Meeting Title: Demilade Agboola’s Zoom Meeting Date: 2025-06-05 Meeting participants: Demilade Agboola, Annie Yu
WEBVTT
1 00:00:21.530 ⇒ 00:00:22.739 Annie Yu: Are they?
2 00:00:23.150 ⇒ 00:00:29.450 Annie Yu: Can you give me one second? I’m gonna run to a restroom. Actually cool.
3 00:01:38.720 ⇒ 00:01:39.980 Annie Yu: hey? I’m back.
4 00:01:41.630 ⇒ 00:01:43.930 Annie Yu: Are you on camera? Okay.
5 00:01:44.160 ⇒ 00:01:54.834 Annie Yu: I usually have my breakfast during our stand up, but I’m wearing invisalign, so I like, try to put them back as soon as possible.
6 00:01:55.390 ⇒ 00:02:04.530 Demilade Agboola: I mean sometimes breakfast during meetings is the way to go, because the the time when I have like meetings back to back I can’t have lunch until like
7 00:02:04.770 ⇒ 00:02:05.810 Demilade Agboola: after we take it.
8 00:02:05.810 ⇒ 00:02:21.413 Annie Yu: I hate having lunch during work I like I I live in a like a small house. I I really try my best to have lunch downstairs, so I don’t have to like work, but that’s hard to like. That’s hard to stick to sometime.
9 00:02:21.760 ⇒ 00:02:24.599 Demilade Agboola: You know. Fair enough it does happen it does happen.
10 00:02:25.990 ⇒ 00:02:33.020 Demilade Agboola: But yeah, so for the cross sale product switching, let me quickly share.
11 00:02:33.560 ⇒ 00:02:39.219 Annie Yu: And I’m I’m gonna look at their conversation from yesterday, cause
12 00:02:40.210 ⇒ 00:02:52.449 Annie Yu: like I. Initially, I thought this like this request was hard for me to really think through what granularities and kind of extra fields we would need.
13 00:02:53.190 ⇒ 00:02:59.750 Annie Yu: but but also would love your input too, instead of just like looking at the ticket that I created.
14 00:03:00.582 ⇒ 00:03:08.330 Demilade Agboola: Cause. So my, my perspective is, are we looking at it from the product perspective? Are we looking at it from the customers perspective?
15 00:03:09.280 ⇒ 00:03:16.440 Demilade Agboola: So are we looking at? How many times do people on this product? Multi that product, for instance, over a given period of time?
16 00:03:16.680 ⇒ 00:03:32.680 Demilade Agboola: Or are we looking at customer, a move to custom, from product, A to product B over time. And like the different customers that did that, and obviously over time, you can aggregate it by the product. So you can say, Oh.
17 00:03:33.100 ⇒ 00:03:40.989 Demilade Agboola: we have 2 customers, or 3 customers, or 10 customers that move from product A to product B and 5 move from product B to product C.
18 00:03:43.210 ⇒ 00:04:06.150 Annie Yu: I honestly think they want both. I I think people talk more about the the ladder that you mentioned like. What products they move from. And then what’s the most popular journey like A to B to C. But I think your 1st question was also like how I thought about it. I would want to know
19 00:04:06.290 ⇒ 00:04:27.059 Annie Yu: when people move from A to B was that like, maybe they stay on A for a very long time, and they move to B, or they might just move away from A after the 1st order. That means like product. A is not like, Retain, not not like good at retention. Right? So so, yeah, I I think there’s like
20 00:04:27.330 ⇒ 00:04:33.300 Annie Yu: space for us to play around with with, like the metrics and how to look at
21 00:04:33.470 ⇒ 00:04:37.729 Annie Yu: things. But with the ticket I created, I I had.
22 00:04:38.590 ⇒ 00:04:46.349 Annie Yu: I had thought that we would want to track kind of like average orders before switching.
23 00:04:47.570 ⇒ 00:04:50.890 Annie Yu: But with what I’ve created I don’t think
24 00:04:51.030 ⇒ 00:04:53.709 Annie Yu: there is an easy way to show
25 00:04:53.960 ⇒ 00:05:00.710 Annie Yu: from A to B to C to D, there, there, that was more like from A to B+B to C.
26 00:05:01.020 ⇒ 00:05:08.880 Demilade Agboola: But quick question, are we trying to ensure that it is a switch? So, for instance, if they want pro product a.
27 00:05:09.000 ⇒ 00:05:11.570 Demilade Agboola: and then they also added, product B,
28 00:05:11.720 ⇒ 00:05:19.270 Demilade Agboola: that doesn’t count as a switch. It’s if they want product, a stops being on product a, and then move to product. B, that that’s a switch right.
29 00:05:20.370 ⇒ 00:05:23.927 Annie Yu: I think that’s important to Joanna.
30 00:05:24.660 ⇒ 00:05:38.660 Annie Yu: I can pull up her initial request to you, because I remember she mentioned she also wanted to see like retention and churn. That’s why I don’t want falsely flagging someone who just had
31 00:05:38.820 ⇒ 00:05:44.169 Annie Yu: multiple plants as like they switched away from one product to another.
32 00:05:46.950 ⇒ 00:05:55.790 Annie Yu: but it’s it was. I was miserable thinking through that. So it would be great to have a second pair of eyes.
33 00:05:59.490 ⇒ 00:06:00.829 Annie Yu: Product, analytics.
34 00:06:02.360 ⇒ 00:06:07.269 Annie Yu: I let me. I can add you here just so.
35 00:06:11.520 ⇒ 00:06:15.730 Annie Yu: I don’t think you’re are you in the product analytics?
36 00:06:17.640 ⇒ 00:06:20.519 Demilade Agboola: No, I am not, and maybe that’s a good thing, too.
37 00:06:21.480 ⇒ 00:06:25.489 Annie Yu: Is it? Is it gonna be okay? If I add you here.
38 00:06:27.105 ⇒ 00:06:27.480 Demilade Agboola: Cool.
39 00:06:28.805 ⇒ 00:06:30.130 Annie Yu: Or
40 00:06:47.268 ⇒ 00:06:53.690 Annie Yu: that’s the that’s the threat. So Joanna was the one who initiate initiated this
41 00:06:54.070 ⇒ 00:06:56.310 Annie Yu: kind of this conversation.
42 00:07:00.060 ⇒ 00:07:01.120 Demilade Agboola: Oh!
43 00:07:04.530 ⇒ 00:07:05.970 Annie Yu: Are you able to see?
44 00:07:05.970 ⇒ 00:07:08.409 Annie Yu: Yeah, I’m the chance. I’m reading it right now.
45 00:07:19.890 ⇒ 00:07:20.630 Annie Yu: sure.
46 00:07:27.310 ⇒ 00:07:27.920 Demilade Agboola: Good stuff.
47 00:07:37.580 ⇒ 00:07:38.310 Demilade Agboola: Hello.
48 00:07:57.360 ⇒ 00:08:05.680 Demilade Agboola: okay, so it doesn’t actually like based off what we’re looking at.
49 00:08:10.010 ⇒ 00:08:15.349 Demilade Agboola: So I’m trying to see. There’s not much talk about switching, though.
50 00:08:18.736 ⇒ 00:08:27.820 Annie Yu: Then in her bullet point for weight loss kids, it’s very likely, remember, it will jump from one to one. We want to map that churn versus.
51 00:08:27.820 ⇒ 00:08:28.819 Demilade Agboola: Or crashed on.
52 00:08:36.240 ⇒ 00:08:38.830 Demilade Agboola: Okay, so let’s see.
53 00:08:41.690 ⇒ 00:08:46.110 Demilade Agboola: Ultimately, I think so. We’ll need to see.
54 00:08:48.430 ⇒ 00:08:53.120 Demilade Agboola: But the question is, how do we? How do we say a customized offer product
55 00:08:55.010 ⇒ 00:08:57.390 Demilade Agboola: like, what is the metric for that.
56 00:09:01.080 ⇒ 00:09:05.869 Annie Yu: I don’t think we. I don’t think we we have the ability.
57 00:09:05.870 ⇒ 00:09:20.839 Demilade Agboola: Yeah, cause it, we would have to make an assumption that, like whatever their scheduled period is, has expired. So normally, maybe they are on a monthly plan with the particular product. And now it’s been over a month. And then that was the last order for that product has like.
58 00:09:20.960 ⇒ 00:09:37.220 Demilade Agboola: So normally they get maybe Medicaid one, and on a monthly basis it’s been over a month. They’ve not gotten a medicaid. So therefore they’ve turned on that product. And now they’re getting Medicaid 2, for instance, so we can then say, Oh, they turn from
59 00:09:37.630 ⇒ 00:09:42.220 Demilade Agboola: make it one to make it 2. But I think that’s a.
60 00:09:44.090 ⇒ 00:09:47.740 Demilade Agboola: My only concern with that is that’s potentially very like
61 00:09:49.910 ⇒ 00:09:58.429 Demilade Agboola: subjective like, it’s it’s possible. Maybe someone didn’t just make an order quickly enough. And then they just made it like a couple of days late, or whatever
62 00:10:03.510 ⇒ 00:10:13.709 Demilade Agboola: I think what we could do is if we ask Joanna, maybe, Rebecca, how they do they like? Do they keep track of people who go off like products?
63 00:10:14.260 ⇒ 00:10:16.250 Demilade Agboola: And what that means like?
64 00:10:16.820 ⇒ 00:10:28.880 Demilade Agboola: So once we have an idea of what that definition is, we can then factor that in into how people are on multiple products or multiple like treatments, basically
65 00:10:29.990 ⇒ 00:10:33.279 Demilade Agboola: because we want to be able to say that you know.
66 00:10:33.740 ⇒ 00:10:40.419 Demilade Agboola: this is not just someone having 2 different products. This is someone who was off one and they moved to another.
67 00:10:42.210 ⇒ 00:10:50.360 Demilade Agboola: Yeah, so we need to know what the cutoff point is or how we define cutoff points for certain products. And then.
68 00:10:51.040 ⇒ 00:10:56.280 Demilade Agboola: if that same customer is on another product or another, like a different product.
69 00:10:58.150 ⇒ 00:11:00.580 Demilade Agboola: Do you? Do you agree with that logic, or do you.
70 00:11:00.580 ⇒ 00:11:02.980 Annie Yu: Yeah, yeah, yeah, I do.
71 00:11:04.090 ⇒ 00:11:09.379 Annie Yu: I’m then thinking, I feel like the conversation between heather and Josh is
72 00:11:10.000 ⇒ 00:11:18.549 Annie Yu: kind of similar, but in a different way. I feel like they care more about less, less. So about churn, like we’re trying to figure out
73 00:11:19.227 ⇒ 00:11:25.690 Annie Yu: but more so about like, what’s the most popular journey. So they can maybe, like
74 00:11:26.040 ⇒ 00:11:31.669 Annie Yu: put marketing to people who bought this a product.
75 00:11:32.550 ⇒ 00:11:44.510 Demilade Agboola: Okay, if if if that’s the case, then we can just make it, hey? Cause? Again, if it’s not about turn, it’s just basically did you? What product did you buy? What did that lead you to.
76 00:11:44.620 ⇒ 00:11:49.890 Demilade Agboola: and what that lead you to? And what that lead you to? Basically. So we can start to say, Hey.
77 00:11:50.390 ⇒ 00:11:54.099 Demilade Agboola: people like 20% of customers who get these products
78 00:11:54.330 ⇒ 00:11:58.739 Demilade Agboola: are likely to move on to that product sort of thing.
79 00:11:59.510 ⇒ 00:12:15.880 Demilade Agboola: so whether they stop using products A and move to product B doesn’t really matter. The fact that they went from product A to product B is what matters. So that product A came 1st and product B came second in that case we don’t. We don’t need to
80 00:12:15.910 ⇒ 00:12:31.279 Demilade Agboola: find out what the cutoff point is, for, like, you know, usage. But we also need to make it very clear that the fact that someone went like people went from this product A to product. B. Does not mean that there was a churn. It just means people
81 00:12:31.660 ⇒ 00:12:40.199 Demilade Agboola: bought more of this stuff like, I think we like that. That clarity like we need to make that clear so that people don’t go. Oh, where this product is churning
82 00:12:40.600 ⇒ 00:12:47.390 Demilade Agboola: to these products rather that they understand that there is potentially a relationship. I thought that was what you did with Cross-sell, though.
83 00:12:48.846 ⇒ 00:12:51.150 Annie Yu: With cross sale. It’s
84 00:12:52.320 ⇒ 00:12:58.817 Annie Yu: can I quickly share my screen? I think, with that one I have to rethink every time
85 00:13:09.110 ⇒ 00:13:10.179 Demilade Agboola: Hey? What’s going to do?
86 00:13:12.730 ⇒ 00:13:18.800 Demilade Agboola: I think I’ve stopped using chrome like I’ll stop it. Chrome is so RAM heavy. It’s crazy.
87 00:13:20.140 ⇒ 00:13:22.848 Annie Yu: Oh, really, I love it!
88 00:13:24.280 ⇒ 00:13:28.664 Annie Yu: I think windows sucks.
89 00:13:29.760 ⇒ 00:13:33.150 Demilade Agboola: No, no cause I I use a macbook, but like it’s still.
90 00:13:33.150 ⇒ 00:13:36.600 Annie Yu: Oh, okay, wait. This is not. This is also a macbook.
91 00:13:36.600 ⇒ 00:13:47.059 Demilade Agboola: Yeah, I use a, I’m a, so I’m a, I’m a tech gadgets. Nerd, that’s another thing that I really like. So I use a 2023 macbook, and 3
92 00:13:47.420 ⇒ 00:13:48.330 Demilade Agboola: pro.
93 00:13:48.840 ⇒ 00:13:49.500 Annie Yu: Oh!
94 00:13:49.500 ⇒ 00:13:52.049 Demilade Agboola: It’s an 18 gig RAM like I like, like.
95 00:13:52.250 ⇒ 00:14:03.470 Demilade Agboola: I’m also getting like the the latest smartbook. Another one I was gonna get like a 64 gig RAM version, or something I I like. I like my gadgets I like
96 00:14:03.975 ⇒ 00:14:05.909 Demilade Agboola: well, even with that like
97 00:14:06.010 ⇒ 00:14:10.230 Demilade Agboola: chrome at the times I will look at my chrome usage and chrome, because I don’t close a lot of my tabs.
98 00:14:10.970 ⇒ 00:14:15.019 Demilade Agboola: and chrome is using like 15 gig of RAM, and it’s like what’s going on.
99 00:14:15.020 ⇒ 00:14:15.670 Annie Yu: Oh!
100 00:14:15.670 ⇒ 00:14:24.079 Demilade Agboola: Yeah, chrome can be really heavy, especially if you’re not closing your tabs. And I struggle to do that. So that’s just another thing by the side. But yeah.
101 00:14:24.340 ⇒ 00:14:25.719 Annie Yu: What do you use? Then?
102 00:14:26.550 ⇒ 00:14:32.114 Demilade Agboola: What do I use? I use to? So I’m using ark. I used to use ark before for my job.
103 00:14:33.067 ⇒ 00:14:36.669 Demilade Agboola: But like, since I started working in Brainforge, I was using chrome.
104 00:14:36.940 ⇒ 00:14:43.079 Demilade Agboola: But like, I think I’m gonna go back to Arc, I think because kind of closes stuff for you as well, so.
105 00:14:43.320 ⇒ 00:15:01.920 Annie Yu: Oh, okay, interesting. I know that. I think Robert also use arc, but I just if like that sidebar will distract me. I get distracted very easily. So I I also can like, have multiple. I can’t. I can’t. A few tabs, but not like a whole lot.
106 00:15:02.470 ⇒ 00:15:07.390 Demilade Agboola: That is fair. I think you can also move it around a bit, but I’m not sure.
107 00:15:08.476 ⇒ 00:15:15.709 Annie Yu: Okay, but for for this cross product adoption here, we don’t track
108 00:15:15.980 ⇒ 00:15:23.999 Annie Yu: the purchase order, though, and I also don’t flag like, if this is people’s 1st order. So we just
109 00:15:24.160 ⇒ 00:15:26.769 Annie Yu: this is a self join. So we just have
110 00:15:27.050 ⇒ 00:15:39.470 Annie Yu: select the timeframe. And then, like, there’s there’s no like any direction here. So we just know, like 2, 2, 55 within this timeframe bought these 2 products.
111 00:15:40.410 ⇒ 00:15:45.529 Demilade Agboola: So there’s no, there’s so what you mean by this is like someone who bought injectable Samar.
112 00:15:45.790 ⇒ 00:15:56.250 Demilade Agboola: We also bought like 150 people who bought injectable summer also bought semruline injection. But there’s no like direction, either ways. You don’t know which they bought first, st
113 00:15:57.160 ⇒ 00:15:59.720 Demilade Agboola: it’s just basically okay. I get what you mean.
114 00:16:00.010 ⇒ 00:16:14.820 Annie Yu: Yeah. And with with the current model, I think we can do that. We can. We can make it. So there’s directional. So we can make sure. People who bought this 1st also bought this. But then my question is, then, how do we know
115 00:16:14.930 ⇒ 00:16:22.369 Annie Yu: before this date they actually bought this first, st like, you know, like I feel like if we
116 00:16:22.730 ⇒ 00:16:29.560 Annie Yu: make the Date range dynamic, then we wouldn’t.
117 00:16:31.240 ⇒ 00:16:32.780 Demilade Agboola: So my, my question is like.
118 00:16:33.080 ⇒ 00:16:35.620 Demilade Agboola: if we, if we already have this.
119 00:16:35.930 ⇒ 00:16:41.090 Demilade Agboola: the only different like, what? What exactly are we doing different with this product switching
120 00:16:42.270 ⇒ 00:16:45.000 Demilade Agboola: because it would appear that like.
121 00:16:46.030 ⇒ 00:16:48.649 Demilade Agboola: especially if we don’t care about like churn.
122 00:16:49.010 ⇒ 00:16:49.680 Annie Yu: Hmm.
123 00:16:49.680 ⇒ 00:16:56.510 Demilade Agboola: I’m not exactly sure, like what the only thing we can do is like for every customer. This was the 1st product.
124 00:16:57.350 ⇒ 00:17:03.829 Annie Yu: Yeah, that we don’t have here. So maybe that’s maybe that’s that’s what we should do.
125 00:17:04.980 ⇒ 00:17:18.489 Demilade Agboola: Yeah, but it will still be kind of like what I mean by that. It will still be kind of similar to this, because we can then just go. This was the 1st product. And then people who had this as a 1st products subsequently got this product as well.
126 00:17:19.380 ⇒ 00:17:48.719 Annie Yu: Yeah, yeah, that’s why like this one is not like officially kind of shared out yet. I just make make a room. But I also asked Robert to give feedback on. I think I want to know how redundant this would be if we move forward with the the other piece of work which I like. I don’t think we necessarily need both, but I think the the one we are trying to work on now is more important than than this.
127 00:17:49.150 ⇒ 00:17:55.780 Demilade Agboola: Yeah, I agree, because at least that gives us some sense of like, hey, if someone joins
128 00:17:57.450 ⇒ 00:18:10.399 Demilade Agboola: our platform and takes this product, maybe in 2 months time. We send them an email trying to get them on this plan because that we’ve seen a relationship of when this is the 1st person.
129 00:18:10.560 ⇒ 00:18:14.940 Demilade Agboola: I think this might just be a bit scattergun like
130 00:18:15.840 ⇒ 00:18:25.130 Demilade Agboola: you don’t know the direction which we will go so like you might end up having to send for people with injectable Samar. Hey? You probably want to get mic
131 00:18:26.210 ⇒ 00:18:32.499 Demilade Agboola: But you don’t necessarily know if it’s actually a thing of people who that get mic first.st Tend to now get injectable. Sema.
132 00:18:33.360 ⇒ 00:18:40.000 Annie Yu: Yeah, that. That’s yeah. That’s what I thought was like, not perfect yet.
133 00:18:40.668 ⇒ 00:18:52.469 Annie Yu: But down here we do have this. But this I think this also has the same problem. But we know that within these 2 months these people bought injectable Sema, and then
134 00:18:52.580 ⇒ 00:19:02.390 Annie Yu: the mic here 2, 55. That only accounts for like 1% here. But then for mic customers here.
135 00:19:03.910 ⇒ 00:19:09.240 Annie Yu: like, 13% also bought injectable. Sema.
136 00:19:11.750 ⇒ 00:19:15.359 Annie Yu: But yeah, again, that’s not like directional.
137 00:19:18.220 ⇒ 00:19:22.189 Demilade Agboola: Yeah, I think the direction will be very helpful.
138 00:19:24.730 ⇒ 00:19:27.360 Demilade Agboola: Like, you really will be very helpful.
139 00:19:28.960 ⇒ 00:19:30.920 Demilade Agboola: Yeah, because, like.
140 00:19:31.120 ⇒ 00:19:44.189 Demilade Agboola: so, yeah, same thing. So like the mic and injectable summer were like all 255 each. But potentially we don’t know the direction of people who go from mic to injectable summer to mic.
141 00:19:44.510 ⇒ 00:19:48.930 Demilade Agboola: and that could be what we would be very helpful instead.
142 00:19:49.990 ⇒ 00:19:57.859 Annie Yu: Yeah, so we can. We don’t like we can think of like we don’t have to keep this at all. Or like, you know, like we.
143 00:19:59.070 ⇒ 00:20:01.820 Annie Yu: Yeah.
144 00:20:05.420 ⇒ 00:20:17.610 Annie Yu: but I don’t know how the like model, or like like what columns, what granularities!
145 00:20:17.790 ⇒ 00:20:20.780 Annie Yu: Well, have to have.
146 00:20:21.310 ⇒ 00:20:28.629 Demilade Agboola: I mean, if we do it from a customer perspective, we can always roll up. But if we do from a product perspective, we can’t roll down so
147 00:20:30.950 ⇒ 00:20:38.090 Demilade Agboola: so like if we if I do like the dates, customer, 1st order.
148 00:20:38.500 ⇒ 00:20:45.310 Demilade Agboola: and then a list of subsequent like second order, 3rd order, 4, th like second product, 3, rd product, 4th product.
149 00:20:46.906 ⇒ 00:20:55.070 Demilade Agboola: for instance, you can always roll up and then say, Hey, giving a date filter
150 00:20:55.986 ⇒ 00:21:03.060 Demilade Agboola: like, give me the 1st products count of all, first, st like unique ids of customers with the 1st product of this.
151 00:21:03.650 ⇒ 00:21:09.520 Demilade Agboola: and then a second product of this, or like you get that you get what I’m trying to say.
152 00:21:09.980 ⇒ 00:21:13.020 Annie Yu: Yeah. So are you saying, each
153 00:21:13.290 ⇒ 00:21:16.650 Annie Yu: sequence will be one kind of one column.
154 00:21:18.870 ⇒ 00:21:26.160 Demilade Agboola: I’m I’m thinking what we can do is for each customer.
155 00:21:26.630 ⇒ 00:21:35.129 Demilade Agboola: Find the 1st product that they bought right, which from the 1st order and then find the second
156 00:21:35.500 ⇒ 00:21:47.519 Demilade Agboola: products, like, you know, basically, if you do a distinct base of each customer distinct products on the dates, we can then do a window function for each customer
157 00:21:47.720 ⇒ 00:21:58.789 Demilade Agboola: and do first, st second, 3.rd So the 1st products would be obviously the one that they got 1st and second product based on the dates would be the second product that they’ve gotten, and the 3rd and the 4th
158 00:22:00.700 ⇒ 00:22:01.240 Annie Yu: Okay.
159 00:22:01.240 ⇒ 00:22:10.319 Demilade Agboola: That we what we can, what a lot, what that allows us to do is to kind of see, we might just do. Top 4 top 5. I think you might start getting messy after like 5.
160 00:22:11.130 ⇒ 00:22:11.750 Annie Yu: Yeah.
161 00:22:12.550 ⇒ 00:22:24.939 Demilade Agboola: And so like, we can put the dates of the product that they got as well as the so second product, second product dates. 3rd product, 3rd product, date, 4, th product, 4, th product, date, 5, th product, 5, th product date.
162 00:22:25.170 ⇒ 00:22:27.420 Demilade Agboola: And then once we have that table
163 00:22:29.080 ⇒ 00:22:38.689 Demilade Agboola: it allows you to be able to do a filter like a date filter, for instance, and that allows you to be able to say, Hey, for every product that was gotten.
164 00:22:39.355 ⇒ 00:22:43.450 Demilade Agboola: The filter will be maybe based off the first.st The 1st product gotten.
165 00:22:44.840 ⇒ 00:22:46.050 Annie Yu: Can you repeat that.
166 00:22:46.050 ⇒ 00:23:11.540 Demilade Agboola: I think, like using the date filter, it will be off. The 1st product like that was gotten. So that means anybody over the last. So when you move the filter so 2 months, for instance, anybody over the last 2 months that got 1st product of this, this is what they’ve subsequently gotten, and if you move it back, you know, to 6 months anybody over the last 6 months with the 1st product of this. This is what they’ve gotten so the date filter will filter by the 1st product kind of
167 00:23:12.110 ⇒ 00:23:19.999 Demilade Agboola: eliminating anyone. Any rows that come like anybody that bought the 1st product before, like before that filter.
168 00:23:20.310 ⇒ 00:23:25.489 Demilade Agboola: that this filter, that that will make sense. Then.
169 00:23:26.110 ⇒ 00:23:28.440 Annie Yu: My follow up question is with that.
170 00:23:28.840 ⇒ 00:23:34.220 Annie Yu: or I don’t even know if this is important. But I I imagine it would be valuable, is
171 00:23:37.070 ⇒ 00:23:43.809 Annie Yu: how how many orders people place for, let’s say, like the 1st product before they
172 00:23:44.320 ⇒ 00:23:53.209 Annie Yu: bought the second, because that would inform kind of the marketing on like when or or that might not matter at all.
173 00:23:56.050 ⇒ 00:23:59.149 Demilade Agboola: I could see it’s being useful.
174 00:23:59.520 ⇒ 00:24:08.799 Demilade Agboola: I think. What might be more important is, what is the time difference? Every time difference.
175 00:24:08.800 ⇒ 00:24:11.340 Annie Yu: So you already have that with the dates, right with each.
176 00:24:12.360 ⇒ 00:24:18.029 Demilade Agboola: Yeah, so what’s the average time difference between the 1st 1st product and second products?
177 00:24:18.780 ⇒ 00:24:28.030 Demilade Agboola: Months or weeks, or whatever. So like, we can start to say, Hey, after about 6 weeks, people who bought this 1st product tend to buy this second product.
178 00:24:28.340 ⇒ 00:24:34.350 Annie Yu: Yeah, yeah, that makes sense that. Yeah, I think that makes much more sense.
179 00:24:38.650 ⇒ 00:24:43.029 Annie Yu: Okay, I’m just gonna see it.
180 00:24:49.430 ⇒ 00:24:52.130 Annie Yu: I don’t think I can find that.
181 00:24:59.880 ⇒ 00:25:02.680 Demilade Agboola: Also gonna say that I know you scheduled a call for.
182 00:25:03.570 ⇒ 00:25:08.220 Demilade Agboola: but I also have a call with Utam at that time.
183 00:25:08.760 ⇒ 00:25:10.650 Annie Yu: Oh, okay, yeah. I
184 00:25:11.120 ⇒ 00:25:23.430 Annie Yu: I also haven’t heard back from Luke. I I was just trying to throw some time there. But I don’t. I’m not positive it’s happening so. But yeah, we can move.
185 00:25:25.200 ⇒ 00:25:31.689 Demilade Agboola: That is fine, I don’t know is, look out of office today.
186 00:25:33.450 ⇒ 00:25:34.280 Annie Yu: Wait, Luke.
187 00:25:34.660 ⇒ 00:25:40.820 Demilade Agboola: I’m asking. I I don’t. I mean, I’m in his calendar, and he doesn’t seem to have anything on today. So I was wondering for that or.
188 00:25:41.480 ⇒ 00:25:43.610 Annie Yu: I think he’s online now. So.
189 00:25:45.320 ⇒ 00:25:47.670 Demilade Agboola: That’s fine. I just just I just didn’t say anything.
190 00:25:49.690 ⇒ 00:25:54.020 Annie Yu: Yeah, I can’t find that thread, Josh and cutter.
191 00:25:56.280 ⇒ 00:26:03.365 Annie Yu: But okay, I I do think this is the right direction and more focus. So thank you so much.
192 00:26:04.000 ⇒ 00:26:06.910 Annie Yu: so then.
193 00:26:11.750 ⇒ 00:26:15.650 Annie Yu: then, I’m not not sure with the ticket now.
194 00:26:18.000 ⇒ 00:26:20.089 Annie Yu: Maybe we should rewrite it.
195 00:26:22.598 ⇒ 00:26:25.830 Demilade Agboola: Sure we could always rewrite it.
196 00:26:27.960 ⇒ 00:26:32.140 Annie Yu: Okay, but I’m gonna.
197 00:26:32.380 ⇒ 00:26:37.729 Demilade Agboola: Potentially. You have a call with Sarah. Right? Are you going to talk to her about this in particular, or just other stuff?
198 00:26:37.730 ⇒ 00:26:39.310 Annie Yu: No no other stuff.
199 00:26:39.310 ⇒ 00:26:47.120 Demilade Agboola: Okay. I was just wondering if yeah, we could definitely try that and then push and.
200 00:26:47.120 ⇒ 00:26:51.479 Annie Yu: Want to meet with Joanna together, or or so to get more.
201 00:26:52.070 ⇒ 00:26:55.530 Annie Yu: If I could buy in or not. I I.
202 00:26:55.530 ⇒ 00:26:56.110 Demilade Agboola: Thank you.
203 00:26:57.210 ⇒ 00:27:04.989 Annie Yu: Very like not strict, though, like she’s like throwing ideas, and we can define whatever that is.
204 00:27:05.370 ⇒ 00:27:14.050 Demilade Agboola: Yeah, sure. I can see I have a call with Rebecca at.
205 00:27:15.270 ⇒ 00:27:20.069 Demilade Agboola: Do you have? Do you have any idea what time works for her, like, generally speaking, have you ever had to meet her.
206 00:27:20.877 ⇒ 00:27:27.999 Annie Yu: Joanna, she in my time zone. So it’s now 9 am. Here.
207 00:27:28.920 ⇒ 00:27:29.630 Demilade Agboola: Oh!
208 00:27:29.840 ⇒ 00:27:32.780 Annie Yu: Probably be like late for you.
209 00:27:32.780 ⇒ 00:27:33.970 Demilade Agboola: Yeah, fair enough.
210 00:27:34.270 ⇒ 00:27:35.190 Demilade Agboola: Oh.
211 00:27:35.190 ⇒ 00:27:45.450 Annie Yu: But yeah, we I I also like, don’t think we we need to meet her in a rush. I don’t. I just don’t know like we can meet her like at some stage, some checkpoint or so.
212 00:27:46.370 ⇒ 00:27:47.160 Demilade Agboola: Yeah.
213 00:27:47.840 ⇒ 00:27:53.110 Demilade Agboola: I think I don’t know if she is she going to be the primary user of this dashboard, like, if we do something like this.
214 00:27:53.310 ⇒ 00:27:59.579 Annie Yu: I think it was her idea, but then I think Heather and Josh also would love to see that. So.
215 00:27:59.580 ⇒ 00:28:00.490 Demilade Agboola: Okay.
216 00:28:01.820 ⇒ 00:28:06.829 Annie Yu: It’s mainly for their like product team. So it’s gonna be cutter. And Joanna.
217 00:28:07.150 ⇒ 00:28:08.650 Demilade Agboola: Oh, okay, that’s fair.
218 00:28:08.920 ⇒ 00:28:14.890 Demilade Agboola: And I’m I’m asking, because, like, if she’s not necessarily the primary user of the dashboard, it wouldn’t be really helpful to meet with her.
219 00:28:16.670 ⇒ 00:28:17.569 Demilade Agboola: Yeah, so
220 00:28:19.150 ⇒ 00:28:26.650 Demilade Agboola: okay, I, I think we could definitely just try this. Instead, I feel like this potentially could
221 00:28:26.880 ⇒ 00:28:34.199 Demilade Agboola: help them in just being able to figure out the time between, you know, 1st product, second product, that sort of thing.
222 00:28:36.480 ⇒ 00:28:42.250 Demilade Agboola: And yeah, I mean, I could try and see if we can meet.
223 00:28:42.940 ⇒ 00:28:48.080 Demilade Agboola: or before the week is up, let me see.
224 00:28:49.010 ⇒ 00:29:05.769 Annie Yu: Okay, I think I’m I have to run now. But I okay, thank you so much. And and I’m just gonna rely on you on like what the model will look like. Because I obviously you have more idea, like better idea than I do in that sense. So.
225 00:29:05.770 ⇒ 00:29:08.069 Demilade Agboola: Yeah, no fine. I’ll I’ll look at that.
226 00:29:08.390 ⇒ 00:29:09.060 Annie Yu: Okay.
227 00:29:09.240 ⇒ 00:29:18.029 Annie Yu: thanks so much. I’ll I’ll let you know if we end up, push the the meeting, or or like even move to tomorrow I’ll let you know.
228 00:29:18.030 ⇒ 00:29:19.160 Demilade Agboola: Alright. Sounds good.
229 00:29:20.140 ⇒ 00:29:20.880 Demilade Agboola: Right.