Meeting Title: Eden West Data Modeling Sync Date: 2026-03-31 Meeting participants: Ashwini Sharma, Awaish Kumar
WEBVTT
1 00:02:24.380 ⇒ 00:02:25.190 Awaish Kumar: Hi.
2 00:02:27.880 ⇒ 00:02:28.780 Awaish Kumar: Hello?
3 00:02:31.340 ⇒ 00:02:32.440 Awaish Kumar: Can you hear me?
4 00:02:40.220 ⇒ 00:02:41.140 Awaish Kumar: Hello?
5 00:02:54.550 ⇒ 00:02:55.290 Ashwini Sharma: You have me.
6 00:02:56.900 ⇒ 00:02:58.299 Awaish Kumar: Yeah, now I can.
7 00:03:01.650 ⇒ 00:03:02.490 Awaish Kumar: Are you still there?
8 00:03:04.060 ⇒ 00:03:05.879 Ashwini Sharma: Hello, hello, hello.
9 00:03:07.240 ⇒ 00:03:07.640 Awaish Kumar: Hello?
10 00:03:08.540 ⇒ 00:03:09.740 Ashwini Sharma: Jay, can you hear me?
11 00:03:10.260 ⇒ 00:03:11.309 Awaish Kumar: I can hear you.
12 00:03:11.940 ⇒ 00:03:13.709 Ashwini Sharma: Okay, okay.
13 00:03:13.710 ⇒ 00:03:18.240 Awaish Kumar: Okay, so what I wanted to discuss is, like, I reviewed the PR,
14 00:03:19.710 ⇒ 00:03:23.589 Awaish Kumar: But I think you’ve seen it, right? I’ve sent you some examples.
15 00:03:24.390 ⇒ 00:03:26.700 Awaish Kumar: Bobby, if you want, Goodbye.
16 00:03:27.290 ⇒ 00:03:29.920 Awaish Kumar: Have you, have you re… like, reviewed it?
17 00:03:31.680 ⇒ 00:03:35.470 Ashwini Sharma: Yeah, I saw the PR. What exactly, I mean,
18 00:03:36.180 ⇒ 00:03:38.850 Ashwini Sharma: You don’t want that wholehe statement, or, like…
19 00:03:39.260 ⇒ 00:03:44.200 Ashwini Sharma: I’m not clear. What is it that you want me to eliminate from that?
20 00:03:45.920 ⇒ 00:03:53.409 Awaish Kumar: Okay, I think I’ve sent you the code, exact code, what it says, but I can show you right now. We can do it together.
21 00:03:56.700 ⇒ 00:04:00.800 Awaish Kumar: Yeah, so, for example… Let me open this file.
22 00:04:09.610 ⇒ 00:04:12.139 Awaish Kumar: So you know what… what it is doing.
23 00:04:12.670 ⇒ 00:04:13.400 Ashwini Sharma: Are you sharing the screen.
24 00:04:14.350 ⇒ 00:04:15.740 Awaish Kumar: Yeah, yeah.
25 00:04:16.540 ⇒ 00:04:22.619 Awaish Kumar: My question is, have you read this, like, the code that is generated?
26 00:04:26.150 ⇒ 00:04:28.600 Ashwini Sharma: Yeah, yeah, I’ve read it. I’ve run it also.
27 00:04:29.820 ⇒ 00:04:35.649 Awaish Kumar: Yeah, but if you see what it is doing, it’s… at the end of it, it is comparing with Basque.
28 00:04:36.270 ⇒ 00:04:39.320 Awaish Kumar: This is a mod EDMI’s order summary table.
29 00:04:39.680 ⇒ 00:04:42.419 Awaish Kumar: I don’t want to compellion in this,
30 00:04:43.880 ⇒ 00:04:48.079 Awaish Kumar: like, has buscode or somebody match, or, like, I don’t need this kind of.
31 00:04:48.080 ⇒ 00:04:57.780 Ashwini Sharma: Okay, the Basque things you don’t need, right? Okay, okay, got it, got it. Yeah, because I was trying to analyze it with Basque, probably, I just added those BASC-related stuff over there.
32 00:04:58.960 ⇒ 00:04:59.580 Ashwini Sharma: True.
33 00:04:59.580 ⇒ 00:05:06.560 Awaish Kumar: But my point is, I’m, like, I want to… we want to create this order summary table from Eden West Data.
34 00:05:06.730 ⇒ 00:05:16.349 Awaish Kumar: this is not a… something to… like, the comparison, the QA that you want to do is just a… just a separate, like, the QA of the model.
35 00:05:16.350 ⇒ 00:05:27.529 Awaish Kumar: But the model itself just have the fields that are needed. Like, I need… or in order summary, there should be order number, customer ID, order total, product name, and things like that.
36 00:05:27.640 ⇒ 00:05:30.210 Awaish Kumar: I think that that’s what you are doing here.
37 00:05:30.350 ⇒ 00:05:33.869 Awaish Kumar: Right? These are the fields should be in the table, right?
38 00:05:34.270 ⇒ 00:05:39.940 Ashwini Sharma: Okay, you don’t… Want, okay, yeah, yeah, okay, got it, got it, got it, got it, yeah.
39 00:05:40.490 ⇒ 00:05:50.449 Awaish Kumar: It’s not about, like, I want it, like, it’s… that’s what the… like, if… when we say, okay, we need something similar to order summary, that means we just need this, right?
40 00:05:51.050 ⇒ 00:05:52.770 Awaish Kumar: The comparison is just rude.
41 00:05:52.770 ⇒ 00:05:54.530 Ashwini Sharma: I’ll remove this one.
42 00:05:54.530 ⇒ 00:05:55.579 Awaish Kumar: Thank you.
43 00:05:59.430 ⇒ 00:06:02.210 Ashwini Sharma: This is, my branch, okay.
44 00:06:03.400 ⇒ 00:06:05.879 Ashwini Sharma: Honored Ravish, yeah, I’ll just remove that.
45 00:06:06.190 ⇒ 00:06:06.930 Ashwini Sharma: It takes a lot of.
46 00:06:08.380 ⇒ 00:06:13.599 Awaish Kumar: What is… And is… yeah, product name is coming, right? So…
47 00:06:14.350 ⇒ 00:06:18.279 Awaish Kumar: There are a few things that are… that are missing, like… like…
48 00:06:18.390 ⇒ 00:06:20.850 Awaish Kumar: I also want to understand how you…
49 00:06:21.040 ⇒ 00:06:25.320 Awaish Kumar: Coming up with product names for the order. Is it a list, or…
50 00:06:26.500 ⇒ 00:06:27.309 Ashwini Sharma: Which one?
51 00:06:28.700 ⇒ 00:06:31.809 Awaish Kumar: Okay, so this is… This is…
52 00:06:32.000 ⇒ 00:06:36.080 Awaish Kumar: Like, we want to do it maybe differently, like, this is not we…
53 00:06:36.760 ⇒ 00:06:38.759 Awaish Kumar: So this is, like, your…
54 00:06:39.320 ⇒ 00:06:46.620 Awaish Kumar: aggregating string? Yeah. Because now that the previous basic structure is a little bit different here.
55 00:06:46.780 ⇒ 00:06:52.850 Awaish Kumar: In the Basque, we only had, for a single item order, we only had a single product.
56 00:06:53.800 ⇒ 00:06:58.009 Awaish Kumar: In our new system, we also have order item table.
57 00:06:58.690 ⇒ 00:07:12.189 Ashwini Sharma: So, okay, let’s not… let’s forget that old thing, right? Because this will always create a confusion. As long as we are looking at the old and then seeing this is not there in the old one, this is not there in the new one, I think it is always going…
58 00:07:12.210 ⇒ 00:07:23.819 Ashwini Sharma: That’s why I’ve been saying since yesterday, right? Let’s have a proper modeling, and define what do we exactly need. Like, what do we need in the order summary, now with the new model?
59 00:07:24.530 ⇒ 00:07:26.819 Ashwini Sharma: Let me take some notes.
60 00:07:27.050 ⇒ 00:07:29.359 Awaish Kumar: I’m going to tell you, Ashwini, if you listen to the end.
61 00:07:29.710 ⇒ 00:07:49.260 Awaish Kumar: A little bit. I’m just trying to convey that information, that if you… I don’t know if you have QA’d the models. If you QA the model, the order table, and then we have order item table, like, the basic model I created, it has both of these tables. Fact order, fact order item.
62 00:07:50.110 ⇒ 00:07:50.980 Awaish Kumar: Right?
63 00:07:53.300 ⇒ 00:07:56.340 Awaish Kumar: Now, if somebody asks me for a summary table.
64 00:07:56.440 ⇒ 00:07:58.849 Awaish Kumar: So, order summary table is, like.
65 00:07:59.460 ⇒ 00:08:08.790 Awaish Kumar: Then there is a question, right? If we want it at an order level, or if we want it at an order item level, right? That is the right question. That’s…
66 00:08:08.970 ⇒ 00:08:10.109 Awaish Kumar: I want, like, you…
67 00:08:10.470 ⇒ 00:08:18.249 Awaish Kumar: That’s something you should have, like, you can ask me, and I have to answer you, but the conversion happens like that, right? If you…
68 00:08:18.630 ⇒ 00:08:19.640 Awaish Kumar: like…
69 00:08:19.800 ⇒ 00:08:34.540 Awaish Kumar: So there is no one, like, if you ask me that, okay, I need these requirements, I want to re… redo everything that is done before, and I want to ask Eden team what are the new requirements and new things.
70 00:08:34.679 ⇒ 00:08:43.319 Awaish Kumar: That’s probably… probably can happen once we start with adding new models, right? For the existing models, I don’t think they are going to…
71 00:08:44.020 ⇒ 00:08:53.170 Awaish Kumar: give us requirements again, right? We have to get requirements ourselves from the existing dashboards, from the existing models.
72 00:08:56.370 ⇒ 00:09:03.499 Awaish Kumar: The client team won’t obviously go again with us, sit with us together again, and define these things for us.
73 00:09:05.130 ⇒ 00:09:10.190 Awaish Kumar: It’s either you, me, or maybe Amber, and it’s all between three of us.
74 00:09:10.190 ⇒ 00:09:23.579 Ashwini Sharma: Okay, let’s do it then. Let’s utilize the meeting that we have. Let’s, you know, sit together, and let’s try to identify what exactly do we need to show in the dashboards, right? That will help us, you know, define these dashboards more… these models more accurately.
75 00:09:26.010 ⇒ 00:09:31.680 Awaish Kumar: Yeah, so, yeah, but, like, at the basics of it, I want to…
76 00:09:32.360 ⇒ 00:09:40.859 Awaish Kumar: Make sure, like, the, for example, product sales summary transaction table, it does not have that complication of having order or order.
77 00:09:41.170 ⇒ 00:09:46.260 Awaish Kumar: Or anything, like, if you… If I can… Show you.
78 00:09:46.900 ⇒ 00:09:47.680 Awaish Kumar: Maybe.
79 00:09:49.250 ⇒ 00:09:52.820 Awaish Kumar: Have you seen the production summary table in BigQuery, basically?
80 00:09:54.510 ⇒ 00:09:56.310 Awaish Kumar: Have you looked at that?
81 00:09:56.310 ⇒ 00:10:01.560 Ashwini Sharma: I might have looked at. Right now, I’m not looking at it, but yeah, in the past, I might have looked at it.
82 00:10:09.930 ⇒ 00:10:11.239 Awaish Kumar: Okay, let me adjust.
83 00:10:11.700 ⇒ 00:10:26.200 Awaish Kumar: show you, right? So, there are some changes, obviously, but then there are some models that we can actually do. That’s… that’s the only thing that I’m actually trying to convey. Like, if I look at this table.
84 00:10:26.470 ⇒ 00:10:30.100 Awaish Kumar: So, you see, it is not dependent on an order.
85 00:10:30.390 ⇒ 00:10:31.979 Awaish Kumar: So, we have a date.
86 00:10:32.160 ⇒ 00:10:33.650 Awaish Kumar: We have a product name.
87 00:10:33.770 ⇒ 00:10:45.859 Awaish Kumar: So, from an order table, you can get order item. From there, we can maybe go back to getting all the items which are sold as part of an order, and we can just get the order date. That is the date.
88 00:10:45.860 ⇒ 00:10:49.519 Ashwini Sharma: What is the grain for this table? Product sales summary by transaction?
89 00:10:49.850 ⇒ 00:10:59.749 Awaish Kumar: Right? Date, product name, These both are basically the same. This is different, mapping for that, but, like.
90 00:11:00.410 ⇒ 00:11:05.549 Awaish Kumar: Ultimately, same green. Date, product, membership plan, and gender. This is the…
91 00:11:05.650 ⇒ 00:11:07.799 Awaish Kumar: This makes the primary key for this table.
92 00:11:09.550 ⇒ 00:11:10.980 Ashwini Sharma: That’s it.
93 00:11:11.700 ⇒ 00:11:13.219 Awaish Kumar: All others are just majors.
94 00:11:13.550 ⇒ 00:11:16.079 Awaish Kumar: Right? These are all counts, averages.
95 00:11:18.600 ⇒ 00:11:24.239 Ashwini Sharma: Why is it called transaction? Why is there a transaction in the name? Summary by transaction?
96 00:11:24.500 ⇒ 00:11:28.889 Awaish Kumar: By transaction. That means, like, we had a different, revenue fields.
97 00:11:29.090 ⇒ 00:11:32.599 Awaish Kumar: Right? This is one of the transactions, revenue is one of the field.
98 00:11:33.090 ⇒ 00:11:37.650 Awaish Kumar: So, that is the name, Product Sales Summary. That’s the table name.
99 00:11:37.910 ⇒ 00:11:40.069 Awaish Kumar: This is for,
100 00:11:40.430 ⇒ 00:11:51.659 Awaish Kumar: giving this table a meaning, because we had multiple versions of product sales summary in our… initially, when we started with this. So, there were multiple ways of calculating revenue.
101 00:11:51.800 ⇒ 00:11:56.060 Awaish Kumar: So, one… one… one of the revenues… revenue fields called transaction revenue.
102 00:11:56.530 ⇒ 00:11:57.850 Awaish Kumar: And that’s why…
103 00:11:58.260 ⇒ 00:12:01.080 Ashwini Sharma: Where is that transaction revenue? It’s there in this table?
104 00:12:01.850 ⇒ 00:12:05.699 Awaish Kumar: It’s in the… in the… in the tables that build this, in the order summary.
105 00:12:05.900 ⇒ 00:12:11.370 Awaish Kumar: in the… Order summary, you can find it in maybe FACT transaction also, like…
106 00:12:11.600 ⇒ 00:12:13.490 Awaish Kumar: So, yeah, that’s a long story.
107 00:12:13.600 ⇒ 00:12:19.220 Awaish Kumar: But, yeah, there were multiple fields for transaction revenue. So, for revenue, there were…
108 00:12:19.860 ⇒ 00:12:27.179 Awaish Kumar: We were debating on multiple fields that, okay, which one is… we should be using for this? So we created a…
109 00:12:27.430 ⇒ 00:12:35.750 Awaish Kumar: Because they were also, obviously, initially, we come up with something, they had their own ways to… the Q8?
110 00:12:36.040 ⇒ 00:12:40.040 Awaish Kumar: And our… they… they have their own models, and they… and our…
111 00:12:40.140 ⇒ 00:12:57.940 Awaish Kumar: models were not matching with their models, and basically, for that reason, we have to go create multiple versions of the same table, and in the end, this one was basically good use. So, for the preserving the meaning where this table comes from, we have by transaction in its name.
112 00:12:58.990 ⇒ 00:13:06.220 Awaish Kumar: But, yeah, but transaction is just for that reason. Actual… Paroxel summary is the model.
113 00:13:06.440 ⇒ 00:13:06.990 Awaish Kumar: And…
114 00:13:06.990 ⇒ 00:13:10.440 Ashwini Sharma: You know, it’s misleading, right, when you say it, like, by transaction.
115 00:13:11.080 ⇒ 00:13:20.379 Ashwini Sharma: It seems to somebody, like, who’s looking at the name, that there is some kind of a transactional information involved, whereas the only information that’s there in this table is
116 00:13:20.680 ⇒ 00:13:23.309 Ashwini Sharma: Basically, a product name and a date.
117 00:13:23.680 ⇒ 00:13:27.689 Ashwini Sharma: And a gender, and gender is probably… what? It’s a customer gender?
118 00:13:28.640 ⇒ 00:13:31.299 Awaish Kumar: Yeah, yeah, it’s a gender of the customer.
119 00:13:31.970 ⇒ 00:13:34.250 Ashwini Sharma: But the customer information is not there.
120 00:13:34.630 ⇒ 00:13:39.129 Awaish Kumar: Yes, yes, but we are trying to get, like, the product
121 00:13:39.380 ⇒ 00:13:42.429 Awaish Kumar: We are on a single date, for a single product.
122 00:13:42.690 ⇒ 00:13:54.329 Awaish Kumar: that is sold, what is the percentage, like, what is the… like, the revenue, for example, between those products were bought by male versus female, right?
123 00:13:58.670 ⇒ 00:14:01.990 Ashwini Sharma: Now we’re just including the gender, like, without… That’s.
124 00:14:01.990 ⇒ 00:14:02.850 Awaish Kumar: Without water.
125 00:14:07.520 ⇒ 00:14:14.500 Awaish Kumar: if you need, like, obviously, if I… if you, if I tell you, actually, I need to know, in,
126 00:14:14.930 ⇒ 00:14:20.010 Awaish Kumar: in Delhi, What is the… Cosmetic sale.
127 00:14:20.760 ⇒ 00:14:27.150 Awaish Kumar: what is the percentage of custom conduct sale between gender… male versus female? What are you going to do?
128 00:14:28.970 ⇒ 00:14:35.960 Ashwini Sharma: I would have included the customer information over here, right? Customer ID, rather than just including Male versus female.
129 00:14:36.470 ⇒ 00:14:41.460 Awaish Kumar: But we are aggregating that, that’s my… the whole point of creating it.
130 00:14:42.270 ⇒ 00:14:45.120 Awaish Kumar: We… we already have a DIM customer table.
131 00:14:45.260 ⇒ 00:14:49.030 Awaish Kumar: We don’t want to go back Like, to that.
132 00:14:49.850 ⇒ 00:14:55.739 Awaish Kumar: We have a Fed transaction, we have a DIM customer, that you can join, you can get exact same information.
133 00:14:56.760 ⇒ 00:15:01.760 Awaish Kumar: But here, we are creating a model for a BI person, which is basically,
134 00:15:01.880 ⇒ 00:15:13.820 Awaish Kumar: want to need this summary table, and, like, maybe it’s not possible for him to do that joining and everything in the BI tool, and he wants us to create that one layer for him.
135 00:15:14.030 ⇒ 00:15:15.639 Awaish Kumar: That’s the… that was the ask.
136 00:15:19.120 ⇒ 00:15:32.900 Awaish Kumar: So this… all information is nothing that is… that could not be found in fact transaction table. So there is a fact transaction, there is a DIM customer, you can join, you can get the date, you can get product name from there, and you can also get,
137 00:15:33.120 ⇒ 00:15:35.289 Awaish Kumar: Membership plan, you can also get gender.
138 00:15:35.390 ⇒ 00:15:37.110 Awaish Kumar: Right? Every… everything is there.
139 00:15:38.620 ⇒ 00:15:40.960 Ashwini Sharma: And this is a mart layer table, right?
140 00:15:41.540 ⇒ 00:15:42.450 Awaish Kumar: Yes, yes.
141 00:15:44.400 ⇒ 00:15:46.130 Awaish Kumar: So, we have MART tables.
142 00:15:46.820 ⇒ 00:15:48.289 Awaish Kumar: Yeah, this is a…
143 00:15:48.690 ⇒ 00:15:54.189 Awaish Kumar: We have mod, where we say the basic table we create are a dim and fact table, right?
144 00:15:54.710 ⇒ 00:16:05.400 Awaish Kumar: that we created. We have effect transactions, DIM products, DIM customers, all of that, DIM shipments. Then comes summary tables. So, there were a lot of
145 00:16:05.680 ⇒ 00:16:07.030 Awaish Kumar: Obviously, the…
146 00:16:07.620 ⇒ 00:16:14.729 Awaish Kumar: The thing is, the simplest thing is that, okay, you have these fact and name tables, just go in BI tool and choose that to generate everything.
147 00:16:14.880 ⇒ 00:16:23.229 Awaish Kumar: But that’s… that’s not how it worked. Obviously, our analysts came up with requirements that, okay, I can’t do this complex joining…
148 00:16:23.770 ⇒ 00:16:30.879 Awaish Kumar: If you look at this model, its itself is really complex in terms of, like, how we’re joining revenue with ads.
149 00:16:32.330 ⇒ 00:16:35.740 Awaish Kumar: when there’s no ads, how we are distributing it, so all that
150 00:16:36.350 ⇒ 00:16:42.040 Awaish Kumar: complexity. It was not possible to do that in Tableau, for example, and then…
151 00:16:42.970 ⇒ 00:16:49.720 Awaish Kumar: the analysts came back to us with their requirement, okay, we need to do this, I’m not able to do it in Tableau.
152 00:16:49.970 ⇒ 00:16:53.710 Awaish Kumar: help me with creating some dbt model which I can use.
153 00:16:57.900 ⇒ 00:17:01.789 Awaish Kumar: Right? So the… If you look at the, for example.
154 00:17:04.349 ⇒ 00:17:06.940 Awaish Kumar: Dashboards that are generated out of it.
155 00:17:07.770 ⇒ 00:17:10.160 Awaish Kumar: Or, like, this one, right?
156 00:17:10.750 ⇒ 00:17:15.789 Awaish Kumar: If I… Go to the hub, and…
157 00:17:19.099 ⇒ 00:17:19.790 Awaish Kumar: What?
158 00:17:21.349 ⇒ 00:17:23.320 Awaish Kumar: What is that?
159 00:17:28.970 ⇒ 00:17:29.650 Awaish Kumar: Hmm.
160 00:17:30.210 ⇒ 00:17:32.879 Awaish Kumar: What’s keep… keep happening for these.
161 00:17:47.770 ⇒ 00:17:55.320 Awaish Kumar: We don’t know what keeps happening with these, How many dashboards are…
162 00:17:55.850 ⇒ 00:17:57.800 Awaish Kumar: Get… get out of them, but…
163 00:17:58.220 ⇒ 00:18:00.870 Awaish Kumar: If you look at… maybe if we can go to Tableau.
164 00:18:56.000 ⇒ 00:18:57.449 Awaish Kumar: Okay, finally.
165 00:18:57.730 ⇒ 00:19:03.130 Awaish Kumar: Soon… For example, this is one of the dashboards which is created on top of
166 00:19:03.700 ⇒ 00:19:05.980 Awaish Kumar: what I’m… the model we are working on.
167 00:19:07.260 ⇒ 00:19:11.860 Awaish Kumar: So… We have ad spend.
168 00:19:12.120 ⇒ 00:19:12.920 Awaish Kumar: Right.
169 00:19:13.070 ⇒ 00:19:19.000 Awaish Kumar: We have new customer count, new customer revenue, refunds… New customer.
170 00:19:19.430 ⇒ 00:19:21.750 Awaish Kumar: COGS, and then we have NK.
171 00:19:23.290 ⇒ 00:19:33.790 Awaish Kumar: This is basically… this… Yeah, this limit… Added spend divided by revenue?
172 00:19:35.240 ⇒ 00:19:47.590 Awaish Kumar: And so, what is… for a single customer… for a new customer to get acquired, we spend this much money? So this is basically for this product. So this is basically what… that’s what we want to support.
173 00:19:47.950 ⇒ 00:19:55.849 Awaish Kumar: Right? They want a similar view that, from new system, what is the new customer acquired? What is the spend? And then…
174 00:19:56.270 ⇒ 00:20:03.020 Awaish Kumar: the ability to create these metrics, MCAC, NOS. So these are, we are not creating inside of our model.
175 00:20:03.240 ⇒ 00:20:12.360 Awaish Kumar: they will be calculated in the BI tool, but obviously we’re supporting the fields that are needed to generate these metrics.
176 00:20:14.730 ⇒ 00:20:18.129 Ashwini Sharma: Refund orders, and everything, influencer spend, and all those things.
177 00:20:21.770 ⇒ 00:20:27.379 Awaish Kumar: Yeah, so we have an… like, we have Edison, we have customer count, new customer revenue.
178 00:20:27.790 ⇒ 00:20:32.280 Awaish Kumar: I don’t know if it has refunds, like, somebody added it, I’m not sure.
179 00:20:33.220 ⇒ 00:20:38.270 Awaish Kumar: But we have new customer cogs, influencer span, everything is in that model.
180 00:20:42.360 ⇒ 00:20:46.890 Awaish Kumar: Production summary by transaction includes all of these fields, so we…
181 00:20:47.490 ⇒ 00:20:50.689 Awaish Kumar: We have a production summary table, which basically brings in date.
182 00:20:50.940 ⇒ 00:20:56.669 Awaish Kumar: You can get product from… going from order to order item, we grab the products. We don’t need to
183 00:20:57.010 ⇒ 00:20:58.849 Awaish Kumar: For example, aggregate.
184 00:21:00.140 ⇒ 00:21:08.150 Awaish Kumar: product information at an order level, because we are basically calculating revenue for individual product on a given day.
185 00:21:08.340 ⇒ 00:21:11.829 Awaish Kumar: We don’t really care about orders in this… in this summary model.
186 00:21:12.110 ⇒ 00:21:20.750 Awaish Kumar: We don’t care about Orders, we don’t care about customer, we don’t care about, like, the… They’re, like, the…
187 00:21:21.100 ⇒ 00:21:26.190 Awaish Kumar: individual information. It’s more about a summary view, which can support this.
188 00:21:26.620 ⇒ 00:21:28.100 Awaish Kumar: this chart…
189 00:21:29.440 ⇒ 00:21:30.600 Ashwini Sharma: What’s the other table?
190 00:21:32.820 ⇒ 00:21:36.400 Ashwini Sharma: Total orders. What’s the difference between this table and the upper table?
191 00:21:36.820 ⇒ 00:21:42.640 Awaish Kumar: Yeah, so there’s a difference of different metrics. So, this is basically, for example, you can see it’s NCAC.
192 00:21:43.180 ⇒ 00:21:45.380 Awaish Kumar: And here it says, CAC.
193 00:21:45.680 ⇒ 00:21:48.269 Awaish Kumar: Only the… the blended CAC, right?
194 00:21:49.210 ⇒ 00:21:49.710 Ashwini Sharma: Okay.
195 00:21:50.260 ⇒ 00:21:53.149 Awaish Kumar: So that’s… actually, and it shows revenue.
196 00:21:53.630 ⇒ 00:22:01.540 Awaish Kumar: It’s… it’s basically total revenue, Right? In the… in that… the range which is selected on top.
197 00:22:02.110 ⇒ 00:22:03.020 Awaish Kumar: This is…
198 00:22:03.020 ⇒ 00:22:06.039 Ashwini Sharma: This is only the new customer count, new customer revenue.
199 00:22:06.400 ⇒ 00:22:08.659 Ashwini Sharma: Don’t refund for new orders, okay.
200 00:22:09.050 ⇒ 00:22:17.060 Awaish Kumar: For the given date range, what is the new customer revenue? The revenue coming from new customers, this is revenue from all the customers.
201 00:22:17.400 ⇒ 00:22:20.369 Awaish Kumar: Including new versus returning.
202 00:22:20.650 ⇒ 00:22:21.789 Awaish Kumar: Then we have…
203 00:22:21.990 ⇒ 00:22:28.059 Awaish Kumar: different LTV and CAC breakdowns and things like that. So these are all being supported by that model.
204 00:22:30.020 ⇒ 00:22:37.550 Ashwini Sharma: And how is that NCAC and other things calculated? Because, like, the column count is kind of limited, right? There are 1, 2, 3, 4, 5…
205 00:22:37.790 ⇒ 00:22:42.970 Ashwini Sharma: 6, 7, 8 columns, and including, data and product name, that’s 9, 10 columns, right?
206 00:22:43.340 ⇒ 00:22:49.199 Ashwini Sharma: Maybe order status is another column, yeah.
207 00:22:49.380 ⇒ 00:22:53.219 Ashwini Sharma: Is there anything else,
208 00:22:56.830 ⇒ 00:23:03.829 Ashwini Sharma: Because the model that is currently there in the Basque, right, Product Sales Summary, that has a lot more than
209 00:23:03.990 ⇒ 00:23:06.720 Ashwini Sharma: Maybe more than 15 columns or so.
210 00:23:07.090 ⇒ 00:23:09.759 Awaish Kumar: Yeah, but it is just, like, few things that…
211 00:23:09.920 ⇒ 00:23:13.960 Awaish Kumar: that I just showed you that might not be used, like the gender column.
212 00:23:14.100 ⇒ 00:23:31.560 Awaish Kumar: So, we added it, like, that is required. That was required to basically support some of their analysis, right? But it might not be in that specific dashboard. It might be part of an individual analysis, or it might be part of another dashboard. I just showed you one of the dashboards.
213 00:23:31.850 ⇒ 00:23:35.569 Awaish Kumar: There are multiple dashboards which are being supported by this model.
214 00:23:37.640 ⇒ 00:23:40.799 Awaish Kumar: In the tableau. You can go in, and you can look at that.
215 00:23:40.950 ⇒ 00:23:49.940 Awaish Kumar: But… and then also, like, this is also, like, they wanted to see how… how they are…
216 00:23:50.240 ⇒ 00:23:54.359 Awaish Kumar: What are… like, what is the… the different… the… the percentage of…
217 00:23:54.920 ⇒ 00:23:59.839 Awaish Kumar: Like, the spread of the revenue between genders, And,
218 00:24:00.820 ⇒ 00:24:06.519 Awaish Kumar: So we added it. Obviously, we don’t want to recreate all this… the whole model, just to…
219 00:24:06.930 ⇒ 00:24:11.550 Awaish Kumar: I have a different grain in a different table with the gender information, so we just added it in.
220 00:24:11.940 ⇒ 00:24:18.950 Awaish Kumar: at a… in the tableau, they just use aggregation. If they don’t want to use gender, they can select date.
221 00:24:19.160 ⇒ 00:24:22.740 Awaish Kumar: Marketing product needs some… The new customer count, and…
222 00:24:23.210 ⇒ 00:24:25.939 Awaish Kumar: Some… every… all other counts, whatever.
223 00:24:28.380 ⇒ 00:24:38.300 Awaish Kumar: So, this is basically… Why we have it, and there are a few things which… are added,
224 00:24:39.050 ⇒ 00:24:44.190 Awaish Kumar: Just, like, these are things, like, we… you see, these are… I don’t know where I opened up.
225 00:24:45.330 ⇒ 00:24:46.680 Awaish Kumar: So many tabs.
226 00:24:48.720 ⇒ 00:24:52.820 Awaish Kumar: Okay, so… There are individual, like, this…
227 00:24:54.440 ⇒ 00:25:00.990 Awaish Kumar: So, yeah, this… these are, like, should not… I won’t ex… Support, like,
228 00:25:01.680 ⇒ 00:25:04.560 Awaish Kumar: Having to do this, but it’s like…
229 00:25:04.990 ⇒ 00:25:13.309 Awaish Kumar: they insisted on, I want to see my total spend for this product, but also my spend for M&T, and I don’t want to…
230 00:25:13.480 ⇒ 00:25:18.380 Awaish Kumar: go from… go to the another dashboard to do that, so…
231 00:25:18.550 ⇒ 00:25:28.390 Awaish Kumar: I want view… view, view here and there, so it’s, like, kind of some of the hard requirements from customer that, and that, like, force us to include
232 00:25:28.520 ⇒ 00:25:32.540 Awaish Kumar: these kind of… Columns in there.
233 00:25:35.660 ⇒ 00:25:40.479 Awaish Kumar: So, to have these as columns in the same table.
234 00:25:40.930 ⇒ 00:25:49.279 Awaish Kumar: It required us to basically… it forced us to add these span… individually span columns in the same model, which is not ideal, but…
235 00:25:49.760 ⇒ 00:25:52.799 Awaish Kumar: like… That’s what’s coming from client.
236 00:25:53.710 ⇒ 00:25:58.850 Awaish Kumar: We are… we are already… once… when there was such request, we are already…
237 00:25:59.380 ⇒ 00:26:04.630 Awaish Kumar: pushed back on… on, okay, let’s create a new table. We already have some tables which can give you
238 00:26:04.740 ⇒ 00:26:11.409 Awaish Kumar: spent by your channel, like MNT and Meta, whatever, so why we want to do that? But, like.
239 00:26:11.950 ⇒ 00:26:15.389 Awaish Kumar: So these are some of the requirements. So they just come from…
240 00:26:16.170 ⇒ 00:26:20.249 Awaish Kumar: Stakeholder, and we are asked to just build it for them.
241 00:26:22.960 ⇒ 00:26:38.369 Ashwini Sharma: Okay, let’s do one thing, right? I’ll create this product sales summary, right? Let’s have a session on the order thing also, after some time, once I’m… once I give you this model. And, you know, I’ll work on the orders.
242 00:26:38.370 ⇒ 00:26:39.010 Awaish Kumar: Thank you.
243 00:26:39.260 ⇒ 00:26:43.450 Awaish Kumar: Okay, yeah, give… let’s talk about… for the order summary.
244 00:26:45.050 ⇒ 00:26:49.530 Awaish Kumar: So, thing is, I’m… I’m… I’m also kind of…
245 00:26:49.860 ⇒ 00:26:59.839 Awaish Kumar: want to have it with Amber, because she’s the one who will be working on dashboard, and I need to know from her what are the requirements in the dashboard, because…
246 00:26:59.990 ⇒ 00:27:09.880 Awaish Kumar: Or, like, now there is a little bit of change, how the previous order summary was. There… there were two things. Number one, we had, like, the journey.
247 00:27:10.040 ⇒ 00:27:14.859 Awaish Kumar: A complete journey of a order, like, advantage team it is, like…
248 00:27:15.000 ⇒ 00:27:17.420 Awaish Kumar: An order placed after that, it is maybe…
249 00:27:17.730 ⇒ 00:27:23.810 Awaish Kumar: In a pending state, and other delivery steps, and all that full journey is there.
250 00:27:24.300 ⇒ 00:27:30.179 Awaish Kumar: We… and there are some dashboards which are actually using that, like, information.
251 00:27:30.590 ⇒ 00:27:36.499 Awaish Kumar: Right? We need to support those dashboards. Like, whatever we come up with, maybe generating similar table.
252 00:27:36.740 ⇒ 00:27:39.540 Awaish Kumar: Maybe some other thing, but we have to support that.
253 00:27:40.370 ⇒ 00:27:41.380 Awaish Kumar: And…
254 00:27:41.720 ⇒ 00:27:47.690 Awaish Kumar: Second thing I wanted to say is, like, now there is a concept of order and the order item in the new system.
255 00:27:49.360 ⇒ 00:27:52.429 Awaish Kumar: That, we just need to take it in order…
256 00:27:52.430 ⇒ 00:28:00.440 Ashwini Sharma: Let’s have a meeting with Amber, and then let’s go from dashboard, and then down towards, you know, what we need in the model, right?
257 00:28:01.310 ⇒ 00:28:01.940 Awaish Kumar: Yep.
258 00:28:02.100 ⇒ 00:28:05.999 Awaish Kumar: I agree on some things, but, like,
259 00:28:06.630 ⇒ 00:28:14.800 Awaish Kumar: we obviously always have to go from dashboard, we also have to always have to do some revisions. That’s not the point.
260 00:28:14.930 ⇒ 00:28:30.109 Awaish Kumar: what I’m actually, like, trying to do here is come up with something so Amber can play with, and she can then tell you, okay, I actually need a little bit more information, a little bit, one more field, like, things like that.
261 00:28:30.450 ⇒ 00:28:35.610 Awaish Kumar: Instead of, like, telling her to go start from scratch, Are you getting my point?
262 00:28:36.830 ⇒ 00:28:38.620 Ashwini Sharma: Yeah, yeah, I get it.
263 00:28:43.320 ⇒ 00:28:43.960 Awaish Kumar: Yeah.
264 00:28:45.940 ⇒ 00:28:53.570 Ashwini Sharma: Yeah, I mean, let’s utilize this time, meeting with Amber, let’s see the dashboards, what is… what is currently being shown.
265 00:28:53.760 ⇒ 00:28:59.579 Ashwini Sharma: what is of interest to the customer, right? And then we can, you know.
266 00:29:00.290 ⇒ 00:29:10.439 Awaish Kumar: I understand, like, but, like, this is something I also want you to go in and check, like, you can log in to this tableau, and this published Tableau is a…
267 00:29:10.590 ⇒ 00:29:16.020 Awaish Kumar: Is a… the folder where all… these are all the dashboards which are being utilized by a customer.
268 00:29:17.760 ⇒ 00:29:23.799 Ashwini Sharma: Can you share me the link to that, the current dashboard that you just showed? Which one was it?
269 00:29:25.040 ⇒ 00:29:28.560 Awaish Kumar: I showed you this autocross LTV.
270 00:29:28.560 ⇒ 00:29:29.980 Ashwini Sharma: Product rows, okay.
271 00:29:31.490 ⇒ 00:29:32.140 Awaish Kumar: Oh, excuse me.
272 00:29:32.140 ⇒ 00:29:32.700 Ashwini Sharma: Oh my god.
273 00:29:33.920 ⇒ 00:29:36.850 Ashwini Sharma: What is that? Product Rose LTV dashboard, right?
274 00:29:36.850 ⇒ 00:29:37.430 Awaish Kumar: pump.
275 00:29:39.310 ⇒ 00:29:39.900 Ashwini Sharma: approach.
276 00:29:39.900 ⇒ 00:29:40.899 Awaish Kumar: I can trade.
277 00:29:46.490 ⇒ 00:29:48.200 Ashwini Sharma: And these are all in draft, Steve?
278 00:29:51.320 ⇒ 00:29:53.839 Awaish Kumar: These are all in published dashboards folder.
279 00:29:54.230 ⇒ 00:29:55.750 Awaish Kumar: I can send you the link.
280 00:30:10.340 ⇒ 00:30:16.079 Awaish Kumar: And… but you can go in inside the published dashboards, So this is,
281 00:30:20.050 ⇒ 00:30:26.280 Awaish Kumar: This is one of the… dashboard for… Looking at…
282 00:30:27.080 ⇒ 00:30:31.829 Awaish Kumar: like, this is what… when I talk about… Order summary, when we have…
283 00:30:32.790 ⇒ 00:30:37.200 Awaish Kumar: the status is, that’s basically this one to… they want to create out of this.
284 00:30:37.770 ⇒ 00:30:42.330 Awaish Kumar: This is being created from these… these timestamps of when it is…
285 00:30:42.330 ⇒ 00:30:43.759 Ashwini Sharma: This is what Auto Sales Summary?
286 00:30:44.480 ⇒ 00:30:45.180 Awaish Kumar: Yes.
287 00:30:45.310 ⇒ 00:30:50.700 Awaish Kumar: So, it is like… When it was created, when an order is created.
288 00:30:51.050 ⇒ 00:30:56.489 Awaish Kumar: When it is in the sent to pharmacy state, when it is in the actual delivered state.
289 00:30:56.630 ⇒ 00:31:06.259 Awaish Kumar: And when it is in the in-between, what is the time frame from actual order placed to actually delivered? So, based on that, basically, this is generated.
290 00:31:07.150 ⇒ 00:31:11.700 Awaish Kumar: And you can actually look at the… Tables here, data sources.
291 00:31:12.900 ⇒ 00:31:14.549 Awaish Kumar: You see? Order somebody.
292 00:31:17.600 ⇒ 00:31:18.260 Ashwini Sharma: Okay.
293 00:31:19.080 ⇒ 00:31:26.939 Ashwini Sharma: All right, all right, yeah, I’ll utilize this call to identify, like, what grain do we need in the orders summary.
294 00:31:28.380 ⇒ 00:31:29.010 Awaish Kumar: Okay.
295 00:31:29.010 ⇒ 00:31:38.649 Ashwini Sharma: to recreate the dashboards that the customers may be needing, but at least I got a clarity on what exactly is needed for the product sales summary. I’ll refactor that.
296 00:31:39.920 ⇒ 00:31:41.069 Awaish Kumar: Okay, thanks.
297 00:31:41.200 ⇒ 00:31:42.320 Ashwini Sharma: Alright.