Meeting Title: LMNT | Retail Data Sync Date: 2026-03-17 Meeting participants: Russell Broere, Uttam Kumaran, Amber Lin
WEBVTT
1 00:00:18.160 ⇒ 00:00:19.300 Amber Lin: Hello!
2 00:00:19.300 ⇒ 00:00:20.910 Russell Broere: Hey, guys!
3 00:00:21.090 ⇒ 00:00:23.219 Amber Lin: Hi, nice to talk again.
4 00:00:23.220 ⇒ 00:00:26.289 Russell Broere: Yes, yes, thanks for setting this all up.
5 00:00:30.330 ⇒ 00:00:31.610 Russell Broere: Can you hear me alright?
6 00:00:31.610 ⇒ 00:00:49.230 Amber Lin: Yeah, I can hear you. I know you took some time to do the what I want to see in the report, so I would love to talk about it later, but want to first make sure I understand how you get to your numbers and why there’s a difference between yours and your numbers and mine.
7 00:00:49.460 ⇒ 00:00:50.650 Russell Broere: Yeah, yeah, for…
8 00:00:50.650 ⇒ 00:01:01.740 Amber Lin: Yeah, let me first make sure you have access to this spreadsheet, so… Copy this link.
9 00:01:02.440 ⇒ 00:01:08.410 Amber Lin: And then… Alright, I dropped it in the Zoom chat, so…
10 00:01:09.000 ⇒ 00:01:13.379 Amber Lin: You can see… this is where you can see the numbers that we have.
11 00:01:14.230 ⇒ 00:01:32.640 Amber Lin: So we have a monthly total, if you scroll to the rightmost… so at the right side of the spreadsheet, there’s the monthly totals, and then we have the weekly totals using the, I think, Saturday to Friday weekly definitions. So…
12 00:01:33.080 ⇒ 00:01:42.969 Amber Lin: Would love to get started on, how you pull your numbers, where you pull it from, what definitions you use, so if you can walk me through that, that would be great.
13 00:01:43.700 ⇒ 00:01:51.989 Russell Broere: Yeah, let me pull… Let me get everything off playing right now.
14 00:02:06.380 ⇒ 00:02:07.870 Russell Broere: Okay.
15 00:02:08.740 ⇒ 00:02:10.399 Russell Broere: Let me share my screen.
16 00:02:33.590 ⇒ 00:02:34.016 Russell Broere: Fair enough.
17 00:02:37.290 ⇒ 00:02:45.150 Russell Broere: So… Essentially, I get a few different reports towards the end of the month.
18 00:02:45.690 ⇒ 00:02:50.789 Russell Broere: And what would be nice is, and I’m sure you guys have seen our OKR sheet.
19 00:02:52.420 ⇒ 00:02:53.130 Uttam Kumaran: Yes.
20 00:02:54.080 ⇒ 00:03:01.939 Russell Broere: So, what… immediately, what would be awesome is if these just automatically populate, and I don’t have to take, like, 3 hours.
21 00:03:02.410 ⇒ 00:03:10.150 Russell Broere: multiple different sessions, because I have to… I essentially receive… through Emerson, I have Omega.
22 00:03:11.470 ⇒ 00:03:15.120 Russell Broere: Right? And so I can go through Target, Walmart.
23 00:03:15.460 ⇒ 00:03:19.739 Russell Broere: Those ones, we can dive deeper into Target.
24 00:03:19.960 ⇒ 00:03:26.569 Russell Broere: I can look at all the different stuff, in-store online provides, kind of.
25 00:03:27.510 ⇒ 00:03:32.670 Russell Broere: deeper view. If I go to Forecasting or Trade Spend Tool…
26 00:03:33.200 ⇒ 00:03:36.150 Russell Broere: Again, that’s, like, an even deeper view.
27 00:03:41.310 ⇒ 00:03:44.480 Russell Broere: Let me go to JBP, which is Joint Business Plan.
28 00:03:46.260 ⇒ 00:03:48.049 Russell Broere: No, it’s forecasting.
29 00:03:49.540 ⇒ 00:03:53.830 Russell Broere: So, this is really mainly where I get… them weekly.
30 00:03:55.430 ⇒ 00:04:06.130 Russell Broere: And so you’ll see each item. I can scroll over here… And you’ll see… Everything in the plan, basically.
31 00:04:06.690 ⇒ 00:04:08.390 Russell Broere: Right, and so then…
32 00:04:08.720 ⇒ 00:04:17.059 Russell Broere: what I’ll do is I can pull it from here, I can export it from here, I can go to Walmart, do the same exact thing, right, the VTR.
33 00:04:17.550 ⇒ 00:04:19.789 Russell Broere: Report pulls the same stuff.
34 00:04:20.050 ⇒ 00:04:21.089 Russell Broere: by week.
35 00:04:21.800 ⇒ 00:04:33.019 Amber Lin: Gotcha. And for this week, I know they use the Saturday to Friday definition. This is what they will show on Emerson, right?
36 00:04:33.340 ⇒ 00:04:37.890 Russell Broere: Yeah, whatever’s in here is what’s pulling from Emerson. Emerson will pull the same thing, so…
37 00:04:37.890 ⇒ 00:04:39.150 Amber Lin: Gotcha, okay.
38 00:04:39.430 ⇒ 00:04:53.030 Russell Broere: But what also I do after that, so I’ll put in just some placeholder numbers, because I get those before accounting has finalized everything and, you know, kind of finished and close the books. So I just kind of look at what
39 00:04:53.140 ⇒ 00:05:02.690 Russell Broere: what I have in Omega, then I send an email, because also what I can do is…
40 00:05:06.300 ⇒ 00:05:08.880 Russell Broere: Oh wait, no, this is not there.
41 00:05:12.540 ⇒ 00:05:23.870 Russell Broere: I can go into the Target portal, and into VendorIQ, And essentially, I can pull… by day.
42 00:05:24.320 ⇒ 00:05:26.610 Russell Broere: They’ve got reports and stuff.
43 00:05:26.900 ⇒ 00:05:28.419 Russell Broere: that I can pull.
44 00:05:33.750 ⇒ 00:05:34.740 Russell Broere: Here.
45 00:05:35.520 ⇒ 00:05:39.629 Russell Broere: So I can pull all kinds of different reports from VendorIQ,
46 00:05:40.200 ⇒ 00:05:59.839 Russell Broere: by day. Usually what I do is I just ask the team, like, as I’m doing these checks, I just send an email, and I say, can you send me all this information by the day in the month, not week ending? And that’s what then I put in, again, into these OKRs, and then what I’ll do… that’s my second check.
47 00:06:00.060 ⇒ 00:06:05.279 Russell Broere: And then the final check, because now I know exactly by day, I know…
48 00:06:05.430 ⇒ 00:06:09.410 Russell Broere: what Emerson’s reporting in their portal is accurate by the week.
49 00:06:09.900 ⇒ 00:06:17.150 Russell Broere: I know it’s gonna be slightly different, and then I come over here, I get this report, First…
50 00:06:18.130 ⇒ 00:06:23.120 Russell Broere: Which is just our allocations for our advertising trade spend.
51 00:06:23.800 ⇒ 00:06:37.159 Russell Broere: I reconcile this, and then the last report I receive is all of the reconciliations and the revenue. So I get, like, our profit and loss statement, and I go by and I look, okay…
52 00:06:37.290 ⇒ 00:06:42.859 Russell Broere: Walmart… Target… sorry, here’s Walmart.
53 00:06:43.860 ⇒ 00:06:53.260 Russell Broere: Target, and you’ll see February… targets 1.323, I come here, 1323…
54 00:06:53.960 ⇒ 00:07:03.149 Russell Broere: Right, and then I come down here, and I’ve already got all of the deductions from this sheet. The last sheet that I use is Vitamin Shop, because we ship
55 00:07:03.600 ⇒ 00:07:13.529 Russell Broere: essentially, we set up Vitamin Shop before Emerson, so Emerson doesn’t manage our Vitamin Shop, so I have to pull it from two different places. And so I find vitamin.
56 00:07:13.530 ⇒ 00:07:17.660 Uttam Kumaran: Where are you getting, Russell, the vitamin Shop data from, by the way?
57 00:07:17.920 ⇒ 00:07:35.090 Russell Broere: That’s directly from Vitamin Shop, and then Bess compiles it into her statement, and so she’ll look at the orders, her revenue, and then any deductions that we’ve received, and then she’ll compile this in the profit and loss statement.
58 00:07:35.090 ⇒ 00:07:38.089 Uttam Kumaran: And is that… is that just an email from their team?
59 00:07:38.090 ⇒ 00:07:46.569 Russell Broere: It usually is. They send us the reports every week, and then, Bess can go in and pull them, but…
60 00:07:46.810 ⇒ 00:07:48.670 Russell Broere: They also…
61 00:07:49.040 ⇒ 00:08:01.020 Russell Broere: send a monthly report to close out the month with Bess, so she kind of confirms what… what everything she’s seeing, so we’re just kind of double-checking on all cylinders.
62 00:08:01.500 ⇒ 00:08:03.239 Uttam Kumaran: Okay, okay, great.
63 00:08:03.240 ⇒ 00:08:03.950 Russell Broere: Yep.
64 00:08:04.310 ⇒ 00:08:07.909 Russell Broere: So I know it’s, it’s like, kind of a funky…
65 00:08:08.330 ⇒ 00:08:10.869 Russell Broere: system, but that’s usually how it is with…
66 00:08:10.870 ⇒ 00:08:12.039 Uttam Kumaran: Well, we’re used to this, I mean…
67 00:08:12.040 ⇒ 00:08:12.930 Russell Broere: Yeah, exactly.
68 00:08:12.930 ⇒ 00:08:31.940 Uttam Kumaran: We’re gonna… I think you’re gonna be interested in some of the stuff we’re gonna… we’re gonna… we’re currently in process on developing, but this is the first time I’ve heard that we actually are getting, some stuff from Vitamin Shop, albeit manual, that’s fine. So I can… we’re already speaking with Beth, so I can see that way, maybe even loading historical data or getting in the loop on that, we can certainly put that in.
69 00:08:32.400 ⇒ 00:08:46.039 Russell Broere: Yeah, there’s so many reports floating around, and we could just dump them all in, and kind of… every time we get a new report, just dump it all in, and then have it auto-populate where we need it. Save us a lot of time.
70 00:08:46.730 ⇒ 00:08:47.860 Russell Broere: So…
71 00:08:48.110 ⇒ 00:09:07.540 Russell Broere: So yeah, that’s… there’s… like I said, there’s ways I can go in and sniff check, you know, mid-month, I can come in here, I can look at different details and stuff like that, but then when I’m actually closing out the month for our OKRs, I want to make sure Bess and I are 100% accurate, and that’s what we’re using with,
72 00:09:07.540 ⇒ 00:09:15.240 Russell Broere: Confido, I think I mentioned that we’re, onboarding… might as well just show you guys real quick.
73 00:09:16.680 ⇒ 00:09:37.819 Russell Broere: this is our accounting and forecasting tool. So essentially, super basic, upload your baselines, your marketing plan, and reconcile them every month. But as you’re seeing, we’re going in manually, and I’m going, hey, what about this one? Hey, what about this one? You know, versus if we set up the whole plan for the year, we anticipate most of these will get
74 00:09:37.820 ⇒ 00:09:39.370 Russell Broere: Approved just without…
75 00:09:39.370 ⇒ 00:09:50.559 Russell Broere: you know, I just say, oh, this is what we charged, this is what we planned, approved. As you can see, this one was not approved, so I’m chasing this one down, right? And so this would all happen within the Confido platform.
76 00:09:51.370 ⇒ 00:09:54.929 Russell Broere: So… So yeah.
77 00:09:54.930 ⇒ 00:10:05.239 Amber Lin: Gotcha. Yeah, I had a few questions as I was listening. So I wanted to ask about the OKR numbers, because I know they’re not directly
78 00:10:05.240 ⇒ 00:10:17.459 Amber Lin: gross sales. When I worked with the wholesale team, there was some manual orders they put in, some deductions. Do you know what the exact logic is that you guys use to get to these OKR numbers?
79 00:10:18.340 ⇒ 00:10:27.840 Russell Broere: I’m not sure… The question, like, wholesale, you said… manually inputs… Orders and stuff.
80 00:10:27.840 ⇒ 00:10:45.239 Amber Lin: Yeah, let me, let me rephrase this. So, we can help you calculate these OKR numbers if we know how to get there. I just want to confirm if this is just gross sales, or if there’s any, like, returns, discounts, or things applied.
81 00:10:45.450 ⇒ 00:10:50.179 Russell Broere: Yeah, so that’s a great question. So, certain things are off-invoice.
82 00:10:50.370 ⇒ 00:10:54.919 Russell Broere: So, yes, like, Target does that.
83 00:10:55.180 ⇒ 00:10:58.010 Russell Broere: But it should all still be within…
84 00:10:59.240 ⇒ 00:11:15.330 Russell Broere: essentially, that would be a question for Bess, because what I’m reporting is gross. So, to answer your question there, yes, but when we dive a level deeper, I reconcile everything, but this 256,000 right here.
85 00:11:15.870 ⇒ 00:11:32.170 Russell Broere: will be most likely off the invoice of what they’re gonna pay us for the… for the product, right? So, it’s not like we’re paying them 256,000 for the product, right? So… Yeah.
86 00:11:32.200 ⇒ 00:11:38.429 Russell Broere: Bess is the one that handles all of that, and that’s kind of, you know, a little bit past where I’m at in terms of accounting.
87 00:11:38.590 ⇒ 00:11:40.660 Russell Broere: But,
88 00:11:41.160 ⇒ 00:11:56.280 Russell Broere: But yes, what I’m reporting from the OKR standpoint is all gross from the reports that, hey, we’ve shipped X amount of units or cases to Target or Walmart, that’s what I put up there. And then you’ll see the trade spend right here.
89 00:11:56.280 ⇒ 00:12:08.080 Russell Broere: I planned 300, because 256 of that was from this 256 unexpected,
90 00:12:08.250 ⇒ 00:12:11.340 Russell Broere: charge, right? Highlighted in yellow over here, so…
91 00:12:11.790 ⇒ 00:12:19.510 Russell Broere: I include it in the OKRs, but then I add a little note saying, we’re disputing this, this was not planned, nor was it communicated. So…
92 00:12:21.200 ⇒ 00:12:29.550 Russell Broere: So yeah, it’s all gross sales from a sales perspective, and then POS, again, is just gross POS sales at store, so…
93 00:12:31.580 ⇒ 00:12:35.160 Amber Lin: Gotcha. Okay. I think then…
94 00:12:35.400 ⇒ 00:12:49.500 Amber Lin: Because I also work with the gross sales numbers, so this means that we’re aligned on what we’re looking at. So, I think next we can take a look at where our numbers are not matching up, and, how we’re getting to those numbers.
95 00:12:49.720 ⇒ 00:12:55.289 Russell Broere: Yeah, yeah, so… Let’s see.
96 00:12:57.870 ⇒ 00:13:00.820 Amber Lin: You can look at… Children.
97 00:13:00.820 ⇒ 00:13:04.560 Russell Broere: So, POS…
98 00:13:09.180 ⇒ 00:13:13.009 Russell Broere: 5… Is that 5, 6, 8, 9…
99 00:13:14.780 ⇒ 00:13:15.480 Amber Lin: Okay.
100 00:13:17.260 ⇒ 00:13:21.210 Uttam Kumaran: And we’re also getting our stuff from Emerson in the backend.
101 00:13:21.350 ⇒ 00:13:28.680 Uttam Kumaran: So we are… we’re basically pulling from, like, a raw data source, so a couple reasons why it may be misaligned is…
102 00:13:28.810 ⇒ 00:13:30.519 Uttam Kumaran: If we’re… of course, this…
103 00:13:30.550 ⇒ 00:13:49.339 Uttam Kumaran: like, if this is closed February, I just want to make sure things like the timestamps are there. Additionally, like, I don’t… I think we also have a store-level understanding of, like, the contribution to this, so if it is off, we can basically go try to pinpoint the store or SKU that’s causing the mismatch.
104 00:13:49.490 ⇒ 00:13:51.490 Uttam Kumaran: So as much of, like, the…
105 00:13:51.910 ⇒ 00:13:57.649 Uttam Kumaran: contributing data to this. Like, if you could pull an export from your Emerson UI, we can match it to ours.
106 00:13:58.160 ⇒ 00:13:58.900 Russell Broere: Yeah.
107 00:13:59.110 ⇒ 00:14:06.949 Russell Broere: I really think it’s… you’re gonna have to just make sure you’re pulling by the day and not week-ending, like you were saying, Amber.
108 00:14:07.290 ⇒ 00:14:08.449 Russell Broere: Saturday or Friday?
109 00:14:08.770 ⇒ 00:14:23.639 Amber Lin: Yeah, so this is the monthly report we do pull by the day, because if we just sum up the different week endings, the number is higher, so the week ending total is, like, 5.7 mil.
110 00:14:23.990 ⇒ 00:14:32.419 Amber Lin: So… It could also be time zone issues that we ran into.
111 00:14:32.420 ⇒ 00:14:32.950 Russell Broere: show me.
112 00:14:32.950 ⇒ 00:14:36.650 Amber Lin: currently using EST time zones, but…
113 00:14:36.850 ⇒ 00:14:42.040 Amber Lin: I don’t know when you pull from Target or pull from Emerson.
114 00:14:42.280 ⇒ 00:14:44.429 Amber Lin: Like, what time zone you are using.
115 00:14:45.690 ⇒ 00:14:49.430 Russell Broere: they’re pulling Central Time, because I’m emailing.
116 00:14:49.430 ⇒ 00:14:50.420 Amber Lin: Mmm.
117 00:14:50.420 ⇒ 00:14:53.910 Russell Broere: team in Minneapolis, but that shouldn’t…
118 00:14:54.660 ⇒ 00:15:00.200 Russell Broere: Matter, because it’s gonna be updating at the same time regardless, but…
119 00:15:00.910 ⇒ 00:15:08.860 Russell Broere: And again, I’m updating… I’m pulling it, give me 2 seconds…
120 00:15:31.100 ⇒ 00:15:42.489 Russell Broere: So yeah, so this is super close. I think it’s gonna be the days or the weeks, and so if we can confirm that, like, this is 5.6895713.
121 00:15:42.720 ⇒ 00:15:45.670 Russell Broere: You know, it’s like $25,000 difference.
122 00:15:45.790 ⇒ 00:15:52.950 Russell Broere: This one is, 819,000, you know?
123 00:15:53.220 ⇒ 00:15:53.900 Amber Lin: Damn.
124 00:15:56.740 ⇒ 00:16:04.000 Amber Lin: Honestly, I think it probably is the time zones of, like, a one-hour difference.
125 00:16:04.130 ⇒ 00:16:10.640 Amber Lin: If the difference is this small, it’s either that, or maybe we’re missing one store, right?
126 00:16:10.790 ⇒ 00:16:13.020 Amber Lin: misclassify one SKU.
127 00:16:13.510 ⇒ 00:16:14.080 Russell Broere: Right.
128 00:16:14.080 ⇒ 00:16:15.579 Amber Lin: That would be the reason.
129 00:16:15.580 ⇒ 00:16:25.220 Russell Broere: Yeah, I agree. So we’re, like, super close. I mean, it’s, what, $1,000 off of… 820.
130 00:16:25.220 ⇒ 00:16:31.459 Uttam Kumaran: Yeah, because what could happen is, like, it could be… if we’re Central Time, we’re doing East Coast, that… it could be an hour that moves into the next day.
131 00:16:31.730 ⇒ 00:16:43.309 Uttam Kumaran: Right. Like, at the start of the month, basically, or something like that. But I also agree, like, in terms of error bars, it’s pretty narrow. So I think that would be helpful, Amber, just to make sure for the past, like.
132 00:16:43.500 ⇒ 00:16:45.389 Uttam Kumaran: However, if we go back 6 months.
133 00:16:45.390 ⇒ 00:16:45.800 Amber Lin: Yeah.
134 00:16:46.320 ⇒ 00:16:52.829 Uttam Kumaran: we can just confirm for Target and Walmart. I would love to talk… to also just check as many
135 00:16:53.010 ⇒ 00:16:56.620 Uttam Kumaran: As far back as we can, so we can be confident, yeah.
136 00:16:56.760 ⇒ 00:17:05.779 Russell Broere: And you guys might be more accurate, honestly. So we’ll find out, you know, as you guys chase down. I’ll go back to our target team, but they pull by day.
137 00:17:06.230 ⇒ 00:17:06.579 Amber Lin: So…
138 00:17:06.779 ⇒ 00:17:15.729 Russell Broere: I feel like, if it’s the timing thing, that might be it, and then we just kind of reset the time to pull it, or whatever we can do.
139 00:17:16.060 ⇒ 00:17:27.050 Amber Lin: Yeah, do you have the daily reports, on that? Because I also have in our database the daily sales, so I would like to compare day by day for February, if you have that.
140 00:17:27.609 ⇒ 00:17:29.299 Russell Broere: Yeah, let’s…
141 00:17:32.479 ⇒ 00:17:33.799 Russell Broere: Where did it go?
142 00:17:38.169 ⇒ 00:17:42.419 Russell Broere: Alright, let me forward that report to you guys.
143 00:17:45.510 ⇒ 00:17:47.420 Russell Broere: I swears.
144 00:17:47.920 ⇒ 00:17:48.600 Russell Broere: Okay.
145 00:17:54.890 ⇒ 00:17:57.660 Amber Lin: I put my email in the chat, just in case.
146 00:18:00.740 ⇒ 00:18:01.660 Russell Broere: X.
147 00:18:09.130 ⇒ 00:18:10.279 Russell Broere: It’s our best.
148 00:18:38.710 ⇒ 00:18:41.460 Russell Broere: Alright, I just sent it over to you guys.
149 00:18:41.460 ⇒ 00:18:42.040 Amber Lin: One second.
150 00:18:43.010 ⇒ 00:18:44.880 Russell Broere: Like, 30 seconds.
151 00:18:45.300 ⇒ 00:18:50.350 Russell Broere: So yeah, that’s the daily report by store, so that’s, like.
152 00:18:51.070 ⇒ 00:18:55.739 Russell Broere: you know, the most accurate drill down I can think of, so…
153 00:18:56.160 ⇒ 00:19:11.199 Russell Broere: actually be the timing if you’re pulling the same, so… And if there’s… if we still can’t find it, I can connect you with John, and you guys can talk for 15 minutes, and he’ll show you actually what he’s looking at, and then that should give us everything that we need.
154 00:19:11.350 ⇒ 00:19:23.340 Russell Broere: But we’re super close. I think, for right now, we can kind of move forward with these numbers until we figure out that one little difference. But, I only have a couple more minutes, but…
155 00:19:24.190 ⇒ 00:19:34.750 Russell Broere: again, I think really just, like, everything Phil and Shivani are kind of working on is probably what I would suggest as well. So, like, from a… from a…
156 00:19:34.980 ⇒ 00:19:46.019 Russell Broere: top-down, I think they’re looking at it really well, and then from my angle, like a bottom-up, is kind of these OKRs, everything I’m reporting from a monthly stance.
157 00:19:46.020 ⇒ 00:20:02.840 Russell Broere: But again, this stuff could change going into next year, so that, you know, I want to make sure that we kind of think through that for contingencies. I think right now, this OKR sheet is kind of what the model we’re using for right now, but Phil might want to recreate the whole
158 00:20:02.970 ⇒ 00:20:22.019 Russell Broere: kind of OKR template as a whole with you guys. I’m not really sure he’s planning, but anything from bottom-up reporting that can get automated, which you guys seem to be doing already, and then drilling down that in-store level data, highlighting those distribution gaps, highlighting inventory at store, you know, compared…
159 00:20:22.070 ⇒ 00:20:24.170 Russell Broere: Relative to their sale-through.
160 00:20:24.570 ⇒ 00:20:35.099 Russell Broere: The… the really, really challenging one is gonna be Target, and as more Costco regions come on.
161 00:20:35.220 ⇒ 00:20:48.679 Russell Broere: those… like, Walmart, Costco, all these other smaller accounts that we’re talking with, too, from grocery, are so easy. Like, I just set up the plan. It’s fairly, you know, they have so much data on
162 00:20:49.120 ⇒ 00:20:57.290 Russell Broere: hey, you do an end cap, you do 2X lift, bake that into your production for 4 weeks, awesome. Target is…
163 00:20:58.390 ⇒ 00:21:02.450 Russell Broere: Much more complicated.
164 00:21:03.100 ⇒ 00:21:08.670 Russell Broere: I can show you real quick… this. So…
165 00:21:09.900 ⇒ 00:21:12.350 Russell Broere: Target calendar, yeah, so we have…
166 00:21:12.740 ⇒ 00:21:17.070 Russell Broere: Multiple off-shelfs, this is not including, like, our on-shelf.
167 00:21:17.650 ⇒ 00:21:30.229 Russell Broere: And then this is where we’re discussing our back half in gray. But we have all of these going… that have already gone live, we have these planned in the future, and then we have, you know, 5, 6 more
168 00:21:30.550 ⇒ 00:21:39.260 Russell Broere: In the end of the year, so… and… and Target will just call us and be like, hey, do you want to do a quarter pallet? And we have to be ready to say yes or no. So…
169 00:21:41.120 ⇒ 00:21:49.039 Russell Broere: the reason why I say this is because from a forecasting standpoint, I’m pretty good at it, but from a looking-back analysis.
170 00:21:49.690 ⇒ 00:21:56.349 Russell Broere: Using that to go forward from inventory, or a promo analysis, or something like that.
171 00:21:56.610 ⇒ 00:21:59.249 Russell Broere: That could help a lot as well, so…
172 00:21:59.370 ⇒ 00:22:05.139 Russell Broere: My point is, is, like, Target, just throws stuff, like, the kitchen sink, and you never know.
173 00:22:05.140 ⇒ 00:22:05.540 Amber Lin: business.
174 00:22:05.540 ⇒ 00:22:08.610 Russell Broere: Forecast jumps up, and then,
175 00:22:08.740 ⇒ 00:22:16.149 Russell Broere: to buy store inventory might go out of whack because you just increased sales by 50%, that’s unexpected, you know?
176 00:22:16.150 ⇒ 00:22:16.840 Amber Lin: see.
177 00:22:17.220 ⇒ 00:22:21.660 Russell Broere: Yeah, so it’s just a lot more involved with Target, so…
178 00:22:24.230 ⇒ 00:22:28.440 Amber Lin: Gotcha, okay. I’m noting these down, so, so far, I know…
179 00:22:28.470 ⇒ 00:22:42.309 Amber Lin: for the near term, we want the OKRs, we want the buy store reporting, and having the inventory of how much they have, what’s the sell… what the sell-through is like, the distribution gaps, and then the
180 00:22:42.310 ⇒ 00:22:50.520 Amber Lin: And I think moving forward, if we have more OKR requirements from Phil, or if… if we…
181 00:22:51.120 ⇒ 00:23:03.620 Amber Lin: would do that type of analysis for, like, looking back at target sales and seeing trends. I think that’s the second step… step after the main reporting, so…
182 00:23:04.210 ⇒ 00:23:10.690 Amber Lin: I think we’re… based on what I’ve been working on, I think we should be able to provide these things
183 00:23:10.800 ⇒ 00:23:18.640 Amber Lin: to you, and I think that will make your life a lot easier, especially if you spend 3 hours pulling all the data from different sources.
184 00:23:19.020 ⇒ 00:23:23.890 Russell Broere: Yeah, it’s just, like, it’s not so much polling, it’s just, like, verifying, and if I just know.
185 00:23:24.450 ⇒ 00:23:33.070 Russell Broere: the same, I’m like, oh, I don’t even have to verify, then that’s perfect. So, that would be ideal. Let me show you real quick
186 00:23:33.610 ⇒ 00:23:44.010 Russell Broere: the inventory… Here we go, perfect. We also get reports for merchandising.
187 00:23:44.180 ⇒ 00:23:44.970 Amber Lin: Huh.
188 00:23:45.160 ⇒ 00:23:53.809 Russell Broere: So it kind of reports, like, side cap, end cap, what’s their execution rate. We also get pictures,
189 00:23:54.700 ⇒ 00:23:58.860 Russell Broere: and stuff like that, so I’m not sure if that’s something that we want to look at, but…
190 00:23:59.580 ⇒ 00:24:14.660 Russell Broere: here’s… here’s what I’ve created. I’ve been using this for, like, 7 years, and it never fails, so it’s super easy. Essentially, you know, you have your… your… where you’re… where you’re selling by store, your item.
191 00:24:14.850 ⇒ 00:24:18.599 Russell Broere: It gets complicated when you have 10 different SKUs on shelf.
192 00:24:18.990 ⇒ 00:24:25.850 Russell Broere: And so… you have your total units on hand, or sorry, total units sold on hand.
193 00:24:25.850 ⇒ 00:24:26.290 Amber Lin: And…
194 00:24:26.290 ⇒ 00:24:30.470 Russell Broere: On the way, so, like, in transit, and then on order, so…
195 00:24:30.770 ⇒ 00:24:39.689 Russell Broere: You know, you have to bake in all your inventory buckets, what are you selling, how long does it take from each stage of inventory to get to shelf?
196 00:24:39.940 ⇒ 00:24:55.659 Russell Broere: Right? And then you bake that into, okay, end of hand, and, like, what’s the inventory on hand at the end of the week, and then weeks on hand. And you look each week forward, and all you do is you say, okay, like, they have 40, they’re selling 15,
197 00:24:55.740 ⇒ 00:25:02.530 Russell Broere: They’re gonna add 20 for the following week, because it’s on order, which means it’s from their warehouse to store.
198 00:25:02.690 ⇒ 00:25:08.250 Russell Broere: Right? And then… what we would have to bake in from there is what’s on PO,
199 00:25:08.780 ⇒ 00:25:22.609 Russell Broere: from us to warehouse, right? But so, essentially, you go 40 plus 20 minus 15 is 45, and then you do the same thing, because there’s no more replenishment, then it goes to 30, 15, 0, and you sell out.
200 00:25:22.790 ⇒ 00:25:26.440 Russell Broere: Right, so you flag all the ones that are negative.
201 00:25:26.910 ⇒ 00:25:35.100 Russell Broere: you set a parameter of, like, I want to keep 4 weeks on hand, right? So anything that’s…
202 00:25:36.580 ⇒ 00:25:47.859 Russell Broere: you know, not 4 weeks on hand gets flagged yellow. Everything, you know, under, like, 2 weeks is red, because it takes 2 weeks to backfill, right? So that’s a distribution gap.
203 00:25:48.950 ⇒ 00:25:50.220 Amber Lin: Gotcha, okay.
204 00:25:51.310 ⇒ 00:25:59.739 Amber Lin: We do have, like, the on-hand, on-transit, on-order inventory data from, I think, both Target and Walmart.
205 00:25:59.980 ⇒ 00:26:04.130 Russell Broere: Okay, perfect. Give me 2 seconds. Sorry, I’m getting…
206 00:27:17.380 ⇒ 00:27:20.140 Russell Broere: Sorry about that. Okay.
207 00:27:20.600 ⇒ 00:27:21.750 Russell Broere: So…
208 00:27:21.920 ⇒ 00:27:27.890 Russell Broere: So yeah, that kind of gives you an idea of, like, what we’re trying to create, but then automating it to,
209 00:27:28.590 ⇒ 00:27:43.979 Russell Broere: to kind of, like, an overview, where it’s like, okay, by order… by SKU, here’s the stores that have zero weeks, and then I can double-click into it, and it’ll highlight it, and then I can take that snapshot and send it to Target.
210 00:27:44.040 ⇒ 00:28:01.529 Russell Broere: And then as we continuously do it, because I don’t want to just have you guys set up stuff that we can’t actually do anything with, so… then we start figuring out… there’s a repeat offenders bucket, and this one has been here for 5 different weeks, and it’s one of the top-selling stores, so…
211 00:28:01.620 ⇒ 00:28:03.010 Russell Broere: We have to…
212 00:28:03.410 ⇒ 00:28:22.120 Russell Broere: A get ahead of the inventory gap, and then increase the presentation minimum at the store, reset, you know, there’s different things that we can start doing if I have the data to go to Target and say, this specific 10% of stores that are usually your most high velocity ones, because they sell out first.
213 00:28:22.350 ⇒ 00:28:24.950 Russell Broere: have, you know, are on this list, let’s work our way.
214 00:28:24.950 ⇒ 00:28:25.380 Amber Lin: Amazing.
215 00:28:25.380 ⇒ 00:28:28.599 Russell Broere: getting improved. So.
216 00:28:28.760 ⇒ 00:28:34.769 Amber Lin: Awesome. If you can share this with me, that we can see what we can do to…
217 00:28:34.920 ⇒ 00:28:36.590 Amber Lin: Put this in a report for you.
218 00:28:36.910 ⇒ 00:28:38.710 Russell Broere: Yeah, yeah, I mean…
219 00:28:48.670 ⇒ 00:28:55.569 Russell Broere: And then the merchandising piece is something we can talk about. I don’t know if we really want to get into it, it’s just a nightmare, so much, but…
220 00:28:55.570 ⇒ 00:28:58.779 Amber Lin: Okay, we’ll conquer in peace at the time.
221 00:28:59.120 ⇒ 00:29:00.659 Amber Lin: The inventory stuffers.
222 00:29:00.660 ⇒ 00:29:06.630 Russell Broere: I agree. But that would be really, really helpful, because that kind of gives you an idea of what I’m thinking from the inventory piece.
223 00:29:06.630 ⇒ 00:29:17.010 Amber Lin: Yeah, awesome. I had the inventory data, and I did put it a little bit in the report, but knowing how you think about it is, like, really helpful.
224 00:29:17.180 ⇒ 00:29:28.910 Russell Broere: Yeah, because, I mean, you can look at, like, percent of stock in stock, it’s like 99.9, but it’s, like, that’s not accurate, because one unit on hand is in stock.
225 00:29:28.980 ⇒ 00:29:35.790 Russell Broere: You have to drill a level deeper, and then a level deeper than that. Then you also need to add what is getting shipped in.
226 00:29:35.840 ⇒ 00:29:51.120 Russell Broere: And then the biggest caveat between that is… I don’t know if it’s at the store, if it’s in the back of the store, or it’s actually on shelf, and then we use our merchandisers, which is this merchandising report that we use to kind of verify, so…
227 00:29:51.120 ⇒ 00:29:51.740 Amber Lin: Gosh.
228 00:29:51.740 ⇒ 00:29:57.190 Russell Broere: Full circle, but there are some gaps, always, in retail, so… Cool.
229 00:29:57.190 ⇒ 00:30:09.970 Amber Lin: Gotcha. Awesome. Well, thank you for your time. I know it’s out of time, so thank you for taking the 30 minutes. It was really helpful. Yeah, of course. And hopefully soon we can deliver those reports for you, at least for your March reconciliation.
230 00:30:09.970 ⇒ 00:30:14.700 Russell Broere: Yeah, that would be great. And then let me know if you guys, you know, want to hop back on,
231 00:30:15.050 ⇒ 00:30:23.879 Russell Broere: I’ll be in Bozeman next week, so I’ll probably have some free time, and then, share what we talked about with Shivani, and we’ll kind of go from there.
232 00:30:24.400 ⇒ 00:30:25.509 Amber Lin: Yeah, awesome.
233 00:30:25.940 ⇒ 00:30:27.799 Russell Broere: Cool, thanks guys.
234 00:30:27.800 ⇒ 00:30:28.470 Uttam Kumaran: Thank you!
235 00:30:28.470 ⇒ 00:30:28.970 Russell Broere: Thank you.
236 00:30:29.300 ⇒ 00:30:29.900 Russell Broere: Right?