Meeting Title: Brainforge Interview w- Amber Date: 2026-03-24 Meeting participants: Amber Lin, Allyson Marks
WEBVTT
1 00:00:05.230 ⇒ 00:00:06.379 Amber Lin: Hi there!
2 00:00:06.800 ⇒ 00:00:08.380 Allyson Marks: Hi, how’s it going?
3 00:00:08.380 ⇒ 00:00:11.819 Amber Lin: Pretty good! One sec, let me turn my camera on.
4 00:00:14.640 ⇒ 00:00:17.320 Amber Lin: Hi! Where are you based in?
5 00:00:17.320 ⇒ 00:00:19.269 Allyson Marks: I’m based out of Denver, how about you?
6 00:00:19.270 ⇒ 00:00:24.110 Amber Lin: Oh, I’m in LA, so it’s, like, 11 for you right now?
7 00:00:24.110 ⇒ 00:00:26.089 Allyson Marks: It’s 10, but it’s earlier…
8 00:00:26.090 ⇒ 00:00:26.440 Amber Lin: That’s you.
9 00:00:26.440 ⇒ 00:00:27.320 Allyson Marks: It wasn’t it?
10 00:00:27.320 ⇒ 00:00:31.480 Amber Lin: Yeah, it’s not o’clock, I just had my first meeting earlier.
11 00:00:32.130 ⇒ 00:00:35.600 Allyson Marks: Well, sorry for making you get on so early to do an interview.
12 00:00:35.600 ⇒ 00:00:38.889 Amber Lin: No, all good, I was already abed.
13 00:00:39.340 ⇒ 00:00:53.910 Amber Lin: Sounds good! Alright, so I think this interview is going to be pretty short. It’s just to, get to know you a little bit more, ask you some questions, and I’ll make sure at the end that you’ll also have time to ask us questions.
14 00:00:53.910 ⇒ 00:01:00.389 Amber Lin: So if we’re ready, we can start off with a quick introduction from you, and then I’ll also introduce myself.
15 00:01:00.970 ⇒ 00:01:14.160 Allyson Marks: Yeah, that sounds great, I’m ready. So, I’m Allison, obviously. I’m a product analyst, I’ve been a product analyst at AirDNA for the past several years, and I was also the only analyst at the company, so I was…
16 00:01:14.170 ⇒ 00:01:26.890 Allyson Marks: in charge of all of the product analytics, like, all of the reporting and dashboards, all of the event tracking and instrumentation and taxonomy, all of our experimentation, and then I’ve also done
17 00:01:26.890 ⇒ 00:01:34.760 Allyson Marks: kind of dabbled in the other departments as well, since I was the only analyst, so marketing, finance, CX, I’ve supported those teams as well.
18 00:01:34.770 ⇒ 00:01:48.009 Allyson Marks: And I also… I started my career at a marketing agency, so I do have some client-facing, like, consulting background. It’s been a little bit, but I have both of those sides, and that’s why I’m excited about this role, because I would like to combine those.
19 00:01:48.350 ⇒ 00:02:04.220 Amber Lin: Awesome! So a quick intro about me. My name is Amber. I joined Brainforge about a year ago. Started in the project management side, because I was helping us set up the PMO, and then I moved on to our strategy team, and I’m doing…
20 00:02:04.260 ⇒ 00:02:13.330 Amber Lin: Not product… product analytics work, but more of, like, consulting, analytics, some dashboard reporting building.
21 00:02:13.440 ⇒ 00:02:17.809 Amber Lin: And so that’s what I do at the company right now. And I think…
22 00:02:18.730 ⇒ 00:02:33.840 Amber Lin: to start off with the questions, I think my goal right now is to figure out where in our different roles would you slot in the best and be most… be the most interested in? So I wanted to open up the questions with.
23 00:02:33.870 ⇒ 00:02:42.480 Amber Lin: What do you want to do? Do you want to keep doing product analytics, or do you want to go more into…
24 00:02:42.600 ⇒ 00:02:50.940 Amber Lin: like, a management role, or want to have more client-facing or sales side? Like, what is more interesting to you?
25 00:02:51.230 ⇒ 00:03:11.020 Allyson Marks: Yeah, great question. I think definitely either the analytics or partially the management. I do really like the technical side of analytics. I think it’s really fun to dig in and find the answers that no one’s ever found before, but I do also really like the people side. Like, I don’t necessarily want to be locked in a closet running SQL all day by myself, like, that’s what’s…
26 00:03:11.020 ⇒ 00:03:19.150 Allyson Marks: drawing me to Brainforge is that I do like interacting with people, and, like, getting dumped to that aha moment, and just having those relationships, so…
27 00:03:19.340 ⇒ 00:03:25.319 Allyson Marks: Yeah, I guess analytics and or management side. I’m not super interested in, like, the sales side.
28 00:03:25.890 ⇒ 00:03:27.010 Amber Lin: Gotcha, okay.
29 00:03:31.100 ⇒ 00:03:45.880 Amber Lin: I think in our company, the way projects are… our project teams are, is we have a pretty lean team of, say, 3 to 5 people, and we pull… we have 3, main areas in the company. There’s the data.
30 00:03:45.880 ⇒ 00:03:56.460 Amber Lin: service, there’s the strategy, more analytics service, and then there’s the AI service. So on any given project, we might pull people from different teams. For example, usually.
31 00:03:56.460 ⇒ 00:04:04.490 Amber Lin: Is we pull someone from the data side to help us set up the connections, do the modeling, and then we pull someone from the analytics side to…
32 00:04:04.490 ⇒ 00:04:07.980 Amber Lin: To get it going, and then there’s a…
33 00:04:08.190 ⇒ 00:04:19.739 Amber Lin: Someone helping… someone that is overseeing the project and the client relationship, and so they will help plan out how things go, and they’re…
34 00:04:19.740 ⇒ 00:04:32.460 Amber Lin: responsible for managing the relationship and, pushing the clients… not pushing the clients, but trying to make the case for renewal and upsells. So, would you be…
35 00:04:32.670 ⇒ 00:04:46.080 Amber Lin: Is that… when I describe sales, is that what you were thinking, or were you thinking more of, like, cold calls and reaching out? Because I think they were thinking of putting you in more of that.
36 00:04:46.600 ⇒ 00:04:53.919 Amber Lin: Leading the project, or… Like, working with the clients and see how that goes.
37 00:04:54.340 ⇒ 00:05:09.870 Allyson Marks: Yeah, I was definitely imagining, like, cold calling, so hearing that it’s working with the clients, is less scary, and I used… I started my career in, like, a client services team. I was an account manager and a project manager, so I’m definitely familiar with that. It’s, been a little bit, but yeah.
38 00:05:11.070 ⇒ 00:05:11.660 Amber Lin: Cool.
39 00:05:11.780 ⇒ 00:05:14.479 Amber Lin: Let me put that down.
40 00:05:18.350 ⇒ 00:05:19.360 Amber Lin: Awesome.
41 00:05:20.930 ⇒ 00:05:25.409 Amber Lin: Let’s see… I think my next question is still…
42 00:05:25.690 ⇒ 00:05:34.790 Amber Lin: more on the work you did before. You said you were mostly doing product analytics. What type of…
43 00:05:35.290 ⇒ 00:05:42.090 Amber Lin: What type of industries did you work in, or was it just for product analytics of your own company?
44 00:05:42.480 ⇒ 00:05:51.039 Allyson Marks: Yeah, so it was… I was internal at our company, so it was, B2C, SaaS, subscription service,
45 00:05:51.580 ⇒ 00:05:55.500 Allyson Marks: Yeah, and then I’ve also done a bit of analytics for a more…
46 00:05:55.820 ⇒ 00:06:02.019 Allyson Marks: B2B enterprise cloud computing software company as well, but yeah. So I’ve been internal.
47 00:06:03.250 ⇒ 00:06:04.200 Amber Lin: Sounds good.
48 00:06:09.870 ⇒ 00:06:20.349 Amber Lin: Gotcha, and… you mainly just do product… you focus on product analytics, right? Not the other branches of analysis?
49 00:06:20.350 ⇒ 00:06:37.560 Allyson Marks: I’d say it’s varied over the years. Definitely the bulk of my experience is product analytics, but I was the only analyst of the company, so I got pulled into finance and marketing and CX, and I would say I’ve spent the last year doing a bit more, business analysis than just strictly product analysis, so…
50 00:06:37.590 ⇒ 00:06:41.539 Allyson Marks: I’ve dabbled in everything, product analytics is definitely, like, my core skill set, though.
51 00:06:42.370 ⇒ 00:06:44.469 Amber Lin: Sounds good.
52 00:06:45.330 ⇒ 00:06:58.239 Amber Lin: Let’s go into one of those… one of those, like, analytics projects, maybe either product analytics or any other project. Can you just walk me through how that went? Just tell me what it was like.
53 00:06:58.570 ⇒ 00:07:01.870 Allyson Marks: Yeah, absolutely. So, I’d say probably…
54 00:07:01.970 ⇒ 00:07:06.460 Allyson Marks: half of my projects are handed down from leadership, and the other half, I’m…
55 00:07:06.790 ⇒ 00:07:13.429 Allyson Marks: going out into the world to determine what’s most helpful for a team and how I can help move the roadmap forward, but…
56 00:07:13.550 ⇒ 00:07:15.979 Allyson Marks: I guess one of the example cases
57 00:07:16.230 ⇒ 00:07:28.940 Allyson Marks: we were going through our annual revenue model to see how much we think we’re gonna get from marketing, and how the product is gonna perform, and how that powers the whole financial future of the business, and I noticed some…
58 00:07:29.080 ⇒ 00:07:34.469 Allyson Marks: assumptions that weren’t really matching up, and so I went off on my own to dig in.
59 00:07:34.960 ⇒ 00:07:48.889 Allyson Marks: I was digging into the product and the finance data and discovered that basically a lot of our customers were purchasing on a later timeline than we had anticipated, and so that one had big implications across the company. We had to…
60 00:07:48.930 ⇒ 00:07:59.179 Allyson Marks: adjust our revenue model quite significantly to account for that and bring it back to the board, so I worked with the CFO on that. It also changed a lot of
61 00:07:59.570 ⇒ 00:08:07.979 Allyson Marks: Decisions we made about the onboarding experience in the product, knowing that customers were going to come back multiple times before purchasing, so we…
62 00:08:08.160 ⇒ 00:08:22.610 Allyson Marks: redesigned some flows, set up some new experiences… experiments, sorry, for onboarding, and then I also worked with the marketing team on their go-to-market strategy, and we ended up shifting a lot of budget into kind of more reactivation campaigns, so…
63 00:08:22.690 ⇒ 00:08:32.090 Allyson Marks: Yeah, that’s, I guess, an example of one that is rooted in product analytics, but ended up impacting more departments across the company and involved pulling in more data.
64 00:08:32.770 ⇒ 00:08:47.559 Amber Lin: That’s exciting. I have a follow-up question of, like, similar to this, you mentioned half of your other projects, you’ll have to go out and figure out what’s helpful. Can you give me more details on that? I’m very interested in, like, how you,
65 00:08:48.200 ⇒ 00:08:55.359 Amber Lin: Like, find out what’s helpful for people, and kind of, advocate for new work streams.
66 00:08:55.360 ⇒ 00:09:10.089 Allyson Marks: Yeah, absolutely. So, as the only analyst, there wasn’t really, like, an analytics roadmap or anyone, like, providing strong direction apart from myself, so mostly I try to embed myself in the processes with the product managers as much as possible, so I hear…
67 00:09:10.090 ⇒ 00:09:15.650 Allyson Marks: What they’re working on in the roadmap, what their priorities are, work with leadership to understand
68 00:09:15.650 ⇒ 00:09:22.320 Allyson Marks: what our goals are for the business, and I guess an example is always retention. Companies always want to improve their retention, and so…
69 00:09:22.410 ⇒ 00:09:23.710 Allyson Marks: I will…
70 00:09:23.750 ⇒ 00:09:38.319 Allyson Marks: dig into our various work streams and understand where… what does our retention look like right now? Where… what are indicators that someone’s going to retain? Work back from there and see where are we losing people on the way to retention.
71 00:09:38.320 ⇒ 00:09:53.909 Allyson Marks: and start kind of digging through those workflows, and then working with the PMs to say, hey, I’m seeing a drop-off in this specific workflow, if we can increase the completion rate by 5%, I think it’ll have this impact on retention, so…
72 00:09:53.910 ⇒ 00:10:00.190 Allyson Marks: can we work in an experiment in the roadmap to make that happen? And then I think that also really helps
73 00:10:00.250 ⇒ 00:10:05.389 Allyson Marks: one, the business performance, but two, the way I also like to communicate about it with the PMs is…
74 00:10:06.150 ⇒ 00:10:07.120 Allyson Marks: like…
75 00:10:07.220 ⇒ 00:10:15.289 Allyson Marks: It helps us divide engineering time, so instead of just having engineers build features and ship them and not know what’s happening, it’s like.
76 00:10:15.580 ⇒ 00:10:26.719 Allyson Marks: if the engineers work on this, we think it’s gonna have X payoff, and it kind of helps them prioritize the workloads as well. So, I guess a mix of capacity prioritization and business outcomes.
77 00:10:27.670 ⇒ 00:10:33.550 Amber Lin: Cool, okay. I… I like hearing that, because in our work.
78 00:10:33.550 ⇒ 00:10:49.210 Amber Lin: We usually start off with some… a slightly smaller engagement where the client has a very clear ask, but our goal is to work our way up, so along the way, a big part of what we do is, especially on the strategy team, is try to see, hey.
79 00:10:49.420 ⇒ 00:11:04.080 Amber Lin: do the clients have a need there? Can we be slotted in? Can we go earn that work stream from them? So it’s, I’m very happy to hear that. That’s kind of your job, essentially, is to find things, find things that you can be helpful in.
80 00:11:04.440 ⇒ 00:11:07.700 Allyson Marks: Yeah, people don’t always know what they need, so you have to help them a little bit.
81 00:11:07.700 ⇒ 00:11:22.749 Amber Lin: Yeah, sounds good. Speaking of that, can you tell me, like, a situation where someone, or one of the stakeholders, or some of your execs didn’t know what they need, but you had to figure out… figure that out for them? What was that like?
82 00:11:23.050 ⇒ 00:11:29.410 Allyson Marks: Yeah, honestly, kind of all the time, I think…
83 00:11:32.570 ⇒ 00:11:35.140 Allyson Marks: I’m trying to think of, like, a good example.
84 00:11:35.620 ⇒ 00:11:41.529 Allyson Marks: This isn’t… fully to your question, but I think… A lot of times.
85 00:11:41.870 ⇒ 00:11:53.369 Allyson Marks: stakeholders, execs have a generic goal. Again, I’ll use the retention one. They’re like, we want to improve retention, because that’s a great way to grow the business. They don’t really understand what that means, or how to get there, and so, again,
86 00:11:53.550 ⇒ 00:12:07.020 Allyson Marks: I’ll start digging in and kind of working backwards, like, alright, we want to increase ARR. What’s the quickest way to do that? Increase retention. What are our contributing behaviors that lead to retention? Kind of just, like, work backwards to the smaller metrics.
87 00:12:07.290 ⇒ 00:12:08.440 Allyson Marks: And then I think…
88 00:12:09.700 ⇒ 00:12:18.499 Allyson Marks: the most important thing with the analysis is helping the stakeholders make a decision, and so I’ve often found, like, I’ll have a
89 00:12:18.590 ⇒ 00:12:32.799 Allyson Marks: I’ll dig through the data, I’ll find a conclusion. Like, I… one example is that I found that over half of our customers, our paying customers, were dormant. They just hadn’t logged in for months, they weren’t doing anything, we were just kind of passively collecting money from them. And so…
90 00:12:32.820 ⇒ 00:12:47.709 Allyson Marks: I brought that to the stakeholders, and I said, here… like, my conclusion is that we have a large group of people we can work on improving their retention, and the stakeholders took it a different way. They were like, maybe we have a more transactional product than we thought, and they ended up
91 00:12:47.710 ⇒ 00:12:53.529 Allyson Marks: going in a different direction from it, and so I think, ultimately, my job as an analyst is not necessarily to
92 00:12:53.740 ⇒ 00:12:56.400 Allyson Marks: tell them what to do, it’s to help them…
93 00:12:57.050 ⇒ 00:13:06.559 Allyson Marks: see the options and what decisions they can make from the data, if that makes sense. And so I guess that’s a case where they didn’t really know what they wanted, and…
94 00:13:07.120 ⇒ 00:13:10.799 Allyson Marks: Finding the data helped them figure out what they did want, and it’s a.
95 00:13:10.800 ⇒ 00:13:11.879 Amber Lin: Okay, like…
96 00:13:11.880 ⇒ 00:13:14.570 Allyson Marks: everyone has different opinions, I guess, so yeah.
97 00:13:15.200 ⇒ 00:13:18.960 Amber Lin: Gotcha, okay. Just writing that down.
98 00:13:23.480 ⇒ 00:13:25.030 Amber Lin: Cool.
99 00:13:25.880 ⇒ 00:13:35.519 Amber Lin: I want to open the floor for you to ask me some questions, and then as I… as we talk, I think some… I… I might come up with more questions along the way, but
100 00:13:36.450 ⇒ 00:13:37.890 Amber Lin: Ask away.
101 00:13:37.890 ⇒ 00:13:45.940 Allyson Marks: Yeah, absolutely. So I guess, I’m curious, so I… you said you’ve shifted roles in your time here, like.
102 00:13:46.250 ⇒ 00:13:50.849 Allyson Marks: what makes you able to do that? Like, I guess, how are people…
103 00:13:51.480 ⇒ 00:14:00.360 Allyson Marks: successful at Brainforge, and, like, what’s the flexibility like for moving roles around, or growth? Because it seems like you’ve done a lot in a short amount of time.
104 00:14:00.840 ⇒ 00:14:09.049 Amber Lin: Yeah, so I think a few parts of that question, what does success look… success looks like, and what is, kind of.
105 00:14:09.160 ⇒ 00:14:14.119 Amber Lin: flexibility look like in the company. So, I think…
106 00:14:14.970 ⇒ 00:14:27.080 Amber Lin: first on flexibility, because I have strong feelings about that. I’ve been able… I’ve been doing so many different things at this company, especially because when I joined a year ago, we were so small.
107 00:14:27.690 ⇒ 00:14:31.400 Amber Lin: It was very easy to change paths, and…
108 00:14:31.400 ⇒ 00:14:50.549 Amber Lin: I did feel a very strong support from the company to say, hey, if this is what you want to do, because you’re already with us, we’re happy to give you a path and work with you to see if you can start owning some different work streams. And even now, as we’re more mature, I think
109 00:14:50.750 ⇒ 00:15:08.230 Amber Lin: as we… we can do our own rules, but they’re so… because our teams are so, intertwined, especially on different projects, there’s always someone from another service that’s on there. It’s very easy to collaborate on that, start learning, start getting interested.
110 00:15:08.530 ⇒ 00:15:09.850 Amber Lin: And…
111 00:15:10.030 ⇒ 00:15:17.439 Amber Lin: So there’s always the ability to learn, and also the ability to officially to say, hey, I want to start on this.
112 00:15:17.440 ⇒ 00:15:39.699 Amber Lin: for example, on AI, and you can say, hey, I want to get started on this, what is the best way for me to get involved? They might put you on a project to say, hey, experiment with some of this, or say, hey, you can start helping with this part of the workstream and see how you feel, and there’s support for getting certificates, support for getting different things, so I do think flexibility is
113 00:15:40.310 ⇒ 00:15:43.329 Amber Lin: It’s pretty high in this company.
114 00:15:43.750 ⇒ 00:15:54.479 Amber Lin: So, like, of course, if we do our… we will do the original part of our work well, but if there… if we’re certain, hey, I want to explore, or I want to pivot, I do think that’s still possible.
115 00:15:55.130 ⇒ 00:16:03.950 Allyson Marks: That’s really nice to hear. I feel like that’s a benefit with small companies, is that you get to wear a lot of hats, which can be a pro or a con, but it sounds like it’s been a pro for you so far.
116 00:16:03.950 ⇒ 00:16:12.620 Amber Lin: Yeah. Well, would you want to, say, explore a… a new field, or want to have… be able to pivot to a different field?
117 00:16:14.170 ⇒ 00:16:26.300 Allyson Marks: I always like the option, you know? Like, I just always want to keep learning. I’m a kind of a nerd who really likes school and, like, taking classes and stuff, so I like to have the option to pivot if something interests me.
118 00:16:26.770 ⇒ 00:16:46.250 Allyson Marks: And I also, I mean, I’m generally intrigued by AI. I feel like now is kind of the time to get involved in AI before the wave passes me by, so I don’t really know what that means, to be honest, but, like, it’s just, I like the opportunity to get exposed to different areas of the business. So, yeah, that’s just a perk for me in general, is that kind of environment where learning is encouraged.
119 00:16:46.250 ⇒ 00:16:51.720 Amber Lin: Yeah, and honestly, I can tell you a little bit as an example of how I was…
120 00:16:51.760 ⇒ 00:17:03.849 Amber Lin: encouraged and kind of forced to learn in this company, especially on the AI side, because they’re… we have an internal team that just develops AI and tries to
121 00:17:03.850 ⇒ 00:17:18.769 Amber Lin: put out things that’s, following the most current stuff, and I felt so much FOMO just watching what they do, and they send out celebrations of, hey, we did this, and I’m like, what is this? And they were… they’re building new tools for
122 00:17:18.990 ⇒ 00:17:32.889 Amber Lin: our internal works, for example, our PM, like, project management workflow is mostly automated right now, so that was really cool to see what they did, and they’re starting to automate some of the…
123 00:17:32.890 ⇒ 00:17:42.109 Amber Lin: Data documentation tasks, so seeing what they do, makes me have… at least always keeps me up to date on the
124 00:17:42.630 ⇒ 00:17:55.160 Amber Lin: basic knowledge of the AI world, and then gives me a slight FOMO, so I’ve been trying to get, get back into developing a little bit of the AI stuff, so I think…
125 00:17:55.340 ⇒ 00:18:03.820 Amber Lin: the company always learns on a very fast pace, so if that’s what you like, I think you’ll find a lot of people that’s also like that in the company.
126 00:18:04.420 ⇒ 00:18:20.849 Allyson Marks: That’s really nice to hear. I have another question about the, like, company makeup. I’ve worked in consulting and client services before. How would you say, like, the work-life balance is? Because I know when clients and timelines and projects get involved, it can be crazy sometimes, so how does Brainforge manage that?
127 00:18:21.330 ⇒ 00:18:25.650 Amber Lin: Yeah, I think, for me personally.
128 00:18:25.770 ⇒ 00:18:40.660 Amber Lin: my work-life balance has improved significantly once I moved from project management to an analyst. So, speaking from an analyst perspective, my work-life balance is pretty good. I work remote, so I save.
129 00:18:40.720 ⇒ 00:18:50.900 Amber Lin: like, an hour or two from… for my personal life each day already. Hours are decently flexible. Our main requirement is that
130 00:18:50.900 ⇒ 00:19:04.479 Amber Lin: We have 4 hours overlap with EST, and usually at night, when you log off, you just log off, because half of our team is in Europe or Asia, so they’re not on anyways.
131 00:19:04.570 ⇒ 00:19:08.020 Amber Lin: I think, usually.
132 00:19:08.110 ⇒ 00:19:26.169 Amber Lin: my days are mostly just 8-hour days. I would say I start a little bit earlier, because I’m on the West Coast. I start about, say, 7.30 or 8, and then I work usually until, say, 4 o’clock, depending on when I start, and…
133 00:19:26.480 ⇒ 00:19:42.840 Amber Lin: if, say, one day I wanted to… I wasn’t… I wasn’t in the right state in the afternoon, I might pause and then work on it later at night. Usually, as long as a deliverable is sent and people know your updates, they know what you’re up to.
134 00:19:42.980 ⇒ 00:19:50.949 Amber Lin: I think it’s usually fine to just… just have a very flexible day, and in terms of pressure from the client.
135 00:19:52.170 ⇒ 00:20:03.509 Amber Lin: I would say it’s mostly restrained inside the workday. There’s no… I haven’t got expectations of, Amber, please work on this, even though it’s 8pm. Like, that has not happened before to me.
136 00:20:04.100 ⇒ 00:20:13.040 Allyson Marks: Okay, that’s really nice to hear, because I’ve experienced a lot of times where client work can kind of blur the boundaries between traditional working hours, so that’s very encouraging to hear.
137 00:20:13.040 ⇒ 00:20:13.590 Amber Lin: Yeah.
138 00:20:14.930 ⇒ 00:20:20.109 Allyson Marks: I guess this is probably… Hard to answer, but, like.
139 00:20:20.310 ⇒ 00:20:32.690 Allyson Marks: Are there common engagements that you have with clients? Like, is there kind of a standard project that a lot of clients opt for, or is it super different across clients and industries?
140 00:20:33.560 ⇒ 00:20:46.530 Amber Lin: I think I can answer that to a certain extent. We have 3 main service lines, and clients usually fall between one, or a combination of two of them. So, we have…
141 00:20:46.740 ⇒ 00:20:56.060 Amber Lin: we started off as a data company, and then soon took on more strategy and AI work. So we, of course, have a lot of basic data work, which is
142 00:20:56.060 ⇒ 00:21:15.279 Amber Lin: hey, help us do all this ingestion, do this modeling, because everything feels like a mess. So there’s a lot of that type of work, which, as analysts, we’re not really involved in that project. And then there’s more and more of, say, help us figure out what should we do
143 00:21:15.370 ⇒ 00:21:24.859 Amber Lin: doing a report, help us establish a BI tool, and work with the execs to establish reporting that helps them. That’s a part of
144 00:21:25.220 ⇒ 00:21:38.269 Amber Lin: the analytics work. And there’s also… I think there’s a small but growing product analytics workstream. We have someone on product analytics, so he works with different companies to
145 00:21:38.420 ⇒ 00:21:56.170 Amber Lin: like, work with the products, and he kind of owns a relationship, so I think when you join, you’ll mostly be on that work stream for product analytics. And then there, of course, there’s AI work, which kind of… there’s… they’re AI-only clients, but there’s also AI clients that
146 00:21:56.340 ⇒ 00:22:01.930 Amber Lin: That, say, the data or strategy client starts to want AI,
147 00:22:02.040 ⇒ 00:22:20.270 Amber Lin: especially this year, so we’ll add that on to their existing service, so mainly to help them, hey, combine all our internal sources to help us establish an agent, or help us use AI on our data so we can answer questions, with AI.
148 00:22:20.650 ⇒ 00:22:32.459 Amber Lin: So I think that’s the main functions of work that we have, and of course, in terms of industries, we have some CPG companies, we have some, pharmacy companies.
149 00:22:32.600 ⇒ 00:22:35.869 Amber Lin: A little bit of SaaS, and a little bit of service.
150 00:22:36.130 ⇒ 00:22:42.040 Allyson Marks: Okay, that’s really cool. When… it probably varies by company and client, too, but, like.
151 00:22:42.070 ⇒ 00:22:58.079 Allyson Marks: do you find that you’re generally filling the gap? Like, they don’t have a role internally to perform this, or is it more supporting one or two roles internally that just don’t have the time to do it? Like, I guess, how technical are your stakeholders, and what kind of stakeholders do you work with?
152 00:22:58.390 ⇒ 00:23:15.440 Amber Lin: Gotcha, I hear you. So I would say it’s a combination, especially for data work. I would say for the bigger companies, there’s always an internal team, but they… the task might be just too big, or
153 00:23:15.550 ⇒ 00:23:18.639 Amber Lin: Like, they’re somewhat technical, but…
154 00:23:20.820 ⇒ 00:23:34.789 Amber Lin: I… I think we… I remember this project for data modeling. We still had, like, we were external helpers that helped their internal team, if that makes sense.
155 00:23:35.480 ⇒ 00:23:42.369 Amber Lin: And on… one of the bigger pharmacy projects were essentially their integrated data team.
156 00:23:42.510 ⇒ 00:23:53.559 Amber Lin: So I think a lot of it is we’re providing expertise. We may start out being, like, a helping hand to fill
157 00:23:53.950 ⇒ 00:24:02.100 Amber Lin: to help with extra hours, but most of the time, I think we’re coming in to say, we’re the experts in this field, let us help you do this the right way.
158 00:24:02.520 ⇒ 00:24:15.750 Allyson Marks: Okay, that’s cool. How… this probably also varies, but, like, is there a general length of contract with the clients? Like, are you working with them for 3 months, or, like, multiple years, or how does that span?
159 00:24:15.940 ⇒ 00:24:26.879 Amber Lin: Yeah, the company’s still pretty young. The company, I think, is 2 to 3 years old right now. We used to have a lot shorter projects, and I would say when we first
160 00:24:26.880 ⇒ 00:24:42.479 Amber Lin: put a client in the pipeline when they’re still pretty small and we’re trying to grow the account. The duration might be, say, a month to two months, like a quick discovery project to see what we could do. And then the project’s usually, like, 6 months… three to six months,
161 00:24:42.520 ⇒ 00:24:46.349 Amber Lin: The 066 was pretty standard, sometimes there’s a little bit more.
162 00:24:47.100 ⇒ 00:24:53.769 Allyson Marks: Okay, that’s fun, I guess, because then you get exposed to a lot of different companies and businesses and ways of working, and it keeps it fresh.
163 00:24:53.770 ⇒ 00:24:54.320 Amber Lin: Yeah.
164 00:24:54.990 ⇒ 00:25:13.610 Amber Lin: Awesome! I think we’re almost at time. Thank you for the conversation. It was really nice talking to you, and I enjoyed your questions, and I think you answered my questions very well. So, I think the next step is the operations team will get back to you in… within a week or two, no matter what the decision is.
165 00:25:13.610 ⇒ 00:25:19.259 Amber Lin: And then I think they already sent you, if you were to move forward, what the interviews would look like.
166 00:25:19.930 ⇒ 00:25:24.400 Allyson Marks: Awesome. Thank you so much. I really appreciate the time. I hope you have a smooth rest of your day.
167 00:25:24.400 ⇒ 00:25:25.810 Amber Lin: You too. Alrighty.
168 00:25:25.810 ⇒ 00:25:26.400 Allyson Marks: Bye.
169 00:25:26.400 ⇒ 00:25:27.160 Amber Lin: Bye!