Meeting Title: ABC Revenue Analysis and Playbooks Review Date: 2026-01-13 Meeting participants: Amber Lin, Robert Tseng
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1 00:00:54.510 ⇒ 00:00:55.560 Robert Tseng: Hey, Amber.
2 00:00:58.110 ⇒ 00:00:59.110 Amber Lin: Hello!
3 00:01:01.380 ⇒ 00:01:02.310 Robert Tseng: How’s it going?
4 00:01:03.750 ⇒ 00:01:06.569 Amber Lin: Pretty good. I’m planning my trip to Portland.
5 00:01:06.690 ⇒ 00:01:10.979 Robert Tseng: Yeah, what’s, what’s, what’s in Portland? Just, just wanted to get a weekend getaway.
6 00:01:11.130 ⇒ 00:01:17.970 Amber Lin: No, Nico’s doing a, magic tournament there, it’s like a card game tournament.
7 00:01:17.970 ⇒ 00:01:19.700 Robert Tseng: Yeah, yeah, I know Magic.
8 00:01:19.700 ⇒ 00:01:21.210 Amber Lin: Yeah, do you play?
9 00:01:21.430 ⇒ 00:01:22.470 Robert Tseng: I used to.
10 00:01:22.750 ⇒ 00:01:24.350 Amber Lin: Oh, wow!
11 00:01:24.350 ⇒ 00:01:24.980 Robert Tseng: Yeah.
12 00:01:25.860 ⇒ 00:01:30.520 Amber Lin: So she, she’s going to the regional championship.
13 00:01:30.950 ⇒ 00:01:34.589 Robert Tseng: Wow, I mean, she must be way better than me, so…
14 00:01:34.590 ⇒ 00:01:39.409 Amber Lin: She’s pretty, pretty good, I’m very impressed. She’s only played for a very short time.
15 00:01:39.430 ⇒ 00:01:58.669 Amber Lin: Huh. And, so I… I’m just going there to see the city, and then I realized, oh, it’s still snow season, and I haven’t snowboarded in a long time since Europe, and I really, really want to go. So I’m gonna figure out how to go ski without a car.
16 00:02:00.700 ⇒ 00:02:02.720 Robert Tseng: Oh.
17 00:02:02.720 ⇒ 00:02:04.299 Amber Lin: If you…
18 00:02:06.710 ⇒ 00:02:10.530 Robert Tseng: If you go to Bend.
19 00:02:12.340 ⇒ 00:02:17.479 Robert Tseng: Oh, I don’t know where you were planning to ski, but Bend, Oregon is a common place for people to ski.
20 00:02:18.810 ⇒ 00:02:19.650 Amber Lin: Mmm.
21 00:02:20.120 ⇒ 00:02:23.720 Amber Lin: I might just end up in Mount Hood, because it’s the closest one.
22 00:02:23.720 ⇒ 00:02:24.730 Robert Tseng: Yeah, hood, okay.
23 00:02:24.990 ⇒ 00:02:30.579 Robert Tseng: I know Bend is a bit of a drive, but I was just gonna say, if you want to go to Bend, I could…
24 00:02:30.970 ⇒ 00:02:37.280 Robert Tseng: I could try to… I mean, I know, I know people, I know many people who, like, have
25 00:02:37.850 ⇒ 00:02:47.060 Robert Tseng: like, vacation homes and stuff there, so probably could get you… get you a place to stay there. And then also, Byron lives in… from… Henney Singer lives in Bend, so I could tell him…
26 00:02:47.060 ⇒ 00:02:47.550 Amber Lin: Yeah.
27 00:02:47.550 ⇒ 00:02:51.680 Robert Tseng: you want to ski, and I’m sure he can get you… I’m sure he can get you a pass.
28 00:02:51.680 ⇒ 00:02:54.739 Amber Lin: Wow, that’s so cool.
29 00:02:54.740 ⇒ 00:03:01.480 Robert Tseng: Yeah. Bend is great, but I mean, no worries. If you wanted to stay close, Mount Hood is… Mount Hood is great, too. I’ve skied there.
30 00:03:01.670 ⇒ 00:03:06.310 Amber Lin: I don’t have a car, so I’m trying to figure out how to bus there.
31 00:03:06.310 ⇒ 00:03:16.799 Robert Tseng: Yeah, yeah, you would just… I know there’s, like, a… you’d probably just bus… you would probably have to bus. I’m sure there are ski buses that kind of take you from… from the city up to… up to the slopes.
32 00:03:17.190 ⇒ 00:03:24.779 Amber Lin: There’s… there’s one, ran by the, by Mount Hood, so…
33 00:03:24.780 ⇒ 00:03:25.270 Robert Tseng: Oh, okay.
34 00:03:25.270 ⇒ 00:03:26.300 Amber Lin: Take that one.
35 00:03:26.750 ⇒ 00:03:27.450 Robert Tseng: Nice.
36 00:03:27.450 ⇒ 00:03:30.560 Amber Lin: Yeah, I wish we could do a ski…
37 00:03:30.700 ⇒ 00:03:31.849 Robert Tseng: a ski shirt.
38 00:03:32.190 ⇒ 00:03:35.269 Amber Lin: I know, I miss it a lot. It was so much easier.
39 00:03:35.270 ⇒ 00:03:35.640 Robert Tseng: Yeah.
40 00:03:35.640 ⇒ 00:03:38.210 Amber Lin: Europe, because everything’s just smaller.
41 00:03:38.210 ⇒ 00:03:45.050 Robert Tseng: LA’s great, too. Like, I have a friend who owns an Airbnb in Big Bear, so whenever I go.
42 00:03:45.680 ⇒ 00:03:46.749 Robert Tseng: Big Bear, yeah.
43 00:03:47.020 ⇒ 00:03:48.870 Amber Lin: That’s so cool. Okay. Yeah.
44 00:03:49.290 ⇒ 00:03:53.549 Amber Lin: Okay, I’m excited for you to come here, it’s been a long time.
45 00:03:53.550 ⇒ 00:03:58.280 Robert Tseng: Yeah, I know, too bad you won’t be there for most of it, but we’ll find time, yeah.
46 00:03:58.280 ⇒ 00:03:58.860 Amber Lin: Yeah.
47 00:03:59.120 ⇒ 00:03:59.690 Robert Tseng: Yeah.
48 00:04:01.720 ⇒ 00:04:02.790 Amber Lin: Exciting.
49 00:04:03.040 ⇒ 00:04:05.129 Amber Lin: Let’s see…
50 00:04:05.130 ⇒ 00:04:12.789 Robert Tseng: I don’t think we’ll do anything big planning. I’ll probably just do, yeah, we’ll do a small co-working thing, and then we’ll just, like, do a bar, a bar thing again, so… Okay.
51 00:04:13.290 ⇒ 00:04:19.159 Amber Lin: You can go to Hannah’s, or you can go to, come to my apartment downstairs, like, they have a co-working space.
52 00:04:19.570 ⇒ 00:04:25.159 Robert Tseng: Yeah, well… well, I haven’t gotten the chance to look at it yet, but I’ll probably think about it tomorrow, but
53 00:04:26.350 ⇒ 00:04:28.019 Robert Tseng: But yes, that would be nice.
54 00:04:29.720 ⇒ 00:04:32.680 Amber Lin: Okay, I should stand too. Good reminder.
55 00:04:34.930 ⇒ 00:04:37.480 Robert Tseng: I’m actually sitting on my stool now, so…
56 00:04:37.480 ⇒ 00:04:38.390 Amber Lin: Oh, okay.
57 00:04:38.390 ⇒ 00:04:41.489 Robert Tseng: I feel like I’m tired of standing.
58 00:04:41.490 ⇒ 00:04:48.180 Amber Lin: Yeah. I’ve been trying to walk during, like, walk on the treadmill during a stand-up, and I think that’s.
59 00:04:48.180 ⇒ 00:04:49.280 Robert Tseng: Yeah, you should.
60 00:04:51.280 ⇒ 00:04:54.309 Amber Lin: Working from home gets very depressing after…
61 00:04:54.310 ⇒ 00:04:55.630 Robert Tseng: Oh. I just… Oh, yeah.
62 00:04:55.630 ⇒ 00:05:05.060 Amber Lin: just looking at it, and then I end the day, and I don’t know why I feel so weird, and I realize that I’ve just not been looking at anything else other than…
63 00:05:05.240 ⇒ 00:05:06.079 Amber Lin: A screen.
64 00:05:06.500 ⇒ 00:05:10.700 Amber Lin: That has different things, so I don’t feel like I’m looking at the same thing, but it’s still…
65 00:05:10.970 ⇒ 00:05:13.240 Amber Lin: Like, physically, it’s the same thing.
66 00:05:13.500 ⇒ 00:05:21.100 Robert Tseng: Yeah, no, I have to break up my day. That’s why I… I’m, like, changing environments. I never stay, like, right here all day anymore, just…
67 00:05:21.140 ⇒ 00:05:21.990 Amber Lin: Oh, really?
68 00:05:21.990 ⇒ 00:05:23.209 Robert Tseng: I would be miserable, yeah.
69 00:05:23.210 ⇒ 00:05:30.090 Amber Lin: How do you do it? Like, what… what… where do you… like, what type of stuff do you do? Any recommendations?
70 00:05:30.480 ⇒ 00:05:33.900 Robert Tseng: Oh, well, I guess, like, this morning, I…
71 00:05:34.490 ⇒ 00:05:43.030 Robert Tseng: gymmed really early, and then my gym has a lounge, so I just, like, worked there. Did my morning calls and stuff over there. It’s got good lighting.
72 00:05:43.310 ⇒ 00:05:50.339 Robert Tseng: Then I came back home to eat lunch, and I’ll be here, and I’ll do some stuff here, and then I’m gonna… I mean…
73 00:05:50.500 ⇒ 00:05:53.829 Robert Tseng: But my… my classes started again, so I’m gonna leave.
74 00:05:53.830 ⇒ 00:05:55.419 Amber Lin: When you’re going back to class?
75 00:05:55.680 ⇒ 00:05:56.440 Robert Tseng: Yes, I’m going.
76 00:05:56.440 ⇒ 00:05:58.560 Amber Lin: Exciting! Exciting!
77 00:05:58.560 ⇒ 00:06:00.929 Robert Tseng: Round 2. I may regret this.
78 00:06:01.150 ⇒ 00:06:05.330 Robert Tseng: I may regret this 4 months from now, but, we’ll try again.
79 00:06:05.330 ⇒ 00:06:13.019 Amber Lin: I mean, once you do this 4 months, I think it’s a lot… a lot harder for you to back out, because then you’ve already invested a whole year.
80 00:06:13.370 ⇒ 00:06:20.530 Robert Tseng: Yeah… I don’t know, I don’t really think about it like that. We’ll see. If I don’t get, like…
81 00:06:21.850 ⇒ 00:06:28.299 Robert Tseng: Well, yeah, I guess… well, we’ll just see. I don’t think I have the conviction to be like, I’m gonna finish it yet. We’ll just…
82 00:06:28.580 ⇒ 00:06:31.000 Robert Tseng: I see. Yeah.
83 00:06:32.000 ⇒ 00:06:33.579 Robert Tseng: Cool. Yeah.
84 00:06:33.660 ⇒ 00:06:39.789 Amber Lin: Okay, I mean, my… recent work has just been on ABC, on the ABC.
85 00:06:39.790 ⇒ 00:06:40.170 Robert Tseng: Yeah.
86 00:06:40.170 ⇒ 00:06:48.029 Amber Lin: I have started… I know we want to review that, so I’ll pull it up, but I have started writing,
87 00:06:48.630 ⇒ 00:07:01.720 Amber Lin: playbooks on what… what I did last year, so on what I can, because I know that’s also something we want to do as a company. I remember we were wanting to get Shreya to write playbooks, so…
88 00:07:01.840 ⇒ 00:07:10.830 Amber Lin: I’m hoping to put some time into that outside of the ABC analysis. Would you be able to review them or give some feedback once I have them?
89 00:07:10.830 ⇒ 00:07:11.520 Robert Tseng: Yeah, of course.
90 00:07:11.520 ⇒ 00:07:13.920 Amber Lin: a more structured thing in place. Cool.
91 00:07:13.920 ⇒ 00:07:14.500 Robert Tseng: Sure.
92 00:07:14.660 ⇒ 00:07:15.360 Amber Lin: Okay.
93 00:07:15.860 ⇒ 00:07:22.180 Amber Lin: It’s mostly, like, lifecycle, email campaigns, And that, so hopefully…
94 00:07:22.570 ⇒ 00:07:25.060 Amber Lin: I can write that down.
95 00:07:25.770 ⇒ 00:07:33.879 Amber Lin: I remember you had a… do you have any playbook examples I can follow, or… I know you have a previous website you were writing on.
96 00:07:34.980 ⇒ 00:07:36.460 Robert Tseng: Oh,
97 00:07:37.640 ⇒ 00:07:44.969 Robert Tseng: Yeah, well, I mean, I think that this… okay, I’ll… I still think this is the best playbook I’ve seen so far, that I…
98 00:07:45.980 ⇒ 00:07:50.520 Robert Tseng: wrote previously. But, alright, let me see.
99 00:07:51.640 ⇒ 00:07:54.500 Robert Tseng: There’s, what is this called?
100 00:08:01.040 ⇒ 00:08:05.440 Robert Tseng: I’m sure you could make this better with GPT, but…
101 00:08:07.110 ⇒ 00:08:10.579 Robert Tseng: I guess, like, why I like this is…
102 00:08:11.640 ⇒ 00:08:19.310 Robert Tseng: There’s, like, a pre-checklist, so before these things, like, what’s the diagnostic… what are the diagnostics that you need to look at?
103 00:08:19.670 ⇒ 00:08:27.889 Robert Tseng: And then… I guess this is for AV experimentation specifically, but then it kind of outlines out, like.
104 00:08:28.670 ⇒ 00:08:32.570 Robert Tseng: Yeah, these are all the things you need to do to set up the actual experiment. Oops.
105 00:08:32.809 ⇒ 00:08:50.769 Robert Tseng: And then there’s, like, a live checklist for, like, once it goes on, like, these are the things you need to be monitoring. And then, I guess what’s not included here that… I don’t know why, maybe it’s in a different folder, different file, is, like, the post checklist, too. So, I feel like a good playbook kind of has those elements, where it’s not just, like.
106 00:08:50.770 ⇒ 00:08:54.159 Robert Tseng: Here are the steps to, like, do the thing, but, like.
107 00:08:54.670 ⇒ 00:08:58.170 Robert Tseng: you’re setting the stage for, like, why are we even doing this thing? Like, if it’s…
108 00:08:58.620 ⇒ 00:09:09.539 Robert Tseng: like, yeah, like, sometimes you have to just, like, you have to see, like, what situation is a good fit to actually run this playbook in, so I think that’s why the pre… pre-live post is important. Yeah.
109 00:09:09.540 ⇒ 00:09:15.540 Amber Lin: I see. And how much, in detail, do you go in the steps of how to do things?
110 00:09:15.740 ⇒ 00:09:16.370 Amber Lin: Like, how…
111 00:09:16.370 ⇒ 00:09:26.560 Robert Tseng: I actually think that’s… that’s probably not as important. I think that’s something you can do in the… in, like, a loom, or, like, you know, those are… I think setting the stage is more important.
112 00:09:27.980 ⇒ 00:09:28.620 Robert Tseng: Yeah.
113 00:09:29.050 ⇒ 00:09:42.210 Amber Lin: Of just say… let’s say for a life cycle analysis, or, like, the conversions that we’ve been looking at. So we would essentially just say, you can look at these
114 00:09:42.300 ⇒ 00:09:50.950 Amber Lin: Dimensions, and, these are common factors that people leave out, like, check for these things for accuracy, so just…
115 00:09:51.300 ⇒ 00:09:58.210 Amber Lin: There’s no need to do any SQL or how to… how to write this query, just, like, what to look at.
116 00:09:58.660 ⇒ 00:09:59.470 Amber Lin: Is enough.
117 00:09:59.470 ⇒ 00:10:04.930 Robert Tseng: Yeah, yeah, I don’t think you need to write that level of detail, yeah.
118 00:10:05.130 ⇒ 00:10:05.770 Amber Lin: Okay.
119 00:10:05.770 ⇒ 00:10:06.499 Robert Tseng: That makes me a bit…
120 00:10:06.500 ⇒ 00:10:10.059 Amber Lin: less scary, because I’ve been pushing it off for a long time.
121 00:10:13.980 ⇒ 00:10:15.980 Robert Tseng: They’re… hmm.
122 00:10:17.130 ⇒ 00:10:18.310 Robert Tseng: Boop, boop, boop.
123 00:10:21.210 ⇒ 00:10:22.929 Robert Tseng: I actually have, like, a…
124 00:10:23.720 ⇒ 00:10:28.969 Robert Tseng: Playbook. Okay, that’s just, like, a mental note I’ll make for now. I don’t know where I put it.
125 00:10:29.570 ⇒ 00:10:33.270 Robert Tseng: But if you’re looking for something that’s lifecycle, sorry, like, what is this for exactly?
126 00:10:33.720 ⇒ 00:10:39.909 Robert Tseng: I’m doing one for lifecycle, and another one for campaign, so the stuff we do for some, yeah.
127 00:10:40.920 ⇒ 00:10:41.520 Robert Tseng: Okay.
128 00:10:46.760 ⇒ 00:11:03.530 Robert Tseng: Okay, yeah, well, I’ll send you a couple things on the lifecycle side, at least, and then, hopefully that’ll help you as you’re building that out. I had my… I know that my wife, Rachel, never met with you. I just, like.
129 00:11:03.570 ⇒ 00:11:22.279 Robert Tseng: I’ve just been hounding her, please write your playbook. She, like, changed jobs recently. I was like, before you leave lifecycle, please write everything that you did the past 3 years into a doc, and I wanted at least to hand that off to you. She says she did it, I was checking my email, I still feel like she didn’t actually do an incentive.
130 00:11:23.140 ⇒ 00:11:29.289 Robert Tseng: But that’s okay, she doesn’t work for me, and that’s… she never does what I want her to do.
131 00:11:29.290 ⇒ 00:11:34.860 Amber Lin: I’ve been, I’ve been pleading Nico to do something for 3 weeks now, it’s not, it’s not happening.
132 00:11:34.860 ⇒ 00:11:40.440 Robert Tseng: Yeah, so I… forgive me, I keep promising something for my partner that’s…
133 00:11:40.440 ⇒ 00:11:49.559 Amber Lin: It’s okay. I can’t do anything else. She does not work for you, and she will not work for you. Yeah. So…
134 00:11:49.560 ⇒ 00:11:53.789 Robert Tseng: I’m gonna… I just… I just texted her, asked her if we send it, so…
135 00:11:53.790 ⇒ 00:11:54.140 Amber Lin: Okay.
136 00:11:54.140 ⇒ 00:11:56.500 Robert Tseng: Hopefully she gets it to me soon, I’ll send it to you.
137 00:11:56.500 ⇒ 00:11:57.400 Amber Lin: Cool, okay.
138 00:11:57.580 ⇒ 00:12:04.559 Amber Lin: Yeah, and if not, I’ll try and send her what I have. If she has something to look at, it might be a bit easier.
139 00:12:04.560 ⇒ 00:12:05.880 Robert Tseng: Yeah. Okay.
140 00:12:07.060 ⇒ 00:12:17.179 Amber Lin: Cool. Okay. I have the… revenue analysis for ABC. I’m trying to make one based on what
141 00:12:17.350 ⇒ 00:12:22.590 Amber Lin: This is, this is the one we’re making based on Sauron’s analysis.
142 00:12:22.690 ⇒ 00:12:28.869 Amber Lin: I made it separate so that it’s… Because it’s based on different data, it talks about different things.
143 00:12:29.420 ⇒ 00:12:44.859 Amber Lin: And the data is not… this data’s kind of limited, because I try to do the P&L, and there’s no cost data, and I just… I just push that off, because I think they want more on the revenue side than the margin side. So…
144 00:12:46.740 ⇒ 00:13:00.989 Amber Lin: it’s essentially of… the first question is, how is it distributed across service and branches? And then the second question, I go look into, where has the growth and shrinkage been coming from?
145 00:13:01.870 ⇒ 00:13:05.679 Amber Lin: And these are just… Different ways to slice
146 00:13:06.040 ⇒ 00:13:13.089 Amber Lin: Slice revenue, and this slices it based on services. This slices it based on their different branches.
147 00:13:13.950 ⇒ 00:13:19.869 Amber Lin: And these are just combined slices of per…
148 00:13:20.310 ⇒ 00:13:36.570 Amber Lin: per branch, what are their services? And per service, what are the branches? And I think there’s limited recommendations I can give at this point. It’s more so of, hey, does this look interesting? Here’s what other questions we can look into.
149 00:13:36.900 ⇒ 00:13:56.389 Amber Lin: And then, I mean, the… I think this growth question is what they would be most interested in, because they were asking, hey, why are we not… why are we not growing in a growing industry? And I think they’re specifically referring to, the one from 2023 to 2024, where their growth dropped significantly.
150 00:13:56.590 ⇒ 00:14:01.840 Amber Lin: So, I’m trying to see why it dropped, trying to see, like, what services caused…
151 00:14:02.600 ⇒ 00:14:11.630 Amber Lin: the differences, but again, like, my data is not the most expansive, so I feel like I’m a little bit limited in what I can do there.
152 00:14:12.270 ⇒ 00:14:12.990 Robert Tseng: I see.
153 00:14:15.640 ⇒ 00:14:16.260 Robert Tseng: Okay.
154 00:14:16.610 ⇒ 00:14:25.210 Robert Tseng: Yeah, I actually have not clicked into this. Do you want to share it with me? I can, like, look at it in more detail, and then leave comments. Yeah. I did actually get the playbook from…
155 00:14:25.330 ⇒ 00:14:26.430 Robert Tseng: operational, so I did actually.
156 00:14:26.430 ⇒ 00:14:27.279 Amber Lin: Please share it with you.
157 00:14:27.280 ⇒ 00:14:28.230 Robert Tseng: your email.
158 00:14:28.230 ⇒ 00:14:29.120 Amber Lin: Exciting.
159 00:14:30.230 ⇒ 00:14:36.160 Amber Lin: Awesome. I’ll try to write my outline, and then read Hertz. I think that will be a better learning.
160 00:14:36.160 ⇒ 00:14:38.850 Robert Tseng: Yes, that would be.
161 00:14:38.850 ⇒ 00:14:39.440 Amber Lin: Yeah.
162 00:14:41.370 ⇒ 00:14:48.930 Amber Lin: I don’t think this is the most polished yet, because we’re presenting on Thursday, so I’ve not been polishing it yet.
163 00:14:49.080 ⇒ 00:14:57.679 Amber Lin: But… I’m trying to see it from the client’s perspective.
164 00:14:58.000 ⇒ 00:15:06.519 Amber Lin: what is actually new to them and what’s interesting. I don’t think they’ve ever did the graphs or distribution, so the visuals might be new.
165 00:15:06.810 ⇒ 00:15:10.100 Amber Lin: But I just don’t know how much of…
166 00:15:10.440 ⇒ 00:15:15.099 Amber Lin: How is revenue distributed? Like, what type of thought it was sparking?
167 00:15:16.220 ⇒ 00:15:16.970 Robert Tseng: Yeah.
168 00:15:17.520 ⇒ 00:15:27.539 Robert Tseng: So, okay, my first time reading through this. Okay, first slide, concentrate a small set of core services, top four, yeah, I mean, that’s a great takeaway. That’s always a good, good one to go with when you’re drilling down.
169 00:15:29.140 ⇒ 00:15:35.519 Robert Tseng: HVAC is at 14%, 21 million… Yeah. Interesting.
170 00:15:35.660 ⇒ 00:15:51.720 Amber Lin: And I would combine this with, Clarence’s market research data. Something that struck me in his research is that HVAC is a lot bigger than GrizzPest, so… Totally. I would combine that and say, hey, your HVAC is…
171 00:15:51.990 ⇒ 00:15:56.389 Amber Lin: is… has a lot of potential, is what I’ll say based on that slide.
172 00:15:56.390 ⇒ 00:15:57.030 Robert Tseng: Yeah.
173 00:15:57.600 ⇒ 00:15:58.370 Robert Tseng: Okay.
174 00:15:58.920 ⇒ 00:16:02.960 Robert Tseng: Yeah, get the revenue distribution across…
175 00:16:03.150 ⇒ 00:16:08.750 Robert Tseng: the different cities, yeah, I think noticing San Antonio is a big, big gap there.
176 00:16:08.880 ⇒ 00:16:17.130 Robert Tseng: Yeah, I think, yeah, the TAM, the TAM would be helpful for that.
177 00:16:21.980 ⇒ 00:16:23.900 Robert Tseng: Commercial pests is…
178 00:16:28.590 ⇒ 00:16:33.349 Robert Tseng: Oh, I see. More mature markets, bigger, bigger portion, so…
179 00:16:33.540 ⇒ 00:16:38.000 Robert Tseng: you would say that would be Rio Grande, Waco.
180 00:16:38.930 ⇒ 00:16:40.190 Amber Lin: Like, they’re… they’re new markets.
181 00:16:40.190 ⇒ 00:16:40.590 Robert Tseng: Yeah.
182 00:16:40.590 ⇒ 00:16:49.879 Amber Lin: have large commercial pests. I just think it’s only because commercial pests is a bigger deal, and because the other service are… are immature.
183 00:16:50.330 ⇒ 00:16:58.319 Robert Tseng: Yeah, I guess, I don’t know if we can see things, like, yeah, we see, like, the overall, like, revenue buckets, but, like, even getting, like, a cost to serve…
184 00:16:58.490 ⇒ 00:17:01.179 Robert Tseng: Kind of, like, view, so…
185 00:17:01.870 ⇒ 00:17:05.920 Robert Tseng: Are these services just more expensive? I mean, I would guess that…
186 00:17:07.380 ⇒ 00:17:14.800 Robert Tseng: actually, I don’t know. Is residential pests more… I mean, I’m assuming it’s more expensive than lawn care, so, like, maybe they are, like…
187 00:17:14.990 ⇒ 00:17:22.160 Robert Tseng: you know, there’s 100… It could be, like, 100 lawn mowing is equal to 10, like.
188 00:17:22.420 ⇒ 00:17:27.989 Robert Tseng: residential pests, like, I’m not sure, like, I think there’s something about, on the supply side, too, like.
189 00:17:27.990 ⇒ 00:17:29.130 Amber Lin: I see.
190 00:17:30.010 ⇒ 00:17:38.410 Robert Tseng: yeah, like, what that distribution looks like. So, I would come up with a metric that’s something along the lines of, like.
191 00:17:38.720 ⇒ 00:17:47.059 Robert Tseng: like, yeah, revenue over, like, the quantity, which… I don’t know if quantity is… there’s no… I have to figure out what the unit is.
192 00:17:48.040 ⇒ 00:17:52.980 Robert Tseng: it beats the number of services, like, I don’t know how you want to normalize it. I think, intuitively, it feels like
193 00:17:54.740 ⇒ 00:17:56.959 Robert Tseng: I mean, I don’t know, by a pool…
194 00:17:57.260 ⇒ 00:18:13.680 Robert Tseng: if residential pass is, like, a single service takes 3 hours, that’s not the same as, like, you know, holiday lights, which may take 30 minutes, like, I don’t know, like, so there needs to be some, like, way of, like, abstracting down to, like, comparing, like, what the cost of service is per…
195 00:18:13.680 ⇒ 00:18:17.680 Robert Tseng: per… per… per service line, but I think that would be an interesting…
196 00:18:18.020 ⇒ 00:18:21.750 Robert Tseng: Yeah, just, yeah, that’ll help you to calculate profitability as well, just like.
197 00:18:21.750 ⇒ 00:18:22.240 Amber Lin: Yeah.
198 00:18:22.240 ⇒ 00:18:24.019 Robert Tseng: Right? Yeah. Yeah. So…
199 00:18:24.470 ⇒ 00:18:29.470 Amber Lin: I think that would be helpful, because I couldn’t figure out what I’m really…
200 00:18:29.830 ⇒ 00:18:48.900 Amber Lin: trying to answer for them, like, I think I’m trying to answer what service they should focus on, and that would combine with Zoran’s analysis, like, hey, you can market these services, but I know we’re not focusing too much on service delivery, rather than just top of funnel and then bottom retention.
201 00:18:50.570 ⇒ 00:19:02.169 Robert Tseng: Yeah, I mean, from his perspective on the marketing side, it is he’s just trying to grow the share, but I think it is important, like, I think the best… when you have a port… doing a easy, like, revenue-based,
202 00:19:03.040 ⇒ 00:19:09.159 Robert Tseng: analysis for the marketing side, it’s LTV CAC, so, like, some version of that, which in…
203 00:19:09.270 ⇒ 00:19:15.529 Robert Tseng: Yeah, what’s, like, the… like, the total revenue for a particular customer in this service line over…
204 00:19:15.870 ⇒ 00:19:16.669 Robert Tseng: kind of liked…
205 00:19:16.690 ⇒ 00:19:22.169 Robert Tseng: Is that… whether it’s cost to acquire, cost to serve, like, some iteration of that to basically have the…
206 00:19:22.170 ⇒ 00:19:38.840 Robert Tseng: Yeah, like, that ratio is a profitability ratio that helps a marketing team clearly see, if I put my dollars into this service line, I’m gonna get a bigger return. So I think that’s a very important, you know, if we have the ability to do that, that’s a very important thing to…
207 00:19:38.840 ⇒ 00:19:46.730 Amber Lin: I got the cost data from UTAM, I need to make sure that it actually has the service lines.
208 00:19:46.730 ⇒ 00:19:47.290 Robert Tseng: Yeah.
209 00:19:47.800 ⇒ 00:19:49.870 Amber Lin: Like, I can… I can check.
210 00:19:50.370 ⇒ 00:19:50.960 Robert Tseng: Okay.
211 00:19:54.280 ⇒ 00:19:54.815 Amber Lin: Hmm…
212 00:20:00.690 ⇒ 00:20:06.410 Amber Lin: No, it has, by branch.
213 00:20:07.560 ⇒ 00:20:08.380 Amber Lin: So…
214 00:20:09.120 ⇒ 00:20:09.690 Robert Tseng: Okay.
215 00:20:09.960 ⇒ 00:20:11.829 Amber Lin: Yeah, it might not be the best.
216 00:20:15.860 ⇒ 00:20:24.610 Robert Tseng: Yeah, and if you don’t know what these assumptions are, I think you should just use AI to help you, like, come up with some estimates,
217 00:20:25.340 ⇒ 00:20:31.050 Robert Tseng: Yeah, and if your assumptions are wrong, like, I think that’s fine. You just… you just… we just call it out, right? So…
218 00:20:31.050 ⇒ 00:20:31.490 Amber Lin: Cool.
219 00:20:31.490 ⇒ 00:20:35.999 Robert Tseng: I could see you making a slide that’s kind of like a…
220 00:20:36.840 ⇒ 00:20:52.569 Robert Tseng: this is how we’re calculating an LTV over CAC, or LTV over cost kind of metric for, like, ABC specifically. Assumption 1, LTV is equal… is basically… we’re defining it as revenue per customer in the service line over a period of time, because maybe
221 00:20:52.780 ⇒ 00:20:54.479 Robert Tseng: You know, like, after…
222 00:20:55.010 ⇒ 00:21:05.970 Robert Tseng: if some of these services, like, they’re… maybe we have to annualize it, maybe you have to… like, I’m not really sure what the frequency of the transaction is, like, maybe it doesn’t really make sense to be treating
223 00:21:05.970 ⇒ 00:21:22.730 Robert Tseng: like, somebody who only gets a home service, like, once every four years as, like, a single customer. Those should just be, like, multiple customers. So, I think there’s just some, like, some light metric definition that you should think about there, so you’re kind of, yeah, like, part… like, I can imagine the left side of the slide talking about the revenue.
224 00:21:22.730 ⇒ 00:21:34.159 Robert Tseng: piece, then yeah, making some cost assumptions here. It’s like, cost to us is there’s the cost to acquire, which we get from the marketing dollars, which maybe that’s not the best view only, because they don’t spend that much on paid marketing.
225 00:21:34.180 ⇒ 00:21:37.829 Robert Tseng: So, cost to acquire, you have to also kind of factor in
226 00:21:37.920 ⇒ 00:21:47.789 Robert Tseng: It’s, like, cost to acquire plus cost to serve, whatever, to get, like, some just baseline, like, COGS metric, right? And then the bottom of the slide, you’re saying.
227 00:21:47.790 ⇒ 00:21:58.259 Robert Tseng: this is what the equation is, and, you know, when we look at it this way, these are your most profitable services. And I think that’s, like, a… that’d be an interesting slide to walk them through, so…
228 00:21:59.480 ⇒ 00:22:16.259 Robert Tseng: Yeah, if you need, like, an example of what that slide could look like, I feel like I’ve sent you some stuff before on, like, slides I built at Ruggable, where, like, I didn’t actually know how to market size the opportunity, but I… I just… and I… so I needed to spend extra time, like, kind of laying out, like, how I… how I did this calculation.
229 00:22:16.400 ⇒ 00:22:22.720 Robert Tseng: And yeah, it was, like, helpful for the C-suite to, like, understand, like, okay,
230 00:22:22.990 ⇒ 00:22:27.400 Robert Tseng: This is how you’re thinking about premium, like, premium-priced rugs versus, like.
231 00:22:27.750 ⇒ 00:22:42.430 Robert Tseng: lower price rugs, like, we’re okay with this assumption. Like, they just needed to see the methodology up front and be okay with it before I went into the actual calculations. So, that’s just probably what I would recommend, kind of just adding that context as well.
232 00:22:42.740 ⇒ 00:22:51.209 Amber Lin: That’s helpful, because right now, like, I’m just talking about, oh, you’re concentrated here, you’re not concentrated there, like, you take up more revenue there, I don’t think that’s the…
233 00:22:51.580 ⇒ 00:23:02.050 Amber Lin: like, that’s not a great basis to make recommendations, so I’ll take Zoran’s data, I’ll take, what I can get, and I’ll make the assumptions for, like, a…
234 00:23:02.050 ⇒ 00:23:02.610 Robert Tseng: Yeah.
235 00:23:03.030 ⇒ 00:23:04.980 Amber Lin: A profit slide.
236 00:23:05.520 ⇒ 00:23:17.430 Robert Tseng: Yeah. I think what would be a successful con… is you’re just trying to create conversation, right? So, like, you don’t have to… your definitions don’t have to be correct. I think they should just… they should be directionally accurate, and let them be like.
237 00:23:17.430 ⇒ 00:23:26.269 Robert Tseng: No, actually, we would think about it this way, though. That’s a productive conversation for each topic, right? And so… I see. Yeah, don’t worry too much about not having, like, it all…
238 00:23:26.950 ⇒ 00:23:40.160 Robert Tseng: like, I’ll… like, ready to go. I think the purpose of these slides is for discussion, and so I think as long as you can kind of create an angle where you can have a productive conversation, I would say that’s, like, that’s a good… that’s a good deck.
239 00:23:40.160 ⇒ 00:23:51.539 Amber Lin: I see. Cool. Yeah. So I’ll… I think I’ll leave the growth slide as is, mostly just so they can talk about, hey, like, what… why has the top line growth
240 00:23:51.600 ⇒ 00:24:00.610 Amber Lin: decline… less services growing, and more services are in decline, so I’ll… Yeah. I’ll talk to them about that.
241 00:24:00.720 ⇒ 00:24:02.900 Amber Lin: That’s, like…
242 00:24:02.970 ⇒ 00:24:11.379 Robert Tseng: it could be just COVID numbers, and then they can talk about, like, what was happening before, and such. Yeah, I’m only seeing 2024 in your numbers.
243 00:24:11.380 ⇒ 00:24:16.929 Amber Lin: 2025, they only gave me data up until October, so I, like, I…
244 00:24:16.930 ⇒ 00:24:21.060 Robert Tseng: Do you have anything going back, like, 3 years, year over year? Like, you know, I’m just…
245 00:24:21.060 ⇒ 00:24:29.409 Amber Lin: Yeah, if you look at slide 8, I added in, going back until 2012, I didn’t add, like.
246 00:24:29.410 ⇒ 00:24:30.360 Robert Tseng: Oh, I see.
247 00:24:30.360 ⇒ 00:24:34.850 Amber Lin: We have 2,000 data, but I just don’t think it… it matters as much.
248 00:24:35.310 ⇒ 00:24:50.449 Robert Tseng: Yeah, 5 years is all that really matters. Yeah. So, like, I think, like, what I would do here is, you know that these are the top… you’re saying top 4 services, right? You take these top four, you look at them over the past 5 years, and then you basically recreate this slide.
249 00:24:50.710 ⇒ 00:25:01.769 Robert Tseng: Right? And you can see, like, has home services been consistently at the top? Or, like, is it, like, over the past few years, you see, like, some trend where one is growing faster than the other, right?
250 00:25:03.540 ⇒ 00:25:21.500 Amber Lin: I actually looked at it, and the distribution of the services is honestly, the top two is very… there’s always consistently residential and then HVAC, and then the… the, like, the third to fifth alternate a little bit up and down, but it’s always been pretty much the same.
251 00:25:22.740 ⇒ 00:25:28.290 Robert Tseng: Okay, I mean, I see you’re trying to do that here, so I can kind of get that.
252 00:25:32.450 ⇒ 00:25:36.060 Robert Tseng: Not super clear to see, but…
253 00:25:36.060 ⇒ 00:25:42.139 Amber Lin: The slide 9, I couldn’t find the best way to illustrate it. It’s more of, like, what services
254 00:25:42.270 ⇒ 00:25:47.219 Amber Lin: Grew in that year, and what services declined, and what is the…
255 00:25:47.330 ⇒ 00:25:50.999 Amber Lin: like, how much do they grow or decline? So it’s…
256 00:25:51.000 ⇒ 00:26:01.610 Robert Tseng: I would filter it out by only, like, kind of the top growth rates. So, like, instead of, like, having to show all of them, so you’re, like, these are the top 5 services in terms of, like.
257 00:26:02.630 ⇒ 00:26:05.109 Robert Tseng: re-revenue sh… like,
258 00:26:06.420 ⇒ 00:26:14.690 Robert Tseng: like, I think it’s, like, the full bar and the sum of the growth makes sense. It should tie out to $12.6 million. I think that makes sense to me.
259 00:26:14.850 ⇒ 00:26:20.739 Robert Tseng: yeah, you can gray out everything that’s other, but then I would say maybe just look… I mean, I guess you kind of, like.
260 00:26:20.850 ⇒ 00:26:23.359 Robert Tseng: You just keep it consistent with…
261 00:26:24.630 ⇒ 00:26:27.639 Robert Tseng: Like, I… yeah, like, I would do, like, the top…
262 00:26:27.760 ⇒ 00:26:33.979 Robert Tseng: Top 3, or top… yeah, probably top 3 is probably the easiest to digest. Top 3 or 4, since…
263 00:26:33.980 ⇒ 00:26:36.810 Amber Lin: I have the top 5 right now, and it’s just a lot.
264 00:26:37.100 ⇒ 00:26:38.190 Amber Lin: 2C.
265 00:26:39.000 ⇒ 00:26:44.479 Robert Tseng: Yeah, like, this one… I mean, I have to go and I have to map out all the ones, so ideally, it’d be, like.
266 00:26:44.980 ⇒ 00:26:51.489 Robert Tseng: ideally, it’s just 3 colors that are consistent across the entire chart, and I’m like, okay, so, like, my biggest…
267 00:26:51.630 ⇒ 00:26:54.610 Robert Tseng: growth has been HVAC year over year.
268 00:26:55.340 ⇒ 00:27:00.039 Robert Tseng: biggest decline year-over-year has been termite, or whatever, whatever it is.
269 00:27:00.040 ⇒ 00:27:00.640 Amber Lin: Yeah.
270 00:27:01.140 ⇒ 00:27:01.690 Robert Tseng: Yeah.
271 00:27:02.030 ⇒ 00:27:03.929 Amber Lin: And they’re not really consistent, which.
272 00:27:03.930 ⇒ 00:27:08.060 Robert Tseng: Yeah, I can tell that it’s not really the same year over year. So…
273 00:27:08.950 ⇒ 00:27:11.920 Robert Tseng: Yeah, how do we make this a little bit clearer?
274 00:27:12.410 ⇒ 00:27:28.400 Robert Tseng: Well, I mean, if you just kind of simplify and just do the top three for each section, I do think that’ll look a bit cleaner. You’ll see the red here, the gray-pink, and then the rest will be kind of closed, and then the back half will be a bit cleaner, too, because you’ll see just, like, a few colors only.
275 00:27:28.700 ⇒ 00:27:29.869 Amber Lin: Yeah, I agree.
276 00:27:30.260 ⇒ 00:27:30.760 Robert Tseng: Okay.
277 00:27:30.760 ⇒ 00:27:31.340 Amber Lin: Cool.
278 00:27:32.450 ⇒ 00:27:35.510 Amber Lin: Okay, and then I have,
279 00:27:35.690 ⇒ 00:27:43.749 Amber Lin: We’re working on Zoran’s slide as well. It’s not that ready for review, but… I can…
280 00:27:43.950 ⇒ 00:27:49.109 Amber Lin: Send you the slide if you have, if you want to take a look at it later.
281 00:27:49.990 ⇒ 00:27:50.580 Robert Tseng: Yeah.
282 00:27:50.950 ⇒ 00:27:51.790 Robert Tseng: Sure.
283 00:27:51.790 ⇒ 00:28:09.260 Amber Lin: Yeah, we’re mostly talking about, one, how the different channels are performing, and two, the conversion funnel, which we… I think we only have one, like, click-to-buy, and he had a really good insight there. Like, one of the… the drop-off rates for click-to-buy, it’s really high, so…
284 00:28:09.480 ⇒ 00:28:10.100 Robert Tseng: Yeah.
285 00:28:10.100 ⇒ 00:28:13.760 Amber Lin: That’s… that would be the main center point that we’ll be talking about.
286 00:28:14.300 ⇒ 00:28:14.900 Robert Tseng: Yeah.
287 00:28:15.920 ⇒ 00:28:23.640 Robert Tseng: A couple more things before I jump to the next call. Just be like, yeah, as you’re putting together these slides, I mean, I’m sure you’re just getting faster at it, but…
288 00:28:23.720 ⇒ 00:28:37.999 Robert Tseng: Yeah, I think for me, what helps is, like, yeah, doing… going from an outline. I will, like, pre-write the headlines of the slides first, but sometimes I’ll just, like, mock up, like, what I… what I want, on the slides, and then I… then I go and build.
289 00:28:39.660 ⇒ 00:28:47.429 Robert Tseng: that sometimes helps me to stay… to keep the narrative a little bit more focused when I’m going through it.
290 00:28:47.630 ⇒ 00:28:51.519 Amber Lin: Do you build the headlines before you do the actual analysis?
291 00:28:51.920 ⇒ 00:28:58.010 Robert Tseng: Well, yeah, so if I… I think it’s like, if, you know, if I were to take this question, answer this question.
292 00:28:58.280 ⇒ 00:29:11.390 Robert Tseng: maybe I’d be like, this is my hypothesis, and then some assumptions I’m making, or, like, kind of the why for, like, why we’re digging into it. And then, like, you know, there’s maybe… I know what types of charts I’m going to be looking at, and…
293 00:29:11.560 ⇒ 00:29:23.699 Robert Tseng: Yeah, so I’ll just kind of, like, pre-write, what I think I’m going to see, if I already have direction. If I don’t really know, then I guess I don’t really write as much. But that just, like, helps me to…
294 00:29:24.950 ⇒ 00:29:27.939 Robert Tseng: Like, yeah, the narrative should be, like.
295 00:29:28.210 ⇒ 00:29:34.620 Robert Tseng: reading the question, I’m understanding how you’re approaching it, what your hypothesis is, and then I, as the reader, I’m asking, like.
296 00:29:34.700 ⇒ 00:29:45.070 Robert Tseng: the why questions. I’m answering… I’m asking why a few times, and each time I look at the next slide, it should answer the why a little bit more for me, so that by the end, I’m like, okay, I think
297 00:29:45.070 ⇒ 00:29:55.250 Robert Tseng: I understand, like, and now there’s a recommendation, like, but yeah, so that’s, you know, that’s kind of why I do it that way. Otherwise, sometimes you’re looking at
298 00:29:56.550 ⇒ 00:30:02.550 Robert Tseng: The same thing from many different angles, but it doesn’t really push you any, like, deeper.
299 00:30:03.050 ⇒ 00:30:05.530 Robert Tseng: Yeah, question. Yeah, I agree. If that makes sense. Yeah.
300 00:30:08.300 ⇒ 00:30:17.229 Amber Lin: Yeah, that’s really helpful. I realized when I was making Zoran’s slide, it was so much faster, because I could… I have the, like, his…
301 00:30:17.430 ⇒ 00:30:21.840 Amber Lin: his outline, essentially, and it just, like, it took 20 minutes.
302 00:30:21.840 ⇒ 00:30:22.380 Robert Tseng: Yeah.
303 00:30:22.600 ⇒ 00:30:24.610 Amber Lin: Instead of a very long time.
304 00:30:25.010 ⇒ 00:30:25.610 Robert Tseng: Yeah.
305 00:30:26.390 ⇒ 00:30:36.090 Robert Tseng: Because otherwise, sometimes you’re just, like, looking at random things, and you’re trying to, like, force a story, and then it’s hard to, like, kind of throw these individual slides up, and then try to stitch them together in the aftermath, right? So…
306 00:30:36.090 ⇒ 00:30:36.720 Amber Lin: Yeah.
307 00:30:36.720 ⇒ 00:30:37.499 Robert Tseng: That’s a good point.
308 00:30:38.140 ⇒ 00:30:49.340 Robert Tseng: Yeah, and then anything that’s, like, an interesting find that you’re like, oh, I don’t really know how to fit it in, I would just throw the append the appendix. The appendix doesn’t need to be, like, a story… doesn’t have to fit the story, but it could be.
309 00:30:49.740 ⇒ 00:30:56.450 Robert Tseng: a useful thing to have, for you to dig deeper into later on. So I’m not saying that you should kind of overrule that, yeah.
310 00:30:56.930 ⇒ 00:30:57.910 Amber Lin: Cool, okay.
311 00:30:58.880 ⇒ 00:31:04.390 Amber Lin: Exciting, and I think I got the onboarding playbook that you shared with me.
312 00:31:04.830 ⇒ 00:31:10.950 Robert Tseng: Cool, alright, well, I haven’t actually looked at it, hopefully it’s helpful as you’re kind of doing it, but yeah.
313 00:31:12.180 ⇒ 00:31:15.550 Amber Lin: All right, thank you so much. It’s really helpful. Bye!
314 00:31:15.550 ⇒ 00:31:16.070 Robert Tseng: Yep.