00:19:24.210 ⇒ 00:19:26.140 Robert Tseng: terrible app or whatever, it’s like…
175 00:19:26.400 ⇒ 00:19:31.739 Robert Tseng: I don’t know, maybe I’m conflating things, but it’s just kind of… there’s just too much in one chunk.
176 00:19:31.740 ⇒ 00:19:39.020 Amber Lin: Yeah, I see. I was very greedy, and I wanted to give you everything I found, because I was like, I found these cool things, but it’s not very digestible.
177 00:19:39.170 ⇒ 00:19:58.299 Robert Tseng: Yeah, the open time stuff, like, I… if you talk to Bertie about this, like, we already formulated an opinion on this. Like, I… she already told her, you need to be sending more later in the day, after 3 p.m, because you were sending too many emails in the morning, people don’t open more… emails in the morning. So I think she already took action on that. Did she ask for a next phase of this? Because if not, then I.
178 00:19:58.300 ⇒ 00:20:08.889 Amber Lin: She wanted to find… she asked to give her the best send times for each channel, and best if per campaign type.
179 00:20:10.250 ⇒ 00:20:13.569 Robert Tseng: Okay, well then, why… did we do that?
180 00:20:14.110 ⇒ 00:20:18.239 Amber Lin: I haven’t looked at SMS.
181 00:20:18.550 ⇒ 00:20:20.770 Robert Tseng: Also… Or, like, for the campaign type.
182 00:20:21.370 ⇒ 00:20:23.520 Amber Lin: Oh, yeah, that one, I have it.
183 00:20:24.620 ⇒ 00:20:29.750 Robert Tseng: But, like, your conclusion is 3 to 7 AM? Like, I just… I can’t believe that that’s true.
184 00:20:29.960 ⇒ 00:20:30.720 Amber Lin: Oh…
185 00:20:30.720 ⇒ 00:20:33.699 Robert Tseng: Like, who’s gonna open an email at 3 to 7 a.m?
186 00:20:34.020 ⇒ 00:20:42.300 Amber Lin: Not 3 to 7 a.m, there’s a lot of people opening at 3 AM, based on Braz’s open data. Like, is that data accurate?
187 00:20:43.310 ⇒ 00:20:44.259 Robert Tseng: I don’t, like.
188 00:20:44.260 ⇒ 00:20:52.210 Amber Lin: Our data in Mother Duck doesn’t have the open times. I went to Braze and used their query, so essentially what we will get through currents.
189 00:20:52.210 ⇒ 00:20:54.320 Robert Tseng: Yeah. They’re open time data.
190 00:20:55.560 ⇒ 00:20:59.780 Robert Tseng: It’s possible, because they are a late-night cookie brand, like, they, they…
191 00:20:59.780 ⇒ 00:21:00.160 Amber Lin: Like, people…
192 00:21:00.160 ⇒ 00:21:04.249 Robert Tseng: We’ll go to insomnia after midnight. Like, that’s the… that’s the thing. But, like…
193 00:21:04.410 ⇒ 00:21:10.169 Robert Tseng: for them to open the email for the first time, maybe they’re just used to… I mean, I don’t… I don’t know, like, I… but…
194 00:21:10.700 ⇒ 00:21:11.880 Robert Tseng: I…
195 00:21:12.280 ⇒ 00:21:25.020 Robert Tseng: Like, maybe, like… okay, maybe you’re right, I just, like, I… what’s… what’s the volume of the emails that we’re sending? If we’re… if it’s just, like, you know, once again, this, like.
196 00:21:25.640 ⇒ 00:21:38.840 Robert Tseng: volume versus conversion kind of situation. Are we saying that it’s actually high revenue per message because it’s only, like, 10 emails versus, like, 100,000? Like, I don’t… I don’t know, like, I just…
197 00:21:38.840 ⇒ 00:21:40.710 Amber Lin: Totally, totally.
198 00:21:40.710 ⇒ 00:21:42.639 Robert Tseng: It feels… it feels strange.
199 00:21:42.910 ⇒ 00:21:44.859 Amber Lin: Yeah, this is the send…
200 00:21:44.980 ⇒ 00:21:55.890 Amber Lin: Send volumes. This graph. We send a lot from 12 and then peak, peak in the afternoon, we send a little bit. Like, this is the send times.
201 00:21:55.890 ⇒ 00:21:58.180 Robert Tseng: And their first open is at 3 AM.
202 00:21:58.560 ⇒ 00:22:01.060 Amber Lin: Not the first open, this is…
203 00:22:01.370 ⇒ 00:22:05.070 Amber Lin: All opens, when is it usually at?
204 00:22:05.310 ⇒ 00:22:08.979 Amber Lin: Usually, people don’t open during the workday. Yeah. Makes sense.
205 00:22:08.980 ⇒ 00:22:09.610 Robert Tseng: That makes sense.
206 00:22:09.610 ⇒ 00:22:15.139 Amber Lin: open in the afternoon, and then there’s a peak at 3 AM. That was very, very…
207 00:22:15.490 ⇒ 00:22:17.350 Robert Tseng: Okay, yeah, maybe, yeah, sure.
208 00:22:17.350 ⇒ 00:22:17.970 Amber Lin: Yeah.
209 00:22:19.270 ⇒ 00:22:23.210 Amber Lin: And they peak on Tuesday because we send the most on Monday.
210 00:22:23.470 ⇒ 00:22:30.170 Amber Lin: And then people… on average, some people delay so that they open the rest on Tuesday.
211 00:22:30.410 ⇒ 00:22:32.289 Amber Lin: Because we send the most here.
212 00:22:32.460 ⇒ 00:22:35.890 Amber Lin: Like, this is… Yeah. …to this graph of…
213 00:22:36.800 ⇒ 00:22:38.850 Amber Lin: What is this? Actually, yeah.
214 00:22:38.850 ⇒ 00:22:50.530 Robert Tseng: Why do we care about them opening it within 30 minutes? What if they just, like, saw the email, and they’re like, alright, well, it’s gonna leave it unread until I go and use the promotional offer or whatever after midnight? Like, then I’ll open my email then.
215 00:22:50.530 ⇒ 00:22:53.790 Amber Lin: I guess I could do the click, because I… that’s…
216 00:22:54.220 ⇒ 00:23:03.529 Amber Lin: my thought behind that is I want to see if we can catch people right then and there, because insomnia could be such an impulse purchase.
217 00:23:03.850 ⇒ 00:23:09.580 Amber Lin: Like, I want… if someone… Pro- sees the promotion, opens it, and clicks it.
218 00:23:09.690 ⇒ 00:23:14.179 Amber Lin: And that… I think that’s a good conversion email.
219 00:23:14.570 ⇒ 00:23:16.599 Amber Lin: Does that logic make sense?
220 00:23:17.650 ⇒ 00:23:18.980 Robert Tseng: Sure…
221 00:23:19.520 ⇒ 00:23:32.979 Robert Tseng: But, like, the way that we’re tracking conversions right now, there’s double counting and everything, right? So if somebody opens a 3AM email, it may be giving credit to the email that was sent 5 days ago, and also the email that was sent 2 days ago.
222 00:23:33.280 ⇒ 00:23:38.480 Amber Lin: Yeah, I wouldn’t use conversion, I was just… I was just going to use clicks.
223 00:23:38.990 ⇒ 00:23:42.359 Amber Lin: Because they have the click time on the links in the email.
224 00:23:42.860 ⇒ 00:23:43.700 Robert Tseng: Okay.
225 00:23:43.700 ⇒ 00:23:46.740 Amber Lin: Yeah, that would be more accurate than conversions.
226 00:23:47.060 ⇒ 00:23:54.700 Robert Tseng: Okay, well, I mean, if you feel strongly that you want to push her to try that, then you can… you can give her that feedback. I think that’s interesting.
227 00:23:55.420 ⇒ 00:23:58.580 Amber Lin: Yeah, this is something we could try, because, like, 3…
228 00:23:58.880 ⇒ 00:24:07.100 Amber Lin: I guess people who study are… I am also confused when I saw this, but there’s, like, two groups of people.
229 00:24:07.100 ⇒ 00:24:07.760 Robert Tseng: Yeah.
230 00:24:08.530 ⇒ 00:24:09.130 Amber Lin: Yeah.
231 00:24:09.660 ⇒ 00:24:18.030 Amber Lin: I think the main insight I wanted to show was… because last… like, last time we talked, we didn’t talk about the…
232 00:24:18.430 ⇒ 00:24:22.329 Amber Lin: Types of cookies, and what they do purchase.
233 00:24:22.640 ⇒ 00:24:26.330 Amber Lin: This one.
234 00:24:29.780 ⇒ 00:24:30.900 Amber Lin: So…
235 00:24:31.460 ⇒ 00:24:38.270 Amber Lin: So after… yesterday, after we talked, I went and looked at how… looked at the line items, didn’t have time to do…
236 00:24:38.630 ⇒ 00:24:43.259 Amber Lin: The graphs you proposed, or the slides, because it was really late.
237 00:24:43.440 ⇒ 00:24:48.289 Amber Lin: Sure. But this is the conversion from 1st to 2nd.
238 00:24:48.580 ⇒ 00:24:49.000 Robert Tseng: Okay.
239 00:24:49.000 ⇒ 00:24:56.080 Amber Lin: So, I would say that if we do get people to get their second cookie, they’re much more likely to get their third cookie.
240 00:24:56.080 ⇒ 00:24:58.100 Robert Tseng: Yeah, that’s huge, yeah.
241 00:24:58.100 ⇒ 00:25:00.239 Amber Lin: This is… this is a range.
242 00:25:00.630 ⇒ 00:25:15.930 Amber Lin: This might be higher than industry average, but I haven’t, like, limited it in, like, second purchase within the next day, so I don’t know how this compares to benchmark, so that’s something I wanted to next.
243 00:25:16.580 ⇒ 00:25:22.440 Amber Lin: We looked at this… Okay.
244 00:25:22.440 ⇒ 00:25:37.190 Robert Tseng: You don’t have to do by industry benchmark, either. You can just do, next order. You… you have… you have your… you benchmark against your internal data. So, second order within 30 days, second order within 60 days, versus just second order at all.
245 00:25:37.630 ⇒ 00:25:38.810 Amber Lin: Oh…
246 00:25:39.040 ⇒ 00:25:42.449 Robert Tseng: Yeah, I wouldn’t look at industry average. I don’t even know where you would find that.
247 00:25:42.450 ⇒ 00:25:44.189 Amber Lin: Okay, okay, cool.
248 00:25:44.920 ⇒ 00:25:56.869 Amber Lin: Okay. Then I looked at line items, which is… which is essentially answering your questions. Do people get more individual cookies, or do they get bundles? What do they get in their first purchase, right?
249 00:25:56.870 ⇒ 00:26:05.730 Amber Lin: Based on their… like, I went into… horizon? I forgot what it’s called. And they have, like, product types, so there’s.
250 00:26:05.730 ⇒ 00:26:06.310 Robert Tseng: Yep.
251 00:26:06.310 ⇒ 00:26:10.120 Amber Lin: They fall under boxes, Classic, Deluxe, and then…
252 00:26:10.120 ⇒ 00:26:11.340 Robert Tseng: Yep.
253 00:26:11.340 ⇒ 00:26:17.479 Amber Lin: So, we have a lot of blocks to use, probably because they had…
254 00:26:17.850 ⇒ 00:26:22.270 Amber Lin: New and old boxes, like, there’s different namings that changed.
255 00:26:22.980 ⇒ 00:26:28.830 Amber Lin: But overall, you can see from approximately
256 00:26:29.000 ⇒ 00:26:42.129 Amber Lin: May of this year. Overall sales spiked, and also, actually, I might need to look at overall purchase data, but right now, boxes are a bigger percentage
257 00:26:42.250 ⇒ 00:26:43.739 Amber Lin: of purchases.
258 00:26:44.690 ⇒ 00:26:47.280 Amber Lin: Than the other types.
259 00:26:47.400 ⇒ 00:26:54.080 Amber Lin: And then there’s less deluxe… And then even… C.
260 00:26:54.210 ⇒ 00:27:04.809 Amber Lin: after that change, usually, like, the percentage of purchases from classes and deluxe are usually the same, the orange and blue line, but right now,
261 00:27:05.070 ⇒ 00:27:16.509 Amber Lin: like, deluxe are… like, more people got classics, and a lot of people get boxes. Probably because they’re heavily promoing boxes. Yeah. That’s the first thing I see on their website.
262 00:27:18.190 ⇒ 00:27:30.550 Amber Lin: And so, I wanted to see, like, does that percentage, like, that distribution change? Do people who purchase more still get a lot of boxes?
263 00:27:30.690 ⇒ 00:27:34.590 Amber Lin: And it seems like repeat purchases
264 00:27:35.320 ⇒ 00:27:39.030 Amber Lin: They start to get more individual cookies, they start to get…
265 00:27:39.750 ⇒ 00:27:45.969 Amber Lin: more classic cookies, specifically. So that’s type 1, the blue bar here.
266 00:27:46.920 ⇒ 00:27:58.819 Amber Lin: And then… so… I wanna see, okay, if they get a lot of boxes, if that’s almost 60%, what type of boxes do they get? And it seems like they get the 6-packs.
267 00:27:59.120 ⇒ 00:28:07.979 Amber Lin: Most likely, because the $20 free shipping, and that’s, like, 6-pack, I think it’s, like, 22-something, and slightly
268 00:28:08.350 ⇒ 00:28:09.470 Amber Lin: the free shipping.
269 00:28:09.470 ⇒ 00:28:13.349 Robert Tseng: So people usually just get that on their first purchase. Yeah.
270 00:28:13.350 ⇒ 00:28:16.059 Amber Lin: And then they might get familiar, and then they won.
271 00:28:17.050 ⇒ 00:28:22.059 Amber Lin: 12 packs as they like the flavor, so that’s a finding there.
272 00:28:23.030 ⇒ 00:28:26.509 Amber Lin: And… this is a… this is a mess of a graph, but…
273 00:28:26.510 ⇒ 00:28:32.050 Robert Tseng: Yeah, next time I would just limit to the top 10. Whatever you have to do, deal with more, like, I would just limit the top 10.
274 00:28:32.260 ⇒ 00:28:32.860 Amber Lin: Yeah.
275 00:28:33.080 ⇒ 00:28:33.670 Robert Tseng: Yeah.
276 00:28:33.850 ⇒ 00:28:41.969 Amber Lin: I… I had a top 10, but then I just wanted to see what grew, because some of these weren’t top 10, and they grew in the…
277 00:28:42.540 ⇒ 00:28:43.090 Robert Tseng: Okay.
278 00:28:43.630 ⇒ 00:28:52.409 Amber Lin: But people start mostly with chocolate chunk on their first order. Like, that’s the biggest…
279 00:28:52.720 ⇒ 00:29:00.019 Amber Lin: Proportion the first orders, probably because it’s most basic, easiest, don’t need to choose, less risk.
280 00:29:00.260 ⇒ 00:29:03.089 Amber Lin: And then when they purchase again.
281 00:29:03.320 ⇒ 00:29:07.969 Amber Lin: They get more and more of classic cookies.
282 00:29:08.110 ⇒ 00:29:16.300 Amber Lin: You have a big percentage of people who get deluxe on their first purchase, and then the deluxe percentage kind of declines.
283 00:29:17.190 ⇒ 00:29:21.310 Amber Lin: Probably… maybe because classes are cheaper?
284 00:29:21.480 ⇒ 00:29:28.100 Amber Lin: Or maybe because, like, there’s also interesting flavors in Classic that I don’t have to go to Deluxe to get the full flavor?
285 00:29:28.100 ⇒ 00:29:34.349 Robert Tseng: Yeah, the deluxe is just kind of novel, and so people are like, oh, okay, great, I tried the caramel apple one, I prefer chocolate chip.
286 00:29:34.350 ⇒ 00:29:39.040 Amber Lin: Yeah, but if they are returning, they’ve probably already tried it, so…
287 00:29:39.040 ⇒ 00:29:39.730 Robert Tseng: Yeah.
288 00:29:39.910 ⇒ 00:29:42.259 Amber Lin: Just go to the ones they like.
289 00:29:42.260 ⇒ 00:29:42.740 Robert Tseng: Yeah.
290 00:29:42.740 ⇒ 00:30:02.310 Amber Lin: And then they, like, some of these flavors, the ones I listed here, you see this chunk that’s growing throughout the purchases? People who return get less of chocolate chunk. They start to get more of these, I would say, cooler flavors or interesting flavors.
291 00:30:02.460 ⇒ 00:30:07.920 Amber Lin: As they come back for repeat individual cookie purchases.
292 00:30:08.140 ⇒ 00:30:10.749 Amber Lin: So that’s, that’s also interesting.
293 00:30:10.950 ⇒ 00:30:11.860 Amber Lin: We’ll see.
294 00:30:14.790 ⇒ 00:30:15.430 Robert Tseng: Okay.
295 00:30:16.230 ⇒ 00:30:16.800 Amber Lin: Yeah.
296 00:30:17.430 ⇒ 00:30:19.550 Amber Lin: Okay.
297 00:30:19.940 ⇒ 00:30:28.850 Robert Tseng: I mean, even this data is great as an evergreen campaign. Like, I would… I mean, if I were her, I’d be like, look, like, you should… your new customers, subscribers, like.
298 00:30:28.980 ⇒ 00:30:43.779 Robert Tseng: hey, like, you know, welcome to Insomnia, like, this is our most popular cookie is, like, chocolate chunk. Here’s a series, or, like, here’s, like, a thing for what makes our chocolate chunks so good. And you, like, literally just hype up your bestseller cookie, and because you know that people are gonna love it.
299 00:30:43.930 ⇒ 00:31:00.219 Robert Tseng: And you know that they’re gonna go get chocolate chunk, and they’re gonna try something else. It’s like, great, you’ve had our, like, best cookie, like, here are some other, like, interesting flavors that, like, people… it… it may sound weird at first, but it grows on our most loyal customers. And, like, I don’t know, like, I think there’s… this is, like, a…
300 00:31:00.590 ⇒ 00:31:08.939 Robert Tseng: you know, that to me is, like, a way that we can… I don’t know if she has enough evergreen campaigns out there, so, a lot of it is just kind of…
301 00:31:09.230 ⇒ 00:31:15.780 Robert Tseng: it’s not tailored to the customer journey. It’s just like, oh, here’s our peanut butter series, whatever, or like, here’s our chocolate.
302 00:31:15.780 ⇒ 00:31:16.130 Amber Lin: Yeah.
303 00:31:16.130 ⇒ 00:31:17.970 Robert Tseng: So, yeah.
304 00:31:18.270 ⇒ 00:31:25.309 Amber Lin: Yeah, because that will tell us who to send, like, what to send to if it’s the first time purchase. After they made the first-time purchase, then it’s.
305 00:31:25.310 ⇒ 00:31:26.160 Robert Tseng: Yeah.
306 00:31:26.160 ⇒ 00:31:30.610 Amber Lin: Promote these cool flavors, and people will try and… will try them.
307 00:31:30.910 ⇒ 00:31:40.569 Robert Tseng: Yeah, so when you talk to her, like, I feel like you should have these types of ideas or stories to kind of, like, nudge her in a direction. And don’t just, like, tell her the data, she’s not that data-driven, she’s gonna look.
308 00:31:40.570 ⇒ 00:31:40.960 Amber Lin: Yeah, I guess.
309 00:31:40.960 ⇒ 00:31:48.660 Robert Tseng: Like, not really know what to make of it. Okay. But, yes, I don’t think this is… this is… yeah, okay, I could see that.
310 00:31:48.660 ⇒ 00:31:49.300 Amber Lin: Cool.
311 00:31:49.300 ⇒ 00:31:57.170 Robert Tseng: I think, I think, yeah, definitely take this section, just share that with her, you know, let her know, kind of, like, some idea around there. The product, yeah, the insights on, like.
312 00:31:57.290 ⇒ 00:32:07.630 Robert Tseng: repeat purchases, like, how the product distribution changes, also really great insight to share with her as well. So I think this stuff is ready to share. Like, I think you could share this with her.
313 00:32:07.630 ⇒ 00:32:12.340 Amber Lin: Okay, what does Emerita need? She needs something different, right?
314 00:32:12.690 ⇒ 00:32:22.709 Robert Tseng: Don’t worry about Amrita, just share it with the broader channel, and just tag Bertie, and Rita will just, like, kind of peek at it, but okay. I mean, Rita would want to see is she would want to see, like, the…
315 00:32:22.960 ⇒ 00:32:31.859 Robert Tseng: I mean, yeah, if you can screenshot a couple of the… I mean, that’s what I’m saying, like, I… the slides format is better. Yeah, the deck is…
316 00:32:31.860 ⇒ 00:32:37.299 Amber Lin: I’ll put it in the deck, like, I already have the images, I have the summary, so I just… I’ll just chuck it in there.
317 00:32:37.300 ⇒ 00:32:41.760 Robert Tseng: Great. Okay, so yeah, put it in the deck, and I think Amrita will look at it as well.
318 00:32:41.760 ⇒ 00:32:42.610 Amber Lin: Okay, awesome.
319 00:32:42.750 ⇒ 00:32:48.740 Robert Tseng: But yeah, remember, start from the driving question. You have to anchor them to, what question is this answering? And,
320 00:32:49.180 ⇒ 00:32:54.010 Robert Tseng: Yeah, otherwise she’s gonna just look at it and not really gonna know why it’s important.
321 00:32:54.010 ⇒ 00:33:04.469 Amber Lin: Okay, should I… I feel like the timing should be in a different slide, right? I can… I feel like this is more interesting. I’ll… I’ll talk about it.
322 00:33:04.470 ⇒ 00:33:10.989 Robert Tseng: Yeah, the timing thing can just be a Slack message if it’s easier to communicate. Like, I think this stuff is more visually easy to communicate over a deck.
323 00:33:10.990 ⇒ 00:33:12.490 Amber Lin: Okay, cool.
324 00:33:12.780 ⇒ 00:33:16.910 Amber Lin: Awesome, I’ll do that, and then I’ll tag you for final review.
325 00:33:17.300 ⇒ 00:33:18.550 Robert Tseng: Cool. Thank you.
326 00:33:18.740 ⇒ 00:33:19.780 Amber Lin: Yeah, thanks.
327 00:33:19.930 ⇒ 00:33:21.280 Robert Tseng: Alright, bye.