Meeting Title: Robert Tseng’s Personal Meeting Room Date: 2025-05-23 Meeting participants: Annie Yu, Awaish Kumar
WEBVTT
1 00:04:16.050 ⇒ 00:04:17.260 Awaish Kumar: Hello, Annie!
2 00:04:19.019 ⇒ 00:04:20.059 Annie Yu: Hello! Weish.
3 00:04:21.029 ⇒ 00:04:22.129 Awaish Kumar: How are you doing.
4 00:04:22.760 ⇒ 00:04:32.110 Annie Yu: Good. I I have some some things that I still wanna get done by this week for for Eden.
5 00:04:33.740 ⇒ 00:04:38.899 Awaish Kumar: Okay. So I think Damalade, Robert, both are not joining. So we can just
6 00:04:39.300 ⇒ 00:04:41.800 Awaish Kumar: go ahead with our phone tickets.
7 00:04:41.920 ⇒ 00:04:46.730 Awaish Kumar: Okay, and write up any updates here. So from
8 00:04:48.880 ⇒ 00:04:54.110 Awaish Kumar: okay, I have wrote here that what cannot be done and
9 00:04:56.240 ⇒ 00:04:59.309 Awaish Kumar: there is no ticket here, so we can just
10 00:04:59.860 ⇒ 00:05:02.720 Awaish Kumar: move to in progress ones. And
11 00:05:02.840 ⇒ 00:05:06.200 Awaish Kumar: so this one is this vlogged, or what.
12 00:05:09.218 ⇒ 00:05:20.219 Annie Yu: Yeah, this one is pending that build out. Ltv, so that’s the message that Robert shared earlier. So right now, we have that Ltv
13 00:05:20.330 ⇒ 00:05:30.069 Annie Yu: kind of heat map chart set up, and I think I then next have to update Mattesh and set up time with him next week.
14 00:05:31.234 ⇒ 00:05:39.050 Annie Yu: So once that once that’s approved, we will also update another dashboard.
15 00:05:40.730 ⇒ 00:05:41.490 Awaish Kumar: Okay.
16 00:05:41.930 ⇒ 00:05:48.330 Awaish Kumar: But for this ticket like, is this tied to one dashboard, or is it for both the dashboards.
17 00:05:48.718 ⇒ 00:06:00.770 Annie Yu: This one is only for this dashboard, but within that other ticket the Ltv, this on the bottom the build out cohort based heat map. That’s for 2 dashboards.
18 00:06:05.940 ⇒ 00:06:09.490 Awaish Kumar: There’s 1. Okay, here. There is only one ticket. Right?
19 00:06:10.270 ⇒ 00:06:14.300 Annie Yu: If you scroll down to the bottom in the comment.
20 00:06:14.870 ⇒ 00:06:25.039 Annie Yu: that’s yeah. That’s the that’s the no, not yeah, that one. So here I have to update, too. But my plan is to meet with Mattesh first, st and then.
21 00:06:25.040 ⇒ 00:06:29.410 Awaish Kumar: It is. But this is in feedback, like you have already. Okay.
22 00:06:30.250 ⇒ 00:06:34.819 Awaish Kumar: this is for 2 dashboards, and you have already done for one, and you are waiting for
23 00:06:35.030 ⇒ 00:06:37.469 Awaish Kumar: client feedback. Basically.
24 00:06:37.470 ⇒ 00:06:38.130 Annie Yu: Yeah, yeah.
25 00:06:38.130 ⇒ 00:06:39.669 Awaish Kumar: To other transport, as well.
26 00:06:39.830 ⇒ 00:06:49.100 Annie Yu: Yeah, but I haven’t sent a message. I thought Robert was gonna send it yesterday, but he did not. So I will send a message to to him, and then.
27 00:06:50.120 ⇒ 00:06:51.460 Awaish Kumar: I think me too.
28 00:06:51.460 ⇒ 00:06:52.390 Annie Yu: That time.
29 00:06:53.050 ⇒ 00:06:55.049 Awaish Kumar: So just give me a second.
30 00:07:35.910 ⇒ 00:07:36.910 Awaish Kumar: Yeah, silly.
31 00:07:37.120 ⇒ 00:07:41.325 Awaish Kumar: So we so so to like
32 00:07:41.940 ⇒ 00:07:43.210 Annie Yu: Update, Mattesh.
33 00:07:43.630 ⇒ 00:07:44.630 Awaish Kumar: We’ve.
34 00:07:44.630 ⇒ 00:07:45.199 Annie Yu: Yeah, okay.
35 00:07:45.200 ⇒ 00:07:51.300 Awaish Kumar: Meet with us right to get past his basically approval.
36 00:07:51.510 ⇒ 00:07:53.670 Annie Yu: Yeah.
37 00:07:58.250 ⇒ 00:08:01.949 Awaish Kumar: But to the dashboard.
38 00:08:02.230 ⇒ 00:08:03.480 Annie Yu: Yeah, correct.
39 00:08:14.540 ⇒ 00:08:15.580 Awaish Kumar: Here.
40 00:08:16.270 ⇒ 00:08:20.630 Awaish Kumar: This one is still like rating.
41 00:08:38.600 ⇒ 00:08:39.510 Annie Yu: Okay.
42 00:08:44.340 ⇒ 00:08:45.269 Awaish Kumar: Where is that?
43 00:08:45.860 ⇒ 00:08:47.469 Annie Yu: Yeah, this one.
44 00:08:48.710 ⇒ 00:08:59.220 Annie Yu: I think this one is done because this one, I ship that kind of document. And we are now already working on this short term. Ltv. So.
45 00:08:59.220 ⇒ 00:09:00.350 Awaish Kumar: As it’s done right.
46 00:09:00.350 ⇒ 00:09:00.990 Annie Yu: Yeah.
47 00:09:06.160 ⇒ 00:09:07.380 Awaish Kumar: Basically.
48 00:09:08.020 ⇒ 00:09:11.610 Awaish Kumar: So this one we will be working on afterwards.
49 00:09:11.610 ⇒ 00:09:13.879 Annie Yu: Yeah, that’s like, longer term.
50 00:09:16.296 ⇒ 00:09:22.490 Awaish Kumar: Blow to just want a lot of about.
51 00:09:23.910 ⇒ 00:09:27.809 Awaish Kumar: So these are not performed. Same reasons.
52 00:09:32.390 ⇒ 00:09:33.310 Awaish Kumar: Okay.
53 00:09:33.620 ⇒ 00:09:40.759 Annie Yu: And then this one product drill down, dash refresh is what I am going to focus today.
54 00:09:44.410 ⇒ 00:09:57.400 Annie Yu: Yeah. So there are several items for this ticket. Yesterday I added that text table. And then.
55 00:09:59.710 ⇒ 00:10:07.300 Annie Yu: yeah, I’m I’m a little stuck on something, though here is the self join.
56 00:10:09.410 ⇒ 00:10:11.479 Annie Yu: I was able to build.
57 00:10:13.900 ⇒ 00:10:15.539 Awaish Kumar: Okay, we can go ahead.
58 00:10:15.540 ⇒ 00:10:29.439 Annie Yu: Yeah, no. I was able to build that cross sale heat map. So basically, they wanted to see the customers who bought product a how. How many of them also bought product BCDE,
59 00:10:30.077 ⇒ 00:10:37.769 Annie Yu: that’s why I built that cross sale heat map. And I did self join. That’s why I could see that. But right now I just
60 00:10:38.040 ⇒ 00:10:48.982 Annie Yu: I think it’s how I set up the self join. I’m now doing inner join on Customer id from both tables, and then product name
61 00:10:49.600 ⇒ 00:10:52.368 Annie Yu: less than product name
62 00:10:53.240 ⇒ 00:10:57.280 Annie Yu: So I think with my current setup I can see.
63 00:10:57.280 ⇒ 00:10:58.600 Awaish Kumar: Plan to look at this.
64 00:11:00.850 ⇒ 00:11:04.720 Awaish Kumar: Wait. What do you plan to do with product, name, list, and product name like you want to
65 00:11:05.940 ⇒ 00:11:08.620 Awaish Kumar: like sorted sort them by names.
66 00:11:10.440 ⇒ 00:11:23.650 Annie Yu: No, no, let me see. Is it? Is it? Does it not make sense to use less than
67 00:11:24.320 ⇒ 00:11:24.970 Annie Yu: you’re like?
68 00:11:24.970 ⇒ 00:11:31.790 Awaish Kumar: Product. Name is a string right product. Name is not a integer value or a for any numeric right.
69 00:11:33.312 ⇒ 00:11:38.519 Annie Yu: Yeah. Oh, oh, here, by product name, I actually meant a standard product name.
70 00:11:39.530 ⇒ 00:11:42.419 Awaish Kumar: Yeah. Yeah. But standard product name is a string. Right?
71 00:11:43.520 ⇒ 00:11:44.410 Annie Yu: Yeah.
72 00:11:45.270 ⇒ 00:11:46.859 Awaish Kumar: This is not a value.
73 00:11:49.240 ⇒ 00:11:50.640 Awaish Kumar: Enterprise, product, name.
74 00:11:51.700 ⇒ 00:11:57.189 Annie Yu: Then how do I filter out then? Can I do not equal.
75 00:11:57.460 ⇒ 00:12:00.259 Awaish Kumar: Yeah, but what that.
76 00:12:01.440 ⇒ 00:12:08.480 Awaish Kumar: But if I I don’t know like what this column means like this, does this column have a if you are saying, it’s a standardized product name.
77 00:12:08.580 ⇒ 00:12:13.430 Awaish Kumar: Then this column basically has these values right everyday plus injectable same out.
78 00:12:13.580 ⇒ 00:12:14.829 Awaish Kumar: These are names.
79 00:12:15.060 ⇒ 00:12:16.119 Annie Yu: Yeah, yeah.
80 00:12:16.480 ⇒ 00:12:21.279 Awaish Kumar: And names like. If we are comparing between names I don’t know. Like
81 00:12:22.160 ⇒ 00:12:24.019 Awaish Kumar: you, you’d want to do that like
82 00:12:27.310 ⇒ 00:12:31.649 Awaish Kumar: I I’m I don’t understand why we are using this condition on a name.
83 00:12:31.890 ⇒ 00:12:43.470 Awaish Kumar: basically what we want to try and achieve. Maybe the logic may be correct, but I don’t get it right now I would use this less than greater than for some numeric columns.
84 00:12:43.970 ⇒ 00:12:45.390 Awaish Kumar: not strings.
85 00:12:48.510 ⇒ 00:12:48.900 Awaish Kumar: Names.
86 00:12:49.396 ⇒ 00:12:59.319 Annie Yu: This is a self join. So I want to see for the same customer. They bought product a, and
87 00:13:00.930 ⇒ 00:13:05.499 Annie Yu: later on, like, after that order what other products they bought.
88 00:13:06.180 ⇒ 00:13:09.240 Annie Yu: So I don’t want them to be the same product.
89 00:13:09.340 ⇒ 00:13:10.780 Annie Yu: Does that make sense.
90 00:13:11.490 ⇒ 00:13:13.320 Awaish Kumar: Yeah, you can say, not equal.
91 00:13:13.580 ⇒ 00:13:14.440 Annie Yu: Okay.
92 00:13:14.710 ⇒ 00:13:17.570 Awaish Kumar: But it’s it’s still wrong, because you are using.
93 00:13:17.970 ⇒ 00:13:21.519 Awaish Kumar: If you are referencing the same
94 00:13:21.940 ⇒ 00:13:24.510 Awaish Kumar: table A, it should be B right.
95 00:13:24.510 ⇒ 00:13:26.350 Annie Yu: Oh, yeah, yeah, you’re right.
96 00:13:26.630 ⇒ 00:13:40.149 Awaish Kumar: And then it should be not equal, because if you compare with self, join and then compare, and then you want them, you want to just ignore the rows where it’s same. Right? Then you can just say, not equal, instead of less than.
97 00:13:40.880 ⇒ 00:13:43.489 Annie Yu: So if I use not equal.
98 00:13:43.990 ⇒ 00:13:45.410 Annie Yu: Okay, here, I’m like.
99 00:13:45.570 ⇒ 00:13:48.360 Awaish Kumar: Something like this, I.
100 00:13:51.250 ⇒ 00:13:52.050 Annie Yu: Okay. Okay.
101 00:13:52.140 ⇒ 00:14:01.410 Awaish Kumar: There’s a space in here because linear was trying to make it something different. But yeah, there’s no space when you use in victory. Just use it together.
102 00:14:01.660 ⇒ 00:14:08.240 Annie Yu: Okay, that makes sense. And okay, I think that that makes sense. But then.
103 00:14:08.340 ⇒ 00:14:20.049 Annie Yu: I think, where I’m stuck also is. So right now with this we only show those customers who have bought other products right. But ideally, I would want to show
104 00:14:20.320 ⇒ 00:14:33.430 Annie Yu: 40% of customers who bought injectable. Sema also bought like Zoframe Otd. Later on. But when when I do the the join, that’s inner join, so I can’t
105 00:14:33.660 ⇒ 00:14:35.560 Annie Yu: really get to that.
106 00:14:37.010 ⇒ 00:14:41.735 Awaish Kumar: Yeah. But you can do this when you when you are joining in customer id,
107 00:14:43.160 ⇒ 00:14:48.860 Awaish Kumar: you are basically saying the product from same customer.
108 00:14:49.870 ⇒ 00:14:53.960 Awaish Kumar: Who bought collectible same app.
109 00:14:53.960 ⇒ 00:14:54.330 Annie Yu: Yeah.
110 00:14:54.330 ⇒ 00:15:01.710 Awaish Kumar: Also about m, 1 CB, 2 b, 12, and at the end we
111 00:15:01.880 ⇒ 00:15:03.669 Awaish Kumar: you have to aggregate them right
112 00:15:04.270 ⇒ 00:15:10.429 Awaish Kumar: by maybe. Just then we have to ignore the customer. Id, we will say the group by by
113 00:15:10.610 ⇒ 00:15:13.460 Awaish Kumar: injectable Sema. And
114 00:15:17.090 ⇒ 00:15:22.140 Awaish Kumar: yeah, this one m. 1 cv. 12. Whatever product name, and then count the customer
115 00:15:22.520 ⇒ 00:15:24.940 Awaish Kumar: just 2 customer ids, or something like that.
116 00:15:25.630 ⇒ 00:15:26.060 Annie Yu: Yes.
117 00:15:26.060 ⇒ 00:15:28.459 Awaish Kumar: That account is going to give you the.
118 00:15:31.320 ⇒ 00:15:37.200 Annie Yu: Yeah. So those sale values are distinct customers, as you said.
119 00:15:37.520 ⇒ 00:15:41.699 Annie Yu: But I think the next step I want to show.
120 00:15:41.940 ⇒ 00:15:52.830 Annie Yu: let’s say, like a hundred like ignore the Sale cross sale. A 100 people bought injectable Sema, and then 40 of them.
121 00:15:53.150 ⇒ 00:16:03.310 Annie Yu: but also bought mic. B. 12. So here we would see the cell value as 40 right. But then my denominator
122 00:16:03.910 ⇒ 00:16:12.120 Annie Yu: within this table is not also 40, because we only get those people who who cross, bought things.
123 00:16:12.520 ⇒ 00:16:15.450 Awaish Kumar: Okay, what is the what, then, should be the denominator.
124 00:16:16.200 ⇒ 00:16:24.120 Annie Yu: A hundred. In this case I I want to show maybe not in this sale table, but maybe in another text table.
125 00:16:24.750 ⇒ 00:16:31.750 Awaish Kumar: Yeah, then you have to find both. Right? Then 1st find the distinct customers group by by injectable same app.
126 00:16:31.870 ⇒ 00:16:45.200 Awaish Kumar: and then distinct customers based on these 2, and then join with injectable Sema. So do you understand? Like I’m I’m going. This is a little fast here. But yeah, like same way you did a join.
127 00:16:45.550 ⇒ 00:16:48.009 Awaish Kumar: you find these 40 people
128 00:16:48.330 ⇒ 00:17:06.260 Awaish Kumar: which basically has, which are distant customers. Right? Just create another city where you will have these district count of district customers by standardized product. Name, right group by that one. So basically, now, you have kind of 2 cities, one which is giving you the kind of number 40, which is like
129 00:17:07.260 ⇒ 00:17:16.929 Awaish Kumar: the customers which bought both products and then the other one CD, which is giving you 100, because now we are only saying that it count the number of customers who bought
130 00:17:17.190 ⇒ 00:17:27.190 Awaish Kumar: product injectable Sema. Now at the end, in the 3rd city you join them both by standardized product name. So now injectable Sema will be will have
131 00:17:27.639 ⇒ 00:17:52.759 Awaish Kumar: injectable. Sema. m. 1 c. 40. In one table, in another table we have injectable Sema in 100. Now we draw an injectable Sema with injectable Sema final table we will get is injectable. Sema. Everyone that’s m. 1 c. Plus v. 1240, and then a column with 100. So basically, then you use you got both columns and same table. Now, you can use them as a nominator and denominator.
132 00:17:53.840 ⇒ 00:18:01.850 Annie Yu: Okay. Okay, okay, I’m gonna try to see if I can do that all with tableau, which.
133 00:18:01.850 ⇒ 00:18:02.639 Awaish Kumar: I don’t know.
134 00:18:03.120 ⇒ 00:18:07.240 Awaish Kumar: I I don’t. I don’t want I don’t think like that’s something you should do in tableau.
135 00:18:07.440 ⇒ 00:18:07.980 Annie Yu: Okay.
136 00:18:07.980 ⇒ 00:18:10.319 Awaish Kumar: And we should have a model
137 00:18:10.680 ⇒ 00:18:14.189 Awaish Kumar: for that. If you want me to assign, or do you want to take it.
138 00:18:16.770 ⇒ 00:18:21.859 Annie Yu: I I probably won’t be able to do it, but I think I I think I.
139 00:18:21.860 ⇒ 00:18:32.030 Awaish Kumar: What do you think like, Will? This model is going to block your complete, your work, or this is something additional thing you want to do
140 00:18:32.420 ⇒ 00:18:35.200 Awaish Kumar: what? What? Just we just what we just discussed.
141 00:18:35.200 ⇒ 00:18:44.190 Annie Yu: Yeah, that’s this one is not with this he map because he map. I can. Only I can show only distinct customers. That’s fine.
142 00:18:44.190 ⇒ 00:18:45.149 Awaish Kumar: Okay, hold on.
143 00:18:45.150 ⇒ 00:18:51.290 Annie Yu: That’s like, yeah, it’s not urgent or anything just good to have.
144 00:18:52.410 ⇒ 00:18:57.999 Awaish Kumar: Okay, the sip. So this task is not blocked and you are going to work on it. So
145 00:18:58.120 ⇒ 00:19:04.370 Awaish Kumar: maybe just create another ticket with with whatever you have described the description.
146 00:19:06.410 ⇒ 00:19:07.145 Awaish Kumar: Just
147 00:19:09.036 ⇒ 00:19:19.469 Awaish Kumar: like. Describe it write it down and and create a ticket here, if possible, and assign it to me. Then I can build, build that model.
148 00:19:19.800 ⇒ 00:19:27.200 Annie Yu: Okay, yeah, that makes sense. I think I’ll I’ll take a bit more time just to make sure. I know, like what I really need and then create that.
149 00:19:27.200 ⇒ 00:19:29.490 Awaish Kumar: I can just then have a placeholder here.
150 00:19:30.060 ⇒ 00:19:32.429 Awaish Kumar: What that model is basically for.
151 00:19:34.984 ⇒ 00:19:42.789 Annie Yu: Or should we assign that ticket to me first? st So I can add things, and then assign to you? Would that make.
152 00:19:42.790 ⇒ 00:19:47.130 Awaish Kumar: I I can assign it to you. No worries, but I just want to like what should be the title.
153 00:19:48.310 ⇒ 00:19:48.940 Awaish Kumar: That’s true.
154 00:19:49.950 ⇒ 00:20:00.160 Annie Yu: Let’s say it’s probably it’s for cross sale. Hemap. So cross sale, model cross sale model. Yeah, he map model.
155 00:20:05.180 ⇒ 00:20:10.629 Awaish Kumar: Okay, I just make like, keep a placeholder just for now and then you can just update it.
156 00:20:10.820 ⇒ 00:20:14.150 Annie Yu: Okay, yeah.
157 00:20:15.200 ⇒ 00:20:18.225 Annie Yu: Another thing, I also think,
158 00:20:18.830 ⇒ 00:20:20.580 Awaish Kumar: Yeah, this item, okay.
159 00:20:20.580 ⇒ 00:20:27.412 Annie Yu: Okay, there’s another thing in that. Actually, the same ticket product rail down. Dash!
160 00:20:29.860 ⇒ 00:20:33.880 Annie Yu: how do I describe this? So the client wants to see
161 00:20:34.760 ⇒ 00:20:51.410 Annie Yu: very similar to cross sale. But cross sale, we really don’t see that journey. If someone bought a 1st and they switch to B and then switch it back to a. So the client wants to see that progression if they switch to something and then switch it back.
162 00:20:52.074 ⇒ 00:20:56.440 Annie Yu: So yesterday I had a quick huddle with Robert.
163 00:20:56.680 ⇒ 00:21:00.370 Annie Yu: He was giving me idea on how to
164 00:21:00.836 ⇒ 00:21:07.440 Annie Yu: flag the data to show that if I can. I’m I’m gonna share this image in a.
165 00:21:07.900 ⇒ 00:21:08.810 Annie Yu: in a chat.
166 00:21:08.810 ⇒ 00:21:09.430 Awaish Kumar: Hi.
167 00:21:09.710 ⇒ 00:21:10.520 Annie Yu: Hmm.
168 00:21:10.520 ⇒ 00:21:15.840 Awaish Kumar: I think, like, just write it down as a something, so we can then.
169 00:21:16.730 ⇒ 00:21:24.809 Awaish Kumar: Well, I I understand what you just said, but that requires like, what? On what level you need like. Do you need it to be on a
170 00:21:26.880 ⇒ 00:21:29.140 Annie Yu: Yeah, I think at this point I haven’t
171 00:21:29.680 ⇒ 00:21:36.520 Annie Yu: figure out if I will need any modeling support or not. I think I can probably do this in tableau.
172 00:21:37.180 ⇒ 00:21:47.539 Awaish Kumar: So. So this is just a customer journey like it’s. It’s kind of very simple thing, right, you know, if I have to write a carry, a skill carry that is, it’s very. It’s not that hard.
173 00:21:47.680 ⇒ 00:21:50.939 Awaish Kumar: but I don’t know if you you can do it in tableau as well.
174 00:21:51.530 ⇒ 00:22:05.149 Annie Yu: Okay, yeah, yeah. I haven’t spent much time on this. So I will explore this today. And if I’m like, really stuck without modeling support. I’ll let you know. But I I’m I’m hoping my hope is I can do this in tableau.
175 00:22:06.190 ⇒ 00:22:11.980 Awaish Kumar: Okay, then, just yeah, just create a ticket ticket here. If you
176 00:22:13.000 ⇒ 00:22:24.150 Awaish Kumar: at the end of the day today, whatever you have on this like if you give your thoughts little bit and just create a ticket if needed. So we are ready for maybe next week. Then.
177 00:22:24.440 ⇒ 00:22:25.719 Annie Yu: Okay. Okay.
178 00:22:26.580 ⇒ 00:22:27.900 Awaish Kumar: Okay. Thank you.
179 00:22:27.900 ⇒ 00:22:28.553 Annie Yu: Thank you.
180 00:22:29.700 ⇒ 00:22:30.990 Annie Yu: Is that all.
181 00:22:31.630 ⇒ 00:22:37.219 Awaish Kumar: Yeah, that’s all like this, like, just for 2 of us. It’s that’s everything.
182 00:22:37.710 ⇒ 00:22:38.970 Annie Yu: Okay. Cool.
183 00:22:40.320 ⇒ 00:22:41.570 Awaish Kumar: Okay. Thanks.
184 00:22:41.570 ⇒ 00:22:49.480 Annie Yu: Okay, thank you. Aish. Oh, sorry. Aish! One more question is there? I saw that you and Luke was
185 00:22:49.590 ⇒ 00:22:56.040 Annie Yu: you were talking about a Meta Point workshop is that something already happened or will be happening.
186 00:22:58.190 ⇒ 00:23:07.020 Awaish Kumar: It’s not it. It does not happen, but it will happen maybe sometime next week.
187 00:23:07.020 ⇒ 00:23:10.699 Annie Yu: Okay. Okay, yeah, just curious. Cause I I would wanna attend.
188 00:23:11.750 ⇒ 00:23:15.169 Awaish Kumar: Yeah, okay, yeah, sure.
189 00:23:15.170 ⇒ 00:23:17.540 Annie Yu: Thank you. Have a good weekend.
190 00:23:18.533 ⇒ 00:23:19.259 Awaish Kumar: You, too.
191 00:23:19.260 ⇒ 00:23:20.000 Annie Yu: Bye.