Meeting Title: PP2G | Standup Date: 2025-05-21 Meeting participants: Luke Daque, Kim Todaro, Amber Lin
WEBVTT
1 00:00:15.220 ⇒ 00:00:16.280 kim todaro: Hey, guys.
2 00:00:16.640 ⇒ 00:00:17.260 Amber Lin: Hi.
3 00:00:17.260 ⇒ 00:00:18.850 Luke Daque: It’s going.
4 00:00:19.020 ⇒ 00:00:20.260 kim todaro: Good! How are you?
5 00:00:20.830 ⇒ 00:00:21.990 Luke Daque: Yeah, doing? Well.
6 00:00:23.150 ⇒ 00:00:26.287 Amber Lin: Kim. I’m I’m at Gutam’s house.
7 00:00:26.810 ⇒ 00:00:28.529 kim todaro: Where is that? Austin?
8 00:00:28.920 ⇒ 00:00:41.996 Amber Lin: Yeah, I I flew in from La to Austin because we had a company event and then is going on vacation. So I’m like, if if you’re not using your house, I can’t use your house.
9 00:00:42.360 ⇒ 00:00:45.950 kim todaro: Oh, that’s awesome! Does he have a pool.
10 00:00:46.868 ⇒ 00:01:01.909 Amber Lin: No, he does not have a pool, but in Austin there is the like. I forgot what it’s called. It’s like a spring that’s outdoors near a river. So it’s pretty famous spot. I might just go there, maybe, on the weekend.
11 00:01:03.230 ⇒ 00:01:08.079 kim todaro: Very cool. That sounds like fun, my friend. My best friend is going to Austin next weekend for a bachelor party.
12 00:01:09.560 ⇒ 00:01:10.430 kim todaro: Yeah.
13 00:01:10.430 ⇒ 00:01:11.660 Amber Lin: So fun.
14 00:01:12.410 ⇒ 00:01:17.160 Amber Lin: Okay, are you gonna go on vacation anytime soon.
15 00:01:17.160 ⇒ 00:01:23.160 kim todaro: I am. I haven’t gone on vacation in a while, but I’m going to the Bahamas at the end of June.
16 00:01:23.160 ⇒ 00:01:24.010 Amber Lin: Hmm.
17 00:01:24.010 ⇒ 00:01:25.352 kim todaro: That’ll be fun.
18 00:01:25.800 ⇒ 00:01:26.610 Amber Lin: Exciting.
19 00:01:26.610 ⇒ 00:01:27.680 Luke Daque: That’s great!
20 00:01:27.680 ⇒ 00:01:31.708 kim todaro: Yeah, so what’s going on.
21 00:01:32.260 ⇒ 00:01:37.060 Amber Lin: Yeah, I’ll let Luke share the screen. And then I think.
22 00:01:37.752 ⇒ 00:01:45.580 Amber Lin: it’s mostly just differences in calculation. That difference is actually, really, really small after we looked at it, screen.
23 00:01:46.130 ⇒ 00:01:48.320 Luke Daque: Sure. Yeah, right.
24 00:01:49.220 ⇒ 00:01:51.290 Luke Daque: Let me share my screen.
25 00:02:02.700 ⇒ 00:02:04.359 Luke Daque: There you go. Can you see my screen?
26 00:02:04.630 ⇒ 00:02:05.300 Amber Lin: Yes.
27 00:02:06.670 ⇒ 00:02:12.919 Luke Daque: So yeah, the this left part is the real dashboard. And this is like shopify.
28 00:02:13.340 ⇒ 00:02:18.500 Luke Daque: and I filter the same dates. May one to 16
29 00:02:19.230 ⇒ 00:02:23.510 Luke Daque: also may one to 16. So for the gross sales it looks like
30 00:02:24.620 ⇒ 00:02:31.839 Luke Daque: in shopify. It’s reporting 160,000 point 14, and which
31 00:02:32.310 ⇒ 00:02:37.230 Luke Daque: it’s also exactly the same for the total sales, which is the gross sales.
32 00:02:37.230 ⇒ 00:02:37.740 kim todaro: Yep.
33 00:02:37.740 ⇒ 00:02:50.330 Luke Daque: So I think we should be accounting everything here. The only I guess thing that I need to look into is the discounts, because, like real, this reporting, like 7,000 compared to like 12,000.
34 00:02:50.720 ⇒ 00:02:51.470 kim todaro: Yeah, I, just.
35 00:02:51.470 ⇒ 00:02:53.409 Luke Daque: So there’s like something missing. There.
36 00:02:53.560 ⇒ 00:03:00.829 kim todaro: Yeah, I really noticed in the in reals ordered sales. The 1 50 number.
37 00:03:01.180 ⇒ 00:03:08.400 Luke Daque: Yeah, that I think should should be similar to the net sales. And that’s what I noticed was was off. Because that’s.
38 00:03:08.610 ⇒ 00:03:10.640 kim todaro: That’s what I report to
39 00:03:12.500 ⇒ 00:03:24.740 kim todaro: report to Ben. So like. Yesterday I made a report, but I I took the net sales from shopify versus real. But then I took the cogs and the marketing costs from
40 00:03:24.960 ⇒ 00:03:27.040 kim todaro: from real to make the report.
41 00:03:27.040 ⇒ 00:03:27.910 Luke Daque: I see.
42 00:03:27.910 ⇒ 00:03:28.149 kim todaro: And
43 00:03:29.030 ⇒ 00:03:34.759 Luke Daque: Okay? And by. And and this is what you reported right, the next sale. This would be the net sales, right?
44 00:03:35.000 ⇒ 00:03:37.760 Luke Daque: Both of this 1, 52 number or.
45 00:03:38.616 ⇒ 00:03:39.650 kim todaro: It’s yeah.
46 00:03:40.150 ⇒ 00:03:44.359 kim todaro: Can you scroll to the left after returns and discounts.
47 00:03:45.340 ⇒ 00:03:46.210 Luke Daque: This one.
48 00:03:46.210 ⇒ 00:03:49.519 kim todaro: Yeah, cause I think total sales includes taxes.
49 00:03:50.180 ⇒ 00:03:55.579 Luke Daque: I see, yeah, makes sense. So I guess this would be. This is like
50 00:03:56.700 ⇒ 00:04:00.730 Luke Daque: lower, because the the discounts is higher here as well as.
51 00:04:01.143 ⇒ 00:04:11.899 Amber Lin: Yeah, I think when we calculated total sales on real, we didn’t sorry net sales on real. We didn’t include taxes. That’s what I remember.
52 00:04:11.900 ⇒ 00:04:16.890 kim todaro: You shouldn’t include taxes. It’s the total sales at the top. It’s confusing.
53 00:04:17.490 ⇒ 00:04:39.170 Amber Lin: I see. Yeah, like we, we can change the total sales into gross sales just to match the different things. So I do agree that it’s a bit confusing, but in terms of gross sales we’re matching shopify exactly. And I think when it comes to ordered sales is the same as total sales right here, and the only difference should be coming from the
54 00:04:39.590 ⇒ 00:04:41.970 Amber Lin: discounts, I believe.
55 00:04:42.170 ⇒ 00:04:53.599 Amber Lin: and then we can also include a column of net sales that includes taxes. If that’s how is that? How the net sales works on shopify.
56 00:04:53.760 ⇒ 00:04:58.970 kim todaro: No, so net sales is total sales minus discounts and returns, and I think
57 00:04:59.220 ⇒ 00:05:08.189 kim todaro: I think the discounts and returns were off a little bit unreal, and I think the order sales should match up with net sales. Right, Luke, or am I wrong?
58 00:05:08.520 ⇒ 00:05:13.980 Luke Daque: Yeah, that should be correct. The only. So, yeah, I’ll have to look into this discounts thing.
59 00:05:14.200 ⇒ 00:05:14.540 kim todaro: Yeah.
60 00:05:14.540 ⇒ 00:05:20.519 Luke Daque: However, like the refund and the returns, though, is probably a bit tricky, because we’re not
61 00:05:21.050 ⇒ 00:05:29.840 Luke Daque: getting any data from shopify for returns, at least from the Api standpoint. We do.
62 00:05:30.140 ⇒ 00:05:32.480 Luke Daque: We do have refunds, though, which is.
63 00:05:33.090 ⇒ 00:05:35.950 Luke Daque: I’m not sure if it’s the same as returns.
64 00:05:36.350 ⇒ 00:05:44.190 Luke Daque: Maybe it’s not like it could be partial refund for a returned item, or something like that.
65 00:05:44.700 ⇒ 00:05:47.950 kim todaro: Can you look at the definition for the total refund amount.
66 00:05:52.870 ⇒ 00:05:59.140 Luke Daque: Total refund amount would be this one.
67 00:06:02.390 ⇒ 00:06:04.419 Luke Daque: It’s basically
68 00:06:11.120 ⇒ 00:06:16.020 Luke Daque: the item refund subtotal plus item refund out, which is.
69 00:06:16.240 ⇒ 00:06:20.170 Luke Daque: it’s it’s not showing what exactly the refund is.
70 00:06:20.820 ⇒ 00:06:33.070 Luke Daque: But yeah, it’s as long as it’s fully funded, basically is what it is.
71 00:06:33.350 ⇒ 00:06:38.530 kim todaro: Yeah, cause I’m just trying to think of why they’re they’re off so much because the customer service team does all that.
72 00:06:39.060 ⇒ 00:06:39.760 Luke Daque: Hmm.
73 00:06:39.760 ⇒ 00:06:43.420 kim todaro: Through shopify directly.
74 00:06:45.150 ⇒ 00:06:47.990 Luke Daque: Yeah, maybe we maybe I can ask.
75 00:06:48.170 ⇒ 00:06:53.499 Luke Daque: like the service team from shopify, how they are calculating returns here.
76 00:06:54.070 ⇒ 00:06:59.990 Luke Daque: And so we can try to match that without we are calculating.
77 00:07:00.130 ⇒ 00:07:00.630 kim todaro: Yeah.
78 00:07:00.630 ⇒ 00:07:01.300 Luke Daque: Points.
79 00:07:02.110 ⇒ 00:07:04.760 kim todaro: Yeah, I mean, it’s not like off by that much. But
80 00:07:07.245 ⇒ 00:07:11.630 kim todaro: I just I just noticed discounts and returns are off, which is why, I just get the
81 00:07:11.750 ⇒ 00:07:15.309 kim todaro: the numbers from shopify. But if we can get real to
82 00:07:15.790 ⇒ 00:07:24.390 kim todaro: whether it’s under ordered sales or another net sales value, I just want those 2 to match, because that’s what I’ll kind of report to Ben and Dan on.
83 00:07:24.390 ⇒ 00:07:28.909 Amber Lin: Hmm him to get a better sense of how best we can
84 00:07:29.260 ⇒ 00:07:38.990 Amber Lin: change this to support you? Can you tell us how like, what specifics. Do you report to Ben? And then, like, we want to make sure all of them are matched up.
85 00:07:39.730 ⇒ 00:07:44.840 kim todaro: Let me see if I have a spreadsheet. I just did one through the.
86 00:07:46.050 ⇒ 00:07:47.500 kim todaro: 19, th I think.
87 00:07:47.500 ⇒ 00:07:48.150 Amber Lin: Hmm!
88 00:07:48.710 ⇒ 00:07:57.919 Amber Lin: If you want to share your screen, we can take a screenshot of what metrics you usually share and where you currently pull them from, so we can make sure that we
89 00:08:00.070 ⇒ 00:08:02.280 Amber Lin: We make your reporting easier.
90 00:08:02.280 ⇒ 00:08:06.461 kim todaro: So another. Yes, I will show you that. And another thing to realize about this
91 00:08:09.510 ⇒ 00:08:18.909 kim todaro: is that real is a little slow. It’s like always a few days behind. So I I made some adjustments myself on like the 18th and 19.th
92 00:08:20.644 ⇒ 00:08:28.040 Amber Lin: Luke, what do you think about that is our real close to real time, or is, how frequent does it update.
93 00:08:30.260 ⇒ 00:08:32.200 Luke Daque: Let me check Will.
94 00:08:34.820 ⇒ 00:08:36.820 Luke Daque: I think it only
95 00:08:40.220 ⇒ 00:08:42.280 Luke Daque: updates once a day. It looks like.
96 00:08:42.510 ⇒ 00:08:45.359 kim todaro: Yeah. So I’m only seeing it through the 17th today.
97 00:08:46.130 ⇒ 00:08:46.870 Luke Daque: Hmm.
98 00:08:47.620 ⇒ 00:08:54.609 Amber Lin: Yeah, I’ve seen that, too, of a few occasions. It’s like 5 days or 3 days behind.
99 00:08:54.610 ⇒ 00:09:00.286 kim todaro: Yeah. So the closer we can get it to like the day before the better. Just because,
100 00:09:01.130 ⇒ 00:09:13.884 kim todaro: we’re really like starting to. We’re like when we have sales. We want to see like how the sales impact profit and like. So that’s what I kind of did on the sheet. I just sent you guys as I kind of looked at profit week over week.
101 00:09:14.680 ⇒ 00:09:16.710 kim todaro: And when we changed prices.
102 00:09:19.690 ⇒ 00:09:25.670 Luke Daque: Okay, yeah, it looks like it’s going up to May 19. But yeah.
103 00:09:26.770 ⇒ 00:09:31.330 Luke Daque: yeah, we can make this refresh, I guess
104 00:09:31.670 ⇒ 00:09:33.529 Luke Daque: faster, like, maybe twice a day.
105 00:09:34.400 ⇒ 00:09:38.150 Luke Daque: That way we’ll have like more up to date numbers.
106 00:09:39.850 ⇒ 00:09:41.766 kim todaro: Yeah, that would be helpful.
107 00:09:45.170 ⇒ 00:09:47.330 Luke Daque: Sounds good. Yeah, I’ll do that.
108 00:09:50.696 ⇒ 00:09:54.730 kim todaro: It was May 15th that I did that. Let me just
109 00:09:56.135 ⇒ 00:10:01.890 kim todaro: and then I’ll just share. This is kind of the sheet that I made yesterday for ben
110 00:10:07.110 ⇒ 00:10:07.770 Luke Daque: Bye.
111 00:10:08.380 ⇒ 00:10:10.840 kim todaro: This I pulled shopify.
112 00:10:12.380 ⇒ 00:10:19.429 kim todaro: This is gross sales, minus returns and discounts and shopify calls from real
113 00:10:21.670 ⇒ 00:10:24.430 kim todaro: And I think I just.
114 00:10:25.900 ⇒ 00:10:32.769 kim todaro: oh, I’m I’m I averaged, based on total sales. What the cogs were for these 2 days, just because
115 00:10:33.310 ⇒ 00:10:36.549 kim todaro: for the 18th and 19, because that data wasn’t in the dashboard yet.
116 00:10:38.330 ⇒ 00:10:45.999 kim todaro: It might even been the 17, th 18, th and 19.th I’m not sure. And then this is just ship station report. I average the total for the month
117 00:10:46.190 ⇒ 00:10:48.359 kim todaro: by day through the 19th
118 00:10:49.178 ⇒ 00:10:52.609 kim todaro: Eunice. This is from Chuck, and this is going to go down to 0 soon.
119 00:10:52.980 ⇒ 00:10:56.630 kim todaro: This is from real. And then this is just profit.
120 00:10:57.370 ⇒ 00:10:59.279 kim todaro: And then I just did it week by week
121 00:11:00.730 ⇒ 00:11:05.059 kim todaro: to see how like the price changes. Impact implemented a sale there.
122 00:11:08.820 ⇒ 00:11:10.240 kim todaro: Hopefully, that’s helpful.
123 00:11:11.550 ⇒ 00:11:13.260 Amber Lin: Yeah, I see, okay.
124 00:11:14.350 ⇒ 00:11:17.880 Luke Daque: Yeah, that that helps a lot. We can double check it there.
125 00:11:18.170 ⇒ 00:11:23.290 Luke Daque: So it looks like, yeah, it looks like it’s just it’s the discounts and refunds that need to be.
126 00:11:23.290 ⇒ 00:11:24.160 kim todaro: Yeah.
127 00:11:24.500 ⇒ 00:11:28.070 Luke Daque: Matching reel basically.
128 00:11:31.980 ⇒ 00:11:32.650 kim todaro: Yep.
129 00:11:33.790 ⇒ 00:11:34.370 Amber Lin: Okay.
130 00:11:35.407 ⇒ 00:11:44.079 Amber Lin: sounds good, Luke. We can talk later about this as well. How long do you think this would take when we.
131 00:11:45.320 ⇒ 00:11:46.710 Amber Lin: when we look at it.
132 00:11:48.670 ⇒ 00:11:52.240 Luke Daque: I guess, for discounts. It shouldn’t.
133 00:11:53.800 ⇒ 00:12:01.390 Luke Daque: I guess it shouldn’t take too long. I maybe I I’ll have something by tomorrow for discounts. But for returns. It might be a little bit tricky.
134 00:12:01.540 ⇒ 00:12:06.410 Luke Daque: I’ll have to check with shopify, perhaps on like how they are calculating that.
135 00:12:07.410 ⇒ 00:12:08.650 Luke Daque: So yeah.
136 00:12:09.320 ⇒ 00:12:18.550 Amber Lin: I see. Will we need anyone that to talk to on the pull bars team? Or are we good to just investigate.
137 00:12:21.760 ⇒ 00:12:23.069 Luke Daque: What do you mean?
138 00:12:23.590 ⇒ 00:12:25.030 Luke Daque: Yeah, I think it should be good.
139 00:12:25.030 ⇒ 00:12:25.640 Amber Lin: Sounds good.
140 00:12:25.640 ⇒ 00:12:26.190 Luke Daque: Yeah.
141 00:12:26.470 ⇒ 00:12:27.220 Amber Lin: Okay.
142 00:12:29.410 ⇒ 00:12:36.080 Amber Lin: Awesome. Thank you, Kim. I hope this helps clear things up a little bit. And then we know what we need to do.
143 00:12:36.660 ⇒ 00:12:38.020 kim todaro: Okay. Sounds good.
144 00:12:38.020 ⇒ 00:12:40.409 Amber Lin: Yeah, thank you for joining the call.
145 00:12:40.410 ⇒ 00:12:42.740 kim todaro: Alright! Thanks, guys. Talk to you soon.
146 00:12:43.210 ⇒ 00:12:44.409 Amber Lin: Bye, bye.
147 00:12:47.490 ⇒ 00:12:53.290 Amber Lin: Oh, Luke, I realize I probably shouldn’t have said that in the meeting I was like, Oh, shit.
148 00:12:53.290 ⇒ 00:12:54.030 Luke Daque: Expired.
149 00:12:54.378 ⇒ 00:13:02.390 Amber Lin: They don’t wanna give you a hard deadline. But I think they’re usually okay with us, just doing things a little slower. But
150 00:13:03.132 ⇒ 00:13:04.659 Amber Lin: is this? Do you.
151 00:13:04.660 ⇒ 00:13:05.440 Luke Daque: No problem.
152 00:13:05.440 ⇒ 00:13:06.850 Amber Lin: Be a lot of work.
153 00:13:08.480 ⇒ 00:13:15.970 Luke Daque: For the for the discounts. I think we should be fine, like
154 00:13:16.310 ⇒ 00:13:24.460 Luke Daque: I can start. I can like, focus on that today the discounts thing, and hopefully, I have something by tomorrow.
155 00:13:25.910 ⇒ 00:13:28.149 Luke Daque: But yeah, it looks like the refunds would
156 00:13:28.630 ⇒ 00:13:30.860 Luke Daque: be a little bit more tricky.
157 00:13:31.110 ⇒ 00:13:32.220 Amber Lin: Yeah, I mean.
158 00:13:32.220 ⇒ 00:13:34.189 Luke Daque: Probably more complex.
159 00:13:34.190 ⇒ 00:13:38.980 Amber Lin: Yeah, I see. I mean, if it’s
160 00:13:39.940 ⇒ 00:13:52.839 Amber Lin: we can also look at refunds versus returns like is which one is actually more accurate. Maybe we can convince them to just use our numbers. And ultimately, I guess, for us is
161 00:13:53.570 ⇒ 00:14:02.860 Amber Lin: once we, I really want this to be our final update. And once we do that, just be like, hey? We have everything in there just.
162 00:14:02.860 ⇒ 00:14:03.700 Luke Daque: Yeah.
163 00:14:04.080 ⇒ 00:14:08.369 Amber Lin: Is this not gonna be 100% match all the time? Because this is not.
164 00:14:08.370 ⇒ 00:14:08.790 Luke Daque: Yeah.
165 00:14:08.790 ⇒ 00:14:11.449 Amber Lin: Having to patch this every time is not great.
166 00:14:12.330 ⇒ 00:14:13.180 Luke Daque: Computer.
167 00:14:13.180 ⇒ 00:14:14.460 Amber Lin: Yeah, okay.
168 00:14:15.068 ⇒ 00:14:23.979 Amber Lin: let me know hopefully, it doesn’t take you too long. If it takes more like a day, probably just let me know, and then we can tell Kim how it is. But.
169 00:14:25.870 ⇒ 00:14:26.700 Luke Daque: Sounds good.
170 00:14:27.940 ⇒ 00:14:32.849 Amber Lin: Okay, for matter, more is mostly Annie working on it right now, right.
171 00:14:32.850 ⇒ 00:14:37.929 Luke Daque: Yeah, she is for the like doing some joins for analytical purposes.
172 00:14:37.930 ⇒ 00:14:42.010 Luke Daque: Okay, no worries. I’ll go call her then oops.
173 00:14:42.010 ⇒ 00:14:43.550 Amber Lin: Do you have kim’s.
174 00:14:43.550 ⇒ 00:14:44.870 Luke Daque: I mean, I can saved.
175 00:14:44.870 ⇒ 00:14:48.470 Luke Daque: I can join. If you’re calling. Maybe we can like sync with that as well.
176 00:14:49.500 ⇒ 00:14:56.630 Amber Lin: Sure, and I’ll see when if she’s able to join the call later. But.
177 00:14:57.380 ⇒ 00:15:06.560 Amber Lin: like, if you’re doing something, I would rather you have focus time than having to be in a meeting. I can always just Async update you.
178 00:15:07.060 ⇒ 00:15:08.259 Luke Daque: Okay. Sounds good.
179 00:15:09.101 ⇒ 00:15:11.840 Amber Lin: Do you have Kim’s linked link saved?
180 00:15:12.090 ⇒ 00:15:13.180 Amber Lin: It’s in the meeting.
181 00:15:13.850 ⇒ 00:15:16.040 Amber Lin: It will disappear after we close this.
182 00:15:17.010 ⇒ 00:15:18.640 Luke Daque: Yeah, let me copy this.
183 00:15:19.780 ⇒ 00:15:21.900 Luke Daque: Yeah, I have it now. You should be good.
184 00:15:21.900 ⇒ 00:15:24.980 Amber Lin: Okay. Alrighty sounds good.
185 00:15:25.660 ⇒ 00:15:26.770 Luke Daque: Thanks.
186 00:15:26.770 ⇒ 00:15:27.770 Amber Lin: Bye.