Meeting Title: Brainforge <> ABC Discovery Discussion Date: 2026-01-16 Meeting participants: MattBurns, read.ai meeting notes, Matt’s Notetaker (Otter.ai), Julie F, Uttam Kumaran, Steven, Amber Lin
WEBVTT
1 00:01:17.030 ⇒ 00:01:17.870 Uttam Kumaran: Hey, Matt.
2 00:01:18.550 ⇒ 00:01:19.830 MattBurns: Who, Tom, how are you?
3 00:01:20.080 ⇒ 00:01:21.180 Uttam Kumaran: Good, how are you?
4 00:01:21.470 ⇒ 00:01:23.340 MattBurns: Doing fine. Doing fine.
5 00:01:24.520 ⇒ 00:01:26.210 Uttam Kumaran: How’s, how’s the month been?
6 00:01:26.970 ⇒ 00:01:30.049 MattBurns: You know, solid so far. Yeah, we’re…
7 00:01:30.810 ⇒ 00:01:35.389 MattBurns: We’re doing a pretty good start to January, a good start to the year, so
8 00:01:36.060 ⇒ 00:01:39.330 MattBurns: All good. We were actually up in Dallas on Monday, Tuesday.
9 00:01:39.960 ⇒ 00:01:46.540 MattBurns: Kind of looking at, the dream system overall, you know, that’s what Dallas uses, and…
10 00:01:47.440 ⇒ 00:01:52.559 MattBurns: Nitesh and his team were up there, so we had some good conversations with them, so…
11 00:01:52.560 ⇒ 00:01:53.250 Uttam Kumaran: Awesome.
12 00:01:54.380 ⇒ 00:02:02.900 Uttam Kumaran: Yeah, I think you guys will be excited to see some of the stuff we have prepared. We’ve been, really diligently working on,
13 00:02:03.210 ⇒ 00:02:07.640 Uttam Kumaran: sharing out, you know, stuff that we’ve discovered from Evolve.
14 00:02:07.760 ⇒ 00:02:17.970 Uttam Kumaran: stuff that we’ve discovered from Google Analytics and the awareness conversion side, as well as, Steven, some updates to our competitive, sort of, industry deck.
15 00:02:18.180 ⇒ 00:02:19.130 Uttam Kumaran: Boom.
16 00:02:19.300 ⇒ 00:02:26.039 Uttam Kumaran: I know we have 2 hours blocked here, if we end up… if we end up like it’s too much, we can cut at any time, but…
17 00:02:26.250 ⇒ 00:02:28.839 Uttam Kumaran: I feel like it’s gonna be action-packed, so…
18 00:02:28.960 ⇒ 00:02:40.330 Uttam Kumaran: like, I’m expecting us to be able to leverage as much of your time as possible to kind of share some of these, but, yeah, I’m pumped. And hey, Julie, I didn’t say hi. Hope you’re doing well.
19 00:02:43.670 ⇒ 00:02:47.340 Julie F: Yo, Tom, I’m here, sort of.
20 00:02:47.340 ⇒ 00:02:49.429 MattBurns: She’s a little under the weather, Tom.
21 00:02:49.430 ⇒ 00:02:50.979 Uttam Kumaran: Oh, I’m sorry, no.
22 00:02:50.980 ⇒ 00:02:52.930 Steven: We won’t make you talk too much.
23 00:02:52.930 ⇒ 00:02:55.120 Uttam Kumaran: No, no, no, yeah.
24 00:02:55.640 ⇒ 00:02:59.459 Uttam Kumaran: Totally happy to chat in the Zoom chat.
25 00:03:00.650 ⇒ 00:03:01.460 MattBurns: Hi, Amber.
26 00:03:03.130 ⇒ 00:03:07.250 Steven: Hey, yeah, what’d y’all do in all… what’d y’all do in Dallas? I heard Yvette’s sick also.
27 00:03:07.610 ⇒ 00:03:12.479 MattBurns: Well, and Tim’s got the flu, and he didn’t go to Dallas, but we were in
28 00:03:12.620 ⇒ 00:03:15.220 MattBurns: College Station on Wednesday night.
29 00:03:15.390 ⇒ 00:03:19.979 MattBurns: He was running the, you know, the voting tabs and everything, and…
30 00:03:21.020 ⇒ 00:03:26.750 MattBurns: Yeah, he left there and cratered as soon as he got back to his house, and… but…
31 00:03:27.050 ⇒ 00:03:32.070 MattBurns: Bobby Bowe and I are fine, so I don’t know… I don’t know what else happened, but anyway.
32 00:03:34.430 ⇒ 00:03:38.299 Amber Lin: I’ve just recovered from the flu, and then this is my…
33 00:03:38.720 ⇒ 00:03:43.299 Amber Lin: paper roll, it… it has… it has came realistic to this.
34 00:03:43.310 ⇒ 00:03:44.880 MattBurns: Oh, wow.
35 00:03:45.060 ⇒ 00:03:50.719 Julie F: We’ve, it’s been going around my team, we’re all taking turns, and I thought I… I thought I dodged it, but…
36 00:03:50.890 ⇒ 00:03:52.100 Steven: Apparently not.
37 00:03:54.070 ⇒ 00:03:54.900 MattBurns: Man…
38 00:03:55.800 ⇒ 00:03:59.320 Steven: I’m glad we’re all in our own space here.
39 00:03:59.320 ⇒ 00:04:04.380 Uttam Kumaran: Half of our, like, team and, like, clients are… everyone’s, like, sick.
40 00:04:04.500 ⇒ 00:04:05.750 MattBurns: Oh…
41 00:04:05.940 ⇒ 00:04:09.389 Uttam Kumaran: I feel fine, I feel fine, I don’t even have allergies, and I know it’s, like, cedar…
42 00:04:09.620 ⇒ 00:04:11.780 MattBurns: Beaver season here in Austin, but…
43 00:04:12.550 ⇒ 00:04:15.040 MattBurns: I’m lucky they don’t bother me either, so…
44 00:04:15.040 ⇒ 00:04:16.329 Uttam Kumaran: Yeah, so…
45 00:04:16.769 ⇒ 00:04:24.189 Uttam Kumaran: All right, Amber, I can let you take it away. I think we have a brief agenda. I sort of told the team, like, kind of, like, what we wanted to start with, so…
46 00:04:24.740 ⇒ 00:04:34.599 Uttam Kumaran: Yeah, I would love for you to… Steven kind of got a sense of, like, how we kind of want to present some things, but I… maybe we can go through… Amber, you can take the lead.
47 00:04:35.070 ⇒ 00:04:51.429 Amber Lin: Yeah, we’re thinking of starting with the sales data that, Julie, you gave us, and then we’ll take a look at marketing. We were able to look at Google Analytics, and then, if we still have time, we have some follow-ups from last week.
48 00:04:51.510 ⇒ 00:04:57.860 Amber Lin: So, let me share screen, and then… Get started.
49 00:05:02.250 ⇒ 00:05:03.400 Amber Lin: Alright.
50 00:05:06.020 ⇒ 00:05:15.009 Amber Lin: So… When I’m looking at the sales data, the question that I’m really trying to answer is,
51 00:05:15.530 ⇒ 00:05:18.569 Amber Lin: Where should we be investing in?
52 00:05:18.590 ⇒ 00:05:28.090 Amber Lin: for, for the best returns. So, for the services, it’s what service should we be investing in, and how
53 00:05:28.090 ⇒ 00:05:39.579 Amber Lin: should we think about, growth for the services? And then later we can look at the branches to say, okay, how or what branch are we going to invest in?
54 00:05:40.380 ⇒ 00:05:43.479 Amber Lin: And so, let’s start off,
55 00:05:43.990 ⇒ 00:05:58.600 Amber Lin: Let me do this. Let’s start off by just looking at revenue distribution. I used the 2024 data, because I think we haven’t finished, the final months for 2025, so…
56 00:05:59.300 ⇒ 00:06:18.420 Amber Lin: looking at here, I think we can see that the top, the revenue is very concentrated in a small set of our core services. So, our top 4 services takes up about 50%, and you can see here, residential pest and HVAC are the biggest ones. And…
57 00:06:19.530 ⇒ 00:06:32.579 Amber Lin: I think, something interesting here is that, HVAC is the second biggest service, and it’s almost as, big as residential pest. And so.
58 00:06:33.640 ⇒ 00:06:38.470 Amber Lin: A question that naturally came up to me is, like, okay, we have…
59 00:06:38.540 ⇒ 00:06:46.340 Amber Lin: some services that’s really, takes a lot of revenue, it’s our core services. So…
60 00:06:46.340 ⇒ 00:07:01.330 Amber Lin: Or are they anywhere near saturation if they have become such a big service for us? And if we were wanting to invest, are we going to invest in these core services, or are we going to invest in those smaller,
61 00:07:01.400 ⇒ 00:07:04.960 Amber Lin: Long-tail services, as we see here.
62 00:07:05.540 ⇒ 00:07:10.130 Amber Lin: So, that’s the… that’s the question that came to my mind.
63 00:07:10.660 ⇒ 00:07:22.170 Amber Lin: And I’m gonna pull up here, this is the market research that, we did on, okay, how big, actually, is the market in Texas? Because,
64 00:07:22.330 ⇒ 00:07:29.500 Amber Lin: That’s the main market that we’re serving, and it has its own geographic, unique attributes that
65 00:07:30.080 ⇒ 00:07:39.420 Amber Lin: cause the service to be different. And comparing this, to what we have Hmm.
66 00:07:39.550 ⇒ 00:07:46.000 Amber Lin: Over here, I essentially want to ask, how far are we in,
67 00:07:46.370 ⇒ 00:07:58.360 Amber Lin: in saying that this service is totally mature, how big of a room of growth is there? And I think something surprising we can find is that, okay, HVAC is 6 times more as
68 00:07:58.360 ⇒ 00:08:07.580 Amber Lin: residential pests, which is our biggest service. And I think, unexpectedly, landscaping, plumbing, and handyman services are also very big.
69 00:08:07.590 ⇒ 00:08:23.529 Amber Lin: And if you look at our current distribution, so the second, our third biggest service is lawn mowing and then commercial pests, and then we do have plumbing and landscaping up there in the list. So.
70 00:08:23.530 ⇒ 00:08:28.590 Steven: Real quick, Amber, sorry to interrupt, so that… can you go to the next slide again, real quick?
71 00:08:28.590 ⇒ 00:08:28.910 Amber Lin: Yeah.
72 00:08:29.060 ⇒ 00:08:32.919 Steven: So that is just in… that’s all of Texas, right?
73 00:08:33.240 ⇒ 00:08:34.109 Amber Lin: Yes, totally.
74 00:08:34.110 ⇒ 00:08:34.780 Steven: Of course I am.
75 00:08:34.789 ⇒ 00:08:41.599 Amber Lin: I know we only cover some of the areas, but I think it’s just a general sense of what’s big, what’s smaller.
76 00:08:41.960 ⇒ 00:08:52.620 Steven: For sure. I assume, like, HVAC, that would include… that’s not split out residential versus commercial, right? That would include all HVAC, so yeah, I’d be curious. I mean, HVAC’s obviously huge in commercial as well.
77 00:08:53.610 ⇒ 00:08:58.720 Steven: Because we don’t do commercial HVAC, so that would skew that a little bit.
78 00:08:59.960 ⇒ 00:09:04.820 Amber Lin: I see. Is it because commercial needs a lot more regulation? Or, like…
79 00:09:04.820 ⇒ 00:09:07.509 Steven: Just way bigger units, it’s a totally different…
80 00:09:07.510 ⇒ 00:09:08.040 Amber Lin: I see.
81 00:09:08.730 ⇒ 00:09:12.449 MattBurns: Dealing with different equipment. Just another comment, too, on.
82 00:09:13.720 ⇒ 00:09:17.009 MattBurns: You know, the pest control numbers, if you’re gonna
83 00:09:19.680 ⇒ 00:09:23.230 MattBurns: You know, from a… from a margin standpoint.
84 00:09:23.580 ⇒ 00:09:30.559 MattBurns: pest control is the most profitable in terms of percentage margin, because HVAC, landscaping.
85 00:09:30.750 ⇒ 00:09:37.050 MattBurns: Even plumbing and handyman, to a certain extent, you’re typically talking about a lot of materials and supplies.
86 00:09:37.350 ⇒ 00:09:41.570 MattBurns: expanse versus pest control, where the M&S is pretty minimal.
87 00:09:41.960 ⇒ 00:09:47.230 MattBurns: So… one of the things, and I know you had asked a question.
88 00:09:48.890 ⇒ 00:09:54.630 MattBurns: relating a little bit to that, and Steven, you were right, I think sending the margin reports
89 00:09:55.090 ⇒ 00:10:00.200 MattBurns: Would be helpful, because… What we do, guys, is,
90 00:10:00.810 ⇒ 00:10:06.030 MattBurns: We really do run a margin report on all the different services, because
91 00:10:06.680 ⇒ 00:10:12.149 MattBurns: Again, it’s not just the raw dollars, it’s, okay, is that service making money for me?
92 00:10:12.370 ⇒ 00:10:16.149 MattBurns: You know, as a… as a… Profit line, so to speak.
93 00:10:16.150 ⇒ 00:10:16.730 Uttam Kumaran: Yeah.
94 00:10:16.830 ⇒ 00:10:17.580 MattBurns: Yeah.
95 00:10:17.660 ⇒ 00:10:23.740 Amber Lin: Totally, that’s… that’s what I… that’s what I would love to know. That’s something that I…
96 00:10:24.310 ⇒ 00:10:43.950 Amber Lin: I would love to see that data. I think that will be really helpful in answering, like, our core question. Later, like, two slides later, I tried to look into that. I made some assumptions, so once we get that report, I would love to put all of these, link them together, and see what we can… see what we can find.
97 00:10:43.950 ⇒ 00:10:46.670 MattBurns: Yeah, for instance, lawn care.
98 00:10:46.790 ⇒ 00:10:47.480 Amber Lin: Yeah?
99 00:10:47.560 ⇒ 00:10:49.909 MattBurns: The fifth one, or sixth one on the list there, lawn care.
100 00:10:49.910 ⇒ 00:10:50.550 Amber Lin: Huh.
101 00:10:50.550 ⇒ 00:10:58.360 MattBurns: also quite profitable in comparison with lawn mowing. Again, same thing. You have lower labor costs, and you have…
102 00:10:58.990 ⇒ 00:11:02.569 MattBurns: a better margin. So, I think when you see the margin reports.
103 00:11:02.880 ⇒ 00:11:08.380 MattBurns: Those will help, so I’ll… and we do have the final 25 revenue figures, so I can send you the final…
104 00:11:08.380 ⇒ 00:11:09.460 Amber Lin: Awesome, okay.
105 00:11:09.460 ⇒ 00:11:12.379 MattBurns: 2025 revenue, and then the margin reports.
106 00:11:12.380 ⇒ 00:11:13.939 Amber Lin: That will be awesome.
107 00:11:13.940 ⇒ 00:11:28.150 Steven: And then, like I said, I did go through… y’all could… y’all are better at this stuff than I am, but I said, Matt, I assume, could send you a final margin report for maybe… I don’t know, would you want more than one year for… across… because our margin reports are split by department and division, then also by branch.
108 00:11:28.150 ⇒ 00:11:39.710 MattBurns: I would say probably on the margin reports last year would be good, because they’re the most recent, they’re the most accurate, because prior to that, some of the older services, we’ve improved the margins, so…
109 00:11:40.490 ⇒ 00:11:42.050 MattBurns: Stop there, and if you want more.
110 00:11:42.050 ⇒ 00:11:59.970 Steven: I mean, I could send you, like I said, I’ve put together one that has, like, the past 5 years just for San Antonio, just to show, you know, honestly, I was a little surprised that even though we were way more profitable a few years ago, our margins actually haven’t… our gross margins haven’t changed that much, so anyways, it might be good just to see over the years, like I said.
111 00:11:59.970 ⇒ 00:12:17.179 Amber Lin: Yeah, totally. I’ll start working with 2025 data, and then… because I’m also trying to make, like, a growth projection, so if I can have past year’s data, I’ll factor that in, in terms of margin improvement, because I know you guys work really hard on that.
112 00:12:17.300 ⇒ 00:12:18.060 Steven: Cool.
113 00:12:18.320 ⇒ 00:12:26.170 Amber Lin: Awesome. So, the next one is, is growth. So, I’m looking at the growth of the different
114 00:12:26.300 ⇒ 00:12:33.820 Amber Lin: services to see, okay, what has happened for these different services in the past 5 years? So…
115 00:12:34.370 ⇒ 00:12:46.940 Amber Lin: on the left, you see this is just the total sales, so dollar amount, and HVAC has really caught up in the past, 3… 3 or 5 years, and it has been,
116 00:12:47.160 ⇒ 00:12:58.950 Amber Lin: closing the gap between, residential pests and HVAC. So, that’s really good news. And then we look on the right of, okay, what has the growth rate been? Has it been consistently
117 00:12:59.140 ⇒ 00:13:00.850 Amber Lin: Continuing to grow.
118 00:13:01.270 ⇒ 00:13:08.020 Amber Lin: And we can see, okay, plumbing and HVAC, had really high growth rates.
119 00:13:08.870 ⇒ 00:13:13.710 Amber Lin: in… 20… about 2019 to…
120 00:13:13.830 ⇒ 00:13:30.260 Amber Lin: 2022, which is around the COVID era. And then afterwards, from 2022 to 2024, the growth rates did drop for plumbing and HVAC, and we saw the, more of our signature services, like.
121 00:13:30.260 ⇒ 00:13:38.079 Amber Lin: pests, rodent, or say lawn mowing, their growth rates have seen a rebound, and they’re, they’re coming back.
122 00:13:38.600 ⇒ 00:13:49.060 Amber Lin: So, when I look at this, and I… and I think, okay, so the changes in growth rates during COVID, is that, just…
123 00:13:49.450 ⇒ 00:13:59.690 Amber Lin: completely macro factors. Did we do anything special in marketing during that time? I guess I’m trying to isolate.
124 00:14:01.150 ⇒ 00:14:07.420 MattBurns: what we did and what happened. Yeah, what we felt happened to a lot of our services during COVID.
125 00:14:07.580 ⇒ 00:14:10.949 MattBurns: Amber, was that people were not traveling, they weren’t spending…
126 00:14:10.950 ⇒ 00:14:11.940 Amber Lin: Mmm.
127 00:14:12.170 ⇒ 00:14:16.260 MattBurns: So they were putting their money into their house, i.e. landscaping, air conditioning.
128 00:14:16.260 ⇒ 00:14:17.060 Amber Lin: Mmm.
129 00:14:17.060 ⇒ 00:14:20.480 MattBurns: Those types… even plumbing, they were spending more time at home.
130 00:14:21.410 ⇒ 00:14:22.150 Amber Lin: Hmm.
131 00:14:22.150 ⇒ 00:14:25.280 MattBurns: Therefore, they were using a lot of those
132 00:14:25.490 ⇒ 00:14:29.240 MattBurns: Resources during the day, because you had people working from home.
133 00:14:30.030 ⇒ 00:14:30.400 Amber Lin: Hmm.
134 00:14:31.150 ⇒ 00:14:37.480 MattBurns: And again, if you’re not going to spend $20,000 on a vacation, well, maybe turn your backyard into a vacation place.
135 00:14:38.550 ⇒ 00:14:39.460 MattBurns: And that car.
136 00:14:40.000 ⇒ 00:14:45.790 MattBurns: Some nice landscaping in the backyard. That was… seemed to kind of be the reason for that.
137 00:14:46.610 ⇒ 00:14:53.940 MattBurns: So, because you’re right, it… particularly landscaping and even HVAC kind of jumped up during the COVID years.
138 00:14:54.200 ⇒ 00:15:04.240 Steven: HVAC also, I don’t know when y’all got into it. San Antonio got into Costco’s, which was a huge driver for us. That accounts for close to half our business, probably, so we started doing Costco.
139 00:15:05.070 ⇒ 00:15:10.039 Steven: 21 or 22, I don’t know when Austin started doing Costco, but that was a big boost for…
140 00:15:10.190 ⇒ 00:15:11.330 MattBurns: Percent.
141 00:15:11.870 ⇒ 00:15:13.960 MattBurns: And that did help overall, yeah.
142 00:15:14.690 ⇒ 00:15:18.760 Amber Lin: I see. Isn’t that more of, like, a commercial HVAC contract?
143 00:15:19.850 ⇒ 00:15:28.570 Steven: No, so we sell the residential units, like, in the handicap. If you’re… if you buy a unit through Costco, you can buy through Costco, and we’re the ones that install it.
144 00:15:28.570 ⇒ 00:15:29.909 Amber Lin: Oh, I see.
145 00:15:30.310 ⇒ 00:15:33.489 MattBurns: It’s a residential service for Costco customers.
146 00:15:33.490 ⇒ 00:15:33.870 Steven: Customer.
147 00:15:33.870 ⇒ 00:15:35.499 Amber Lin: I see, okay.
148 00:15:36.200 ⇒ 00:15:41.749 MattBurns: Yeah, and what they do is… it’s popular because If you’re a Costco
149 00:15:42.020 ⇒ 00:15:47.570 MattBurns: aficionado, a Costco customer, you want your Costco reward points?
150 00:15:48.130 ⇒ 00:15:48.760 Amber Lin: Hmm.
151 00:15:48.760 ⇒ 00:15:50.859 MattBurns: points on your Costco card.
152 00:15:51.190 ⇒ 00:15:55.409 MattBurns: for doing it, and then you can use those points to do other things with Costco. I mean.
153 00:15:55.590 ⇒ 00:15:58.920 MattBurns: You can buy a car from Costco, you can take a vacation.
154 00:15:59.480 ⇒ 00:16:00.920 MattBurns: from Costco, so…
155 00:16:01.590 ⇒ 00:16:09.020 Amber Lin: Yeah, and, like, I would love to go look in more. I remember, I think I do see Costco accounts on…
156 00:16:09.090 ⇒ 00:16:25.360 Amber Lin: On the reports that Julie had, so maybe if we isolate that to see how much is it from new contracts, or how much is it just from macro changes in demand? And ideally, we can find a way to replicate that, because if… if that’s the…
157 00:16:25.360 ⇒ 00:16:29.160 Amber Lin: main driver of growth. We still have other markets that’s…
158 00:16:29.160 ⇒ 00:16:37.299 Amber Lin: pretty big that maybe we should go in for those contracts. Maybe not just at Costco, but at different… other different partners as well.
159 00:16:37.440 ⇒ 00:16:37.970 MattBurns: Yep.
160 00:16:38.290 ⇒ 00:16:39.500 Amber Lin: Yeah, cool.
161 00:16:39.660 ⇒ 00:16:44.409 Uttam Kumaran: Yeah, I wonder, Amber, also, you mentioned, like, in your thing, is it…
162 00:16:44.770 ⇒ 00:16:56.670 Uttam Kumaran: Is it… what service… what, like, is it lasting behavior changes? I would be interested to see, of the customers acquired during that period, one, like, how many of them still
163 00:16:56.780 ⇒ 00:17:02.149 Uttam Kumaran: like, remain customers of ABC, and is that split… is, like, that sort of retention
164 00:17:02.760 ⇒ 00:17:06.789 Uttam Kumaran: within certain categories, high or low.
165 00:17:07.150 ⇒ 00:17:14.420 Uttam Kumaran: But I also think that overall, like, remote work, especially in Austin, has really not, like.
166 00:17:14.750 ⇒ 00:17:29.159 Uttam Kumaran: it’s not gone down super significantly. Yes, there’s a lot of people that are now going to office, but, like, anecdotally, in, like, I don’t know, I just meet with a lot of business folks here, like, people, a lot of people are still working from home.
167 00:17:29.210 ⇒ 00:17:42.979 Uttam Kumaran: And so… and, like, the number of people working from home has actually, I think, increased, the number of homes have increased, so I would be surprised if, like, yes, I think there’s probably a decrease in some of the people that are working from home, but, like.
168 00:17:43.320 ⇒ 00:17:46.080 Uttam Kumaran: I don’t know, the behaviors should be mostly the same.
169 00:17:47.480 ⇒ 00:17:48.020 MattBurns: Yeah.
170 00:17:51.700 ⇒ 00:17:52.290 Amber Lin: Cool.
171 00:17:53.500 ⇒ 00:18:01.220 MattBurns: We saw Julie’s out there. There are specific codes in the system. There are Costco-related service codes, so we can specifically get those.
172 00:18:02.300 ⇒ 00:18:02.860 Amber Lin: Yeah.
173 00:18:04.060 ⇒ 00:18:11.430 Amber Lin: So the next one I was trying to look at, especially for marketing spend.
174 00:18:11.710 ⇒ 00:18:15.350 Amber Lin: How much would the ROI be per…
175 00:18:15.370 ⇒ 00:18:32.790 Amber Lin: marketing dollar that we spend on services. And right here, I made the assumptions about customer acquisition cost, based on what we currently have in Google Analytics, and also, some services require higher trust, it’s a bigger
176 00:18:32.790 ⇒ 00:18:47.559 Amber Lin: contracted or job, so people are more hesitant, so the customer acquisition cost would vary. And then I looked at, okay, how… so if a customer gets acquired, how many times would they
177 00:18:47.640 ⇒ 00:18:52.029 Amber Lin: do that service in a 12-month period, and I based that on
178 00:18:52.030 ⇒ 00:19:11.899 Amber Lin: say, the ratios of our monthly contracts versus, say, annual contracts, and the services that I marked that have a lot higher repeat multipliers have a lot more people that’s on, say, monthly contracts. And that will be the case, say, for lawn mowing, or for commercial.
179 00:19:12.090 ⇒ 00:19:26.989 Amber Lin: So, based on that, and I also did an assumption on margin here, which I would love to update with the actual numbers for 2025, and using that, I went ahead and calculated, okay, so if we acquire a customer.
180 00:19:27.380 ⇒ 00:19:41.180 Amber Lin: what is their, say, 1-year value? I’m not looking at 5 years, because I don’t yet know the retention numbers, but even just from a simple estimate, 1 year, you can see that
181 00:19:41.720 ⇒ 00:19:56.080 Amber Lin: commercial pass has a bigger one-year value per customer. Landscaping has a very high value per customer, probably because the single revenue per job is very high. Yeah.
182 00:19:56.230 ⇒ 00:19:57.740 Amber Lin: And also similar.
183 00:19:57.740 ⇒ 00:19:59.990 Steven: Those margin numbers will go down quite a bit.
184 00:20:00.800 ⇒ 00:20:01.910 Amber Lin: Alright.
185 00:20:02.560 ⇒ 00:20:11.009 Amber Lin: I, I, I didn’t know, but I’m glad… I thought I did a conservative estimate, I think I was probably too optimistic.
186 00:20:11.250 ⇒ 00:20:26.340 Amber Lin: And then based on that, and using the customer acquisition cost assumptions, we can sort of look at, okay, for the dollar amount that we’re gonna spend, which service might have the biggest return?
187 00:20:26.670 ⇒ 00:20:27.200 MattBurns: Yep.
188 00:20:27.440 ⇒ 00:20:27.840 Amber Lin: So…
189 00:20:27.840 ⇒ 00:20:34.040 Steven: Yeah, it’s not… not a surprise that obviously on the, yeah, the return, commercial and residential pests would be the highest. Yeah, landscape…
190 00:20:34.180 ⇒ 00:20:37.520 Steven: There’s definitely some struggles on the margin, so that one will probably go down.
191 00:20:37.520 ⇒ 00:20:44.589 Amber Lin: I see, I see. It is a pretty big, like, labor investment, and there’s a lot of equipment involved.
192 00:20:44.590 ⇒ 00:20:45.120 MattBurns: Yep.
193 00:20:45.520 ⇒ 00:20:46.670 Amber Lin: Cool.
194 00:20:46.670 ⇒ 00:20:56.949 Steven: But it is interesting to kind of confirm, you know, we know that, that in this, especially when you’re looking at 1 year, when you’re looking at five years, probably would increase even more, because again, you expect to keep those customers on…
195 00:20:57.150 ⇒ 00:20:57.580 Amber Lin: Yeah.
196 00:20:57.580 ⇒ 00:20:58.539 Steven: a long time.
197 00:20:58.560 ⇒ 00:21:15.650 Amber Lin: So, once we have the retention numbers, we can factor that in, and then we can start to think about, okay, if we were to increase retention for 5%, what change would that do? And then I can… I think that’s part of how we can motivate
198 00:21:15.650 ⇒ 00:21:27.909 Amber Lin: our CSRs, or have our folks focus on retention, because then they’ll know a dollar amount that’s attached to, like, the efforts that they’re putting in, instead of, this is just something that they have to do.
199 00:21:29.200 ⇒ 00:21:34.939 Amber Lin: So, I think next, I wanted to look at…
200 00:21:35.130 ⇒ 00:21:47.310 Amber Lin: which branch deserves the most investment? I would say, definitely, I would love to do a bit more research here. I put a lot… I put most of my time into the services analysis, but…
201 00:21:47.730 ⇒ 00:21:53.419 Amber Lin: say, curb branch, here’s just a visualization of how big
202 00:21:53.650 ⇒ 00:22:08.440 Amber Lin: In terms of dollar amount sales each branch is. And on the right is just as a percentage of Austin. So, we can see that San Antonio is only about 33, 32% of how big
203 00:22:08.560 ⇒ 00:22:10.670 Amber Lin: Austin is.
204 00:22:11.280 ⇒ 00:22:12.460 Amber Lin: And then…
205 00:22:14.270 ⇒ 00:22:31.209 Amber Lin: we know that Austin is our currently biggest and most mature market, but the question that we have here is, okay, why is Austin so dominant? Is it because of market size? Is it because of the services, or just that we had a lot of
206 00:22:31.210 ⇒ 00:22:39.370 Amber Lin: history there, or where we have, we’re more mature in how we operate in that market.
207 00:22:39.510 ⇒ 00:22:40.650 Amber Lin: And…
208 00:22:41.480 ⇒ 00:22:49.299 Amber Lin: So we can learn… we can learn from that and apply it to expanding other markets. And then the second question I have is.
209 00:22:49.300 ⇒ 00:23:11.250 Amber Lin: Okay, so for these markets that’s a lot smaller, what is the growth potential there? Should we be investing more in Austin, who’s, where we have an established base, or should we be investing in, say, San Antonio, or even should we be investing more in Waco? Is the market there really that small?
210 00:23:11.250 ⇒ 00:23:15.739 Amber Lin: Because we currently have a very small percentage of revenue coming from there.
211 00:23:17.740 ⇒ 00:23:23.549 MattBurns: Well, it’s… it’s… certainly Austin has been operating here the longest. This is…
212 00:23:24.350 ⇒ 00:23:28.499 Amber Lin: You know, San Antonio actually was where ABC started, but then we had…
213 00:23:28.500 ⇒ 00:23:36.020 MattBurns: A 20-year interruption, where… We were operating under… the business was sold there when Bobby’s dad.
214 00:23:36.020 ⇒ 00:23:36.830 Amber Lin: That’s all.
215 00:23:36.830 ⇒ 00:23:38.210 MattBurns: And so on, so we’re back.
216 00:23:38.210 ⇒ 00:23:39.199 Amber Lin: I see, I see.
217 00:23:39.630 ⇒ 00:23:47.850 MattBurns: And in Waco, we’ve only been in there a year and a half, so… and we didn’t make an acquisition. That was a pure startup from scratch.
218 00:23:48.080 ⇒ 00:23:55.800 MattBurns: You know, our feeling also is that Austin proper.
219 00:23:56.440 ⇒ 00:24:00.349 MattBurns: Isn’t growing so much because of the expensive real estate and the fact that.
220 00:24:00.350 ⇒ 00:24:00.830 Amber Lin: Hmm.
221 00:24:00.830 ⇒ 00:24:03.760 MattBurns: It’s matured, but the surrounding
222 00:24:04.350 ⇒ 00:24:14.760 MattBurns: you know, smaller areas around Austin, i.e. San Marcos, Bastrop, even Marble Falls, and so on. We’re getting some pretty good growth there. Georgetown.
223 00:24:15.490 ⇒ 00:24:16.060 Amber Lin: Hmm.
224 00:24:16.060 ⇒ 00:24:23.449 MattBurns: Some of those, but, yeah, certainly San Antonio, you would… You would tend to believe…
225 00:24:23.780 ⇒ 00:24:33.270 MattBurns: probably has the most potential because of the market size in comparison with Austin, and the revenue is certainly not, but like you pointed out, about a third of Austin.
226 00:24:34.110 ⇒ 00:24:41.750 MattBurns: Interesting, Steve and I were talking the other day about… and we have in the past about areas out where he lives, you know, out in Burney.
227 00:24:42.330 ⇒ 00:24:48.320 MattBurns: just… those growth areas out to the north and out to the west, because the income…
228 00:24:48.510 ⇒ 00:24:50.740 MattBurns: The incomes are high out there.
229 00:24:52.060 ⇒ 00:24:57.699 MattBurns: And, that it’s… you have customers there that can do all the services that we.
230 00:24:57.700 ⇒ 00:24:58.320 Amber Lin: Yeah.
231 00:24:58.320 ⇒ 00:25:01.479 MattBurns: You know, they’ve got the disposable income there, so…
232 00:25:01.480 ⇒ 00:25:15.179 Amber Lin: Yeah. Yeah, and when I looked at this, and we also got the actual marketing spend from 2025, and when I look at this, and I compare it to the marketing
233 00:25:15.210 ⇒ 00:25:26.109 Amber Lin: Well, this is the marketing budget for 2026. We are just spending pretty much approximately the portion according to its current market size.
234 00:25:26.110 ⇒ 00:25:27.120 MattBurns: So…
235 00:25:27.120 ⇒ 00:25:31.440 Amber Lin: Like, my question there, then, is when we are…
236 00:25:31.600 ⇒ 00:25:47.399 Amber Lin: if we want to put a lot more growth… we want to see a lot more growth in San Antonio, should we just be investing more in San Antonio? Like, what is… should we make a strategic bet there to increase the budget there, or should we just continue doing,
237 00:25:47.930 ⇒ 00:25:55.450 Amber Lin: steady, say, proportional investment according to our current sales. So…
238 00:25:55.450 ⇒ 00:26:01.649 MattBurns: That’s a good question, because we did not grow in 2025 in Austin proper.
239 00:26:01.770 ⇒ 00:26:02.500 Amber Lin: Mmm.
240 00:26:02.500 ⇒ 00:26:05.019 MattBurns: Yet, we spent the most dollars there.
241 00:26:05.350 ⇒ 00:26:05.920 Amber Lin: Yeah.
242 00:26:05.920 ⇒ 00:26:06.460 MattBurns: So…
243 00:26:06.460 ⇒ 00:26:16.340 Steven: Obviously, the problem comes in, though, it’s… obviously, you don’t know this, but you’ve got to at least maintain… Austin’s such a beast, if you were to be down 5%, but up 30% in San Antonio, well…
244 00:26:16.430 ⇒ 00:26:26.590 Steven: it’s still… you’re still in a world of hurt. So Austin’s such a beast that you have to at least… is it gonna have the growth potential to grow 10 or 15% in a year? Probably not, but you need to go
245 00:26:26.730 ⇒ 00:26:31.259 Steven: 3, 4, 5% in a year to allow, you know, because it’s just so… so big.
246 00:26:31.600 ⇒ 00:26:45.240 Amber Lin: Yeah, and especially, I think, a certain percent of marketing is more defensive, because there’s a lot of competitors in Austin, because it’s such a big and profitable market. But yeah, I think we should…
247 00:26:45.340 ⇒ 00:27:02.419 Amber Lin: maybe model it out to see what would happen if we invested a bit more in even, say, the smallest market, if we will see significant returns there. So it’s still an ROI question, and I think that’s something interesting that we can model out later.
248 00:27:02.800 ⇒ 00:27:05.550 MattBurns: Yeah, conversely, the…
249 00:27:05.820 ⇒ 00:27:16.209 MattBurns: retention effort, you would think, let’s really concentrate on Austin for the retention effort, because if I change that retention, or change the loss.
250 00:27:16.360 ⇒ 00:27:21.399 MattBurns: percentage, you know, a bit, you’d have a really big effect in Austin.
251 00:27:21.400 ⇒ 00:27:22.719 Amber Lin: Yeah.
252 00:27:23.140 ⇒ 00:27:30.630 Steven: Yeah, it’s like, really, your focused efforts are maintaining, yeah, retention and maintaining in Austin and growing.
253 00:27:30.630 ⇒ 00:27:32.299 Uttam Kumaran: The markets where the potential is.
254 00:27:32.880 ⇒ 00:27:48.410 Amber Lin: Yeah, I’ve actually started a similar model of, okay, for conversion, retention, and bundling, for the same amount of, say, percentage increase or dollar amount that we invest in.
255 00:27:48.490 ⇒ 00:27:56.789 Amber Lin: what is going to return the best results, and I… I’m also comparing the different markets, to see
256 00:27:57.000 ⇒ 00:28:02.580 Amber Lin: Which one, or different services to see which one would have, would…
257 00:28:02.610 ⇒ 00:28:16.550 Amber Lin: Should we do more of a retention, conversion, so based on existing market optimization, or should we look into more of a strategic… a new market penetration
258 00:28:16.550 ⇒ 00:28:29.909 Amber Lin: So, like, that’s something that I started working on. I’m not very close to finish, so once I get updated numbers and from our discussion today, I think that will be something interesting to talk about next time.
259 00:28:30.400 ⇒ 00:28:38.120 MattBurns: Yeah, and I know, Les, for instance, has always pointed out the difference If you’re doing the…
260 00:28:38.660 ⇒ 00:28:40.050 MattBurns: the pay-per-click.
261 00:28:40.070 ⇒ 00:28:42.930 Amber Lin: Type advertising, the difference between cost…
262 00:28:43.760 ⇒ 00:28:52.300 MattBurns: of a click on advert… on HVAC versus pest control or some… because HVAC during the season can be really high.
263 00:28:52.560 ⇒ 00:28:53.570 MattBurns: You know, for a…
264 00:28:54.430 ⇒ 00:28:56.970 MattBurns: pay-per-click, it can really be up there, so…
265 00:28:57.190 ⇒ 00:28:57.540 Amber Lin: Yeah.
266 00:28:57.540 ⇒ 00:28:58.559 MattBurns: Right there, yeah.
267 00:28:58.760 ⇒ 00:29:18.480 Amber Lin: Yeah, I wanna… I think this is the perfect transition to go talk about our next set of slides is about marketing. We got this from Google Analytics. Of course, right now, it’s limited on Google Analytics, it’s not broken down by service, because we… I don’t think we’ve set up tracking for that.
268 00:29:18.480 ⇒ 00:29:21.500 Amber Lin: But it will be very interesting to say.
269 00:29:21.500 ⇒ 00:29:35.200 Amber Lin: to see that, okay, HVAC just needs a lot more money to be put in to get a customer compared to, say, lawn mowing, which requires a lot lower trust. So, it’ll be interesting to see that.
270 00:29:35.730 ⇒ 00:29:36.320 MattBurns: Yep.
271 00:29:36.850 ⇒ 00:29:52.450 Amber Lin: Cool. Uzam, if you’re good, we can transition to talk about the other slides, or any remaining questions. I know, Julie, I would love to hear your feedback, but I would… maybe I’ll email you for the feedback. I don’t want to force you to talk right now.
272 00:29:54.720 ⇒ 00:29:58.740 Uttam Kumaran: Yeah, I’m curious, Matt and Steven, if there’s any other…
273 00:29:58.770 ⇒ 00:30:12.249 Uttam Kumaran: Directions that you heard about that you’d like us to go, deeper on. But, you know, for me, it’s really just getting an understanding of how have services and the revenue and profit for them has changed over time.
274 00:30:12.250 ⇒ 00:30:25.279 Uttam Kumaran: Additionally, it’s understanding, you know, one thing that we always talked about, Amber, and this is really where, when we get to… to dream, is gonna be understanding, like, service mix among customers.
275 00:30:25.350 ⇒ 00:30:28.570 Uttam Kumaran: And that’s, like, one of the highlights of our initial conversations was.
276 00:30:29.290 ⇒ 00:30:42.650 Uttam Kumaran: proving the fact that bundling services leads to a more sticky and more valuable customer, and we want to… we want to prove that and show, like, how… how dramatic it is. And then the other piece that I wanted to…
277 00:30:42.650 ⇒ 00:30:51.880 Uttam Kumaran: you know, kind of look at a lot as well, is, like, retention and churn. I think that’s kind of next, is to sort of show how customers flow in and out of ABC.
278 00:30:51.880 ⇒ 00:31:09.149 Uttam Kumaran: And how long they typically stick, and really doing a lot of the work, Amber, that we do for other customers, just figure out, like, what is the combination of activities that makes a great customer, and how do we get more people to do those activities? You know, like, is buying X and Y service and scheduling it during June, like.
279 00:31:09.150 ⇒ 00:31:20.559 Uttam Kumaran: the best combination, and therefore, we have to lead that, right? So, like, it’s similar to, like, what Amber, like, Greg on our team is talking about. What is a golden set of activities? I would really like to…
280 00:31:20.880 ⇒ 00:31:23.589 Uttam Kumaran: Find that out, you know?
281 00:31:27.060 ⇒ 00:31:27.790 MattBurns: Yeah.
282 00:31:29.570 ⇒ 00:31:30.110 Amber Lin: Cool.
283 00:31:30.230 ⇒ 00:31:36.379 Amber Lin: Alright, I’m gonna pull up the other set of slides we have. So…
284 00:31:37.050 ⇒ 00:31:40.150 Amber Lin: This is based on what we did in…
285 00:31:40.370 ⇒ 00:31:45.409 Amber Lin: Google Analytics. So, that takes a look at,
286 00:31:45.660 ⇒ 00:31:50.240 Amber Lin: Where people have been coming from, and…
287 00:31:50.620 ⇒ 00:32:02.090 Amber Lin: how they convert. So, whatever activity they’ve done on the website, we’re… we’re able to track part of that through Google Analytics. So.
288 00:32:02.240 ⇒ 00:32:12.349 Amber Lin: the question that I had here, first, Zoran led this analysis, and then so our question is, which channel
289 00:32:12.810 ⇒ 00:32:19.390 Amber Lin: Is doing the best, gives us the best demand, and what channel should we
290 00:32:19.640 ⇒ 00:32:27.410 Amber Lin: Invest in, improving, expanding, so similarly, of where do we put our next dollar?
291 00:32:27.980 ⇒ 00:32:37.349 Amber Lin: So, let’s take a look at… this slide. So… This is the engagement.
292 00:32:37.390 ⇒ 00:32:54.950 Amber Lin: for the different channels. So, when we talk about, channels, there’s organic search, which is when people search on Google or search on, I think, Google Maps as well. And there’s paid search, which is, when we pay Google to put it up on
293 00:32:54.950 ⇒ 00:33:07.580 Amber Lin: To put it as a sponsored listing at the top. There’s also direct traffic, which is when people type in the website name or click on their bookmark.
294 00:33:08.210 ⇒ 00:33:19.540 Amber Lin: And of course, there’s other, other sources, such as, the social media, organic, social media, email, video, etc.
295 00:33:20.120 ⇒ 00:33:28.149 Amber Lin: So, looking at all of this, I think what we found is that the organic search has the highest,
296 00:33:28.620 ⇒ 00:33:36.910 Amber Lin: Volume of traffic and the highest engagement rate, which is the red line right here, which… and followed by paid search.
297 00:33:37.580 ⇒ 00:33:42.380 Amber Lin: Another thing that we found is that direct traffic, has…
298 00:33:42.790 ⇒ 00:33:58.890 Amber Lin: unusually low engagement rate, which usually doesn’t make sense, because if someone types in your website link, or clicks on their bookmark, they usually will go through a lot of different pages, they’ll spend a lot of time on the website.
299 00:33:58.890 ⇒ 00:34:03.160 Amber Lin: So this is likely indicative of bot traffic.
300 00:34:03.230 ⇒ 00:34:19.769 Amber Lin: Which I think makes sense, because I… we looked at the cities where the traffic is coming from. There is a big portion coming from China, from… from, like, a city that’s really known for their ramen, and I was like, oh, I don’t think… I don’t think they’re getting ABC services over there.
301 00:34:20.710 ⇒ 00:34:22.690 Amber Lin: So…
302 00:34:22.699 ⇒ 00:34:24.569 MattBurns: Not that we’re aware of.
303 00:34:26.900 ⇒ 00:34:43.779 Amber Lin: And so, based on that, I think a simple recommendation is that we’ll continue prioritizing our top engagement and traffic channels, which is organic search and paid search, and then we’ll audit the other channels to see,
304 00:34:44.050 ⇒ 00:34:48.539 Amber Lin: If we can filter out some bot traffic to make this diagram more clean.
305 00:34:49.230 ⇒ 00:34:58.339 Amber Lin: And so, the next part, I really want to focus on, organic search, because I don’t think we have enough data on paid search.
306 00:34:59.630 ⇒ 00:35:18.119 Uttam Kumaran: Yeah, I just want to say one thing, like, in terms of… this organic search volume is amazing. It’s not very common to see, a dominant segment be organic search. This doesn’t mean people coming to you directly, you did not pay for that traffic. Most of the people on the internet are paying for the traffic that are coming to their site.
307 00:35:18.150 ⇒ 00:35:32.810 Uttam Kumaran: And so that’s a really, really good signal. For us, what we want to do is basically understand the situations in which the organic search folks are coming to ABC, and replicate that on paid, and sort of boost that using paid channels.
308 00:35:32.810 ⇒ 00:35:45.830 Uttam Kumaran: And also, what we… what, like, one of our recommendations will be to implement methods to kind of, like, cut the bot traffic out, and so there’s some ways of filtering that out as well.
309 00:35:46.520 ⇒ 00:35:51.770 Uttam Kumaran: But overall, this is… it was really great to see that the organic search is such a high segment.
310 00:35:54.190 ⇒ 00:36:11.959 Amber Lin: Yeah, and I think building off of that, the paid… the stuff that we pay for performs really well on conversion, so that’s an even better sign to know that, our dollars are actually turning into leads. And I think pointing out another thing here, so…
311 00:36:11.960 ⇒ 00:36:18.530 Amber Lin: this section that says unassigned, it’s… I believe it’s display retargeting, and it drives…
312 00:36:18.770 ⇒ 00:36:29.729 Amber Lin: very significant amount of phone calls, which is this green column right here. So, I think the next steps we have there is, okay, we’ll definitely continue investing in
313 00:36:29.860 ⇒ 00:36:48.209 Amber Lin: paid search. We’ll figure out what’s the best way to do that. And also, we’re gonna look at this section that has a significant phone call conversion to see what we’re doing well there. So, I think those are some next steps or next interesting questions that we have.
314 00:36:51.250 ⇒ 00:37:02.490 Amber Lin: Cool. So, next, we took a look at the organic traffic for the different services, and…
315 00:37:03.160 ⇒ 00:37:12.819 Amber Lin: I think pest control is the biggest volume of organic search that comes up, followed by plumbing and HVAC.
316 00:37:13.280 ⇒ 00:37:24.330 Amber Lin: And I think this is… This is the matrix on two things. So, click-through rate is the Sorry, if…
317 00:37:24.900 ⇒ 00:37:31.020 Amber Lin: Is if people, click on the link.
318 00:37:31.060 ⇒ 00:37:47.800 Amber Lin: after they see the traffic, and then the position is where they rank on the search results. So, it’s if we come up as the first option after they type it in into Google. And actually, this is… this is really good news for us, is because it tells us
319 00:37:47.900 ⇒ 00:38:02.869 Amber Lin: hey, we have a lot of opportunity to improve there. So, small tweaks in how you present your website, small tweaks in SEOs, can really change the positions where you rank up. So I think that’s a…
320 00:38:03.500 ⇒ 00:38:20.279 Amber Lin: it’s a really good ROI for every dollar you put in to improve, improve the SEO, improve the website ranking, because those are more execution, and it’s not really our brand. So we are… it sounds like…
321 00:38:21.390 ⇒ 00:38:37.830 Amber Lin: there’s a lot of room for us to improve there. So, I think Zoran will take a bit deeper dive into this, and then we’ll come up with better strategies on how to do that, but I think it just tells us that there’s a big opportunity here for improvement.
322 00:38:43.400 ⇒ 00:38:47.149 Amber Lin: And then… Next slide here is the…
323 00:38:48.810 ⇒ 00:38:58.220 Amber Lin: the trend over time. So, I think something that was alarming, what caught my attention here was, okay, the organic
324 00:38:58.460 ⇒ 00:39:12.209 Amber Lin: Sessions, peaked in 2024, but had a sharp decline in 2025, and the other channels grew slightly, but it didn’t really compensate for the volume of
325 00:39:12.490 ⇒ 00:39:31.390 Amber Lin: organic search. And then, by looking at that, then our question is, okay, is it… is it because of demand shifts? Is it because of algorithm shifts or content? And so, if we were able to find what causes the issue, and if it’s something that’s
326 00:39:31.400 ⇒ 00:39:41.570 Amber Lin: about our site structure, how we word things on the sites. Those are relatively easy changes to implement that will have a significant impact on,
327 00:39:41.690 ⇒ 00:39:53.449 Amber Lin: how many people we confer, or how many people that actually go look at our services. So I just want to point it out there. I think this is… this is… this is a great opportunity for us.
328 00:39:54.540 ⇒ 00:39:56.609 Amber Lin: Okay.
329 00:39:57.010 ⇒ 00:40:05.030 Amber Lin: The… the last one, we found that the location-specific landing pages, so when they… when they search
330 00:40:05.650 ⇒ 00:40:20.290 Amber Lin: ABC, or say, Pest Control Austin, and they land on the Austin landing page, I think they have the best engagement and the best conversion rates. I think that just tells us that our local reputation, our local
331 00:40:20.330 ⇒ 00:40:30.759 Amber Lin: marketing is going pretty well. So, that’s just a confirmation of what we have. I’m gonna pause here, before I dive into the other sections.
332 00:40:35.210 ⇒ 00:40:39.170 Uttam Kumaran: So this was a lot of, like, what Les went through, which is, like.
333 00:40:39.680 ⇒ 00:40:56.040 Uttam Kumaran: people are searching for XYZ Austin, and they’re going to the landing pages, and so, like, really making those super conversion-driven is, like, our real big push, versus pushing the generic ABC
334 00:40:56.220 ⇒ 00:41:08.349 Uttam Kumaran: you know, homepage. You know, and so making that… the fact that when someone goes there, that they’re really, you know, pushed to convert is super, super important, that we… we kind of, like.
335 00:41:08.670 ⇒ 00:41:10.149 Uttam Kumaran: Make that a reality.
336 00:41:11.230 ⇒ 00:41:22.590 Amber Lin: Yeah, and I think the purpose of us going through this is… is to find, okay, so if we were to optimize our website, where should we put the effort in for the
337 00:41:22.740 ⇒ 00:41:36.129 Amber Lin: the biggest impact, and I think this tells us we should focus on the location landing pages, and this tells us, okay, we should focus on, maybe pest control and plumbing, which has already the biggest organic traffic.
338 00:41:37.620 ⇒ 00:41:55.690 Amber Lin: So the next question, I know we were very interested in, how are we performing in generative search? So, when AI gets involved in the search, and we have experts on the team that specialize in this. They help us, improve our own generative search results.
339 00:41:55.690 ⇒ 00:42:10.399 Amber Lin: So, we did a sample search, just a quick search on one of the biggest LLMs we searched on perplexity, and we didn’t find ABC on this list. So, when we searched for San Antonio, it recommended
340 00:42:10.730 ⇒ 00:42:16.660 Amber Lin: some other services, and I said, oh, if you want this, go to this store.
341 00:42:17.330 ⇒ 00:42:20.669 Amber Lin: So, I think that… That tells us that
342 00:42:21.710 ⇒ 00:42:36.110 Amber Lin: we should check other LLMs, but maybe that we’re not… we haven’t put effort into optimizing for AI and generative search, and it’s quite different than the traditional SEOs, SEO that we’ve been optimizing for.
343 00:42:36.410 ⇒ 00:42:54.310 Amber Lin: So, if we want to take a deeper look into that, we can totally do an audit of our current performance, and we have a set of recommendations that we do ourselves, and we recommend other people doing. And, for example, right here on the left, this is the…
344 00:42:55.190 ⇒ 00:42:58.769 Amber Lin: This is our view of what traffic
345 00:42:59.050 ⇒ 00:43:02.250 Amber Lin: comes to our site from the AI,
346 00:43:02.300 ⇒ 00:43:21.229 Amber Lin: from the AI brand. So, you can see that ChatGPT leads a certain level of traffic there. We can see that Gemini and Claude lead some traffic to our site, so we can definitely do the same thing for ABC and see how we’re performing on generative search.
347 00:43:21.770 ⇒ 00:43:22.340 MattBurns: Good.
348 00:43:23.860 ⇒ 00:43:32.500 Julie F: No, I wanted to jump in on that one. I did a chat GPT search a couple months ago on recommendations.
349 00:43:32.610 ⇒ 00:43:36.290 Julie F: or the best pest control service in Austin.
350 00:43:36.500 ⇒ 00:43:37.470 Amber Lin: Awesome.
351 00:43:37.470 ⇒ 00:43:39.680 Julie F: And it did not list ABC.
352 00:43:40.010 ⇒ 00:43:42.039 Julie F: Of course, I’m like, okay, why?
353 00:43:42.170 ⇒ 00:43:48.080 Julie F: And, the biggest reason was that it offers a lot of other services other than pest control.
354 00:43:48.080 ⇒ 00:43:48.500 Amber Lin: Mmm.
355 00:43:48.500 ⇒ 00:43:51.410 Julie F: So, like, the focus isn’t on pest control.
356 00:43:51.700 ⇒ 00:43:52.630 Julie F: So I thought that was.
357 00:43:52.630 ⇒ 00:43:53.220 Amber Lin: I see you.
358 00:43:53.220 ⇒ 00:43:55.499 Julie F: I don’t know if you have answers to.
359 00:43:55.500 ⇒ 00:44:00.630 Uttam Kumaran: Yeah, I can talk to that. So, all of this is literally mainly…
360 00:44:00.830 ⇒ 00:44:05.449 Uttam Kumaran: how we have to frame the content on the landing page. It’s actually…
361 00:44:05.550 ⇒ 00:44:18.579 Uttam Kumaran: in the way you described it, you’re giving the AI too much credit for, like, deducing. It just knows what it sees. And so, if we’re able to make our landing pages more, like,
362 00:44:18.770 ⇒ 00:44:37.920 Uttam Kumaran: more specific around how we are the best in pest control, and how we compare to other pest controls, and you basically are answering those questions you have, like, why you should recommend ABC over others, it will start to change. So there’s just a set of changes we have to make to the website content and copy.
363 00:44:37.950 ⇒ 00:44:47.780 Uttam Kumaran: That will basically change the way you come up. And you’d be surprised, it may not be that the other competitors ever did anything anyways, they may have also just
364 00:44:48.400 ⇒ 00:44:59.850 Uttam Kumaran: led their landing page with, we’re the best pest control in Austin, and then it… it just sort of shows up that way. So, it… you have to… we have to design it with… with this intention in mind.
365 00:45:01.550 ⇒ 00:45:02.959 MattBurns: Yeah, makes sense.
366 00:45:03.410 ⇒ 00:45:06.040 MattBurns: Because it’s just like you said, Utam, it’s just…
367 00:45:06.360 ⇒ 00:45:09.860 MattBurns: Aggregating the information that’s already out there, so we just need to change.
368 00:45:09.860 ⇒ 00:45:13.310 Uttam Kumaran: Yeah, it’s really not… not making, like.
369 00:45:13.310 ⇒ 00:45:14.299 MattBurns: It’s not thinking…
370 00:45:14.300 ⇒ 00:45:32.170 Uttam Kumaran: not, making a recommendation in the way we expect humans to, given the set of information. It’s a lot less sophisticated… in this example, it’s a lot less sophisticated than that, because it doesn’t even need to do that. One person says they’re the best, one person doesn’t say they’re the best. And
371 00:45:32.200 ⇒ 00:45:37.599 Uttam Kumaran: And so there’s a lot of low-hanging fruit, on optimizing this.
372 00:45:37.670 ⇒ 00:45:41.350 Uttam Kumaran: And so this is, again, really focused on just, like.
373 00:45:41.440 ⇒ 00:45:59.470 Uttam Kumaran: where are people coming to ABC? And in the moment that they are coming to the website, you have a… you have a time in between they… you want… like, they are interested in purchasing a service, right? Like, nobody’s, like, going onto ABC and, like, scrolling…
374 00:46:00.500 ⇒ 00:46:03.459 Uttam Kumaran: They’re interested, something’s happening, and they’re like.
375 00:46:06.570 ⇒ 00:46:15.580 Uttam Kumaran: what is the distance between them getting to the purchase scheduling flow and where they are right now? And what is the friction in between? Like, that is…
376 00:46:15.690 ⇒ 00:46:21.740 Uttam Kumaran: that is everything from awareness to conversion that we are… we are gonna figure out.
377 00:46:22.580 ⇒ 00:46:27.040 Uttam Kumaran: And then, really, it’s like, what we will find, most likely, is that
378 00:46:27.140 ⇒ 00:46:34.739 Uttam Kumaran: We have… we are getting great people, we are not making it easy for them to purchase, and that’s something we’ve already discussed, but…
379 00:46:34.760 ⇒ 00:46:49.690 Uttam Kumaran: less than just, like, that as a statement, in what way are we not, you know, making them… making it easy to purchase? And what do we change, right? And, like, how do we leverage Monkey Boy, or Click to Buy, or other systems to sort of drive that, you know?
380 00:46:51.240 ⇒ 00:47:04.049 Amber Lin: Yeah, and I think this is a perfect point. I want to drop in this slide of what we did for the conversion on click-to-buy. So, taking Austin as an example, on the right, you see the drop-off rate by step, and
381 00:47:04.910 ⇒ 00:47:18.560 Amber Lin: it is so high, during service selection and during order review. Like, order… 50-60% drop-off during order review is a very, very, very high amount, that…
382 00:47:18.570 ⇒ 00:47:30.790 Amber Lin: likely means that, okay, people are already clicking on click to buy. That’s very, very high intent. They’re ready to buy the service, but they’re abandoning their cart before their commitment, and
383 00:47:30.830 ⇒ 00:47:40.470 Amber Lin: means that if we do fix our UX issues, we fix our clarity issues, then we can really get back a lot of that demand, and it’s…
384 00:47:40.790 ⇒ 00:47:50.829 Amber Lin: It apparently is 50% here that people… of people that has dropped off, so if we’re able to fix that, it’s gonna be a huge win.
385 00:47:51.270 ⇒ 00:47:58.119 MattBurns: Is… is that… and you don’t think that’s because there’s… they don’t see the price until then?
386 00:47:58.340 ⇒ 00:48:06.169 MattBurns: Or is it… are they seeing the pri… in other words, is it price sticker shock, or they’ve already got that information, they’re just… something else is causing it?
387 00:48:06.830 ⇒ 00:48:26.420 Amber Lin: I think that’s a really interesting point to look into. I want to see what’s available during the service selection, and that’s why we want to do… do A-B tests, following this, so we want to see if we put the price before, does it make people drop off less, if they have
388 00:48:26.430 ⇒ 00:48:38.000 Amber Lin: transparency early, or is it best that they have a lot of investment into typing all the forms and then doing it later? So, those are interesting tests we can totally run.
389 00:48:38.670 ⇒ 00:48:42.639 MattBurns: Yeah, I know when I’m on a particular website, it’s like.
390 00:48:42.830 ⇒ 00:48:51.720 MattBurns: I don’t want to put in all my information first, and tell me what it’s going to cost, and if I’m interested, I’ll go from there, you know? Yeah.
391 00:48:52.270 ⇒ 00:48:55.259 Uttam Kumaran: And so we just need to test those variables. Okay.
392 00:48:55.260 ⇒ 00:48:55.790 MattBurns: Good, good.
393 00:48:55.790 ⇒ 00:49:06.499 Uttam Kumaran: and improve one way or another. In typical B2B software, it’s not uncommon to have, like, just a few percentage of conversion rate.
394 00:49:06.500 ⇒ 00:49:16.109 Uttam Kumaran: But this is where it’s like, we want to be able to slice this by what are the combinations of services they’re buying, what channel are they coming from.
395 00:49:16.110 ⇒ 00:49:27.399 Uttam Kumaran: Like, what page did they come from, and from that channel? And really try to central on finding where the high conversion is, and replicating that motion.
396 00:49:27.490 ⇒ 00:49:30.640 Uttam Kumaran: So one thing I’m happy about is at least…
397 00:49:30.870 ⇒ 00:49:48.210 Uttam Kumaran: you know, since we started, we have the data, right? So I’m very, very happy. I don’t… we’re not, like, weeks away from finding that answer out. And Monkey Boy, they’ve been super, super helpful with nailing this, and I feel really confident in Joe and that team to, like, help us
398 00:49:48.240 ⇒ 00:49:55.249 Uttam Kumaran: Basically, do these tests, and so, yeah, I feel like we’re… we’re making good progress here.
399 00:49:55.870 ⇒ 00:49:59.630 MattBurns: Yeah, we’re… we’re big fans of Monkey Boy. Both Aaron and Joe are…
400 00:49:59.760 ⇒ 00:50:03.090 MattBurns: top… top shelf, in my view. They’re… they’re… they’re good.
401 00:50:07.280 ⇒ 00:50:07.850 Amber Lin: Yep.
402 00:50:08.020 ⇒ 00:50:21.529 Amber Lin: I think that we also had… we noticed a few limitations on Google Analytics, and one of it is what we talked about, bot traffic, and secondly is we don’t really have the per-service
403 00:50:21.830 ⇒ 00:50:26.219 Amber Lin: knowledge for, say, com…
404 00:50:26.590 ⇒ 00:50:41.749 Amber Lin: for the, say, acquisition cost, and for conversions, we’re really only able to see click-to-buy right now. We don’t really see how, what happens to, say, offline channels. We don’t really see the full
405 00:50:41.750 ⇒ 00:50:58.859 Amber Lin: e-commerce setup, we’re only… we’re tracking this through the different pages that people go through, but I think there’s an even better way to set it up in Google Analytics to get more detailed data. And lastly, if we want to look at demographics, Google doesn’t have the
406 00:50:58.860 ⇒ 00:51:10.130 Amber Lin: best demographic information, because people don’t enter in all their information on Google, but if it’s through Meta, for through Facebook, if we do have that data, that will have a
407 00:51:10.130 ⇒ 00:51:19.539 Amber Lin: A lot better demographics if we want to analyze things by age, by, by gender, by income, per se.
408 00:51:19.880 ⇒ 00:51:22.700 Amber Lin: So, those are just improvements we can make.
409 00:51:22.820 ⇒ 00:51:29.249 Amber Lin: to the current setup in Google Analytics that lets us have even better insights.
410 00:51:30.070 ⇒ 00:51:35.320 Amber Lin: Gotcha. Yeah, so I think to… oops, to sum it off, I think…
411 00:51:35.680 ⇒ 00:51:38.070 Amber Lin: Our next steps is to
412 00:51:38.290 ⇒ 00:51:55.910 Amber Lin: look at the channel performance in an even bigger time period, and also to start looking, start setting up those A-B tests to know what we wanna… what we want to test, and to find where we can improve things.
413 00:51:57.880 ⇒ 00:52:05.989 Amber Lin: So… Any questions, any concerns that we wanna, or any areas you wanna look into?
414 00:52:09.750 ⇒ 00:52:10.220 MattBurns: No.
415 00:52:10.220 ⇒ 00:52:10.959 Steven: I don’t think so.
416 00:52:10.960 ⇒ 00:52:19.340 MattBurns: Like I said, we can… I’ll send you the revenue data, we’ll send you the margin reports,
417 00:52:20.830 ⇒ 00:52:23.580 MattBurns: U-Tam, I… we did see, it looked like.
418 00:52:23.730 ⇒ 00:52:27.119 MattBurns: Evolve was getting you the links you needed.
419 00:52:27.330 ⇒ 00:52:29.229 Uttam Kumaran: Yeah, we’re closer.
420 00:52:29.230 ⇒ 00:52:29.650 MattBurns: Good there.
421 00:52:29.650 ⇒ 00:52:36.200 Uttam Kumaran: We’re, like, we’re, like, right at the end. Okay. So, that should, that should help,
422 00:52:36.370 ⇒ 00:52:45.230 Uttam Kumaran: with us going even further back historically. And so on both of these presentations, we still have probably another 20-30%
423 00:52:45.230 ⇒ 00:52:59.500 Uttam Kumaran: to kind of go one step deeper. But I just kind of, like, first, I wanted to just gut check a lot of, like, the way we were looking at things, and kind of get the immediate reaction on our assumptions, and so I think that was really helpful today.
424 00:53:00.000 ⇒ 00:53:02.279 Uttam Kumaran: I can tell that, like, the mo… like.
425 00:53:02.370 ⇒ 00:53:06.189 Uttam Kumaran: Probably the least awareness internally is probably on, like.
426 00:53:06.260 ⇒ 00:53:25.309 Uttam Kumaran: the website traffic side. Like, I think y’all have a pretty good understanding on the margin and how the revenue is segmented by service. Like, I wouldn’t, you know? So, more of what I think I want to look at is, the combination of services, like, what leads to a true sticky customer.
427 00:53:25.480 ⇒ 00:53:28.319 Uttam Kumaran: And then we, we want to just, like.
428 00:53:28.510 ⇒ 00:53:31.269 Uttam Kumaran: Finish the tackle on everything on the,
429 00:53:31.740 ⇒ 00:53:46.539 Uttam Kumaran: awareness and conversion side, and basically leave you… leave you with, like, a menu of things that we can do and what we expect the ROI to be. I don’t know, Amber, if you… if you wanted to share any updates to the market
430 00:53:46.640 ⇒ 00:53:52.760 Uttam Kumaran: slides, or if you… if you prefer, like, maybe we… Clarence can go through it next week.
431 00:53:52.980 ⇒ 00:53:54.500 Uttam Kumaran: I know there were some…
432 00:53:55.760 ⇒ 00:53:56.389 Amber Lin: updates, but…
433 00:53:56.390 ⇒ 00:53:57.279 Uttam Kumaran: up to you.
434 00:53:57.280 ⇒ 00:54:10.279 Amber Lin: Yeah, let me look at what he has extra here. I think I’ve… I used half of his research on, say, market sizes for different services and geo… geographies.
435 00:54:10.280 ⇒ 00:54:27.879 Amber Lin: The other two follow-ups he did, one is on the door-to-door pest services, and next is the competitive analysis, say, on, like, buyerboys, on salt, or John Wayne Services, Taco, like, that’s… those are the two main areas.
436 00:54:27.880 ⇒ 00:54:35.150 Amber Lin: He… he did, so I think we can save it for next time, because I can’t cover… I don’t want to miss any points that he had.
437 00:54:35.660 ⇒ 00:54:41.819 Uttam Kumaran: Okay, okay, great. Yeah, Matt, we answered some of the questions that we had from our presentation with Clarence.
438 00:54:43.330 ⇒ 00:54:47.989 Uttam Kumaran: So… and in terms of, like, the overall project, I still… I think we’re…
439 00:54:48.100 ⇒ 00:55:05.789 Uttam Kumaran: we’re gunning to try to do some type of final, sort of, like, wrap-up of things, you know, the end of this month, or potentially the first week of Feb. So we’re, you know, I feel like really comfortable with the amount of data we’ve received. You know, I…
440 00:55:05.920 ⇒ 00:55:22.039 Uttam Kumaran: in this whole part, like, that’s my job, is to kind of bridge that gap, so Amber, Zoran on her team, Clarence can… can sort of pick up the baton, and so I think we’re… we’re… we’re getting into a better place there. We… what I told the team is we’re not gonna be able to go
441 00:55:22.040 ⇒ 00:55:39.139 Uttam Kumaran: 100, you know, 100 miles into any of these areas, but I do want to start to canvas and then pick a couple to go deeper on. And really, what you can expect from us at the end is, like, we’re just gonna show, like, okay, here’s the ROI of a bunch of things that we can do.
442 00:55:39.170 ⇒ 00:55:42.909 Uttam Kumaran: here’s the ROI, and the effort, and, like.
443 00:55:43.320 ⇒ 00:55:48.889 Uttam Kumaran: Let’s choose some that we can try to go after, you know, and make a difference, but…
444 00:55:48.960 ⇒ 00:56:03.669 Uttam Kumaran: I… I think, again, like, there’s… there’s a ton of opportunity. Like, we’re seeing really, really, some really simple, and some more complex ways to really move the needle. So it’s not, like, so far we’re, like.
445 00:56:03.770 ⇒ 00:56:10.099 Uttam Kumaran: okay, we haven’t found anything, like, nothing we can do. It’s, like, sort of like picking the right thing.
446 00:56:10.250 ⇒ 00:56:12.590 MattBurns: No, I agree, and I… I think the…
447 00:56:12.820 ⇒ 00:56:16.029 MattBurns: presentation where Clarence was there, that had a lot of…
448 00:56:17.860 ⇒ 00:56:28.259 MattBurns: you know, you fine-tune that, and that’s gonna… that’s gonna be very helpful. And then, you know, today, things like, just the fact that those website
449 00:56:29.030 ⇒ 00:56:35.339 MattBurns: you know, with click-to-buy, those figures are… yeah, we can turn… you can turn that around a little bit. That’s a… that’s a…
450 00:56:35.500 ⇒ 00:56:38.169 MattBurns: That’ll be very… that’ll be very helpful, yeah.
451 00:56:38.320 ⇒ 00:56:39.070 MattBurns: Yeah.
452 00:56:39.070 ⇒ 00:56:50.239 Steven: Yeah, do you know… is there any update? I know Bobby’s had a few interviews, but when is y’all’s goal to have… I’m just wondering if we’ll have a new marketing director… well, no, we won’t have anyone in place by the time y’all do the…
453 00:56:50.470 ⇒ 00:56:53.229 Steven: y’all’s first week of February, whatever, but…
454 00:56:53.230 ⇒ 00:56:53.580 MattBurns: Well…
455 00:56:53.580 ⇒ 00:56:53.990 Steven: timeline.
456 00:56:53.990 ⇒ 00:57:00.340 MattBurns: He’s trying to make a decision by the end of January, so that he’ll have one month with less.
457 00:57:00.660 ⇒ 00:57:03.619 MattBurns: Okay. So, he’s had some good interviews, so…
458 00:57:04.430 ⇒ 00:57:08.930 MattBurns: It’s possible they could… I mean, obviously.
459 00:57:09.030 ⇒ 00:57:13.449 MattBurns: We’ll show them the results of the presentation, whether they’re there for it live or not.
460 00:57:13.940 ⇒ 00:57:14.360 Steven: No.
461 00:57:14.360 ⇒ 00:57:20.190 MattBurns: Because even if he makes an offer at the end of the month, can that person start immediately? Do they have to wait 2 weeks, whatever, but…
462 00:57:20.330 ⇒ 00:57:22.370 MattBurns: But obviously, we’re gonna use the…
463 00:57:22.890 ⇒ 00:57:25.149 MattBurns: The results that we get from
464 00:57:25.940 ⇒ 00:57:32.640 MattBurns: this project, that’ll… that’ll shape a lot of what we do next year, or this year, so yeah, for sure.
465 00:57:33.670 ⇒ 00:57:37.350 MattBurns: But I just don’t know, you know, how soon can that person start, and so on.
466 00:57:37.820 ⇒ 00:57:38.440 Steven: Yeah.
467 00:57:40.640 ⇒ 00:57:47.110 Uttam Kumaran: Yeah, and also, if you guys have any feedback on, you know, any part of this presentation that,
468 00:57:47.320 ⇒ 00:57:59.640 Uttam Kumaran: Bobby would, like, you know, definitely double-click into that we can even prepare and go deeper on, that would be helpful. You know, I… I reflect a lot on our initial conversations about
469 00:57:59.640 ⇒ 00:58:14.510 Uttam Kumaran: what he mentioned around bundling and really, like, that, and so that’s where I’m kind of focused on. I know, like, everything around Click2Buy and the website is all sort of, like, greenfield, but if there’s any other parts of what we saw that you’re, like.
470 00:58:14.530 ⇒ 00:58:20.020 Uttam Kumaran: he’s gonna double-click into that, and we could go a step deeper and save some slides, like, I would love to hear that.
471 00:58:20.170 ⇒ 00:58:27.420 MattBurns: Well, our feeling, Utam, is… in fact, we’re moving a little more in that direction this year in terms of
472 00:58:27.660 ⇒ 00:58:31.050 MattBurns: Sending out…
473 00:58:31.390 ⇒ 00:58:44.109 MattBurns: outside salespeople so that they can bundle on most every lead we get. In other words, in the previous few years, we’ve channeled a lot of the
474 00:58:44.290 ⇒ 00:58:52.750 MattBurns: Phone calls, to in-house, An in-house… our in-house salespeople primarily only sell one thing.
475 00:58:53.630 ⇒ 00:59:00.709 MattBurns: Because if somebody calls for a pest issue, or a lawn care, or a lawn mowing, that’s what in-house sells.
476 00:59:01.040 ⇒ 00:59:04.969 MattBurns: what Bo and Bobby really are saying is, well, I’d rather
477 00:59:05.100 ⇒ 00:59:10.140 MattBurns: If I can get out there quickly, most of the people are gonna say, yeah, send out an inspector.
478 00:59:11.090 ⇒ 00:59:16.659 MattBurns: And the feeling is that inspector is going to do a better job of selling more than just one thing.
479 00:59:16.900 ⇒ 00:59:18.429 MattBurns: i.e. the bundling.
480 00:59:18.790 ⇒ 00:59:24.250 MattBurns: And particularly if we make the offer of the bundle attractive.
481 00:59:24.490 ⇒ 00:59:31.920 MattBurns: I.e, they get a discount, they get more reward points, they get whatever. Yeah. That kind of thing, because…
482 00:59:32.040 ⇒ 00:59:37.179 MattBurns: The strategy, which we still think makes sense.
483 00:59:37.530 ⇒ 00:59:43.800 MattBurns: Is if we acquire… if we… in particular, if we acquire a pest control customer.
484 00:59:44.720 ⇒ 00:59:48.190 MattBurns: The pest control customers tend to be pretty darn loyal.
485 00:59:48.690 ⇒ 00:59:51.879 MattBurns: Particularly if they really like their service tech.
486 00:59:52.180 ⇒ 00:59:58.370 MattBurns: And that allows that service tech to do the lead line.
487 00:59:58.690 ⇒ 01:00:03.649 MattBurns: For us to offer that customer services via…
488 01:00:03.760 ⇒ 01:00:10.970 MattBurns: You know, emails, via offers, and whatever, so the thought has been to build off of the pest control.
489 01:00:11.330 ⇒ 01:00:19.239 MattBurns: Give me the pest control, I can build the other stuff. And that’s really how we built a lot of the other services that we have. It was…
490 01:00:19.240 ⇒ 01:00:19.810 Uttam Kumaran: Yeah.
491 01:00:19.810 ⇒ 01:00:25.410 MattBurns: you’re an existing pest control customer, I’m gonna market to you for all the services. Totally. So…
492 01:00:25.530 ⇒ 01:00:26.840 Uttam Kumaran: Yeah, I mean, what we…
493 01:00:26.840 ⇒ 01:00:33.839 Steven: And again, part of some of the beginning stuff y’all went over, I think pest control, that same theory can work for some of the other services, and that’s.
494 01:00:33.840 ⇒ 01:00:34.889 MattBurns: through that.
495 01:00:34.890 ⇒ 01:00:49.739 Steven: ROI comes in, the pool customer is probably the same, basically the same way, so does the ROI on pool customer, you know, advertising them, a better ROI than a pest. But anyway, so that could kind of translate to a few of the other services we have as well. We’ve… yeah.
496 01:00:49.740 ⇒ 01:00:50.280 MattBurns: Yup.
497 01:00:50.410 ⇒ 01:00:51.000 Julie F: Easily.
498 01:00:51.000 ⇒ 01:00:52.140 Uttam Kumaran: Yeah, so I…
499 01:00:52.140 ⇒ 01:01:00.419 Julie F: If you’re looking for some sort of tracking on that, on bundling, when you get access, there is a code in there that’s an accounting discount code called bundle.
500 01:01:01.290 ⇒ 01:01:03.579 Julie F: So if you want to see how much we’re doing with that.
501 01:01:04.730 ⇒ 01:01:05.990 Uttam Kumaran: Okay, okay, perfect.
502 01:01:09.180 ⇒ 01:01:17.580 Uttam Kumaran: Yeah, I am interested to see, like, what other bundling offers that we can, recommend, given, like, what we’re seeing in the data, and that will really…
503 01:01:17.760 ⇒ 01:01:21.960 Uttam Kumaran: you know, that’ll be huge. And it’s also, like.
504 01:01:22.100 ⇒ 01:01:29.679 Uttam Kumaran: The time, like, in between whatever the person’s purchased first, at what point should we offer them a bundle, or what should a bundle include?
505 01:01:29.780 ⇒ 01:01:32.379 Uttam Kumaran: So that’s all things I think we’ll look into, so…
506 01:01:32.780 ⇒ 01:01:36.570 Steven: I think another piece that we didn’t really touch on today that kind of came up.
507 01:01:36.680 ⇒ 01:01:57.410 Steven: some of the ROI numbers and stuff, I think commercial is still something really on our minds, you know, how can we possibly market, and what does that look like? Because we know there’s some, even in Austin, where we are fairly saturated, I think there’s still room to grow in some of the divisions, but we think commercial is just somewhere across the board that we really can attack.
508 01:01:57.990 ⇒ 01:02:04.409 Uttam Kumaran: Yeah, so that’s something I think I left off, but similarly to, like, the website, like, kind of greenfield, so I think Amber…
509 01:02:04.620 ⇒ 01:02:08.990 Uttam Kumaran: one thing that we can work with Clarence on is putting together a deck purely on, like.
510 01:02:09.340 ⇒ 01:02:13.610 Uttam Kumaran: like, how you would architect a B2B, like, commercial sort of strategy.
511 01:02:13.960 ⇒ 01:02:17.380 Uttam Kumaran: You know, I know a lot of it is sort of direct sales, but
512 01:02:17.420 ⇒ 01:02:22.570 Uttam Kumaran: What… what we’ll kind of present on is… is… and in most of our businesses that we consult with.
513 01:02:22.570 ⇒ 01:02:45.400 Uttam Kumaran: B2B always lags the kind of consumer marketing, but it’s mostly the same sort of flows. In fact, in B2B, you actually have a lot more data, because you can easily say, like, our ICP is the, like, sort of X person within a hotel. They usually have the facilities manager title, and we’re gonna find all the facility managers in Austin.
514 01:02:45.400 ⇒ 01:02:50.760 Uttam Kumaran: And we’re gonna send them an offer in this way, and we’re gonna create landing pages around facility management.
515 01:02:50.940 ⇒ 01:03:02.540 Uttam Kumaran: And so there’s a lot of strategies from the consumer side that we can learn, that we can execute, like, really similarly to how we’re doing the consumer stuff. And so, totally, like, I would love to present on that.
516 01:03:03.790 ⇒ 01:03:04.910 MattBurns: Great. Cool.
517 01:03:07.320 ⇒ 01:03:08.280 Uttam Kumaran: Okay, so…
518 01:03:08.280 ⇒ 01:03:13.729 Steven: Y’all are coming… y’all are coming, San Antonio, we do a 9.30 Wednesday, is that right?
519 01:03:13.730 ⇒ 01:03:15.030 Uttam Kumaran: Yes, that’s correct.
520 01:03:15.470 ⇒ 01:03:18.200 Steven: Okay, were you gonna send out a cal- I don’t think I got a calendar in.
521 01:03:18.200 ⇒ 01:03:20.800 Uttam Kumaran: I was going to send that, I…
522 01:03:21.010 ⇒ 01:03:25.430 Uttam Kumaran: There is a placeholder on my account, but I will send that over, yeah.
523 01:03:25.810 ⇒ 01:03:26.800 Steven: Cool, yeah.
524 01:03:26.950 ⇒ 01:03:28.870 Uttam Kumaran: Yeah, we’ll be there at 9.30.
525 01:03:28.870 ⇒ 01:03:31.529 Steven: Clarence… is Clarence coming from that one, too?
526 01:03:31.530 ⇒ 01:03:32.630 Uttam Kumaran: Yeah, me, Clarence?
527 01:03:33.310 ⇒ 01:03:36.919 Steven: Do you want David in the whole presentation, or do you want to meet with him afterwards, or I don’t…
528 01:03:36.920 ⇒ 01:03:44.449 Uttam Kumaran: So we’re talking to David right after this, actually, or in an hour or so. I…
529 01:03:48.300 ⇒ 01:03:51.539 Uttam Kumaran: We’ll be going through most, you know, data.
530 01:03:52.930 ⇒ 01:03:55.710 MattBurns: The beginning, we’ll probably start with a lot of the…
531 01:03:55.750 ⇒ 01:03:59.740 Uttam Kumaran: Like, core data work, so… Cool.
532 01:04:00.340 ⇒ 01:04:04.460 Uttam Kumaran: But we’re also talking to him right today, and I can arrange with him.
533 01:04:04.980 ⇒ 01:04:05.650 Steven: Gotcha.
534 01:04:05.910 ⇒ 01:04:06.420 Uttam Kumaran: Okay.
535 01:04:06.420 ⇒ 01:04:07.010 Steven: Okay.
536 01:04:07.950 ⇒ 01:04:11.630 Steven: Yeah, I know Bo’s coming down, obviously I’ll be here,
537 01:04:12.570 ⇒ 01:04:22.030 Steven: if there’s anything from Dream you need, again, Natasha’s right down the road. If you wanted him to come by or anything, he’d probably be happy to. Obviously, David will be here, Yvette will be here, so…
538 01:04:22.530 ⇒ 01:04:23.100 Uttam Kumaran: Okay.
539 01:04:25.450 ⇒ 01:04:26.210 Uttam Kumaran: Okay.
540 01:04:26.460 ⇒ 01:04:30.860 Uttam Kumaran: Great, well… Thank you so much, this was great. Yeah, I appreciate it.
541 01:04:30.860 ⇒ 01:04:31.410 MattBurns: Thanks, guys.
542 01:04:31.680 ⇒ 01:04:32.749 Uttam Kumaran: Okay, talk to you soon.
543 01:04:33.200 ⇒ 01:04:33.690 MattBurns: Bye-bye.