Meeting Title: Brainforge x LMNT: Wholesale Reporting Date: 2026-01-12 Meeting participants: Uttam Kumaran, Awaish Kumar, Madison Vausbinder
WEBVTT
1 00:00:07.230 ⇒ 00:00:08.090 Uttam Kumaran: English.
2 00:00:09.630 ⇒ 00:00:10.280 Awaish Kumar: No.
3 00:00:19.690 ⇒ 00:00:22.970 Uttam Kumaran: Hey, Madison. Sorry, it’s just, like, back-to-backs.
4 00:00:22.970 ⇒ 00:00:29.019 Madison Vausbinder: That’s okay, I just wanted to make sure that happened to me the other day, and someone sent the wrong link, so I just wanted to make sure.
5 00:00:29.540 ⇒ 00:00:29.910 Uttam Kumaran: Oh my god.
6 00:00:29.910 ⇒ 00:00:31.370 Madison Vausbinder: the background.
7 00:00:31.660 ⇒ 00:00:35.870 Uttam Kumaran: Thank you, yeah, he’s done… done absolutely nothing all day, except that.
8 00:00:38.680 ⇒ 00:00:51.239 Uttam Kumaran: Cool, it’s great to connect again. I think we’ve made some progress on our end since our last time we chatted, but really, I think this call is probably more tactical just about,
9 00:00:51.450 ⇒ 00:01:05.480 Uttam Kumaran: you know, getting understanding of, like, what the workflows are within the Google Sheet, and then, yeah, we’re basically looking to replicate some of that from the source data and sort of be able to, you know, automate some of those pieces for you.
10 00:01:05.590 ⇒ 00:01:10.489 Uttam Kumaran: So I don’t know, Waish, like, maybe I can… you want to lead? You have kind of an agenda on your side.
11 00:01:11.310 ⇒ 00:01:12.630 Awaish Kumar: Yep.
12 00:01:13.670 ⇒ 00:01:17.210 Awaish Kumar: So yeah, so the last time, like, we kind of discussed…
13 00:01:17.360 ⇒ 00:01:28.830 Awaish Kumar: what are the pain points and why they matter? So today is most like… mostly we will be discussing about, like, what, like, what are different tabs in that CRM sheet.
14 00:01:29.120 ⇒ 00:01:34.069 Awaish Kumar: Spreadsheet, and then, how you are, like, trying to calculate, like.
15 00:01:34.390 ⇒ 00:01:36.869 Awaish Kumar: Trying to, like, segment the customers.
16 00:01:37.420 ⇒ 00:01:41.929 Awaish Kumar: Try, like, the… filling out the tags for each.
17 00:01:43.880 ⇒ 00:01:44.990 Awaish Kumar: And things like that.
18 00:01:46.100 ⇒ 00:01:50.540 Awaish Kumar: So… Yeah, like, maybe if you can, like…
19 00:01:52.030 ⇒ 00:01:57.579 Awaish Kumar: like, give an overview of the spreadsheet, maybe if I could share that.
20 00:01:58.340 ⇒ 00:02:05.519 Awaish Kumar: So we have different tabs, like all partners, and wholesale application data, and, like.
21 00:02:06.520 ⇒ 00:02:11.230 Awaish Kumar: tab is used for what, like, purpose? That’s… Unless we wanted to know.
22 00:02:12.970 ⇒ 00:02:23.030 Madison Vausbinder: Yeah, I guess, just so I’m aware, too, so it sounds like your guys’ focus is on… well, your focus on the CRM is the goal to, like…
23 00:02:23.250 ⇒ 00:02:32.150 Madison Vausbinder: perfect the application CRM process, or to help use that to create, like, a partner spent…
24 00:02:33.100 ⇒ 00:02:33.700 Awaish Kumar: Yeah.
25 00:02:33.700 ⇒ 00:02:40.660 Madison Vausbinder: like, database, I guess, just so I can answer the questions correctly, and show you guys where I’m at. Let me open this, too.
26 00:02:40.660 ⇒ 00:02:46.840 Awaish Kumar: So the purpose is mostly… is, like, use CRM data and Shopify data, and then
27 00:02:47.290 ⇒ 00:02:52.039 Awaish Kumar: top of it. Like, the kind of things that we are trying to do, like, get the historical…
28 00:02:52.370 ⇒ 00:02:58.709 Awaish Kumar: Customers and analyze… And, like, segment them there by bulk buyers, or…
29 00:03:00.240 ⇒ 00:03:09.540 Awaish Kumar: and then find out their LTV, or things like that. And I, like, analyze the churn versus new, and things like that.
30 00:03:09.540 ⇒ 00:03:12.020 Uttam Kumaran: It’s less of, like, replacing the input step.
31 00:03:12.020 ⇒ 00:03:12.500 Madison Vausbinder: growth.
32 00:03:12.500 ⇒ 00:03:24.720 Uttam Kumaran: more about, like, taking some of that input manual data, combining it with a real Shopify data, to then produce, like, the reporting on top is, like, really our… our goal here.
33 00:03:24.720 ⇒ 00:03:31.069 Madison Vausbinder: Okay, yeah, no, totally makes sense. I was just asking, because I’m also working on cleaning up some of the…
34 00:03:31.130 ⇒ 00:03:47.220 Madison Vausbinder: way that that current CRM for application tracking works, because now we are bringing on all three segments. So, like I said, just let me know where I’m helpful, and I can explain. Let me just let me know.
35 00:03:47.290 ⇒ 00:03:57.050 Madison Vausbinder: It’s, I can help explain the spreadsheet, or answer specific questions, or, whatever’s best for you guys.
36 00:03:58.310 ⇒ 00:04:04.480 Uttam Kumaran: Yeah, I think what, which, which, which sheets in the spreadsheet at which you want to start on first?
37 00:04:05.830 ⇒ 00:04:16.909 Awaish Kumar: like, I want to, like, first of all, understand, like, this sheet has a lot of tabs, so which one are actual… the active partners, or the… like, the… is all partners…
38 00:04:17.160 ⇒ 00:04:19.620 Awaish Kumar: Give us the correct result.
39 00:04:20.740 ⇒ 00:04:31.420 Madison Vausbinder: Got it. So, I think that’s where the gap is. Like, for me, this is a tracker of applications that we’ve had from basically 2023 to 20…
40 00:04:31.470 ⇒ 00:04:56.459 Madison Vausbinder: 25, let’s say, but it’s still going. You know, let’s cut off there. And those are applications of people and emails that have applied, and then whether or not we’ve set them up or emailed them. It’s not essentially a list of all of our partners, and in fact, it has… it doesn’t have most of our partners on there, because anyone who’s been onboarded before 2023
41 00:04:56.460 ⇒ 00:05:06.060 Madison Vausbinder: or anybody who’s changed emails is not included on this list. So what I’m trying to figure out is a way to have that data
42 00:05:07.150 ⇒ 00:05:21.069 Madison Vausbinder: essentially be… like, the only reason I like this data is because it has information from their application that I would, in theory, want to go into their, like, Shopify account. Yes. But Shopify is the source of the actual partners.
43 00:05:21.840 ⇒ 00:05:23.740 Uttam Kumaran: So,
44 00:05:24.080 ⇒ 00:05:35.170 Uttam Kumaran: like, I guess, is… would… what part of this data is helpful in addition to the Shopify data, or is all of this should basically be there as well? Like, I guess, what isn’t there?
45 00:05:35.300 ⇒ 00:05:58.239 Madison Vausbinder: Yeah, we’ve… we’ve done a lot recently where we have meta fields going in for the company name, so I think that’s one of the biggest gaps, is since we’re not using Shopify B2B, people are in as individuals, not companies. So, the company name’s really helpful. We’ve started trying to import that into the account creation, but again, it’s only for people, like, 2024 onward.
46 00:05:58.240 ⇒ 00:06:20.700 Madison Vausbinder: the address that they apply for is sometimes helpful, because if we get someone who’s suddenly changed addresses to a home address, that’s helpful. If we have their business category, because they’re answering that in the application, that’s super helpful. So, I think it’s… we’ve tried to import it in through, meta fields where we can,
47 00:06:21.400 ⇒ 00:06:33.470 Madison Vausbinder: The other thing is, like, onboard date, so Shopify, since we… our accounts are, like, adding tags, so that we add a wholesale 270 tag, it’s not creating a new account. I don’t have, like.
48 00:06:33.900 ⇒ 00:06:40.719 Madison Vausbinder: detailed records on everybody’s onboard date. So that’s where I get that from, is their application.
49 00:06:41.150 ⇒ 00:06:52.199 Madison Vausbinder: And one thing, I think it may be helpful not to confuse, because it’s not, set up yet, but I’m try… I can show you what I’m looking at from a simplified application.
50 00:06:52.200 ⇒ 00:07:02.149 Madison Vausbinder: To show you kind of where… what I would actually be looking at data-wise, and what would be helpful for me. Just because I think this is, like, a lot of things in one that, like…
51 00:07:02.150 ⇒ 00:07:10.430 Uttam Kumaran: be trimmed down. Yeah, so the kind of the… what we’re gonna do is we’re gonna pull… we’re gonna… we’re pulling this into Snowflake, the data warehouse, but we’re just gonna take…
52 00:07:10.570 ⇒ 00:07:19.700 Uttam Kumaran: the pieces that are not in Shopify from here. So if some of it’s duplicated, we’re gonna go to Shopify as a source of truth.
53 00:07:19.700 ⇒ 00:07:33.980 Uttam Kumaran: Think of it as, like, we’re just basically joining these together, but there are gonna be pieces, like, whatever is not there that is living here, is gonna be the source of truth for that information, like, potentially onboard date, and so we’ll want to make sure that we bring that in from this side.
54 00:07:34.210 ⇒ 00:07:35.110 Madison Vausbinder: Yeah.
55 00:07:35.110 ⇒ 00:07:41.250 Uttam Kumaran: Give us sort of a cohesive view of, like, all information about… about these, like, these partners.
56 00:07:42.340 ⇒ 00:08:02.780 Madison Vausbinder: Yeah, that’s perfect. And really, like, the main data that we’re missing is the stuff in Shopify. Like, I think this is supplemental, and it’s stuff that I like to tie in, but when I’m looking for partner information, it’s what’s in Shopify, and the biggest gap right now is every time I want that, I have to go download a report that’s specifically customized to what I want.
57 00:08:02.960 ⇒ 00:08:03.840 Uttam Kumaran: Okay, okay.
58 00:08:04.200 ⇒ 00:08:10.360 Awaish Kumar: Okay, and how do you identify the wholesale partners in Shopify? Is it using the tag wholesale?
59 00:08:10.360 ⇒ 00:08:20.880 Madison Vausbinder: So we have a tag that’s wholesale 270 that all our partners have, and now that we have 3 segments, everyone has an additional tag. So we have
60 00:08:20.880 ⇒ 00:08:45.850 Madison Vausbinder: everyone has Wholesale 270, but then some have Wholesale 270 in Trusted Health, some have Wholesale 270 in Bulk Buyer, some have Wholesale 270 in Specialty Retail. So they all… it’s all tags, but then, like, somebody, for example, if you made an account 2 years ago for personal purchases, but you used your company email, and then you reached out to apply to, like, carry it at your company, sometimes we’re just adding a tag onto that account.
61 00:08:45.850 ⇒ 00:08:58.599 Madison Vausbinder: So the account may look like it’s 2 years old, but we actually only added their wholesale status 6 months ago. And Shopify doesn’t track, from what I found, when the tag was added, so it’s a little bit complicated sometimes.
62 00:08:58.960 ⇒ 00:09:02.029 Awaish Kumar: Okay, so there are also tags on the…
63 00:09:02.410 ⇒ 00:09:07.520 Awaish Kumar: orders as well. Yes, correct. I’m not sure if that can help, like, somebody…
64 00:09:08.210 ⇒ 00:09:15.570 Awaish Kumar: ordered before… Like, like, 2 years earlier, it might not be tagged with wholesale or something.
65 00:09:15.570 ⇒ 00:09:30.290 Madison Vausbinder: Exactly. Yeah, the tag is wholesale, and that’s usually how I’ll figure out first purchase date for people, or first wholesale purchase date. I just end up doing it through formulas in Excel, so wherever we could automate, that would be better.
66 00:09:30.940 ⇒ 00:09:31.909 Awaish Kumar: Okay. Go ahead.
67 00:09:35.970 ⇒ 00:09:40.040 Uttam Kumaran: Yeah, and in Shopify, we’ll have the… we’ll have the tag creation dates as well.
68 00:09:40.210 ⇒ 00:09:41.949 Madison Vausbinder: Cool. Yeah, that would be awesome.
69 00:09:43.500 ⇒ 00:10:03.590 Madison Vausbinder: Yeah, that’s… I think that’s a hard part for me, is they don’t track that, just because it’s, you know, it’s… you have to play a guessing game, or do it off the first order date. And then it’s like, I don’t know if they actually, like, got onboarded a year ago and just didn’t place an order, so, that’s, like, the only reason that all this application data really is helpful. It’s because it’s, like, gives me a source of
70 00:10:03.590 ⇒ 00:10:04.819 Madison Vausbinder: when they onboarded?
71 00:10:04.820 ⇒ 00:10:05.570 Uttam Kumaran: Yeah, onboarded, yeah.
72 00:10:05.770 ⇒ 00:10:11.350 Madison Vausbinder: Most of the stuff we care about is more what’s happening after they actually become a partner.
73 00:10:11.350 ⇒ 00:10:11.860 Uttam Kumaran: Okay.
74 00:10:11.860 ⇒ 00:10:14.250 Madison Vausbinder: Or we know about them as a partner.
75 00:10:14.790 ⇒ 00:10:21.510 Awaish Kumar: So, from these Google Sheets, we only use, like, wholesale application data, mostly. All the columns from here.
76 00:10:21.840 ⇒ 00:10:22.690 Awaish Kumar: for…
77 00:10:24.590 ⇒ 00:10:28.610 Madison Vausbinder: For your guys’ reference, yes. Everything else has, like, purposes that…
78 00:10:28.680 ⇒ 00:10:45.849 Madison Vausbinder: are probably irrelevant to what you’re looking at. So, ideally they would trim down. I’d say the only one that I like to keep track of, and, I don’t know if it would make sense in the thing you’re working on, or how it’ll end, but is, like, our monthly onboarding numbers, so how many people are
79 00:10:46.510 ⇒ 00:10:55.919 Madison Vausbinder: like, joining the program, like, how many new wholesale tag accounts are created, and how many people… how many applications we got. So, some of.
80 00:10:55.920 ⇒ 00:11:01.849 Uttam Kumaran: Yeah, so this monthly partner account is exactly the thing we would replicate and kind of get out of… take out of this, basically.
81 00:11:02.280 ⇒ 00:11:02.650 Madison Vausbinder: Exactly.
82 00:11:02.650 ⇒ 00:11:05.660 Awaish Kumar: So this is… okay, this is based on Shopify data.
83 00:11:06.410 ⇒ 00:11:19.979 Madison Vausbinder: No, that’s all manual, so it’s literally based on how many people, have added. If you look at the, main application tab, we add an onboard date when we onboard them, so it’s literally counting how many people were onboarded in that month.
84 00:11:20.730 ⇒ 00:11:21.720 Awaish Kumar: Okay, it’s…
85 00:11:21.990 ⇒ 00:11:24.509 Madison Vausbinder: I mean, that’s a manual ad from our team.
86 00:11:25.630 ⇒ 00:11:26.380 Awaish Kumar: Okay.
87 00:11:26.810 ⇒ 00:11:29.759 Awaish Kumar: So this is coming from, this sheet.
88 00:11:30.610 ⇒ 00:11:38.099 Awaish Kumar: And one thing you mentioned, that you’re not able to match CRM data with the Shopify.
89 00:11:38.570 ⇒ 00:11:43.610 Awaish Kumar: For, for, like, more than 2,000… Something partners.
90 00:11:46.640 ⇒ 00:11:53.529 Madison Vausbinder: Yes, I don’t know… I guess that’s a guess number,
91 00:11:53.870 ⇒ 00:12:02.539 Madison Vausbinder: It is definitely high, and it probably is in the thousands, but it’s because most of the time, people will apply
92 00:12:02.670 ⇒ 00:12:12.560 Madison Vausbinder: And then the email changes along the way, which is the most difficult thing, because we let people change accounts, like, if their manager changes, or,
93 00:12:12.660 ⇒ 00:12:24.180 Madison Vausbinder: You know, things like that. So we have a lot of partners that are no longer tied to an original application because we don’t know who they were at that time. One thing I’m starting…
94 00:12:24.180 ⇒ 00:12:37.889 Madison Vausbinder: to… hopefully this month, we’ll… our team will start adding customer ID into one of the columns, so we can go back and track. I would love to have had that happen 5 years ago, I think it would have made it a lot easier, but, I think that’s, like.
95 00:12:38.500 ⇒ 00:12:46.109 Madison Vausbinder: exactly what I’m trying to figure out, is, like, how can… customer ID is always my source of truth, not emails, so wherever that can match.
96 00:12:46.460 ⇒ 00:12:49.670 Uttam Kumaran: So I think, Awash, we’ll probably have to do… we can probably…
97 00:12:49.970 ⇒ 00:12:55.870 Uttam Kumaran: probably speed some of that up by doing email, and then getting the ID and adding it back to the spreadsheet.
98 00:12:55.870 ⇒ 00:12:56.740 Madison Vausbinder: Brilliant.
99 00:12:57.070 ⇒ 00:12:59.049 Madison Vausbinder: Yeah, true, that would be helpful.
100 00:12:59.050 ⇒ 00:13:05.340 Uttam Kumaran: And then forever, that way, at least, you’ll find something that maybe we can’t match, and then those are the manual ones versus, like, everything.
101 00:13:06.020 ⇒ 00:13:26.079 Madison Vausbinder: Yeah, exactly, and I’ve done that, a couple times when we were trying to figure out… I don’t know how much we dove into, like, our segmentation we did prior, but, like, when we met back to try and guess everyone’s categories. So I have done that, and it’s helpful, but again, it’s… our data’s kind of unclean, especially pre-2023, so it’s…
102 00:13:26.080 ⇒ 00:13:42.320 Madison Vausbinder: Also, all that to say, like, I still am mostly focused now on the data of people as partners, not so much tying in the application, but I want to have good practices going forward for the applications that come in, so that we don’t have that issue later down the road.
103 00:13:42.760 ⇒ 00:13:43.190 Uttam Kumaran: No fan.
104 00:13:43.190 ⇒ 00:13:43.740 Awaish Kumar: Good.
105 00:13:44.210 ⇒ 00:13:54.049 Awaish Kumar: And, like, so, for example, you mentioned that the customer had tagged wholesale, and then recently they have been added with the extra tags.
106 00:13:54.050 ⇒ 00:13:54.740 Madison Vausbinder: Hmm.
107 00:13:55.210 ⇒ 00:13:59.429 Awaish Kumar: specialty retail, etc. So, like, do we want to
108 00:14:00.020 ⇒ 00:14:07.260 Awaish Kumar: Like, have that journey of the customers that they have moved from Like, different tags, like…
109 00:14:07.690 ⇒ 00:14:11.440 Awaish Kumar: From wholesale to service-specific one, or…
110 00:14:11.560 ⇒ 00:14:16.549 Awaish Kumar: Yeah, we are okay with having just the latest recent category they are in.
111 00:14:17.320 ⇒ 00:14:30.020 Madison Vausbinder: Yeah, I think the most recent category they’re in, most of… ideally, like, no one should change going forward, because they’ll be onboarded with the correct one. So it’s mostly whenever the Wholesale 270 tag is added.
112 00:14:31.820 ⇒ 00:14:34.340 Awaish Kumar: Okay. Wholesale status for all of them.
113 00:14:37.610 ⇒ 00:14:42.949 Awaish Kumar: Okay, but, like, I’m also, like, asking if… a wholesale partner.
114 00:14:43.350 ⇒ 00:14:45.950 Awaish Kumar: Which is assigned a specialty retail tag.
115 00:14:47.130 ⇒ 00:14:50.670 Awaish Kumar: So it should just be as a specialty retail order.
116 00:14:51.300 ⇒ 00:14:52.290 Madison Vausbinder: Right.
117 00:14:52.290 ⇒ 00:14:52.780 Awaish Kumar: Categorize.
118 00:14:52.780 ⇒ 00:14:59.230 Madison Vausbinder: Correct. They’ll still have the wholesale tag. So I think there’s, like, two ways we look at data. There’s all partners, which is…
119 00:14:59.230 ⇒ 00:15:13.009 Madison Vausbinder: everyone with the wholesale 270 tag, and then there would be which segment they’re in, which would be the other one. In theory, everyone, at least going forward, should always receive both tags at the same time, so, like, there shouldn’t be a difference in their date of onboarding with that.
120 00:15:13.010 ⇒ 00:15:22.889 Madison Vausbinder: Some people will change throughout the time, but it shouldn’t really. But yeah, the specialty retail tag is more, like, defining what segment they’re in.
121 00:15:23.420 ⇒ 00:15:23.980 Awaish Kumar: Great.
122 00:15:24.400 ⇒ 00:15:28.340 Awaish Kumar: And if there are orders made before they become a…
123 00:15:28.600 ⇒ 00:15:33.289 Awaish Kumar: wholesale partner? Like, do we want to… Like, filter those out?
124 00:15:33.560 ⇒ 00:15:35.570 Madison Vausbinder: Yeah, if we could, preferably.
125 00:15:36.370 ⇒ 00:15:37.010 Awaish Kumar: Okay.
126 00:15:40.850 ⇒ 00:15:46.460 Awaish Kumar: Yeah, so… Apart from that, I have some…
127 00:15:48.760 ⇒ 00:15:54.010 Awaish Kumar: Yeah, so, like, these are questions more, like, on the reporting side.
128 00:15:54.790 ⇒ 00:15:55.929 Awaish Kumar: Do you have any…
129 00:15:56.070 ⇒ 00:16:03.860 Awaish Kumar: sheet or something which you are creating as part of your reporting exercise, if you can, like, show us that or share.
130 00:16:04.880 ⇒ 00:16:08.230 Awaish Kumar: Nice. So we are going to kind of replicate that.
131 00:16:08.720 ⇒ 00:16:11.810 Madison Vausbinder: Yeah. Like, what would be mo-
132 00:16:11.930 ⇒ 00:16:22.470 Madison Vausbinder: most helpful, like, just an order one, or you want… I just pulled recently, when I was doing the segmentation, a… it’s, like, order data and segment and company.
133 00:16:23.030 ⇒ 00:16:28.149 Uttam Kumaran: Yeah, I guess what’s… yeah, but I guess more importantly is also, like, what is the output you’re going for?
134 00:16:28.520 ⇒ 00:16:40.030 Uttam Kumaran: on your… especially your shared, we just saw that one tab with some reporting, but yeah, more… more… we’re gonna have this access to the kind of the same things you have. More, I’m thinking, like, what is the…
135 00:16:40.170 ⇒ 00:16:50.310 Uttam Kumaran: sort of structure of your final table, or, like, your final analysis. Because if you scroll up a ways, basically, we’re gonna drive to creating, like, a few tables that can…
136 00:16:50.430 ⇒ 00:17:01.280 Uttam Kumaran: sort of support this, right? So, like, this is an example customer table that will have, like, when was their first date, when were they onboarded, and then all these, like, summary metrics.
137 00:17:01.540 ⇒ 00:17:07.819 Uttam Kumaran: So, our job is, we’re just making sure that this satisfies, like, any of your cuts, basically.
138 00:17:08.250 ⇒ 00:17:12.510 Madison Vausbinder: Yeah, let me show you real quick.
139 00:17:14.980 ⇒ 00:17:22.469 Madison Vausbinder: I would say, since it’s manual, it changes every time, but, like, I just did a data poll for 2025.
140 00:17:22.619 ⇒ 00:17:23.049 Uttam Kumaran: Brave.
141 00:17:23.069 ⇒ 00:17:39.160 Madison Vausbinder: What I did, and this… most of them do look like this. This is probably the most detailed version, but it’s, like, company name, and customer ID, which is the main thing I pull by. This is their segment that we’ve defined.
142 00:17:39.500 ⇒ 00:17:53.499 Madison Vausbinder: lifetime spend is usually in there. Account creation date. Like I said, I don’t have the ability to do the wholesale tag, so right now I just go off of account creation, but I’m not… I can’t, like, bank on that being a wholesale. It could be a normal account.
143 00:17:53.720 ⇒ 00:18:18.689 Madison Vausbinder: Last order date, and then I usually do, like, spend by year, because that’s the threshold we’ll usually do for different sends and, like, flavor launches. Another thing that I’d like to do that’s not on here is, like, sparkling spend versus drink mix spend, so we have two different product categories. So, like, somebody who maybe is, like, one of our top purchasers may have never even tried our sparkling products, so I like to see that.
144 00:18:18.690 ⇒ 00:18:23.919 Madison Vausbinder: And then in this one, I have, like, growth year over year.
145 00:18:24.010 ⇒ 00:18:31.619 Madison Vausbinder: Which is just formula-based, so that’s something that’s helpful as well. And then I have, like, dashboards on here.
146 00:18:31.620 ⇒ 00:18:50.870 Madison Vausbinder: Like, this one brings in sparkling spend, but this is, like, spend by month. But I have dashboards on here that are more segment-based, so, like, being able to look at just specialty retail as a whole, or, like, bulk buyer as a whole is super helpful as well, so that we can compare performance across the segments.
147 00:18:52.130 ⇒ 00:18:54.209 Madison Vausbinder: Does that answer most questions?
148 00:18:54.480 ⇒ 00:18:58.140 Awaish Kumar: Yeah, so it… yeah, if you can share that sheet with us.
149 00:18:58.140 ⇒ 00:18:58.930 Madison Vausbinder: Yeah.
150 00:18:59.060 ⇒ 00:18:59.940 Madison Vausbinder: Definitely.
151 00:19:00.420 ⇒ 00:19:10.929 Madison Vausbinder: And just so you know, like, the way I pull this now is I took a… basically just a data pull from Shopify of…
152 00:19:12.200 ⇒ 00:19:20.269 Madison Vausbinder: all partners with Wholesale 270 and their spend, and then I manually put in formulas for their segment and their company name from our segment.
153 00:19:20.270 ⇒ 00:19:20.889 Uttam Kumaran: Okay.
154 00:19:20.890 ⇒ 00:19:28.720 Madison Vausbinder: and then I have a different tab that’s all order data, and then I did formulas to pull in the order data. So that’s what I say when I mean it’s manual, like, this isn’t.
155 00:19:28.720 ⇒ 00:19:41.909 Uttam Kumaran: So this is… so this is all, like, that… both… both of those pieces of logic, like the segmentation logic and the name logic, we’re gonna just rep… we’re gonna write in SQL, so then you won’t have to just… you just basically won’t have to do this again.
156 00:19:41.910 ⇒ 00:19:42.300 Madison Vausbinder: Yeah.
157 00:19:42.300 ⇒ 00:19:48.900 Uttam Kumaran: I mean, as, like, as segments come up or change, it’s really easy for us to do that. So you’ll just have, like, a live version of this from, like.
158 00:19:49.220 ⇒ 00:20:00.490 Uttam Kumaran: basically any point in time, so that’s all we’re basically replicating and driving… driving to, like… and then we’ll give you sort of a wide table, and then you can create the summaries directly on top of that.
159 00:20:00.790 ⇒ 00:20:22.289 Madison Vausbinder: Yeah, I mean, that’s perfect. And, like, these segments are all based… like, they… this is what their tag is now. This was… when I did this, it was before we had the tags in there. So, like, I think, too, and we have a company, Metafield, on Shopify, my goal would be to import all the old ones so we have the company names on there. And so, like, almost making it like Shopify always has this data on the account.
160 00:20:22.290 ⇒ 00:20:22.800 Uttam Kumaran: The latest.
161 00:20:22.800 ⇒ 00:20:23.400 Madison Vausbinder: That way, it makes.
162 00:20:23.400 ⇒ 00:20:23.880 Uttam Kumaran: Yeah.
163 00:20:23.880 ⇒ 00:20:32.010 Madison Vausbinder: to manage. But yeah, it’s usually, like, I don’t look at super complicated things, it’s just I have to pull 3 different reports to get to this.
164 00:20:32.350 ⇒ 00:20:33.919 Madison Vausbinder: Jake’s been summary.
165 00:20:34.220 ⇒ 00:20:35.120 Uttam Kumaran: Yeah, okay.
166 00:20:35.710 ⇒ 00:20:36.109 Awaish Kumar: So there’.
167 00:20:36.110 ⇒ 00:20:38.310 Uttam Kumaran: So yeah, if we can… Got it, got it.
168 00:20:39.210 ⇒ 00:20:43.330 Awaish Kumar: So these company names are not coming from Shopify, they are coming from CRM.
169 00:20:43.840 ⇒ 00:21:06.719 Madison Vausbinder: Yeah, and some of them are in Shopify, but most of them are manual, which, like, are from the CRM, the application CRM, or my segmentation data. So, like, right now, they just live in a spreadsheet for me, but I think probably the best step is importing them into Shopify. I just don’t want to override current ones, so I have to find space to do that, where it doesn’t, override any that may be different, so…
170 00:21:06.720 ⇒ 00:21:10.319 Uttam Kumaran: Yeah, so which probably we can pull from the CRM for now.
171 00:21:10.320 ⇒ 00:21:12.119 Madison Vausbinder: And then, eventually.
172 00:21:12.180 ⇒ 00:21:18.569 Uttam Kumaran: Like, once we basically update the meta fields, we can just pull from there and sort of
173 00:21:18.670 ⇒ 00:21:22.909 Uttam Kumaran: cut the CRM off, basically, as, like, the source of any information.
174 00:21:23.330 ⇒ 00:21:23.670 Awaish Kumar: No.
175 00:21:23.670 ⇒ 00:21:48.399 Madison Vausbinder: Yeah, and I almost think for the data you guys are doing, this is my, segmentation data thing. So this is customer ID, company, and segment. Like, to me, this is way more of a source of truth than the application, because we’ve done so much research into this. Okay. As opposed to, like, the application doesn’t have segment, it’s just, like, a loose category that the person chose. So I almost think that
176 00:21:48.400 ⇒ 00:21:51.039 Madison Vausbinder: as much as we live in this CRM.
177 00:21:51.040 ⇒ 00:21:51.400 Uttam Kumaran: Yep.
178 00:21:51.400 ⇒ 00:21:55.830 Madison Vausbinder: I think it really relates to as much of what you’re doing as, like, the segmentation data.
179 00:21:56.100 ⇒ 00:21:59.870 Uttam Kumaran: Okay, so yeah, I think if we can get access to the data pool and then the segmentation.
180 00:21:59.870 ⇒ 00:22:00.870 Madison Vausbinder: -
181 00:22:00.870 ⇒ 00:22:01.649 Uttam Kumaran: We’ll bring that.
182 00:22:01.650 ⇒ 00:22:03.540 Madison Vausbinder: This one’s on here, so I’ll just give you this.
183 00:22:03.540 ⇒ 00:22:04.569 Uttam Kumaran: Oh, okay, great.
184 00:22:04.570 ⇒ 00:22:05.840 Madison Vausbinder: All imported into it.
185 00:22:05.840 ⇒ 00:22:06.459 Uttam Kumaran: Okay, okay, I see.
186 00:22:06.460 ⇒ 00:22:07.260 Madison Vausbinder: Yes, ma’am.
187 00:22:07.760 ⇒ 00:22:09.479 Madison Vausbinder: So I think that’ll be the most helpful.
188 00:22:09.480 ⇒ 00:22:15.280 Uttam Kumaran: And is the segmentation, like… is it… it’s not rule-based right now, right? It’s all… it’s kind of like…
189 00:22:16.060 ⇒ 00:22:19.470 Uttam Kumaran: As someone gets added, you then manually put the right… put the segment.
190 00:22:19.940 ⇒ 00:22:24.039 Madison Vausbinder: Yeah, it’s based off what category they’re in, so it’s…
191 00:22:24.040 ⇒ 00:22:24.450 Uttam Kumaran: Okay.
192 00:22:24.450 ⇒ 00:22:32.220 Madison Vausbinder: loosely rule-based based on what they choose in the application, but then it can change based on conversation. So it’s like… it’s…
193 00:22:32.360 ⇒ 00:22:34.859 Madison Vausbinder: Human-chosen category.
194 00:22:34.860 ⇒ 00:22:35.260 Uttam Kumaran: Yeah.
195 00:22:35.940 ⇒ 00:22:39.380 Uttam Kumaran: Are you planning on managing that in Shopify eventually, too?
196 00:22:40.010 ⇒ 00:22:43.510 Madison Vausbinder: Well, it’ll just always be the tag by segment, and so…
197 00:22:43.510 ⇒ 00:22:44.520 Uttam Kumaran: Oh, yeah, yeah, yeah.
198 00:22:44.520 ⇒ 00:22:55.190 Madison Vausbinder: Can we switch to Shopify B2B at some point? I don’t know what that’ll look like. I don’t know if they’re tagged the same, but it will always live in Shopify, as some version of the tag.
199 00:22:55.550 ⇒ 00:22:56.170 Uttam Kumaran: Okay.
200 00:22:56.590 ⇒ 00:22:59.730 Awaish Kumar: And this account creation date, where is it coming from?
201 00:23:00.640 ⇒ 00:23:02.420 Madison Vausbinder: Sorry, which part of it?
202 00:23:02.770 ⇒ 00:23:04.520 Awaish Kumar: Account creation date.
203 00:23:04.520 ⇒ 00:23:19.029 Madison Vausbinder: Oh, yeah, so that’s the actual Shopify account creation date that I exported, so it’s, like, the customer account. I just, like, make this note for people looking at it, because it doesn’t… it’s not when the wholesale status tag was added.
204 00:23:19.030 ⇒ 00:23:19.660 Awaish Kumar: Okay.
205 00:23:19.660 ⇒ 00:23:20.500 Madison Vausbinder: creation.
206 00:23:21.620 ⇒ 00:23:29.389 Madison Vausbinder: So I would ideally have the tag added date, not the wholesale, or not the account creation date.
207 00:23:30.130 ⇒ 00:23:32.639 Awaish Kumar: For a customer, if there are, like.
208 00:23:32.940 ⇒ 00:23:41.949 Awaish Kumar: 12 orders, and at the 12th order, we see a tag. So we can select that date, right? This is when a tag was added for this customer.
209 00:23:42.070 ⇒ 00:24:00.979 Madison Vausbinder: Yes, except that technically would be their first wholesale order date, not their, like, wholesale account creation date. And I only say that because we… and I don’t use it too much right now, but, like, we used to look at how long is it taking someone to place their first order from when we add their tag? And it could be 6 months to a year.
210 00:24:00.980 ⇒ 00:24:09.640 Madison Vausbinder: So that would be helpful information. But compared to account creation date, yeah, that’s probably more helpful at this moment.
211 00:24:12.740 ⇒ 00:24:13.450 Awaish Kumar: Okay.
212 00:24:16.730 ⇒ 00:24:33.819 Madison Vausbinder: Yeah, I think that’s most of it on here. I… I, like, at some point would love to have… I think, like, my goal is anything that I find important from the application is fed into Shopify so that it’s as a meta field, and then it’s just, like, this is the sort of… It’s then just… yeah.
213 00:24:34.170 ⇒ 00:24:52.000 Uttam Kumaran: Exactly, so I think, like, that’s where we’re not… it’s… that’s still probably going to be a manual process, but, like, you hopefully have more time just to, like, nail that. Then that will flow through all the reporting, basically. That way, also, you can just change things if things change, and it’ll flow through. I think we’ll get the nuances of, like.
214 00:24:52.680 ⇒ 00:25:01.210 Uttam Kumaran: if they change tags, like, what to do historically, but all this seems, like, totally doable for us. I think we’re pretty close.
215 00:25:01.760 ⇒ 00:25:02.330 Awaish Kumar: Okay.
216 00:25:02.330 ⇒ 00:25:26.809 Madison Vausbinder: Just for your context, because I saw a question on there that was, like, what makes the categories different. They are category-based, but one of the main differences is bulk buyer, or people not reselling, and then the other two categories are reselling, but different types. So, like, when someone switches, it usually would be from, like, bulk buyer to trusted health, let’s say, because maybe they just want to supply for their employees versus sell it.
217 00:25:26.810 ⇒ 00:25:30.179 Madison Vausbinder: So it’s never spend-based, it’s not like, what product.
218 00:25:30.180 ⇒ 00:25:30.750 Uttam Kumaran: Okay.
219 00:25:30.750 ⇒ 00:25:47.450 Madison Vausbinder: It’s not location, it’s just, like, their category, and really the only time someone should change is, like, if, I don’t know, like, a pharmacy was buying for their employees, and suddenly they want to resell it. So then they would go into a specialty retail instead of being bulk buyer.
220 00:25:47.680 ⇒ 00:25:48.600 Uttam Kumaran: Okay, okay.
221 00:25:49.440 ⇒ 00:25:52.450 Awaish Kumar: Okay, now I have a few questions on the…
222 00:25:52.450 ⇒ 00:25:53.210 Madison Vausbinder: Yeah.
223 00:25:53.580 ⇒ 00:26:02.529 Awaish Kumar: Like, when we say revenue, what revenue you are, like, calculating here? Is it across, net, or…
224 00:26:02.930 ⇒ 00:26:19.670 Madison Vausbinder: To be honest, I just… it just depends what I’m pulling. I think usually gross, I… it doesn’t… for us, since we don’t charge shipping, it’s kind of usually roughly the same. I usually don’t pay attention to taxes, so I’ll take that out, but everything else is pretty the same… much the same.
225 00:26:20.230 ⇒ 00:26:23.310 Awaish Kumar: So, like, gross minus taxes.
226 00:26:24.070 ⇒ 00:26:31.329 Madison Vausbinder: Yes, and I don’t know if that’s technically just a different thing, but I think I usually use gross.
227 00:26:32.300 ⇒ 00:26:37.530 Awaish Kumar: So we take values, like, without the taxes, but then whatever is coming from Shopify.
228 00:26:37.930 ⇒ 00:26:40.330 Awaish Kumar: If it is before Texas or after a year.
229 00:26:41.010 ⇒ 00:26:50.889 Madison Vausbinder: I… yeah, usually it doesn’t… it doesn’t really matter. I think I usually pull from Shopify the one without, but I’ll usually have both on there, just in case. But…
230 00:26:51.500 ⇒ 00:26:57.350 Awaish Kumar: Okay, and then, like, in terms of, like, do we offer any discounts or anything?
231 00:26:58.060 ⇒ 00:27:13.299 Madison Vausbinder: We don’t, so we’re… we’re easy in that way that we’re all on… all of our partners are on the same pricing. Granted, each segment is on different pricing, and now specialty retail has different product lines than… not product, but SKU lines.
232 00:27:13.300 ⇒ 00:27:18.140 Madison Vausbinder: than the other two. But all the pricing is the same across the segments, so…
233 00:27:18.140 ⇒ 00:27:24.650 Madison Vausbinder: Makes it easy. So there’s no discounts, there’s no promotions, they never have any different pricing.
234 00:27:25.000 ⇒ 00:27:25.510 Uttam Kumaran: Okay, good.
235 00:27:26.060 ⇒ 00:27:31.979 Awaish Kumar: One thing I recall is, like, that, like, I think refrigerator… refrigerator cost was…
236 00:27:33.020 ⇒ 00:27:35.690 Awaish Kumar: Shows up as the revenue, or something like that.
237 00:27:37.550 ⇒ 00:27:49.629 Madison Vausbinder: I don’t know. Our refrigerators are technically a gift, but they pay for shipping, so it goes into the shipping cost, which is different, because we don’t charge shipping on anything else.
238 00:27:50.230 ⇒ 00:27:50.990 Awaish Kumar: Okay.
239 00:27:51.440 ⇒ 00:27:59.029 Madison Vausbinder: But I don’t know, like, usually that won’t show up on the revenue without shipping in it, but, it’s not technically revenue.
240 00:27:59.850 ⇒ 00:28:05.460 Madison Vausbinder: From, like, my perspective, when I’m looking at a partner that is spending, and how much they’ve spent with us.
241 00:28:06.650 ⇒ 00:28:12.580 Awaish Kumar: Okay, so we are excluding anything related to refrigerators from our reports, right?
242 00:28:12.580 ⇒ 00:28:20.660 Madison Vausbinder: I think so, yeah. We… I like to look at who has one, but besides that, it wouldn’t be really attributed into their spend.
243 00:28:21.270 ⇒ 00:28:24.080 Awaish Kumar: Okay, and apart from revenue, is there…
244 00:28:25.130 ⇒ 00:28:29.159 Awaish Kumar: Like, anything we are… any dashboards on cost, like…
245 00:28:30.340 ⇒ 00:28:33.599 Awaish Kumar: Like, refrigerator costs of maintaining some…
246 00:28:33.950 ⇒ 00:28:41.080 Madison Vausbinder: I mean, refrigerators in our displays are probably the only things that…
247 00:28:41.830 ⇒ 00:28:50.050 Madison Vausbinder: would vary, and most people are getting them, so I think it’d be interesting to see if we had… I don’t even know if we have a cost assigned to them, so I don’t
248 00:28:50.570 ⇒ 00:28:52.590 Madison Vausbinder: I… probably not,
249 00:28:52.950 ⇒ 00:29:06.880 Madison Vausbinder: But we… and for us, since our products are… since we charge the same across all, we don’t usually evaluate our partners based on, how much, like, the cost is. It’s more so, but just based on, like, how they’re… how much they’ve spent and, where we can support them.
250 00:29:08.960 ⇒ 00:29:11.570 Awaish Kumar: And one last question, like, in the…
251 00:29:11.920 ⇒ 00:29:16.030 Awaish Kumar: Reports to show, like, it was saying some spend.
252 00:29:16.250 ⇒ 00:29:16.920 Madison Vausbinder: Yeah.
253 00:29:17.200 ⇒ 00:29:20.329 Awaish Kumar: So, is there, like, marketing spend, or…
254 00:29:21.020 ⇒ 00:29:26.300 Madison Vausbinder: No, sorry, that’s the customer’s spend. Okay. So, spend on product.
255 00:29:26.630 ⇒ 00:29:28.170 Awaish Kumar: Okay, it’s revenue.
256 00:29:28.330 ⇒ 00:29:29.310 Madison Vausbinder: Yeah.
257 00:29:30.760 ⇒ 00:29:33.209 Awaish Kumar: So we are not doing any marketing or anything.
258 00:29:33.210 ⇒ 00:29:34.050 Madison Vausbinder: -
259 00:29:35.340 ⇒ 00:29:38.539 Madison Vausbinder: Hopefully it’ll make it easy for you on that part.
260 00:29:38.540 ⇒ 00:29:39.790 Uttam Kumaran: Yes.
261 00:29:41.290 ⇒ 00:30:02.829 Madison Vausbinder: Yeah, no, we don’t… we don’t do any promotions and, or marketing spend into our partners, so really, I’m just, like, only looking at their spend, which is technically revenue, but since it’s not, like, revenue after cost, I just usually call it lifetime spend, so I can look at it and it counts. But that’s really all we look at, and then, like, spend frequency, or, like, last order date.
262 00:30:03.390 ⇒ 00:30:04.010 Uttam Kumaran: Okay.
263 00:30:04.010 ⇒ 00:30:04.580 Madison Vausbinder: So…
264 00:30:05.770 ⇒ 00:30:06.480 Awaish Kumar: Okay.
265 00:30:06.710 ⇒ 00:30:14.490 Awaish Kumar: So, yeah, like, I’m pretty clear, kind of, on what is needed. So if you get… we get access to those Excel sheets, we…
266 00:30:14.630 ⇒ 00:30:16.710 Awaish Kumar: We will try to, like, replicate those.
267 00:30:16.830 ⇒ 00:30:32.149 Madison Vausbinder: Cool. Yeah, that would be awesome. And then I think if there’s just any questions on the sheets, or, like, what’s in them, let me know, or, like, where the source of it comes from, because it is very pieced together, so just let me know where I can help but answer any questions, but hopefully it gives…
268 00:30:32.150 ⇒ 00:30:35.150 Uttam Kumaran: Are you putting that together every month, Madison, or is it every, like…
269 00:30:35.150 ⇒ 00:30:45.920 Madison Vausbinder: Not that detailed. So, every month we do, a revenue report, which is totally separate from our finance team, but probably, like.
270 00:30:45.980 ⇒ 00:31:04.950 Madison Vausbinder: it probably ends up being every month I do, like, spend. Specifically, I’ll do lifetime spend or year-to-date, because we’ll do, like, send-outs of new flavor launches and such. I don’t usually go into as much detail, in terms of growth, but last order date is usually one of the big things as well, because it’s…
271 00:31:04.950 ⇒ 00:31:08.760 Madison Vausbinder: We, like, won’t send to inactive partners or something.
272 00:31:08.760 ⇒ 00:31:09.450 Uttam Kumaran: Okay, okay.
273 00:31:09.790 ⇒ 00:31:16.369 Madison Vausbinder: So, I do monthly, sometimes multiple times a month, poll, like, spend, but not in as detailed as this, and not usually.
274 00:31:16.370 ⇒ 00:31:16.800 Uttam Kumaran: Okay.
275 00:31:16.800 ⇒ 00:31:18.140 Madison Vausbinder: Or company name.
276 00:31:18.500 ⇒ 00:31:23.870 Uttam Kumaran: Yeah, I think it will make it… I think this will just make it a lot… like, this new system will just make it a lot easier for you to pull that.
277 00:31:23.870 ⇒ 00:31:24.250 Madison Vausbinder: Hmm.
278 00:31:24.250 ⇒ 00:31:26.880 Uttam Kumaran: Kind of, like, whenever you need with the most up-to-date information.
279 00:31:26.880 ⇒ 00:31:28.029 Madison Vausbinder: Yeah, totally.
280 00:31:28.290 ⇒ 00:31:45.159 Madison Vausbinder: And one other thing that’s not on our… it’s probably in the sheet somewhere, but that I also have to pull often is address. And we have a lot of difficulty with that, because when we are sending out gifts, we… it goes usually to the default address, but sometimes people…
281 00:31:45.180 ⇒ 00:31:57.800 Madison Vausbinder: like, or it used to sometimes we’d send to the last address they shipped to, but people would be shipping to their home once, or, like, their store once. So I think for us, getting clearer about the default addresses on the accounts,
282 00:31:57.800 ⇒ 00:31:58.380 Uttam Kumaran: Okay.
283 00:31:58.770 ⇒ 00:32:04.949 Madison Vausbinder: In that way, like, we have a source of truth for when we’re shipping to partners, or, like, where they’re actually located.
284 00:32:05.130 ⇒ 00:32:08.449 Uttam Kumaran: Yeah, once we get the reporting down, you’ll see that, like, the…
285 00:32:08.700 ⇒ 00:32:15.450 Uttam Kumaran: flywheel of basically, like, oh, that’s wrong, okay, let’s go update it back in Shopify, and then it’ll, like, sync again. We’ll be, like, a lot…
286 00:32:15.590 ⇒ 00:32:17.520 Uttam Kumaran: You know, quicker, you know, so…
287 00:32:19.350 ⇒ 00:32:20.590 Madison Vausbinder: Perfect.
288 00:32:20.590 ⇒ 00:32:21.270 Awaish Kumar: Okay.
289 00:32:23.830 ⇒ 00:32:28.429 Uttam Kumaran: So I think, yeah, maybe let’s, maybe, Awash, on our side, we can just summarize the…
290 00:32:28.670 ⇒ 00:32:36.320 Uttam Kumaran: kind of cool reporting, and I think we can just drive towards one table that, if Madison can just probably pull from that snowflake, we’ll have all the joins made.
291 00:32:36.480 ⇒ 00:32:38.989 Madison Vausbinder: And then that’ll become, like, a master table that…
292 00:32:38.990 ⇒ 00:32:49.229 Uttam Kumaran: at least short-term, you can just basically export that into a spreadsheet, or we can help write it to a spreadsheet, and then we should have, like, a BI tool in place by, like, end of next month.
293 00:32:49.390 ⇒ 00:32:51.250 Uttam Kumaran: For you to actually just, like, do a lot of the…
294 00:32:51.680 ⇒ 00:32:55.579 Uttam Kumaran: Summing and reporting, and you can just have a live dashboard on this.
295 00:32:56.220 ⇒ 00:33:00.309 Uttam Kumaran: And sort of… work on other stuff, I guess. Yeah.
296 00:33:00.310 ⇒ 00:33:01.870 Madison Vausbinder: No.
297 00:33:01.870 ⇒ 00:33:02.990 Uttam Kumaran: Take a break.
298 00:33:02.990 ⇒ 00:33:11.179 Madison Vausbinder: Every time, like, and one other thing that I get asked a lot is, like, how many partners are in San Diego? And it’s like, it takes me so long.
299 00:33:11.180 ⇒ 00:33:12.130 Uttam Kumaran: Yes, yeah.
300 00:33:12.130 ⇒ 00:33:15.240 Madison Vausbinder: So, like, wherever I can make that part easy.
301 00:33:15.240 ⇒ 00:33:19.989 Uttam Kumaran: That’s a good one, Awish. We should, like, try to, like, find that use case, we should just say, like.
302 00:33:20.360 ⇒ 00:33:24.179 Uttam Kumaran: To show side-by-side of what finding out that question was before and after.
303 00:33:24.180 ⇒ 00:33:29.570 Madison Vausbinder: No, totally. People ask me it just, like, offhandedly, and they probably think it’s, like.
304 00:33:30.340 ⇒ 00:33:34.870 Madison Vausbinder: And I’m like, it takes me, like, multiple hours to go re-sort everyone, so…
305 00:33:35.330 ⇒ 00:33:39.580 Madison Vausbinder: If you can fix that problem for me, that would be amazing.
306 00:33:39.580 ⇒ 00:33:40.480 Uttam Kumaran: Perfect.
307 00:33:40.480 ⇒ 00:33:48.810 Madison Vausbinder: But yeah, but yeah, I’ll send… I’ll send over this wholesale data poll, and then just… is it best to send it through that Slack channel, or email.
308 00:33:48.810 ⇒ 00:33:52.340 Uttam Kumaran: Yeah, you can even throw it into,
309 00:33:52.520 ⇒ 00:33:56.710 Uttam Kumaran: this Google Drive that I will… Okay, perfect. …put in our Slack thread.
310 00:33:57.140 ⇒ 00:34:02.390 Madison Vausbinder: Sounds good. And do you guys already have access to our files, or do I just need to share that manually?
311 00:34:02.390 ⇒ 00:34:06.590 Uttam Kumaran: I think you’ll need to just share it manually, or if you put it in here.
312 00:34:07.360 ⇒ 00:34:10.020 Uttam Kumaran: It may prompt you, I’m not sure.
313 00:34:10.210 ⇒ 00:34:11.010 Madison Vausbinder: Okay, yeah.
314 00:34:11.010 ⇒ 00:34:11.840 Uttam Kumaran: But I will just…
315 00:34:11.840 ⇒ 00:34:12.279 Madison Vausbinder: Let me know.
316 00:34:12.280 ⇒ 00:34:15.359 Uttam Kumaran: Yeah, I’m just gonna send you, yeah, this one.
317 00:34:16.840 ⇒ 00:34:17.739 Uttam Kumaran: Perfect.
318 00:34:19.699 ⇒ 00:34:27.939 Madison Vausbinder: Awesome, alright, I’ll drop it in there. Just so you know, I’ll drop it in, but it won’t be… well, I don’t really live update that one often.
319 00:34:27.949 ⇒ 00:34:29.949 Uttam Kumaran: I don’t even mind if it’s a copy.
320 00:34:29.949 ⇒ 00:34:31.099 Madison Vausbinder: Okay. We’re just…
321 00:34:31.100 ⇒ 00:34:31.800 Uttam Kumaran: Yeah, it works.
322 00:34:31.800 ⇒ 00:34:35.149 Madison Vausbinder: I don’t know if I ever updated it, I’ll try and keep you updated on yours.
323 00:34:35.159 ⇒ 00:34:37.789 Uttam Kumaran: No, no, no, I just need a one version of it, because, yeah, yeah.
324 00:34:37.790 ⇒ 00:34:38.170 Madison Vausbinder: Okay.
325 00:34:38.179 ⇒ 00:34:39.019 Uttam Kumaran: Yeah, that’s fine.
326 00:34:39.020 ⇒ 00:34:40.500 Awaish Kumar: Sounds good.
327 00:34:40.679 ⇒ 00:34:41.580 Awaish Kumar: we…
328 00:34:42.560 ⇒ 00:34:51.459 Awaish Kumar: Yeah, I don’t know if we just need one version, because she mentioned that segmentation sheet, where they’re adding, like, company names.
329 00:34:52.729 ⇒ 00:34:56.879 Uttam Kumaran: But we’re gonna get a copy of the segmentation sheet in that, right? So that’s… yeah.
330 00:34:56.880 ⇒ 00:35:01.010 Madison Vausbinder: It… the segmentation sheet is a… a…
331 00:35:01.380 ⇒ 00:35:11.050 Madison Vausbinder: spreadsheet where I figured out everyone’s company name in segment, and that data is what this data pull is using. So, like, the segmentation sheet…
332 00:35:11.140 ⇒ 00:35:13.639 Awaish Kumar: It’s all included in this data poll.
333 00:35:13.760 ⇒ 00:35:15.490 Madison Vausbinder: There’s no other missing info.
334 00:35:15.490 ⇒ 00:35:19.429 Awaish Kumar: My question is, is it just a one-time thing, or are you continuing… continue to.
335 00:35:19.430 ⇒ 00:35:37.689 Madison Vausbinder: Well, I surely hope it’s a one-time thing, because I had to go through all 13,000 and choose a segment. Anybody who’s being onboarded now, we have their category, and they’ll, like, have the right tag, so it’ll… in theory, we won’t ever have to do it again. It will be assigned upon their account creation. Okay.
336 00:35:37.990 ⇒ 00:35:51.100 Madison Vausbinder: And the same with company name, like, now our… in the application, they tell us their company name, so ideally, we never have to go back and figure that out. It’s… it’s just in our system. So hopefully, whenever to do it.
337 00:35:51.100 ⇒ 00:35:51.670 Uttam Kumaran: Okay.
338 00:35:52.850 ⇒ 00:36:01.829 Madison Vausbinder: Cool. Alright, well, I’ll send this over. Again, if there’s any questions at all, feel free to reach out. But thank you, Aizen. Hopefully that helped.
339 00:36:02.120 ⇒ 00:36:03.979 Uttam Kumaran: No, thank you for the time, I appreciate it.
340 00:36:05.320 ⇒ 00:36:07.120 Uttam Kumaran: Okay. Alright, talk soon. Talk soon.
341 00:36:07.330 ⇒ 00:36:07.970 Uttam Kumaran: Bye.