Meeting Title: Stackblitz | Sync-up Date: 2025-03-19 Meeting participants: Luke Daque, Sahanaasokan, Amber Lin
WEBVTT
1 00:00:38.490 ⇒ 00:00:39.680 Amber Lin: Hi! There!
2 00:00:41.650 ⇒ 00:00:43.460 sahanaasokan: Hello! How are you?
3 00:00:44.100 ⇒ 00:00:44.540 Amber Lin: Good.
4 00:00:44.540 ⇒ 00:00:44.970 Luke Daque: 11
5 00:00:44.970 ⇒ 00:00:51.270 Amber Lin: And actually I am getting my wisdom teeth removed in 45 min
6 00:00:51.510 ⇒ 00:00:53.529 sahanaasokan: Oh, my God! Are you ready
7 00:00:54.130 ⇒ 00:00:56.400 Amber Lin: I am. I don’t know.
8 00:00:56.610 ⇒ 00:00:59.989 Amber Lin: I’m getting all 4 of them out
9 00:00:59.990 ⇒ 00:01:00.840 sahanaasokan: Account owner.
10 00:01:01.540 ⇒ 00:01:02.570 Luke Daque: One time.
11 00:01:03.030 ⇒ 00:01:12.689 Amber Lin: I I want to, because I just don’t want to recover many times, because I want I don’t want to eat baby food like 2 times a year for a week each
12 00:01:13.020 ⇒ 00:01:23.879 sahanaasokan: Yeah, I honestly, I think that’s the move, like, when I got mine out I only got the bottom 2 out, and now I’m feeling the top 2 coming in, and I’m like, I wish I just kind of got them all out at the same time. So
13 00:01:23.880 ⇒ 00:01:24.510 Amber Lin: Oh!
14 00:01:24.510 ⇒ 00:01:26.809 sahanaasokan: I think you’re what you’re doing is right.
15 00:01:26.980 ⇒ 00:01:29.570 Amber Lin: I see that is great.
16 00:01:31.250 ⇒ 00:01:32.340 Amber Lin: Where are you guys?
17 00:01:34.090 ⇒ 00:01:37.310 Amber Lin: It is. I have it bye.
18 00:01:42.350 ⇒ 00:01:46.140 Luke Daque: Yeah, I’m doing well, everything’s well. How about you? Sahana? How are you
19 00:01:46.690 ⇒ 00:01:49.409 sahanaasokan: Good. Yeah. Everything’s good. Just been busy.
20 00:01:50.226 ⇒ 00:01:53.440 sahanaasokan: Yeah, just yeah. It’s just been busy. I guess.
21 00:01:54.750 ⇒ 00:01:57.110 Amber Lin: What do you do at your other job?
22 00:01:57.680 ⇒ 00:01:59.810 sahanaasokan: I’m a data scientist at calendly
23 00:01:59.810 ⇒ 00:02:00.370 Amber Lin: One.
24 00:02:01.090 ⇒ 00:02:01.720 Luke Daque: Thanks.
25 00:02:01.720 ⇒ 00:02:03.109 Amber Lin: Challengely, wow.
26 00:02:03.630 ⇒ 00:02:09.198 sahanaasokan: Yeah, it’s like the Cal, like, the scheduling company, like, I don’t know. Used it.
27 00:02:10.729 ⇒ 00:02:11.400 sahanaasokan: Yeah.
28 00:02:11.400 ⇒ 00:02:16.779 Luke Daque: We’re using it in Brainforge, right or like. Aren’t you using it in Brainforge calendly.
29 00:02:17.550 ⇒ 00:02:19.220 Luke Daque: Like to track your time
30 00:02:19.460 ⇒ 00:02:30.159 Amber Lin: I don’t know. That’s clockify. I think we’re trying to use some another another
31 00:02:31.590 ⇒ 00:02:35.390 sahanaasokan: Yeah, usually, people use it to sync up calendars to like schedule meetings.
32 00:02:36.970 ⇒ 00:02:43.180 sahanaasokan: yeah, I feel like, maybe sales uses it. That’s kind of the use case. I see for it here, but I’m not sure
33 00:02:45.320 ⇒ 00:02:45.900 Luke Daque: Cool.
34 00:02:46.500 ⇒ 00:02:47.260 sahanaasokan: Yeah.
35 00:02:51.100 ⇒ 00:02:52.989 Amber Lin: I think today.
36 00:02:53.380 ⇒ 00:03:02.149 Amber Lin: I’m just gonna check on progress with Luke. And then, Shahanna, we can go over the product analytic answers that we jumped in
37 00:03:03.350 ⇒ 00:03:03.940 sahanaasokan: Okay.
38 00:03:05.690 ⇒ 00:03:10.699 Luke Daque: Sure. So yeah, maybe I can go 1st with my updates.
39 00:03:11.140 ⇒ 00:03:13.469 Luke Daque: let me share my screen real quick.
40 00:03:14.900 ⇒ 00:03:19.389 Luke Daque: So I did send out a Pr. Yesterday to add a couple of
41 00:03:21.070 ⇒ 00:03:21.650 Amber Lin: Hmm.
42 00:03:21.650 ⇒ 00:03:23.989 Luke Daque: Matrices in the little dashboard.
43 00:03:24.803 ⇒ 00:03:32.539 Luke Daque: So we already have the like for the subscriptions overview. We already have this the split of
44 00:03:32.690 ⇒ 00:03:38.449 Luke Daque: revenue type, whether it’s coming from subscriptions or token reloads. So we can like, if you look at.
45 00:03:39.070 ⇒ 00:03:41.209 Luke Daque: go to the revenue.
46 00:03:44.380 ⇒ 00:03:49.919 Luke Daque: I mean, I yeah, maybe I can create another one that’s just total red for me. But like, there’s
47 00:03:50.820 ⇒ 00:03:54.411 Luke Daque: here’s basically a split of the subscription revenue versus
48 00:03:55.090 ⇒ 00:04:01.789 Luke Daque: token reload revenue for basically this one, and then monthly recurring revenue would be subscriptions.
49 00:04:03.217 ⇒ 00:04:07.400 Luke Daque: Yeah, also added the like, whether.
50 00:04:07.630 ⇒ 00:04:10.499 Luke Daque: if it’s a new subscription or not, it’s just like a
51 00:04:11.688 ⇒ 00:04:19.199 Luke Daque: filter here, so we’ll be able to determine which subscriptions are new, which subscriptions are
52 00:04:21.290 ⇒ 00:04:22.160 sahanaasokan: With
53 00:04:22.350 ⇒ 00:04:27.660 Luke Daque: What? Which subscriptions are like, basic.
54 00:04:28.590 ⇒ 00:04:32.259 Luke Daque: Yeah, if it’s a 1st subscription or reactivated subscription.
55 00:04:32.880 ⇒ 00:04:37.289 Luke Daque: basically, when a subscription gets cancelled and then sometime later the
56 00:04:37.660 ⇒ 00:04:44.930 Luke Daque: customer reactivated it. It’s also here, and whether it’s an upgrade or downgrade. So if they’re in a plan
57 00:04:45.960 ⇒ 00:04:49.480 Luke Daque: pro plan, for example, which is a lower tier, and then they
58 00:04:49.780 ⇒ 00:04:56.990 Luke Daque: have another subscription after the pro plan to an upgraded one. Then that’s an upgraded subscription. So as a downgrade.
59 00:04:57.170 ⇒ 00:05:02.320 Luke Daque: which is very which is like what they have for
60 00:05:03.510 ⇒ 00:05:08.450 Luke Daque: what they’re asking for, what they have in bare metrics at the moment. There’s my line here.
61 00:05:11.560 ⇒ 00:05:13.449 Luke Daque: Yeah, I think it’s in a different.
62 00:05:13.830 ⇒ 00:05:24.549 Luke Daque: And so there these were the missing stuff, the upgrades downrates, new subscriptions.
63 00:05:24.940 ⇒ 00:05:32.320 Luke Daque: So what I’m still currently working on is the returns and refunds and failed charges like these last 3 matrices.
64 00:05:32.540 ⇒ 00:05:35.410 Luke Daque: coupons, redeemed fare charges and refunds.
65 00:05:35.530 ⇒ 00:05:41.440 Luke Daque: and then we should have all the metrics available in our rail dashboard
66 00:05:42.492 ⇒ 00:06:00.089 Luke Daque: the only other thing, though, which I might need help with I don’t know. We’d be able to help me. Here is the data validation piece just like this is a screenshot from their bare metrics for monthly recurring revenue for January 2025. But if we look at our
67 00:06:01.440 ⇒ 00:06:11.020 Luke Daque: dashboard that I created. If I just trigger this to January, we’d be able to
68 00:06:11.730 ⇒ 00:06:14.760 Luke Daque: C for active subscriptions.
69 00:06:17.440 ⇒ 00:06:23.219 Luke Daque: We’re at like 1.6, 1 million. So we’re like 10, a hundred, 10,000,
70 00:06:23.450 ⇒ 00:06:28.710 Luke Daque: or a hundred 1,000 off compared to like 1.7 million.
71 00:06:29.160 ⇒ 00:06:30.770 Luke Daque: We’re at 1.6.
72 00:06:31.510 ⇒ 00:06:42.139 Luke Daque: So yeah, and then total subscriptions as well, or total customers who are at like in our real dashboard. It’s 48,000 versus what they have raised, like 52
73 00:06:43.700 ⇒ 00:06:45.250 Luke Daque: active customers
74 00:06:47.100 ⇒ 00:06:48.819 Luke Daque: So I might be like, Yeah.
75 00:06:48.820 ⇒ 00:07:00.419 Amber Lin: Be able to help you validate or do you? Would you want someone who’s like a data analyst to help you validate the data? Who do you have in mind? I can go ask you, Tom.
76 00:07:02.020 ⇒ 00:07:04.303 Luke Daque: I actually don’t know at the moment,
77 00:07:05.290 ⇒ 00:07:16.639 Luke Daque: but maybe as well like, if we can get access to their bare metrics, then it would be easier to validate that way, like, maybe like, do some filters, because all we have right now are just screenshots.
78 00:07:16.810 ⇒ 00:07:22.820 Luke Daque: And like, Yeah, I I’m not sure. Maybe I’m just. Maybe they have a different
79 00:07:23.877 ⇒ 00:07:33.819 Luke Daque: logic for determining whether a subscription is active or not. Or maybe it’s like both active and past due. So maybe past due is like
80 00:07:34.270 ⇒ 00:07:43.659 Luke Daque: still an active subscription. But it’s just past the due date or something. So because if I I feel that both of them. It’s pretty close now. 51 against 52.
81 00:07:44.090 ⇒ 00:07:49.160 Luke Daque: But it’s still like a bit far like the others. A 1,000 difference or something.
82 00:07:49.723 ⇒ 00:07:54.159 Luke Daque: And maybe it could be time zone, because we’re using Esd.
83 00:07:54.860 ⇒ 00:07:57.320 Luke Daque: and maybe this is Utc, I don’t know.
84 00:07:58.700 ⇒ 00:08:00.229 Luke Daque: So yeah, stuff like that
85 00:08:01.410 ⇒ 00:08:07.270 Amber Lin: Okay. So this seems like something we need to ask the client mostly if
86 00:08:07.720 ⇒ 00:08:08.130 Luke Daque: Yeah.
87 00:08:08.130 ⇒ 00:08:20.160 Amber Lin: Their exact data cause. I don’t know when they took the screenshot, or if the data is also matched up, because this could be, say, a month ago, and now they got a lot more customers, which means we’ll be even more off
88 00:08:20.530 ⇒ 00:08:26.040 Luke Daque: Yeah. But this is just this is just for the month of January, though, so it shouldn’t change right? Like
89 00:08:26.040 ⇒ 00:08:26.540 Amber Lin: Okay.
90 00:08:26.540 ⇒ 00:08:28.580 Luke Daque: Yeah, that would still be the same. Yeah.
91 00:08:28.820 ⇒ 00:08:37.429 Luke Daque: And yeah, from what I like having meetings with Mitch before, he doesn’t also really know the details like how bare metrics is
92 00:08:37.980 ⇒ 00:08:41.979 Luke Daque: calculating stuff. So what we did before was like, we’re looking into like
93 00:08:42.570 ⇒ 00:08:57.470 Luke Daque: specific subscriptions like going to a specific subscription id or specific customer, and looking into that specific customer in their metrics as well and like, try to figure out what’s different between them.
94 00:08:57.690 ⇒ 00:08:58.720 Luke Daque: The 2,
95 00:08:58.870 ⇒ 00:09:06.740 Luke Daque: like one of the things that we noticed basically or before was that there are customers that have
96 00:09:07.240 ⇒ 00:09:12.700 Luke Daque: upgraded their subscription multiple multiple times in one month, basically so.
97 00:09:13.190 ⇒ 00:09:19.020 Luke Daque: and in in my previous model I was like showing all of those for subscriptions, for example.
98 00:09:19.250 ⇒ 00:09:33.790 Luke Daque: for that specific month. But then, in bare metrics, it’s just like it’s being what do you call this? It’s being prorated like, although there’s only one active subscription which is the latest one. But then the Mrr is being prorated
99 00:09:33.990 ⇒ 00:09:37.030 Luke Daque: for the different subscriptions.
100 00:09:38.030 ⇒ 00:09:42.650 Luke Daque: So yeah, it’s it’s pretty complicated a bit.
101 00:09:42.760 ⇒ 00:09:45.140 Luke Daque: It’s it’s complicating it a bit basically
102 00:09:46.200 ⇒ 00:09:56.450 Amber Lin: Okay. Luke, can you tell me what’s the deliverable, or what the client is looking for for this? And maybe how far away are we from that
103 00:09:56.820 ⇒ 00:10:05.849 Luke Daque: Yeah, the basically the yeah, like, having the real dashboard for the subscriptions overview.
104 00:10:06.170 ⇒ 00:10:17.039 Luke Daque: And then the ability to be able to drill down to the plans, because this is missing in bare metrics at the moment. It’s just if you see in the screenshots it’s just like the whole
105 00:10:17.290 ⇒ 00:10:20.260 Luke Daque: thing, the total monthly recurring revenue
106 00:10:21.370 ⇒ 00:10:21.990 Amber Lin: Okay.
107 00:10:21.990 ⇒ 00:10:31.179 Luke Daque: He, he! They basically want to be able to drill down like for the pro plan. What’s the total number of subscriptions, or what’s the total monthly.
108 00:10:31.720 ⇒ 00:10:38.780 Luke Daque: And then, yeah. And for the customer overview dashboard. It’s like
109 00:10:38.900 ⇒ 00:10:41.739 Luke Daque: the biggest thing that they wanted was really to
110 00:10:43.580 ⇒ 00:10:47.119 Luke Daque: be able to determine which are high value. Customers
111 00:10:47.990 ⇒ 00:10:54.760 Luke Daque: like, either based on the token reload amount or the lifetime value, or the Mrr
112 00:10:54.860 ⇒ 00:10:59.439 Luke Daque: right? So like, if you filter it, based on the token reloads
113 00:11:00.854 ⇒ 00:11:09.559 Luke Daque: amount or value in dollars. This would be like the high, the customer that’s like the top 10, I guess, or like how many it is
114 00:11:09.710 ⇒ 00:11:24.579 Luke Daque: we expand this. We can see like the top 10 customers with the highest token reload amounts, and they’re interested in this because I don’t know what their plan is for the high value customers. Maybe they they’re gonna provide a a plan for them, or something.
115 00:11:25.000 ⇒ 00:11:29.419 Luke Daque: or like to retain, you know, find a way to retain them and stuff.
116 00:11:30.490 ⇒ 00:11:35.845 Luke Daque: And if we look at like, based on the Mrr, that’ll be the monthly recurring revenue, then.
117 00:11:36.500 ⇒ 00:11:44.870 Luke Daque: well, I guess these would be the highest plan customers with 1 25 per dollar upper per month plan.
118 00:11:45.290 ⇒ 00:11:48.030 Luke Daque: I guess we can check lifetime value
119 00:11:50.060 ⇒ 00:11:58.600 Luke Daque: then, based on lifetime value. This would be the yeah, highest valued customers.
120 00:12:00.460 ⇒ 00:12:01.370 Luke Daque: So
121 00:12:01.690 ⇒ 00:12:03.830 Amber Lin: Okay, yeah, like something like that.
122 00:12:05.070 ⇒ 00:12:16.520 Amber Lin: Okay, sounds good. And so essentially, right now, we’re just fine tuning. We have the data. But we’re just trying to make it say accurate and app up to standard essentially
123 00:12:16.780 ⇒ 00:12:17.830 Luke Daque: Yeah, and
124 00:12:17.830 ⇒ 00:12:18.440 Amber Lin: Okay.
125 00:12:18.440 ⇒ 00:12:23.909 Luke Daque: It doesn’t necessarily also mean that we, it need to be exactly the same with what
126 00:12:24.370 ⇒ 00:12:25.866 Luke Daque: bare metrics is because
127 00:12:27.190 ⇒ 00:12:36.019 Luke Daque: Right cause like maybe maybe we just stick to what we have. And then we just be able to explain why it’s different from their metrics like.
128 00:12:36.250 ⇒ 00:12:47.939 Luke Daque: how is it any different? How we are calculating monthly, recurring revenue that’s different from from how their metrics is calculating, monthly, recurring revenue, something like that
129 00:12:47.940 ⇒ 00:12:56.109 Amber Lin: Okay, sounds good. And how long has this? Was this the only project essentially for them?
130 00:12:58.560 ⇒ 00:12:59.210 Amber Lin: Well.
131 00:12:59.840 ⇒ 00:13:08.629 Luke Daque: So far. This is what we have. But there were like, we go to the bulk metrics sheet. There’s still a couple of stuff that they
132 00:13:09.250 ⇒ 00:13:10.670 Luke Daque: wanted like
133 00:13:12.310 ⇒ 00:13:12.980 Amber Lin: Oh!
134 00:13:13.710 ⇒ 00:13:15.029 Luke Daque: Oh, boy, was that?
135 00:13:16.560 ⇒ 00:13:20.230 Luke Daque: Yeah, like sign ups would be another model that
136 00:13:21.060 ⇒ 00:13:22.210 Luke Daque: I guess I could. I have
137 00:13:22.210 ⇒ 00:13:22.680 Amber Lin: Okay.
138 00:13:22.680 ⇒ 00:13:31.300 Luke Daque: Actually started. Yet sessions, like events related, and page views, which is also events related. So these are like
139 00:13:31.850 ⇒ 00:13:38.680 Luke Daque: lower priority, I guess, than than the users and organizations and subscriptions, but
140 00:13:40.184 ⇒ 00:14:00.930 Amber Lin: I, I think the last thing I want to understand is that what is the what’s the timeline for the client are they expecting something soon? Have they been waiting for a while, or have we just updated them on something new like, do we think, do we think we’re moving fast, or do you think we’re moving too slow for the client? They might get a little annoyed. What do you think
141 00:14:00.930 ⇒ 00:14:06.240 Luke Daque: Yeah, that’s a good question, because, like, Mitch is also pretty busy. We don’t really have like we. Only
142 00:14:06.460 ⇒ 00:14:10.179 Luke Daque: the last time we met was last Thursday, and we
143 00:14:10.180 ⇒ 00:14:10.570 Amber Lin: Hmm.
144 00:14:10.570 ⇒ 00:14:21.419 Luke Daque: Did provide a couple of updates from the real dashboards, and from what I understand, they didn’t really. I’ll also ask for a specific due date for some updates or whatnot. But
145 00:14:21.730 ⇒ 00:14:25.389 Luke Daque: yeah, we definitely definitely need to improve our like.
146 00:14:25.690 ⇒ 00:14:32.484 Luke Daque: like communicating to them what what’s been done so far? And like, like even this
147 00:14:34.285 ⇒ 00:14:36.659 Luke Daque: data validation piece, where we can
148 00:14:37.080 ⇒ 00:14:42.770 Luke Daque: probably communicate to them that we we have. It’s pretty close, but it’s still not
149 00:14:42.950 ⇒ 00:14:47.130 Luke Daque: exactly the same. So we might need to like, do some further investigation.
150 00:14:48.540 ⇒ 00:14:52.140 Luke Daque: Maybe. Yeah. Spend time with Mitch as well
151 00:14:52.800 ⇒ 00:14:55.690 Luke Daque: to investigate the discrepancies and stuff
152 00:14:55.690 ⇒ 00:14:59.020 Amber Lin: Good. I’ll note that I’ll note that down.
153 00:15:01.510 ⇒ 00:15:05.190 Amber Lin: Let me just from you.
154 00:15:05.900 ⇒ 00:15:13.499 Amber Lin: Okay, so. And, Luke, what do you plan to do for the rest of this week. I know we have around like 2 to 3 days
155 00:15:14.300 ⇒ 00:15:21.969 Luke Daque: Yeah, I’m planning to see if I can add all of these missing ones coupons redeem failed charges and refunds.
156 00:15:22.110 ⇒ 00:15:28.090 Luke Daque: And then, yeah, also the data validation I’ve been like trying to figure out like what’s causing
157 00:15:28.220 ⇒ 00:15:30.829 Luke Daque: all of the discrepancies. So basically that
158 00:15:32.930 ⇒ 00:15:35.869 Luke Daque: Yeah. And if I have, I still have time, I can try to
159 00:15:36.350 ⇒ 00:15:41.999 Luke Daque: start poking with the sign ups and event related models as well
160 00:15:42.860 ⇒ 00:15:48.720 Amber Lin: Okay, sounds good. Let me just write that down validation
161 00:15:50.570 ⇒ 00:15:56.220 Amber Lin: data validation. I’ll say that’s for end of this week.
162 00:16:00.310 ⇒ 00:16:04.170 Amber Lin: You said to add the refunds and
163 00:16:04.630 ⇒ 00:16:07.789 Luke Daque: Coupons, redeem refunds and failed charges
164 00:16:07.790 ⇒ 00:16:11.799 Amber Lin: Oh, essentially the all, the the in progress. Once right in the near
165 00:16:11.800 ⇒ 00:16:14.039 Luke Daque: Yeah, yeah, all of these
166 00:16:14.040 ⇒ 00:16:14.830 Amber Lin: Okay.
167 00:16:15.330 ⇒ 00:16:16.199 Luke Daque: Is this one so
168 00:16:16.200 ⇒ 00:16:26.560 Amber Lin: Then I will, I see I will add a 10th to do of adding other metrics.
169 00:16:26.940 ⇒ 00:16:31.980 Amber Lin: I will put this in there as well.
170 00:16:33.740 ⇒ 00:16:35.129 Amber Lin: Great issue.
171 00:16:38.070 ⇒ 00:16:38.850 Amber Lin: Yeah.
172 00:16:42.120 ⇒ 00:16:42.950 Amber Lin: Great
173 00:16:43.660 ⇒ 00:16:48.199 Luke Daque: Yeah, I guess we can add a sub issue here. That’s like data validation
174 00:16:49.620 ⇒ 00:16:54.310 Amber Lin: Oh, is this connected in real like? Is this notion in real? Wow!
175 00:16:54.310 ⇒ 00:16:55.070 Luke Daque: And
176 00:16:55.500 ⇒ 00:17:02.120 Amber Lin: Oh, you’re in! You’re in real. I thought this was notion for so long. I’m so sorry. I’m very glad you’re
177 00:17:02.120 ⇒ 00:17:03.470 Luke Daque: Already it.
178 00:17:03.880 ⇒ 00:17:04.690 Luke Daque: It’s weird.
179 00:17:04.690 ⇒ 00:17:05.379 Amber Lin: I know it’s a little
180 00:17:05.380 ⇒ 00:17:05.710 Luke Daque: Okay.
181 00:17:05.710 ⇒ 00:17:06.440 Amber Lin: Got it
182 00:17:08.950 ⇒ 00:17:09.700 Luke Daque: Yeah.
183 00:17:12.530 ⇒ 00:17:17.800 Amber Lin: Okay, I’ll I’ll delete the issue. That, I added. Then I think you know the you know the best
184 00:17:18.829 ⇒ 00:17:19.500 Amber Lin: kidding.
185 00:17:20.839 ⇒ 00:17:35.049 Amber Lin: Okay, so, Hannah, I want. I know we don’t have much time. I hope this gave you a little bit more context on the dashboards we have which will help us talk about the product analytics. What’s your? Have you read the document yet?
186 00:17:35.600 ⇒ 00:17:45.890 sahanaasokan: I’m not. I just finished sending over the like work on the Eden side. So I’m free today to work on anything for this. But no, I haven’t had a chance to look at all the answers
187 00:17:45.890 ⇒ 00:17:46.620 Amber Lin: Sure.
188 00:17:49.040 ⇒ 00:17:51.989 Amber Lin: Okay. Why don’t we
189 00:17:52.770 ⇒ 00:18:04.170 Amber Lin: just take a quick view at them? Now, since we still have around 1012 min or so? Let’s go look at them. Let me try and share my screen.
190 00:18:04.580 ⇒ 00:18:11.169 Amber Lin: And I hope the do. You have a sense of our real dashboard? And what kind of data we have what kind of data models we have
191 00:18:11.660 ⇒ 00:18:12.510 Amber Lin: no cool
192 00:18:12.950 ⇒ 00:18:14.259 sahanaasokan: Yeah, that would be really helpful.
193 00:18:14.559 ⇒ 00:18:18.149 Amber Lin: Oh, no, we we just looked at that. So what Ryan was
194 00:18:18.150 ⇒ 00:18:19.120 sahanaasokan: Oh yes, yes.
195 00:18:19.120 ⇒ 00:18:20.119 Amber Lin: All right. Yeah.
196 00:18:20.120 ⇒ 00:18:26.839 sahanaasokan: Yes, yes, I thought you were like, if you like, you were kind of talking about net new. But yes, now I kind of have some context
197 00:18:28.670 ⇒ 00:18:35.109 Amber Lin: Okay, so this is the document. This is the 6 month roadmap, and in there.
198 00:18:35.790 ⇒ 00:18:39.180 Amber Lin: Here is the answers that utham put
199 00:18:39.700 ⇒ 00:18:40.840 sahanaasokan: So.
200 00:18:41.730 ⇒ 00:18:44.270 Amber Lin: Let’s let’s look at them.
201 00:18:44.870 ⇒ 00:18:50.770 Amber Lin: So he wants 2 dashboards. Can you just run through them? You know this much more than I do.
202 00:18:51.330 ⇒ 00:18:57.820 Amber Lin: I will really just words, but I think they’ll
203 00:18:58.260 ⇒ 00:19:01.089 Amber Lin: much more meaning to you than they do to me.
204 00:19:01.090 ⇒ 00:19:11.909 sahanaasokan: Yeah, here we did. Yeah. So 2 dashboards, we have our North Star metrics. We have our product Kps, our product area. So for the North Star Metrics. Okay, so 3 to 5 Nsms
205 00:19:12.170 ⇒ 00:19:19.340 sahanaasokan: token usage that makes sense daily, active, weekly, active, monthly, active users. That makes sense.
206 00:19:19.590 ⇒ 00:19:34.479 sahanaasokan: I’m gonna talk out loud. Some of the data requirements, Luke, just for like context to. So you can kind of see if, like we have it, if we kind of need to define new scope. I think it’s kind of it’ll be effective if we just kind of have that conversation. Now.
207 00:19:34.590 ⇒ 00:19:35.660 Luke Daque: Sure.
208 00:19:35.960 ⇒ 00:19:44.419 sahanaasokan: So, okay, so yeah, we probably need some kind of activity log to help us understand active users
209 00:19:44.420 ⇒ 00:19:45.140 Luke Daque: Hmm.
210 00:19:45.630 ⇒ 00:19:52.700 sahanaasokan: And then getting into C Mrr over arr. So yes, this is where we’re gonna kind of need
211 00:19:55.660 ⇒ 00:20:02.660 sahanaasokan: to calculate recurring revenue at a monthly aggregate.
212 00:20:03.900 ⇒ 00:20:08.290 sahanaasokan: And then we’re basically going to have to define each of these metrics.
213 00:20:08.630 ⇒ 00:20:18.161 sahanaasokan: So I think we’re better off. I don’t know what we have right now. But if we have some kind of product sales summary table like, similar to what we’ve done for Eden.
214 00:20:18.730 ⇒ 00:20:22.850 sahanaasokan: that’s probably the kind of data format we need for
215 00:20:23.090 ⇒ 00:20:26.120 sahanaasokan: this one. And it’s it’s definitely.
216 00:20:26.470 ⇒ 00:20:40.850 sahanaasokan: it’s really important that the architecture is right. But the quality of the data is also right. Because I know with Eden the numbers didn’t really align. And that’s very, that’s the key. So there’s never going to be a lot of Qc work here.
217 00:20:41.310 ⇒ 00:20:45.830 sahanaasokan: okay, getting into the next one org plan customers.
218 00:20:47.360 ⇒ 00:20:51.870 sahanaasokan: Okay, that’s fine compound monthly growth rate. That’s fine
219 00:20:52.860 ⇒ 00:21:07.159 sahanaasokan: customer concentration by revenue. Yeah, that’s fine. Just like segmentation. Yeah, so I would say, amber, we can do we? We should do these in like rollouts, so I would say, e like f through
220 00:21:07.290 ⇒ 00:21:11.390 sahanaasokan: h are nice to have, but that would that would come more in like a v 2.
221 00:21:25.780 ⇒ 00:21:29.780 sahanaasokan: So that’s like a yeah, let’s just keep it as like a v 2 like iteration. So
222 00:21:29.930 ⇒ 00:21:38.870 sahanaasokan: I think we should to like, make sure we’re not taking too long on execution. Just prioritize like like 2 to 3 key components. And then we could do like.
223 00:21:38.980 ⇒ 00:21:46.289 sahanaasokan: you know, cus like, these are kind of more analyses versus dashboards. Okay, cool. So getting into the next section.
224 00:21:48.930 ⇒ 00:21:58.399 sahanaasokan: Okay, so growth marketing. CEO sales. Okay? And let’s let’s just organize this by like priorities. So like CEO would be one.
225 00:21:59.290 ⇒ 00:22:01.740 sahanaasokan: Let’s do. I would say
226 00:22:01.930 ⇒ 00:22:06.849 sahanaasokan: sales as 2, and then growth and then marketing. And then we can kind of just combine.
227 00:22:08.820 ⇒ 00:22:11.070 sahanaasokan: Actually, no, we can just keep them separate. Okay.
228 00:22:11.480 ⇒ 00:22:23.380 sahanaasokan: that’s fine. And then I think a follow up question I would like to like, want to ask is for these specific business areas. Do they want to see metrics aggregated
229 00:22:23.770 ⇒ 00:22:29.850 sahanaasokan: specifically to answer like their specific business problems, like, for example, for sales like.
230 00:22:30.000 ⇒ 00:22:32.989 sahanaasokan: are there specific sales, metrics they want to see for growth.
231 00:22:33.710 ⇒ 00:22:42.199 sahanaasokan: like, do they want to like be able to like filter by product like I don’t know. Just like, you know, I kind of wanted this. This needs a little bit more granularity
232 00:22:42.750 ⇒ 00:22:46.010 Amber Lin: Yeah, I think right now we are going to.
233 00:22:46.150 ⇒ 00:22:53.470 Amber Lin: There’s 1 stakeholders interested which is in sales. So I believe right now sales would be the most
234 00:22:54.710 ⇒ 00:23:14.370 Amber Lin: priority, just because maybe after the CEO, because that’s someone we want to onboard. And yes, we do want to answer those specific sales questions. That’s kind of what was talking about, I believe. Let me check linear to make sure, because I have 2 clients, and I’m I’m trying to make sure if I keep getting them mixed up. So let’s see
235 00:23:14.820 ⇒ 00:23:19.160 Amber Lin: projects. We are in stack woods.
236 00:23:19.880 ⇒ 00:23:22.779 Amber Lin: Let’s see all projects.
237 00:23:31.220 ⇒ 00:23:36.139 Amber Lin: this oh, system here!
238 00:23:37.590 ⇒ 00:23:39.100 Amber Lin: Oh, my goodness.
239 00:23:42.120 ⇒ 00:23:46.969 Amber Lin: sorry! Go ahead, but I do think that we do. We are onboarding a
240 00:23:48.350 ⇒ 00:23:49.939 Amber Lin: Someone in sales
241 00:23:50.110 ⇒ 00:24:00.560 sahanaasokan: Okay, that’s cool. Yeah. So I’m gonna just make I need to add some like more context here. But I kinda wanna think about how we can make it more like sales focus. Then.
242 00:24:00.780 ⇒ 00:24:02.810 sahanaasokan: just because I do think like
243 00:24:03.120 ⇒ 00:24:17.439 sahanaasokan: they, they probably want to see the views sliced in a certain in certain ways, like a like a like a more granular like table chart. To actually understand some of these metrics. Okay, sounds good. And then, we can scroll down
244 00:24:17.980 ⇒ 00:24:26.359 sahanaasokan: 2, 3, 4 product has only been live.
245 00:24:29.160 ⇒ 00:24:33.720 sahanaasokan: Okay? Okay. So for 4, 4, a, this is more of like
246 00:24:34.120 ⇒ 00:24:38.480 sahanaasokan: like, how do they default. Want to see the data? No, no, no. So scroll up.
247 00:24:39.020 ⇒ 00:24:39.760 sahanaasokan: Yeah
248 00:24:39.940 ⇒ 00:24:56.270 sahanaasokan: to that one. Yeah. So like, what is the ideal time horizon for tracking these metrics. So it’s more about like, what is the default view like? Do they want to see data at the Weekly View? They want to see it at the daily view. Like, what is the default view? Like, we’ll include all
249 00:24:57.460 ⇒ 00:25:01.079 sahanaasokan: like aggregates. But they have to like, it’s more of like a default.
250 00:25:01.280 ⇒ 00:25:05.229 sahanaasokan: Yeah, thank you, and then 5 a
251 00:25:14.180 ⇒ 00:25:20.339 Luke Daque: So it looks like if this can. If this is, this would require us to meet with Mitch. For, like, yeah.
252 00:25:20.660 ⇒ 00:25:22.670 Luke Daque: what he wants to see right
253 00:25:22.940 ⇒ 00:25:23.810 sahanaasokan: Are there? Yeah.
254 00:25:23.810 ⇒ 00:25:24.339 Luke Daque: Ask him.
255 00:25:24.850 ⇒ 00:25:29.710 sahanaasokan: I mean, if this is fine, we can start building regardless. It’s just more of like when we do build it like.
256 00:25:30.120 ⇒ 00:25:41.619 sahanaasokan: what’s the default. I’m assuming it’s usually week. But again, if it’s like a daily snapshot they want to see every day, then it’s gonna have to be day, right? So I think that’s context, we need
257 00:25:42.220 ⇒ 00:25:46.770 sahanaasokan: And yeah, are there any other comparative? You? Wanna
258 00:25:48.010 ⇒ 00:25:53.569 sahanaasokan: okay, 5 is an optional. It’s just more of like a context. It’s not something we have to address
259 00:25:54.260 ⇒ 00:25:57.660 sahanaasokan: granular Kpi questions
260 00:26:00.280 ⇒ 00:26:02.850 Amber Lin: Sorry. What did you say was optional
261 00:26:03.270 ⇒ 00:26:10.500 sahanaasokan: 5, a like, that’s just more for context. It’s just it’s not like something we’re actually gonna deliver.
262 00:26:15.700 ⇒ 00:26:19.780 sahanaasokan: Can you break down, hey, Luke.
263 00:26:23.500 ⇒ 00:26:29.069 sahanaasokan: okay? And then I guess we have the Kpi stuff in real. So I’ll take a look at that again.
264 00:26:30.060 ⇒ 00:26:34.520 sahanaasokan: But I guess product areas was more of like, okay, like, you know.
265 00:26:35.110 ⇒ 00:26:47.190 sahanaasokan: for this specific product, are there different components of the product we want to focus on? Like, for example, at calendly right like when I think calendly, there’s scheduling. But there’s also workflows. But there’s also
266 00:26:49.860 ⇒ 00:26:59.080 sahanaasokan: integrations like we work with like Linkedin and other companies for surfacing calendar. Right? So that’s the same kind of question I’m asking here, like, what are the actual different
267 00:26:59.370 ⇒ 00:27:01.279 sahanaasokan: parts of the product
268 00:27:04.140 ⇒ 00:27:08.109 Luke Daque: Yeah. From what I understand, they’re just they just have plans. That’s it.
269 00:27:08.540 ⇒ 00:27:08.930 sahanaasokan: This plan.
270 00:27:08.930 ⇒ 00:27:12.520 Luke Daque: So it, yeah, just yeah. Subscription plans. And then
271 00:27:12.950 ⇒ 00:27:17.039 Luke Daque: anything outside plans would be token reloads like tokens.
272 00:27:18.027 ⇒ 00:27:20.610 Luke Daque: Yeah, they can. They can print something like that.
273 00:27:21.210 ⇒ 00:27:27.800 sahanaasokan: So let’s add that to one a so one A should probably be tokens, and then subscriptions
274 00:27:28.070 ⇒ 00:27:29.430 Luke Daque: Yeah, something like that.
275 00:27:29.430 ⇒ 00:27:34.480 sahanaasokan: Token usage and subscriptions are the 2 product areas. Yeah, there we go.
276 00:27:34.810 ⇒ 00:27:36.880 sahanaasokan: Okay. And then.
277 00:27:39.940 ⇒ 00:27:42.089 sahanaasokan: okay, done, done, done.
278 00:27:42.800 ⇒ 00:27:47.140 sahanaasokan: And then we have all the different actions. Right? A user can take in real
279 00:27:49.180 ⇒ 00:27:51.600 Luke Daque: What do you mean like filtering and stuff
280 00:27:51.800 ⇒ 00:27:54.129 sahanaasokan: Like how they can engage with the token
281 00:27:56.900 ⇒ 00:27:59.390 Luke Daque: I’m not sure I got. I get what you’re making
282 00:27:59.390 ⇒ 00:28:05.169 sahanaasokan: Is there multiple like? Is there different actions a user can perform with a token? Or is it just like a purchase
283 00:28:07.756 ⇒ 00:28:12.330 Luke Daque: I guess the yeah, like, what does the usage mean?
284 00:28:13.360 ⇒ 00:28:16.670 Luke Daque: I guess. Like they use their.
285 00:28:16.880 ⇒ 00:28:21.569 Luke Daque: This is like an AI company, right? So they they use tokens to
286 00:28:21.760 ⇒ 00:28:24.859 Luke Daque: like, ask AI questions and stuff like that, and then
287 00:28:25.140 ⇒ 00:28:27.266 Luke Daque: if they run out of it, then
288 00:28:27.570 ⇒ 00:28:28.270 sahanaasokan: Buy more.
289 00:28:28.270 ⇒ 00:28:31.790 Luke Daque: Yeah, they need to reload or like, buy more here
290 00:28:31.790 ⇒ 00:28:42.020 sahanaasokan: Okay, let’s just keep that as a Tbd, I kind of want more context. There, amber and I’m also, gonna I think we yeah, we needed. I think we need to talk to someone on the product side.
291 00:28:42.390 ⇒ 00:28:43.030 Luke Daque: Yeah.
292 00:28:43.790 ⇒ 00:28:44.190 sahanaasokan: Okay?
293 00:28:44.700 ⇒ 00:28:48.390 sahanaasokan: And then, yeah, sorry.
294 00:28:48.390 ⇒ 00:28:50.270 Luke Daque: Yeah. No go ahead.
295 00:28:51.880 ⇒ 00:28:55.669 sahanaasokan: Okay? And then again, I think, 5 and 6 here.
296 00:28:55.980 ⇒ 00:29:00.379 sahanaasokan: actually, can we move 5 to below? 6, yeah.
297 00:29:01.510 ⇒ 00:29:11.310 sahanaasokan: And then so for 5, let’s do filter by all of this. So membership plan, product, customer, yeah, everything.
298 00:29:11.700 ⇒ 00:29:13.429 sahanaasokan: date everything. Yeah.
299 00:29:13.730 ⇒ 00:29:16.930 sahanaasokan: And then for 6, this is going to be
300 00:29:17.292 ⇒ 00:29:25.060 sahanaasokan: a second, this is going to be in a different dashboard. So we can just put nice to have v, 2. So this is also going to be part of an iteration.
301 00:29:29.120 ⇒ 00:29:32.759 sahanaasokan: Yeah, it’s gonna be. And then we can just put like time to activation
302 00:29:32.890 ⇒ 00:29:37.150 sahanaasokan: time to conversion and then another dashboard for retention
303 00:29:43.820 ⇒ 00:29:50.349 sahanaasokan: and then retention cool. So those are 3 different dashboards. Okay?
304 00:29:50.490 ⇒ 00:29:52.879 sahanaasokan: Sounds good. So I think I have some
305 00:29:53.160 ⇒ 00:29:58.220 sahanaasokan: steps. I think, amber. I’m gonna we’re gonna have to work together to create tickets for all of this.
306 00:30:02.830 ⇒ 00:30:03.730 sahanaasokan: Hello.
307 00:30:06.630 ⇒ 00:30:08.250 Luke Daque: Yeah, I think Amber’s on mute.
308 00:30:09.050 ⇒ 00:30:10.060 Luke Daque: But yeah.
309 00:30:13.170 ⇒ 00:30:13.800 sahanaasokan: Okay.
310 00:30:17.362 ⇒ 00:30:19.890 Amber Lin: Yeah. So I together on
311 00:30:21.950 ⇒ 00:30:22.380 sahanaasokan: Sorry, what
312 00:30:22.380 ⇒ 00:30:27.130 Amber Lin: Of course, you said. We will need to work together on
313 00:30:27.920 ⇒ 00:30:32.669 sahanaasokan: Creating tickets for these and linear cause, it’s gonna be like a multi step project. So it’s gonna be like
314 00:30:32.670 ⇒ 00:30:33.290 Luke Daque: Here.
315 00:30:33.410 ⇒ 00:30:42.100 sahanaasokan: Creating like, first, st just understanding what data we have. Like, I don’t even know what data we have like, what data models we have.
316 00:30:42.560 ⇒ 00:30:52.479 sahanaasokan: So we need to kind of scope all of that out. See what needs to be built out. And then we probably need to create mock ups for everything, and figma for the dashboards.
317 00:30:53.170 ⇒ 00:30:56.210 sahanaasokan: and then start rolling out dashboards one by one.
318 00:30:59.390 ⇒ 00:31:04.829 sahanaasokan: But the, I think, the the hardest step here, at least from experience is
319 00:31:05.040 ⇒ 00:31:13.689 sahanaasokan: understanding data and creating the data requirements like the data models. Because Eden had Eden’s data model was very tricky.
320 00:31:14.040 ⇒ 00:31:15.759 sahanaasokan: So data model
321 00:31:16.740 ⇒ 00:31:22.069 sahanaasokan: a data tracking plan, but I, according to Utam, they already have tracking. So that’s not necessary.
322 00:31:24.990 ⇒ 00:31:34.800 Amber Lin: I mean, Luke knows a bit more about this, right? Because we’ve been already doing the data models. Or are we just referring to the fines, data models.
323 00:31:35.395 ⇒ 00:31:40.129 Amber Lin: Cause. We, I assume we already have some understanding here. Right, Luke, is that true?
324 00:31:40.130 ⇒ 00:31:48.180 Luke Daque: Yeah, we should have data models already, especially for the subscriptions and customer models.
325 00:31:49.132 ⇒ 00:31:54.320 Luke Daque: Did you already have access to the snowflake for this client already.
326 00:31:57.160 ⇒ 00:31:59.410 Luke Daque: Sahana or amber? I don’t know. Maybe
327 00:31:59.410 ⇒ 00:32:00.520 sahanaasokan: Hey? What were you saying?
328 00:32:01.290 ⇒ 00:32:03.310 Luke Daque: Do you already have access to Snowflake
329 00:32:03.740 ⇒ 00:32:04.549 sahanaasokan: I don’t think so.
330 00:32:04.960 ⇒ 00:32:06.430 Amber Lin: I don’t think so.
331 00:32:06.430 ⇒ 00:32:07.080 Amber Lin: That’s right.
332 00:32:07.080 ⇒ 00:32:07.550 Amber Lin: So
333 00:32:08.060 ⇒ 00:32:09.720 Luke Daque: Yeah, let’s put that in.
334 00:32:09.890 ⇒ 00:32:14.919 Luke Daque: and maybe the real dashboards as well. I can add you in. If you’re not in there yet.
335 00:32:20.000 ⇒ 00:32:24.430 sahanaasokan: Yeah, if you can add me to that, that’d be great, and I believe I have access to real
336 00:32:25.290 ⇒ 00:32:26.720 Luke Daque: Can you confirm?
337 00:32:27.550 ⇒ 00:32:33.529 Luke Daque: Yeah, you should have access to real already. I I don’t think you accepted the invite yet, but there was
338 00:32:33.530 ⇒ 00:32:34.480 sahanaasokan: Okay, let me do that.
339 00:32:34.480 ⇒ 00:32:38.369 Luke Daque: Team right after this call. Also, guys, I do have to drop
340 00:32:39.000 ⇒ 00:32:44.869 Amber Lin: Yeah, don’t worry. I I have another call, too. I think there’s still some stuff to
341 00:32:45.030 ⇒ 00:32:51.059 Amber Lin: go through. Can you just look at the Doc, maybe just leave some comments here and there and then
342 00:32:51.300 ⇒ 00:32:54.000 sahanaasokan: This is. It’s a 6 month roadmap, right?
343 00:32:56.880 ⇒ 00:33:01.260 sahanaasokan: Since those are gonna be more for like v, 2 as well. So that’s more future
344 00:33:01.970 ⇒ 00:33:08.359 Amber Lin: Okay, sounds good. These are also the questions you gave Utam. So if you can have some time, just look over them, and I will
345 00:33:08.360 ⇒ 00:33:08.730 sahanaasokan: Yeah, yeah.
346 00:33:08.730 ⇒ 00:33:11.039 Amber Lin: Time when you will do the linear
347 00:33:11.260 ⇒ 00:33:16.240 sahanaasokan: Okay, yeah, I will look. I think those are all metric definitions. So, yes, I will look at those. But thank you.
348 00:33:16.240 ⇒ 00:33:21.889 Amber Lin: Okay. Sounds good. I think just now take a look at real, and then I’ll book a call with you
349 00:33:22.040 ⇒ 00:33:23.400 sahanaasokan: Thanks, bye.
350 00:33:23.400 ⇒ 00:33:24.460 Amber Lin: Alrighty bye.
351 00:33:26.220 ⇒ 00:33:29.090 Amber Lin: Okay, Luke, on to our next meeting.
352 00:33:30.280 ⇒ 00:33:30.850 Amber Lin: Yeah.