Meeting Title: Javy-Data-Engineering-Weekly Date: 2024-11-05 Meeting participants: Nicolas Sucari, Uttam Kumaran, Aman Nagpal, Payas Parab, Robert Tseng
WEBVTT
1 00:03:35.080 ⇒ 00:03:36.020 Nicolas Sucari: Hi, guys.
2 00:03:36.160 ⇒ 00:03:37.110 Nicolas Sucari: Hi, Robert.
3 00:03:39.350 ⇒ 00:03:40.320 Robert Tseng: Hey? Nico.
4 00:03:42.170 ⇒ 00:03:42.970 Nicolas Sucari: How are you?
5 00:03:44.600 ⇒ 00:03:46.220 Robert Tseng: I’m good. How are you?
6 00:03:47.600 ⇒ 00:03:48.830 Nicolas Sucari: Good thanks.
7 00:05:05.980 ⇒ 00:05:07.209 Aman Nagpal: Hey, guys, how’s it going.
8 00:05:08.200 ⇒ 00:05:09.000 Nicolas Sucari: Came on
9 00:05:09.890 ⇒ 00:05:11.340 Nicolas Sucari: doing good. How are you?
10 00:05:12.060 ⇒ 00:05:13.380 Aman Nagpal: Doing, good thanks.
11 00:05:24.730 ⇒ 00:05:25.510 Robert Tseng: Hey, everyone!
12 00:05:27.630 ⇒ 00:05:28.250 Robert Tseng: Yay.
13 00:05:28.250 ⇒ 00:05:28.920 Aman Nagpal: Take one.
14 00:05:31.630 ⇒ 00:05:32.530 Nicolas Sucari: Hey? Pay us?
15 00:05:33.260 ⇒ 00:05:34.860 Nicolas Sucari: Okay? I think.
16 00:05:35.380 ⇒ 00:05:35.900 Payas Parab: Hey! How are you?
17 00:05:35.900 ⇒ 00:05:36.919 Nicolas Sucari: Ryan is with some.
18 00:05:41.090 ⇒ 00:05:43.950 Nicolas Sucari: Give me a minute. I’ll ping Ryan so that he can join.
19 00:05:48.030 ⇒ 00:05:49.929 Nicolas Sucari: But yeah, we can get starting
20 00:05:51.310 ⇒ 00:05:56.650 Nicolas Sucari: So for today, guys, we have a small agenda of stuff.
21 00:05:57.728 ⇒ 00:05:59.719 Nicolas Sucari: For the data updates.
22 00:06:00.710 ⇒ 00:06:02.269 Nicolas Sucari: We have
23 00:06:02.470 ⇒ 00:06:16.210 Nicolas Sucari: gorgeous. And again, the dashboards there in real now. We are still we. We just added the the dashboards. In order to explore the data, we need to to keep matching that with the orders from shopify and Amazon.
24 00:06:16.300 ⇒ 00:06:25.529 Nicolas Sucari: so that we can add dimensions into the other dashboards. But it will be good. I don’t know, Aman, if you were able to take a look at those those new dashboards that we added in real
25 00:06:25.720 ⇒ 00:06:26.500 Nicolas Sucari: yep.
26 00:06:27.500 ⇒ 00:06:29.899 Aman Nagpal: Not yet. No, I’ll try to take a look.
27 00:06:31.300 ⇒ 00:06:45.690 Nicolas Sucari: Excellent. Okay, let us know if you, if you can, take a look, and answer any of those questions that you that you sent us and if if there is any dimension missing, or any measure that you’d like to add, let us know. And we can work on that, too. Okay.
28 00:06:46.350 ⇒ 00:06:47.880 Nicolas Sucari: that sounds good, awesome.
29 00:06:48.020 ⇒ 00:06:55.579 Aman Nagpal: I know we’ll get into. Sorry. I know we’ll get into, or probably will amplitude a little bit later. But just for these specific items.
30 00:06:55.630 ⇒ 00:06:56.839 Aman Nagpal: I would check
31 00:06:57.160 ⇒ 00:07:03.610 Aman Nagpal: basically the table set up in real and as long as everything looks good, then we can take the next steps in amplitude right.
32 00:07:04.810 ⇒ 00:07:06.340 Nicolas Sucari: Yeah, yeah, perfect.
33 00:07:07.880 ⇒ 00:07:08.730 Nicolas Sucari: Okay.
34 00:07:09.369 ⇒ 00:07:34.530 Nicolas Sucari: also, we added the product category in the all orders line. Sorry in the orders table we can. Now check the product category and try to look into more detailed stuff there. We are still working to add that into the order line. As each order has different products with different product categories. That’s kind of a little bit difficult. I don’t know if that’s
35 00:07:34.530 ⇒ 00:07:41.929 Nicolas Sucari: something you will need. But we can try to take a look and see how we can add there in the in the other dashboard, too.
36 00:07:42.315 ⇒ 00:07:52.919 Nicolas Sucari: And then the we already, added Tiktok shops as an app source as we spoke today, yesterday. Pay us so if you go into all orders
37 00:07:53.120 ⇒ 00:08:05.619 Nicolas Sucari: dashboard right now you’ll see that you have 3 different sources Tiktok, Tiktok, shopify and Amazon. And you can filter down just the tiktok source by clicking on it. Okay?
38 00:08:06.184 ⇒ 00:08:12.810 Nicolas Sucari: Then we had the Google sheet for the Cox analysis, where we created the folder where.
39 00:08:12.810 ⇒ 00:08:26.900 Payas Parab: Nico just wanted to add the note of I’m on. We. We updated the logic so that the Tiktok order is not based on the tags that are coming in in the order they’re based on whether a Tiktok order Id is assigned. So I wanna make sure that that’s a
40 00:08:27.130 ⇒ 00:08:31.400 Payas Parab: like a reasonable way to do it rather than relying on the is Tiktok shop tag.
41 00:08:31.830 ⇒ 00:08:32.780 Aman Nagpal: So
42 00:08:33.230 ⇒ 00:08:35.559 Aman Nagpal: from my understanding. It’s
43 00:08:35.630 ⇒ 00:08:37.900 Aman Nagpal: the Tiktok order. Id
44 00:08:38.090 ⇒ 00:08:46.419 Aman Nagpal: is only found within the shopify tag. A different shopify tag. So is that what we’re looking at, or is there? Is it from somewhere else?
45 00:08:48.860 ⇒ 00:09:05.610 Payas Parab: there’s like there was a column that came through from the raw data from shopify that had a Tiktok order. Id. And then there was also like a tag that was like is Tiktok Shop, that was like one of the tags that, like it was among the other tags you guys had like is Snapchat is Klaviyo, etcetera.
46 00:09:06.146 ⇒ 00:09:12.530 Payas Parab: We’re using. That is Tiktok order Id. And I imagine that comes from the integration between Shopify and Tiktok.
47 00:09:13.130 ⇒ 00:09:18.400 Aman Nagpal: Let me just share my screen real quick, because I I think it’s an important point to make sure we’re all.
48 00:09:18.590 ⇒ 00:09:20.070 Aman Nagpal: Yes, okay.
49 00:09:20.070 ⇒ 00:09:21.900 Payas Parab: Yeah, sorry, Nico, sorry to interrupt. I just wanna make sure we.
50 00:09:21.900 ⇒ 00:09:23.330 Nicolas Sucari: No, no, that’s perfect, perfect.
51 00:09:23.330 ⇒ 00:09:27.651 Payas Parab: We get that right so that there’s no discrepancy when they log into Tiktok.
52 00:09:28.280 ⇒ 00:09:31.490 Aman Nagpal: Do you guys see this order? Screen 1, 8, 8 0, 5.
53 00:09:32.450 ⇒ 00:09:33.080 Nicolas Sucari: Yeah.
54 00:09:34.120 ⇒ 00:09:35.950 Aman Nagpal: Sorry one. Sec.
55 00:09:36.920 ⇒ 00:09:42.120 Aman Nagpal: Okay, yeah. So from my understanding, the integration is only adding
56 00:09:42.370 ⇒ 00:09:54.040 Aman Nagpal: the order id as a tag. So we had this Tiktok shop tag, which I think we were using before shipped by Tiktok. I think that’s just for the Fbt. Orders. And then the Tiktok order Id. It’s
57 00:09:54.160 ⇒ 00:09:57.459 Aman Nagpal: saved on Tiktok Seller Central, and
58 00:09:57.560 ⇒ 00:09:59.079 Aman Nagpal: within the
59 00:09:59.434 ⇒ 00:10:10.829 Aman Nagpal: you know, the the integration is able to pull it up from there. But in terms of the actual order. I don’t think it’s anywhere else except for this tag. So is that where we’re getting it from now.
60 00:10:11.510 ⇒ 00:10:13.249 Payas Parab: I believe so right. Nico.
61 00:10:14.370 ⇒ 00:10:19.288 Nicolas Sucari: Yeah, I believe so. And yeah, we’re, we’re just getting that id. So maybe
62 00:10:19.940 ⇒ 00:10:28.639 Nicolas Sucari: maybe it’s the same. And I’m looking at the numbers, and I think the it’s the same whether we are getting it from the stick to shop.
63 00:10:28.750 ⇒ 00:10:30.400 Nicolas Sucari: a tag or the
64 00:10:30.420 ⇒ 00:10:32.730 Nicolas Sucari: Tiktok order id tag. So maybe if
65 00:10:33.273 ⇒ 00:10:43.410 Nicolas Sucari: we we should take a look and see if there is a case where we can find one tag or not the other. But if that doesn’t happen this, the number should be the same. Yep.
66 00:10:43.930 ⇒ 00:11:02.470 Payas Parab: One thing I also wanted to clarify. Now that I see this here as well is like I know we have an is Tiktok shop order like Boolean in the table. Right? This whole shipped by Tiktok right like, what do you guys consider to be a Tiktok order like? Is there a scenario here where there’s a tiktok order id assigned, but it’s not shipped or fulfilled by Tiktok.
67 00:11:03.210 ⇒ 00:11:19.719 Aman Nagpal: No, so so I think those columns, I assume, was taking it from wherever. So, if if it says, is Tiktok order, I think what we had before was, does it have a Tiktok shop at? And then the bullion would be true. Right? So I think that’s where these items were coming from.
68 00:11:20.830 ⇒ 00:11:25.609 Aman Nagpal: you know, I ideally. There’s also the Channel tick tock, which
69 00:11:26.040 ⇒ 00:11:36.720 Aman Nagpal: I think I I forget. If we discuss it, I feel like we might have where I think this is probably better to use. The actual channel is tick tock rather than tiktok shop, even though it’s the same thing, just because
70 00:11:37.250 ⇒ 00:11:45.170 Aman Nagpal: we are. I believe we’re adding these Tiktok shop tags with shopify flow once it scans for the channel. So the channel is.
71 00:11:45.280 ⇒ 00:11:46.020 Aman Nagpal: It should be the same.
72 00:11:46.020 ⇒ 00:11:47.390 Payas Parab: Channel is a source of truth.
73 00:11:47.390 ⇒ 00:11:50.539 Aman Nagpal: Yeah, exactly in terms of
74 00:11:50.810 ⇒ 00:11:57.799 Aman Nagpal: what’s considered anything that comes in from Tiktok Channel or Tiktok shop tag is from Tiktok.
75 00:11:57.840 ⇒ 00:12:01.620 Aman Nagpal: but the ones that are, whether we, if we fulfill them, then
76 00:12:01.940 ⇒ 00:12:10.280 Aman Nagpal: there’s no extra tag. If Tick Tock’s fulfilling them, it has shipped by Tiktok Tag is my understanding. So that’s the only difference of how it’s being shipped. But
77 00:12:10.290 ⇒ 00:12:14.970 Aman Nagpal: any Tiktok or any order that comes in with the Channel. Tiktok is from Tiktok Shop.
78 00:12:15.230 ⇒ 00:12:16.559 Payas Parab: Got it. Okay.
79 00:12:17.140 ⇒ 00:12:17.540 Aman Nagpal: Thank you.
80 00:12:17.540 ⇒ 00:12:18.380 Payas Parab: Sure. Yeah.
81 00:12:18.380 ⇒ 00:12:19.670 Aman Nagpal: The channel instead of.
82 00:12:19.670 ⇒ 00:12:20.290 Payas Parab: Channel.
83 00:12:20.510 ⇒ 00:12:21.140 Aman Nagpal: Yeah.
84 00:12:21.140 ⇒ 00:12:29.829 Payas Parab: Is there? Is there a situation? Because if you guys use on Tiktok like closed loop and open loop ads where it’s like the Channel attribution goes to tick, tock. But
85 00:12:29.880 ⇒ 00:12:43.559 Payas Parab: the order wasn’t from Tiktok. If that makes sense, because, like in the closed loop, you do have the order like process through Tiktok. But in the event that you run an ad on Tiktok that then directs to your shopify landing page with the Channel attribution still be Tiktok.
86 00:12:43.960 ⇒ 00:12:53.490 Aman Nagpal: No, so the channel only uses when it’s purchased within Tiktok shop. But any Tiktok ads we run, and they purchase it on our landing pages that comes in normally
87 00:12:53.510 ⇒ 00:12:54.910 Aman Nagpal: without the Tiktok Channel.
88 00:12:54.910 ⇒ 00:12:55.360 Payas Parab: Okay.
89 00:12:55.709 ⇒ 00:13:00.600 Aman Nagpal: But I think it. It’s also important. Yes, we should use the channel, but
90 00:13:00.640 ⇒ 00:13:03.650 Aman Nagpal: it would be ideal for Tiktok orders to have
91 00:13:04.030 ⇒ 00:13:07.550 Aman Nagpal: the Tiktok Id being taken from the tag as well. So we have both.
92 00:13:07.550 ⇒ 00:13:09.459 Payas Parab: Yes, yep, okay.
93 00:13:09.710 ⇒ 00:13:17.779 Payas Parab: clear. Sorry to interrupt, Nico. I just wanted to make sure this, because we’ve had some issues before where it’s like the Jared’s been like the Tiktok numbers. Just look off. So I wanna make sure we’re all
94 00:13:17.920 ⇒ 00:13:19.880 Payas Parab: blind on how to how to do that.
95 00:13:21.030 ⇒ 00:13:21.680 Payas Parab: Yeah.
96 00:13:21.680 ⇒ 00:13:22.065 Nicolas Sucari: Okay.
97 00:13:22.450 ⇒ 00:13:23.330 Payas Parab: Yeah, perfect.
98 00:13:23.330 ⇒ 00:13:29.870 Aman Nagpal: Move on. The thing you mentioned about the categories did when you say categories? Do you mean the
99 00:13:29.930 ⇒ 00:13:31.820 Aman Nagpal: shopify collections?
100 00:13:32.960 ⇒ 00:13:38.310 Nicolas Sucari: The let me share. But what we can see now that we have there is
101 00:13:38.410 ⇒ 00:13:40.810 Nicolas Sucari: in. Let me check here.
102 00:13:41.260 ⇒ 00:13:43.900 Nicolas Sucari: I’ll give you an example of what we have so that you can check.
103 00:13:44.840 ⇒ 00:13:52.670 Nicolas Sucari: We have product type. It’s the. It’s the product type that is like coffee concentrates, accessories, protein coffee shakes and meal replacements
104 00:13:52.820 ⇒ 00:13:54.610 Nicolas Sucari: are those ones? Okay.
105 00:13:54.610 ⇒ 00:13:56.570 Aman Nagpal: So that’s coming from.
106 00:13:57.130 ⇒ 00:13:59.239 Aman Nagpal: I don’t know if this has it. For example.
107 00:13:59.970 ⇒ 00:14:01.480 Aman Nagpal: right here, this product type that we’re.
108 00:14:01.480 ⇒ 00:14:03.810 Nicolas Sucari: Yeah, exactly. That product type. Yeah.
109 00:14:04.260 ⇒ 00:14:05.410 Aman Nagpal: Can we?
110 00:14:06.080 ⇒ 00:14:13.750 Aman Nagpal: Is it possible to call it the same thing and shopify so that we know we can compare apples to apples, so that we know in the data warehouse. It says product type
111 00:14:13.800 ⇒ 00:14:14.920 Aman Nagpal: that it’s it’s.
112 00:14:14.920 ⇒ 00:14:20.200 Nicolas Sucari: It’s product type. Sorry I I mentioned product categories. But did they mention that we haven’t available this product type? Yeah.
113 00:14:20.200 ⇒ 00:14:21.699 Aman Nagpal: That’s perfect. Then thank you.
114 00:14:22.500 ⇒ 00:14:27.199 Nicolas Sucari: I can. Okay, yeah, I can share, if not the real dashboard here. But I’ll show it.
115 00:14:27.270 ⇒ 00:14:28.409 Nicolas Sucari: Give me a minute.
116 00:14:29.880 ⇒ 00:14:31.779 Nicolas Sucari: but you will see it here.
117 00:14:31.890 ⇒ 00:14:33.659 Nicolas Sucari: What you can do now is
118 00:14:35.230 ⇒ 00:14:37.089 Nicolas Sucari: Are you seeing the screen? Yes.
119 00:14:38.220 ⇒ 00:14:44.680 Nicolas Sucari: okay. So now you’ll see the product name. And also you’ll see the product type. So if you want to just check like boarding coffee
120 00:14:45.163 ⇒ 00:14:56.610 Nicolas Sucari: you just can click and see, like all of the order lines. And that’s kind of stuff, and this is in the All order line metrics. But if we go to the All order dashboard, what we can see is a tiktok here that’s coming from that
121 00:14:56.966 ⇒ 00:15:01.209 Nicolas Sucari: tick, Tock, id tag, and we can check for the other channel value.
122 00:15:01.370 ⇒ 00:15:08.870 Nicolas Sucari: And we’re keeping this one for now just to compare. But yeah, you have both here to to check.
123 00:15:09.710 ⇒ 00:15:10.315 Nicolas Sucari: Okay.
124 00:15:10.920 ⇒ 00:15:11.680 Aman Nagpal: Thank you.
125 00:15:12.620 ⇒ 00:15:13.460 Aman Nagpal: Cool.
126 00:15:13.881 ⇒ 00:15:21.040 Nicolas Sucari: Great. So that’s the data update. We have the drive folder. Whenever you have that cogs
127 00:15:21.466 ⇒ 00:15:36.369 Nicolas Sucari: spreadsheet that you want to share with us. We can try that flow into using this spreadsheet and and building that into Snowflake. We’ll just need to map out those columns on that sheet so that we can do that
128 00:15:36.814 ⇒ 00:15:40.935 Nicolas Sucari: and then we are still working on the returns and refunds
129 00:15:41.800 ⇒ 00:15:47.070 Nicolas Sucari: data tables that we have there from shopify? So yeah, I don’t know.
130 00:15:47.410 ⇒ 00:15:47.870 Uttam Kumaran: Yeah.
131 00:15:47.870 ⇒ 00:15:48.569 Nicolas Sucari: Ryan, if you want to.
132 00:15:48.570 ⇒ 00:15:57.690 Uttam Kumaran: I had one question. There I’m on. Are people allowed to do partial? I guess. Let’s talk about refunds, returns, and partial.
133 00:15:57.760 ⇒ 00:15:59.190 Uttam Kumaran: like partial
134 00:15:59.300 ⇒ 00:16:06.220 Uttam Kumaran: refunds and stalking if you can just go through those situations. So one, if maybe we can start with like
135 00:16:06.350 ⇒ 00:16:09.890 Uttam Kumaran: we’ve we’ve seen some cases where people are doing full returns.
136 00:16:10.200 ⇒ 00:16:13.329 Uttam Kumaran: Do you guys always restock? And is, is that a decision?
137 00:16:13.450 ⇒ 00:16:15.750 Uttam Kumaran: Or, yeah, if you could talk about that.
138 00:16:16.710 ⇒ 00:16:25.000 Aman Nagpal: Yes, so I don’t know what the Cx policies are, what to check Mark when they do the returns. I mean, I can always find out. But
139 00:16:25.260 ⇒ 00:16:31.270 Aman Nagpal: since we don’t pay attention to any of the inventory within shopify itself as of right now.
140 00:16:31.480 ⇒ 00:16:32.330 Aman Nagpal: I
141 00:16:32.610 ⇒ 00:16:37.229 Aman Nagpal: don’t think it necessarily makes a difference. So if if someone has a return
142 00:16:37.607 ⇒ 00:16:42.969 Aman Nagpal: whether or not they hit restock, it’s half of our products. Say, negative inventory, anyway, in shopify. So
143 00:16:43.278 ⇒ 00:16:46.999 Aman Nagpal: yeah, I would, I would think it probably doesn’t matter right. What do you think.
144 00:16:48.490 ⇒ 00:16:58.809 Uttam Kumaran: Yeah, I mean, that makes sense. I I, yeah, I I just in case the restock matters. Then yeah, I would say, let’s skip that. And then the other question is, do you guys allow for partial refunds
145 00:16:59.780 ⇒ 00:17:02.169 Uttam Kumaran: like one item in the order.
146 00:17:02.470 ⇒ 00:17:05.779 Aman Nagpal: Yeah, we we definitely a lot partial. Or if
147 00:17:05.839 ⇒ 00:17:07.230 Aman Nagpal: you know, I’m sure
148 00:17:07.290 ⇒ 00:17:11.759 Aman Nagpal: I would guess there’s scenarios where we, wanna, you know, refund X amount to a customer. Just.
149 00:17:12.410 ⇒ 00:17:16.430 Aman Nagpal: you know, for that one product, or whatever it may be. Yeah, we we definitely do partial.
150 00:17:17.240 ⇒ 00:17:18.800 Uttam Kumaran: Okay? Cause we’re
151 00:17:18.829 ⇒ 00:17:24.659 Uttam Kumaran: yeah, we’re basically, we are seeing kind of like 2 situations. We’re seeing sometimes where there are order.
152 00:17:25.079 ⇒ 00:17:36.819 Uttam Kumaran: I order line level refunds. And then some situations where there are complete refunds. And we’re just like doing some modeling there to get the numbers right. We we’ve tracked everything that’s like a complete refund.
153 00:17:37.247 ⇒ 00:17:40.852 Uttam Kumaran: But we’re noticing there are some cases where there’s individual items that get
154 00:17:41.300 ⇒ 00:17:42.440 Uttam Kumaran: refunded.
155 00:17:43.176 ⇒ 00:17:46.940 Uttam Kumaran: But just. But frankly, most of them look like full
156 00:17:47.800 ⇒ 00:17:49.240 Uttam Kumaran: full refunds.
157 00:17:50.490 ⇒ 00:17:52.830 Uttam Kumaran: But okay, that helps. That’s all we needed.
158 00:17:53.970 ⇒ 00:17:55.378 Aman Nagpal: I have one more
159 00:17:56.060 ⇒ 00:17:57.490 Aman Nagpal: situation, that’s
160 00:17:57.790 ⇒ 00:18:01.229 Aman Nagpal: or question. I guess that’s kind of related. But maybe not. Is.
161 00:18:01.570 ⇒ 00:18:06.340 Aman Nagpal: does it take into account removed items. So customer places an order.
162 00:18:06.770 ⇒ 00:18:13.749 Aman Nagpal: We remove items, and we add items later from the admin panel. Does all of that will sync up correctly after the fact? Right?
163 00:18:14.660 ⇒ 00:18:19.189 Uttam Kumaran: Yes, so I mean, basically, it’s all the items
164 00:18:19.310 ⇒ 00:18:26.209 Uttam Kumaran: in the order. What I’ve seen the data is, there’s like a lot of just like cancellations that come in as
165 00:18:26.500 ⇒ 00:18:27.680 Uttam Kumaran: our refund.
166 00:18:28.342 ⇒ 00:18:33.149 Uttam Kumaran: So I wonder? I guess this is where we’ll probably talk about recharge, if, like we can.
167 00:18:33.160 ⇒ 00:18:40.130 Uttam Kumaran: we should look at the recharge data to see the cancellation, because right right now, this is all in shopify coming in as as
168 00:18:40.470 ⇒ 00:18:47.060 Uttam Kumaran: refunds. And but I’m seeing in the actual refund in the notes that it’s like
169 00:18:47.710 ⇒ 00:18:50.060 Uttam Kumaran: subscription cancel subscription cancel
170 00:18:50.538 ⇒ 00:18:52.030 Uttam Kumaran: so that’s what I wanted.
171 00:18:52.200 ⇒ 00:18:58.419 Uttam Kumaran: I assume those are situations where someone orders, and they’re like, Oh, I didn’t realize I was still on auto order. I want to cancel that one.
172 00:18:58.610 ⇒ 00:19:03.630 Uttam Kumaran: Once we get the recharge data, and I want to see whether that’s come in as a cancellation on on recharge or not.
173 00:19:04.380 ⇒ 00:19:12.030 Aman Nagpal: Yeah. So for there’s 2 processes processes, for you know that situation is one they would.
174 00:19:13.160 ⇒ 00:19:22.590 Aman Nagpal: There’s a difference between refund and cancel, right. So we might refund, I guess an order in some cases, E, even though it’s been fully shipped. So we’re not going to cancel it at that point
175 00:19:23.140 ⇒ 00:19:32.019 Aman Nagpal: money other cases where somebody, I guess, just places an order, and maybe before it ships, we decide to cancel it. Or maybe it’s
176 00:19:32.536 ⇒ 00:19:46.020 Aman Nagpal: we have digital items in there that are partially fulfilled. So maybe we catch it at that stage where the digital booklet is fulfilled. But the actual products aren’t, and we’ll cancel it there and refund it there. So there are a few different variations of that
177 00:19:46.593 ⇒ 00:20:05.399 Aman Nagpal: but in the case of you know that you brought up with the subscription. If an order is cancelled in shopify, the Cx. Rep also has to go into recharge and cancel it there separately, so it is 2 different kind of areas where it would be cancelled. But yeah, we would get that data from the the recharge. Api.
178 00:20:06.190 ⇒ 00:20:08.883 Uttam Kumaran: Okay. Okay? Great. Yeah. Cause I’m I’m seeing
179 00:20:09.290 ⇒ 00:20:15.539 Uttam Kumaran: I’m seeing some situations where there’s like, there’s no notes on anything. And it’s just like the entire order
180 00:20:15.580 ⇒ 00:20:18.650 Uttam Kumaran: is refunded in some situations where
181 00:20:18.920 ⇒ 00:20:20.340 Uttam Kumaran: it is like a
182 00:20:20.460 ⇒ 00:20:27.610 Uttam Kumaran: we didn’t want the upsell. There’s a partial refund, and then also the cancellations. But you’re right, like, some of the cancellations are post fulfillment.
183 00:20:27.910 ⇒ 00:20:28.475 Uttam Kumaran: Yeah.
184 00:20:30.320 ⇒ 00:20:38.120 Uttam Kumaran: okay, cool. Yeah. Just like, sometimes some. Everybody kind of does this differently. And has, like some people allow for partial refunds.
185 00:20:38.200 ⇒ 00:20:39.560 Uttam Kumaran: Some people
186 00:20:39.790 ⇒ 00:20:43.409 Uttam Kumaran: don’t. Some people allow for like, you can replace individual orders.
187 00:20:43.420 ⇒ 00:20:44.650 Uttam Kumaran: So I think that helps
188 00:20:45.061 ⇒ 00:20:48.320 Uttam Kumaran: we’re close. We just have a couple more cases to sort out.
189 00:20:49.120 ⇒ 00:21:01.769 Aman Nagpal: Yeah, no, that makes sense. There’s there’s always gonna be these these little cases. The reason I asked about the removal is we’re running a new upsell right now. We kinda had a hack it together. But basically what we’re doing is.
190 00:21:02.040 ⇒ 00:21:09.361 Aman Nagpal: 1st of all, we switched to after sell from zipify, which is what we were using for post purchase upsells before.
191 00:21:10.510 ⇒ 00:21:21.660 Aman Nagpal: but the the way we’re doing it is if somebody buys a 4 bottle, concentrate subscription from the landing page. We give them a post purchase up post purchase, offer
192 00:21:21.720 ⇒ 00:21:22.770 Aman Nagpal: to
193 00:21:22.810 ⇒ 00:21:28.210 Aman Nagpal: change to 8 bottles every 3 months. So not only are we
194 00:21:28.230 ⇒ 00:21:39.340 Aman Nagpal: tacking on an extra 4 bottles to their existing order that’s already been created. But we’re changing the frequency and recharge to 3 months or to 90 days instead of 30 days.
195 00:21:39.635 ⇒ 00:21:46.199 Aman Nagpal: But the whole reason I bring this up and I ask is the way we’re doing this is, we sell them an item
196 00:21:46.720 ⇒ 00:21:59.080 Aman Nagpal: in the post purchase called Javi upgrade. Once they purchase that it triggers our script where we remove, then remove that item from their order, add on the extra concentrate bottles.
197 00:21:59.110 ⇒ 00:22:11.249 Aman Nagpal: And we do this before the order goes to fulfillment. So I just wanna make sure that those changes that we’re making removing a product and adding additional products with their correct discounts, is, will all feed in in sync.
198 00:22:11.870 ⇒ 00:22:16.499 Uttam Kumaran: Yeah, maybe we should take that. And I’ll we’ll look at a couple of examples.
199 00:22:16.560 ⇒ 00:22:31.650 Uttam Kumaran: So basically, our process for debugging is we actually just we literally just look at at like, okay, here’s an order where there’s a partial refund. How is this getting aggregated on our side? And I want to actually note this in documentation, that there are these different cases. So let me look at how those upsells
200 00:22:31.720 ⇒ 00:22:32.890 Uttam Kumaran: are
201 00:22:33.170 ⇒ 00:22:34.270 Uttam Kumaran: coming in
202 00:22:34.665 ⇒ 00:22:40.779 Uttam Kumaran: and seeing like cause. Again we see every single order item, and then, but as you mentioned, some make it.
203 00:22:41.420 ⇒ 00:22:55.820 Uttam Kumaran: I don’t know. Some may get removed, and then others are added and so, like, I think we’ll, we’ll take that and make sure what those cases are. And basically for documentation. What we’ll do is we just have, like every single example. So here’s like an example of a
204 00:22:55.900 ⇒ 00:23:01.629 Uttam Kumaran: a full order refund that got fulfilled. And we didn’t restock. Here’s
205 00:23:01.650 ⇒ 00:23:09.279 Uttam Kumaran: basically the link to it in shopify. Here’s how it shows up in our data. We’re good. And we kind of like go one by one by one. So.
206 00:23:10.330 ⇒ 00:23:11.239 Aman Nagpal: That’s perfect.
207 00:23:13.240 ⇒ 00:23:43.029 Robert Tseng: Okay, retam, as we’re building that out, I just wanna kind of like type on this. And since it’s related to the recharge, so post fulfillment. It seems like recharge is gonna be our best source of truth to kind of figure out like any of those edge cases on the subscription side are on the that related to refunds and returns. So it feels like that effort will kind of help us clean that side up. And then the pre fulfillment piece mostly shopify. But then, because we have this edge case with after sell like.
208 00:23:43.220 ⇒ 00:23:51.910 Robert Tseng: and I don’t know if there are any other custom scripts that we’re running to edit orders pre fulfillment amount. If those, if you have any of those, I think that would help
209 00:23:52.312 ⇒ 00:23:54.740 Robert Tseng: Rutan be able to go and like map that out.
210 00:23:55.430 ⇒ 00:23:58.240 Aman Nagpal: Yeah, I think Pre is just that one. But
211 00:23:58.300 ⇒ 00:24:12.209 Aman Nagpal: I mean, I don’t. I don’t know. Do you think it is still 2 different buckets, though, with shopify and recharge. Right? It’s, you know, recharge we’re using for active cancel. You know, churn subscribers all that info. But in terms of actual
212 00:24:12.320 ⇒ 00:24:18.590 Aman Nagpal: placing orders, canceling orders, refunds that would still be pulled from shopify. Right? So I guess.
213 00:24:18.590 ⇒ 00:24:22.969 Uttam Kumaran: That would come from shop. The only reason I want to make sure is that
214 00:24:23.000 ⇒ 00:24:24.609 Uttam Kumaran: there’s a difference between
215 00:24:25.150 ⇒ 00:24:36.259 Uttam Kumaran: like I want to map out what’s cancelled versus what’s a partial refund versus? What’s a full refund? Because you guys both have the subscriptions and like the ad hoc buying right? So that’s the thing. I want to make sure
216 00:24:36.450 ⇒ 00:24:41.500 Uttam Kumaran: that one on our side will confirm that all cancellations on
217 00:24:42.317 ⇒ 00:24:46.850 Uttam Kumaran: cause cause. Again, there are some people that they they canceled after
218 00:24:47.030 ⇒ 00:24:50.930 Uttam Kumaran: they got sent the materials for that like subscription period.
219 00:24:50.940 ⇒ 00:25:00.949 Uttam Kumaran: So I wanna just check how that actually results. And then also, when we are showing true like, here are all the cancelled users. How do we make sure that?
220 00:25:01.200 ⇒ 00:25:04.979 Uttam Kumaran: Because sometimes people, again, they’re canceling after they get something shipped?
221 00:25:05.010 ⇒ 00:25:10.460 Uttam Kumaran: So I think there’s just a couple of scenarios that I want to map out. Basically. So we have, like.
222 00:25:10.470 ⇒ 00:25:13.070 Uttam Kumaran: okay, they canceled after shipping.
223 00:25:13.120 ⇒ 00:25:16.349 Uttam Kumaran: Here’s like a situation they canceled, basically
224 00:25:16.560 ⇒ 00:25:23.939 Uttam Kumaran: during. And we didn’t fulfill the order. Here’s like the exact example. Here’s how it shows up in recharge. Here’s how it shows up in in shopify?
225 00:25:24.408 ⇒ 00:25:36.339 Uttam Kumaran: Because you don’t want the situation where you look at your shopify data. And you’re like, we expect that we have this many active subscribers. But then that’s different than what’s in recharge. I just wanna make sure that that’s
226 00:25:37.320 ⇒ 00:25:38.710 Uttam Kumaran: that’s ironed out.
227 00:25:39.420 ⇒ 00:25:46.920 Aman Nagpal: Yeah, I think the biggest thing there is just making sure we have that distinction. Top of mind is a canceled order versus a canceled subscription. Right?
228 00:25:46.920 ⇒ 00:25:47.430 Uttam Kumaran: Yes.
229 00:25:47.430 ⇒ 00:25:55.170 Aman Nagpal: The order is shopify. Only canceled subscription is recharge only, so you can cancel an order, but still have your subscription active, and you can also.
230 00:25:55.170 ⇒ 00:25:55.820 Uttam Kumaran: Okay.
231 00:25:55.820 ⇒ 00:25:59.869 Aman Nagpal: Subscription without canceling the order. So I think that distinction is the most important. There.
232 00:25:59.870 ⇒ 00:26:05.830 Uttam Kumaran: I see. Okay, that makes a lot of sense. Yeah. And I and I see both in the order notes. And then I would say, the one thing is like
233 00:26:05.920 ⇒ 00:26:08.159 Uttam Kumaran: the team’s doing a great job of keeping great
234 00:26:08.230 ⇒ 00:26:17.720 Uttam Kumaran: like refund or order notes, because there’s a lot of clients we’ve had where that’s like super messy. So that’s like helping us a lot to debug. So okay.
235 00:26:19.280 ⇒ 00:26:20.130 Aman Nagpal: Sounds good.
236 00:26:20.130 ⇒ 00:26:24.965 Uttam Kumaran: And then the last question is, are you guys doing anything to manage? I see, refund co
237 00:26:25.770 ⇒ 00:26:28.990 Uttam Kumaran: like, are you guys? Is there a platform you guys are using to manage
238 00:26:29.520 ⇒ 00:26:30.870 Uttam Kumaran: like refunds.
239 00:26:33.670 ⇒ 00:26:35.220 Uttam Kumaran: is that important.
240 00:26:35.220 ⇒ 00:26:39.360 Aman Nagpal: We use this? Was it this disputifier for chargebacks?
241 00:26:39.360 ⇒ 00:26:40.600 Uttam Kumaran: Okay. Yeah.
242 00:26:40.830 ⇒ 00:26:42.270 Aman Nagpal: But for refunds.
243 00:26:42.590 ⇒ 00:26:45.540 Aman Nagpal: I think we’re just doing it directly within shopify.
244 00:26:46.660 ⇒ 00:26:47.600 Uttam Kumaran: Okay. Okay.
245 00:26:48.020 ⇒ 00:26:53.669 Uttam Kumaran: yeah. I think some of these are from, I’m seeing some from 2021 that are like, says, refund code. So maybe that’s just an old tool.
246 00:26:53.840 ⇒ 00:26:55.330 Aman Nagpal: Yeah, yeah, yeah, that’s probably it.
247 00:26:55.330 ⇒ 00:26:56.190 Uttam Kumaran: Okay. Okay.
248 00:27:00.660 ⇒ 00:27:01.440 Uttam Kumaran: Cool.
249 00:27:01.760 ⇒ 00:27:20.949 Nicolas Sucari: Okay? Yeah, we’ll keep working on that logic that we are trying to identify so that we have the, we have match the data between shopify and what we have in Snowflake. And apart from that, if you can share the the credentials for recharge we can start looking into that, too.
250 00:27:21.397 ⇒ 00:27:25.520 Nicolas Sucari: From the data side. I don’t think we have anything else for now.
251 00:27:26.620 ⇒ 00:27:29.020 Nicolas Sucari: Yeah, we’ll keep working on those
252 00:27:29.080 ⇒ 00:27:31.950 Nicolas Sucari: on those topics and let you know when we have any update.
253 00:27:32.387 ⇒ 00:27:35.689 Nicolas Sucari: Robert, do you wanna go with the amplitude stuff.
254 00:27:36.128 ⇒ 00:27:39.200 Robert Tseng: I can take it from here. Thanks.
255 00:27:39.200 ⇒ 00:27:41.099 Aman Nagpal: Before we jump into that, they’re just.
256 00:27:41.100 ⇒ 00:27:41.670 Nicolas Sucari: Yeah, we can.
257 00:27:41.670 ⇒ 00:27:44.221 Aman Nagpal: In the sheet. So I was looking at
258 00:27:44.840 ⇒ 00:27:47.810 Aman Nagpal: a Google sheet Jared’s message. It seemed like he said.
259 00:27:48.850 ⇒ 00:27:53.449 Aman Nagpal: I’m trying to pull up his message, any Google sheet works. So I don’t know if
260 00:27:53.810 ⇒ 00:28:03.010 Aman Nagpal: how you know, if you guys have like a layout in mind or anything basic, we can just throw together and run by him. Cause he would be the one to update that. So
261 00:28:03.240 ⇒ 00:28:05.260 Aman Nagpal: I mean, it sounds like we would need
262 00:28:06.020 ⇒ 00:28:13.849 Aman Nagpal: you you guys know best how you would usually do it. But we’re seems like we’re we’re gonna do cogs within that sheet right instead of pulling from shopify.
263 00:28:13.870 ⇒ 00:28:16.720 Aman Nagpal: So that’s 1. And then, I guess, any sort of
264 00:28:16.880 ⇒ 00:28:33.859 Aman Nagpal: shipping assumptions that, you know. Maybe we were adding from the the work, the shopify flow, but like pick and pack cost label cost box costs, etc, based on weight. So it sounds like a lot of those assumptions would be in this sheet, too, if I’m not mistaken.
265 00:28:35.190 ⇒ 00:28:36.820 Robert Tseng: So what a good like
266 00:28:37.170 ⇒ 00:28:39.681 Robert Tseng: version of this be?
267 00:28:40.890 ⇒ 00:29:09.079 Robert Tseng: cause we’ve already been in taking your the flows, automation file and kind of breaking that apart that has all the cogs, data and cost modeling there. So what if we just took that? And we we just extracted all the different like metrics that we were calculating out of that, and that that would just go in the sheet, and we could send that to Jared and see if that covers. Like all these, we basically want to capture every cost, assumption, or calculation that we already have in flows right.
268 00:29:10.120 ⇒ 00:29:12.610 Aman Nagpal: Yeah, I I think that’s exactly it. All of those.
269 00:29:13.190 ⇒ 00:29:24.089 Aman Nagpal: Then there we throw in a sheet. Ask him if this looks good. Ask him if there’s anything missing and then separately, I guess maybe on another tab or another sheet will be all the cogs
270 00:29:24.230 ⇒ 00:29:27.760 Aman Nagpal: for all the products themselves, which
271 00:29:28.750 ⇒ 00:29:31.250 Aman Nagpal: I guess we can do by skew.
272 00:29:33.760 ⇒ 00:29:40.019 Aman Nagpal: that would probably be the easiest. But yeah, I think you know what you just said. Just if we can run it by him, we should be good to go.
273 00:29:40.560 ⇒ 00:29:48.679 Robert Tseng: Okay. So yeah, let’s do that team. I’m just gonna repeat it just for us. So we’ll do. We’ll do 2 sheets on the Google Sheet. The 1st tab will be
274 00:29:49.072 ⇒ 00:30:05.180 Robert Tseng: just every every metric that comes out of that flows file that monset us, and the second one maybe pies. You know we could go. Just make sure we’ll go back into one of the shopify orders just the way that the cogs is presented. There, let’s just have that as like the template for the
275 00:30:05.240 ⇒ 00:30:11.359 Robert Tseng: for the for the cog side, and then let’s send that to Jared and and Jam to sign off on it.
276 00:30:13.190 ⇒ 00:30:13.910 Robert Tseng: Okay.
277 00:30:14.900 ⇒ 00:30:19.700 Aman Nagpal: If you can just tag me on that sheet as well, or when you send it I’ll take a look also.
278 00:30:21.810 ⇒ 00:30:22.430 Robert Tseng: Okay?
279 00:30:24.190 ⇒ 00:30:35.739 Robert Tseng: Great. Okay? Well, then, other things I had on the agenda recharge we talked about already. So yeah, I mean, just the team knows Justin’s asking for this one. So we wanna make sure this gets prioritized.
280 00:30:36.040 ⇒ 00:30:38.939 Robert Tseng: I think just with the work that we’re doing around
281 00:30:39.810 ⇒ 00:30:45.599 Robert Tseng: refunds, returns and like and recharge. I feel like this is a good bucket of work for us to kind of
282 00:30:46.224 ⇒ 00:30:52.070 Robert Tseng: maybe in our next check in with Justin team we could, we could share kind of the
283 00:30:52.570 ⇒ 00:31:01.159 Robert Tseng: the yeah, we we can show him that we’ve captured all the different scenarios. I think that would be a great thing for us to present on, probably next week, or something.
284 00:31:02.750 ⇒ 00:31:16.060 Robert Tseng: on the snowflake amplitude native side, I think Amana mentioned to you Zach has turned it on, or as of yesterday, he told me he turned it on, but might. Maybe it’ll take like a day for it to actually turn on
285 00:31:16.767 ⇒ 00:31:23.622 Robert Tseng: from there. Yeah, I think I’m ready to kind of pick to to to demo.
286 00:31:24.310 ⇒ 00:31:26.049 Robert Tseng: on that side, I think
287 00:31:26.250 ⇒ 00:31:30.199 Robert Tseng: just the way to think about the the capability here is.
288 00:31:30.280 ⇒ 00:31:47.299 Robert Tseng: yeah. Once again, the data lives in Snowflake, even though amplitude will be. It’s basically going to be querying on top of Snowflake the data. So it’s not going to move into amplitude and then move back to Snowflake. It’s in Snowflake. But what amplitude is going to do is pretty much just like.
289 00:31:48.520 ⇒ 00:31:53.579 Robert Tseng: add the visual layer on it. That’s it’s still sequel, based kind of like what we’ve seen with Meta base
290 00:31:54.488 ⇒ 00:31:56.560 Robert Tseng: and any like custom
291 00:31:56.790 ⇒ 00:32:19.459 Robert Tseng: logic that we’ve built into Snowflake. So, for example, like the type of order that we’ve created now, we don’t need to separate like, we don’t have need to have multiple order events across platforms. We’re working towards having an order that’s that can be properly tagged as Amazon shopify or Tiktok like that can just be pulled directly into an amplitude report.
292 00:32:19.750 ⇒ 00:32:21.029 Robert Tseng: And so
293 00:32:21.280 ⇒ 00:32:33.049 Robert Tseng: yeah, we’re thinking that the yeah, we’ll just pick a few of these like more complex cross source events that we’ve built out. And I think that could be a great use case, for
294 00:32:33.130 ⇒ 00:32:48.069 Robert Tseng: this is how order reporting worked in amplitude before this before we before we went on this adventure, and then kind of what it is now. And I think that’s that’s kind of what I’m thinking for the 1st demo of that capability.
295 00:32:48.450 ⇒ 00:32:49.599 Robert Tseng: How does that sound.
296 00:32:50.110 ⇒ 00:33:03.459 Aman Nagpal: Sounds great. So just to confirm this whole amplitude, snowflake integration that they have. It’s literally just the fact that amplitude can query that data from Snowflake is, that is that pretty much all it is.
297 00:33:04.160 ⇒ 00:33:09.639 Robert Tseng: Yeah, without any. Yeah? I mean, it’s it’s just the fact that it would other sources.
298 00:33:09.710 ⇒ 00:33:17.129 Robert Tseng: You’re pulling data into amplitude. And then amplitude is having to like model on top of it for it, but because amplitude.
299 00:33:18.000 ⇒ 00:33:22.659 Robert Tseng: their organization is completely platform on Snowflake itself.
300 00:33:22.690 ⇒ 00:33:45.560 Robert Tseng: Now that we have our own snowflake instance, we’re just like substituting rather than them ingesting data from 3rd party into their snowflake and having to do all the stuff that they got there, we just have, like a direct like way to to query on visualize the data from our warehouse, so it’ll be faster, and I guess the controls are in our hands rather than the way that amplitude models it.
301 00:33:46.760 ⇒ 00:33:55.599 Aman Nagpal: That makes sense. And then I know. So you spoke with Zach. We we got. I guess it’s a separate project for a free trial. Basically, is that how we have.
302 00:33:56.453 ⇒ 00:33:59.459 Robert Tseng: Yeah, I think we just wanna make sure that we can.
303 00:33:59.670 ⇒ 00:34:02.959 Robert Tseng: Yeah, we’re gonna we’re having it in a separate project. First.st
304 00:34:03.360 ⇒ 00:34:23.810 Aman Nagpal: Okay, so let me know if I’m understanding this process correctly. We’re going to test everything or a few items right to start with in that separate project. So for example, order data, right? So in our current attitude, we have the order created event. Which you know, maybe I guess it’ll still be an order created event in the new one.
305 00:34:24.101 ⇒ 00:34:43.349 Aman Nagpal: Unless there’s a different way. You want to do it, but we will test it. See how it goes in there. Once everything looks good, we can bring in a new order, created event in our existing amplitude, and then go into our charts. Let’s say our old reports, old charts, and start, maybe replacing the old order, Creator event with the new one. Right.
306 00:34:43.560 ⇒ 00:34:44.150 Robert Tseng: Yeah.
307 00:34:44.780 ⇒ 00:34:48.249 Aman Nagpal: Okay, yeah, that sounds great. And what about and we spoke about
308 00:34:48.270 ⇒ 00:34:55.530 Aman Nagpal: gorgeous before that data. Do you want to bring it into this test, Amp. As well? Or do you want to just bring into it the old one.
309 00:34:56.290 ⇒ 00:34:58.580 Robert Tseng: Yeah, I think for every data.
310 00:34:58.910 ⇒ 00:35:03.010 Robert Tseng: every data source that we have in Snowflake. Let’s, I’m thinking that we should just.
311 00:35:04.077 ⇒ 00:35:07.679 Robert Tseng: I guess it’s a staging environment now, where we’re testing out
312 00:35:08.485 ⇒ 00:35:11.250 Robert Tseng: what it looks like in in the.
313 00:35:11.920 ⇒ 00:35:21.150 Robert Tseng: in, in the native. So we can do a comparison between like what we exist. We currently have versus what, what? We’re not, what we’re able to bring with the integration. So I think.
314 00:35:21.260 ⇒ 00:35:31.650 Robert Tseng: yeah, maybe we can pick. I mean, I want to create is is the obvious one. But maybe we can pick a few that we can go and and build out for the for the next week in in the in the demo.
315 00:35:32.320 ⇒ 00:35:39.909 Aman Nagpal: That’d be great. So, for example, let’s say we makes a few cords, reports, or dashboards. That’ll be easy to move over to the other project.
316 00:35:40.270 ⇒ 00:35:40.920 Robert Tseng: Yeah.
317 00:35:41.430 ⇒ 00:35:45.100 Aman Nagpal: Okay. Great. Did that give you any pushback on the free trial, or anything.
318 00:35:46.500 ⇒ 00:35:53.670 Robert Tseng: No, no, I think he he was like, yeah, I mean, he wants. Obviously he wants to use the product more so.
319 00:35:53.670 ⇒ 00:35:54.380 Aman Nagpal: We just.
320 00:35:54.380 ⇒ 00:35:54.960 Robert Tseng: Excited.
321 00:35:54.960 ⇒ 00:35:59.260 Aman Nagpal: Like you said signed up for a whole lot more events. So.
322 00:35:59.460 ⇒ 00:36:00.130 Robert Tseng: Yeah.
323 00:36:00.270 ⇒ 00:36:02.120 Aman Nagpal: Yeah, everything sounds good there, though.
324 00:36:02.620 ⇒ 00:36:14.159 Robert Tseng: Cool. The last piece there is, because I know that you guys just renewed also, if you bundle different packages, I think other things that amplitude has that could be good
325 00:36:14.380 ⇒ 00:36:22.290 Robert Tseng: for you to consider. I don’t know how your other tools are working. One is on the ex their experimentation product, and the other one is their Cdp product.
326 00:36:22.530 ⇒ 00:36:37.389 Robert Tseng: So maybe I just will ask a couple of questions like on the experimentation side, what do you use as your experimentation platform right now. And like, yeah, is that something that you want to explore like what ampl like? What having it run? Amplitude would be.
327 00:36:38.190 ⇒ 00:36:45.790 Aman Nagpal: So I I initially, I didn’t look into it too much. I thought it was included in what we had, but maybe not either way definitely something
328 00:36:45.910 ⇒ 00:37:00.739 Aman Nagpal: down the road I would be interested in looking at. But I think we just don’t have the bandwidth to switch right now to a new process. Right now it’s fully custom. We run all the traffic through a cloudflare worker which handles the routing similar to a regular landing page routing. But
329 00:37:01.024 ⇒ 00:37:17.760 Aman Nagpal: yeah. And then all the Ab test data gets fed straight into amplitude. So I think we’ll probably just keep that. As for now, as is for now. But I definitely down the road. Wanna take a look at that. I saw your message thing. If you could break that down for me, I’d be curious what that is.
330 00:37:18.180 ⇒ 00:37:29.330 Robert Tseng: Yeah. So that’s kind of maybe the question leading questions up to that would be kind of how are you pushing audience data or cohort data from amplitude into like Okendo. That’s your main messaging platform right.
331 00:37:31.210 ⇒ 00:37:33.500 Aman Nagpal: Pulling amplitude data into Okendo.
332 00:37:33.500 ⇒ 00:37:37.490 Robert Tseng: Or are you even doing that at all like, how how do you use
333 00:37:37.610 ⇒ 00:37:43.680 Robert Tseng: like the for the for the targeting that you’re using at Oquendo, does it? Does it rely on any like
334 00:37:44.250 ⇒ 00:37:47.778 Robert Tseng: Audiences or cohorts that you’ve built in amplitude?
335 00:37:48.830 ⇒ 00:37:49.570 Robert Tseng: yeah.
336 00:37:50.592 ⇒ 00:37:52.349 Aman Nagpal: You mean okendo right not klaviyo.
337 00:37:53.020 ⇒ 00:37:53.880 Robert Tseng: Yeah.
338 00:37:54.000 ⇒ 00:37:54.760 Robert Tseng: right because.
339 00:37:54.760 ⇒ 00:37:57.929 Aman Nagpal: So reviews, it’s pretty much
340 00:37:57.970 ⇒ 00:38:01.850 Aman Nagpal: set and forget user places in order.
341 00:38:01.940 ⇒ 00:38:09.109 Aman Nagpal: They okay, no knows what products they ordered. And then it sends out an email from with its Clavio integration
342 00:38:09.210 ⇒ 00:38:13.199 Aman Nagpal: to those users after X amount of days have passed.
343 00:38:13.450 ⇒ 00:38:14.170 Aman Nagpal: I’m saying.
344 00:38:14.170 ⇒ 00:38:18.160 Robert Tseng: I also meant for Klavio, too. I’m sorry. I know Kendall was just like one piece of it. Yeah.
345 00:38:18.160 ⇒ 00:38:20.199 Aman Nagpal: No, no, yeah. So that’s that’s how. Okay, no setup. And.
346 00:38:20.200 ⇒ 00:38:20.520 Robert Tseng: Okay.
347 00:38:20.850 ⇒ 00:38:21.509 Aman Nagpal: To go
348 00:38:21.640 ⇒ 00:38:23.750 Aman Nagpal: definitely. Want to do
349 00:38:24.207 ⇒ 00:38:32.430 Aman Nagpal: always open to doing a lot more right now. It’s I mean, all of our lists are pretty much just based on
350 00:38:32.970 ⇒ 00:38:35.589 Aman Nagpal: where they’re signing up. For example, what
351 00:38:35.630 ⇒ 00:38:44.640 Aman Nagpal: landing page they’re put on one list or what they’re purchasing. We’re creating segments and lists that way. Whether it’s protein coffee, whether it’s concentrate
352 00:38:44.780 ⇒ 00:38:48.249 Aman Nagpal: whether they’re active, subscriber or not. So a lot of it
353 00:38:48.340 ⇒ 00:38:51.179 Aman Nagpal: is just coming straight from shopify recharge
354 00:38:51.410 ⇒ 00:38:53.590 Aman Nagpal: nothing from amplitude itself.
355 00:38:54.010 ⇒ 00:38:58.641 Robert Tseng: Got it. Okay? Well, I mean the most the most commonly used, like marketing.
356 00:38:59.778 ⇒ 00:39:02.470 Robert Tseng: Like feature in amplitude, is
357 00:39:02.750 ⇒ 00:39:23.820 Robert Tseng: once we have, we already have a lot of events in there. But once you build more complex funnel reports, and you can. You can select the drop off audience as a cohort, and that becomes like a list that you use for targeting. Right? So that’s kind of how you get that next level of personalization. Since it sounds like right now, it’s really just personalization off of like the 1st touch point, which is mostly just a landing page.
358 00:39:24.485 ⇒ 00:39:28.759 Robert Tseng: So I think that’s kind of the value of the of the Cvp product.
359 00:39:29.352 ⇒ 00:39:32.070 Robert Tseng: Since you already have all the data in Snowflake.
360 00:39:32.600 ⇒ 00:39:40.799 Robert Tseng: Their their yeah, their Cdp product only works if their client is is on snowflake. And basically it has
361 00:39:41.970 ⇒ 00:39:56.360 Robert Tseng: connectors to like Playview and other or braze or other messaging platforms. That you can push cohorts that you that you create an amplitude reports directly into those into those tools. And so that’s
362 00:39:56.440 ⇒ 00:40:03.399 Robert Tseng: I mean, that’s that’s kind of the in a nutshell like what the the Cdp product would would help with.
363 00:40:03.840 ⇒ 00:40:14.200 Aman Nagpal: So pretty much, you know, whatever we’re doing now, all the segments we’re creating with shopify and or, you know, recharge data. We can do the same thing, but with amplitude data as well.
364 00:40:14.850 ⇒ 00:40:26.413 Robert Tseng: Yeah, so you would get everything that you can our existing do in in shopify and recharge. But then you can add more complexity to it with the behaviors that you’re able to track in in the amplitude events.
365 00:40:27.240 ⇒ 00:40:33.149 Aman Nagpal: I. That sounds like it would be amazing, right? So it it sounds like we could do something like people who
366 00:40:33.540 ⇒ 00:40:38.549 Aman Nagpal: bought, concentrate and cancel on the 3rd month. Send them this specific email, right?
367 00:40:38.550 ⇒ 00:40:39.740 Robert Tseng: Yeah, exactly.
368 00:40:39.740 ⇒ 00:40:43.870 Aman Nagpal: Yeah, I think we’d 100% be interested in, you know, trying that out.
369 00:40:44.290 ⇒ 00:41:10.479 Robert Tseng: Okay, yeah. Well, I mean, I just want to put that on your radar since because it looks like you upgraded or you like renewed your amplitude contract during this quarter. If you are able. If you know we can, we can talk about this Async as well. But if you do end up bundling and up and and purchasing either the experiment or Cdp. You’ll get a better rate on it this quarter than you would if you, because if you bought it in q 1 or Q. 2, it would pretty much just be like
370 00:41:10.756 ⇒ 00:41:15.999 Robert Tseng: they wouldn’t consider it like a like a package because you didn’t buy it in the same quarter. So.
371 00:41:16.480 ⇒ 00:41:24.400 Aman Nagpal: Okay, that’s great. Yeah, I’ll get out. And I mean, yeah, hopefully knock it out this month or next month. If if we decide to go forward with it.
372 00:41:25.040 ⇒ 00:41:26.590 Robert Tseng: Yeah. Okay, cool.
373 00:41:29.070 ⇒ 00:41:34.240 Robert Tseng: Sorry. I’m reading the messages from Utam.
374 00:41:36.410 ⇒ 00:41:40.081 Robert Tseng: Alright. Any any other kind of thoughts from people?
375 00:41:41.880 ⇒ 00:41:47.800 Aman Nagpal: I wanted to ask what were some of the action items from last time. I think we pretty much
376 00:41:48.010 ⇒ 00:42:00.339 Aman Nagpal: discuss all of them. So we did, Okendo. Gorgeous. One of them that comes to mind is the Amazon stuff. But let me know if there’s anything else missing, or that we’re forgetting. But the whole fuzzy matching, or a different way to match
377 00:42:00.390 ⇒ 00:42:05.190 Aman Nagpal: Amazon buyers to shopify. How did we? How are we doing with that.
378 00:42:05.890 ⇒ 00:42:11.909 Nicolas Sucari: Yeah, we still have that in our backlog. We are gonna definitely keep working on that after the meeting. And
379 00:42:12.080 ⇒ 00:42:28.309 Nicolas Sucari: yeah, once we have the other stuff figured out. But yeah, we have the logic that Brian was working on for Ryan, and we will keep trying to match on the streets on the address formalization. Yeah. And the email matching. If we have.
380 00:42:28.980 ⇒ 00:42:37.669 Nicolas Sucari: don’t wanna, I don’t wanna say we’re gonna get to somewhere. But we’re still gonna keep trying to have a list that we can share with you. Okay.
381 00:42:38.180 ⇒ 00:42:46.069 Aman Nagpal: Okay. Thank you. Yeah, that that works. You know, it’s even as when we tried manually, it’s we were coming up with very little, but there has to be
382 00:42:46.080 ⇒ 00:42:51.050 Aman Nagpal: at least a couple of 1,000 that match. So I think it’s we just gotta figure out how. I guess I know it’ll take a bit.
383 00:42:52.150 ⇒ 00:42:52.730 Nicolas Sucari: Yeah.
384 00:42:53.480 ⇒ 00:43:02.769 Robert Tseng: Do you have a record of like how you’ve done it before? Just so we can, you know, just have, like a benchmark for what we need to be with as we’re tracking progress.
385 00:43:03.160 ⇒ 00:43:05.850 Aman Nagpal: So I didn’t do it, but I think he was
386 00:43:05.970 ⇒ 00:43:13.410 Aman Nagpal: just downloading order sheets from shopify and Amazon manually, and then trying to. I don’t know. Run them.
387 00:43:13.410 ⇒ 00:43:14.229 Robert Tseng: I see.
388 00:43:14.230 ⇒ 00:43:18.219 Aman Nagpal: Tool or AI, or you know, whatever it is, match up the addresses.
389 00:43:18.500 ⇒ 00:43:19.959 Aman Nagpal: or and or the names.
390 00:43:21.870 ⇒ 00:43:22.530 Robert Tseng: Okay.
391 00:43:23.410 ⇒ 00:43:26.300 Robert Tseng: cool. Yeah. Let’s keep. Let’s keep running on that team.
392 00:43:28.320 ⇒ 00:43:31.830 Aman Nagpal: Thanks, guys. Anything else? Items that we’re forgetting from last time.
393 00:43:35.280 ⇒ 00:43:37.479 Nicolas Sucari: Think so. I think we covered all of it. Yeah.
394 00:43:38.170 ⇒ 00:43:44.349 Aman Nagpal: Okay, if we have a few. I mean, everyone doesn’t need to stay out for this, but I can try to get you the recharge.
395 00:43:44.350 ⇒ 00:43:45.719 Uttam Kumaran: Yeah, let’s do that.
396 00:43:46.270 ⇒ 00:43:47.080 Uttam Kumaran: Yep.
397 00:43:47.337 ⇒ 00:43:52.749 Aman Nagpal: I have the menu open. I want as a store owner. So you said admin, is the way to go.
398 00:43:53.400 ⇒ 00:43:56.119 Uttam Kumaran: Yes, you should just be able to go to apps.
399 00:44:00.440 ⇒ 00:44:02.520 Aman Nagpal: Okay, I see the article
400 00:44:05.215 ⇒ 00:44:06.160 Aman Nagpal: token.
401 00:44:07.770 ⇒ 00:44:13.720 Aman Nagpal: Yeah, this might be a little outdated because they have 2 different options. But let’s see which looks the closest.
402 00:44:15.410 ⇒ 00:44:15.990 Uttam Kumaran: Okay.
403 00:44:35.460 ⇒ 00:44:40.089 Aman Nagpal: Yeah, let’s try with admin. And then, if it doesn’t work, just just let me know. So let me grab this now, for you.
404 00:44:56.970 ⇒ 00:45:02.116 Uttam Kumaran: And another random question, cause I’m just looking at stuff. Now, you guys
405 00:45:02.860 ⇒ 00:45:05.099 Uttam Kumaran: in shopify. They have like
406 00:45:05.340 ⇒ 00:45:11.100 Uttam Kumaran: stuff based on the exchange rate. They have, like current versus what it was when you bought.
407 00:45:11.310 ⇒ 00:45:13.649 Uttam Kumaran: Do you guys have an opinion on
408 00:45:14.990 ⇒ 00:45:17.100 Uttam Kumaran: what we use?
409 00:45:21.710 ⇒ 00:45:27.280 Aman Nagpal: I think we would need to. I mean for international orders. We would need to take into account
410 00:45:27.380 ⇒ 00:45:28.960 Aman Nagpal: what the rate was.
411 00:45:29.230 ⇒ 00:45:32.209 Uttam Kumaran: When they purchased. Okay, okay, cool. Cause we’ve
412 00:45:32.270 ⇒ 00:45:34.650 Uttam Kumaran: been maintaining 2. And I
413 00:45:35.250 ⇒ 00:45:37.299 Uttam Kumaran: want to get rid of one because it’s confusing.
414 00:45:37.790 ⇒ 00:45:42.459 Aman Nagpal: Yeah, I think. No, that’s a good question. I think it. It makes sense just to use what it was back
415 00:45:42.710 ⇒ 00:45:43.790 Aman Nagpal: for each order
416 00:45:45.870 ⇒ 00:45:47.100 Aman Nagpal: limitations.
417 00:46:03.810 ⇒ 00:46:06.320 Aman Nagpal: Should I just call this 5 Tran? I guess.
418 00:46:07.250 ⇒ 00:46:08.960 Uttam Kumaran: Yeah, that’s perfect.
419 00:46:10.680 ⇒ 00:46:13.720 Aman Nagpal: And then do you need read access for everything? Not right?
420 00:46:14.610 ⇒ 00:46:16.690 Uttam Kumaran: It’s just read access. Yeah.
421 00:46:24.370 ⇒ 00:46:25.949 Aman Nagpal: Would it help if I
422 00:46:26.030 ⇒ 00:46:30.790 Aman Nagpal: sent you a screenshot of the options for both Apis while you try one? I guess.
423 00:46:33.250 ⇒ 00:46:34.998 Uttam Kumaran: sure, I mean in terms of
424 00:46:36.290 ⇒ 00:46:41.900 Uttam Kumaran: Yeah. Cause if it doesn’t match what they have in theirs, then I could try running the one that that you
425 00:46:42.030 ⇒ 00:46:43.860 Uttam Kumaran: I don’t know if you sent it already, but.
426 00:46:44.620 ⇒ 00:46:46.331 Aman Nagpal: No, let me just
427 00:46:46.770 ⇒ 00:46:52.270 Aman Nagpal: no, it’s a it’s the ones without a read access option. Like.
428 00:46:52.270 ⇒ 00:46:54.230 Uttam Kumaran: We we don’t like. We don’t need.
429 00:46:54.280 ⇒ 00:46:59.180 Uttam Kumaran: Yeah. I mean, we mainly just need read access and everything. But even some of them
430 00:46:59.430 ⇒ 00:47:02.060 Uttam Kumaran: I can double check which ones we need.
431 00:47:03.020 ⇒ 00:47:08.659 Aman Nagpal: There’s 2 here that don’t have a read which is customer notifications and retention strategies. But
432 00:47:08.770 ⇒ 00:47:10.190 Aman Nagpal: I don’t know that we need those.
433 00:47:10.190 ⇒ 00:47:10.850 Uttam Kumaran: Fine.
434 00:47:11.420 ⇒ 00:47:14.659 Uttam Kumaran: We mainly need, like orders, discount subscriptions.
435 00:47:14.800 ⇒ 00:47:16.210 Uttam Kumaran: customers.
436 00:47:17.710 ⇒ 00:47:19.440 Nicolas Sucari: Until next year.
437 00:47:19.650 ⇒ 00:47:20.320 Aman Nagpal: Here.
438 00:47:20.470 ⇒ 00:47:21.400 Aman Nagpal: in chat.
439 00:47:22.930 ⇒ 00:47:23.600 Uttam Kumaran: That’s fine!
440 00:47:24.380 ⇒ 00:47:25.519 Aman Nagpal: This is
441 00:47:25.560 ⇒ 00:47:27.180 Aman Nagpal: the Api key.
442 00:47:29.220 ⇒ 00:47:29.990 Aman Nagpal: yeah.
443 00:47:30.350 ⇒ 00:47:32.750 Aman Nagpal: that is an Admin Api key.
444 00:47:33.150 ⇒ 00:47:37.130 Aman Nagpal: and then it’s storefront Api. This info.
445 00:47:40.840 ⇒ 00:47:41.790 Uttam Kumaran: Okay.
446 00:47:41.790 ⇒ 00:47:43.809 Aman Nagpal: Should be that.
447 00:47:48.130 ⇒ 00:47:51.939 Uttam Kumaran: Do you like the storefront? Api key is like for man.
448 00:47:52.420 ⇒ 00:47:56.689 Uttam Kumaran: I’m not gonna well, I don’t know. I’m not. If I had to guess. Okay, it looks like this worked
449 00:47:56.900 ⇒ 00:47:58.990 Uttam Kumaran: the 1st one you signed for me.
450 00:47:58.990 ⇒ 00:47:59.890 Aman Nagpal: That.
451 00:47:59.890 ⇒ 00:48:00.650 Nicolas Sucari: Yeah.
452 00:48:01.740 ⇒ 00:48:02.430 Aman Nagpal: Secret.
453 00:48:05.360 ⇒ 00:48:06.260 Aman Nagpal: There it is!
454 00:48:06.260 ⇒ 00:48:09.520 Uttam Kumaran: Okay. It looks like the 1st one you sent me worked.
455 00:48:12.470 ⇒ 00:48:15.589 Uttam Kumaran: And then I’m just gonna take it off and see
456 00:48:15.870 ⇒ 00:48:17.619 Uttam Kumaran: and let’s go back to it.
457 00:48:24.740 ⇒ 00:48:26.200 Aman Nagpal: I’m just gonna send in slack
458 00:48:26.360 ⇒ 00:48:27.809 Aman Nagpal: screenshots of this.
459 00:48:28.100 ⇒ 00:48:29.100 Aman Nagpal: Yeah.
460 00:48:47.460 ⇒ 00:48:48.070 Aman Nagpal: okay.
461 00:48:48.070 ⇒ 00:48:49.009 Uttam Kumaran: Looks like,
462 00:48:50.910 ⇒ 00:48:53.000 Uttam Kumaran: mean, it looks like it’s totally fine.
463 00:48:55.540 ⇒ 00:48:59.840 Uttam Kumaran: Okay? So we’ll I’ll confirm when this finishes today. But it looks like
464 00:48:59.900 ⇒ 00:49:01.040 Uttam Kumaran: we’re good.
465 00:49:02.640 ⇒ 00:49:10.709 Uttam Kumaran: And then it looks like there’s a 5 trend error with the gorgeous connector. But we’re gonna look into that. It doesn’t look like it’s a pro challenge on our side looks like something that they need to restart.
466 00:49:12.660 ⇒ 00:49:15.042 Aman Nagpal: Cool sounds good. I just sent
467 00:49:16.140 ⇒ 00:49:20.099 Aman Nagpal: or it’s sending now the screenshots of the differences in the options
468 00:49:20.320 ⇒ 00:49:22.712 Aman Nagpal: forefront versus admin.
469 00:49:24.100 ⇒ 00:49:25.449 Aman Nagpal: yeah, I mean.
470 00:49:26.270 ⇒ 00:49:29.330 Aman Nagpal: as long as Admin has all the info that
471 00:49:30.310 ⇒ 00:49:32.789 Aman Nagpal: storefront has, I feel like we should be good.
472 00:49:33.770 ⇒ 00:49:36.410 Uttam Kumaran: Yeah, I believe it’s the admin that we need
473 00:49:37.168 ⇒ 00:49:38.960 Uttam Kumaran: I don’t think we need
474 00:49:40.430 ⇒ 00:49:45.390 Uttam Kumaran: the storefront. But let’s see, because it looks like that Ui is different than the one they had in the docs.
475 00:49:45.660 ⇒ 00:49:50.519 Uttam Kumaran: And I’ll I’ll probably just send them a message to tell them to update it, too, because it’s confusing. Now.
476 00:49:53.690 ⇒ 00:49:54.570 Aman Nagpal: Yeah.
477 00:49:55.850 ⇒ 00:49:58.110 Nicolas Sucari: Yeah, it’s already syncing. I can see it, too.
478 00:49:58.110 ⇒ 00:49:58.730 Uttam Kumaran: Okay.
479 00:49:58.730 ⇒ 00:49:59.240 Aman Nagpal: Awesome.
480 00:49:59.240 ⇒ 00:50:00.648 Uttam Kumaran: Okay, cool. So we’ll we’ll
481 00:50:01.040 ⇒ 00:50:02.390 Uttam Kumaran: We’ll probably look to just.
482 00:50:02.410 ⇒ 00:50:07.730 Uttam Kumaran: I know, get something really simple for you to explore in real. And then also we’ll start to weave that in.
483 00:50:09.000 ⇒ 00:50:11.139 Aman Nagpal: Sweet. I also, yeah, go ahead.
484 00:50:11.818 ⇒ 00:50:14.630 Aman Nagpal: I think to you, Nico, and
485 00:50:14.680 ⇒ 00:50:21.920 Aman Nagpal: maybe Brian. It was just some email I got from 5 Tran about Amazon table update, or something like that.
486 00:50:22.030 ⇒ 00:50:23.939 Aman Nagpal: Just wanted to forward it over.
487 00:50:26.070 ⇒ 00:50:26.580 Uttam Kumaran: Yeah, I.
488 00:50:26.580 ⇒ 00:50:27.300 Nicolas Sucari: They said they said.
489 00:50:27.300 ⇒ 00:50:31.686 Uttam Kumaran: They send a lot, they send a lot of spam. So we may just take you off because
490 00:50:31.960 ⇒ 00:50:39.250 Uttam Kumaran: they’re also their. Their emails are very alarming. It’s like, we’re gonna remove this table. And it’s like some obscure table name.
491 00:50:39.550 ⇒ 00:50:45.670 Uttam Kumaran: None of those tables affect us. But and we’re also getting all those emails so.
492 00:50:45.670 ⇒ 00:50:46.140 Nicolas Sucari: Think we’ll just.
493 00:50:46.140 ⇒ 00:50:48.200 Uttam Kumaran: Turk, you off of that. If that’s okay.
494 00:50:48.320 ⇒ 00:50:48.869 Aman Nagpal: No! No! That one.
495 00:50:48.870 ⇒ 00:50:50.270 Nicolas Sucari: Yeah. The one that we.
496 00:50:50.350 ⇒ 00:50:56.250 Nicolas Sucari: the one from today was about a financial fee component. I think that’s the table that we are not.
497 00:50:56.250 ⇒ 00:50:59.129 Uttam Kumaran: But they’re they usually just say, like, we’re changing this. And it’s like.
498 00:50:59.130 ⇒ 00:50:59.660 Nicolas Sucari: Yeah.
499 00:50:59.660 ⇒ 00:51:02.570 Uttam Kumaran: Nothing changes. Really. So okay.
500 00:51:03.325 ⇒ 00:51:07.324 Uttam Kumaran: the other thing I do. Nico, do we have questions from Aman on
501 00:51:07.860 ⇒ 00:51:10.069 Uttam Kumaran: on the recharge side.
502 00:51:10.090 ⇒ 00:51:11.150 Uttam Kumaran: You know how we we have.
503 00:51:11.150 ⇒ 00:51:24.809 Nicolas Sucari: We don’t. No, no, it will be really useful for you, Aman, if you can just slack us with some questions that you’d like to answer with the recharge information so that we can make sure we we add, the dimensions and measures that we can
504 00:51:25.283 ⇒ 00:51:32.129 Nicolas Sucari: bring from there into a real dashboard so that you can explore. So if you have like, 2 min and you can gather around some questions.
505 00:51:32.320 ⇒ 00:51:34.160 Nicolas Sucari: let us know once it’s like us. Okay.
506 00:51:34.160 ⇒ 00:51:35.840 Aman Nagpal: We want the data to answer.
507 00:51:36.570 ⇒ 00:51:42.750 Nicolas Sucari: Yeah, exactly like, if you were able to go into the data and try to answer some questions, what? That those questions should be.
508 00:51:42.750 ⇒ 00:51:44.640 Uttam Kumaran: That’ll just help us prioritize.
509 00:51:44.650 ⇒ 00:51:47.579 Uttam Kumaran: kind of like what we did on the akendo on the other side.
510 00:51:47.640 ⇒ 00:51:55.479 Uttam Kumaran: Yeah, 5 or 10. We’ll just make sure that those are answered. We’ll we’ll form. We’re going to be forming the data model, anyways. But I noticed that recharges like a ton of tables
511 00:51:55.880 ⇒ 00:51:56.830 Uttam Kumaran: that we just wanna
512 00:51:57.470 ⇒ 00:51:58.490 Uttam Kumaran: prioritize
513 00:51:58.510 ⇒ 00:51:59.970 Uttam Kumaran: eyes a little bit. Yeah.
514 00:51:59.970 ⇒ 00:52:02.039 Aman Nagpal: For sure I’ll try to send some. I feel like
515 00:52:02.150 ⇒ 00:52:06.460 Aman Nagpal: Robert and Pius would probably know what’s most.
516 00:52:06.460 ⇒ 00:52:07.689 Uttam Kumaran: Okay, if.
517 00:52:08.880 ⇒ 00:52:15.120 Aman Nagpal: Rid of blood. But yeah, like a lot of it is just active subscribers cancelled. Even if you go to the
518 00:52:15.530 ⇒ 00:52:24.539 Aman Nagpal: recharge dashboard. I don’t know if you guys have access to that. But you know there’s a bunch of analytics there that they give us. Maybe we could emulate that. But
519 00:52:24.838 ⇒ 00:52:28.489 Aman Nagpal: I think Robert probably know the best ones, but I’ll try to shoot some over to.
520 00:52:29.220 ⇒ 00:52:32.200 Nicolas Sucari: Cool, excellent. Okay, I’ll ask. Pay us. And Robert.
521 00:52:32.490 ⇒ 00:52:33.450 Nicolas Sucari: thanks.
522 00:52:35.550 ⇒ 00:52:54.089 Uttam Kumaran: Okay, awesome. I think we’ll probably make sure all these notes get updated in the notion, Doc. Or notion homepage as well. So we’ll just make sure to keep that priorities. And then I know we have the bunch of lists of stuff from here, so we’ll just make sure things get updated there. And then, yeah, we’re we’re almost done with the refunds. I. We’ve. I’ve just been basically looking at it while we’re on this call. And
523 00:52:54.110 ⇒ 00:53:01.639 Uttam Kumaran: there’s just something unique about like we. There’s some refunds that have order lines as refunds or some that don’t.
524 00:53:01.730 ⇒ 00:53:06.669 Uttam Kumaran: I’m just trying to make sure that there isn’t really a difference doesn’t seem to be a difference. So we should be done with that
525 00:53:07.170 ⇒ 00:53:08.010 Uttam Kumaran: this week.
526 00:53:08.900 ⇒ 00:53:10.539 Aman Nagpal: Awesome. That sounds great
527 00:53:10.560 ⇒ 00:53:11.989 Aman Nagpal: in terms of
528 00:53:12.480 ⇒ 00:53:13.660 Aman Nagpal: priorities.
529 00:53:15.200 ⇒ 00:53:16.819 Aman Nagpal: how do we kind of have
530 00:53:17.560 ⇒ 00:53:18.480 Aman Nagpal: things.
531 00:53:18.480 ⇒ 00:53:22.339 Uttam Kumaran: Yeah, Nico, do you want to share the notion? Let’s let’s just make sure that the notion reflects
532 00:53:23.480 ⇒ 00:53:24.489 Uttam Kumaran: what we want to take on.
533 00:53:24.490 ⇒ 00:53:25.040 Nicolas Sucari: Yeah.
534 00:53:26.120 ⇒ 00:53:28.720 Nicolas Sucari: sure, let me share my screen.
535 00:53:30.880 ⇒ 00:53:32.639 Nicolas Sucari: I was just updating the
536 00:53:32.930 ⇒ 00:53:34.769 Nicolas Sucari: thinking data sources.
537 00:53:35.050 ⇒ 00:53:35.700 Aman Nagpal: Hmm!
538 00:53:36.370 ⇒ 00:53:37.510 Aman Nagpal: Here great.
539 00:53:38.880 ⇒ 00:53:40.280 Nicolas Sucari: Let me know when you’re seeing.
540 00:53:41.430 ⇒ 00:53:42.210 Aman Nagpal: Yep.
541 00:53:42.660 ⇒ 00:53:50.149 Nicolas Sucari: Cool. So I added, here the dashboards that we have. I didn’t, added the okay and then, gorgeous one, just because we’re chat. We’re already.
542 00:53:50.430 ⇒ 00:54:15.589 Nicolas Sucari: We’re still looking into the the data. But you can click here and go to the folder, the complete, real folder that we have with all of the dashboards, and then these are directly to go to those dashboards, and if you want to go access any of the tools that we have. We have all of the links here? The roadmap or the boards that we are using is this one? So right now, we’re working in the refunds and returns. And we have this task here to
543 00:54:15.590 ⇒ 00:54:28.670 Nicolas Sucari: keep comparing the Meta base and real data that we have so that we can match all of the differences that we can find. And then I have, like these backlog column here where we are adding all of the
544 00:54:28.982 ⇒ 00:54:42.999 Nicolas Sucari: tasks that we have, we have the product categories or yeah product type that we discussed today. We already added to the order table. We need to add it to the other one. And we have here the match Amazon versus shopify customers data, the Google sheet stuff.
545 00:54:43.030 ⇒ 00:55:07.590 Nicolas Sucari: adding shipments, table into snowflake recharge data, north data and anything else that we discuss. I tried to add it here, so if you have anything that you’re not seeing here, let me know. I can add it ideally. What we are doing with Ryan is we discuss every day on how these tasks are moving and what is in progress, and how we can complete anything. So yeah, we’re going through all of that every day.
546 00:55:07.942 ⇒ 00:55:26.990 Nicolas Sucari: And then I have some documentation here in the resources where we where we are adding, like each of the data sources that we have and when they are syncing. When was the initial sync, and how frequent is syncing, so that we know what to expect from each of them. Okay?
547 00:55:27.870 ⇒ 00:55:38.289 Nicolas Sucari: And then we’re working on this documentation with about metrics where we have, like the tables that we have with all of the metrics, and how they are calculated. We are trying to
548 00:55:38.390 ⇒ 00:55:48.650 Nicolas Sucari: add some stuff here and add some context for each of the metrics. But ideally, this is kind of the place where you can find any measure that you have and how it was calculated, and a little bit of context about it.
549 00:55:48.830 ⇒ 00:55:49.500 Nicolas Sucari: Okay.
550 00:55:51.370 ⇒ 00:55:54.970 Uttam Kumaran: Does that priority? List make sense? I’m on
551 00:55:56.100 ⇒ 00:55:59.960 Nicolas Sucari: So this is low, but we are just working on it. So move it here.
552 00:56:00.100 ⇒ 00:56:00.960 Nicolas Sucari: But yeah.
553 00:56:01.770 ⇒ 00:56:08.099 Aman Nagpal: Yeah, no, I think the order makes complete sense. The only thing I think, that we discussed was
554 00:56:09.890 ⇒ 00:56:16.740 Aman Nagpal: Well, so it says, gorgeous and okendo modeling are done. So that data, you said, is already in real right.
555 00:56:17.520 ⇒ 00:56:26.114 Nicolas Sucari: So yeah, we have like, this is for the initial thing that we do. And to create the 1st tables, we need to create a new one, a new task, so that we can start
556 00:56:26.840 ⇒ 00:56:31.840 Nicolas Sucari: using that data or that tables to match with what we have in shopify. And Amazon. Yeah.
557 00:56:32.300 ⇒ 00:56:37.800 Aman Nagpal: I would just record this card for getting that through amplitude, I guess. Yeah.
558 00:56:37.800 ⇒ 00:56:38.880 Uttam Kumaran: Let’s do that.
559 00:56:39.240 ⇒ 00:56:40.910 Aman Nagpal: Yeah. But otherwise
560 00:56:41.230 ⇒ 00:56:49.980 Aman Nagpal: the order is looks good. Gordon. Google Sheet is good. What’s that stable in Snowflake? Is that outgoing shipments.
561 00:56:50.940 ⇒ 00:57:15.289 Nicolas Sucari: Yeah. Our idea was to create kind of a shipments dashboard where you can go in and see for all of the sources that you have how they were shipped. If we have that data we know we have it for shopify, but we don’t have for Amazon. But if someone wants to go in and try to look into shipments. Add, like a specific dashboard, where you can see all of the measures regarding shipping and the orders, and how those ship?
562 00:57:15.662 ⇒ 00:57:27.940 Nicolas Sucari: It’s something that we we use and and with with other clients, and they find useful to go and look on how we are doing on shipping costs and that kind of stuff. So if you want, we can work on that too.
563 00:57:28.758 ⇒ 00:57:35.930 Aman Nagpal: I think that’s definitely a great idea really good. Nice to have. I would put it lower than the other 2. But
564 00:57:36.677 ⇒ 00:57:40.100 Aman Nagpal: definitely after these would be good to have.
565 00:57:41.730 ⇒ 00:57:50.799 Uttam Kumaran: And also, like as you have, you know, the modeling that we did for Oquendo, and gorgeous like that was our 1st pass. We’re gonna constantly have, like, we want to add new columns or new logic.
566 00:57:50.810 ⇒ 00:57:54.749 Uttam Kumaran: So that’s gonna come from. You know your side as well as from Pius
567 00:57:54.770 ⇒ 00:57:56.490 Uttam Kumaran: on what they need. So
568 00:57:57.115 ⇒ 00:58:00.899 Uttam Kumaran: we’ll probably continue again. I don’t. Wanna we don’t want to swamp.
569 00:58:00.960 ⇒ 00:58:26.689 Uttam Kumaran: This would like, Okay, there’s like 15 priorities, and they’re all in progress. So as much as possible, we just have like one or 2 things we’re doing. And we’ll just like push those through right and right now, basically for today and tomorrow, I think a lot of is on closing out this refund and then making sure we’re still very, very confident between Meta, base and real and shopify and snowflake, and so like. That may be a little bit ongoing, but I feel like the refunds ties into that. And then
570 00:58:26.955 ⇒ 00:58:37.590 Uttam Kumaran: just continuing to do? I think we’re. We’re also we have the query we’ve done for the Amazon versus shopify customers data. So we’ll make a couple more changes, and then at least show you like what we’re seeing.
571 00:58:37.870 ⇒ 00:58:39.119 Uttam Kumaran: Terms of results.
572 00:58:39.630 ⇒ 00:58:40.950 Aman Nagpal: Oh, that sounds great!
573 00:58:42.640 ⇒ 00:59:11.150 Nicolas Sucari: Yeah, ideally, is what there will be changes every day on all of the models. And that’s you have questions. We’ll try to figure out what we need to change on the models. The the model tasks here is just to have the initial ones. And then we can just go with specific specific things or tasks here. So yeah, ideally, is that we’re we’re doing it. Yeah, we. And as I said, we are discussing about this every day and trying to track everything. How we are. We’re doing
574 00:59:11.490 ⇒ 00:59:12.740 Nicolas Sucari: And yeah, that’s it.
575 00:59:13.900 ⇒ 00:59:14.720 Aman Nagpal: That works
576 00:59:15.870 ⇒ 00:59:16.610 Aman Nagpal: cool.
577 00:59:17.673 ⇒ 00:59:23.829 Nicolas Sucari: Also just to mention on Friday that we sent the
578 00:59:25.090 ⇒ 00:59:38.029 Nicolas Sucari: the like. Like an update. I I usually send like a weekly update with all of what we’ve been doing on the week. We’re gonna try to add their kind of hours that we’ve been working on for all of those tasks so that we can start to track.
579 00:59:38.680 ⇒ 00:59:39.820 Nicolas Sucari: The weekly
580 00:59:39.850 ⇒ 00:59:45.159 Nicolas Sucari: our spend that we are doing with all of those tasks. Okay, so that’s something you should expect. This Friday.
581 00:59:46.350 ⇒ 00:59:47.709 Aman Nagpal: That sounds good. Thank you.
582 00:59:49.020 ⇒ 00:59:56.469 Nicolas Sucari: Excellent. Okay, I don’t have anything else, guys. I don’t know if you have any other question or aman. You want to ask something.
583 00:59:56.470 ⇒ 00:59:57.090 Uttam Kumaran: That’s it.
584 00:59:57.740 ⇒ 00:59:59.940 Aman Nagpal: Nope, that’s it for me. Thank you. Guys.
585 01:00:00.390 ⇒ 01:00:01.410 Aman Nagpal: Cool.
586 01:00:01.410 ⇒ 01:00:02.100 Nicolas Sucari: Thank you very much.
587 01:00:02.100 ⇒ 01:00:02.899 Uttam Kumaran: See you guys soon.
588 01:00:02.900 ⇒ 01:00:03.570 Nicolas Sucari: Bye, bye.
589 01:00:03.840 ⇒ 01:00:05.039 Aman Nagpal: Have a good one. Bye.