2025-11-26_honey_stinger_sales_analysis_sync

Meeting Title: Honey Stinger Sales Analysis Sync Date: 2025-11-26 Meeting participants: Amber Lin, Henry Zhao

WEBVTT

1 00:00:14.690 ⇒ 00:00:15.820 Henry Zhao: Hello!

2 00:00:16.290 ⇒ 00:00:17.020 Amber Lin: Hi!

3 00:00:17.250 ⇒ 00:00:21.760 Amber Lin: Sorry, I totally forgot. I was working on the analysis.

4 00:00:21.880 ⇒ 00:00:24.319 Amber Lin: Did you get a tag I sent you?

5 00:00:24.790 ⇒ 00:00:29.409 Henry Zhao: Yeah, I’m still looking at it. I think I already saw this one.

6 00:00:29.410 ⇒ 00:00:31.940 Amber Lin: All right.

7 00:00:31.940 ⇒ 00:00:34.770 Henry Zhao: I don’t know if I want to put anything in the deck for now.

8 00:00:34.770 ⇒ 00:00:37.850 Amber Lin: I think today we just wanted to share something for…

9 00:00:38.850 ⇒ 00:00:42.329 Henry Zhao: For them to think of… think about over the weekend, right?

10 00:00:42.640 ⇒ 00:00:53.089 Amber Lin: Yeah, I think this is just for Robert’s review, because I eventually want to put it in text, and I think having images is easier than a chunk of text, as I have…

11 00:00:53.090 ⇒ 00:01:05.209 Amber Lin: experimented before, like, when I send a chunk of text, like, even if it’s organized, they get really confused. So, if it’s, like, separated by slide deck and there’s images and stuff, it’ll be easier for them.

12 00:01:05.519 ⇒ 00:01:11.979 Amber Lin: But we probably don’t have to send it out. What findings did you realize at the end?

13 00:01:12.210 ⇒ 00:01:24.459 Henry Zhao: Nothing. So, I don’t really know if there’s anything interesting from here, so remember we talked about a few things, well, actually, let’s go over Han339 first, I just want to close the loop on this.

14 00:01:24.460 ⇒ 00:01:24.980 Amber Lin: Okay.

15 00:01:24.980 ⇒ 00:01:28.749 Henry Zhao: Do you think we’re done with this? I think you already did the search term analysis, right?

16 00:01:29.170 ⇒ 00:01:34.329 Amber Lin: I did a little bit… let me show you the… yeah, it’s on the bottom, slide 15.

17 00:01:34.650 ⇒ 00:01:37.500 Amber Lin: Like, that’s the search term specifically for the…

18 00:01:37.690 ⇒ 00:01:43.110 Amber Lin: shoes, because remember there was a big spike on shoes? .

19 00:01:43.680 ⇒ 00:01:44.120 Henry Zhao: On what?

20 00:01:44.120 ⇒ 00:01:48.160 Amber Lin: On the… on the energy shoes.

21 00:01:48.160 ⇒ 00:01:49.059 Henry Zhao: Don’t choose an ad.

22 00:01:49.060 ⇒ 00:01:54.339 Amber Lin: Yeah, but, like, there is some, but…

23 00:01:55.460 ⇒ 00:02:06.039 Amber Lin: like, it also happened the week of the PO order, like, I can’t make a significant conclusion, because the difference is just, I guess, like, it went from…

24 00:02:06.210 ⇒ 00:02:11.939 Amber Lin: About 8 or 8 searches to, like, 20-something searches.

25 00:02:12.050 ⇒ 00:02:13.939 Henry Zhao: Oh, you were talking about searches, okay, cool.

26 00:02:14.100 ⇒ 00:02:16.009 Amber Lin: Yeah, so slide 15.

27 00:02:16.790 ⇒ 00:02:34.579 Amber Lin: So, like, we could conclude something there, but I don’t think it… because the data is so small, I don’t know if I can make a significant conclusion, because there’s only 20 differences, and they sell so much product, and I don’t really know, like, why it’s so low.

28 00:02:34.930 ⇒ 00:02:37.310 Amber Lin: So.

29 00:02:37.310 ⇒ 00:02:37.990 Henry Zhao: more…

30 00:02:38.170 ⇒ 00:02:38.600 Amber Lin: Yeah. For sure.

31 00:02:38.600 ⇒ 00:02:40.650 Henry Zhao: Shopify is probably…

32 00:02:41.500 ⇒ 00:02:46.780 Henry Zhao: So, we probably want to look at… alright, so let’s… for orders by item, let’s take a look at this.

33 00:02:47.010 ⇒ 00:02:47.390 Amber Lin: It’s good.

34 00:02:47.390 ⇒ 00:02:52.200 Henry Zhao: Click filter… Jeez.

35 00:02:54.410 ⇒ 00:02:56.859 Henry Zhao: How do I filter a pivot tab out?

36 00:02:59.770 ⇒ 00:03:00.680 Henry Zhao: out here.

37 00:03:01.070 ⇒ 00:03:02.919 Henry Zhao: It’s so different from Excel, so it’s like, sometimes.

38 00:03:02.920 ⇒ 00:03:04.210 Amber Lin: Yeah, it is.

39 00:03:04.780 ⇒ 00:03:08.250 Amber Lin: Okay, so what were we saying? Product…

40 00:03:11.410 ⇒ 00:03:15.549 Henry Zhao: Text contains, let’s just do shoe.

41 00:03:18.320 ⇒ 00:03:19.220 Amber Lin: Hmm…

42 00:03:19.370 ⇒ 00:03:22.910 Henry Zhao: What the… Are there not shoes here? Shoes.

43 00:03:25.610 ⇒ 00:03:26.630 Henry Zhao: That’s weird.

44 00:03:31.250 ⇒ 00:03:32.360 Henry Zhao: What am I doing wrong?

45 00:03:35.810 ⇒ 00:03:37.130 Henry Zhao: Expatine Shoes.

46 00:03:39.620 ⇒ 00:03:40.160 Amber Lin: Hmm.

47 00:03:48.590 ⇒ 00:03:50.769 Henry Zhao: There we go.

48 00:03:51.200 ⇒ 00:03:53.569 Henry Zhao: So what period were you looking at for yours?

49 00:03:53.870 ⇒ 00:03:57.569 Amber Lin: The search term was from… starting from September.

50 00:03:58.650 ⇒ 00:04:03.089 Henry Zhao: Okay. But here we can look at, like, the actual order account for choose.

51 00:04:03.090 ⇒ 00:04:03.830 Amber Lin: Okay.

52 00:04:04.070 ⇒ 00:04:10.799 Henry Zhao: Oh, it is pretty interesting, like, can you see, like, there’s a big spike here? Oh, this is very old, though. Let’s look at more recent data.

53 00:04:11.480 ⇒ 00:04:12.980 Amber Lin: That’s May.

54 00:04:13.690 ⇒ 00:04:16.740 Henry Zhao: But it just kind of, like, fluctuates over time.

55 00:04:16.740 ⇒ 00:04:19.039 Amber Lin: Yeah, can I also see the month?

56 00:04:19.660 ⇒ 00:04:20.589 Henry Zhao: Yeah, sorry.

57 00:04:27.920 ⇒ 00:04:30.180 Amber Lin: Well, this is… this goes back.

58 00:04:31.320 ⇒ 00:04:32.620 Amber Lin: Okay.

59 00:04:32.620 ⇒ 00:04:36.179 Henry Zhao: Yeah, that’s why I think this is good, because we have a lot more data for Shopify.

60 00:04:36.480 ⇒ 00:04:39.870 Henry Zhao: But let’s categorize these,

61 00:04:40.030 ⇒ 00:04:44.439 Henry Zhao: Do you… can you go in and take a look at these products and categorize them also?

62 00:04:45.230 ⇒ 00:04:46.309 Henry Zhao: The way you did?

63 00:04:46.680 ⇒ 00:04:47.340 Henry Zhao: Or…

64 00:04:47.340 ⇒ 00:04:53.059 Amber Lin: Yeah, I think I can do that. I will need to run Kirscher, though.

65 00:04:53.210 ⇒ 00:04:53.750 Henry Zhao: Okay.

66 00:05:01.220 ⇒ 00:05:03.109 Amber Lin: Oh, I think I…

67 00:05:03.110 ⇒ 00:05:03.949 Henry Zhao: Any classified?

68 00:05:03.950 ⇒ 00:05:18.770 Amber Lin: them. I’m just using their original classifications, like, they had a classification in Shopify. They have, like, nutrition bars versus nutrition gels and chews, so some things are grouped

69 00:05:18.990 ⇒ 00:05:22.320 Amber Lin: together, so I can also run it based on

70 00:05:22.800 ⇒ 00:05:25.930 Amber Lin: Like, they’re naming, and group them for.

71 00:05:25.930 ⇒ 00:05:26.490 Henry Zhao: There.

72 00:05:27.270 ⇒ 00:05:32.429 Henry Zhao: Yeah, but, like, this big spike here for choose was, like, this one big order, so these are things we probably want to point out.

73 00:05:32.870 ⇒ 00:05:39.209 Henry Zhao: This is why I did it by item, because I don’t want to just be like, oh, there’s a big spike, because probably people wanted shoes. No, this is a good word.

74 00:05:39.290 ⇒ 00:05:40.770 Amber Lin: Can we look at 201?

75 00:05:40.770 ⇒ 00:05:51.749 Henry Zhao: fluctuates over time, so I need to know, kind of… I don’t know what the recommendation is, because I don’t know how they prepare or stock their inventory, and I don’t know if you guys talked about whether Shopify has inventory issues.

76 00:05:51.750 ⇒ 00:05:55.449 Amber Lin: I’m not… we didn’t talk about Shopify, it was all Amazon.

77 00:05:56.360 ⇒ 00:05:59.350 Amber Lin: Just… Demi was…

78 00:05:59.740 ⇒ 00:06:04.689 Amber Lin: the Amazon person, and she knew a lot about how Amazon did their orders and stuff.

79 00:06:07.190 ⇒ 00:06:14.119 Amber Lin: Do you see a big spike here? It still seems like it’s natural fluctuations, it goes from 500 to 1,000.

80 00:06:15.350 ⇒ 00:06:18.520 Henry Zhao: And I would expect that Shopify is kind of like that.

81 00:06:18.640 ⇒ 00:06:21.140 Henry Zhao: There’s not as much, just, like, random fluctuates.

82 00:06:21.410 ⇒ 00:06:22.160 Henry Zhao: You know?

83 00:06:24.530 ⇒ 00:06:28.329 Henry Zhao: Let’s see if, like, Black Friday or whatever’s higher, which probably should be, right? Yeah.

84 00:06:28.530 ⇒ 00:06:30.549 Henry Zhao: So, like, November, I’m not surprised that it’s higher.

85 00:06:32.650 ⇒ 00:06:34.440 Henry Zhao: However, it’s not tracking for you.

86 00:06:34.640 ⇒ 00:06:38.320 Amber Lin: I don’t think we… do we have all the data for November?

87 00:06:38.540 ⇒ 00:06:43.479 Henry Zhao: No, but I’m saying it’s not Black Friday yet, so I’m wondering if, like, the rest is gonna come in all at once, like, 800 orders.

88 00:06:44.780 ⇒ 00:06:45.730 Henry Zhao: Next week, you know?

89 00:06:45.930 ⇒ 00:06:53.219 Henry Zhao: But, like, I don’t want to say that even if inventory’s not an issue for them. Like, what is the problem we’re trying to solve? So that’s why I wanted to get this meeting with you.

90 00:06:55.130 ⇒ 00:06:56.590 Amber Lin: I see. So.

91 00:06:56.590 ⇒ 00:06:58.669 Henry Zhao: That’s why we can look it up by day, actually, so…

92 00:06:58.670 ⇒ 00:06:59.590 Amber Lin: Yeah, okay.

93 00:06:59.660 ⇒ 00:07:01.439 Henry Zhao: That’ll be nice. Can we just look at.

94 00:07:01.440 ⇒ 00:07:05.149 Amber Lin: that for, like… No offense. I don’t know.

95 00:07:05.150 ⇒ 00:07:05.970 Henry Zhao: Exactly.

96 00:07:06.310 ⇒ 00:07:10.989 Henry Zhao: 2024, 11.01… And let’s look at it by day.

97 00:07:12.840 ⇒ 00:07:15.359 Henry Zhao: So let’s see if, like, all the ones came in at the end of the month, see? Yeah.

98 00:07:15.770 ⇒ 00:07:18.020 Henry Zhao: So right now, it’s November 26th.

99 00:07:18.790 ⇒ 00:07:21.160 Amber Lin: It is, though? It came… a lot of it came.

100 00:07:21.160 ⇒ 00:07:22.270 Henry Zhao: Oh, wellness.

101 00:07:23.160 ⇒ 00:07:24.100 Amber Lin: 18th.

102 00:07:24.720 ⇒ 00:07:27.130 Henry Zhao: Oh, wow. Yeah.

103 00:07:28.780 ⇒ 00:07:35.470 Amber Lin: Well, it depends on who’s ordering, right? Are these individual orders, or are these business orders?

104 00:07:35.860 ⇒ 00:07:37.899 Henry Zhao: These are just numbers of orders.

105 00:07:38.470 ⇒ 00:07:51.519 Amber Lin: I see, because if this business is ordering from them, they may order a week or two earlier before, they have their sale, right? So if, say, Amazon starts their sale at.

106 00:07:51.520 ⇒ 00:07:52.460 Henry Zhao: Oh, you’re right.

107 00:07:52.460 ⇒ 00:07:55.530 Amber Lin: 20th. So if these… this is, like,

108 00:07:55.810 ⇒ 00:08:02.789 Amber Lin: retail store, and they want to stock this. Can we see the average order size?

109 00:08:03.590 ⇒ 00:08:05.869 Amber Lin: Can we do a…

110 00:08:07.300 ⇒ 00:08:10.149 Henry Zhao: But I can probably just tell from the price divided by the orders.

111 00:08:10.150 ⇒ 00:08:11.850 Amber Lin: Yeah, can we do that?

112 00:08:11.980 ⇒ 00:08:16.179 Amber Lin: Can we do a column that’s that? I just want to see, like, who’s ordering.

113 00:08:16.650 ⇒ 00:08:17.840 Henry Zhao: Yeah, average.

114 00:08:18.060 ⇒ 00:08:18.740 Henry Zhao: Total.

115 00:08:18.950 ⇒ 00:08:21.790 Henry Zhao: document presented on me.

116 00:08:21.980 ⇒ 00:08:23.310 Amber Lin: If that doesn’t show us.

117 00:08:23.310 ⇒ 00:08:25.249 Henry Zhao: Why would they order from Shopify, though?

118 00:08:25.570 ⇒ 00:08:32.179 Amber Lin: Why not? Like, there’s no other way for them to order other than ordering from Amazon.

119 00:08:33.809 ⇒ 00:08:36.159 Henry Zhao: No, I’m saying, why would Amazon order from Shopify?

120 00:08:36.159 ⇒ 00:08:48.929 Amber Lin: No, no, not Amazon, but other stores. Say there’s a retail store next to me, they want to stock this place, or say my gym wants to stock them. Can we look at, maybe, the max order size?

121 00:08:51.260 ⇒ 00:08:54.669 Amber Lin: I don’t think we would do that. We probably would look at a…

122 00:08:54.670 ⇒ 00:08:56.470 Henry Zhao: This wouldn’t tell you anything, I think.

123 00:08:56.470 ⇒ 00:09:02.849 Amber Lin: Yeah, we’ll need to look at the… just the PO order. I’m sorry, not just the average order, like, max order.

124 00:09:04.200 ⇒ 00:09:05.449 Amber Lin: During that day?

125 00:09:06.060 ⇒ 00:09:09.489 Henry Zhao: Yeah, it’s not, not, not, it’s not that high, 46.

126 00:09:09.640 ⇒ 00:09:15.370 Amber Lin: I don’t think this… does this do anything? Like, this… this max formula?

127 00:09:15.750 ⇒ 00:09:16.490 Henry Zhao: Yeah, it does.

128 00:09:16.700 ⇒ 00:09:17.649 Henry Zhao: Tells, what’s the biggest…

129 00:09:17.650 ⇒ 00:09:18.440 Amber Lin: Gross.

130 00:09:23.480 ⇒ 00:09:26.560 Amber Lin: What’s the thing inside the bracket?

131 00:09:28.480 ⇒ 00:09:33.590 Henry Zhao: Don’t worry about it, this is the total cost of the orders, this is the total highest order cost.

132 00:09:35.780 ⇒ 00:09:36.790 Henry Zhao: Aren’t just random?

133 00:09:41.030 ⇒ 00:09:41.650 Amber Lin: Hmm.

134 00:09:43.350 ⇒ 00:09:46.139 Henry Zhao: They probably did, like, a pre-Black Friday sale that did really well.

135 00:09:46.820 ⇒ 00:09:47.650 Henry Zhao: Because this is what I’m saying.

136 00:09:47.650 ⇒ 00:09:48.190 Amber Lin: Vinegar.

137 00:09:48.190 ⇒ 00:09:53.549 Henry Zhao: I don’t think people actually care that much about Black Friday. And this is… no, this is Cyber Monday, I think.

138 00:09:54.310 ⇒ 00:09:58.369 Henry Zhao: I think Cyber Monday’s bigger for an online site, right? See, 12.2? This is Cyber Monday.

139 00:09:58.900 ⇒ 00:10:03.010 Henry Zhao: So there we go. So Cyber Monday does well, and this year’s Cyber Mondays when?

140 00:10:03.550 ⇒ 00:10:06.729 Henry Zhao: So we’ll see it this year on, like, December 1st, yeah, so…

141 00:10:07.100 ⇒ 00:10:09.350 Amber Lin: Wait, where’s Cyber Monday? Can you show me again?

142 00:10:10.010 ⇒ 00:10:13.019 Amber Lin: Like, on the… on the Mother’s Day.

143 00:10:13.770 ⇒ 00:10:16.700 Amber Lin: 5… 10.

144 00:10:17.460 ⇒ 00:10:21.069 Henry Zhao: So more orders, but the same-ish average order size, so… which makes sense.

145 00:10:21.070 ⇒ 00:10:21.710 Amber Lin: Right, like a…

146 00:10:21.710 ⇒ 00:10:23.759 Henry Zhao: Cheaper, but don’t buy more.

147 00:10:23.960 ⇒ 00:10:25.879 Henry Zhao: So this all makes a lot of sense to me, actually.

148 00:10:26.150 ⇒ 00:10:26.870 Amber Lin: Okay.

149 00:10:27.080 ⇒ 00:10:32.370 Amber Lin: So, maybe we can add a plot that’s, like, the number of…

150 00:10:32.370 ⇒ 00:10:34.059 Henry Zhao: Let’s look at it in 2023.

151 00:10:34.060 ⇒ 00:10:34.820 Amber Lin: Okay.

152 00:10:34.820 ⇒ 00:10:35.900 Henry Zhao: Obviously, the same thing.

153 00:10:38.870 ⇒ 00:10:43.120 Henry Zhao: So Black Friday was November 24th, so Cyber Monday was probably the 29th.

154 00:10:43.400 ⇒ 00:10:44.579 Henry Zhao: Oh, sorry, 27.

155 00:10:45.160 ⇒ 00:10:47.220 Henry Zhao: 27, alright, so 24, 27.

156 00:10:48.760 ⇒ 00:10:50.750 Henry Zhao: Yeah, a lot higher on 27 also.

157 00:10:51.470 ⇒ 00:10:52.160 Amber Lin: Hmm.

158 00:10:53.650 ⇒ 00:10:57.019 Henry Zhao: But this is what I’m saying, like, this is not probably that interesting to them, like, they probably know this.

159 00:10:57.680 ⇒ 00:10:59.619 Amber Lin: Yeah, that’s right.

160 00:11:00.560 ⇒ 00:11:04.349 Amber Lin: Because they’re probably already preparing for…

161 00:11:04.460 ⇒ 00:11:11.509 Amber Lin: Like, even if it applies to Amazon, too, like, they probably already are preparing, and Amazon has made their purchase.

162 00:11:11.850 ⇒ 00:11:13.410 Amber Lin: A while back, already.

163 00:11:15.210 ⇒ 00:11:21.309 Henry Zhao: Yeah, and I did figure out that, So, one… Amazon…

164 00:11:22.040 ⇒ 00:11:24.670 Henry Zhao: Did we look at Amazon versus traffic yet, and Walmart yet?

165 00:11:25.070 ⇒ 00:11:26.100 Amber Lin: No.

166 00:11:26.260 ⇒ 00:11:26.950 Amber Lin: Perfect.

167 00:11:27.110 ⇒ 00:11:37.080 Henry Zhao: But let’s look at that together also. Sales trends… Spikes… in Amazon, but… s,

168 00:11:38.290 ⇒ 00:11:43.580 Henry Zhao: Kind of revolves around a stable average in Shopify.

169 00:11:43.840 ⇒ 00:11:47.630 Henry Zhao: Sea spikes… on Cyber Monday.

170 00:11:50.110 ⇒ 00:11:51.559 Henry Zhao: And not Black Friday.

171 00:11:51.690 ⇒ 00:12:06.960 Henry Zhao: As an example… Shopify data… is already in the Klaviyo events table, but we have more historical data, Klaviyo

172 00:12:07.090 ⇒ 00:12:11.249 Henry Zhao: Allows us to join on demographic data.

173 00:12:15.260 ⇒ 00:12:18.699 Henry Zhao: Do we want to look at, Walmart together? Walmart was pretty easy, right?

174 00:12:18.880 ⇒ 00:12:19.440 Henry Zhao: Yes.

175 00:12:19.440 ⇒ 00:12:23.840 Amber Lin: There was not too much on Walmart. I looked at the sales…

176 00:12:24.040 ⇒ 00:12:26.680 Amber Lin: We can look at the visitors.

177 00:12:27.450 ⇒ 00:12:29.449 Amber Lin: Yeah, I can grab that.

178 00:12:31.630 ⇒ 00:12:32.970 Henry Zhao: What was Branch Up again?

179 00:12:33.570 ⇒ 00:12:40.009 Amber Lin: I can do the traffic for Walmart, do you want to do that for Shopify? I haven’t looked at ShopTech data at all.

180 00:12:41.660 ⇒ 00:12:47.899 Henry Zhao: Shopify data’s pretty… Pretty standard. We already have that, actually, from Klaviyo.

181 00:12:49.180 ⇒ 00:12:50.819 Henry Zhao: Right? So, we already have that.

182 00:12:51.380 ⇒ 00:12:52.809 Henry Zhao: This is the traffic data.

183 00:12:57.220 ⇒ 00:12:58.630 Amber Lin: Oh, cool, okay.

184 00:12:59.740 ⇒ 00:13:02.849 Amber Lin: This is definitely November. So yeah, we see the same spikes in November.

185 00:13:03.670 ⇒ 00:13:06.860 Henry Zhao: And I don’t know what February… or there was a spike here.

186 00:13:07.550 ⇒ 00:13:08.049 Henry Zhao: Well, I want.

187 00:13:08.050 ⇒ 00:13:10.050 Amber Lin: That looks… that looks like me.

188 00:13:10.050 ⇒ 00:13:11.609 Henry Zhao: March, March, yeah.

189 00:13:12.000 ⇒ 00:13:13.869 Henry Zhao: March is another spike.

190 00:13:13.870 ⇒ 00:13:20.339 Amber Lin: Maybe that’s, like, mid… yeah, that’s mid… Mid-year March, and then around…

191 00:13:20.340 ⇒ 00:13:20.750 Henry Zhao: awful.

192 00:13:20.750 ⇒ 00:13:22.250 Amber Lin: July, June…

193 00:13:22.250 ⇒ 00:13:23.360 Henry Zhao: 3rd, March…

194 00:13:23.570 ⇒ 00:13:30.889 Henry Zhao: Yeah, March and November. So, second peak is March, and then June 2 years ago, I don’t know if I cared that much about that.

195 00:13:31.990 ⇒ 00:13:35.040 Amber Lin: So there’s 3 peaks each year, essentially.

196 00:13:35.200 ⇒ 00:13:39.230 Henry Zhao: But the biggest peak is obviously… I wonder if June is because of these email blasts.

197 00:13:41.050 ⇒ 00:13:41.850 Amber Lin: Hmm.

198 00:13:42.070 ⇒ 00:13:44.310 Amber Lin: What’s… what’s this graph for?

199 00:13:45.060 ⇒ 00:13:48.550 Henry Zhao: Emails. This is all emails. This email subscribes over time.

200 00:13:49.080 ⇒ 00:13:51.829 Henry Zhao: So December is probably from Cyber Monday.

201 00:13:52.540 ⇒ 00:13:54.010 Henry Zhao: This makes a lot of sense, actually.

202 00:13:56.310 ⇒ 00:13:59.050 Henry Zhao: We can say Cyber Monday is, like, their biggest,

203 00:14:04.900 ⇒ 00:14:09.999 Henry Zhao: And the opening emails spikes after those… the month after the spikes, right?

204 00:14:10.940 ⇒ 00:14:14.040 Henry Zhao: But again, like, I’m just looking at data, I don’t know what problem we’re trying to solve, so…

205 00:14:17.070 ⇒ 00:14:19.299 Amber Lin: I see, what’s the most interesting to you?

206 00:14:20.850 ⇒ 00:14:23.759 Henry Zhao: You have to pick, like, one or three.

207 00:14:25.220 ⇒ 00:14:28.869 Henry Zhao: these emails, these spikes in November and June?

208 00:14:32.510 ⇒ 00:14:37.309 Henry Zhao: These orders by item is fine, because I already have that in here, actually, so… checkouts.

209 00:14:38.880 ⇒ 00:14:41.010 Henry Zhao: And then where was orders?

210 00:14:42.290 ⇒ 00:14:43.750 Henry Zhao: We’re adding to cart…

211 00:14:47.280 ⇒ 00:14:48.949 Henry Zhao: No, we can do it here.

212 00:14:53.090 ⇒ 00:14:55.540 Henry Zhao: And do we see spikes in November? Let’s see.

213 00:14:56.140 ⇒ 00:14:57.379 Henry Zhao: Not… not a lot.

214 00:15:02.500 ⇒ 00:15:06.890 Henry Zhao: Refunds are very low, so these are the refunds. It’s very low, which is good.

215 00:15:08.550 ⇒ 00:15:15.500 Henry Zhao: So I wouldn’t even really look at refunds anymore, because it’s pretty trivial, right? Like, any company’s gonna have refunds, this doesn’t seem like something of concern.

216 00:15:15.500 ⇒ 00:15:18.589 Amber Lin: Is this, like, number of orders?

217 00:15:19.020 ⇒ 00:15:21.800 Henry Zhao: Yeah, this is the number of orders, but I can easily change it to.

218 00:15:21.890 ⇒ 00:15:22.990 Amber Lin: Amounts.

219 00:15:23.160 ⇒ 00:15:25.260 Amber Lin: Yeah, let’s see amounts, it might be different.

220 00:15:29.320 ⇒ 00:15:29.960 Amber Lin: Okay.

221 00:15:30.560 ⇒ 00:15:31.799 Henry Zhao: Again, very trivial, like…

222 00:15:31.800 ⇒ 00:15:36.789 Amber Lin: spikes in… June, June, July.

223 00:15:37.220 ⇒ 00:15:42.930 Amber Lin: You see that? I think there are spikes in June, July around… March… in April.

224 00:15:43.750 ⇒ 00:15:44.850 Amber Lin: Right.

225 00:15:45.190 ⇒ 00:15:50.520 Amber Lin: For 2024, as well, and… There’s spikes around there.

226 00:15:51.180 ⇒ 00:15:51.840 Amber Lin: And then…

227 00:15:51.840 ⇒ 00:15:56.130 Henry Zhao: Yeah, so you see something spikes in March, June, and November for Cyber Monday.

228 00:15:56.130 ⇒ 00:15:56.860 Amber Lin: Yeah.

229 00:15:57.190 ⇒ 00:16:01.659 Amber Lin: Do you want to look at the PO order? So if you go to slide 6…

230 00:16:01.990 ⇒ 00:16:08.160 Amber Lin: Sopl orders on Amazon. I assume Amazon probably is a bit earlier.

231 00:16:08.430 ⇒ 00:16:09.620 Henry Zhao: Then…

232 00:16:10.250 ⇒ 00:16:12.819 Amber Lin: Like, the spikes that will happen.

233 00:16:13.590 ⇒ 00:16:17.629 Amber Lin: So, it’s earlier or later, so I’m not sure.

234 00:16:17.880 ⇒ 00:16:19.110 Amber Lin: Like, there is also…

235 00:16:19.110 ⇒ 00:16:21.039 Henry Zhao: There’s what I see, right?

236 00:16:21.040 ⇒ 00:16:21.880 Amber Lin: bikes.

237 00:16:22.040 ⇒ 00:16:36.989 Henry Zhao: here’s what I see. I see that summer is a low… no, because we might not have full historical data, but I was saying, like, we probably see more activity from Amazon… well, what actually this tells me is that Amazon is just ramping up starting summer of this year.

238 00:16:38.540 ⇒ 00:16:41.800 Henry Zhao: Because before, it wasn’t really… like, there wasn’t that much stuff from Amazon, unless.

239 00:16:41.800 ⇒ 00:16:55.979 Amber Lin: Yeah, it’s only started from May, so I think we can make a conc- we can tell them that you should expect something to, happen for next year’s March on Amazon as well.

240 00:16:56.730 ⇒ 00:17:05.029 Amber Lin: then we need to tell them the difference in time that it will happen. So, if we look here, the first spike happens around…

241 00:17:05.740 ⇒ 00:17:08.540 Amber Lin: Like, on slide 15.

242 00:17:08.829 ⇒ 00:17:12.439 Amber Lin: If you can go there, we can look at it together.

243 00:17:12.960 ⇒ 00:17:16.490 Amber Lin: Slide… sorry, not 15, slide, 6.

244 00:17:17.810 ⇒ 00:17:22.819 Amber Lin: Slight 6. So, ignore everything before, like…

245 00:17:25.710 ⇒ 00:17:33.319 Amber Lin: So, the first… like, anything… I think there wasn’t any data before… The… Let’s see…

246 00:17:34.260 ⇒ 00:17:42.910 Amber Lin: May. So, the spike actually happened in May, and do you think that’s… a bit before…

247 00:17:43.040 ⇒ 00:17:47.340 Amber Lin: Like, that’s about a month or two before the June-July spike.

248 00:17:49.580 ⇒ 00:17:54.939 Amber Lin: And then… like, I think that’s… the next ramp up…

249 00:17:55.080 ⇒ 00:18:02.999 Amber Lin: is for… I think it’s for Prime, and then again for… like, I don’t know what the last bike is. That could be… that’s a spike.

250 00:18:03.000 ⇒ 00:18:03.370 Henry Zhao: Thank you.

251 00:18:03.370 ⇒ 00:18:05.079 Amber Lin: Yeah, early November.

252 00:18:05.080 ⇒ 00:18:11.010 Henry Zhao: I went back to school on Labor Day and resolved an increase in, like, energy chews and waffles, though.

253 00:18:11.170 ⇒ 00:18:15.210 Amber Lin: I don’t know, like, people make sports, like, they buy…

254 00:18:15.210 ⇒ 00:18:15.870 Henry Zhao: Oh, yeah, that’s true.

255 00:18:15.870 ⇒ 00:18:21.370 Amber Lin: I don’t know. But it’s… you can also just say… we can also say that it’s for Prime Day.

256 00:18:21.690 ⇒ 00:18:23.669 Amber Lin: Like, the prime big deals day.

257 00:18:24.050 ⇒ 00:18:27.729 Amber Lin: So, that’s the major event for Amazon.

258 00:18:27.730 ⇒ 00:18:37.680 Henry Zhao: Yeah, and it looks like for Shopify, it’s, it’s heavy in the summer, and it’s heavy on Cyber Monday, and then there’s also usually spike in March.

259 00:18:37.680 ⇒ 00:18:44.109 Amber Lin: Yeah, I do want to see their, like, promotional cadence, because that’s very even.

260 00:18:45.730 ⇒ 00:18:49.670 Amber Lin: Put… Oh my god.

261 00:18:51.020 ⇒ 00:18:54.999 Henry Zhao: Yeah, and I think we should join this stuff with, like, the stuff we see in Klaviyo, so, like…

262 00:18:56.740 ⇒ 00:18:59.839 Henry Zhao: Yeah, I see the spikes here too, but not, like, that dramatically.

263 00:19:01.140 ⇒ 00:19:17.150 Amber Lin: Do you think we can say that there will be increased demand next year in March? Like, do you think that is because the promotions they run on Shopify, or is it just natural demand? Because I don’t know how that would translate to Amazon.

264 00:19:19.890 ⇒ 00:19:25.149 Henry Zhao: I don’t really know either, because we don’t have enough data, and I don’t understand Amazon purchasing behaviors enough.

265 00:19:25.410 ⇒ 00:19:29.179 Henry Zhao: to… To opinionate on that, but…

266 00:19:29.320 ⇒ 00:19:34.079 Henry Zhao: Let’s take a look at this, actually. Let’s look at orders and order amounts.

267 00:19:35.200 ⇒ 00:19:36.520 Henry Zhao: For the last…

268 00:19:36.690 ⇒ 00:19:41.289 Henry Zhao: let’s say 2 years. Let me just get rid of this status, because I don’t care… I just want to look at page, actually.

269 00:19:41.290 ⇒ 00:19:45.970 Amber Lin: It seems that they have a Waffle Madness sale, in March.

270 00:19:47.010 ⇒ 00:19:47.950 Henry Zhao: That’s…

271 00:19:48.250 ⇒ 00:19:54.670 Amber Lin: Like… college athletes. Wait, when do… when do school start?

272 00:19:55.800 ⇒ 00:19:57.180 Amber Lin: When does school start?

273 00:19:57.930 ⇒ 00:20:00.080 Henry Zhao: School starts in August or September.

274 00:20:03.300 ⇒ 00:20:06.059 Henry Zhao: We’ll start with March 2023, actually. Alright.

275 00:20:06.760 ⇒ 00:20:12.399 Henry Zhao: here’s what I want to look at. Let’s look at this with Klaviyo for… this might actually be an interesting insight.

276 00:20:15.990 ⇒ 00:20:19.119 Henry Zhao: Product viewed. Products viewed, active on site.

277 00:20:19.510 ⇒ 00:20:20.350 Henry Zhao: Interesting, okay.

278 00:20:20.870 ⇒ 00:20:28.180 Henry Zhao: I cannot say it, I think it’s pretty straightforward. So this is, purchases… Amount.

279 00:20:28.510 ⇒ 00:20:30.989 Henry Zhao: So let’s look at how many purchases

280 00:20:31.390 ⇒ 00:20:34.589 Henry Zhao: Come out of each, product’s view.

281 00:20:36.350 ⇒ 00:20:37.940 Henry Zhao: You understand what I’m trying to do here?

282 00:20:38.650 ⇒ 00:20:39.960 Amber Lin: No.

283 00:20:41.040 ⇒ 00:20:45.099 Henry Zhao: So this is the number of purchases for each month, and this is the amount of, like, views.

284 00:20:45.470 ⇒ 00:20:49.379 Henry Zhao: So I want to look at how many product views result in a purchase, right?

285 00:20:49.380 ⇒ 00:20:50.480 Amber Lin: Okay, cool.

286 00:20:51.080 ⇒ 00:20:54.769 Henry Zhao: And then we can also look at average amount per purchase, per viewer.

287 00:20:56.200 ⇒ 00:21:05.089 Henry Zhao: this could tell us, like, for each person that, right, how much money are they worth, right? Okay. So that way, like, I would be willing to spend this much on marketing. This could actually be a useful…

288 00:21:07.520 ⇒ 00:21:11.770 Henry Zhao: I don’t know how this month has 3,012 product views, but 3,472 orders.

289 00:21:13.290 ⇒ 00:21:14.870 Amber Lin: Maybe people came back.

290 00:21:16.310 ⇒ 00:21:20.260 Amber Lin: Or they made multiple orders. Doesn’t make sense.

291 00:21:21.370 ⇒ 00:21:25.649 Henry Zhao: So then I… maybe I don’t like this number that much, but it’s not that… it’s not that insightful anyway.

292 00:21:26.580 ⇒ 00:21:30.780 Henry Zhao: But, like, this amount per viewer is probably good.

293 00:21:30.970 ⇒ 00:21:31.800 Amber Lin: Mmm.

294 00:21:31.800 ⇒ 00:21:32.480 Henry Zhao: Viewer.

295 00:21:33.160 ⇒ 00:21:35.889 Amber Lin: Seems like they’re overall getting better.

296 00:21:36.560 ⇒ 00:21:37.880 Henry Zhao: Yeah, is it?

297 00:21:39.400 ⇒ 00:21:41.770 Henry Zhao: Let’s, graph this and see what it looks like.

298 00:21:43.440 ⇒ 00:21:45.749 Henry Zhao: This could actually be a useful insight.

299 00:21:50.520 ⇒ 00:21:54.119 Henry Zhao: Revenue… Per product viewer.

300 00:21:55.990 ⇒ 00:22:01.600 Henry Zhao: I’m gonna bring this up, because now I want to also look at per site active person, okay?

301 00:22:03.820 ⇒ 00:22:04.850 Henry Zhao: Go away!

302 00:22:05.790 ⇒ 00:22:07.749 Henry Zhao: Alright, so now let’s take this amount.

303 00:22:10.470 ⇒ 00:22:16.430 Henry Zhao: And line it up here… Revenue.

304 00:22:18.510 ⇒ 00:22:21.049 Henry Zhao: Rev per impression.

305 00:22:21.820 ⇒ 00:22:25.760 Henry Zhao: rep per visitor, right? So this is how many people visited the site.

306 00:22:28.140 ⇒ 00:22:31.399 Henry Zhao: And how many… what ended up being the revenue, right? So…

307 00:22:33.180 ⇒ 00:22:35.669 Henry Zhao: I would be willing to pay, like, this much.

308 00:22:36.650 ⇒ 00:22:37.610 Henry Zhao: click.

309 00:22:37.870 ⇒ 00:22:40.949 Henry Zhao: In my, in my, advertising. Does that make sense?

310 00:22:44.340 ⇒ 00:22:47.820 Henry Zhao: I think this is something we could definitely share. This is insights that they definitely need to know.

311 00:22:49.320 ⇒ 00:22:51.240 Henry Zhao: And this actually has a business impact.

312 00:22:54.480 ⇒ 00:22:56.639 Henry Zhao: Revenue per site visitor.

313 00:22:58.550 ⇒ 00:23:00.540 Henry Zhao: It looks like it’s growing now, so…

314 00:23:01.870 ⇒ 00:23:03.999 Henry Zhao: That allows them to spend more on marketing, you know?

315 00:23:04.430 ⇒ 00:23:05.330 Amber Lin: Oh, that’s good.

316 00:23:06.080 ⇒ 00:23:10.809 Henry Zhao: So I would be willing to spend around $40 t o$ 60, for an impression, for a click.

317 00:23:12.660 ⇒ 00:23:15.010 Henry Zhao: So the CPC that they do should be…

318 00:23:15.150 ⇒ 00:23:18.119 Henry Zhao: In the 40 or below that range, you know?

319 00:23:19.110 ⇒ 00:23:22.359 Henry Zhao: Because for each visitor, I’m expected to generate about $40 of revenue.

320 00:23:23.720 ⇒ 00:23:26.560 Henry Zhao: And then they might actually end up buying in Walmart, or…

321 00:23:26.760 ⇒ 00:23:31.070 Henry Zhao: Or Amazon, etc, right? So the revenue per visitor is probably a little bit higher than this.

322 00:23:31.070 ⇒ 00:23:39.520 Amber Lin: Okay. You want to copy and paste that graph in the slides so we know, like, where… what we want to talk about?

323 00:23:39.830 ⇒ 00:23:40.235 Henry Zhao: Hmm?

324 00:23:44.820 ⇒ 00:23:46.710 Amber Lin: Like, maybe…

325 00:23:47.340 ⇒ 00:23:48.159 Henry Zhao: Bring it here.

326 00:24:09.840 ⇒ 00:24:16.999 Henry Zhao: Understanding revenue… revenue… First site visitor…

327 00:24:17.000 ⇒ 00:24:18.680 Amber Lin: Can I? Can we…

328 00:24:18.860 ⇒ 00:24:26.709 Amber Lin: Back check the number of visitors. I don’t want this to be because they’re just getting less visitors, and we have faulty data.

329 00:24:26.940 ⇒ 00:24:32.019 Amber Lin: Like, the number of visitors and number of orders. Is the number of orders increasing, do you think?

330 00:24:33.420 ⇒ 00:24:38.650 Henry Zhao: Number of orders is not really increasing, it’s just kind of… All over the place.

331 00:24:38.910 ⇒ 00:24:40.030 Amber Lin: We have that shot.

332 00:24:40.630 ⇒ 00:24:46.159 Amber Lin: And why is the… why is the revenue per visitor going up? Are they just getting.

333 00:24:46.160 ⇒ 00:24:49.350 Henry Zhao: Let’s look at the revenue over time.

334 00:24:51.470 ⇒ 00:24:53.629 Henry Zhao: Create a pivot chart, how do I do that?

335 00:24:59.980 ⇒ 00:25:01.709 Henry Zhao: How do I create a baby chart from this?

336 00:25:04.400 ⇒ 00:25:08.199 Henry Zhao: I mean, There used to be, like, a button that, like, let’s like, create picture chart.

337 00:25:08.200 ⇒ 00:25:14.650 Amber Lin: Yeah, I think it’s on the… if you click on the pivot, Something in the top? Okay.

338 00:25:24.380 ⇒ 00:25:26.759 Henry Zhao: Well, I gotta pick them out, okay? So…

339 00:25:27.970 ⇒ 00:25:29.520 Amber Lin: What is Faco Melt?

340 00:25:30.340 ⇒ 00:25:31.950 Henry Zhao: I just don’t… I just don’t want you to look at it.

341 00:25:35.790 ⇒ 00:25:36.779 Henry Zhao: Dang it.

342 00:25:41.660 ⇒ 00:25:42.919 Henry Zhao: It’s not really going up.

343 00:25:43.510 ⇒ 00:25:46.569 Amber Lin: Okay, so that’s something happening with their visitors, then.

344 00:25:47.080 ⇒ 00:25:48.950 Amber Lin: Why are they getting less visitors?

345 00:25:49.280 ⇒ 00:25:51.280 Henry Zhao: But it is slightly going up, let’s look at the trend line.

346 00:25:53.950 ⇒ 00:25:57.069 Amber Lin: Oh… Like, the patterns are so rated.

347 00:25:57.070 ⇒ 00:25:58.070 Henry Zhao: You’re really going up.

348 00:25:58.820 ⇒ 00:25:59.500 Amber Lin: Huh?

349 00:25:59.900 ⇒ 00:26:01.440 Henry Zhao: It’s barely going up.

350 00:26:01.440 ⇒ 00:26:08.580 Amber Lin: Yeah. But you see, the pattern is so interesting. The first two years, especially, it almost looks identical.

351 00:26:08.690 ⇒ 00:26:10.200 Amber Lin: Spike, spike, spike.

352 00:26:10.590 ⇒ 00:26:17.620 Henry Zhao: I’d rather visit there, though? Then… Kind of, yeah, it’s like…

353 00:26:17.660 ⇒ 00:26:20.820 Amber Lin: Traffic are going down.

354 00:26:21.560 ⇒ 00:26:22.560 Amber Lin: Oh.

355 00:26:24.800 ⇒ 00:26:26.119 Henry Zhao: Yeah, it is. See?

356 00:26:26.840 ⇒ 00:26:28.549 Amber Lin: Yeah, that’s bad.

357 00:26:30.210 ⇒ 00:26:34.599 Henry Zhao: Because we should have… Aventura’s going down, but orders are not, so they’re getting better visitors.

358 00:26:34.620 ⇒ 00:26:35.490 Amber Lin: Huh.

359 00:26:36.400 ⇒ 00:26:38.190 Amber Lin: What’s the conversion rate?

360 00:26:40.570 ⇒ 00:26:43.050 Henry Zhao: The conversion rate was…

361 00:26:43.960 ⇒ 00:26:47.910 Amber Lin: Like, I know it’s not that accurate, but it’s still probably helpful for us to look at.

362 00:26:51.130 ⇒ 00:26:52.279 Henry Zhao: Just keep a second.

363 00:26:55.540 ⇒ 00:26:56.590 Henry Zhao: Months?

364 00:26:57.800 ⇒ 00:27:07.510 Henry Zhao: Visitors… I probably want to do a join in Klaviyo and just actually look at visitors and, purchasers…

365 00:27:09.050 ⇒ 00:27:10.540 Henry Zhao: It’ll be around the same.

366 00:27:12.170 ⇒ 00:27:13.050 Henry Zhao: Yes.

367 00:27:13.430 ⇒ 00:27:17.709 Henry Zhao: Purchases… oh, this is why, because purchases could be more than one, that’s why.

368 00:27:18.080 ⇒ 00:27:19.800 Amber Lin: I see, so not orders.

369 00:27:20.200 ⇒ 00:27:23.160 Henry Zhao: Should be purchasers, yeah, not orders, should be purchasers.

370 00:27:23.380 ⇒ 00:27:25.360 Henry Zhao: So let’s do that real quick,

371 00:27:26.110 ⇒ 00:27:33.029 Henry Zhao: Let’s do, instead of count, count, I can just…

372 00:27:34.550 ⇒ 00:27:37.570 Henry Zhao: distinct order, so those are good.

373 00:27:37.570 ⇒ 00:27:42.229 Amber Lin: You can do distinct per- no. Like, distinct purchase times?

374 00:27:42.640 ⇒ 00:27:43.900 Amber Lin: If that works?

375 00:27:43.900 ⇒ 00:27:46.400 Henry Zhao: Oh, we wanna just think purchasers, so…

376 00:27:46.400 ⇒ 00:27:47.219 Amber Lin: Let’s see…

377 00:27:49.930 ⇒ 00:27:51.829 Henry Zhao: Customer ID, sign.

378 00:27:55.360 ⇒ 00:28:02.550 Amber Lin: Wait, in that case, wouldn’t it be… if a customer ordered multiple times a month, it still would be the same, like, you would only count it once?

379 00:28:03.030 ⇒ 00:28:05.140 Henry Zhao: Yep, exactly. That’s what we want to do.

380 00:28:07.380 ⇒ 00:28:08.389 Henry Zhao: Because we want to see, actually.

381 00:28:08.390 ⇒ 00:28:11.049 Amber Lin: Oh, like, if more people join, okay.

382 00:28:14.100 ⇒ 00:28:14.700 Amber Lin: Hmm.

383 00:28:18.890 ⇒ 00:28:19.520 Henry Zhao: It’s okay.

384 00:28:19.520 ⇒ 00:28:22.609 Amber Lin: So that doesn’t account for if you get repeat customers.

385 00:28:23.480 ⇒ 00:28:26.709 Henry Zhao: Right, exactly. Because you don’t… because that’s why it was going over 100%.

386 00:28:27.890 ⇒ 00:28:30.080 Henry Zhao: Because people can purchase more than once. Okay.

387 00:28:30.080 ⇒ 00:28:30.540 Amber Lin: Okay.

388 00:28:30.540 ⇒ 00:28:33.070 Henry Zhao: So, conversion rate, essentially, right?

389 00:28:34.640 ⇒ 00:28:35.910 Henry Zhao: Let me get rid of this now.

390 00:28:38.560 ⇒ 00:28:39.549 Henry Zhao: No way.

391 00:28:42.450 ⇒ 00:28:45.389 Henry Zhao: There’s still stuff over 100%, so this bothers me.

392 00:28:53.670 ⇒ 00:28:57.060 Henry Zhao: Can you purchase without going to the site? Maybe you can, right?

393 00:28:57.940 ⇒ 00:29:00.520 Henry Zhao: So yeah, definitely conversion rate is going up, because it used to be in the 50s.

394 00:29:00.520 ⇒ 00:29:03.500 Amber Lin: Can you purchase without going to the site, though?

395 00:29:07.540 ⇒ 00:29:09.960 Amber Lin: Like, isn’t the checkout on the site?

396 00:29:10.160 ⇒ 00:29:13.370 Henry Zhao: I think you can, I think there’s, like, recharge and stuff like that.

397 00:29:13.370 ⇒ 00:29:16.970 Amber Lin: Oh, so if they had a subscription?

398 00:29:17.220 ⇒ 00:29:17.810 Henry Zhao: Yeah.

399 00:29:18.550 ⇒ 00:29:19.190 Amber Lin: Oh…

400 00:29:19.470 ⇒ 00:29:20.190 Henry Zhao: I might meet with it.

401 00:29:20.190 ⇒ 00:29:21.269 Amber Lin: Do they have a subscription?

402 00:29:21.270 ⇒ 00:29:23.960 Henry Zhao: Obviously, that’ll increase over time, because subscriptions stay.

403 00:29:23.960 ⇒ 00:29:27.149 Amber Lin: Yeah, hi stingers.

404 00:29:27.560 ⇒ 00:29:28.860 Amber Lin: description…

405 00:29:30.290 ⇒ 00:29:31.450 Henry Zhao: We could also, let’s just double check.

406 00:29:31.450 ⇒ 00:29:36.189 Amber Lin: Oh, yeah, subscribe and save on your favorites. Okay, fine, that works.

407 00:29:36.190 ⇒ 00:29:38.560 Henry Zhao: No, 100% is very shady to me.

408 00:29:38.910 ⇒ 00:29:39.620 Amber Lin: Yeah.

409 00:29:39.920 ⇒ 00:29:41.270 Henry Zhao: Very shady.

410 00:29:41.620 ⇒ 00:29:43.859 Henry Zhao: Because I don’t think there are that many subscriptions.

411 00:29:44.230 ⇒ 00:29:45.570 Amber Lin: Yeah…

412 00:29:50.530 ⇒ 00:29:56.220 Henry Zhao: I would… I would maybe ask, like, can they buy… Without going to the site.

413 00:29:57.760 ⇒ 00:30:00.939 Henry Zhao: I just say, because we’re seeing more purchasers than site visitors.

414 00:30:01.370 ⇒ 00:30:04.650 Amber Lin: When does the 100% start happening? That’s only…

415 00:30:04.710 ⇒ 00:30:06.230 Henry Zhao: May of this year.

416 00:30:06.390 ⇒ 00:30:09.520 Amber Lin: That’s so interesting, because that’s when the PO order spiked.

417 00:30:10.750 ⇒ 00:30:15.760 Amber Lin: Like, that’s… I think that’s when the demand went a lot… up a lot more.

418 00:30:15.760 ⇒ 00:30:18.640 Henry Zhao: I wonder if Amazon is included in these purchasers here.

419 00:30:19.180 ⇒ 00:30:23.950 Amber Lin: Don’t… oh, I actually think so, because there’s…

420 00:30:24.620 ⇒ 00:30:32.889 Amber Lin: Like, when I looked at Shopify data, I think some of their manufacturers had Amazon on a name, so do they count the Amazon purchases?

421 00:30:32.890 ⇒ 00:30:38.399 Henry Zhao: Let’s see if orders… let’s see if orders has, like, where the order came from, like, if it came from the site or not.

422 00:30:46.440 ⇒ 00:30:51.249 Henry Zhao: Also, I’m 6 minutes late for a meeting, sorry. Do you want to continue to help me look into this?

423 00:30:51.610 ⇒ 00:30:53.530 Amber Lin: Yeah, when are you done with your meeting?

424 00:30:54.510 ⇒ 00:30:58.520 Henry Zhao: Oh, actually, this might be a fake meeting.

425 00:30:59.470 ⇒ 00:31:00.870 Henry Zhao: This is a fake meeting, right?

426 00:31:01.900 ⇒ 00:31:03.299 Amber Lin: Well…

427 00:31:04.050 ⇒ 00:31:09.249 Amber Lin: declined, so I don’t know what we’re gonna do. We can hop on and see, but he declined.

428 00:31:09.250 ⇒ 00:31:12.919 Henry Zhao: Top on NC, and then we’ll come back here. Yeah. Or we can just stay on there. Let’s just stay on there, right?

429 00:31:12.920 ⇒ 00:31:15.230 Amber Lin: I’ll stay here, let me know if you need me there.

430 00:31:15.230 ⇒ 00:31:17.980 Henry Zhao: Yeah, let’s go to that one, and then we can just continue talking there if there’s nobody there.

431 00:31:18.660 ⇒ 00:31:19.259 Henry Zhao: You know what I mean?

432 00:31:19.260 ⇒ 00:31:23.640 Amber Lin: Okay, I do want, like, I do want the recording to be in one place, because then you can…

433 00:31:23.640 ⇒ 00:31:24.120 Henry Zhao: Oh, okay.

434 00:31:24.120 ⇒ 00:31:25.370 Amber Lin: Summarize the insights.

435 00:31:25.370 ⇒ 00:31:27.250 Henry Zhao: Okay, I’ll be right back then. I’ll be right back.

436 00:31:27.580 ⇒ 00:31:28.200 Amber Lin: Cool.

437 00:32:12.560 ⇒ 00:32:13.509 Henry Zhao: Are you there?

438 00:32:15.340 ⇒ 00:32:16.879 Henry Zhao: You’re on mute if you’re talking.

439 00:32:17.400 ⇒ 00:32:19.840 Amber Lin: Oh, I am on mute.

440 00:32:20.180 ⇒ 00:32:21.350 Amber Lin: Cool, okay.

441 00:32:21.560 ⇒ 00:32:22.330 Amber Lin: Woo.

442 00:32:22.940 ⇒ 00:32:33.189 Amber Lin: Hmm. Can we plot these two trend lines together? Like, can we… can you do, like, 3 trend lines on… on these, just so that we can see?

443 00:32:33.330 ⇒ 00:32:36.230 Amber Lin: It’s hard to check the numbers just by eye.

444 00:32:37.270 ⇒ 00:32:37.910 Henry Zhao: Sure.

445 00:32:42.190 ⇒ 00:32:43.309 Henry Zhao: Why is my…

446 00:32:44.040 ⇒ 00:32:50.979 Henry Zhao: Why would any tool ever put month as a data point? That should be programmed to just not even be as big.

447 00:32:54.280 ⇒ 00:32:55.120 Henry Zhao: Oops.

448 00:33:02.240 ⇒ 00:33:04.230 Henry Zhao: For diabet.

449 00:33:04.340 ⇒ 00:33:06.190 Henry Zhao: I’ll put the secondary access.

450 00:33:17.750 ⇒ 00:33:25.720 Amber Lin: Hmm, wow, so cool. Okay, so we’re seeing… Visitors and purchases go down?

451 00:33:26.620 ⇒ 00:33:27.650 Henry Zhao: No.

452 00:33:27.650 ⇒ 00:33:29.419 Amber Lin: Like, they’re overlapping.

453 00:33:29.850 ⇒ 00:33:33.170 Henry Zhao: They’re going up. Okay, so rare rates obviously going up.

454 00:33:35.240 ⇒ 00:33:36.820 Henry Zhao: Purchases? Purchases are going up.

455 00:33:37.030 ⇒ 00:33:38.049 Henry Zhao: It’s for sure going up.

456 00:33:40.040 ⇒ 00:33:40.840 Amber Lin: Okay.

457 00:33:40.960 ⇒ 00:33:42.500 Henry Zhao: There’s those definitely going down, yeah.

458 00:33:43.480 ⇒ 00:33:44.969 Henry Zhao: That’s why the conversion rate’s going up.

459 00:33:46.150 ⇒ 00:33:53.490 Amber Lin: Yeah… This is a good graph, it’s just a little messy, but it’s a good graph.

460 00:33:56.250 ⇒ 00:33:59.950 Amber Lin: I think per month is good, though. Like, I think we want to see the spikes.

461 00:34:00.370 ⇒ 00:34:00.860 Henry Zhao: Yeah.

462 00:34:00.860 ⇒ 00:34:02.490 Amber Lin: But overall, we can…

463 00:34:03.710 ⇒ 00:34:05.429 Henry Zhao: We knew that it was still… What?

464 00:34:05.430 ⇒ 00:34:07.269 Amber Lin: Visitors going down.

465 00:34:08.260 ⇒ 00:34:10.009 Amber Lin: That’s not good for them.

466 00:34:11.570 ⇒ 00:34:25.899 Amber Lin: Like, are they tightening their marketing? Like, if conversion rate’s going up, are they tightening their marketing strategy? Are they more targeted? But if they’re getting more orders, shouldn’t it be because they’re becoming, say, more viral and more people get them? Like, they…

467 00:34:25.909 ⇒ 00:34:26.819 Henry Zhao: The way I interpret it?

468 00:34:26.820 ⇒ 00:34:27.130 Amber Lin: down.

469 00:34:27.149 ⇒ 00:34:34.889 Henry Zhao: Either data issue, or, their visitors are being… they’re targeting better, right? So they’re bringing on better visitors.

470 00:34:35.009 ⇒ 00:34:36.369 Amber Lin: Yeah…

471 00:34:36.530 ⇒ 00:34:42.769 Henry Zhao: Which is… which is common for a company, like, when you start out, right, you don’t know who to target, so you just kind of throw your marketing out there all over the place.

472 00:34:42.770 ⇒ 00:34:43.270 Amber Lin: True.

473 00:34:43.270 ⇒ 00:34:50.440 Henry Zhao: You get a lot of visitors, but not everyone’s interested in the honey waffles or whatever. Over time, you’re gonna get better visitors also, because you’re gonna get repeat visitors.

474 00:34:50.570 ⇒ 00:34:51.620 Henry Zhao: It’s one.

475 00:34:51.850 ⇒ 00:35:10.469 Henry Zhao: Secondly, you’re gonna be better at targeting. You’re gonna be able to use lookalike audiences, you’re gonna know, kind of, who responds better to your advertising, and only do that, and you’re gonna be doing retargeting, right? So… and in the beginning, you’re, like, just kind of throwing a wide net out there, but then when you realize these are the people that actually care, you start targeting those people only, and you get better results.

476 00:35:10.470 ⇒ 00:35:12.349 Amber Lin: I like that graph, can we put that in?

477 00:35:13.210 ⇒ 00:35:19.239 Amber Lin: Like, Robert will… Robert will say it’s messy, but I think I just want… I just want to let him know, like, what we found.

478 00:35:20.190 ⇒ 00:35:20.850 Henry Zhao: Yeah.

479 00:35:32.040 ⇒ 00:35:32.930 Henry Zhao: What the heck?

480 00:35:39.730 ⇒ 00:35:40.700 Henry Zhao: No.

481 00:36:04.000 ⇒ 00:36:05.379 Henry Zhao: Then we need to, like…

482 00:36:35.510 ⇒ 00:36:38.420 Henry Zhao: I’m just, like, putting my… our notes from today,

483 00:36:59.630 ⇒ 00:37:00.890 Henry Zhao: Yeah, makes sense.

484 00:37:28.510 ⇒ 00:37:32.770 Henry Zhao: Something like that, yeah. This is not clean, but, like, this is kind of what we’ve so far found.

485 00:37:34.470 ⇒ 00:37:43.759 Henry Zhao: And then now that we have Shopify data and we have, like, the customer’s data, we can figure out, like, is there any demographic stuff we can probably analyze and join with that data, right? So…

486 00:37:44.070 ⇒ 00:37:56.399 Henry Zhao: All this stuff is demographic, so I don’t know… we can look at, email marketing consent versus no consent, like, how likely they are to purchase versus their average revenue. We have the orders count here, so that’s actually pretty easy to do.

487 00:37:57.030 ⇒ 00:37:58.989 Henry Zhao: SMS marketing consent also…

488 00:38:04.470 ⇒ 00:38:07.310 Henry Zhao: Active subscriber, we can see there at that.

489 00:38:09.890 ⇒ 00:38:12.600 Henry Zhao: Yeah, we can probably look at that already, actually.

490 00:38:13.150 ⇒ 00:38:14.309 Henry Zhao: Wanna look at that right now?

491 00:38:22.030 ⇒ 00:38:28.299 Henry Zhao: So, consent updated at… Optin level. Alright, let’s look at what are the possible opt-in levels.

492 00:38:30.180 ⇒ 00:38:31.560 Henry Zhao: I’m not gonna consent.

493 00:38:35.360 ⇒ 00:38:37.459 Henry Zhao: Honey Stinger, are you still there?

494 00:38:39.910 ⇒ 00:38:42.269 Amber Lin: I was muted, sorry, I said yes.

495 00:38:45.590 ⇒ 00:38:47.550 Henry Zhao: Email RC.

496 00:38:48.080 ⇒ 00:38:58.170 Henry Zhao: And… total… Orders account… These mosquito bites are killing me. Total spent.

497 00:38:59.950 ⇒ 00:39:00.760 Henry Zhao: It’s tough.

498 00:39:03.280 ⇒ 00:39:07.219 Henry Zhao: You know, marketing spent.optInLevel.

499 00:39:14.600 ⇒ 00:39:22.160 Amber Lin: What does the opt-in mean? So they receive the marketing and then say, I want to unsubscribe or subscribe?

500 00:39:24.160 ⇒ 00:39:25.110 Henry Zhao: Yeah.

501 00:39:25.270 ⇒ 00:39:30.770 Henry Zhao: Look, it’s… Mmm, it’s not really that… It’s not really that impactful.

502 00:39:42.840 ⇒ 00:39:46.749 Henry Zhao: I mean, this is pretty obvious, right? Like, if you’re not opted in, you’re probably not buying anything.

503 00:39:51.130 ⇒ 00:39:51.840 Henry Zhao: Hmm.

504 00:39:52.440 ⇒ 00:39:56.629 Henry Zhao: I don’t know, this is… this is more… another one of those, like, duh things, but I can maybe…

505 00:39:57.220 ⇒ 00:40:03.889 Henry Zhao: Take a look at orders, and then joinhoneySinger.polytomic.com.

506 00:40:04.180 ⇒ 00:40:09.479 Henry Zhao: Shopify.customers, C on o dot customer ID, system.id.

507 00:40:11.700 ⇒ 00:40:13.270 Henry Zhao: That’s not right.

508 00:40:25.570 ⇒ 00:40:28.680 Amber Lin: Cool. I put in slide 12.

509 00:40:28.980 ⇒ 00:40:32.669 Henry Zhao: The weekly sales and weekly traffic for Walmart.

510 00:40:33.040 ⇒ 00:40:35.689 Amber Lin: Big traffic…

511 00:40:37.200 ⇒ 00:40:37.820 Henry Zhao: Okay.

512 00:40:39.100 ⇒ 00:40:41.019 Henry Zhao: Yeah, again, it’s very high in the summer, right?

513 00:40:41.940 ⇒ 00:40:47.979 Amber Lin: Yeah, big spike in around July for traffic, however, sales remained pretty high.

514 00:40:47.980 ⇒ 00:40:52.229 Henry Zhao: No, no, no, don’t say July. So this is what we were seeing in,

515 00:40:52.750 ⇒ 00:40:55.990 Henry Zhao: Oh, you’re right, okay, so your axis is a little bit more different, but…

516 00:40:56.710 ⇒ 00:40:59.800 Henry Zhao: Because it looks like it bleeds into August also, and we saw that in Shopify as well.

517 00:40:59.800 ⇒ 00:41:11.400 Amber Lin: A little… a little bit, so between… this is weekly, right? So it’s, like, the month of July, and then it slowly declines off for visitors, but their sales are still…

518 00:41:11.530 ⇒ 00:41:20.060 Amber Lin: pretty high. Like, their stills are… they’re still the same. So that also probably means they’re… they have better targeting, because the conversion rates must have went up.

519 00:41:20.370 ⇒ 00:41:20.929 Amber Lin: If that.

520 00:41:20.930 ⇒ 00:41:24.490 Henry Zhao: No, this is a little weird, honestly. This looks a little more weird to me.

521 00:41:26.110 ⇒ 00:41:26.940 Amber Lin: Why is that?

522 00:41:27.180 ⇒ 00:41:29.139 Amber Lin: Because there’s a significant drop?

523 00:41:29.890 ⇒ 00:41:32.870 Henry Zhao: Yeah, this, this, look, and that… I just…

524 00:41:33.080 ⇒ 00:41:36.429 Henry Zhao: Something doesn’t sit right with me, like, it doesn’t feel right, you know?

525 00:41:36.430 ⇒ 00:41:37.060 Amber Lin: Hmm.

526 00:41:38.600 ⇒ 00:41:40.889 Henry Zhao: Because we’ve worked over 10 years now, we’ve seen, like.

527 00:41:41.490 ⇒ 00:41:46.100 Henry Zhao: Actual data versus, like, 8 data errors, this looks to me more like a data error of some sort.

528 00:41:46.240 ⇒ 00:41:49.429 Amber Lin: Hmm. Yeah, let me… I can go check.

529 00:41:49.800 ⇒ 00:41:52.919 Henry Zhao: Yeah, like… In my mind, it doesn’t seem to sit right.

530 00:41:54.020 ⇒ 00:41:56.259 Henry Zhao: But glad that you put this slide, I think this is good.

531 00:42:00.780 ⇒ 00:42:05.170 Henry Zhao: And do we want anything on subscribers? I think we can wait for next week, when we have the attentive date and stuff like that.

532 00:42:05.170 ⇒ 00:42:08.010 Amber Lin: Yeah, I see. Some of,

533 00:42:08.340 ⇒ 00:42:13.860 Amber Lin: Like, there’s brand shop data until… the date range is until, like.

534 00:42:14.180 ⇒ 00:42:22.130 Amber Lin: November, but some of the energy chews and stuff, their… their date range is lower, but if they go to the brand shop, they…

535 00:42:23.390 ⇒ 00:42:28.529 Amber Lin: they probably… say, because the energy choose date range ends in September.

536 00:42:28.750 ⇒ 00:42:36.209 Amber Lin: So, and there’s a few, like, Energy Waffles also ends in September, but I think the brand shop is a separate thing.

537 00:42:37.500 ⇒ 00:42:38.900 Amber Lin: And if they…

538 00:42:39.150 ⇒ 00:42:44.179 Amber Lin: If they were to go to see the energy chews, they probably already went through the brand shop.

539 00:42:58.590 ⇒ 00:43:03.889 Henry Zhao: Let me just do a chart on… since it’s about to be Black Friday, let’s just do one on Black Monday, and then I think we’re good.

540 00:43:03.970 ⇒ 00:43:04.810 Amber Lin: Okay.

541 00:43:11.070 ⇒ 00:43:16.369 Henry Zhao: Can you… not give me these months, please? How do I cut off, like…

542 00:43:23.640 ⇒ 00:43:25.439 Amber Lin: You might need to do two graphs.

543 00:43:26.630 ⇒ 00:43:28.893 Henry Zhao: I don’t want to!

544 00:43:30.870 ⇒ 00:43:35.030 Henry Zhao: I just don’t think it’ll look as nice as two graphs. Honestly, I’ll just keep this as one graph.

545 00:43:36.160 ⇒ 00:43:39.550 Henry Zhao: cyber Monday spike.

546 00:43:40.540 ⇒ 00:43:44.990 Henry Zhao: The past 2 years… Tells us we can expect

547 00:43:45.340 ⇒ 00:43:49.290 Henry Zhao: buyer sales this upcoming Monday. This might be.

548 00:43:49.290 ⇒ 00:43:51.970 Amber Lin: confusing for them, if we don’t have the middle part

549 00:43:53.090 ⇒ 00:43:59.270 Amber Lin: Like, they don’t know how much it compares. Like, yes, it is a spike, but how much does it compare to the usual months?

550 00:44:02.300 ⇒ 00:44:05.029 Henry Zhao: I don’t want them to see their months, though, so I’m gonna…

551 00:44:05.300 ⇒ 00:44:06.819 Amber Lin: Let’s just do two graphs, then.

552 00:44:09.160 ⇒ 00:44:10.120 Henry Zhao: Fine.

553 00:44:25.380 ⇒ 00:44:27.880 Henry Zhao: What is 1111? Why is there a spike there?

554 00:44:28.970 ⇒ 00:44:36.040 Amber Lin: 11.11 is the singles day sale. It’s pretty popular in Asia, I don’t know if it’s here, though.

555 00:44:45.750 ⇒ 00:44:48.839 Henry Zhao: Well, this year, they had a… this year they had a different… last year, they had a different day.

556 00:44:50.020 ⇒ 00:44:54.590 Henry Zhao: So it looks like maybe it’s a pre… Maybe a pre-Black Friday sale.

557 00:44:54.590 ⇒ 00:44:59.559 Amber Lin: Yeah, how many days is it before Black Friday? Because it’s a different day in both years.

558 00:45:00.250 ⇒ 00:45:03.050 Henry Zhao: So, 27, 13, that’s 2 weeks.

559 00:45:03.670 ⇒ 00:45:06.419 Henry Zhao: 2… Two weeks again!

560 00:45:06.900 ⇒ 00:45:11.070 Amber Lin: Yeah, so they probably did Black Friday and then Cyber Monday, so those are just a few.

561 00:45:11.070 ⇒ 00:45:17.760 Henry Zhao: No, they did a… they did a… it’s two weeks until Cyber Monday, when we have a pre-sale. That’s literally probably what it is. You see this up here, too? That’s one week.

562 00:45:18.240 ⇒ 00:45:18.590 Amber Lin: Cool.

563 00:45:18.590 ⇒ 00:45:20.380 Henry Zhao: Yeah. See? Same thing. Alright.

564 00:45:21.030 ⇒ 00:45:21.830 Amber Lin: Okay.

565 00:45:22.280 ⇒ 00:45:24.670 Amber Lin: Let’s put those two graphs on.

566 00:45:24.670 ⇒ 00:45:27.860 Henry Zhao: Cyber Monday 2023 spike.

567 00:45:30.450 ⇒ 00:45:33.960 Henry Zhao: Cyber Monday 2024 spike.

568 00:45:41.600 ⇒ 00:45:43.099 Henry Zhao: Can you create a new slide for me, please?

569 00:45:43.660 ⇒ 00:45:45.380 Henry Zhao: I can bash it.

570 00:46:07.200 ⇒ 00:46:09.890 Henry Zhao: The heck, I don’t know, it’s why it’s so hard for me to copy this?

571 00:46:18.490 ⇒ 00:46:20.500 Amber Lin: Cool, I can add the pointers.

572 00:46:28.780 ⇒ 00:46:32.200 Amber Lin: Can you give me the exact dates for these bikes?

573 00:46:32.560 ⇒ 00:46:33.220 Henry Zhao: Yeah.

574 00:46:56.250 ⇒ 00:46:58.809 Amber Lin: Okay, and then the spike is on…

575 00:46:59.940 ⇒ 00:47:02.899 Amber Lin: Good. I’ll create another small one for you.

576 00:47:13.170 ⇒ 00:47:16.349 Henry Zhao: Yeah, for that one, I would just put 2 weeks before, because that’s what it always is.

577 00:47:16.350 ⇒ 00:47:16.980 Amber Lin: Okay.

578 00:47:18.000 ⇒ 00:47:18.780 Amber Lin: Cool.

579 00:47:19.590 ⇒ 00:47:22.080 Amber Lin: I want to move them.

580 00:47:22.620 ⇒ 00:47:27.050 Amber Lin: To the other side… So, 2023 first.

581 00:47:27.050 ⇒ 00:47:28.002 Henry Zhao: This is okay.

582 00:47:28.980 ⇒ 00:47:29.790 Henry Zhao: mine.

583 00:47:30.130 ⇒ 00:47:31.560 Henry Zhao: I’m just kidding, I’m just messing with you.

584 00:47:31.830 ⇒ 00:47:33.190 Amber Lin: That’s fine, you know.

585 00:47:33.970 ⇒ 00:47:35.679 Henry Zhao: Okay, you can do it, I won’t mess with you.

586 00:47:37.820 ⇒ 00:47:46.690 Amber Lin: I’ve used… I think I’ve inherited some of Robert’s pickiness, because I foresee his… his, rejections.

587 00:47:46.690 ⇒ 00:47:47.260 Henry Zhao: Hmm?

588 00:47:51.010 ⇒ 00:47:53.749 Henry Zhao: Yeah, buyer by Northropiciously. Alright.

589 00:47:55.670 ⇒ 00:48:01.590 Amber Lin: Day of… And… Two weeks before…

590 00:48:03.780 ⇒ 00:48:09.829 Henry Zhao: And this year’s Cyber… they probably already planned for this, Cyber…

591 00:48:09.830 ⇒ 00:48:13.820 Amber Lin: 25… This year is December 1st.

592 00:48:14.560 ⇒ 00:48:15.170 Amber Lin: Mmm.

593 00:48:15.170 ⇒ 00:48:20.309 Henry Zhao: Did we see a spike two weeks ago, which was… the 20th… 17th?

594 00:48:20.660 ⇒ 00:48:21.319 Henry Zhao: Let’s see.

595 00:48:22.470 ⇒ 00:48:23.000 Amber Lin: Mmm.

596 00:48:23.000 ⇒ 00:48:25.379 Henry Zhao: We don’t have data on the 17th yet, so… can’t tell.

597 00:48:25.380 ⇒ 00:48:26.250 Amber Lin: Oh, okay.

598 00:48:26.480 ⇒ 00:48:27.330 Henry Zhao: Probably did.

599 00:48:28.100 ⇒ 00:48:32.010 Amber Lin: I think we do have the 17th data, do we?

600 00:48:36.510 ⇒ 00:48:38.419 Henry Zhao: I can maybe find it in Klaviyo.

601 00:48:43.820 ⇒ 00:48:46.339 Henry Zhao: Man, I wish I remembered where all the queries were.

602 00:48:47.960 ⇒ 00:49:07.369 Amber Lin: I feel like this is… our findings are a little bit late, because they’re… they know it will spike on Cyber Monday, but I, like, maybe the insight was that it spiked two weeks before, but they also probably know that, so… like, what can we reasonably make from this slide? It’s a finding, but I don’t know what we can tell them.

603 00:49:09.120 ⇒ 00:49:11.739 Henry Zhao: To prepare for low volumes next month.

604 00:49:12.000 ⇒ 00:49:13.340 Henry Zhao: That is, that is…

605 00:49:13.340 ⇒ 00:49:15.350 Amber Lin: That’s… that is true.

606 00:49:16.540 ⇒ 00:49:19.139 Amber Lin: Like, what are they gonna do with the inventory?

607 00:49:19.140 ⇒ 00:49:26.020 Henry Zhao: ask them to reorder, like, opportunity for December promotions, such as reorder a gift for Christmas, those sort of things.

608 00:49:26.190 ⇒ 00:49:27.769 Amber Lin: I see. So, to boost.

609 00:49:27.770 ⇒ 00:49:28.290 Henry Zhao: See that?

610 00:49:28.700 ⇒ 00:49:30.150 Amber Lin: Boost of falloff.

611 00:49:30.960 ⇒ 00:49:41.309 Henry Zhao: So, recommendation would be, like, boost… Boost December sales… With gift and reorder campaigns.

612 00:49:44.940 ⇒ 00:49:47.250 Henry Zhao: Because those are, like, opportunities, right? If they want to grow.

613 00:49:47.250 ⇒ 00:49:47.870 Amber Lin: Hmm.

614 00:49:47.870 ⇒ 00:49:53.959 Henry Zhao: They’re, like, doing the same thing every year, pretty much, which is fine, but if you want to grow, the opportunities are in December, etc, right?

615 00:49:53.960 ⇒ 00:49:59.800 Amber Lin: Yeah, the magnitude is also very similar. Like, they haven’t got that much better.

616 00:50:00.420 ⇒ 00:50:04.600 Amber Lin: from, like… Okay, I’m gonna put that there, too.

617 00:50:05.540 ⇒ 00:50:11.249 Amber Lin: Similar… Oh, I mean, this is 2024 and 2020.

618 00:50:12.570 ⇒ 00:50:13.919 Henry Zhao: Right, so they gotta be ready.

619 00:50:13.920 ⇒ 00:50:21.309 Amber Lin: So they’re growing this year. I do want to see if the magnitude changed this year, like, even if we don’t have that data.

620 00:50:22.920 ⇒ 00:50:30.589 Amber Lin: What is the… Have they grown since 2024 to 2025, essentially, is what I want to see.

621 00:50:30.590 ⇒ 00:50:31.130 Henry Zhao: Okay.

622 00:50:31.290 ⇒ 00:50:35.560 Henry Zhao: Let me revive my Klaviyo data, then.

623 00:50:37.170 ⇒ 00:50:43.929 Amber Lin: I think we can still read the dates from the one that you had that stopped at the 14th.

624 00:50:44.710 ⇒ 00:50:54.560 Amber Lin: Okay, do you want, yeah, that’s fine. Let’s see… So it’s… 800… 5,000…

625 00:50:58.130 ⇒ 00:51:01.060 Amber Lin: December… Hmm.

626 00:51:08.540 ⇒ 00:51:09.540 Henry Zhao: Everybody day here.

627 00:51:12.660 ⇒ 00:51:16.900 Henry Zhao: Borders, total, price… Average price.

628 00:51:19.040 ⇒ 00:51:23.300 Henry Zhao: Let’s grab a look at this… Insert chart…

629 00:51:33.340 ⇒ 00:51:38.060 Henry Zhao: Now, my day is really messy, but it doesn’t look… Like, it’s grown overall.

630 00:51:38.230 ⇒ 00:51:39.010 Henry Zhao: See?

631 00:51:39.430 ⇒ 00:51:40.060 Amber Lin: Hmm.

632 00:51:40.840 ⇒ 00:51:42.359 Henry Zhao: It’s very just stable over time.

633 00:51:42.940 ⇒ 00:51:45.239 Henry Zhao: This literally looks like a, like, an earthquake.

634 00:51:45.660 ⇒ 00:51:46.840 Amber Lin: I see.

635 00:51:47.010 ⇒ 00:51:49.760 Henry Zhao: It shouldn’t look like this, like, it should be going up, but it’s not, so…

636 00:51:49.760 ⇒ 00:51:50.760 Amber Lin: Yeah.

637 00:51:51.610 ⇒ 00:51:53.920 Henry Zhao: So they want to grow, they need to start doing something better.

638 00:51:54.490 ⇒ 00:52:01.559 Amber Lin: Like, they, you know, they have been telling us that, oh, our orders have been growing significantly, but that could just be Amazon.

639 00:52:02.600 ⇒ 00:52:03.649 Henry Zhao: Yeah, it probably is.

640 00:52:05.380 ⇒ 00:52:07.600 Henry Zhao: And their spikes seem to be higher now.

641 00:52:08.050 ⇒ 00:52:12.980 Henry Zhao: But the overall, it doesn’t seem to me like it is doing that much better.

642 00:52:17.610 ⇒ 00:52:18.499 Henry Zhao: Why is there… oh.

643 00:52:19.250 ⇒ 00:52:21.310 Henry Zhao: Like, it is growing, but not that much.

644 00:52:27.690 ⇒ 00:52:28.460 Henry Zhao: square.

645 00:52:29.250 ⇒ 00:52:36.470 Henry Zhao: What’s the, yeah, it’s growing by, like.016 per day.

646 00:52:37.790 ⇒ 00:52:39.560 Henry Zhao: So, that’s not significant.

647 00:52:41.230 ⇒ 00:52:46.920 Amber Lin: Can that chart… can you smooth that chart by, like, week or something?

648 00:52:46.920 ⇒ 00:52:47.490 Henry Zhao: Of course!

649 00:52:47.490 ⇒ 00:52:52.470 Amber Lin: Or we can just paste our chart in and ask them, like, hey, how’s your sales doing?

650 00:52:52.470 ⇒ 00:52:57.090 Henry Zhao: By week. Because of the spike in the monthly, I can definitely do it by week.

651 00:53:00.240 ⇒ 00:53:02.330 Henry Zhao: We had some arrests.

652 00:53:02.970 ⇒ 00:53:04.620 Amber Lin: Yeah, I’ll make a slide there.

653 00:53:09.650 ⇒ 00:53:13.410 Henry Zhao: Week orders… Total USD…

654 00:53:13.410 ⇒ 00:53:17.090 Amber Lin: Is this… oh, total order value… total revenue?

655 00:53:18.010 ⇒ 00:53:31.049 Amber Lin: I also, like, because I’m very new to, like, all these analysis, I would recently read, like, we should also look at the GMV versus their discount. I don’t know how much they’re discounting, but I don’t know if we have that data.

656 00:53:31.550 ⇒ 00:53:34.660 Henry Zhao: I don’t know, but it doesn’t seem like it would be that useful, honestly. Alright.

657 00:53:34.880 ⇒ 00:53:36.489 Henry Zhao: This is fine.

658 00:53:38.990 ⇒ 00:53:41.010 Amber Lin: Is there a trend line we can add?

659 00:53:41.350 ⇒ 00:53:42.479 Henry Zhao: Of course.

660 00:53:43.170 ⇒ 00:53:44.330 Henry Zhao: Two orders now.

661 00:53:53.880 ⇒ 00:53:54.540 Amber Lin: Mmm.

662 00:53:56.080 ⇒ 00:54:01.049 Amber Lin: Should we just add one for total USD? I don’t think we can see it for sales.

663 00:54:01.940 ⇒ 00:54:06.020 Amber Lin: Or orders. Like, I don’t… it’s way too low for us to see anything.

664 00:54:06.230 ⇒ 00:54:08.190 Henry Zhao: Yeah, yeah, we’ll do it one by one.

665 00:54:08.670 ⇒ 00:54:10.200 Henry Zhao: Oh, that’s probably why it’s not… okay.

666 00:54:13.810 ⇒ 00:54:18.809 Henry Zhao: Let’s do orders first, I would say. Alright, so we do see increase… over weeks.

667 00:54:19.280 ⇒ 00:54:21.439 Henry Zhao: What is this? It’s the same thing, right? It’s,

668 00:54:22.430 ⇒ 00:54:25.730 Henry Zhao: 0.109, which is just that times 7, so same thing,

669 00:54:26.220 ⇒ 00:54:29.750 Henry Zhao: every 10 weeks, you get one extra order, you know? So, I don’t.

670 00:54:29.750 ⇒ 00:54:31.709 Amber Lin: Wow, that’s not very helpful.

671 00:54:32.120 ⇒ 00:54:38.159 Amber Lin: Can you copy the… Or we can just do the total USD. Actually, let’s just…

672 00:54:38.540 ⇒ 00:54:39.870 Henry Zhao: Are you totally see? Okay.

673 00:54:39.870 ⇒ 00:54:40.440 Amber Lin: Yeah.

674 00:54:42.900 ⇒ 00:54:43.640 Amber Lin: Cool.

675 00:54:43.930 ⇒ 00:54:45.709 Henry Zhao: Make 5 extra dollars every week.

676 00:54:45.870 ⇒ 00:54:48.330 Amber Lin: Okay, let’s put that graph in. Let’s tell them.

677 00:54:48.950 ⇒ 00:55:00.699 Amber Lin: He was… well, he was… I think he was talking about Amazon, but they were saying that they had a lot of… they’re getting better this month, I don’t know if they are.

678 00:55:01.000 ⇒ 00:55:10.780 Amber Lin: And, like, I mean, the worst their problem, they’re… we’re consultants, so we’re kind of, like, they need to have a problem to need us, so I think we should put that in.

679 00:55:21.350 ⇒ 00:55:22.509 Henry Zhao: Skip that, alright.

680 00:55:29.660 ⇒ 00:55:33.119 Henry Zhao: And these charts are all in the spreadsheet, so if you want to, like, reformat or anything.

681 00:55:33.120 ⇒ 00:55:39.960 Amber Lin: Yeah, cool, sounds good. Overall, Shopify revenue…

682 00:55:40.290 ⇒ 00:55:41.900 Henry Zhao: It’s only growing slightly, maybe?

683 00:55:44.200 ⇒ 00:55:45.560 Amber Lin: Yeah.

684 00:55:46.750 ⇒ 00:55:50.010 Amber Lin: All pretty… what do we say, almost stagnant?

685 00:55:50.770 ⇒ 00:55:52.869 Henry Zhao: No, you can say it’s growing, but just slightly, right? Like…

686 00:55:53.370 ⇒ 00:55:57.179 Henry Zhao: We need to figure out the opportunities for growth, you know?

687 00:55:57.480 ⇒ 00:56:02.509 Henry Zhao: Which is, like, December, in my opinion. Like, you see how, like, it’s, like, so low each December?

688 00:56:04.550 ⇒ 00:56:07.660 Amber Lin: Okay, let’s put in significant dip.

689 00:56:08.670 ⇒ 00:56:10.809 Amber Lin: In September…

690 00:56:11.560 ⇒ 00:56:12.680 Henry Zhao: December.

691 00:56:12.680 ⇒ 00:56:13.700 Amber Lin: Just.

692 00:56:13.700 ⇒ 00:56:14.790 Henry Zhao: Winter’s not too bad.

693 00:56:16.280 ⇒ 00:56:21.879 Henry Zhao: September’s sometimes actually pretty hot. It’s just December. December’s really bad. They gotta start thinking about, like, gifting and things like that.

694 00:56:23.880 ⇒ 00:56:30.060 Amber Lin: Because they… I think most likely is that they maxed out their orders, or they made… pulled them ahead.

695 00:56:30.410 ⇒ 00:56:33.219 Amber Lin: For… Every Monday, that’s fine.

696 00:56:33.370 ⇒ 00:56:35.249 Amber Lin: Yeah. And that’s what they care about.

697 00:56:35.670 ⇒ 00:56:37.490 Amber Lin: $5 per week.

698 00:56:38.280 ⇒ 00:56:40.030 Amber Lin: Per week.

699 00:56:41.560 ⇒ 00:56:45.129 Amber Lin: Putting that in here…

700 00:56:46.580 ⇒ 00:56:48.000 Henry Zhao: Yeah, and the point of.

701 00:56:48.000 ⇒ 00:56:49.580 Amber Lin: Overtime, yeah.

702 00:56:51.080 ⇒ 00:56:53.520 Amber Lin: Cool. This is… this is really cool.

703 00:56:53.790 ⇒ 00:56:55.200 Amber Lin: I’m gonna put this…

704 00:56:59.920 ⇒ 00:57:00.770 Amber Lin: Okay.

705 00:57:02.810 ⇒ 00:57:03.830 Henry Zhao: Yes, we do have some new data.

706 00:57:03.830 ⇒ 00:57:17.710 Amber Lin: Same recommendation as what we had before. Can we use… I mean, we didn’t change the recommendation. Can we change for slide 10 of, like, overall consider strategies to increase sales?

707 00:57:19.180 ⇒ 00:57:19.790 Amber Lin: Mmm…

708 00:57:20.030 ⇒ 00:57:22.890 Henry Zhao: How do we do it the right way?

709 00:57:27.580 ⇒ 00:57:33.099 Amber Lin: Yeah, because just telling them they’re not growing is not helpful. Yeah, let’s see.

710 00:57:33.300 ⇒ 00:57:41.480 Henry Zhao: Don’t want them to start, like, again, throwing big nets out to just bring in more visitors, but maybe we do want to if we can stay within that forecast.

711 00:57:41.480 ⇒ 00:57:43.420 Amber Lin: Overall, visitors are not…

712 00:57:43.570 ⇒ 00:57:52.669 Amber Lin: not going up. Like, I think we conclude that they have better targeting, but their top funnel is shrinking.

713 00:57:53.150 ⇒ 00:57:59.050 Henry Zhao: I would say you can look at this and say we can increase our advertising as long as we stay within the recommended CPC.

714 00:57:59.780 ⇒ 00:58:01.790 Amber Lin: Yeah.

715 00:58:02.440 ⇒ 00:58:11.050 Henry Zhao: And as long as it’s in our, like, production… production… Right? Because you don’t want to just bring on more people, but then you, like, don’t have enough inventory.

716 00:58:11.400 ⇒ 00:58:16.960 Amber Lin: Yeah. So… So, what’s our conclusion for the first slide?

717 00:58:18.740 ⇒ 00:58:20.020 Henry Zhao: Our conclusion…

718 00:58:20.020 ⇒ 00:58:21.260 Amber Lin: Top of funnel?

719 00:58:22.880 ⇒ 00:58:25.219 Henry Zhao: What was the first slide? Can you show your screen now?

720 00:58:25.220 ⇒ 00:58:27.609 Amber Lin: Slide… slide 10. Yeah, I can do that.

721 00:58:29.660 ⇒ 00:58:30.350 Amber Lin: So…

722 00:58:30.350 ⇒ 00:58:31.600 Henry Zhao: the work. I just wanna…

723 00:58:32.970 ⇒ 00:58:38.219 Amber Lin: Hello? I am not doing all the work. You did all the queries, I’m editing your slides.

724 00:58:38.230 ⇒ 00:58:39.700 Henry Zhao: I feel like we’re a good team, though, like…

725 00:58:39.700 ⇒ 00:58:56.140 Amber Lin: I do think so, I like the back and forth. Okay, so we start… so this is a great story… I think this is a great story to tell. So we start here, we see… we say, okay, we see that your sales are not, like, your revenue is not growing.

726 00:58:56.370 ⇒ 00:59:03.660 Henry Zhao: Can you say significant dip every December? Because I wanted to be clear that every December it’s dip. Like, look at the low point in every year. It’s always December, you see that?

727 00:59:03.660 ⇒ 00:59:05.680 Amber Lin: Yeah, it’s right here.

728 00:59:06.100 ⇒ 00:59:07.789 Henry Zhao: It’s literally the bottom every day, so…

729 00:59:07.790 ⇒ 00:59:14.899 Amber Lin: Yeah. And they sort of go in the… in this shape, it’s like a little semicircle, like, oval.

730 00:59:14.900 ⇒ 00:59:15.699 Henry Zhao: Go up and down.

731 00:59:15.700 ⇒ 00:59:17.250 Amber Lin: jump and down.

732 00:59:17.250 ⇒ 00:59:19.539 Henry Zhao: There’s definitely opportunity for growth there, I would say, it might be.

733 00:59:19.540 ⇒ 00:59:20.170 Amber Lin: Yeah.

734 00:59:20.980 ⇒ 00:59:23.020 Amber Lin: Okay.

735 00:59:24.130 ⇒ 00:59:27.670 Amber Lin: Significant dip in September… December.

736 00:59:27.830 ⇒ 00:59:36.280 Amber Lin: What’s her recommendation? So… Like, if it’s $5 for a week, can we just say it’s stagnant?

737 00:59:37.060 ⇒ 00:59:44.050 Amber Lin: I guess they… how much do they grow per year? 22 times 5. Okay, they grow 260 per year.

738 00:59:44.300 ⇒ 00:59:46.439 Amber Lin: I was just gonna say, it’s, like, growth.

739 00:59:46.780 ⇒ 00:59:48.380 Amber Lin: Stagnant.

740 00:59:51.210 ⇒ 00:59:56.730 Amber Lin: And… Like, we can tell them why, I guess we can pull in…

741 00:59:56.950 ⇒ 00:59:58.829 Amber Lin: Like, this slide is just about comparison.

742 00:59:58.830 ⇒ 01:00:02.790 Henry Zhao: Look at an average… Per year, and let me just see if it’s actually…

743 01:00:03.460 ⇒ 01:00:06.109 Henry Zhao: Yeah, let’s do a year-over-year increase.

744 01:00:07.540 ⇒ 01:00:09.750 Henry Zhao: Okay, so… oh, it pretty much is.

745 01:00:10.600 ⇒ 01:00:18.850 Henry Zhao: What is it? 2023, they sold $235 K . L a s t y e a r, t h ey so l d 227 K, an d t hi sy e a r, t h ey so l d$ 226K. So it really is…

746 01:00:19.290 ⇒ 01:00:24.209 Amber Lin: Can you drop the numbers in the speaker notes and give me the year-over-year, like.

747 01:00:24.210 ⇒ 01:00:28.680 Henry Zhao: No, I don’t want to, I’m not com- I’m not confident about this amount.

748 01:00:28.680 ⇒ 01:00:29.909 Amber Lin: about the numbers?

749 01:00:29.910 ⇒ 01:00:32.460 Henry Zhao: Yeah, it doesn’t look right. Okay, so I don’t want to share. Let’s just…

750 01:00:33.220 ⇒ 01:00:40.230 Henry Zhao: Because the average order was really big in 2024, something is wrong with my query, probably.

751 01:00:43.080 ⇒ 01:00:44.450 Amber Lin: Can I also look at it?

752 01:00:45.270 ⇒ 01:00:45.810 Henry Zhao: Yeah.

753 01:00:48.230 ⇒ 01:00:50.209 Henry Zhao: This is really weird. What am I doing wrong?

754 01:00:56.430 ⇒ 01:00:59.360 Henry Zhao: So, 2022, 2023, 2024, 2025…

755 01:00:59.560 ⇒ 01:01:06.640 Henry Zhao: 2.3 million in 23, 2.27 million in 24, 2.26 million this year, which is not done yet.

756 01:01:06.770 ⇒ 01:01:08.219 Amber Lin: Yeah, makes sense.

757 01:01:08.220 ⇒ 01:01:10.929 Henry Zhao: Average order price. Why is this one $9,000?

758 01:01:11.390 ⇒ 01:01:13.419 Amber Lin: It’s max orders, not average.

759 01:01:13.420 ⇒ 01:01:15.469 Henry Zhao: Oh, Max, that’s… okay, okay, okay, that’s why.

760 01:01:16.050 ⇒ 01:01:18.109 Henry Zhao: Okay, thank you for clarifying that.

761 01:01:18.920 ⇒ 01:01:20.110 Henry Zhao: Okay, there we go.

762 01:01:20.230 ⇒ 01:01:28.880 Henry Zhao: Okay, there we go. Because also, energy choose to not cost $9,000, so you’re right. Okay, that’s, that’s okay, that’s good. Now I feel better. Alright, good, good, thank you.

763 01:01:29.410 ⇒ 01:01:31.799 Henry Zhao: So, their total sales in 2024 win?

764 01:01:32.030 ⇒ 01:01:33.580 Amber Lin: Damn.

765 01:01:33.580 ⇒ 01:01:38.909 Henry Zhao: 2024 went down, but 2025 is going up because the year’s not over yet, and we’re just $10,000 shy.

766 01:01:39.160 ⇒ 01:01:40.689 Henry Zhao: But you know we get that in, like…

767 01:01:40.690 ⇒ 01:01:45.640 Amber Lin: Okay, so tomorrow it’ll… Dollars. Like, I’m comparing it between…

768 01:01:46.080 ⇒ 01:01:52.830 Amber Lin: Yeah, I still think… I don’t think they’re gonna boost up until, like, 30… something?

769 01:01:52.980 ⇒ 01:01:56.290 Amber Lin: I think it’s still gonna be pretty much the same.

770 01:01:56.440 ⇒ 01:01:57.729 Amber Lin: Maybe as 2018.

771 01:01:57.730 ⇒ 01:02:00.270 Henry Zhao: It’s stagnant. It’s stagnant, you’re right, it’s stagnant.

772 01:02:00.380 ⇒ 01:02:01.659 Henry Zhao: We can put that, yeah.

773 01:02:01.760 ⇒ 01:02:04.330 Henry Zhao: I just wanted to double-check the 250, because it’s so low.

774 01:02:04.330 ⇒ 01:02:09.190 Amber Lin: Yeah, yeah, totally. Let’s put the overall numbers in there somewhere.

775 01:02:09.340 ⇒ 01:02:12.949 Amber Lin: You can also just drop a screenshot, because Robert’s looking at this.

776 01:02:13.340 ⇒ 01:02:15.679 Henry Zhao: No, I’m gonna copy and paste this, if you have the actual numbers.

777 01:02:15.900 ⇒ 01:02:16.570 Amber Lin: Okay.

778 01:02:17.480 ⇒ 01:02:26.420 Amber Lin: Over… over… 2023, 20… 25…

779 01:02:29.020 ⇒ 01:02:31.210 Henry Zhao: I’ll put it here…

780 01:02:31.680 ⇒ 01:02:40.010 Amber Lin: You can put in speaker notes, like, what are the strategies for them to increase revenue? They can have more sales, they can boost the average

781 01:02:40.360 ⇒ 01:02:42.220 Amber Lin: Like, average order value.

782 01:02:42.220 ⇒ 01:02:44.310 Henry Zhao: Or advertising, now that we know the CPC.

783 01:02:44.990 ⇒ 01:02:49.069 Amber Lin: Yeah, which goes into more orders, which is, like, more…

784 01:02:49.420 ⇒ 01:02:54.799 Amber Lin: It’s either they get more new people, or they have more repeat purchases.

785 01:02:55.080 ⇒ 01:03:00.380 Amber Lin: And obviously, they’re not getting more new people, are they?

786 01:03:00.880 ⇒ 01:03:02.300 Henry Zhao: No, they’re getting less new people.

787 01:03:02.550 ⇒ 01:03:03.420 Amber Lin: Okay.

788 01:03:04.550 ⇒ 01:03:12.669 Amber Lin: Is there a way we can look at, like, new customers per… Per week, or per month?

789 01:03:14.230 ⇒ 01:03:15.070 Henry Zhao: Yes?

790 01:03:15.460 ⇒ 01:03:16.500 Henry Zhao: Should be able to.

791 01:03:18.920 ⇒ 01:03:24.100 Henry Zhao: I think that’s just customers, and then… Created at, yeah.

792 01:03:26.310 ⇒ 01:03:34.860 Henry Zhao: New customers per week. Alright, so select a trunk week… Created that… count.

793 01:03:35.360 ⇒ 01:03:45.300 Henry Zhao: From Honey Stinger… dot polyatomic, dot underscore shopify, dot customers, grouped by 1, order by 1.

794 01:03:46.610 ⇒ 01:03:51.140 Henry Zhao: Yeah, that is probably a good thing to look at. Actually, I think I already have that in… again.

795 01:03:55.960 ⇒ 01:03:58.529 Henry Zhao: I think I already had that in one of the Klaviyo ones. Alright, new customers.

796 01:03:58.530 ⇒ 01:04:00.910 Amber Lin: Gosh, let me copy this…

797 01:04:01.090 ⇒ 01:04:08.429 Amber Lin: into our channel, so Utam and Robert knows… well, not Robert, Robert is sleeping, so Utam knows that we’re actually doing something.

798 01:04:08.860 ⇒ 01:04:10.440 Amber Lin: Oh, I have. Okay, never mind.

799 01:04:10.440 ⇒ 01:04:11.809 Henry Zhao: Doing a lot of stuff.

800 01:04:11.810 ⇒ 01:04:16.910 Amber Lin: Yeah, I’m gonna move my follow-up to the end, because I just don’t think…

801 01:04:17.110 ⇒ 01:04:19.440 Amber Lin: Like, there’s not much I can make from it.

802 01:04:19.570 ⇒ 01:04:24.219 Amber Lin: I’m gonna put it at the end, so I can say we did follow-ups.

803 01:04:35.170 ⇒ 01:04:36.110 Amber Lin: I’ll see…

804 01:04:36.110 ⇒ 01:04:37.540 Henry Zhao: Same thing, pretty much.

805 01:04:38.700 ⇒ 01:04:41.880 Henry Zhao: Big spike in this week, I don’t know what that was.

806 01:04:42.800 ⇒ 01:04:43.960 Henry Zhao: Located by month…

807 01:04:44.000 ⇒ 01:04:46.549 Amber Lin: Promotion, most likely.

808 01:04:47.660 ⇒ 01:04:50.290 Amber Lin: And whenever they get a promotion, they have more.

809 01:04:54.360 ⇒ 01:04:57.350 Amber Lin: Yeah, can I get the weekly slide?

810 01:04:57.520 ⇒ 01:05:00.120 Amber Lin: Or monthly, whichever makes more sense.

811 01:05:00.120 ⇒ 01:05:01.540 Henry Zhao: That one’s gonna be better, yeah.

812 01:05:01.540 ⇒ 01:05:07.799 Amber Lin: I think the weekly should be better, because it shows it on the baseline, it’s pretty much the same, because monthly will get the spikes in, too.

813 01:05:08.370 ⇒ 01:05:10.640 Amber Lin: Let’s check. I’ll go, I’ll go copy over.

814 01:05:10.640 ⇒ 01:05:11.990 Henry Zhao: You can check for reels.

815 01:05:11.990 ⇒ 01:05:12.570 Amber Lin: Yeah.

816 01:05:12.790 ⇒ 01:05:14.019 Henry Zhao: Wow, which one do you like better?

817 01:05:16.610 ⇒ 01:05:17.740 Henry Zhao: or monthly?

818 01:05:18.550 ⇒ 01:05:25.219 Amber Lin: I honestly like the weekly, because it tells us that, like, the spikes are very, very slim.

819 01:05:25.350 ⇒ 01:05:27.969 Amber Lin: It’s, like, one day, two days.

820 01:05:29.240 ⇒ 01:05:30.819 Henry Zhao: One week, you don’t know days, but…

821 01:05:30.820 ⇒ 01:05:32.200 Amber Lin: That’s true.

822 01:05:32.770 ⇒ 01:05:34.000 Amber Lin: promotions.

823 01:05:35.220 ⇒ 01:05:40.890 Amber Lin: Also means their promotions last very short, because it doesn’t trail off, it just ends.

824 01:05:40.890 ⇒ 01:05:41.680 Henry Zhao: Yeah…

825 01:05:44.270 ⇒ 01:05:47.110 Amber Lin: Okay, great. That ties together with slide 5.

826 01:05:47.640 ⇒ 01:05:48.760 Amber Lin: Can you add the spouse?

827 01:05:48.760 ⇒ 01:05:50.520 Henry Zhao: I could. Guys and tell me where to fit.

828 01:05:50.980 ⇒ 01:05:53.029 Amber Lin: Are you on the right slides?

829 01:05:53.030 ⇒ 01:05:55.219 Henry Zhao: No, I’m not. Okay.

830 01:05:55.650 ⇒ 01:05:58.450 Amber Lin: I will copy… I’ll send in the…

831 01:05:58.630 ⇒ 01:06:02.719 Henry Zhao: Also, this would be so easy if we were in the same, were you working, though, yeah.

832 01:06:02.950 ⇒ 01:06:09.580 Amber Lin: Oh, that’s so funny, yeah. But it’s okay, we’re working visually, we’re not making paper slides. I’m gonna make a…

833 01:06:09.580 ⇒ 01:06:11.500 Henry Zhao: You want me to put it? Yeah, because I know I’ll put it.

834 01:06:15.930 ⇒ 01:06:17.830 Amber Lin: New users…

835 01:06:20.920 ⇒ 01:06:23.879 Amber Lin: Weekly, new, user, custom…

836 01:06:23.880 ⇒ 01:06:25.300 Henry Zhao: The customers, no.

837 01:06:25.300 ⇒ 01:06:26.600 Amber Lin: Summerge…

838 01:06:28.120 ⇒ 01:06:29.520 Amber Lin: Also…

839 01:06:30.270 ⇒ 01:06:38.980 Amber Lin: I guess they’re saying more… like, they have… they’re getting spikes in 2024, and probably means that they started doing promotions then.

840 01:06:38.980 ⇒ 01:06:39.920 Henry Zhao: Yeah.

841 01:06:40.680 ⇒ 01:06:42.150 Henry Zhao: I understand, yeah.

842 01:06:43.530 ⇒ 01:06:45.340 Henry Zhao: Or just, like, better promotions, right?

843 01:06:45.630 ⇒ 01:06:52.420 Amber Lin: Yeah. I do think, like, year over year, it should be more in, like, 2025.

844 01:06:52.570 ⇒ 01:06:55.820 Amber Lin: It’s just the scale is so high, so that…

845 01:06:56.140 ⇒ 01:06:59.759 Amber Lin: The… like, we can’t really see the differences.

846 01:07:01.610 ⇒ 01:07:05.279 Amber Lin: Over time…

847 01:07:17.150 ⇒ 01:07:18.030 Amber Lin: Okay.

848 01:07:18.760 ⇒ 01:07:25.410 Amber Lin: Cool. Is there a summary number I can… oh, I guess they have… 0.1…

849 01:07:25.640 ⇒ 01:07:30.310 Amber Lin: So, average one more… 0.1 more customer?

850 01:07:33.140 ⇒ 01:07:34.680 Amber Lin: Like, what is the…

851 01:07:35.490 ⇒ 01:07:36.040 Henry Zhao: What is the…

852 01:07:36.040 ⇒ 01:07:37.990 Amber Lin: The formula on me and the gun?

853 01:07:38.390 ⇒ 01:07:39.890 Henry Zhao: Don’t know…

854 01:07:40.880 ⇒ 01:07:45.720 Amber Lin: Well, yeah, an extra .14 customers, per week.

855 01:07:45.990 ⇒ 01:07:46.730 Henry Zhao: What’s on it?

856 01:07:47.410 ⇒ 01:07:52.980 Henry Zhao: Does that sound right. It’s pretty much a stagnant, it just means it’s stagnant, but it’s kind of going up over time.

857 01:07:52.980 ⇒ 01:07:53.700 Amber Lin: Yeah.

858 01:07:54.030 ⇒ 01:07:59.159 Amber Lin: Okay, so that means that… 1 to 5 by 2.

859 01:08:00.780 ⇒ 01:08:13.550 Amber Lin: So every 7 weeks, they get one new customer, like, one more cuss… On average, right? It’s on average. Sometimes they go down, sometimes they go up, but on average, it’s, like, pretty much the same.

860 01:08:13.820 ⇒ 01:08:19.500 Amber Lin: Cool. So they’re… Not acquiring significantly more customers.

861 01:08:19.700 ⇒ 01:08:22.160 Amber Lin: Each week. There’s a problem.

862 01:08:24.350 ⇒ 01:08:25.289 Amber Lin: Like, they’re not correct.

863 01:08:25.290 ⇒ 01:08:31.470 Henry Zhao: That Y… Y-intercept is very low, so… That tells me that…

864 01:08:33.250 ⇒ 01:08:34.639 Amber Lin: What does that mean? It’s negative.

865 01:08:34.640 ⇒ 01:08:35.460 Henry Zhao: That’s something.

866 01:08:36.270 ⇒ 01:08:38.799 Henry Zhao: It is, but what does that tell me?

867 01:08:44.189 ⇒ 01:08:45.499 Henry Zhao: I don’t remember what I learned…

868 01:08:45.990 ⇒ 01:08:54.460 Amber Lin: I think that doesn’t mean much, it just means at the beginning of time, there’s negative 5,000, 5,000 customers.

869 01:08:54.740 ⇒ 01:09:01.059 Amber Lin: Like, in 0BC, or 0AC, I forgot. Like, there’s… there’s negative.

870 01:09:01.069 ⇒ 01:09:02.309 Henry Zhao: Oh, baby.

871 01:09:03.939 ⇒ 01:09:07.609 Henry Zhao: But that formula should be based off of, like, the first data point, not…

872 01:09:07.629 ⇒ 01:09:08.809 Amber Lin: Oh, really?

873 01:09:08.809 ⇒ 01:09:11.049 Henry Zhao: Not extrapolated to the end of time.

874 01:09:11.529 ⇒ 01:09:12.139 Amber Lin: Hmm.

875 01:09:12.529 ⇒ 01:09:17.029 Amber Lin: That shouldn’t be, though. Like, it is a trend line, so… I think it’s…

876 01:09:17.029 ⇒ 01:09:17.589 Henry Zhao: That’s true.

877 01:09:17.590 ⇒ 01:09:19.500 Amber Lin: Infinitely extends in theory.

878 01:09:19.750 ⇒ 01:09:22.129 Amber Lin: Okay, so the first one in slide 5…

879 01:09:22.130 ⇒ 01:09:25.930 Henry Zhao: No, I think… oh, no, never mind, never mind. Nevermind, never mind, continue.

880 01:09:25.939 ⇒ 01:09:28.849 Amber Lin: Okay, let’s write our summary.

881 01:09:29.239 ⇒ 01:09:36.069 Amber Lin: Let’s write our summary in slide… Once.

882 01:09:37.549 ⇒ 01:09:43.249 Amber Lin: And… slide… 8. So, what’s the story we’re gonna tell?

883 01:09:44.499 ⇒ 01:09:47.089 Amber Lin: So, we say, like, overall…

884 01:09:48.250 ⇒ 01:09:49.510 Henry Zhao: I’ll go back to my…

885 01:09:49.810 ⇒ 01:09:54.250 Henry Zhao: doubles monitor so I can share my screen, but let me just be right back. Okay.

886 01:09:54.590 ⇒ 01:09:55.749 Henry Zhao: It’s really dirty.

887 01:09:58.760 ⇒ 01:10:00.270 Henry Zhao: Oh, my screen is so dirty.

888 01:10:05.070 ⇒ 01:10:05.900 Henry Zhao: Whoo!

889 01:10:11.810 ⇒ 01:10:16.640 Henry Zhao: I feel good. After this, I’m gonna stop working, so that maybe tomorrow, if I’m bored, I’ll go on and do the Omni stuff.

890 01:10:17.750 ⇒ 01:10:23.979 Amber Lin: Cool, okay. I just need to do this, and I’m gonna do insomnia, and that’s it for today.

891 01:10:24.350 ⇒ 01:10:27.710 Henry Zhao: I feel like I’ve gotten so much Eden and Honey Stinger stuff done this week.

892 01:10:27.710 ⇒ 01:10:33.929 Amber Lin: Yeah, and Fred, I saw the done list this morning, I was like, wow, that’s so much stuff, and you were working so late last night.

893 01:10:34.970 ⇒ 01:10:39.620 Henry Zhao: Yeah, because I can’t do it during the day when, like, people are doing meetings and all this stuff. Yeah, yeah.

894 01:10:40.350 ⇒ 01:10:46.059 Henry Zhao: Like, I need to focus, I’m gonna work. And I was just worried, like, I think as a team, we only have 40 hours a month for Honey Stinger, right?

895 01:10:46.410 ⇒ 01:10:56.429 Amber Lin: I don’t… I don’t know the allocations. I’ve just been working, because, like… ultimately, yes, there’s time limits, but I’m not even assigned to other clients, and there’s no work.

896 01:10:56.430 ⇒ 01:10:57.199 Henry Zhao: I don’t think.

897 01:10:57.200 ⇒ 01:10:58.560 Amber Lin: You have more work.

898 01:10:58.850 ⇒ 01:11:03.820 Amber Lin: I only have the analysis stuff, so it’s like, I… like, my hours go to waste.

899 01:11:04.160 ⇒ 01:11:07.670 Henry Zhao: Like, I feel like I did so much for Eden today, and it’s like, an hour and 38 minutes, like…

900 01:11:07.670 ⇒ 01:11:09.496 Amber Lin: Huh.

901 01:11:10.590 ⇒ 01:11:21.270 Amber Lin: Okay, so we say that sales grow stagnant over time, it’s significant, and…

902 01:11:28.350 ⇒ 01:11:30.949 Amber Lin: Only short.

903 01:11:31.720 ⇒ 01:11:34.220 Amber Lin: Spikes Overhaul.

904 01:11:34.680 ⇒ 01:11:38.869 Amber Lin: Holiday or promotional periods.

905 01:11:39.450 ⇒ 01:11:40.190 Amber Lin: M.

906 01:11:40.470 ⇒ 01:11:47.340 Amber Lin: And… Significant drop.

907 01:11:47.730 ⇒ 01:11:48.630 Amber Lin: Off.

908 01:11:48.930 ⇒ 01:11:50.650 Amber Lin: Afterwards…

909 01:11:55.860 ⇒ 01:11:57.130 Henry Zhao: Which slide are you working on?

910 01:11:57.130 ⇒ 01:11:58.969 Amber Lin: I’m working on slide 8.

911 01:11:59.870 ⇒ 01:12:02.650 Henry Zhao: I see your little thingy, I forgot that you can tell.

912 01:12:02.910 ⇒ 01:12:09.999 Amber Lin: Cool, we have that significant drop of December.

913 01:12:10.790 ⇒ 01:12:21.770 Amber Lin: So, our recommendation is… Like, how… How can we… Grace Gross.

914 01:12:22.190 ⇒ 01:12:24.369 Amber Lin: You’ll screw up?

915 01:12:25.210 ⇒ 01:12:28.179 Amber Lin: That’s too broad. I guess, like…

916 01:12:37.050 ⇒ 01:12:41.239 Henry Zhao: I mean, I’m looking at this data, and I’m thinking about it with, like, a consultant’s, like, mindset, right?

917 01:12:41.240 ⇒ 01:12:42.090 Amber Lin: Yes.

918 01:12:42.090 ⇒ 01:12:43.330 Henry Zhao: Pretty much what I would say.

919 01:12:43.980 ⇒ 01:12:45.240 Amber Lin: Revenue…

920 01:12:45.240 ⇒ 01:12:50.129 Henry Zhao: whether I was, like, Accenture or Deloitte or something, I wouldn’t say the same thing. Like, this is what we…

921 01:12:50.130 ⇒ 01:12:59.290 Amber Lin: Revenue per visitor… Growing… Since Q2…

922 01:13:07.200 ⇒ 01:13:12.200 Amber Lin: To, however… Conversion.

923 01:13:21.710 ⇒ 01:13:25.960 Amber Lin: However, we see lower traffic, decreasing traffic.

924 01:13:41.870 ⇒ 01:13:50.900 Amber Lin: Like, if… If we see… Increasing conversion rates, increasing revenue, and then lower traffic.

925 01:13:51.180 ⇒ 01:13:54.980 Amber Lin: Does that mean, like, increased repeat purchases?

926 01:13:55.900 ⇒ 01:14:01.999 Henry Zhao: Not necessarily, not necessarily. Could be many things, and I would like to check on that. I don’t want to, like, make an assumption there.

927 01:14:02.000 ⇒ 01:14:02.730 Amber Lin: Okay.

928 01:14:02.940 ⇒ 01:14:04.360 Henry Zhao: Yeah.

929 01:14:05.370 ⇒ 01:14:12.570 Amber Lin: So, let’s… Changed, so, in the next steps,

930 01:14:16.450 ⇒ 01:14:17.150 Amber Lin: Okay.

931 01:14:24.190 ⇒ 01:14:26.529 Amber Lin: So we acquire…

932 01:14:29.540 ⇒ 01:14:36.170 Amber Lin: Guess what you say suggesting… Improved targeting.

933 01:14:43.850 ⇒ 01:14:48.420 Amber Lin: Growing from $40 t o$ 70? Is that the right number?

934 01:14:51.810 ⇒ 01:14:52.790 Henry Zhao: Which number?

935 01:14:53.060 ⇒ 01:14:55.399 Amber Lin: Like, revenue per visitor right now.

936 01:14:55.690 ⇒ 01:14:57.570 Amber Lin: Where, like, did it grow from.

937 01:14:57.570 ⇒ 01:14:58.769 Henry Zhao: I think it should… I think it is.

938 01:14:58.770 ⇒ 01:15:01.320 Amber Lin: From 40 to 70. Okay, cool.

939 01:15:01.760 ⇒ 01:15:03.879 Amber Lin: Acquire…

940 01:15:04.480 ⇒ 01:15:06.850 Henry Zhao: Just to double-think.

941 01:15:07.880 ⇒ 01:15:18.260 Amber Lin: Opportunity for… But… For more revenue, if… Acquire more…

942 01:15:35.890 ⇒ 01:15:36.750 Amber Lin: Okay.

943 01:15:37.090 ⇒ 01:15:40.540 Amber Lin: How do you… what do you think? Two chunks of the recap.

944 01:15:41.150 ⇒ 01:15:42.340 Amber Lin: Choir…

945 01:15:46.140 ⇒ 01:15:47.400 Amber Lin: Cedar-ish?

946 01:15:50.060 ⇒ 01:15:52.560 Henry Zhao: I would say $40 t o$ 60, not $70.

947 01:15:53.430 ⇒ 01:15:54.680 Amber Lin: Okay, cool.

948 01:16:03.290 ⇒ 01:16:09.170 Amber Lin: More visitors… Like, how do I frame the.

949 01:16:09.170 ⇒ 01:16:11.980 Henry Zhao: I don’t even know if they want to acquire more visitors, though, like…

950 01:16:12.110 ⇒ 01:16:16.990 Henry Zhao: I haven’t gotten that feeling from Byron, or, like, from looking at the data, it doesn’t look like that’s their priority.

951 01:16:17.460 ⇒ 01:16:19.520 Amber Lin: What would their priority be?

952 01:16:20.230 ⇒ 01:16:25.180 Henry Zhao: That’s what I want to understand from Byron. That’s why I wasn’t in any of the meetings, and it just doesn’t look obvious to me.

953 01:16:25.180 ⇒ 01:16:28.340 Amber Lin: I see. He wants to… I think…

954 01:16:28.570 ⇒ 01:16:36.070 Amber Lin: We were only talking about Amazon in that meeting, so probably didn’t get his insights on this side, but he wanted to forecast demand.

955 01:16:36.680 ⇒ 01:16:40.109 Amber Lin: Like, he… he… I don’t know what… what’s his…

956 01:16:41.790 ⇒ 01:16:42.239 Henry Zhao: So we have…

957 01:16:42.240 ⇒ 01:16:46.950 Amber Lin: I’m gonna go spot his LinkedIn, I don’t know what his job title is.

958 01:16:47.850 ⇒ 01:16:49.910 Henry Zhao: Yeah, let’s look at it. That’s a good idea.

959 01:16:50.300 ⇒ 01:16:51.539 Henry Zhao: What’s his last name?

960 01:16:51.840 ⇒ 01:16:52.689 Amber Lin: Don’t know.

961 01:16:52.840 ⇒ 01:16:58.580 Amber Lin: Byron Pitam? Yeah, I just heard his name. Okay, Byron Pittam.

962 01:17:00.750 ⇒ 01:17:02.060 Henry Zhao: So…

963 01:17:02.090 ⇒ 01:17:05.490 Amber Lin: Hahaha. He’s a director of e-commerce.

964 01:17:06.020 ⇒ 01:17:09.190 Amber Lin: He was the e-comm growth manager.

965 01:17:10.770 ⇒ 01:17:22.759 Amber Lin: He was an e-com growth manager from 2021 to 2022, and then he became the Director of Digital Marketing from 2022 to 24, and now he’s a director of e-commerce.

966 01:17:23.420 ⇒ 01:17:25.140 Henry Zhao: You went to the University of Georgia.

967 01:17:27.340 ⇒ 01:17:28.610 Henry Zhao: He’s a dog walker.

968 01:17:32.000 ⇒ 01:17:36.479 Amber Lin: Okay, so he can care about growth, I think?

969 01:17:37.160 ⇒ 01:17:44.230 Henry Zhao: But it just seemed like from the first meeting that I watched the recording of, he just, he cares more about, like, not running out of product than actual overall growth.

970 01:17:44.230 ⇒ 01:17:48.330 Amber Lin: Yeah, I think that’s… the result of…

971 01:17:48.610 ⇒ 01:17:54.410 Amber Lin: like, overall, Growthy is, like, running out of product because they’ve been growing recently.

972 01:17:54.410 ⇒ 01:17:57.109 Henry Zhao: We’re doing a good job growing, but I don’t… I’m not seeing them growing.

973 01:17:57.110 ⇒ 01:18:03.359 Amber Lin: That’s what I’m… that’s what I’m curious about. He’s… they say they’re growing. It doesn’t look like they’re growing.

974 01:18:04.300 ⇒ 01:18:07.509 Henry Zhao: You know what it is? I feel like it’s not distributed well.

975 01:18:08.030 ⇒ 01:18:15.869 Henry Zhao: Oh, right, right, because, okay, think about it. If you’re seeing spikes now that you weren’t seeing before, and your overall, by year, by week, it’s not growing.

976 01:18:16.120 ⇒ 01:18:23.060 Henry Zhao: That means what happened is instead of every day I’m selling 10, I’m selling 5 every day, but then there’s these spikes where I’m selling 50 or 80.

977 01:18:23.060 ⇒ 01:18:26.149 Amber Lin: And that’s why, that’s why… oh, oh, oh.

978 01:18:26.150 ⇒ 01:18:29.169 Henry Zhao: Maybe… boy, so maybe our recommendation is spread out…

979 01:18:29.370 ⇒ 01:18:32.340 Amber Lin: Yes, so it’s more predictable.

980 01:18:32.340 ⇒ 01:18:34.649 Henry Zhao: Prepared for that increased inventory when you have…

981 01:18:34.650 ⇒ 01:18:37.020 Amber Lin: Okay, that’s… that’s so awesome. So we say…

982 01:18:37.020 ⇒ 01:18:37.529 Henry Zhao: That’s right.

983 01:18:37.530 ⇒ 01:18:41.580 Amber Lin: slide 3, significant spikes.

984 01:18:43.240 ⇒ 01:18:46.929 Henry Zhao: It’s like, spikes exist now, but overall hasn’t grown, so…

985 01:18:47.610 ⇒ 01:18:48.870 Amber Lin: It seems like…

986 01:18:48.870 ⇒ 01:18:51.720 Henry Zhao: Meth Pass forward is, like, to manage the spikes better, right?

987 01:18:51.720 ⇒ 01:18:52.780 Amber Lin: Yes, so…

988 01:18:52.780 ⇒ 01:18:54.349 Henry Zhao: Without your earner promotions.

989 01:18:54.860 ⇒ 01:18:59.959 Amber Lin: Smooth out… Promotions for more…

990 01:18:59.960 ⇒ 01:19:01.429 Henry Zhao: Yes, what about from Auckland?

991 01:19:01.430 ⇒ 01:19:03.549 Amber Lin: Double demand.

992 01:19:04.880 ⇒ 01:19:05.470 Amber Lin: Okay.

993 01:19:05.470 ⇒ 01:19:13.199 Henry Zhao: We learned something in business school. Okay, so we learned something in business school where it’s like, why do you… do some companies allow you to, like, pre-book stuff, or like…

994 01:19:13.200 ⇒ 01:19:13.590 Amber Lin: Yeah.

995 01:19:13.590 ⇒ 01:19:15.639 Henry Zhao: Like, delay your tour dates.

996 01:19:15.640 ⇒ 01:19:16.230 Amber Lin: Go there.

997 01:19:16.230 ⇒ 01:19:16.790 Henry Zhao: Okay.

998 01:19:16.790 ⇒ 01:19:17.430 Amber Lin: Yeah, they’re…

999 01:19:17.430 ⇒ 01:19:21.430 Henry Zhao: Forecasting must be so shit.

1000 01:19:21.430 ⇒ 01:19:22.100 Amber Lin: Yeah.

1001 01:19:22.510 ⇒ 01:19:34.150 Henry Zhao: Say you’re Six Flags, right? Say you’re Six Flags, and you want… you want to do a promotion where people want to come to your amusement park. But if you do a really successful promotion, like, a million people are going to come to your park, they’re going to be waiting in lines, there’s not going to be enough…

1002 01:19:34.150 ⇒ 01:19:38.379 Amber Lin: Yeah, I actually just went to Six Flags, like, last… Last week.

1003 01:19:38.380 ⇒ 01:19:48.410 Henry Zhao: So we learned a business class where it’s like, they have promotions where it’s like, you can buy a ticket, but you can use that on a later date, and they do that to smooth out the demand so that they can, like, actually…

1004 01:19:48.600 ⇒ 01:19:51.820 Henry Zhao: like, cater to all the customers, and that’s… here.

1005 01:19:51.820 ⇒ 01:19:53.989 Amber Lin: On demand for more.

1006 01:19:55.070 ⇒ 01:19:57.070 Amber Lin: Predictable.

1007 01:20:02.300 ⇒ 01:20:08.520 Henry Zhao: Or, like, stock up big time before you do a promotion, right? Because this is… all of this you see is very, like, spike-based.

1008 01:20:13.630 ⇒ 01:20:16.859 Henry Zhao: I don’t think… I don’t think I would have come up to this conclusion if we hadn’t worked together on this.

1009 01:20:16.860 ⇒ 01:20:20.430 Amber Lin: I know, now we just looked at it and say, okay, it looks… like…

1010 01:20:20.430 ⇒ 01:20:22.210 Henry Zhao: 30 minutes ago, I would have stopped working on this, yeah.

1011 01:20:22.630 ⇒ 01:20:23.370 Henry Zhao: to, like.

1012 01:20:23.550 ⇒ 01:20:24.900 Amber Lin: Very interesting, though.

1013 01:20:25.080 ⇒ 01:20:27.590 Amber Lin: Now, this type of stuff is really cool.

1014 01:20:27.590 ⇒ 01:20:29.069 Henry Zhao: This is, like, super exciting. This is awesome.

1015 01:20:29.790 ⇒ 01:20:34.589 Amber Lin: Yeah. How do I conclude it? How do I… like…

1016 01:20:34.590 ⇒ 01:20:38.189 Henry Zhao: I don’t know how to do… I don’t know how to write this stuff, like…

1017 01:20:38.190 ⇒ 01:20:42.640 Amber Lin: Significant spikes, skew perception?

1018 01:20:42.830 ⇒ 01:20:49.870 Amber Lin: Or significant spikes… Okay, followed… Bye.

1019 01:20:53.930 ⇒ 01:20:59.310 Amber Lin: Here. Followed by… Steep. Drop.

1020 01:20:59.860 ⇒ 01:21:00.910 Amber Lin: Ofts.

1021 01:21:02.580 ⇒ 01:21:13.640 Amber Lin: Cause… Thing inventory stock counts… And failed… Okay.

1022 01:21:15.370 ⇒ 01:21:16.320 Amber Lin: Cool.

1023 01:21:18.530 ⇒ 01:21:19.220 Henry Zhao: M.

1024 01:21:19.690 ⇒ 01:21:24.510 Amber Lin: So… If people buy…

1025 01:21:25.050 ⇒ 01:21:32.919 Amber Lin: I’m thinking about what this means. So, if people only buy a lot when there’s significant promotions and not buy at all.

1026 01:21:33.060 ⇒ 01:21:42.289 Amber Lin: What does that mean for their customer base, or for their pricing, or for, like, how people perceive their product? Does that mean they’re heavily value-based?

1027 01:21:44.700 ⇒ 01:21:47.030 Amber Lin: Does that mean that they don’t care about the brand?

1028 01:21:47.520 ⇒ 01:21:48.209 Amber Lin: Let me see…

1029 01:21:48.210 ⇒ 01:21:48.769 Henry Zhao: Yeah. It’s like…

1030 01:21:48.770 ⇒ 01:21:53.010 Amber Lin: Energy, waffle… Competitors…

1031 01:21:56.930 ⇒ 01:22:01.649 Henry Zhao: I’d be curious to know, like, what is the cadence people buy energy waffles?

1032 01:22:01.650 ⇒ 01:22:07.290 Amber Lin: Yeah, and like, why do they buy honey stinger? Like, why, like, why do they care? If they’re buying energy…

1033 01:22:07.290 ⇒ 01:22:09.460 Henry Zhao: What is the value prop, right?

1034 01:22:09.460 ⇒ 01:22:10.320 Amber Lin: Huh.

1035 01:22:10.510 ⇒ 01:22:14.360 Amber Lin: There’s so many energy waffles out there.

1036 01:22:14.850 ⇒ 01:22:16.769 Henry Zhao: Then there’s protein bars.

1037 01:22:16.940 ⇒ 01:22:25.119 Henry Zhao: Yeah, but it’s like, do you buy a free workout every day? Do you stock up for a few months, and then when you’re out, you’re out? Like, what is the value prop, or, like, buying cadence, right?

1038 01:22:25.120 ⇒ 01:22:27.960 Amber Lin: Okay, so that’s, like, suggesting…

1039 01:22:27.960 ⇒ 01:22:31.949 Henry Zhao: I feel like I can kind of tell that from the emails, so I subscribe to their emails.

1040 01:22:31.950 ⇒ 01:22:32.770 Amber Lin: Huh.

1041 01:22:32.770 ⇒ 01:22:38.739 Henry Zhao: And I was just trying to kind of look at their promotions and, like, what they kind of say, so let me Google… let me search my Gmail for Honey Stinger.

1042 01:22:42.690 ⇒ 01:22:47.329 Henry Zhao: Oh, wow. Did you know that I had a Honey Stinger ad in my… in my Target ad a few weeks ago?

1043 01:22:48.590 ⇒ 01:22:51.309 Henry Zhao: Here’s a glass thing. Whoa! Yeah, take a look at this.

1044 01:22:52.100 ⇒ 01:22:53.080 Henry Zhao: One second.

1045 01:22:53.080 ⇒ 01:22:58.040 Amber Lin: I cannot see your screen. You don’t have to share if it’s your personal email.

1046 01:22:58.040 ⇒ 01:22:59.290 Henry Zhao: I’m sharing my phone again.

1047 01:22:59.840 ⇒ 01:23:03.350 Amber Lin: Value-driven customer?

1048 01:23:03.540 ⇒ 01:23:04.390 Henry Zhao: You see this?

1049 01:23:04.390 ⇒ 01:23:05.290 Amber Lin: Space…

1050 01:23:07.360 ⇒ 01:23:18.200 Amber Lin: Cool. Yeah, people only… if people only buy if there’s, like, promotions, that means they don’t have brand loyalty, and maybe that’s why they have so little subscribers.

1051 01:23:19.620 ⇒ 01:23:20.969 Amber Lin: If that’s true.

1052 01:23:21.900 ⇒ 01:23:27.559 Amber Lin: And… limited brand loyalty? Do they have a loyalty program?

1053 01:23:28.040 ⇒ 01:23:35.250 Henry Zhao: Alright, so here it says, sports field trusted by 2,000 plus pro plus college teams. So I wonder if this is, like, schools buy this also for their athletes?

1054 01:23:35.250 ⇒ 01:23:40.560 Amber Lin: Cool, that’s why I was looking at, the…

1055 01:23:40.900 ⇒ 01:23:49.500 Amber Lin: like, the school terms. You know the spike in March? There’s no significant… There’s no significant…

1056 01:23:49.920 ⇒ 01:23:58.160 Amber Lin: days, like, special events around there, but, like, March is a few months before May. Isn’t… could that be, like, competition?

1057 01:23:59.320 ⇒ 01:24:00.770 Henry Zhao: Like, would… would…

1058 01:24:00.770 ⇒ 01:24:04.280 Amber Lin: Competitions be around, like, 2 months before…

1059 01:24:04.570 ⇒ 01:24:11.399 Henry Zhao: Yes. So that’s a good point then, because look at this. Honey Stinger products are used by over 250 NCAA athletic programs.

1060 01:24:11.400 ⇒ 01:24:12.210 Amber Lin: Okay.

1061 01:24:12.750 ⇒ 01:24:14.839 Henry Zhao: And NIL deals, that’s cool.

1062 01:24:15.040 ⇒ 01:24:16.110 Amber Lin: Cool.

1063 01:24:16.680 ⇒ 01:24:19.980 Amber Lin: Right, that’s interesting. When do college…

1064 01:24:20.460 ⇒ 01:24:26.060 Amber Lin: College, sports, competition, usually for season?

1065 01:24:26.620 ⇒ 01:24:30.290 Amber Lin: Competition usually happen.

1066 01:24:41.330 ⇒ 01:24:42.100 Amber Lin: Oh.

1067 01:24:42.610 ⇒ 01:24:48.740 Amber Lin: Okay. Championship tournaments like March Madness in March, so is it, like… I don’t know.

1068 01:24:48.740 ⇒ 01:24:53.090 Henry Zhao: So, like, in the summer, in the summer, they have, like, summer training and stuff like that, so obviously they’re gonna need these.

1069 01:24:53.090 ⇒ 01:24:53.950 Amber Lin: Huh.

1070 01:24:54.270 ⇒ 01:24:55.070 Henry Zhao: to restock.

1071 01:24:55.070 ⇒ 01:24:59.820 Amber Lin: Okay, that’s… that’s interesting. Let’s put it… Where do we put it?

1072 01:25:00.000 ⇒ 01:25:02.730 Henry Zhao: Those spikes might be because of these partnerships with colleges and stuff.

1073 01:25:02.950 ⇒ 01:25:11.430 Amber Lin: They do think so, except for the, like, winter… I’m gonna…

1074 01:25:11.430 ⇒ 01:25:14.639 Henry Zhao: It never makes sense, right? Everyone’s going home from school, they don’t need that much.

1075 01:25:14.640 ⇒ 01:25:24.840 Amber Lin: spikes in March… In summer, and… In… November.

1076 01:25:25.600 ⇒ 01:25:30.010 Henry Zhao: I wonder if we can look at our customers and see…

1077 01:25:30.490 ⇒ 01:25:33.159 Henry Zhao: Maybe we can look at the customers that have bought the most?

1078 01:25:33.900 ⇒ 01:25:35.330 Henry Zhao: Let’s see what those are.

1079 01:25:35.330 ⇒ 01:25:38.100 Amber Lin: Okay, they’re likely, like, business customers.

1080 01:25:38.100 ⇒ 01:25:38.789 Henry Zhao: Gotta stick of.

1081 01:25:38.790 ⇒ 01:25:45.299 Amber Lin: Individuals cannot buy 10K, 1,000, like, even 2,000 honey scales.

1082 01:25:45.300 ⇒ 01:25:47.880 Henry Zhao: Maybe not. Let’s take a look at.

1083 01:25:47.880 ⇒ 01:25:48.500 Amber Lin: Hmm.

1084 01:26:04.940 ⇒ 01:26:06.890 Henry Zhao: Shoot, shoot, shoot, shoot, shoot. Cancel, cancel, cancel, cancel.

1085 01:26:11.150 ⇒ 01:26:13.300 Henry Zhao: Let’s look at the 100 biggest customers, alright.

1086 01:26:15.760 ⇒ 01:26:21.760 Henry Zhao: Did some guy at Harvard ordered… 72 orders already.

1087 01:26:28.070 ⇒ 01:26:29.870 Henry Zhao: I’m not seeing a lot of, like.

1088 01:26:31.080 ⇒ 01:26:35.260 Amber Lin: It could be a person, like, it’s only gonna be a person ordering.

1089 01:26:36.280 ⇒ 01:26:36.720 Henry Zhao: Let’s do…

1090 01:26:36.720 ⇒ 01:26:38.410 Amber Lin: Can we look at their emails?

1091 01:26:39.300 ⇒ 01:26:45.520 Henry Zhao: That’s what I was looking at, the emails. I was expecting… oh, here we go, here we go, here we go! Dolphins, so this is, like, the Miami Dolphins.

1092 01:26:45.530 ⇒ 01:26:46.540 Amber Lin: Oh, really?

1093 01:26:46.540 ⇒ 01:26:53.349 Henry Zhao: Yeah, Scott Boas bought… Purchased… how much? 13,000 of a Honey Stinger products.

1094 01:26:53.350 ⇒ 01:26:55.650 Amber Lin: Can we look at, like, membership?

1095 01:27:00.830 ⇒ 01:27:02.360 Henry Zhao: What do you mean by membership?

1096 01:27:02.360 ⇒ 01:27:03.979 Amber Lin: Oh, that’s so interesting.

1097 01:27:04.440 ⇒ 01:27:05.280 Henry Zhao: Javio.

1098 01:27:06.030 ⇒ 01:27:08.269 Henry Zhao: This is interesting for us, they probably already know this stuff.

1099 01:27:08.700 ⇒ 01:27:09.790 Henry Zhao: Yeah.

1100 01:27:13.020 ⇒ 01:27:15.520 Henry Zhao: Yeah, there’s profiles, I think, here.

1101 01:27:16.930 ⇒ 01:27:19.930 Henry Zhao: Profiles and events. Alright, let’s my self-profile data.

1102 01:27:24.020 ⇒ 01:27:26.520 Henry Zhao: Alright, let’s take a look at Kavio, then.

1103 01:27:45.470 ⇒ 01:27:50.390 Henry Zhao: How do I want to join that email, maybe? Or…

1104 01:27:55.080 ⇒ 01:27:58.300 Henry Zhao: How was I joining this? Good to come up, yeah.

1105 01:28:04.120 ⇒ 01:28:05.220 Henry Zhao: Hangs on…

1106 01:28:19.250 ⇒ 01:28:22.310 Henry Zhao: Good memberships, you said? Okay, order placed successfully on recharge.

1107 01:28:27.240 ⇒ 01:28:32.289 Amber Lin: Because I think one of the claims we want to make is that they’re, like, their loyalty…

1108 01:28:32.880 ⇒ 01:28:37.520 Amber Lin: is also not good? I don’t know. Like, I don’t know if they’re members…

1109 01:28:38.400 ⇒ 01:28:45.939 Amber Lin: I guess one of the… the one number… one, we want to look at how many people are members, and then two, do the members purchase

1110 01:28:46.560 ⇒ 01:28:47.470 Amber Lin: more.

1111 01:28:48.240 ⇒ 01:28:53.879 Amber Lin: I guess, which they should, but I don’t know how many people become members.

1112 01:28:56.730 ⇒ 01:28:58.410 Henry Zhao: PRI for these orgs should be pretty good.

1113 01:29:15.080 ⇒ 01:29:16.560 Henry Zhao: Alright, why is it so slow?

1114 01:29:20.460 ⇒ 01:29:21.480 Henry Zhao: Right back, okay?

1115 01:29:21.700 ⇒ 01:29:22.280 Amber Lin: Okay.

1116 01:29:22.470 ⇒ 01:29:26.940 Amber Lin: I also have to drop soon, but I think we have a lot of stuff already.

1117 01:29:27.060 ⇒ 01:29:29.879 Henry Zhao: We’re gonna look at this little file stuff for next week, maybe.

1118 01:29:30.080 ⇒ 01:29:35.039 Amber Lin: Yeah, cool, okay. We can, we can put it in, like, next steps.

1119 01:29:36.060 ⇒ 01:29:39.640 Amber Lin: Customer loyalty…

1120 01:30:03.450 ⇒ 01:30:04.460 Henry Zhao: Right?

1121 01:30:27.080 ⇒ 01:30:27.790 Amber Lin: Okay.

1122 01:30:27.910 ⇒ 01:30:32.470 Amber Lin: So what are your ques… what is… what are we looking at here?

1123 01:30:33.750 ⇒ 01:30:42.300 Henry Zhao: Out of the highest, so let me go properties dot, border… price.

1124 01:30:43.820 ⇒ 01:30:49.989 Henry Zhao: Group by 1, order by 2 descending, limit 50. Let’s look at the top 50 purchasers.

1125 01:30:53.740 ⇒ 01:30:56.060 Henry Zhao: event properties.data.tool twice?

1126 01:30:57.190 ⇒ 01:30:58.070 Henry Zhao: Wait, hold on.

1127 01:31:04.290 ⇒ 01:31:05.040 Henry Zhao: What?

1128 01:31:07.940 ⇒ 01:31:09.100 Henry Zhao: Shouldn’t have.

1129 01:31:10.140 ⇒ 01:31:10.800 Henry Zhao: Right.

1130 01:31:18.980 ⇒ 01:31:19.940 Henry Zhao: Okay.

1131 01:31:21.550 ⇒ 01:31:22.950 Henry Zhao: Total prices are here.

1132 01:31:25.230 ⇒ 01:31:27.440 Amber Lin: Okay, I have about 5 minutes.

1133 01:31:29.450 ⇒ 01:31:30.020 Henry Zhao: price.

1134 01:31:49.690 ⇒ 01:31:51.359 Henry Zhao: Oh, there we go. Alright.

1135 01:31:51.780 ⇒ 01:31:55.870 Henry Zhao: ejoinhoneyStinger.polytomic.

1136 01:31:56.580 ⇒ 01:32:00.840 Henry Zhao: I have to memorize this. Avio.profiles, I gotta do this fast.

1137 01:32:01.080 ⇒ 01:32:04.360 Henry Zhao: P on E dot relationship.

1138 01:32:05.820 ⇒ 01:32:09.819 Henry Zhao: profile.data.id equals p.id.

1139 01:32:10.110 ⇒ 01:32:11.029 Henry Zhao: Is that right?

1140 01:32:13.990 ⇒ 01:32:15.710 Henry Zhao: Leadership’s profile.

1141 01:32:18.190 ⇒ 01:32:21.039 Henry Zhao: Relationshipsprofile.data.id, that’s right.

1142 01:32:23.310 ⇒ 01:32:24.980 Henry Zhao: Relationships profile.

1143 01:32:25.790 ⇒ 01:32:34.360 Henry Zhao: Okay, then we want p.email… by 1, order by 2 descending… Shoot!

1144 01:32:34.950 ⇒ 01:32:35.520 Amber Lin: Hmm?

1145 01:32:36.870 ⇒ 01:32:41.789 Henry Zhao: sum. Yeah, good thing that was an error. Has total spend…

1146 01:32:42.090 ⇒ 01:32:47.280 Henry Zhao: What about your descending limit… 75… whatever, 87. It’s fine.

1147 01:32:52.930 ⇒ 01:32:54.470 Henry Zhao: This is the last thing, and I’ll let you go.

1148 01:32:55.200 ⇒ 01:32:57.279 Henry Zhao: That’s my next meeting. I don’t anymore.

1149 01:33:00.710 ⇒ 01:33:02.349 Henry Zhao: This is good, alright.

1150 01:33:02.460 ⇒ 01:33:04.169 Henry Zhao: Let’s what it says.

1151 01:33:04.840 ⇒ 01:33:06.610 Henry Zhao: Do queries?

1152 01:33:09.490 ⇒ 01:33:17.190 Henry Zhao: ops vendors… my email… In, Recharge by email.

1153 01:33:18.850 ⇒ 01:33:20.220 Henry Zhao: Okay, I’ll leave that there.

1154 01:33:22.130 ⇒ 01:33:23.929 Henry Zhao: We can go over this on Monday if you need to go.

1155 01:33:24.810 ⇒ 01:33:32.070 Amber Lin: Okay, cool. I looked at slide 3 and 4. If you notice, like, on slide 4,

1156 01:33:32.160 ⇒ 01:33:46.439 Amber Lin: the spikes didn’t really happen, especially in 2023 and 2024, the spikes did not happen in… on the winter holidays. Like, I know this, like, 2025 is a little bit different.

1157 01:33:46.560 ⇒ 01:33:51.410 Amber Lin: But in the past 2 years, you see the spike here, in the…

1158 01:33:51.410 ⇒ 01:33:54.219 Henry Zhao: Nice. Like, in more sales?

1159 01:33:54.220 ⇒ 01:34:00.240 Amber Lin: more sales into November, but not more new customers in November.

1160 01:34:00.860 ⇒ 01:34:08.570 Amber Lin: That… that could be that, at least for the past 2 years, like, they’re… These were driven

1161 01:34:08.700 ⇒ 01:34:11.180 Amber Lin: By new customers, this is.

1162 01:34:11.180 ⇒ 01:34:11.920 Henry Zhao: Pete.

1163 01:34:12.650 ⇒ 01:34:15.749 Amber Lin: Like, this could be people who already subscribed.

1164 01:34:16.010 ⇒ 01:34:18.689 Amber Lin: Saw the promotion, went back and bought.

1165 01:34:19.650 ⇒ 01:34:22.179 Amber Lin: That’s, like, that… that could be something.

1166 01:34:23.170 ⇒ 01:34:23.770 Henry Zhao: Yeah.

1167 01:34:28.570 ⇒ 01:34:29.480 Henry Zhao: That’s true.

1168 01:34:29.870 ⇒ 01:34:38.130 Amber Lin: Yeah. Anyways, okay, we’ll make… we’ll… we’ll… Okay, my, my recharge just came out, and, there’s not that… nothing interesting.

1169 01:34:38.390 ⇒ 01:34:39.040 Amber Lin: Okay.

1170 01:34:39.280 ⇒ 01:34:42.580 Amber Lin: Cool, sounds good. Thanks, this is really cool.

1171 01:34:42.970 ⇒ 01:34:44.079 Henry Zhao: It was, thank you so much.

1172 01:34:44.080 ⇒ 01:34:44.840 Amber Lin: Alright.

1173 01:34:45.020 ⇒ 01:34:45.890 Henry Zhao: Yeah.

1174 01:34:46.160 ⇒ 01:34:46.880 Henry Zhao: Bye.

1175 01:34:46.880 ⇒ 01:34:47.890 Amber Lin: Bye.

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