Meeting Title: Insomnia Email Campaign Optimization Sync Date: 2025-11-12 Meeting participants: Hannah Wang
WEBVTT
1 00:00:04.610 ⇒ 00:00:07.510 Hannah Wang: Alright, this is for insomnia.
2 00:00:09.500 ⇒ 00:00:10.810 Hannah Wang: Let’s make a comment.
3 00:00:14.710 ⇒ 00:00:16.269 Hannah Wang: What’s the one that we didn’t?
4 00:00:17.190 ⇒ 00:00:20.190 Hannah Wang: This is fleshless fleshes.
5 00:00:23.180 ⇒ 00:00:24.260 Hannah Wang: Mmm.
6 00:00:28.040 ⇒ 00:00:39.530 Hannah Wang: Right now, we’re only doing owns channel, and more specifically email. I don’t know by the time when we published this case study, if we would have done, like, SMS and push.
7 00:00:40.580 ⇒ 00:00:52.029 Hannah Wang: Which is… what’s the difference between SMS and push? I feel like they’re the same. Oh, push notifications are, like, the notifications you get on their phone through the app, but SMS is text. Oh, okay.
8 00:00:52.880 ⇒ 00:01:04.680 Hannah Wang: So we’re doing email campaigns? Like, owned channel… what’s that? Yeah, that’s very confusing. Let’s just say email campaign. Email campaign…
9 00:01:06.310 ⇒ 00:01:15.040 Hannah Wang: analysis, optimization, and, like, so either that or segmenting, customer segmentation.
10 00:01:15.870 ⇒ 00:01:34.429 Hannah Wang: for campaigns. Okay. I think that’s better. That makes sense. For more, like, email campaigns, either include or exclude the email, depending on what you think. Do you want to edit the header? So do we know what this one’s for? Customer… customer…
11 00:01:35.280 ⇒ 00:01:36.799 Hannah Wang: segment link.
12 00:01:36.980 ⇒ 00:01:38.410 Hannah Wang: Analysis?
13 00:01:39.020 ⇒ 00:01:41.170 Hannah Wang: Customer segmentation analysis?
14 00:01:44.790 ⇒ 00:01:54.680 Hannah Wang: Or emailed… Email campaign… Targeting? Optimization?
15 00:01:56.040 ⇒ 00:02:05.750 Hannah Wang: Targeting and optimization. Like, optimizing who you go after, right? Like, targeting any, I guess, or just channel optimization, you decide.
16 00:02:07.050 ⇒ 00:02:19.650 Hannah Wang: Yeah, still performance marketing, marketing ops, yeah, so… or Customer Insights. Like, project types customer insights, specifically.
17 00:02:22.070 ⇒ 00:02:27.220 Hannah Wang: Yeah, it’s a month. Industry… What is QSR?
18 00:02:28.650 ⇒ 00:02:33.710 Hannah Wang: I think that, like, cons… wait, are we in, like, food and beverage?
19 00:02:35.360 ⇒ 00:02:39.040 Hannah Wang: They’re not a quick service restaurant, that was all…
20 00:02:39.180 ⇒ 00:02:42.649 Hannah Wang: Like, are we really in consumer delivery? We’re in food and beverage.
21 00:02:42.960 ⇒ 00:02:45.190 Hannah Wang: Beer and food and beverage.
22 00:02:51.030 ⇒ 00:02:53.250 Hannah Wang: Okay,
23 00:02:57.370 ⇒ 00:02:58.650 Hannah Wang: 10.
24 00:02:59.510 ⇒ 00:03:07.330 Hannah Wang: Well, they’ve been seeing declining performances in their, you know, marketing campaigns.
25 00:03:08.110 ⇒ 00:03:13.400 Hannah Wang: they’ve… Not been able to…
26 00:03:13.900 ⇒ 00:03:28.089 Hannah Wang: revive it, and they don’t know what to change, because they don’t know what’s wrong. So they can… they’ve only done very, very small tweaks that has not made a difference. I see.
27 00:03:28.430 ⇒ 00:03:41.270 Hannah Wang: like, just copy tweaks? Yeah, like, copy tweaks, small timing tweaks, but very small scale. They mostly changed based on what they did last year, and the same doesn’t… doesn’t mean much.
28 00:03:41.460 ⇒ 00:03:47.709 Hannah Wang: They did introduce their… like, segmentation.
29 00:03:48.310 ⇒ 00:03:55.650 Hannah Wang: Game 1’s, like, start of 2025, I’m not sure. Like, they introduced it, but yeah, it’s not doing much.
30 00:03:56.210 ⇒ 00:04:05.020 Hannah Wang: Segmentation as in customer segmenting. What does that mean? Like, who do they go, like… Yeah, so segmentation is essentially,
31 00:04:05.160 ⇒ 00:04:05.970 Hannah Wang: like…
32 00:04:06.680 ⇒ 00:04:26.149 Hannah Wang: in the people who buy your products, your customers, they’re different people, right? For insomnia, students, like, college students, is a big segment for them, right? And then there’s, like, young professionals, or there’s, like, business customers who buy in bulk for events. So that’s what segmentation means.
33 00:04:27.000 ⇒ 00:04:38.159 Hannah Wang: And Dan… So, that’s the context, like, email not doing well, and…
34 00:04:38.270 ⇒ 00:04:42.789 Hannah Wang: a lot of… I believe Robert did analysis, like, a lot of their…
35 00:04:44.060 ⇒ 00:04:51.440 Hannah Wang: Revenue from the own channel comes from email, so email is an important channel, but declining.
36 00:04:52.990 ⇒ 00:04:59.380 Hannah Wang: And, like, why is phone channel important is because You…
37 00:04:59.550 ⇒ 00:05:07.820 Hannah Wang: own it, versus, like, own channel, meaning you don’t… you own this channel, versus you go through food delivery apps. Like, you don’t own…
38 00:05:07.930 ⇒ 00:05:15.090 Hannah Wang: Uber, you don’t own DoorDash, but you own your app, you own your emails, you own your messages.
39 00:05:15.270 ⇒ 00:05:24.450 Hannah Wang: That’s what it’s called phone. I see. And that’s why, like, it’s important for them, because that’s where they have the most flexibility. They can send out
40 00:05:24.730 ⇒ 00:05:43.550 Hannah Wang: like, people will subscribe to your emails, and then you can send out messages. So that’s a place that they’re most in touch, but they’re loyal customers, they can have conversations, they can have, like, branding, versus on-food delivery as it’s just, there’s DoorDash versus ACOG, right?
41 00:05:43.740 ⇒ 00:05:48.210 Hannah Wang: Okay, and so that’s the context. I think it’s a calic…
42 00:05:48.350 ⇒ 00:06:00.810 Hannah Wang: recovery challenges of, like, they’ve not been able to find anything to change its performance, but it’s been declining. They don’t know why it’s been declining,
43 00:06:02.010 ⇒ 00:06:14.070 Hannah Wang: like, the CMO or, like, director of marketing really wants to know how to improve it. They’ve tried segmentation, it still haven’t worked. They don’t know who to target, how to target their email.
44 00:06:14.290 ⇒ 00:06:20.070 Hannah Wang: Their email, campaigns, have…
45 00:06:20.250 ⇒ 00:06:29.979 Hannah Wang: high open rates, but really low click rates. Below industry average click rates, which means that, I guess that itself, it’s meaningful.
46 00:06:30.370 ⇒ 00:06:35.240 Hannah Wang: Okay, and then our solution.
47 00:06:36.770 ⇒ 00:06:45.259 Hannah Wang: Well, first of all, we… we’re a data company, so we help them put their data together, as in, okay, what are…
48 00:06:45.440 ⇒ 00:06:53.870 Hannah Wang: Well, oh, another challenge. Their conversion track… their conversion tracking an email through their app.
49 00:06:54.330 ⇒ 00:06:59.030 Hannah Wang: Like, not their app, but the software they use for email marketing is off.
50 00:06:59.400 ⇒ 00:07:13.479 Hannah Wang: So, which means, when you say this email led to this many sales, it’s off. Why was it off? That’s what we found, that’s what we investigated, right? Like, they knew that there was something off, and what we found is that
51 00:07:14.790 ⇒ 00:07:31.580 Hannah Wang: attribution? Do you know what attribution is? It’s like seeing which, like, where it came from, right? Yeah, so I made an order, I received 5 emails. How do you attribute my order to? Does email 1 cause my order? Do email 5 cause my order?
52 00:07:32.440 ⇒ 00:07:38.929 Hannah Wang: right now, let’s say I made The order within 7 days.
53 00:07:39.180 ⇒ 00:07:48.999 Hannah Wang: of the email saying, if I receive all 5 emails in the same week, right now, I get a tribute to each individual one of them, so each of them gets $5.
54 00:07:49.540 ⇒ 00:07:52.330 Hannah Wang: Wow, which means that it gets duplicated. Right.
55 00:07:52.820 ⇒ 00:08:08.710 Hannah Wang: Usually there’s a deduplication process of, say, you can only get attribute to one right now, not all of them. Yeah, but they don’t have it, so… so right now, it’s, like, by magnitude, two times, so that’s…
56 00:08:08.990 ⇒ 00:08:18.829 Hannah Wang: Like, they have that issue. So they always… they’ve thought… which means that they’ve thought their return from this channel has been two times higher than what they thought it was.
57 00:08:18.870 ⇒ 00:08:34.309 Hannah Wang: Which is an issue, right? Interesting. Anyways, so that was very interesting when I found out. I was like, oh, wow. Yeah. And then when it comes to descriptions, well, first, we set up the data. We need to make data accurate.
58 00:08:34.419 ⇒ 00:08:37.029 Hannah Wang: So, we found another solution to
59 00:08:37.169 ⇒ 00:08:44.229 Hannah Wang: get the data directly from the… I’m still working on that, but just assume that we already have it.
60 00:08:44.400 ⇒ 00:08:53.889 Hannah Wang: So, that way we can have accurate purchase data, accurate engagement and conversion data, so making sure that things are accurate to start with.
61 00:08:54.160 ⇒ 00:08:58.989 Hannah Wang: And then, what’s a challenge.
62 00:08:59.300 ⇒ 00:09:00.700 Hannah Wang: Oh.
63 00:09:00.990 ⇒ 00:09:12.859 Hannah Wang: And then we… help them… Reclassify their campaign types, and analyze the best, like…
64 00:09:13.860 ⇒ 00:09:19.260 Hannah Wang: Best send times, best performance, which campaign is best.
65 00:09:19.450 ⇒ 00:09:24.859 Hannah Wang: So that’s, like, campaign-type analysis.
66 00:09:25.200 ⇒ 00:09:30.709 Hannah Wang: And now we’re introducing… we’re wanting to introduce a new segmentation.
67 00:09:30.960 ⇒ 00:09:34.100 Hannah Wang: Because before, their segmentation is just based on…
68 00:09:35.410 ⇒ 00:09:39.849 Hannah Wang: How recent they purchased, how frequent they purchased, how much they purchase, which…
69 00:09:40.070 ⇒ 00:09:49.930 Hannah Wang: That doesn’t mean I will click on their email. That just means I maybe live close by to sign a story, just a cookie. Doesn’t mean I’m loyal to the brand. So…
70 00:09:50.690 ⇒ 00:10:03.490 Hannah Wang: We’re trying to create a new segmentation that’s more helpful for their email campaign targeting, because new customers need a different email than a very old customer, and a new customer is, like.
71 00:10:03.600 ⇒ 00:10:22.209 Hannah Wang: you made a first purchase, let’s try and get you to make a second one. This is an old customer, if I’m very loyal, I might want reward points, like, I’m gonna go for that. Or if I haven’t got it in a while, if you remind me of nostalgia and, like, whatever, the cool deal, I might come back. So…
72 00:10:22.760 ⇒ 00:10:27.430 Hannah Wang: That’s what we’re trying to introduce, something that’s lifecycle-based, and so that
73 00:10:27.690 ⇒ 00:10:43.670 Hannah Wang: It helps them create better targeting campaigns, and then further on will help segment a bit further, like, oh, these people likes new flavors, these people like rewards, and helping them
74 00:10:45.040 ⇒ 00:10:54.130 Hannah Wang: Separate the customer into small bunches, so not only they can target email campaigns better, they can also, like.
75 00:10:54.820 ⇒ 00:11:04.199 Hannah Wang: run experiments… experiments and learn, oh, these people like this, but not that. But right now, if they’re targeting everybody, they will never know, because
76 00:11:04.410 ⇒ 00:11:07.410 Hannah Wang: They just got a mush of data, yeah.
77 00:11:07.760 ⇒ 00:11:10.540 Hannah Wang: How did you fix attribution?
78 00:11:10.730 ⇒ 00:11:17.980 Hannah Wang: Did we not fix that? That’s not me. What is that? That’s gonna be them a lot of it. So, we’re going to…
79 00:11:18.420 ⇒ 00:11:29.630 Hannah Wang: So, fixing attribution means we need to get the raw source of data, so the purchase and campaign data, and then we rewrite the logic of
80 00:11:29.910 ⇒ 00:11:43.650 Hannah Wang: This is what’s currently attributed, we just do a deduplication logic of if this order has already been attributed, we don’t attribute it again. That’s the SSD logic.
81 00:11:44.030 ⇒ 00:11:51.400 Hannah Wang: That’s most of it. Results, probably just better campaign performance, leaner, like.
82 00:11:51.770 ⇒ 00:12:05.479 Hannah Wang: Do you do data attribution? I can ask you that later, though. Yeah. Any tools you use? Cursor? Can’t put that. We also use, like, other dog or brains. Okay, probably similar? Okay.
83 00:12:05.900 ⇒ 00:12:11.340 Hannah Wang: Who are the team members? PM is Robert. PM is Joe Utem. I think us.
84 00:12:12.140 ⇒ 00:12:20.690 Hannah Wang: Bhutan, casey, still help? Maybe Sam helped as well? Or engineer.
85 00:12:21.300 ⇒ 00:12:27.690 Hannah Wang: Engineering, and then… like, analytics or data engineering, I’m getting laude.
86 00:12:27.890 ⇒ 00:12:29.889 Hannah Wang: And I’m a data analyst.
87 00:12:30.470 ⇒ 00:12:31.770 Hannah Wang: Okay. Yeah.
88 00:12:31.970 ⇒ 00:12:37.340 Hannah Wang: I always thought they were the same thing, until you explained it to me. Analyst versus
89 00:12:37.530 ⇒ 00:12:41.730 Hannah Wang: analysis. Yeah, I don’t… I don’t… It’s blank.
90 00:12:42.120 ⇒ 00:12:55.919 Hannah Wang: take the data, of course, move it here, and then make it nice and tidy. I just… I look at it. I just look at it, like, oh, this goes here, this goes here. Okay. Yeah. Oh, this stuff is pretty interesting. It is pretty interesting.
91 00:12:56.330 ⇒ 00:12:59.240 Hannah Wang: Much more interesting than being a PM.
92 00:12:59.950 ⇒ 00:13:04.139 Hannah Wang: Alright, cool. Yeah. Alright, that’s it, right?