Meeting Title: Nicolas Sucari’s Zoom Meeting Date: 2024-09-17 Meeting participants: Nicolas Sucari, Uttam Kumaran, Bpeiair, Aman Nagpal, Robert Tseng
WEBVTT
1 00:00:43.110 ⇒ 00:00:44.610 Nicolas Sucari: Hey, guys, can you hear me?
2 00:00:45.400 ⇒ 00:00:46.879 Aman Nagpal: Hey? Yep! How’s it going?
3 00:00:47.850 ⇒ 00:00:48.530 Aman Nagpal: Hello!
4 00:00:48.530 ⇒ 00:00:49.850 Nicolas Sucari: Good. How are you, man?
5 00:00:50.280 ⇒ 00:00:51.480 Aman Nagpal: Doing. Well, thanks.
6 00:00:53.750 ⇒ 00:00:55.660 Nicolas Sucari: Excellent. Okay.
7 00:00:55.980 ⇒ 00:00:58.460 Nicolas Sucari: Robert. Do you know, if Russan was gonna join.
8 00:01:00.310 ⇒ 00:01:03.029 Robert Tseng: I don’t think he said he was going to.
9 00:01:03.550 ⇒ 00:01:04.780 Robert Tseng: but we’re staying.
10 00:01:05.740 ⇒ 00:01:06.330 Nicolas Sucari: Okay.
11 00:01:07.050 ⇒ 00:01:08.450 Nicolas Sucari: okay, so
12 00:01:09.088 ⇒ 00:01:30.240 Nicolas Sucari: Aman, Brian. Let’s go for introductions first.st So Brian is working with the the Brainforge team with us as he’s gonna be working on all of the data, modeling and engineering regarding the integrations and everything we need. And yeah, Aman, if you want to tell him a little bit about you, and about Javi Coffee.
13 00:01:30.830 ⇒ 00:01:40.720 Aman Nagpal: Yeah, of course. So you know, Jovi coffee, we’re an Ecom company. We sell coffee, concentrate protein coffee. I handle everything on the tech side. So
14 00:01:41.276 ⇒ 00:01:42.859 Aman Nagpal: yeah. Excited to get started.
15 00:01:45.140 ⇒ 00:01:45.850 Aman Nagpal: Cool.
16 00:01:46.110 ⇒ 00:02:13.880 bpeiair: I’ll do a little intro I for for the 1st phase of the project I’ll I’ll be the primary data engineer. So nice to meet you my name is Brian. Been doing this for 8 ish years in the past 3 years? I think it’s been a lot of what we’re doing here, which is great. So I’m very comfortable with implementing 5 tran dbt, dbt, cloud snowflake. Just the data warehousing.
17 00:02:13.890 ⇒ 00:02:15.090 bpeiair: getting a model
18 00:02:15.490 ⇒ 00:02:31.376 bpeiair: getting a nice model into a nice reporting layer. And I’m coming from a contract with Instacart. And before that I was at Reebok and Adidas, and then athletic greens, which is very similar as an e-commerce kind of like product.
19 00:02:32.306 ⇒ 00:02:54.099 bpeiair: And for that I was at we work, which is where utumn and I met and that’s where I was. I was full time at we work, and then for anyone who knows the story. They fired everybody. But that’s where I learned Dbt and Snowflake. So that’s a good thing. So yeah, I’ve been. I’ve been contracting for a bit now, this project. Looks
20 00:02:55.053 ⇒ 00:02:55.936 bpeiair: very.
21 00:02:57.900 ⇒ 00:03:03.140 bpeiair: Things can move very quickly, which is good. In the in the sense that
22 00:03:03.210 ⇒ 00:03:23.259 bpeiair: it’s 1 of my 1st projects where it’s kind of like blank slate, a lot of the projects I go to. There was an old something that needs to be refactored, or something that’s already there, that’s really like complicated so hopefully, with starting from the point that we are now with the
23 00:03:23.280 ⇒ 00:03:37.759 bpeiair: experience that we have. And then using these, you know, enterprise tools that are just so easy to hook things up. There won’t be any like crazy custom, python, that we leave you. With that you won’t be able to decode later. It’s thankfully in the
24 00:03:37.760 ⇒ 00:03:54.181 bpeiair: really, in the past 5 years all these tools just make it really easy to click a button and set up an Api refresh it in a data warehouse. All your data is there runs on whatever schedule that you want and Kpis come out clean. So
25 00:03:54.913 ⇒ 00:04:08.596 bpeiair: I’m I’m rambling now, but I did. Wanted to do a bigger introduction since I’ll be working for the next couple of months, or a couple of weeks, or whatever it is, on this, and I will probably be in the slack channel asking a lot of stupid questions. So
26 00:04:09.000 ⇒ 00:04:11.829 bpeiair: that’s my intro. I’ll throw it back to
27 00:04:12.130 ⇒ 00:04:13.569 bpeiair: Nico, or whoever.
28 00:04:13.820 ⇒ 00:04:20.772 Aman Nagpal: Awesome good to meet you, man. Yeah, I think slack is great. Any questions you have send as many, you know, as you need. And
29 00:04:21.370 ⇒ 00:04:24.569 Aman Nagpal: yeah, I hope it’s it’s because we’re starting
30 00:04:24.710 ⇒ 00:04:26.519 Aman Nagpal: kind of fresh with.
31 00:04:27.066 ⇒ 00:04:40.129 Aman Nagpal: the whole data warehouse solution. I hope it is pretty straightforward. So yeah, I’m looking forward to getting started also. Have you seen the I’m guessing you’ve seen it. The the We work show that was insane. I had a friend who worked at. We work as well.
32 00:04:40.130 ⇒ 00:04:42.429 bpeiair: Yeah, yeah, I
33 00:04:42.811 ⇒ 00:04:48.270 bpeiair: we. I watched the 1st half of the season with, like some colleagues, and then finished it.
34 00:04:48.290 ⇒ 00:04:53.120 bpeiair: There’s also documentary, I think I watched everything. The the show was
35 00:04:53.622 ⇒ 00:04:57.240 bpeiair: hilarious. The real life was a lot sadder.
36 00:04:57.300 ⇒ 00:04:59.930 bpeiair: but the show did a good job of
37 00:05:00.960 ⇒ 00:05:02.800 bpeiair: characterizing why
38 00:05:02.910 ⇒ 00:05:04.619 bpeiair: he dropped the ball so bad.
39 00:05:05.040 ⇒ 00:05:05.920 Aman Nagpal: Yeah, so.
40 00:05:06.050 ⇒ 00:05:06.650 Aman Nagpal: what?
41 00:05:06.650 ⇒ 00:05:07.170 Uttam Kumaran: Jump on the.
42 00:05:07.170 ⇒ 00:05:10.210 bpeiair: No, the we crashed we whatever.
43 00:05:10.560 ⇒ 00:05:11.460 Uttam Kumaran: Oh!
44 00:05:11.460 ⇒ 00:05:12.170 bpeiair: Anne Hathaway.
45 00:05:12.170 ⇒ 00:05:14.359 Aman Nagpal: Saying that you guys used to, you guys met there.
46 00:05:14.720 ⇒ 00:05:20.040 Uttam Kumaran: Yeah, I didn’t. I could never finish it. Dude. It was like just too much too close to home, like
47 00:05:21.910 ⇒ 00:05:27.100 Uttam Kumaran: it was tough, like, I don’t know. I we were involved in all that. I don’t know how like I just couldn’t watch it because I
48 00:05:27.480 ⇒ 00:05:43.299 Uttam Kumaran: it was like it was. It made it so dramatic. Yeah. And I remember, just like, I like worked like 24 h days for like weeks. And that wasn’t that. That was just painful like. It’s not that my life wasn’t like yachts, and like like all that stuff.
49 00:05:43.300 ⇒ 00:05:48.090 bpeiair: They spent a lot of money on stuff that we didn’t get to go to. I treated it like fiction.
50 00:05:48.090 ⇒ 00:05:49.210 Aman Nagpal: Camp or no.
51 00:05:50.400 ⇒ 00:05:52.799 Uttam Kumaran: Yeah, we went to Summer camp. I went to.
52 00:05:52.800 ⇒ 00:05:53.449 bpeiair: 2 of them.
53 00:05:53.450 ⇒ 00:05:55.250 Uttam Kumaran: Brian went to to. I think I went.
54 00:05:55.250 ⇒ 00:05:56.890 bpeiair: 3 shit.
55 00:05:58.170 ⇒ 00:05:59.949 Uttam Kumaran: It was fun, and then it was busy.
56 00:05:59.950 ⇒ 00:06:00.900 bpeiair: Our campus cool.
57 00:06:01.120 ⇒ 00:06:02.529 bpeiair: I’ll say that much.
58 00:06:04.180 ⇒ 00:06:09.720 Aman Nagpal: Yeah, my friend, who he was in the finance department. He told me the same thing. He said it was like the craziest experience. So
59 00:06:11.500 ⇒ 00:06:12.190 Aman Nagpal: once in a while.
60 00:06:12.190 ⇒ 00:06:12.860 Uttam Kumaran: Way
61 00:06:13.250 ⇒ 00:06:15.910 Uttam Kumaran: how he was in. He was in the computer Science Department.
62 00:06:16.060 ⇒ 00:06:17.480 Aman Nagpal: No, no! He was in finance.
63 00:06:17.680 ⇒ 00:06:19.509 Uttam Kumaran: Oh, finance. Okay, okay.
64 00:06:19.590 ⇒ 00:06:23.060 Uttam Kumaran: yeah, I don’t. We worked with a bunch of people in the finance. So yeah, but
65 00:06:23.080 ⇒ 00:06:26.900 Uttam Kumaran: they were particularly like under the gun, because.
66 00:06:27.010 ⇒ 00:06:30.140 Uttam Kumaran: of course, that company was just like a creative finance game.
67 00:06:30.260 ⇒ 00:06:31.300 Uttam Kumaran: And
68 00:06:31.360 ⇒ 00:06:34.080 Uttam Kumaran: there’s just a like. Frankly, there’s just a lot of fraud
69 00:06:35.380 ⇒ 00:06:38.489 Uttam Kumaran: and, like fraud, comes in like many different flavors, which is like
70 00:06:38.730 ⇒ 00:06:42.949 Uttam Kumaran: we should change this metric like we should change the definition, or like.
71 00:06:43.050 ⇒ 00:06:46.330 Uttam Kumaran: move this to a different category, right? Like that sort of
72 00:06:46.850 ⇒ 00:06:47.750 Uttam Kumaran: stuff.
73 00:06:47.910 ⇒ 00:06:55.383 Aman Nagpal: Yeah, I remember even once he was talking about. We live. And he was just like we need all these people who aren’t paying rent. To start paying rent is just a mess.
74 00:06:56.022 ⇒ 00:06:58.950 Uttam Kumaran: Yeah. And then it’s like, Why are we becoming like.
75 00:06:59.020 ⇒ 00:07:03.629 Uttam Kumaran: why are we becoming like an apartment landlord? I thought we were doing office space.
76 00:07:04.490 ⇒ 00:07:14.180 bpeiair: That one was weird. They they jacked up the occupancy in the We live by letting early employees live there for a year for free.
77 00:07:14.180 ⇒ 00:07:15.429 Aman Nagpal: Just to get the numbers up.
78 00:07:16.520 ⇒ 00:07:23.060 bpeiair: Get the numbers up and make it look feel lively for people who are actually renting, who then didn’t end up paying, anyway?
79 00:07:24.420 ⇒ 00:07:29.949 bpeiair: anyway, we could talk about this for a long time. We don’t have to. What what was your friend’s name? May. Maybe maybe we know him.
80 00:07:30.391 ⇒ 00:07:33.480 Aman Nagpal: Jescar, and sink. He goes by jazzy
81 00:07:34.950 ⇒ 00:07:36.109 Aman Nagpal: in the afternoon.
82 00:07:36.110 ⇒ 00:07:39.079 bpeiair: Utah. You worked with finance more than he may, Utah. We.
83 00:07:39.080 ⇒ 00:07:40.000 Uttam Kumaran: Maybe.
84 00:07:42.270 ⇒ 00:07:44.020 bpeiair: Feel like we knew a lot of the people. But anyway.
85 00:07:44.020 ⇒ 00:07:45.059 Uttam Kumaran: Lots of luck, but.
86 00:07:45.060 ⇒ 00:07:46.740 bpeiair: Sometimes the name game works.
87 00:07:47.390 ⇒ 00:07:50.510 Aman Nagpal: Yeah. Yeah. Well, we went on a big tangent there. Sorry.
88 00:07:51.200 ⇒ 00:07:51.940 bpeiair: It’s all good.
89 00:07:52.870 ⇒ 00:07:54.040 Nicolas Sucari: Oh, it’s all good.
90 00:07:54.340 ⇒ 00:08:02.440 Nicolas Sucari: Yeah, okay. So talking about Javi, 1st steps that we were discussing on Thursday.
91 00:08:02.785 ⇒ 00:08:19.330 Nicolas Sucari: I think you a man were able to create the Snowflake account and the 5 turn account. I think utam we don’t get the password for the Snowflake account. I don’t know. I know that, Brian. You couldn’t access, and I was not able to access, too. So maybe you can add us as a user
92 00:08:19.739 ⇒ 00:08:31.189 Nicolas Sucari: so we can access snowflake and then, Brian, I think you need the 5 tran, also login, so that you can start setting up the shopify account right?
93 00:08:32.289 ⇒ 00:08:34.862 bpeiair: Yeah, I haven’t logged into Fivetran yet.
94 00:08:35.449 ⇒ 00:08:47.879 bpeiair: I’ve been setting up some of the just the template dbt stuff. But I saw there was a thread about 5 train and shopify do we? Do we have the key and everything? Or is that still being generated? Or
95 00:08:48.619 ⇒ 00:08:49.409 bpeiair: because what I was.
96 00:08:49.410 ⇒ 00:08:50.810 Uttam Kumaran: Everything should be.
97 00:08:51.040 ⇒ 00:08:54.790 Uttam Kumaran: Everything should be in 5 train, hooked up except for Amazon.
98 00:08:55.600 ⇒ 00:08:56.000 bpeiair: But.
99 00:08:56.000 ⇒ 00:08:58.360 Uttam Kumaran: We? We just need to hook up the destination.
100 00:08:58.660 ⇒ 00:08:59.320 Uttam Kumaran: and then.
101 00:08:59.320 ⇒ 00:09:00.139 bpeiair: Oh, got it? Okay.
102 00:09:00.140 ⇒ 00:09:04.219 Uttam Kumaran: You can kick off shopify. And yeah, that should probably take like a day or 2, right? Usually. So.
103 00:09:04.590 ⇒ 00:09:12.560 Aman Nagpal: I just ended up using the 5 parent app. But if we decide, we need to switch to a private app and use Api key. Then, you know, just let me know we can switch
104 00:09:12.819 ⇒ 00:09:20.390 Aman Nagpal: Amazon. I’m still getting that error. It says check back in 15 min, but it’s been like that all day, so I’ll give it another day or 2 and see what’s going on.
105 00:09:21.001 ⇒ 00:09:33.860 Aman Nagpal: And then with 5 trend is, is that the kind of piece that’s going to keep re-syncing old data. So anytime orders are funded or anything, it just after the big sync, it just keeps checking all the old data.
106 00:09:35.840 ⇒ 00:09:36.780 Aman Nagpal: correct it.
107 00:09:36.780 ⇒ 00:09:37.319 bpeiair: Does
108 00:09:38.200 ⇒ 00:09:46.500 bpeiair: unless you specify. Otherwise it does one big historical sync. And then it does incremental syncs based on the schedule that you set
109 00:09:46.750 ⇒ 00:09:53.889 bpeiair: so it won’t like truncate. Replace all the data every time. For the most part it’ll be daily or hourly whatever you want.
110 00:09:54.310 ⇒ 00:09:58.849 bpeiair: incremental inserts. And then, if, like a data source
111 00:09:59.100 ⇒ 00:10:02.089 bpeiair: in, let’s say in shopify, there’s some sort of
112 00:10:02.770 ⇒ 00:10:13.919 bpeiair: data that changed in the past. Then it’s a manual thing in 5 trend where you can say, like backfill all historical data. But you, you shouldn’t have to do that unless there’s like some change in past data.
113 00:10:14.700 ⇒ 00:10:24.580 Aman Nagpal: So when what’s considered past data, right? So let’s say, I have an order from 30 days ago that I’m refunding. Is that considered pass? Or will it automatically sync that refund.
114 00:10:25.590 ⇒ 00:10:26.490 bpeiair: So
115 00:10:27.905 ⇒ 00:10:28.960 bpeiair: like
116 00:10:29.640 ⇒ 00:10:48.009 bpeiair: action, or I call it a change log for for an order that I placed last week that is like pending, and then gets shipped to me, and then, if I and I refund it next week, that that’s not like a big historical thing. In in 5 trend it should, for each
117 00:10:49.600 ⇒ 00:10:59.030 bpeiair: id record will track an update, and then it’ll incrementally like, even if I place the order last year and I did something today. It’ll it’ll track that.
118 00:11:00.810 ⇒ 00:11:02.290 bpeiair: I’m talking about more like
119 00:11:02.400 ⇒ 00:11:04.100 bpeiair: systemic level.
120 00:11:04.670 ⇒ 00:11:13.220 bpeiair: like adding in a whole new category metadata column or something like that, that we that you didn’t have before that that’s something that would require a
121 00:11:13.620 ⇒ 00:11:14.700 bpeiair: a backfill. Sorry.
122 00:11:15.030 ⇒ 00:11:23.359 Aman Nagpal: Got it. One issue we ran into this week. I figured I’d mention it to you guys and see if in the future there’s any way to resolve this, but
123 00:11:23.400 ⇒ 00:11:35.640 Aman Nagpal: we made some changes with, so we use L of R for server side tracking. We were changing some of their you know what what events we were firing for. L. Of R. Within our landing pages, and
124 00:11:36.100 ⇒ 00:11:53.489 Aman Nagpal: for whatever change we made, it kind of, broke something for over the weekend. And we were not collecting visits to our listicles. So for 2 days, that data shows 0, right? Because we just didn’t fire the events. And typically that data is stored right within amplitude. Is there something?
125 00:11:54.542 ⇒ 00:12:03.920 Aman Nagpal: Not now, once this is all set up, will we continue to send that data. I guess that’ll go to the data warehouse as well. But is there a way
126 00:12:04.240 ⇒ 00:12:09.799 Aman Nagpal: to avoid an issue like that happening? Or is that kind of just on us that we can’t, you know, cause an issue like that.
127 00:12:13.990 ⇒ 00:12:16.116 Uttam Kumaran: Yeah, I can answer. I mean, there’s
128 00:12:16.610 ⇒ 00:12:21.429 Uttam Kumaran: it’s kind of like, there’s only so much we can do like if the things aren’t recorded.
129 00:12:21.940 ⇒ 00:12:26.799 Uttam Kumaran: then there’s nothing right. But if let’s say you do record them, and you’re like
130 00:12:26.870 ⇒ 00:12:30.449 Uttam Kumaran: we do have those events somewhere, but like they’re not
131 00:12:30.510 ⇒ 00:12:37.170 Uttam Kumaran: perfectly merged. But, like we have a Csv of like every event we can, we can then, after the fact, stitch those in.
132 00:12:37.310 ⇒ 00:12:40.190 Uttam Kumaran: That’s something that’s like the benefit of this. For example.
133 00:12:40.210 ⇒ 00:12:49.330 Uttam Kumaran: I mean, Brian, we’ve done this before, where it’s like we’re missing. We’re missing like 2 days worth of data. We have a Csv of that data when we build the data model for that
134 00:12:49.380 ⇒ 00:12:59.069 Uttam Kumaran: part of that building process is like for those days reference, the Csv. For all other days. Reference this, and then we kind of like merge the columns and everything. So if you do have
135 00:12:59.590 ⇒ 00:13:01.110 Uttam Kumaran: those events somewhere.
136 00:13:01.310 ⇒ 00:13:02.969 Uttam Kumaran: we can attempt to do that.
137 00:13:03.310 ⇒ 00:13:07.859 Uttam Kumaran: That’s a little bit of like the surgery stuff that we could do. But if there’s no events.
138 00:13:08.280 ⇒ 00:13:11.020 Uttam Kumaran: then you’re kind of Sol. So.
139 00:13:11.810 ⇒ 00:13:16.260 Aman Nagpal: The only thing I thought of was Google analytics. We just pull up that URL or those Urls.
140 00:13:16.260 ⇒ 00:13:17.590 Uttam Kumaran: Having a Ga.
141 00:13:18.260 ⇒ 00:13:22.630 Uttam Kumaran: I mean, again, we can bring in the Ga stuff and see whether, like, we can
142 00:13:23.700 ⇒ 00:13:26.030 Uttam Kumaran: see anything on those pages.
143 00:13:26.260 ⇒ 00:13:27.130 Uttam Kumaran: Okay.
144 00:13:27.590 ⇒ 00:13:29.470 Uttam Kumaran: especially because now that
145 00:13:29.660 ⇒ 00:13:31.889 Uttam Kumaran: oh, it’s like, Are you guys on J. 4.
146 00:13:32.510 ⇒ 00:13:36.010 Aman Nagpal: We are. We don’t really use Ga. 4, since we do everything in amplitude, but.
147 00:13:36.010 ⇒ 00:13:36.350 Uttam Kumaran: Yeah.
148 00:13:37.120 ⇒ 00:13:37.940 Uttam Kumaran: turn on.
149 00:13:38.120 ⇒ 00:13:39.640 Aman Nagpal: Yeah, it is turned on. So.
150 00:13:39.640 ⇒ 00:13:44.930 Uttam Kumaran: Okay, then it’ll basically nice thing with Ga, 4. It’s all events, it’s like one big events. Table.
151 00:13:45.316 ⇒ 00:13:55.339 Uttam Kumaran: So that’s something like, if you. If you want to send us the details we could get. I mean, we could probably put a note, Nico, for like next week to hook up. Ga, 4.
152 00:13:56.130 ⇒ 00:13:56.690 Uttam Kumaran: Yeah.
153 00:13:57.140 ⇒ 00:13:59.169 Uttam Kumaran: And we can bring that data into
154 00:13:59.555 ⇒ 00:14:04.059 Uttam Kumaran: and then we can stop it. And just look at the specific example. If we need to.
155 00:14:04.440 ⇒ 00:14:08.389 Aman Nagpal: Do you think that’s useful, or should we just keep it as a backup in case something happens.
156 00:14:08.560 ⇒ 00:14:11.330 Uttam Kumaran: Oh, I would leave it as about I would. Totally. I mean, it’s
157 00:14:11.350 ⇒ 00:14:13.690 Uttam Kumaran: it’s free. I would totally. There’s a backup.
158 00:14:15.970 ⇒ 00:14:21.399 Uttam Kumaran: yeah, I mean, I kind of like on any for most clients we’ve seen that that’s typically what they do
159 00:14:21.420 ⇒ 00:14:23.670 Uttam Kumaran: is that they’re using like amplitude or heap.
160 00:14:23.850 ⇒ 00:14:26.930 Uttam Kumaran: And then as a backup. They have ga, or like.
161 00:14:27.120 ⇒ 00:14:29.340 Uttam Kumaran: yeah, in case they just they
162 00:14:29.520 ⇒ 00:14:33.949 Uttam Kumaran: they need to do some small stuff they keep. Ga, but I think that’s fine. The only
163 00:14:34.250 ⇒ 00:14:37.940 Uttam Kumaran: downside is like, if you notice, it’s like affecting page performance.
164 00:14:38.160 ⇒ 00:14:42.029 Uttam Kumaran: But like, you can look at that specifically, it’s usually pretty. Okay.
165 00:14:43.080 ⇒ 00:14:43.570 Aman Nagpal: Cool, so.
166 00:14:43.570 ⇒ 00:14:46.059 Uttam Kumaran: The only reason not to have, like 10 of these is cause
167 00:14:46.400 ⇒ 00:14:51.390 Uttam Kumaran: your your site is going to be firing off like 100 events every time someone like moves their cursor.
168 00:14:51.430 ⇒ 00:14:52.880 Uttam Kumaran: Right? That’s the
169 00:14:53.780 ⇒ 00:14:54.245 Uttam Kumaran: yeah.
170 00:14:56.130 ⇒ 00:14:56.960 Aman Nagpal: Make, sense.
171 00:14:58.646 ⇒ 00:15:06.399 Uttam Kumaran: I think one thing, while we’re on this call I just double check Snowflake, Aman. Do you mind if you open up Snowflake? If you could just change
172 00:15:06.470 ⇒ 00:15:14.379 Uttam Kumaran: my default role to account. Admin the way to do that is, if you open snowflake, you go to users.
173 00:15:14.450 ⇒ 00:15:17.040 Uttam Kumaran: and you actually just click the 3 dots
174 00:15:17.570 ⇒ 00:15:19.539 Uttam Kumaran: next to my name.
175 00:15:19.710 ⇒ 00:15:31.170 Uttam Kumaran: You can. You should either say edit or grant a role, and if you could just grant me account, admin. I’m going to be able to invite Brian. And then, Brian, you’ll be able to execute.
176 00:15:31.470 ⇒ 00:15:32.830 Uttam Kumaran: to set up scripts.
177 00:15:34.140 ⇒ 00:15:36.178 Aman Nagpal: Yup, let me do that right now.
178 00:15:36.470 ⇒ 00:15:43.640 Uttam Kumaran: And then, Brian, I think you have the set of the set of scripts. You’ll be able to set up the raw. dB, let’s just dump all of
179 00:15:43.750 ⇒ 00:15:45.649 Uttam Kumaran: the 5 trend stuff into there.
180 00:15:45.740 ⇒ 00:15:49.150 Uttam Kumaran: You could just create a shopify, folder, shopify schema.
181 00:15:49.420 ⇒ 00:15:50.270 Uttam Kumaran: and then.
182 00:15:51.190 ⇒ 00:15:58.489 bpeiair: Yeah, I think so. Did did I hear that? The shopify 5 tran connection is already running.
183 00:15:58.510 ⇒ 00:16:00.310 bpeiair: So, isn’t it? It’s already.
184 00:16:00.310 ⇒ 00:16:04.550 Uttam Kumaran: It’s not running. No, we didn’t hook up. We didn’t do the snowflake hookup. It’s just like.
185 00:16:04.730 ⇒ 00:16:06.049 Uttam Kumaran: yeah. It’s just like.
186 00:16:06.349 ⇒ 00:16:06.649 bpeiair: Cool!
187 00:16:07.270 ⇒ 00:16:11.190 Uttam Kumaran: It’s just check we we didn’t like actually run the initial sync.
188 00:16:11.500 ⇒ 00:16:14.790 bpeiair: I’ll yeah, I’ll set all that up then.
189 00:16:15.206 ⇒ 00:16:17.349 bpeiair: Thanks for getting all the connections in
190 00:16:17.778 ⇒ 00:16:22.849 bpeiair: and then I was gonna say, with the 5 train issue that you were having with Amazon.
191 00:16:23.294 ⇒ 00:16:32.589 bpeiair: We can definitely look into it. But when when that happens to you, I think especially for new clients you can. 5 trends like technical support.
192 00:16:32.780 ⇒ 00:16:37.570 bpeiair: I believe, is free, and they are pretty like we use them all the time, because
193 00:16:37.924 ⇒ 00:16:58.050 bpeiair: you know, they know better than us. We can read the logs and try to figure it out. But that’s kind of just like a free service. You could just ping them and be like, Hey, can you look at this error? They they’ll log in and and just check it out, and usually they they fix it on their end. So instead of I I just meant like, instead of waiting a day to see if it patches you could just
194 00:16:58.080 ⇒ 00:17:00.480 bpeiair: send them a message. They’re pretty responsive.
195 00:17:01.060 ⇒ 00:17:09.689 Aman Nagpal: So I mean, maybe they can still help with us. It seems I haven’t done anything anytime. I load the settings page. That’s the error it’s giving me on Amazon. I haven’t.
196 00:17:09.690 ⇒ 00:17:10.990 Uttam Kumaran: Like an Amazon thing.
197 00:17:11.200 ⇒ 00:17:12.819 Aman Nagpal: Yeah, it seems more of an Amazon thing.
198 00:17:12.829 ⇒ 00:17:15.613 bpeiair: Oh, never mind, then. Okay.
199 00:17:18.589 ⇒ 00:17:22.139 bpeiair: I thought the connector was up, but erroring out.
200 00:17:23.490 ⇒ 00:17:27.009 Aman Nagpal: The username is case sensitive. I can’t seem to get in.
201 00:17:29.540 ⇒ 00:17:33.550 Uttam Kumaran: It’s not, but it should just be your your 1st name, Amn.
202 00:17:38.820 ⇒ 00:17:41.320 Aman Nagpal: Yep, that was it. For some reason. My email says.
203 00:17:41.550 ⇒ 00:17:45.160 Aman Nagpal: my username something else. Okay, I’m gonna change that now.
204 00:17:45.390 ⇒ 00:17:45.980 Uttam Kumaran: Okay.
205 00:17:54.950 ⇒ 00:17:58.420 Aman Nagpal: It says your account admin you wanted to change it to something else.
206 00:18:00.980 ⇒ 00:18:04.459 Uttam Kumaran: Well, let me see if I can.
207 00:18:04.780 ⇒ 00:18:07.560 Uttam Kumaran: It doesn’t allow me to switch to
208 00:18:07.830 ⇒ 00:18:09.320 Uttam Kumaran: account I’m in.
209 00:18:09.360 ⇒ 00:18:10.612 Uttam Kumaran: does it? Say,
210 00:18:11.390 ⇒ 00:18:13.400 Aman Nagpal: User admin sysadmin security.
211 00:18:13.400 ⇒ 00:18:14.080 Uttam Kumaran: Have.
212 00:18:14.470 ⇒ 00:18:15.330 Aman Nagpal: Org admin.
213 00:18:17.590 ⇒ 00:18:19.729 Uttam Kumaran: It says. I’m already account admin.
214 00:18:20.860 ⇒ 00:18:21.920 Uttam Kumaran: What?
215 00:18:24.560 ⇒ 00:18:26.060 Aman Nagpal: Maybe I push it through again.
216 00:18:26.910 ⇒ 00:18:28.980 Uttam Kumaran: Yeah, maybe try it again.
217 00:18:29.560 ⇒ 00:18:32.589 Uttam Kumaran: And are you able to see the default role?
218 00:18:33.390 ⇒ 00:18:34.590 Uttam Kumaran: If you hit edit.
219 00:18:36.240 ⇒ 00:18:42.340 Aman Nagpal: Yeah, okay, so in the column before I click it, it says your account admin. But default role was
220 00:18:42.510 ⇒ 00:18:43.330 Aman Nagpal: blank. So now.
221 00:18:43.330 ⇒ 00:18:43.880 Uttam Kumaran: Yeah.
222 00:18:43.880 ⇒ 00:18:44.880 Aman Nagpal: Admin, again.
223 00:18:45.440 ⇒ 00:18:47.299 Uttam Kumaran: Yeah, there’s a
224 00:18:48.950 ⇒ 00:18:53.522 Uttam Kumaran: we can just skip talking about snowflake rolls. It’s
225 00:18:54.230 ⇒ 00:18:54.759 Uttam Kumaran: it’s just.
226 00:18:54.760 ⇒ 00:18:56.630 Aman Nagpal: Just did it for both of you. So.
227 00:18:56.630 ⇒ 00:18:58.340 Uttam Kumaran: Okay, cool. It worked.
228 00:19:00.810 ⇒ 00:19:03.320 Uttam Kumaran: Okay, cool. Yeah. Nico, you can go ahead. That’s all I need.
229 00:19:04.490 ⇒ 00:19:10.620 Nicolas Sucari: Okay, perfect. Yeah. So when when Brian has access to snowflake, we can start
230 00:19:10.720 ⇒ 00:19:20.709 Nicolas Sucari: running that sync from for 5 trend and snowflake and should be fine. And let’s wait and see what happens with Amazon. If you want to ping them a month, that will be
231 00:19:20.890 ⇒ 00:19:31.470 Nicolas Sucari: great. So if we can. Yeah, use that the time? And then, if if there, any other source where you’re selling, that we should be
232 00:19:31.560 ⇒ 00:19:37.410 Nicolas Sucari: taking care of, or should be trying to sync the data right now, or with shopify and Amazon.
233 00:19:37.480 ⇒ 00:19:38.670 Nicolas Sucari: We’re good to go.
234 00:19:39.550 ⇒ 00:19:40.630 Aman Nagpal: So.
235 00:19:41.300 ⇒ 00:19:49.019 Aman Nagpal: Amazon. We’re doing us only as of now. By end of year I think we’ll be doing Canada and Uk as well. But not as of yet.
236 00:19:49.460 ⇒ 00:19:56.070 Aman Nagpal: Tiktok Shop is a big one for us. We’re currently syncing all orders into shopify.
237 00:19:57.980 ⇒ 00:19:59.440 Aman Nagpal: so I guess we can just
238 00:20:00.020 ⇒ 00:20:03.749 Aman Nagpal: get them there unless there’s a way to get it from Tiktok and kind of
239 00:20:04.603 ⇒ 00:20:13.669 Aman Nagpal: organize the data that way in case things are missing on shopify side. I also know we tested we talked about this before fulfilled by Tik Tok.
240 00:20:14.050 ⇒ 00:20:15.280 Aman Nagpal: I think
241 00:20:15.420 ⇒ 00:20:22.040 Aman Nagpal: those orders are still coming into shopify. But yeah, I don’t know. Does it make sense to connect tick, tock directly as well.
242 00:20:22.970 ⇒ 00:20:23.460 Uttam Kumaran: Yes.
243 00:20:23.460 ⇒ 00:20:24.319 Nicolas Sucari: I think.
244 00:20:24.500 ⇒ 00:20:25.030 Nicolas Sucari: yeah, I agree.
245 00:20:25.030 ⇒ 00:20:27.940 Uttam Kumaran: Livetran can only do tiktok ads.
246 00:20:28.100 ⇒ 00:20:30.830 Uttam Kumaran: But, Brian, I’m gonna email.
247 00:20:31.190 ⇒ 00:20:34.970 Uttam Kumaran: the account person on 5 train. And we can just ask them
248 00:20:35.640 ⇒ 00:20:40.840 Uttam Kumaran: like, what’s the deal with Tiktok shops? Cause I’m sure they have a bunch of other clients that are doing that. And then let’s
249 00:20:41.230 ⇒ 00:20:43.039 Uttam Kumaran: we can just chase that on our end.
250 00:20:43.710 ⇒ 00:20:47.049 bpeiair: If the if the Tiktok stuff is
251 00:20:47.080 ⇒ 00:20:53.620 bpeiair: synced to shopify in shopify, can we just can we access the Tiktok data through the shopify
252 00:20:54.620 ⇒ 00:20:55.380 bpeiair: extracts.
253 00:20:56.640 ⇒ 00:21:06.230 Aman Nagpal: On my side. Yes, but it’s you know we may change in the future. We may use a different app right now. We’re using Tiktok’s native app. It’s really shitty the the setup. So
254 00:21:06.700 ⇒ 00:21:17.339 Aman Nagpal: there’s the Tiktok order number, and then the shopify order number. So our 3 PL. As an issue, and all of that data is is really just saved. As shopify tag. So it’ll have a tick tock
255 00:21:17.690 ⇒ 00:21:19.840 Aman Nagpal: order tag. It’ll have a Tiktok
256 00:21:19.980 ⇒ 00:21:32.339 Aman Nagpal: tag with the Tiktok order number in it. It’ll have, I think we’re automatically tagging free orders as sample orders tick tock sample orders. So it’s it’s a bit of a mess. We can definitely do it for now. But
257 00:21:32.600 ⇒ 00:21:34.120 Aman Nagpal: ideally.
258 00:21:34.290 ⇒ 00:21:35.839 Aman Nagpal: there’s a better way to do this.
259 00:21:36.400 ⇒ 00:21:39.150 bpeiair: Got it. Okay? Yeah. Utahm, I would say. Then.
260 00:21:40.100 ⇒ 00:21:43.619 Uttam Kumaran: Let’s call. Let’s yeah. Let’s call them. And then
261 00:21:44.450 ⇒ 00:21:48.259 Uttam Kumaran: I’m gonna call some other friends and see what they’re doing.
262 00:21:52.530 ⇒ 00:21:55.200 Aman Nagpal: And in terms of other data sources.
263 00:21:56.020 ⇒ 00:21:56.865 Aman Nagpal: nothing
264 00:21:58.090 ⇒ 00:22:01.970 Aman Nagpal: on the top of my head. In terms of sales. I know we have
265 00:22:02.050 ⇒ 00:22:06.549 Aman Nagpal: our whole operation 3 pl. Side. I don’t know if we want to get into that yet.
266 00:22:08.140 ⇒ 00:22:11.509 Aman Nagpal: but as of right now, we’re using extensive
267 00:22:12.010 ⇒ 00:22:28.139 Aman Nagpal: to pull all of our orders from Shopify and Amazon, wherever into extensive order management, and then we also have extensive. I am. I forgot what that stands for, but that pushes all the orders to our current warehouses, and within the next
268 00:22:28.530 ⇒ 00:22:38.990 Aman Nagpal: 2 months or so we’ll be switching to netsuite instead of extensive, and we will probably only use flow space as our 3 pl, that’s my understanding.
269 00:22:39.110 ⇒ 00:22:40.360 Aman Nagpal: So
270 00:22:41.220 ⇒ 00:22:43.889 Aman Nagpal: yeah, I expect that to happen within the next
271 00:22:44.210 ⇒ 00:22:45.580 Aman Nagpal: 2 months or so.
272 00:22:45.580 ⇒ 00:22:47.770 Uttam Kumaran: Do you guys already have netsuite set up.
273 00:22:48.850 ⇒ 00:22:49.570 Aman Nagpal: It’s
274 00:22:50.430 ⇒ 00:22:59.230 Aman Nagpal: probably I. I’m actually totally away from that project that the operation side is handling, that I know it’s been set up, and they’ve been testing, but we’re not using it for anything as of yet.
275 00:22:59.230 ⇒ 00:22:59.860 Uttam Kumaran: Okay.
276 00:23:00.560 ⇒ 00:23:07.729 Uttam Kumaran: okay. So Netsuite and 5 train have, like a really good connector. I don’t know. Brian forgot we did Netsuite before 1st or something.
277 00:23:09.560 ⇒ 00:23:10.370 Uttam Kumaran: But
278 00:23:10.840 ⇒ 00:23:14.155 Uttam Kumaran: let’s I think let’s take a note, Nico. What we can do is
279 00:23:14.410 ⇒ 00:23:14.810 Nicolas Sucari: Yeah.
280 00:23:14.810 ⇒ 00:23:18.419 Uttam Kumaran: We can message the extensive folks and ask them if they
281 00:23:18.600 ⇒ 00:23:20.060 Uttam Kumaran: if they have a
282 00:23:20.310 ⇒ 00:23:23.709 Uttam Kumaran: like, a scheduled reports, functionality, or an Api.
283 00:23:23.780 ⇒ 00:23:25.730 Uttam Kumaran: And let’s just start a thread with them.
284 00:23:25.960 ⇒ 00:23:28.910 Uttam Kumaran: And then when we do the cut over to Netsuite.
285 00:23:29.290 ⇒ 00:23:32.050 Uttam Kumaran: we this that should actually be a little bit easier.
286 00:23:32.260 ⇒ 00:23:37.989 Uttam Kumaran: So let’s let’s email them about that. And then we can email 5 train about the Tiktok shop as well.
287 00:23:38.480 ⇒ 00:23:39.920 Uttam Kumaran: And then we’ll just
288 00:23:40.180 ⇒ 00:23:42.580 Uttam Kumaran: we could get an answer for both of those.
289 00:23:43.330 ⇒ 00:23:46.579 bpeiair: We did netsuite for that weird physical therapy company.
290 00:23:47.330 ⇒ 00:23:48.000 Uttam Kumaran: Yes.
291 00:23:48.000 ⇒ 00:23:49.760 bpeiair: And it. It works great.
292 00:23:52.830 ⇒ 00:23:53.769 Aman Nagpal: Good to hear
293 00:23:54.184 ⇒ 00:24:01.179 Aman Nagpal: outside of that I can’t. I mean, Robert, do you have any ideas? Anything else we mentioned before, that I may be forgetting.
294 00:24:03.223 ⇒ 00:24:05.090 Robert Tseng: I think those are the
295 00:24:05.130 ⇒ 00:24:07.859 Robert Tseng: I mean even the I know Tiktok
296 00:24:08.020 ⇒ 00:24:14.110 Robert Tseng: was kind of raised and last call, but I know shopify and Amazon were the main things. We wanted to get up and running.
297 00:24:15.830 ⇒ 00:24:23.499 Robert Tseng: But yeah, mom, maybe we can kind of chat separately about the L of our stuff here. I just I just went in and I was looking around. I would like to see kind of why
298 00:24:23.900 ⇒ 00:24:28.330 Robert Tseng: the tracking fell through. See if we can do anything more proactively. So to prevent that from
299 00:24:28.520 ⇒ 00:24:30.064 Robert Tseng: from breaking down.
300 00:24:30.950 ⇒ 00:24:34.269 Robert Tseng: yeah. Because I think that’s definitely something that
301 00:24:34.710 ⇒ 00:24:43.050 Robert Tseng: we tell me, the guys can’t really necessarily do anything about if the data is just like not flowing through. So I’d like to see if we can do anything about it more upstream.
302 00:24:43.330 ⇒ 00:24:46.770 Aman Nagpal: Yeah, definitely. And how about all of the.
303 00:24:47.140 ⇒ 00:24:52.100 Aman Nagpal: you know, like performance marketing data? All the page visits
304 00:24:52.310 ⇒ 00:24:55.680 Aman Nagpal: Clavio, attentive data, things like that.
305 00:24:56.550 ⇒ 00:25:10.667 Robert Tseng: I would say, Clavio, data, just like page visits. And just a lot of this the the marketing tech data that gets scraped in. Maybe you and I can. We can go through like what we want to bring in, because what I’ve seen with other clients is
306 00:25:11.050 ⇒ 00:25:27.039 Robert Tseng: yeah, we we bring in this data. It’s massive, you know, it’s like millions of rows like per month that are synced. And they don’t use most of it. So, yeah, I think Mark, marketing data specifically has is notorious for just like collecting a bunch of stuff that you don’t actually need.
307 00:25:27.494 ⇒ 00:25:30.545 Robert Tseng: So I think if we can kind of get ahead of that
308 00:25:31.430 ⇒ 00:25:35.559 Robert Tseng: then I think that could be helpful for for the guys when they’re setting up the connectors there.
309 00:25:35.620 ⇒ 00:25:43.399 Robert Tseng: but I think, from my understanding, we were not trying to capture that in the 1st month I mean we we can bring it in. I don’t know. Maybe we, Tom, this is your call on.
310 00:25:43.400 ⇒ 00:25:48.429 Uttam Kumaran: Yeah, I I think I agree with you, basically like, I don’t want it to sit stale. So when we get to it.
311 00:25:48.620 ⇒ 00:25:49.330 Uttam Kumaran: will
312 00:25:50.290 ⇒ 00:26:04.879 Uttam Kumaran: We’ll bring that all in the Clavio in particular. You’re right like, there’s just so much data. And it’s gonna run up the bill. So I’m gonna be really careful about what we’re bringing in from Clavio. If all we need is like summary reports.
313 00:26:04.950 ⇒ 00:26:11.240 Uttam Kumaran: There’s other ways of doing that. If we want to do more specific like which client open what email and linking that to other stuff
314 00:26:11.250 ⇒ 00:26:17.409 Uttam Kumaran: can do that as well. But I think we take that after take. I think we just take that next month.
315 00:26:17.846 ⇒ 00:26:27.319 Uttam Kumaran: Unless we get through this earlier, it’ll just take a day or 2 to sync everything. It’ll be a very similar Api process. So we have that running list on our side.
316 00:26:27.578 ⇒ 00:26:30.499 Uttam Kumaran: So when we get to that, we’ll get to that, I think, for now
317 00:26:30.800 ⇒ 00:26:34.210 Uttam Kumaran: across shopify, which
318 00:26:34.260 ⇒ 00:26:37.839 Uttam Kumaran: again, well, now, it’s helpful to know that the Tiktok, tagging.
319 00:26:37.840 ⇒ 00:26:38.240 Nicolas Sucari: Yeah.
320 00:26:38.240 ⇒ 00:26:40.910 Uttam Kumaran: Is ha! The Tiktok stuff is happening through the tags. Cause.
321 00:26:41.030 ⇒ 00:26:51.069 Uttam Kumaran: Brian, that’s how we’ll like, basically try to bifurcate stuff, or like, we’ll add a dimension, for like where it’s coming from. That’s helpful. And then the Amazon stuff
322 00:26:51.505 ⇒ 00:26:53.399 Uttam Kumaran: as soon as we can get
323 00:26:53.440 ⇒ 00:26:56.520 Uttam Kumaran: that through that’d be great. And I think that’s all we need to basically
324 00:26:56.980 ⇒ 00:26:58.400 Uttam Kumaran: sprint towards like
325 00:26:58.920 ⇒ 00:27:00.450 Uttam Kumaran: the initial deliverable.
326 00:27:02.380 ⇒ 00:27:11.559 Uttam Kumaran: so yeah, I I think I’ll kind of leave it to Brian to chat if we need anything in particular, but he’ll probably sending. Once he looks into the tags he’ll probably send a couple of questions
327 00:27:12.760 ⇒ 00:27:18.129 Uttam Kumaran: and then, if there’s anything unique about the data, we’ll just talk in slack. So I think that should be all we need.
328 00:27:18.230 ⇒ 00:27:19.710 Uttam Kumaran: least for another week or so.
329 00:27:20.120 ⇒ 00:27:31.149 Aman Nagpal: Yeah, I I don’t mind pushing that back after everything else. I think that makes sense. Just a used case. For example, Justin wants to see we send our
330 00:27:31.370 ⇒ 00:27:41.279 Aman Nagpal: subscribers and initial welcome email, and I think maybe their 1st rebuild. We send them an email. How many people are clicking the.
331 00:27:41.380 ⇒ 00:27:47.536 Aman Nagpal: you know, clicking a link in that email to go to the user portal. And how many people are actually cancelling
332 00:27:47.830 ⇒ 00:27:48.470 Uttam Kumaran: Yeah.
333 00:27:48.470 ⇒ 00:27:56.909 Aman Nagpal: Like. So stuff like that. And even that I don’t like you said I don’t know if we need all the Clavio data. Maybe we just need. We’re recording the clicks based on the Utm and.
334 00:27:57.210 ⇒ 00:27:57.760 Uttam Kumaran: Yeah.
335 00:27:57.930 ⇒ 00:28:02.179 Aman Nagpal: And things like that. But we can definitely do that later on. So.
336 00:28:02.180 ⇒ 00:28:15.159 Uttam Kumaran: Yeah, I think the only other thing Robert, for maybe later this month is to give Nico a little bit of overview on, like what’s being leveraged heavily in amplitude reports that he could start to plan for
337 00:28:15.180 ⇒ 00:28:18.559 Uttam Kumaran: next month, while Brian’s working through the
338 00:28:18.880 ⇒ 00:28:23.370 Uttam Kumaran: stuff for revenue. So that’d be my only ask.
339 00:28:23.810 ⇒ 00:28:24.380 Robert Tseng: Yeah.
340 00:28:25.820 ⇒ 00:28:26.460 Aman Nagpal: Other things.
341 00:28:26.460 ⇒ 00:28:27.650 Nicolas Sucari: Can get us.
342 00:28:27.800 ⇒ 00:28:29.160 Aman Nagpal: Sorry. Go. Ahead. Yeah.
343 00:28:29.160 ⇒ 00:28:32.500 Nicolas Sucari: Oh, yeah, Robert, if you can get us access to amplitude, or
344 00:28:32.950 ⇒ 00:28:38.960 Nicolas Sucari: yes, and that’s some information on what’s there, so that we can take a look that would be great, too.
345 00:28:40.046 ⇒ 00:28:48.759 Robert Tseng: A mom won’t have to be the one to give you access to evidence of my admin, but once you’re in, I can give you walk through what most of these reports are. Yeah.
346 00:28:49.140 ⇒ 00:29:15.295 Aman Nagpal: Yep, I’ll get you guys added before I forget just other things that are coming to mind. I mentioned before. We have a lot of cloudflare workers that are grabbing data from certain Apis and throwing it into amplitude. So all of our Okendo reviews, we have a dashboard of like average review per week. Rolling averages things like that. The way that we’re getting that data is a cloud flare worker that’s grabbing the data from the Okendo Api sending it to amplitude.
347 00:29:16.140 ⇒ 00:29:23.260 Aman Nagpal: Couple of other gorgeous. We definitely like to get, you know, gorgeous data to say, you know
348 00:29:23.310 ⇒ 00:29:27.949 Aman Nagpal: what types of orders are damaged, is it? You know? Mostly 3 bottle orders, is it?
349 00:29:28.277 ⇒ 00:29:33.510 Aman Nagpal: You know how many tickets are? Of what type things like that we definitely want as well.
350 00:29:34.010 ⇒ 00:29:41.120 Uttam Kumaran: Okay, yeah, we’ve worked with the gorgeous folks before. So I actually sent them an email. They have a 5 Tran light Connector
351 00:29:41.270 ⇒ 00:29:45.039 Uttam Kumaran: Nico. So let’s also in our message to 5 trend. Let’s just like
352 00:29:45.480 ⇒ 00:29:52.217 Uttam Kumaran: hit them with like 50 things. And we they also have an okendo connector, too. The nice thing is, I think,
353 00:29:52.790 ⇒ 00:30:05.529 Uttam Kumaran: we’ll see like again, if we can move that reporting. So you don’t have to use the cloudflare worker, and we’ll get basically all of okendo, all of gorgeous, into the data warehouse. Then we can kick that stuff off as well.
354 00:30:07.080 ⇒ 00:30:08.560 Uttam Kumaran: so yeah, that’s really helpful.
355 00:30:10.160 ⇒ 00:30:16.119 Uttam Kumaran: Nico, we can add that. And we can. We can get whatever access we need to set those up again. The biggest things is like, I want to
356 00:30:16.350 ⇒ 00:30:30.269 Uttam Kumaran: get those stuff set up when Brian has capacity to kind of like work on those, but just knowing that they have the connector, and then anywhere where, for example, gorgeous, is like, is that a light mode which we may need to ask? Ask for a request?
357 00:30:30.530 ⇒ 00:30:38.879 Uttam Kumaran: We can just kick that stuff off. So that’s helpful. And yeah, again, ideally, where you have a cloud for a worker, where it’s like, Hey, I wish we had this many a little bit more data, or
358 00:30:38.970 ⇒ 00:30:45.049 Uttam Kumaran: the cloud for worker like messes up. That’s something that will just move all to the warehouse, and you can kind of turn those off
359 00:30:45.445 ⇒ 00:30:51.320 Uttam Kumaran: unless they’re being used for like any sort of like process automation, it’s being used for reporting
360 00:30:51.637 ⇒ 00:30:56.410 Uttam Kumaran: and it’s and it doesn’t. And we can duplicate it, or we can remove it, and that’s what we’ll go for.
361 00:30:56.940 ⇒ 00:31:03.139 Aman Nagpal: Yeah, no, I think a lot of these. If we can just replace the cloudflare worker, it’s a lot easier to maintain.
362 00:31:03.577 ⇒ 00:31:12.139 Aman Nagpal: How about north beam. How does that fit into all of this? Because I know the media buying guys just live and die by north beam data and its attribution. And compare.
363 00:31:12.140 ⇒ 00:31:12.910 Uttam Kumaran: Yeah.
364 00:31:16.010 ⇒ 00:31:17.050 Uttam Kumaran: So
365 00:31:17.210 ⇒ 00:31:19.849 Uttam Kumaran: I don’t know. Yeah, Robert, if you want to go ahead, I have.
366 00:31:20.230 ⇒ 00:31:25.080 Uttam Kumaran: I’m kind of I I guess my perspective is that north beam attribution.
367 00:31:25.230 ⇒ 00:31:40.949 Uttam Kumaran: You. I don’t think we’re going to get anywhere trying to replicate their like attribution model. I think, where we’re going to be able to help is basically on measuring, like all the combined marketing costs and measuring all the combined marketing like performance.
368 00:31:41.200 ⇒ 00:31:46.700 Uttam Kumaran: But North Beam is really really amazing, just for the marketers to live in. I don’t
369 00:31:46.900 ⇒ 00:31:55.660 Uttam Kumaran: like. I don’t think it’s going to be in our world to like replace that product, I think, in terms of reporting on marketing, spend
370 00:31:55.690 ⇒ 00:31:58.900 Uttam Kumaran: like on Channel spend on channel performance
371 00:31:58.980 ⇒ 00:32:03.449 Uttam Kumaran: and on like looking at basic attribution, we’ll be able to do.
372 00:32:03.560 ⇒ 00:32:04.330 Uttam Kumaran: But.
373 00:32:04.980 ⇒ 00:32:13.429 Aman Nagpal: I think that’s perfect doing now with amplitude. Right? So we’re syncing. And again with the cloudflare worker, our daily spend from north beam into amplitude.
374 00:32:13.460 ⇒ 00:32:20.350 Aman Nagpal: And then we’re able to make bunch of charts with Cac, you know, based on our actual order information. So it sounds like that process can be replicated here.
375 00:32:21.500 ⇒ 00:32:22.080 Uttam Kumaran: Yeah.
376 00:32:22.600 ⇒ 00:32:44.570 Robert Tseng: Yeah, I mean that that reporting process can be replicated. And I think, like, where we could be looking forward to on how? Yeah, we’re not. We’re not. Gonna replace Northbe like what U top said. I think with the data that we have control of, that, we can model in the warehouse we can get. We could do like user level attribution, which which North beat doesn’t do what they’re do. What they’re good at doing is
377 00:32:44.917 ⇒ 00:33:09.220 Robert Tseng: modeling for like strategic channel spending for like unidentified users. And that’s because they’re they have models running on, you know, hundreds of 1,000 clients tuned into different to all the different channels they know, like within their portfolio, how everybody is making optimizations for these channels. And that’s how they’re tuning their models. So you’re always going to get like the baseline of like what
378 00:33:09.983 ⇒ 00:33:20.340 Robert Tseng: like, how how they, how everyone else in your category and their in their and their model is is performing and that’s like just helpful for
379 00:33:20.880 ⇒ 00:33:26.370 Robert Tseng: situations where you’re trying to do attribution with limited user level tracking.
380 00:33:26.636 ⇒ 00:33:37.140 Robert Tseng: That might be. I don’t know what what share of your users you’re not able to track, but I would guess you know at least 40% or something. But then the other half that you are able to capture.
381 00:33:37.900 ⇒ 00:33:59.609 Robert Tseng: Yeah, any whatever. We are able to identify it. I would say that we we would be using. You could build attribution internally with the data that we have in the warehouse to identify them and do attribution modeling without without using without relying on north beam. So I do think that ends up kind of being going hand in hand.
382 00:34:00.950 ⇒ 00:34:09.359 Robert Tseng: where? Yeah, we’ll end up using like different lenses of attribution to like supplement each other. But yeah, it’s not going to replace me.
383 00:34:09.949 ⇒ 00:34:27.439 Uttam Kumaran: Yeah, like north beam and triple whale. They do a ton of stuff behind the scenes, and marketing attribution is like an endless struggle that those companies that’s like all they do is figure out how to like track people on the Internet. The biggest things that we’re gonna help with is like again, if you’re like, we want to add up all of our marketing, spend and see
384 00:34:27.579 ⇒ 00:34:29.639 Uttam Kumaran: total customers on one side.
385 00:34:29.699 ⇒ 00:34:33.559 Uttam Kumaran: total spend, and like those broken down by channels
386 00:34:33.659 ⇒ 00:34:39.147 Uttam Kumaran: that’s perfect, like, that’s stuff that will will actually get the spend directly from
387 00:34:39.689 ⇒ 00:34:43.699 Uttam Kumaran: from the platforms themselves. So yeah.
388 00:34:44.440 ⇒ 00:34:47.690 Aman Nagpal: Cool. That sounds great to me, I think what will be helpful is
389 00:34:48.141 ⇒ 00:34:59.810 Aman Nagpal: just asking me asking every department. You know what’s kind of the main dashboards and reports that you use within amplitude to make sure that you know everything’s smooth sailing as we move toward the next month or 2.
390 00:35:00.420 ⇒ 00:35:11.959 Robert Tseng: Yeah, I think it also be helpful to see like, what else do they? What else did do they not have that they want? Yeah, that like, they’re burning to have like, I think that’d be helpful for prioritization as well.
391 00:35:12.660 ⇒ 00:35:19.866 Uttam Kumaran: Yeah. And I think the nice thing among we can use that we we’ve put together like questions, Doc. And I actually like
392 00:35:21.380 ⇒ 00:35:49.149 Uttam Kumaran: I like starting from like these business questions. Instead of being like, we want to see this metric or this metric is like, we’re actually going to solve a question. The way we solve it is like the way we’re going to solve it. But I that’s why I think it’s easier to interact with the different business stakeholders with these questions in mind, because some of these questions have overlaps, and these questions will use the same sources, the same metrics that’s for us to figure out the biggest thing is like, I want to make sure we go check by check
393 00:35:49.150 ⇒ 00:36:02.190 Uttam Kumaran: and understand that like this is solvable with this dashboard, or with these 3 models. And like, I think, having this like questions, Doc helps that so feel free just to toss more in there, or, as you chat with people and you get ideas.
394 00:36:02.230 ⇒ 00:36:05.790 Uttam Kumaran: That’s where I’m gonna kind of have the team centralize, like all of our
395 00:36:06.120 ⇒ 00:36:07.260 Uttam Kumaran: questions. So.
396 00:36:07.960 ⇒ 00:36:17.370 Robert Tseng: Yeah. And I’m on. If you wanna bring me into like calls with some of these guys, cause I know not. Everybody like jumps to the docs to fill things out. So if they prefer to just like talk about it. And I.
397 00:36:17.370 ⇒ 00:36:17.769 Uttam Kumaran: The right.
398 00:36:17.770 ⇒ 00:36:25.030 Robert Tseng: Kind of yeah, like Round table with them. Like I I think that’s something I would I would want to. I would want to do as well. So.
399 00:36:25.620 ⇒ 00:36:30.320 Aman Nagpal: I think. Yeah, that’ll be super helpful. Definitely, with some specific stakeholders.
400 00:36:32.030 ⇒ 00:36:39.229 Aman Nagpal: And the what was I going to say? The docs? I don’t think they’re editable. Should I make a new doc with the duplicate, or what should I do? There.
401 00:36:39.580 ⇒ 00:36:43.050 Nicolas Sucari: I can. We can give you access to it. Maybe you tell me what you think.
402 00:36:43.360 ⇒ 00:36:43.920 Uttam Kumaran: Yeah, yeah.
403 00:36:43.920 ⇒ 00:36:51.729 Nicolas Sucari: We will work on on those, doc or or we can copy it and create a new one. But yeah, I can edit it. I can make you edit.
404 00:36:51.920 ⇒ 00:36:56.850 Uttam Kumaran: No, yeah. Just make sure that. Yeah, everything in that client page is editable.
405 00:36:57.020 ⇒ 00:37:02.009 Uttam Kumaran: And then, yeah, if you’re not able to add, people let me know we can add them. But
406 00:37:02.190 ⇒ 00:37:11.660 Uttam Kumaran: yeah, I mean, I want to make sure that all that stuff is like super open and shareable. And that, like that kind of bridges, the gap between some of the technical work. And then.
407 00:37:12.040 ⇒ 00:37:15.459 Uttam Kumaran: like, I wanna make sure that. Okay, we we went from like
408 00:37:15.550 ⇒ 00:37:18.370 Uttam Kumaran: these source tables to like these reporting tables. And
409 00:37:18.590 ⇒ 00:37:22.780 Uttam Kumaran: these tables actually go to like check box these like 10 questions. We have
410 00:37:22.890 ⇒ 00:37:34.610 Uttam Kumaran: also, it puts us in a frame of reference. So when we’re collecting further requirements, we start from like a what are the questions instead of like, Oh, I want to see. I want to see like this metric. It’s like, what’s the question you’re trying to solve.
411 00:37:34.730 ⇒ 00:37:41.280 Uttam Kumaran: Okay, that’ll help Brian basically frame like, Oh, we can pull that from this table. Oh, we already have that here.
412 00:37:41.548 ⇒ 00:37:47.999 Uttam Kumaran: Otherwise we end up with like 50. That it’s what happens is like you end up with 50 dashboards to answer the same 10 questions
413 00:37:48.590 ⇒ 00:37:55.140 Uttam Kumaran: after doing this like 50 times. Just try to avoid that on the 51, st you know. So.
414 00:37:55.590 ⇒ 00:38:05.520 Aman Nagpal: 100%. Yeah, to to Robert’s point, I think certain people, it’s just going to be easier to hop on a call and kind of extract and translate what they’re aiming for rather than just getting that laundry list. So
415 00:38:07.940 ⇒ 00:38:08.660 Aman Nagpal: yeah.
416 00:38:09.080 ⇒ 00:38:16.040 Nicolas Sucari: Perfect. Okay, and you, you already have edit access on notion. So let me know if that works.
417 00:38:16.050 ⇒ 00:38:21.780 Nicolas Sucari: If you want to invite anyone else there to add more information, let me know. And we can. We can add them.
418 00:38:22.202 ⇒ 00:38:29.810 Nicolas Sucari: Yeah. So next step for us is gonna be thinking all the data from shopify which is snowflake and pipetran.
419 00:38:30.241 ⇒ 00:38:36.369 Nicolas Sucari: And yeah, and we are, gonna start with that. And then, if we need anything else we’ll be slacking. Okay.
420 00:38:37.590 ⇒ 00:38:39.429 Aman Nagpal: Sweet. Thank you guys so much.
421 00:38:40.310 ⇒ 00:38:41.879 Uttam Kumaran: Thanks. Guys. Talk soon.
422 00:38:41.880 ⇒ 00:38:43.530 Nicolas Sucari: Thanks, guys, thanks everyone. Bye, bye.
423 00:38:43.870 ⇒ 00:38:44.600 Nicolas Sucari: but.