Meeting Title: Mack Weldon x Brainforge Data Integration Date: 2025-12-12 Meeting participants: Fireflies.ai Notetaker Joules, Joules Asuncion, Robert Tseng, Kelli Peluso
WEBVTT
1 00:01:53.510 ⇒ 00:01:54.780 Joules Asuncion: Hi, Robert.
2 00:01:56.180 ⇒ 00:01:57.139 Robert Tseng: Hey, Jed.
3 00:02:00.160 ⇒ 00:02:06.510 Joules Asuncion: Would it be okay if I have my note-taker in here, or should I… Should I kick him?
4 00:02:06.720 ⇒ 00:02:10.949 Robert Tseng: It’s fine.
5 00:02:11.520 ⇒ 00:02:15.919 Robert Tseng: I mean, we’ll ask, we’ll ask. If she says no, then we can kick it.
6 00:02:16.680 ⇒ 00:02:17.640 Joules Asuncion: Okay, awesome.
7 00:02:17.910 ⇒ 00:02:18.490 Robert Tseng: Yeah.
8 00:02:27.010 ⇒ 00:02:33.049 Joules Asuncion: I already pulled out a report for, for the Q4 2025, all the years coming in.
9 00:02:33.390 ⇒ 00:02:34.550 Joules Asuncion: Okay.
10 00:02:34.740 ⇒ 00:02:37.660 Joules Asuncion: Yeah, I tried pulling in the report yesterday afternoon.
11 00:02:38.440 ⇒ 00:02:39.250 Robert Tseng: Great.
12 00:02:41.810 ⇒ 00:02:45.460 Robert Tseng: I am meeting with Luke later. Maybe I’ll just add you to that call.
13 00:02:45.690 ⇒ 00:02:48.500 Robert Tseng: Okay. If you want to just sit into it, yeah.
14 00:02:49.280 ⇒ 00:02:53.069 Joules Asuncion: I think I have a meeting with Gabe later today.
15 00:02:53.070 ⇒ 00:02:54.009 Robert Tseng: Oh, at the same time.
16 00:02:54.130 ⇒ 00:02:58.590 Robert Tseng: Okay, well, if it doesn’t conflict, you can join. I’ll just… I’ll just put it in. It’s optional for you.
17 00:05:28.980 ⇒ 00:05:30.869 Robert Tseng: I just sent her an edge, you’ll see.
18 00:05:56.220 ⇒ 00:05:57.309 Robert Tseng: Hey, Kelly!
19 00:05:57.310 ⇒ 00:05:58.440 Kelli Peluso: Hey, how’s it going?
20 00:05:58.760 ⇒ 00:05:59.750 Robert Tseng: Good, how are you?
21 00:05:59.750 ⇒ 00:06:04.079 Kelli Peluso: Good! Thanks so much for, getting this call set up.
22 00:06:04.080 ⇒ 00:06:05.150 Robert Tseng: Yeah, of course.
23 00:06:06.300 ⇒ 00:06:08.180 Robert Tseng: I saw that you’re based in New York.
24 00:06:09.270 ⇒ 00:06:15.850 Kelli Peluso: Yes, yeah, our office is right in Nomad, and then I’m on Long Island, so I think.
25 00:06:15.850 ⇒ 00:06:16.409 Robert Tseng: Oh, cool.
26 00:06:16.410 ⇒ 00:06:18.789 Kelli Peluso: Well, times a week, yeah. Are you New York as well?
27 00:06:18.790 ⇒ 00:06:20.880 Robert Tseng: I am, yeah, I’m by Columbus Circle.
28 00:06:21.030 ⇒ 00:06:26.910 Kelli Peluso: Oh, nice. Yeah, it’s, brutally cold today and yesterday.
29 00:06:26.910 ⇒ 00:06:29.450 Robert Tseng: Yeah, I haven’t stepped outside yet.
30 00:06:29.450 ⇒ 00:06:40.960 Kelli Peluso: Don’t. There’s… yeah, it’s… it’s very, very cold. I was surprised yesterday because the temperature was a little higher than it had been, but it was really, really brutal in the city.
31 00:06:40.960 ⇒ 00:06:41.640 Robert Tseng: Yeah.
32 00:06:41.720 ⇒ 00:06:46.820 Kelli Peluso: Oh, by the way, this is my colleague, Jed. I’m just having him sit in. He kind of helps with, kind of.
33 00:06:46.820 ⇒ 00:06:53.330 Robert Tseng: Yeah, just all things admin for us, and, I guess, are you okay with him having his note-taker here?
34 00:06:53.330 ⇒ 00:06:54.610 Kelli Peluso: Oh, yeah. Okay.
35 00:06:54.610 ⇒ 00:06:55.220 Robert Tseng: Alright.
36 00:06:55.510 ⇒ 00:06:57.199 Kelli Peluso: Hi, it’s great to meet you.
37 00:06:57.630 ⇒ 00:07:00.350 Kelli Peluso: Awesome.
38 00:07:00.350 ⇒ 00:07:02.540 Joules Asuncion: Thank you, Kelly. Hi.
39 00:07:02.720 ⇒ 00:07:03.530 Kelli Peluso: Hi.
40 00:07:04.300 ⇒ 00:07:10.620 Kelli Peluso: Yeah, so I really appreciate you taking the time to chat with me,
41 00:07:10.660 ⇒ 00:07:28.050 Kelli Peluso: Ethan’s awesome in, like, connecting everyone in his network, yeah. That’s great. So just a little bit, I guess, like, about myself and where we are. So I work for Mack Weldon, we’re a menswear e-commerce company. We have one brick and mortar… well, we have one brick and mortar store in Hudson Yards,
42 00:07:28.220 ⇒ 00:07:31.549 Kelli Peluso: But we heavily rely on Shopify for…
43 00:07:31.750 ⇒ 00:07:40.709 Kelli Peluso: all of our, business-related revenue data and order counts and everything. We previously had been using Snowplow, for.
44 00:07:40.710 ⇒ 00:07:41.040 Robert Tseng: Yeah.
45 00:07:41.040 ⇒ 00:07:58.910 Kelli Peluso: web analytics. I’m a team of one. I have a part-time contractor right now. We’re very lean, so Snowplow, unfortunately, we loved the platform, but it was difficult in terms of a process. I don’t, you know, put the pa- the,
46 00:07:59.430 ⇒ 00:08:18.030 Kelli Peluso: the pixels, you know, on, like, the site and organize that, so we were partnering with our third-party engineering, and it was just a really broken process. So last year, we migrated to Amplitude, to have kind of more of a space for our e-commerce team and our non-technical users to be able to use the platform.
47 00:08:18.320 ⇒ 00:08:20.530 Kelli Peluso: And unfortunately, we’ve had…
48 00:08:21.360 ⇒ 00:08:28.689 Kelli Peluso: so many issues with their, like, their Shopify plugin, that we are no longer even paying for our contract. They’ve…
49 00:08:29.230 ⇒ 00:08:42.690 Kelli Peluso: zeroed out the contract, for us, because we found several bugs in how they’re tracking sessions, and, we were never able to get accurate data, so we’re looking to move, potentially, to MixedPanel,
50 00:08:42.690 ⇒ 00:08:43.220 Robert Tseng: Sure.
51 00:08:43.220 ⇒ 00:08:58.379 Kelli Peluso: and try a different platform, one of the most important things is having that kind of usability for our non-technical users to be able to create events and track them. Then on my end, I’m just ingesting it into our data warehouse. We use Omni right now as our BI tool.
52 00:08:58.380 ⇒ 00:08:59.350 Robert Tseng: Oh, great, yeah.
53 00:08:59.350 ⇒ 00:09:00.330 Kelli Peluso: So I couldn’t…
54 00:09:00.530 ⇒ 00:09:09.370 Kelli Peluso: get that all joined to our order data and everything. We’ve really been without it now since April, so we’re looking to get this off the ground,
55 00:09:09.530 ⇒ 00:09:17.480 Kelli Peluso: And do our due diligence a bit more on… with people who have used the tool, and if you have any experience with it in the e-commerce space.
56 00:09:17.480 ⇒ 00:09:17.960 Robert Tseng: Yeah.
57 00:09:17.960 ⇒ 00:09:24.960 Kelli Peluso: we’re pretty bummed, because there’s so much about Amplitude that’s great, and features that I think would have really benefited us,
58 00:09:25.490 ⇒ 00:09:31.229 Kelli Peluso: And… Yeah, so it’s a bit, kind of, about me and where we are right now,
59 00:09:31.900 ⇒ 00:09:36.620 Kelli Peluso: So would love to get, kind of, some… any information that you have, or…
60 00:09:36.620 ⇒ 00:09:37.290 Robert Tseng: Okay.
61 00:09:37.390 ⇒ 00:09:50.670 Robert Tseng: Yeah, I’ll do… I’ll just do a quick intro about, like, kind of me and… So, yeah, like I… like, kind of Eden mentioned, we’ve worked with Portable on a couple e-com clients as well. Funny enough, they were also kind of putting it into Amplitude.
62 00:09:50.670 ⇒ 00:09:51.350 Kelli Peluso: Oh, interesting.
63 00:09:51.350 ⇒ 00:10:05.819 Robert Tseng: Yeah, that wasn’t through Portable, like, that was just, they had, they had… I mean, typically, we recommend using, like, a CDP, like a segment, or rudder stack, depending on how technical your team is, to push data into these tools.
64 00:10:05.990 ⇒ 00:10:11.600 Robert Tseng: I suspect if you ran into issues with the direct, kind of, like, Shopify to
65 00:10:11.650 ⇒ 00:10:31.100 Robert Tseng: Amplitude integration, you may run into similar challenges with Mixpanel. I just… I mean, I think we just… we just biased towards, like, using… using a… using a CDP, and then, like, if, you know, you’re still… I don’t exactly know what problems you face in Amplitude, but typically in the e-comm world, common problems we’ve seen are, like.
66 00:10:31.100 ⇒ 00:10:49.820 Robert Tseng: you know, you’re just not able to identify a lot of visitors, the identity stitching is pretty weak, and stuff like that. So, just on the attribution and tagging and tracking side, there just seem to be consistent issues, even if you use ZDP. And so, like, what my team does is we use… we build, like, a custom edge layer, solution.
67 00:10:49.820 ⇒ 00:10:53.929 Robert Tseng: Which basically is, like, kind of like a,
68 00:10:56.130 ⇒ 00:11:07.789 Robert Tseng: like, a Cloudflare-based, like, rerouting kind of solution. We’re able to just, like… it’s… it operates like a client-side pixel, but it basically… we capture all the traffic, but then we’re able to, kind of.
69 00:11:07.960 ⇒ 00:11:24.970 Robert Tseng: it’s almost lossless. It’s, like, 2-3%, kind of, like, traffic that we can’t identify anymore, as opposed to, you know, previously it was maybe, like, 20, 20%, 20-plus percent traffic that we can identify. So, we kind of use, like, a couple of these different pieces that… so I guess, like, my…
70 00:11:24.970 ⇒ 00:11:30.820 Robert Tseng: point of sharing that is, like, I don’t think an out-of-the-box solution is going to get you to, like, 100% accuracy.
71 00:11:30.820 ⇒ 00:11:43.039 Robert Tseng: And, like, it often takes these, like, different, different, kind of steps to get there. So I don’t know if that’s the problem that you’re exactly facing, but I guess we have plenty of experience in working with eTom and Mixpan, so…
72 00:11:43.290 ⇒ 00:11:58.060 Kelli Peluso: Okay, that’s great, that’s good to know, and that’s, I think, you know, part of this move to Amplitude was it went off of my plate, it went onto our e-commerce team’s plate. Yeah. So I don’t think that there’s as much of a…
73 00:11:59.270 ⇒ 00:12:05.819 Kelli Peluso: understanding of, like, the technical side of that data, and just wanted, like, a plug-and-play, like.
74 00:12:05.820 ⇒ 00:12:06.270 Robert Tseng: Yeah.
75 00:12:06.270 ⇒ 00:12:09.090 Kelli Peluso: let’s get all of the data here, why isn’t it working, type of…
76 00:12:09.120 ⇒ 00:12:10.639 Robert Tseng: Yeah, and it’s not, yeah.
77 00:12:10.640 ⇒ 00:12:18.010 Kelli Peluso: Yeah, so this is actually the first time that I’ve even heard about, like, having a layer kind of in between to help with that. Yeah.
78 00:12:18.170 ⇒ 00:12:21.010 Kelli Peluso: So that’s really interesting to consider.
79 00:12:21.010 ⇒ 00:12:21.460 Robert Tseng: Yeah.
80 00:12:21.460 ⇒ 00:12:23.360 Kelli Peluso: Because, yeah.
81 00:12:23.360 ⇒ 00:12:42.980 Robert Tseng: I mean, I don’t know how much reporting you’ve built out in Amplitude already, but, like, the other e-commer brands we work with, like, they were, like, the… I get why people like these tools, like, it’s just so easy to create segments, and then if you set it up well, you can push those audiences into your, kind of, whatever, kind of customer engagement tool as well. So, like, that…
82 00:12:42.980 ⇒ 00:13:02.569 Robert Tseng: rather than, like, for other e-commerce that we work with, we try to push them into Omni, ironically, as well, Omni database, some other, like, lighter cloud-native, like, BI tool, but their team would not use it. They would still prefer to use Amplitude. And so, like, yeah, I think, like, just having it all in one place, and just the speed at which they can
83 00:13:02.570 ⇒ 00:13:11.390 Robert Tseng: kind of create their own reports, like, that… that workflow, we could never really bring them out of, and so we’re just like, well, might as well just try to make it… make it better, so…
84 00:13:11.390 ⇒ 00:13:11.730 Kelli Peluso: Yeah.
85 00:13:11.730 ⇒ 00:13:21.230 Robert Tseng: Yeah, that was… yeah, those are… those are… those are some of the considerations, that… that we… we had to kind of wrestle with, as we were working with these types of clients.
86 00:13:21.740 ⇒ 00:13:23.430 Kelli Peluso: Okay, that makes sense.
87 00:13:23.550 ⇒ 00:13:32.639 Kelli Peluso: I mean, it’s tough now, anyway, with the privacy, the different privacy layers and everything, as you mentioned, like, the different cookie…
88 00:13:32.800 ⇒ 00:13:36.029 Kelli Peluso: Restrictions and whatnot that…
89 00:13:36.420 ⇒ 00:13:49.300 Kelli Peluso: it’s hard for us to even tell what is accurate for our sessions anymore, and with that, like, how much inaccuracy can we accept? Yeah. Because there’s always going to be a level of that, but,
90 00:13:50.050 ⇒ 00:13:54.310 Kelli Peluso: That’s interesting. I haven’t really looked into CDPs before.
91 00:13:54.550 ⇒ 00:13:55.060 Robert Tseng: Yeah.
92 00:13:55.060 ⇒ 00:14:03.419 Kelli Peluso: So that… might be something for us to… to consider. That… that side of my…
93 00:14:03.680 ⇒ 00:14:06.940 Kelli Peluso: Engineering acumen is not.
94 00:14:07.340 ⇒ 00:14:25.529 Robert Tseng: Yeah, I mean, tagging and tracking is not really, like, data engineering work, so, yeah, I think, I don’t know if you have a MarTech specialist or someone on your team, like, you know, we… yeah, I’d probably recommend you probably need someone, someone there who’s, like, who understands, like.
95 00:14:25.750 ⇒ 00:14:28.649 Robert Tseng: I mean, you kind of need to know some JavaScript,
96 00:14:28.650 ⇒ 00:14:29.000 Kelli Peluso: Right.
97 00:14:29.000 ⇒ 00:14:29.950 Robert Tseng: no…
98 00:14:30.320 ⇒ 00:14:46.850 Robert Tseng: you’re, like, constantly QAing the data layer with these web-based pixels, and trying to see, like, what’s coming through, what’s not. So, I… I mean, I often, like, when I staff, like, people on my engagements, like, I do typically pair, like, a MarTech
99 00:14:46.850 ⇒ 00:14:54.780 Robert Tseng: specialist with, like, a data person. So, yeah, like, that just seems to be, like, the need all the time, yeah.
100 00:14:54.780 ⇒ 00:14:55.530 Kelli Peluso: Excellent.
101 00:14:55.690 ⇒ 00:14:59.069 Kelli Peluso: Okay, let me bring that to… so we use,
102 00:14:59.180 ⇒ 00:15:05.700 Kelli Peluso: We outsource for our engineering right now, but they do all of our on-site everything.
103 00:15:05.700 ⇒ 00:15:06.160 Robert Tseng: Yup.
104 00:15:06.160 ⇒ 00:15:09.149 Kelli Peluso: So let me connect with them and see,
105 00:15:09.460 ⇒ 00:15:13.110 Kelli Peluso: If any of them have specialty in that area.
106 00:15:13.360 ⇒ 00:15:13.850 Robert Tseng: Okay.
107 00:15:14.200 ⇒ 00:15:22.000 Robert Tseng: No, I mean, if you… it sounds like… I mean, I don’t know the deal of what exactly the econ team was seeing as that was broken in amplitude, but if you want, like.
108 00:15:22.250 ⇒ 00:15:42.119 Robert Tseng: me to have, like, a deeper discovery conversation with them to understand, like, what exactly was the problem they were facing. If it’s related to this stuff, like, I mean, we could… we could easily help with this. And yeah, I think, you know, before, like, just, like, yeah, kind of moving to another tool, like, I feel like you may run into the same… same issues, it sounds like.
109 00:15:42.120 ⇒ 00:15:43.480 Kelli Peluso: Right? Yeah. Okay.
110 00:15:43.480 ⇒ 00:15:44.150 Robert Tseng: Yeah.
111 00:15:44.150 ⇒ 00:15:48.189 Kelli Peluso: No, that’s super helpful, and that’s what I’ve been concerned about, because… Yeah.
112 00:15:49.070 ⇒ 00:16:08.880 Kelli Peluso: I, unfortunately, it was, like, when my team leaned out, so I had to take a step back, and my e-commerce manager, or our VP of e-com was handling all of this. Yeah. So I’m trying to jump in now to help vet whatever it is a bit better that we move to, but I’ve had that same concern that, like, if you have this massive tool like Amplitude, who
113 00:16:09.360 ⇒ 00:16:12.430 Kelli Peluso: Many people use, and are really happy with it.
114 00:16:12.430 ⇒ 00:16:12.800 Robert Tseng: Yeah.
115 00:16:12.800 ⇒ 00:16:18.290 Kelli Peluso: I think we’re probably going to run into the same Same problem.
116 00:16:18.500 ⇒ 00:16:18.840 Robert Tseng: Yeah.
117 00:16:18.840 ⇒ 00:16:22.110 Kelli Peluso: We connect with her and see,
118 00:16:22.310 ⇒ 00:16:28.599 Kelli Peluso: what her availability looks like, too, for next week. So I’m sure she’d be really interested to hear
119 00:16:28.740 ⇒ 00:16:30.680 Kelli Peluso: Kind of this perspective of…
120 00:16:31.310 ⇒ 00:16:31.710 Robert Tseng: Yeah.
121 00:16:31.710 ⇒ 00:16:35.019 Kelli Peluso: like, because we re-engage with HEAP as well,
122 00:16:35.200 ⇒ 00:16:47.919 Kelli Peluso: They were… we had been looking at heap and amplitude last year when we made a decision. Yeah. I’ve heard really great things about post hoc, but I know it’s a bit more of an engineering kind of lift, I think, right? Yeah. Yeah.
123 00:16:48.440 ⇒ 00:16:54.790 Kelli Peluso: So… I per… I wanna… I would like to work with post hoc.
124 00:16:54.790 ⇒ 00:16:55.130 Robert Tseng: Yeah.
125 00:16:55.130 ⇒ 00:17:01.440 Kelli Peluso: It would, you know, be… be more of a lift for everybody. So let me connect with her.
126 00:17:01.890 ⇒ 00:17:08.209 Kelli Peluso: And get back to you about, next steps. But I really appreciate even just the, you know.
127 00:17:09.690 ⇒ 00:17:13.709 Kelli Peluso: the feedback that we’ll probably run into this and other tools as well.
128 00:17:13.710 ⇒ 00:17:21.160 Robert Tseng: Yeah. I think a couple other things I’ll say, I’m like, yeah, I mean, just as far as, like, figuring out the trade-offs that you’re making, right? Like, so, yeah, the…
129 00:17:21.329 ⇒ 00:17:32.930 Robert Tseng: in terms of, like, setup and maintenance, like, a tool like a mixed panel or Amplitude is easier to set up and maintain than a Post Hog. Like, I mean, we use Post Hog for our team because we’re mostly engineers, and
130 00:17:32.930 ⇒ 00:17:45.879 Robert Tseng: We just, you know, it is easy, or, like, it, you know, it’s not, not a huge lift for us to maintain. But, yeah, I, you know, typically e-com teams, data team, you know, data, data teams are pretty, pretty, pretty.
131 00:17:46.140 ⇒ 00:18:02.600 Robert Tseng: pretty tight already, and, like, you know, I don’t know if it’s worth your time to, like, maintain that, and, you know, I guess you’re saving, like, 50 grand a year. Like, I think it’s, you know, it’s kind of, up to you whether or not that trade-off is worth it.
132 00:18:02.650 ⇒ 00:18:20.409 Robert Tseng: And then, yeah, as far as, like, your users, kind of, like, really understanding, like, how is Amplitude already being used? If it’s, like, mostly a non-technical audience using it, we actually find that, yeah, Mixpanel is a better tool for, like, marketers than, Amplitude is. Amplitude just is, like, it is kind of complicated. There are a lot of…
133 00:18:20.420 ⇒ 00:18:23.400 Robert Tseng: different things going on in there, whereas MixPanel’s a bit…
134 00:18:23.470 ⇒ 00:18:29.920 Robert Tseng: easier for marketers to use. Right. But, like, technical PMs and, like, people who are, like.
135 00:18:30.070 ⇒ 00:18:48.330 Robert Tseng: more advanced in, like, doing kind of predictive analytics work, or, like, wanting to use some of the more advanced features in Amplitude, really like Amplitude. So, it’s kind of just, like, you know, I think those are some of the considerations as well to, as you’re making that, kind of choosing the right tool.
136 00:18:48.330 ⇒ 00:18:56.210 Kelli Peluso: Yeah. No, and that’s great feedback, too, because the users that are in there are mostly, like, our UX designer.
137 00:18:56.210 ⇒ 00:18:56.920 Robert Tseng: Hmm.
138 00:18:56.970 ⇒ 00:19:15.060 Kelli Peluso: and our VP of e-com, I mean, even that team is fairly light. Nobody is doing, like, predictive analytics. Our attribution marketing is in there sometimes, and our growth marketing. But they also use Klaviyo and, like, have other tools that they’re using for all of,
139 00:19:15.410 ⇒ 00:19:23.000 Kelli Peluso: their customer segmentation and everything, so they’re using it more for, like, sanity checks on their attribution and making sure that.
140 00:19:23.260 ⇒ 00:19:23.620 Robert Tseng: Yeah.
141 00:19:23.620 ⇒ 00:19:26.760 Kelli Peluso: Order is being attribute… attributed correctly.
142 00:19:27.040 ⇒ 00:19:35.490 Kelli Peluso: So, I don’t think, from the technical perspective, there’s that, like, value add, right now, for…
143 00:19:35.490 ⇒ 00:19:37.560 Robert Tseng: I’m assuming you have Snowflake, or…
144 00:19:37.560 ⇒ 00:19:38.200 Kelli Peluso: Yeah, we’re on Zoom.
145 00:19:38.200 ⇒ 00:19:45.360 Robert Tseng: Yeah, okay, because you were using SnowBot before, so… Yeah. Have you done the warehouse native amplitude integration?
146 00:19:46.000 ⇒ 00:19:46.890 Kelli Peluso: Yes.
147 00:19:47.040 ⇒ 00:19:47.730 Robert Tseng: Okay.
148 00:19:47.930 ⇒ 00:19:51.070 Kelli Peluso: Yeah, that’s how we, that’s how we were bringing the data in.
149 00:19:51.070 ⇒ 00:19:51.760 Robert Tseng: Okay.
150 00:19:52.060 ⇒ 00:20:03.070 Kelli Peluso: But unfortunately, it was duplicated, we weren’t able to… whatever was broken with the SDK, we weren’t able to,
151 00:20:03.550 ⇒ 00:20:06.340 Kelli Peluso: Basically, match our sessions back to the visitors.
152 00:20:06.340 ⇒ 00:20:06.790 Robert Tseng: Okay.
153 00:20:06.940 ⇒ 00:20:10.050 Kelli Peluso: And we were having issues with that, so…
154 00:20:10.190 ⇒ 00:20:10.700 Robert Tseng: Yeah.
155 00:20:10.700 ⇒ 00:20:16.910 Kelli Peluso: The data that we have, we have it, it just isn’t really telling us much at the moment. Yeah.
156 00:20:17.570 ⇒ 00:20:21.510 Kelli Peluso: So… We’re, yeah.
157 00:20:22.260 ⇒ 00:20:22.870 Robert Tseng: Okay.
158 00:20:24.280 ⇒ 00:20:29.750 Kelli Peluso: Cool. Okay, so let me…
159 00:20:31.040 ⇒ 00:20:35.039 Kelli Peluso: do… I’ll do a bit more digging in terms of the CDP
160 00:20:35.730 ⇒ 00:20:39.209 Kelli Peluso: Kind of side of things. And then…
161 00:20:39.790 ⇒ 00:20:45.019 Kelli Peluso: connect with my… my VP, I think, should… should probably like to just have a conversation with you anyway.
162 00:20:45.020 ⇒ 00:20:46.270 Robert Tseng: Sure, yeah, that’d be great.
163 00:20:46.270 ⇒ 00:20:53.319 Kelli Peluso: We check, for all of this, so let me, loop her into this, and I will…
164 00:20:53.530 ⇒ 00:20:54.649 Kelli Peluso: Follow up with you.
165 00:20:55.870 ⇒ 00:21:09.600 Robert Tseng: Sounds good. Yeah, I’ll send over a couple resources, too. One would be, like, kind of an example, and we have, like, a case study that feels very similar to what you’re describing, and, see if that’s, like, a helpful way to kind of tee up the conversation, and, yeah, you know, we’ll…
166 00:21:09.600 ⇒ 00:21:10.190 Kelli Peluso: Gotcha.
167 00:21:10.190 ⇒ 00:21:11.720 Robert Tseng: Happy to be helpful however I can.
168 00:21:12.350 ⇒ 00:21:18.179 Kelli Peluso: That’s great. I really appreciate the time, in chatting with me about this. It’s very helpful.
169 00:21:18.340 ⇒ 00:21:19.160 Kelli Peluso: Yeah.
170 00:21:19.330 ⇒ 00:21:21.230 Robert Tseng: Okay, sounds good. Thanks, Kelly.
171 00:21:21.230 ⇒ 00:21:23.819 Kelli Peluso: Thank you so much. Thanks, you too.
172 00:21:23.820 ⇒ 00:21:24.620 Robert Tseng: Bye.