Meeting Title: Uttam <> Robert <> Jakob Date: 2024-12-20 Meeting participants: Robert Tseng, Jakob Kagel, Uttam Kumaran
WEBVTT
1 00:01:07.270 ⇒ 00:01:08.490 Uttam Kumaran: Yeah, oh, dude.
2 00:01:08.740 ⇒ 00:01:09.869 Jakob Kagel: Hey? How’s it going.
3 00:01:09.870 ⇒ 00:01:10.803 Uttam Kumaran: What up.
4 00:01:11.957 ⇒ 00:01:13.389 Jakob Kagel: Like the background.
5 00:01:13.920 ⇒ 00:01:16.530 Uttam Kumaran: Thanks. We had our little Christmas party yesterday.
6 00:01:16.530 ⇒ 00:01:17.949 Jakob Kagel: Yeah, yeah.
7 00:01:19.770 ⇒ 00:01:23.769 Jakob Kagel: The brain for it’s branded background is.
8 00:01:24.221 ⇒ 00:01:26.480 Uttam Kumaran: It’s a small details, dude.
9 00:01:26.480 ⇒ 00:01:28.790 Jakob Kagel: It is, it is for sure.
10 00:01:29.247 ⇒ 00:01:30.619 Uttam Kumaran: How are you?
11 00:01:30.975 ⇒ 00:01:32.040 Jakob Kagel: Well, doing well.
12 00:01:32.686 ⇒ 00:01:33.979 Uttam Kumaran: Hey, Robert.
13 00:01:34.640 ⇒ 00:01:35.470 Robert Tseng: Hey, guys.
14 00:01:35.830 ⇒ 00:01:36.940 Jakob Kagel: Hey? How’s it going.
15 00:01:37.700 ⇒ 00:01:39.060 Robert Tseng: Good to meet you, Jacob.
16 00:01:39.280 ⇒ 00:01:40.730 Jakob Kagel: Great to meet you, great to meet you.
17 00:01:41.750 ⇒ 00:01:51.474 Uttam Kumaran: Cool. I I guess. I’ve given, you know, Jacob given you some context and Roberts context. But kind of circle on everything. So Jacob Roberts,
18 00:01:52.179 ⇒ 00:02:06.149 Uttam Kumaran: Robert actually ran. Is close partner with us on a couple of clients, but ran a data analytics company. And we’re actually sort of merging under one house under Brainforge, sort of expanding our team, but also
19 00:02:06.170 ⇒ 00:02:17.079 Uttam Kumaran: basically trying to get from the bronze medal to this to the gold, and you know, running towards that and expanding and doing a lot of things. I think one of the
20 00:02:17.080 ⇒ 00:02:39.417 Uttam Kumaran: you know reasons I wanted to call you is, we’re expanding into a lot of new work on the data analyst side. You know, we we are. Robert brings a ton of experience there. And we’re not. Only we have a few sort of products and services we’re offering that hopefully, I think it’s going to be a little bit more straightforward than the the pool parts work. Kind of like. Maybe maybe I’ll let Robert
21 00:02:39.710 ⇒ 00:02:54.123 Uttam Kumaran: sort of give maybe a little bit of intro of sort of the stuff that we’re doing for clients. But I just to give you context. Robert Jacob, as I mentioned, is a killer on the home depot team in terms of data, analytics and
22 00:02:54.430 ⇒ 00:03:13.029 Uttam Kumaran: but that job, of course, is very boring compared to the cool stuff we do but also Jacob, what Jacob was here in Austin, and he’s in New York now. Actually, yeah. So moved recently, I’m trying to chase his dreams of being you know, New York City Star. So.
23 00:03:13.030 ⇒ 00:03:13.579 Jakob Kagel: I mean, you know.
24 00:03:13.580 ⇒ 00:03:18.840 Uttam Kumaran: Left a small left, a small left, a small town, but he’s also from Georgia, so he’s.
25 00:03:18.840 ⇒ 00:03:19.250 Jakob Kagel: Yeah.
26 00:03:19.250 ⇒ 00:03:21.989 Uttam Kumaran: He’s a fish out of water in New York.
27 00:03:21.990 ⇒ 00:03:26.450 Jakob Kagel: Yeah, I mean, you know, just just just new experiences. Man, I mean, awesome.
28 00:03:26.450 ⇒ 00:03:26.790 Uttam Kumaran: That’s all.
29 00:03:26.790 ⇒ 00:03:31.199 Jakob Kagel: Great. But you know, everyone gotta go see New York and whatnot.
30 00:03:31.200 ⇒ 00:03:42.049 Uttam Kumaran: And Jacob’s a big foodie. I feel like I’m pretty big Foodie Jacobs. I got a different level like thinks like, we think very similar like, oh, yeah, you.
31 00:03:42.050 ⇒ 00:03:42.940 Jakob Kagel: I mean.
32 00:03:43.140 ⇒ 00:03:52.740 Uttam Kumaran: Cause. I I was like, Hey, I want to get Ceviche like, do I have 5 Ceviche places in Austin? You need to go. It was like a very specific request. I still haven’t been to all of them, but.
33 00:03:52.940 ⇒ 00:03:53.460 Jakob Kagel: Absolutely.
34 00:03:53.460 ⇒ 00:03:56.589 Uttam Kumaran: Little foodie we’re talking about. And so I would say, Jacob, you’re probably.
35 00:03:56.590 ⇒ 00:03:57.979 Jakob Kagel: You gotta eat. You gotta eat.
36 00:03:57.980 ⇒ 00:03:58.480 Uttam Kumaran: Yeah, but.
37 00:03:58.480 ⇒ 00:04:02.194 Jakob Kagel: Yeah, great, great to meet you. Yeah. Like it was home said like,
38 00:04:02.540 ⇒ 00:04:30.624 Jakob Kagel: So I started like, in data at expedia, like working for verbo is like a data scientist. And then, yeah, now, I work like, I manage like customer intelligence team at home depot. And yeah, like you said. I’ve been done some work like for Brainforge in the past like for cool parts to go a lot of like dashboarding. We did like a little bit of like customer segmentation. All kind of stuff. But yeah, I think I’ll be more than happy to sort of help
39 00:04:30.960 ⇒ 00:04:39.369 Jakob Kagel: you as you guys sort of are expanding. And yeah, I think I have like a pretty wide skill set. So
40 00:04:39.450 ⇒ 00:04:43.699 Jakob Kagel: you know, I probably feel like I can. I can do whatever you all need me to do.
41 00:04:44.370 ⇒ 00:04:50.341 Robert Tseng: Sure. Yeah, no, I think that yeah. Sounds like you have like a pretty broad set of bi experiences. And
42 00:04:51.090 ⇒ 00:05:03.669 Robert Tseng: I guess, for us at least like what the clients that we’re bringing in kind of just give you some of the tool stack that we use. So we’re we have strong vendor partnerships with mixed panel and amplitude. So they bring, like
43 00:05:03.780 ⇒ 00:05:06.755 Robert Tseng: decent number of leads to us. And
44 00:05:07.300 ⇒ 00:05:23.860 Robert Tseng: yeah. So I think a good number of our analytics engagements start in the product analytics realm. So a lot of it is usually like Sas or app companies like wanting to measure how users are using their product. But yeah, if it’s more on the e-commerce.
45 00:05:23.860 ⇒ 00:05:26.509 Jakob Kagel: Lot of a, BAB testing, or like.
46 00:05:26.760 ⇒ 00:05:51.510 Robert Tseng: Yeah. So for the earlier, like earlier stage companies, they don’t have any ab testing. And you can’t run into significant experiment experiments, anyway. So it’s like something that’s a nice to have. But I wouldn’t say is, the biggest thing for them is really just customer segmentation upfront being able to understand? Like, okay, you have all these active users like, how do we start to like make sense of them? So we apply some pretty general frameworks to it things like
47 00:05:51.510 ⇒ 00:06:07.429 Robert Tseng: growth accounting so just breaking that out to, you know, active, retained chur churned or churn signal customers, users that have been resurrected as well, so, stuff like that. That’s just like different time, based cuts of like user activity and trying to bucket them that way.
48 00:06:08.148 ⇒ 00:06:34.890 Robert Tseng: And then a lot of them are really trying to answer the question like, what gets our users to stick? And so, yeah, a lot of it is kind of like early stage retention, reporting as well trying to figure out what are like those best views for them. So we find that those are probably like the highest value projects kind of in the 1st couple of months. And then yeah, I mean, if the if the client is growing and the the needs evolve. That’s usually when we bring in. Like, the
49 00:06:34.890 ⇒ 00:06:46.569 Robert Tseng: more I mean by that point, we’ve already looked at all their data, and we have, we may push them towards the full stack kind of like data engineering pipeline all the way through, like what? I guess Tom’s team did for it.
50 00:06:47.720 ⇒ 00:06:53.980 Robert Tseng: Yeah. And so once that’s in place, and there’s a lot more flexibility to go after other types of projects. But
51 00:06:54.751 ⇒ 00:06:57.240 Robert Tseng: so yeah, I would say, like with those.
52 00:06:57.650 ⇒ 00:07:01.979 Robert Tseng: you know on with the product analytics, focused clients. We do have a pretty like
53 00:07:02.250 ⇒ 00:07:05.159 Robert Tseng: predictable process at this point. So it almost be.
54 00:07:05.160 ⇒ 00:07:05.790 Robert Tseng: That’s great
55 00:07:05.790 ⇒ 00:07:15.249 Robert Tseng: kind of like plugging you in and just kind of showing you like, yeah, these are kind of the core deliverables that we drive to. And you know you will have the liberty to kind of like, take it to that point.
56 00:07:15.653 ⇒ 00:07:20.750 Robert Tseng: But yeah, I think what I would be curious to know is, Yeah, I guess, like, from your
57 00:07:20.920 ⇒ 00:07:36.179 Robert Tseng: what would be? Yeah, I mean, you have broad experiences, but like what’s like your special like, what would you describe as like your specialty? So like, I know I can like tap you if they if they’re asking that specific question. And we, you can be like that strategy or architect partner for them on.
58 00:07:36.180 ⇒ 00:07:45.009 Jakob Kagel: Yeah, I mean, definitely, like, segmentation is something like I’ve done a lot of work on. I mean, at Home depot. Obviously, I think our segmentation is
59 00:07:45.010 ⇒ 00:08:09.979 Jakob Kagel: pretty like sophisticated, like, you know, we built like a whole machine learning model with like light. Gbm, where we use, you know, like 170 features to split like pro versus consumer. But I mean, when we were doing like segmentation, like for pool parts. You know, it was a lot sort of more simple like, we didn’t have as many data points and as good of data to even run something like that. So we had to rely a lot on things like.
60 00:08:09.980 ⇒ 00:08:28.029 Jakob Kagel: you know, their email address and how they were kind of like self identifying and things like that. But yeah, I mean, I think anything sort of like related to customer data. That’s all that I do like at home depot like I just finished up my customer lifetime value for them. And
61 00:08:28.210 ⇒ 00:08:40.207 Jakob Kagel: we do a lot of sort of like demographics analysis like with new stores opening. We also work a lot with like customer survey and like feedback data, and we actually use like a lot of that in segmentation, too.
62 00:08:40.500 ⇒ 00:08:40.900 Uttam Kumaran: Nice.
63 00:08:40.909 ⇒ 00:08:49.099 Jakob Kagel: So yeah, I work heavily, sort of with this customer data right now. But you know, I’m really pretty comfortable. I think across
64 00:08:49.359 ⇒ 00:09:02.289 Jakob Kagel: a lot of things, I mean at Verbo, you know, my work was more like around, like forecasting, like occupancy, forecasting for like flywheel markets, and then also do, did like a lot of like compliance work and things like for them as well.
65 00:09:03.140 ⇒ 00:09:03.840 Robert Tseng: Got it.
66 00:09:04.240 ⇒ 00:09:12.939 Robert Tseng: Yeah, I mean, I think definitely that industry experience in the hospitality sector would probably be helpful because you maybe like we could leverage. We could leverage that like.
67 00:09:13.070 ⇒ 00:09:16.460 Robert Tseng: you know, he knows the compliance around. Like, you know, we we come across.
68 00:09:16.460 ⇒ 00:09:17.020 Jakob Kagel: Right.
69 00:09:17.020 ⇒ 00:09:30.589 Robert Tseng: You know, hospitality based kind of clients here and there. On the segmentation front. Super helpful, I think. Yeah. In my mind. I wanna like Peg, you as like the segmentation expert. I will say that.
70 00:09:30.590 ⇒ 00:09:31.100 Jakob Kagel: Guys.
71 00:09:31.100 ⇒ 00:09:34.130 Robert Tseng: Yeah, I mean, no one’s using 170 features for the class.
72 00:09:34.130 ⇒ 00:09:39.860 Jakob Kagel: Right? Exactly. I mean, it just depends, like, you know, what kind of data that you have really, that you can segment on.
73 00:09:39.860 ⇒ 00:09:46.090 Uttam Kumaran: Most of our most of the clients we have care about their customers, and definitely don’t have like a
74 00:09:46.380 ⇒ 00:09:57.022 Uttam Kumaran: you know, the common things like, I want a 3 60 view customer. I want to know the different touch points we want. You know, definitely, it’s something that I think everybody is so so interested in
75 00:09:57.510 ⇒ 00:09:58.470 Uttam Kumaran: totally.
76 00:09:58.970 ⇒ 00:10:18.140 Jakob Kagel: Sure. I mean, I think with like pool parts to like our experience right? It’s like we’re trying to show like retention, like across sort of like the different segments, and like, you know, which segments will spend more like over time, like who has higher, like average order values like things like that kind of just like, know all the classic like cuts and stuff that we probably want to show.
77 00:10:19.070 ⇒ 00:10:45.510 Robert Tseng: Cool. Yeah. Now, I would say, like, you know, as we kind of prepare like things are, no one’s kicking off in the next week. So we’re like, really gearing up for q 1 and trying to figure out where we’re staffing people on different clients. Now. Yeah, I think leading up to that. It’d be great like I can show you like kind of some internal stuff that we’ve done like. I have this whole like go to market motion on like ab experimentation, and how we, how we pitch it, and how we run it.
78 00:10:45.510 ⇒ 00:10:45.960 Jakob Kagel: Right.
79 00:10:46.202 ⇒ 00:10:52.740 Robert Tseng: But I would love to kind of build something like that out on the customer segmentation side as well. So we can start to kind of like.
80 00:10:52.880 ⇒ 00:10:54.690 Robert Tseng: yeah, like, kind of build that.
81 00:10:55.110 ⇒ 00:11:05.429 Jakob Kagel: So you want to build like a standard like basically like process for segmentation, right where you say, like for basically any client that this is how you’re gonna sort of do segmentation right?
82 00:11:05.840 ⇒ 00:11:18.589 Robert Tseng: Yeah, I guess it doesn’t have to be like so nitty, gritty, and like how it’s executed. But really, I mean, what are those like, deliver. What are what are the deliverables that like I could put in front of an executive? And they’ll be like, Yeah, I want that.
83 00:11:18.590 ⇒ 00:11:40.444 Jakob Kagel: Say, like, yeah, this is like, this is realistically like, these are realistic things that we can deliver. And yeah, okay, yeah, no. I’m definitely happy to work with you on that. And yeah, I think that’s really good. Like, sort of expectation. Setting is really important. Obviously, so it all basically comes back to. You know, what kind of data are you collecting? What do we have that we can use?
84 00:11:40.740 ⇒ 00:11:53.910 Uttam Kumaran: Yeah, the nice thing is we’re doing like full stack now. So our pitch is like, we pitch basically like, you guys have these problems. Let us solve it. But then we’re coming in. Not only on the analyst side, we’re coming in across the entire stack, which is great.
85 00:11:53.910 ⇒ 00:11:54.240 Jakob Kagel: Right.
86 00:11:54.240 ⇒ 00:12:21.910 Uttam Kumaran: We can actually solve these problems we’re not relying on. We’re not saying like, Oh, we can only do what’s in our scope. We we will have like pretty much the full scope. The other thing is the name of our game for this next year is like standardization. So we’re trying to standardize and create clear service offerings for everything we do. And so you’ll also be working with other. We have, like one other analyst that’s most likely gonna start. And then we have another person that’s also working. So there’ll actually be like a crew of people. And then.
87 00:12:21.910 ⇒ 00:12:22.390 Jakob Kagel: Sounds good.
88 00:12:22.390 ⇒ 00:12:43.764 Uttam Kumaran: I’ll be working on clients and then the other thing we’re doing is we actually do have like engineering meetings now every week across, like the entire engineering crew. Which helps. Because if we want to do more cross functional stuff, basically, I’m like kind of leading some of the more platform things like, how does data ends work with the analysts, work with the aes. So
89 00:12:44.070 ⇒ 00:12:44.530 Jakob Kagel: Sounds good.
90 00:12:44.530 ⇒ 00:12:45.190 Uttam Kumaran: Yeah.
91 00:12:47.580 ⇒ 00:12:50.960 Jakob Kagel: Well, yeah, I mean, I’m really happy to help out, you know, whatever y’all need me to do.
92 00:12:51.220 ⇒ 00:12:53.349 Jakob Kagel: I’m here to do it. So let’s.
93 00:12:53.350 ⇒ 00:12:59.929 Uttam Kumaran: Cool, and then I think, Jacob, I still have all your details and stuff, so I’ll just like turn the lights back on and stuff
94 00:13:00.368 ⇒ 00:13:07.931 Uttam Kumaran: and then maybe we can plan next week. This next 2 weeks is kind of holiday stuff. So maybe let’s plan something for
95 00:13:08.390 ⇒ 00:13:12.909 Uttam Kumaran: I don’t know sometime, maybe the week before New Year’s, or that this week or weekend.
96 00:13:12.910 ⇒ 00:13:14.010 Uttam Kumaran: Okay.
97 00:13:14.010 ⇒ 00:13:17.630 Jakob Kagel: Whenever you, whenever you all want to jump on, I’m I’m ready. So you know.
98 00:13:17.630 ⇒ 00:13:18.130 Uttam Kumaran: Cool.
99 00:13:18.130 ⇒ 00:13:26.469 Jakob Kagel: I’m ready to go. But yeah, I guess so. Like you don’t have like. There’s no like specific clients that you have like lined up yet, or like.
100 00:13:26.790 ⇒ 00:13:27.700 Uttam Kumaran: We do?
101 00:13:28.230 ⇒ 00:13:35.419 Jakob Kagel: Okay, yeah, it’s just like high level, just like what kind of businesses like we’re talking about.
102 00:13:36.620 ⇒ 00:13:42.789 Robert Tseng: Yeah, I mean, I think your background probably fits like we just we just closed like a E-com client that’s also
103 00:13:43.480 ⇒ 00:13:49.219 Robert Tseng: moving to brick and mortar as well. So they’re in like the telehealth space or like.
104 00:13:49.220 ⇒ 00:13:50.610 Jakob Kagel: Interesting. Okay, okay.
105 00:13:50.610 ⇒ 00:13:59.280 Robert Tseng: Like a health health based E-com company like the G. The glp. One semaglutide kind of like company, like weight loss. Kind of pills. Kind of stuff.
106 00:13:59.650 ⇒ 00:14:05.100 Jakob Kagel: Interesting? Is it like main mainly like subscription based? Or is it like.
107 00:14:05.310 ⇒ 00:14:16.070 Robert Tseng: Yeah, subscription base. And yeah, they just they’re opening a few brick and mortar like in the in q, 1 of 2025. And so I think, like having your kind of experience
108 00:14:16.490 ⇒ 00:14:17.710 Robert Tseng: there. I’m like kind of knowing.
109 00:14:17.710 ⇒ 00:14:18.140 Uttam Kumaran: How to come.
110 00:14:18.140 ⇒ 00:14:20.860 Jakob Kagel: They’re selling that ozempic, or what’s.
111 00:14:20.860 ⇒ 00:14:23.510 Uttam Kumaran: Yeah, but dude, there’s they’re selling it on your block. Dude good
112 00:14:23.510 ⇒ 00:14:24.909 Uttam Kumaran: could walk down the street and get it.
113 00:14:24.910 ⇒ 00:14:27.479 Jakob Kagel: You’re on bio something. Yeah, yeah.
114 00:14:29.780 ⇒ 00:14:30.330 Jakob Kagel: Yeah.
115 00:14:30.330 ⇒ 00:14:37.420 Jakob Kagel: Okay, okay, that’s interesting. I mean, it’s just helpful, like, you know, to for me, sort of like to think about like what kind of businesses like sort of.
116 00:14:37.420 ⇒ 00:14:45.240 Uttam Kumaran: It’s like that. And then, of course, we have some like, we have some b 2 b Saas, where it’s just like normal digital companies.
117 00:14:47.730 ⇒ 00:14:52.429 Uttam Kumaran: That’s it’s it’s probably will lead more like Ecom, and that for the short term we have
118 00:14:52.890 ⇒ 00:14:55.673 Uttam Kumaran: their proposals out for folks.
119 00:14:56.850 ⇒ 00:14:58.420 Uttam Kumaran: But we’ll see.
120 00:14:58.420 ⇒ 00:15:17.689 Jakob Kagel: Is like interesting, because it’s like, yeah, then it’s like, it depends kind of to like how much like, what kind of data you can have on your customers, because you really can segment them, too, by like kind of like share of wallet to where it’s like, you know, you’re like, okay, you know, they maybe only have, like this many, you know, licenses or whatever. But like.
121 00:15:18.320 ⇒ 00:15:21.050 Jakob Kagel: You know their company is this big
122 00:15:21.440 ⇒ 00:15:45.589 Uttam Kumaran: And the nice thing for b 2 b Saas is like a lot. All the data is online. So it’s all available whether it’s the Pla product data, whether it’s the financial data. You know, everything is basically there. A lot of, I think, what the b 2 b Saas companies want to know is like, how does product usage affect churn and stuff like that like sort of looking at activity? And how that correlates to.
123 00:15:46.000 ⇒ 00:15:46.679 Jakob Kagel: For sure.
124 00:15:46.680 ⇒ 00:15:48.249 Uttam Kumaran: Activity or something like that. That’s.
125 00:15:48.550 ⇒ 00:15:51.140 Uttam Kumaran: you know, a lot of what we’ve seen. People ask.
126 00:15:52.440 ⇒ 00:15:55.650 Jakob Kagel: Sounds good. Sounds good. Yeah. I mean, definitely, I think.
127 00:15:56.020 ⇒ 00:16:10.259 Jakob Kagel: yeah, we’ll we’ll make it work. But yeah, I think I don’t know. Yeah, I definitely think, yeah, I understand. Like your goal, like with the standardized sort of like segmentation offering. And yeah, we should just sort of figure out, kind of like
128 00:16:10.390 ⇒ 00:16:24.509 Jakob Kagel: how to best frame that like across sort of like these different industries. And like, you know, B, 2 b versus B to C, like, sort of maybe have, like, you know, sort of different considerations. There. But yeah, happy to help with the.
129 00:16:25.170 ⇒ 00:16:32.260 Robert Tseng: Yeah, I mean, I I was like, you know, things are, gonna be a bit slow to start in the in the New Year, as people kind of trickle in from the holidays, but.
130 00:16:32.260 ⇒ 00:16:32.760 Jakob Kagel: Sure.
131 00:16:32.760 ⇒ 00:16:44.300 Robert Tseng: I I’d love to get. You know we mentioned kind of putting together that playbook for the customer segmentation. I also heard you mentioned kind of like voice of customer like you’re, you know, kind of the like. The.
132 00:16:44.300 ⇒ 00:17:04.080 Jakob Kagel: I mean, yeah, that’s like a big one. I mean, I think that’s something like we were. We had, I mean, we only had kind of like one question, basically a checkout like for pool parts to go. And that was something kind of, we’re trying to figure out. It’s like, you know, getting customer feedback data so valuable if you can get, you know, if you have good questions and you can get.
133 00:17:04.089 ⇒ 00:17:06.079 Uttam Kumaran: Even strategy around survey.
134 00:17:06.079 ⇒ 00:17:06.729 Jakob Kagel: Right.
135 00:17:06.730 ⇒ 00:17:12.919 Uttam Kumaran: Some sort of offering around how to implement, survey, how how to implement, post purchase survey like something.
136 00:17:12.920 ⇒ 00:17:15.229 Jakob Kagel: I think that’s yeah. That’s the that’s the.
137 00:17:15.230 ⇒ 00:17:17.730 Uttam Kumaran: Mps. Maybe it’s all around like Nps.
138 00:17:17.730 ⇒ 00:17:18.569 Jakob Kagel: Yeah.
139 00:17:18.730 ⇒ 00:17:30.939 Jakob Kagel: yeah, I mean, yeah, definitely, because that’s like, I mean, a lot of these companies like you said, like, they don’t know anything about their their customers, and they don’t collect any information either. So it’s like, it’s really hard kind of
140 00:17:31.230 ⇒ 00:17:56.570 Uttam Kumaran: Yeah. And and the but the thing we’re struggling with is like we, I mean. And to kind of give you a sense of like the people we’re trying to bring on. One is me and Robert can talk at like the strategy level, right? But there’s a lot of data people who are like I’ll just execute like, just tell me what to execute. I’ll execute. So we’re we’re looking for kind of both. But I feel like you’re you. I mean, you clearly work with you work with a bunch of execs. And you know, sort of like, how
141 00:17:57.310 ⇒ 00:18:02.079 Uttam Kumaran: these ideas and then getting it done like we’re gonna get it done. That’s that’s.
142 00:18:02.080 ⇒ 00:18:12.760 Jakob Kagel: Definitely. I’m getting dragged through the ringer like, for sure. So I mean, I’m definitely like my boss is like whatever I build like with customer lifetime value. Man. It was like
143 00:18:13.140 ⇒ 00:18:24.120 Jakob Kagel: man. They just don’t poke like a million holes like, you know, and whatever you’re trying to do, so it’s like, because it’s like, yeah, we like present so much like the Elt. And like, you know.
144 00:18:24.120 ⇒ 00:18:24.590 Uttam Kumaran: Yeah.
145 00:18:24.590 ⇒ 00:18:32.759 Jakob Kagel: It’s. But it’s like, Yeah, I mean, you kind of you learn a lot about sort of all the considerations and like everything that you really kind of have to like.
146 00:18:33.220 ⇒ 00:18:36.179 Jakob Kagel: you know. Think about before you could just sort of like
147 00:18:36.440 ⇒ 00:18:38.539 Jakob Kagel: hand something over or whatnot. But
148 00:18:38.890 ⇒ 00:19:02.059 Uttam Kumaran: Yeah. And we’re looking for sort of like, people like, you are like willing to go into strategy sessions with, like the Cmo the CEO and talk about Ltv. As a concept right? And then the implementation is also there. But some of the stuff that is customer facing, because the things that me and Robert want to go spend time was like account management like, how is this whole project going? How do we expand? Renew a lot of.
149 00:19:02.060 ⇒ 00:19:02.480 Jakob Kagel: Retirement.
150 00:19:02.480 ⇒ 00:19:12.939 Uttam Kumaran: Spent in the strategic stuff, which is great, but it never allows us to go one step beyond and be like. Can we renew this customer. Can we expand to other parts of the business
151 00:19:13.690 ⇒ 00:19:28.200 Uttam Kumaran: profile, especially around the data analyst side, especially around Ltv customer. And you know, customer happiness survey. You could totally own that, even from like a how do you go from 0 to one and create like a survey program? How do you go.
152 00:19:28.200 ⇒ 00:19:28.580 Jakob Kagel: Right.
153 00:19:28.580 ⇒ 00:19:48.600 Uttam Kumaran: One on like Ltv. You have no knowledge of Ltv. Or segmentation. How do we even start and craft like a plan towards that? That’s like perfect. And then, basically again, we’ll we’ll standardize that. And then when we go to clients, and they’re like, Tell me some of the sort of things that you guys do. It’s like we can start these programs for you. And then we kind of get a sense of like.
154 00:19:48.760 ⇒ 00:19:55.569 Uttam Kumaran: how much time and how much effort, and then that allows us to sort of just sell. You know better if we have those kind of like.
155 00:19:56.280 ⇒ 00:19:59.470 Uttam Kumaran: you know, plans and and services. So that’s kind of like, where.
156 00:20:00.750 ⇒ 00:20:13.889 Jakob Kagel: Yeah, for sure. I mean, definitely, I can help you upsell, for sure. I think, yeah, it’s like, I think, with Ltv, like, I remember. So we met. I met with this guy that you had introduced me to the guy is company was called like wild, I think right.
157 00:20:13.890 ⇒ 00:20:15.989 Uttam Kumaran: Yeah, so Robert knows. Clint, too. Yeah.
158 00:20:15.990 ⇒ 00:20:16.359 Robert Tseng: Oh, you have!
159 00:20:16.360 ⇒ 00:20:33.719 Jakob Kagel: Yeah. So I think, like, I mean, I never like, you know, they wouldn’t like spill like the secret sauce or whatever. But I’m like 95%. Sure. Kinda like, I understand, like what they’re like, their model is basically. And I think it’s like very similar sort of like what we ended up.
160 00:20:33.720 ⇒ 00:20:35.640 Uttam Kumaran: See, they’re looking at frequency.
161 00:20:35.640 ⇒ 00:20:36.330 Jakob Kagel: Yeah, it’s like.
162 00:20:36.330 ⇒ 00:20:37.989 Robert Tseng: Buy till you die. Model. Yeah.
163 00:20:37.990 ⇒ 00:20:45.509 Jakob Kagel: Right, exactly, exactly. And the reason like it wouldn’t work like with pool parts was like, you just had too many people that only transacted once.
164 00:20:45.510 ⇒ 00:20:45.840 Jakob Kagel: but
165 00:20:45.840 ⇒ 00:20:57.879 Jakob Kagel: and like your distribution is just like way, too skewed. But yeah, I mean, that’s like, that’s obviously something you have to consider to like when you’re tell people that you can do customer lifetime values like you have to look at like
166 00:20:58.370 ⇒ 00:21:01.089 Jakob Kagel: if they only have people, that if people only transact.
167 00:21:01.090 ⇒ 00:21:02.709 Uttam Kumaran: It’s like, once a year. Yeah.
168 00:21:02.710 ⇒ 00:21:08.480 Jakob Kagel: If they only if they don’t have any customer attention, basically like full boards that like basically 0 customers.
169 00:21:08.480 ⇒ 00:21:11.439 Uttam Kumaran: No, it’s very. It’s a very like enigma company, like very.
170 00:21:11.440 ⇒ 00:21:14.819 Jakob Kagel: At least that we could like that. We could track, you know. I mean.
171 00:21:14.820 ⇒ 00:21:20.530 Uttam Kumaran: But see, the problem with them is they should have start. They should have sold. They should realize that and been like, we need to sell subscription based products.
172 00:21:20.530 ⇒ 00:21:21.050 Uttam Kumaran: Right?
173 00:21:21.050 ⇒ 00:21:27.159 Uttam Kumaran: We, I told them, you should actually get into selling cleaner or like maintenance products and shit like that.
174 00:21:27.600 ⇒ 00:21:28.120 Uttam Kumaran: Bye-bye.
175 00:21:28.120 ⇒ 00:21:43.270 Jakob Kagel: Yeah, I mean, I think, too, like they probably had like, not just sort of rehash the whole thing or whatever. But they probably had because of that, too. And like the way that their website was set up, they probably did have repeat customers. But people are probably just using different emails.
176 00:21:43.270 ⇒ 00:21:44.650 Jakob Kagel: you know, every time.
177 00:21:44.650 ⇒ 00:21:48.800 Jakob Kagel: so that they could get like the discount or whatever the 10%. So it’s like.
178 00:21:48.800 ⇒ 00:21:49.430 Uttam Kumaran: Yeah, I, just.
179 00:21:49.430 ⇒ 00:21:53.904 Jakob Kagel: I don’t know but that’s just like a flaw with their business. They kinda like, you know. But anyway.
180 00:21:54.140 ⇒ 00:22:00.980 Uttam Kumaran: Yeah, they’re an enigma customer. But we’re not. Gonna let’s see, we’re not gonna run any new clients are not running. That’s kind of a shit show client.
181 00:22:01.450 ⇒ 00:22:03.450 Uttam Kumaran: They like us. They like us a lot, but like.
182 00:22:03.450 ⇒ 00:22:05.889 Jakob Kagel: Are you all? Are you all still working with us?
183 00:22:05.890 ⇒ 00:22:07.439 Uttam Kumaran: Yeah, yeah, we’re still working for that.
184 00:22:07.440 ⇒ 00:22:09.239 Uttam Kumaran: Okay, yeah, yeah, we’re still good
185 00:22:09.240 ⇒ 00:22:10.990 Uttam Kumaran: for them. And but like.
186 00:22:10.990 ⇒ 00:22:11.730 Jakob Kagel: Good.
187 00:22:11.730 ⇒ 00:22:15.299 Uttam Kumaran: I have an alliance with them because they were like that. They gave me a shot, you know.
188 00:22:15.300 ⇒ 00:22:17.550 Jakob Kagel: For sure. No, I don’t think they’re bad people at all.
189 00:22:17.550 ⇒ 00:22:19.490 Uttam Kumaran: No, no, but it’s a tough. It’s like
190 00:22:19.600 ⇒ 00:22:35.189 Uttam Kumaran: every every 3 months. I basically, I’m like, Yo, we gotta think about like, how do we do this long term call me he’d be like dude. I need every skew on every platform by 4 o’clock. I’m like Bro. I can’t. But whatever.
191 00:22:35.190 ⇒ 00:22:35.680 Jakob Kagel: Yeah.
192 00:22:35.680 ⇒ 00:22:41.390 Uttam Kumaran: Yeah, the new clients we’re running like we’re gonna run everything by the book and kind of have a very clear offering.
193 00:22:41.390 ⇒ 00:22:44.080 Jakob Kagel: I mean, Dom. You know I’m I’m scrappy, too. Man.
194 00:22:44.080 ⇒ 00:22:44.690 Uttam Kumaran: No, I know
195 00:22:45.170 ⇒ 00:22:58.159 Uttam Kumaran: that’s that’s what will save us. That stuff will save us when they when there is like an urgent thing, or they’re like something. That’s where we’re all gonna be really great. Other firms will be like, oh, we have a 2 day sla like.
196 00:22:58.160 ⇒ 00:22:59.030 Jakob Kagel: Reactivity.
197 00:22:59.030 ⇒ 00:23:02.879 Uttam Kumaran: We still have that, but for our sanity too, and.
198 00:23:02.880 ⇒ 00:23:03.230 Jakob Kagel: Yeah.
199 00:23:03.230 ⇒ 00:23:05.169 Uttam Kumaran: Start standardizing where we can.
200 00:23:05.470 ⇒ 00:23:09.339 Jakob Kagel: But yeah, telehealth ozempic, whatever we’ll figure it out, you know.
201 00:23:09.340 ⇒ 00:23:12.730 Jakob Kagel: Yeah, whatever they need, we’ll we’ll make it work, you know.
202 00:23:12.730 ⇒ 00:23:14.040 Uttam Kumaran: Yeah, yeah.
203 00:23:15.145 ⇒ 00:23:16.250 Jakob Kagel: Alright!
204 00:23:16.539 ⇒ 00:23:28.400 Uttam Kumaran: Alright. So let’s chat, Robert. And then, yeah, I’ll just let you know, Jacob, when we want to have like a 1st meeting. And then, yeah, we’re planning on staffing what all the staffing looks like for Jan. So like
205 00:23:28.790 ⇒ 00:23:29.880 Uttam Kumaran: back to you in the next 2 weeks.
206 00:23:29.880 ⇒ 00:23:55.029 Jakob Kagel: Sounds great. Yeah, I mean exactly. I mean the, you know, even if if you’ll have any information or whatever that you can share over like before we meet, just like in general, like about the client. This about like I said, like the type of business. Whatever like, you know, the client name or whatever. Just so I can get sort of like a feel or like an idea of like what I think. Sort of like, you know the data is gonna look like or whatnot is good, too. So but sounds great.
207 00:23:55.410 ⇒ 00:24:01.510 Jakob Kagel: sure, awesome. Well, great to meet you. And yeah, y’all have a great weekend and happy holidays.
208 00:24:02.366 ⇒ 00:24:03.519 Robert Tseng: Thanks guys.
209 00:24:03.770 ⇒ 00:24:04.290 Jakob Kagel: Yeah.
210 00:24:04.290 ⇒ 00:24:04.730 Uttam Kumaran: Bye.