Meeting Title: ABC | Data Sync Date: 2025-04-08 Meeting participants: Annie Yu, Amber Lin
WEBVTT
1 00:01:00.100 ⇒ 00:01:01.320 Amber Lin: Hello!
2 00:01:01.320 ⇒ 00:01:02.140 Annie Yu: Hi.
3 00:01:02.390 ⇒ 00:01:05.010 Amber Lin: So let me share my screen on the near.
4 00:01:05.019 ⇒ 00:01:05.609 Annie Yu: Okay.
5 00:01:06.440 ⇒ 00:01:12.020 Amber Lin: So this is for ABC. And
6 00:01:12.690 ⇒ 00:01:18.382 Amber Lin: we’re having a meeting with Brian later and ideally, we want to discuss.
7 00:01:19.850 ⇒ 00:01:25.559 Amber Lin: what is it? Oh, I keep forgetting let me go look at my.
8 00:01:25.560 ⇒ 00:01:31.540 Annie Yu: Like how to join the 8 by 8, and the bot performance.
9 00:01:31.540 ⇒ 00:01:33.910 Amber Lin: Yes, that one
10 00:01:34.316 ⇒ 00:01:52.689 Amber Lin: help him with a dashboard, because you now know a lot about it. Now and then. The 8 by 8 Api 8 by Api probably will be just on him. I know Casey had more idea of it, but he’s really busy with the internal stuff. So I’m just gonna let Brian say what he knows, and then relay that to Casey.
11 00:01:53.470 ⇒ 00:01:54.000 Amber Lin: Don’t be.
12 00:01:54.000 ⇒ 00:02:04.469 Annie Yu: Okay. So when we say, Kpi, does that mean they will start, I guess, feeding their 8 by 8 data into our
13 00:02:05.060 ⇒ 00:02:08.919 Annie Yu: probably Github Snowflake.
14 00:02:09.690 ⇒ 00:02:23.990 Amber Lin: Essentially. Yes. So right now, it’s a manual export. Last time they just exported the march data right? So right now, probably, as we continue testing. We probably want them to give us data, maybe this week.
15 00:02:24.290 ⇒ 00:02:39.999 Amber Lin: which would be ideal. If we get data from, say, last week, we can say, okay, how many? How’s the agents performing? If we can separate by user type like a lot of exploration ideally, we should get that from them
16 00:02:40.310 ⇒ 00:02:47.050 Amber Lin: for last week, and then, okay, let me create a ticket for that good.
17 00:02:47.960 ⇒ 00:02:48.820 Amber Lin: Oh.
18 00:02:49.470 ⇒ 00:02:58.171 Annie Yu: So I also do have a follow up question. So I still don’t really know how to think of the roles I’m playing, just because,
19 00:02:58.720 ⇒ 00:02:59.730 Annie Yu: I think
20 00:02:59.920 ⇒ 00:03:10.270 Annie Yu: in this case I will need to do a lot of data modeling work, which is fine. But I’m not like a like a data engineer. So that will mean things
21 00:03:13.180 ⇒ 00:03:23.800 Annie Yu: on things like I can figure out like maybe how we could join them. But then to actually do it, that would take me definitely more time than like a a data engineer would.
22 00:03:24.607 ⇒ 00:03:38.150 Annie Yu: So I’m not sure how to think of the role of like, what? What kind of the role I will be playing versus like, how, what kind of work we can rely on Brian or their team to do.
23 00:03:38.470 ⇒ 00:03:59.289 Amber Lin: I see. Can you bring that up in the meeting? Because essentially, you’re gonna be our data lead on the ABC project because Casey and Miguel doesn’t do data. They do a lot of engineering. It will take them 3 times more for 5 times more time, as it will take you. And I know data modeling stuff might take you some more time. So next meeting.
24 00:03:59.290 ⇒ 00:04:10.150 Amber Lin: coming up in a few in 10 min. Can you just ask Brian what he can take on. I bet he’s pretty happy to take on those stuff, and he seems very experienced and knowledgeable.
25 00:04:10.190 ⇒ 00:04:14.149 Amber Lin: So whatever you can delegate to him do that.
26 00:04:14.755 ⇒ 00:04:28.739 Amber Lin: You probably will be working pretty close with him, and then he seems pretty responsive in a channel as well. So if you don’t know how to do the data modeling work. Just throw that to him. Let him figure out some parts.
27 00:04:28.740 ⇒ 00:04:35.750 Annie Yu: And okay. And to that point also, just because if let’s say, do they have
28 00:04:36.080 ⇒ 00:04:42.960 Annie Yu: access to our Github and all that cause? If they don’t I? I’m pretty sure I will be the one to do everything.
29 00:04:44.047 ⇒ 00:04:49.400 Amber Lin: Yeah, we want to give them access. I believe they do have access. If not, we will grant them access.
30 00:04:49.400 ⇒ 00:04:50.669 Annie Yu: Okay. Okay.
31 00:04:51.240 ⇒ 00:04:53.159 Amber Lin: So. Just.
32 00:04:54.090 ⇒ 00:04:59.559 Amber Lin: You probably have to keep on autumn’s feet on that, because he needs to give the Snowflake Grant.
33 00:04:59.800 ⇒ 00:05:02.370 Amber Lin: I was asking the past few days, but
34 00:05:02.800 ⇒ 00:05:06.569 Amber Lin: they should have it so essentially, they would be able to do it with you.
35 00:05:07.370 ⇒ 00:05:14.830 Annie Yu: Yeah, yeah, okay, yeah, I just, I’m just not so sure about the kind of how to differentiate or like.
36 00:05:15.670 ⇒ 00:05:20.169 Annie Yu: think of my role versus their role.
37 00:05:21.590 ⇒ 00:05:27.239 Annie Yu: Yeah, I know there wouldn’t be like a fine line, I mean for data modeling, like sometimes in Javi.
38 00:05:27.350 ⇒ 00:05:46.659 Annie Yu: Sometimes a wish would do like a complicated model. And then eventually, I realized, okay, I I actually want these 2 to be like this. Not that. Then I would go change it and then have him review it. Then, if I have to build like a model from scratch, and if it’s like
39 00:05:47.040 ⇒ 00:06:03.629 Annie Yu: an easy one, it should be fine. But I’m just saying like, if when data gets larger and when there are like multiple tables that will that will like, take me some time, which I’ll be happy to do. But that would just probably more time than a data.
40 00:06:03.630 ⇒ 00:06:18.250 Amber Lin: And I’m glad you bring it up because I have no clue. So now that you bring it up, we’ll really work on getting Brian to do the work, because I think this is what this was his job. Last time we met he was like, oh, if we don’t have models that create those. I think he’s pretty good at it.
41 00:06:18.790 ⇒ 00:06:44.029 Amber Lin: Take him a very little time. So what you do is actually just give him very specific instructions of how you want to look like, and you guys can discuss it. I kind of want you to lead the next meeting with him, because I don’t want I’m not. I don’t know too much about the data, so I don’t want to mislead the conversation in any way. I’ll just chime in about operational stuff if I need.
42 00:06:44.190 ⇒ 00:06:45.620 Annie Yu: Okay. Yeah.
43 00:06:45.620 ⇒ 00:06:46.200 Amber Lin: Yeah.
44 00:06:46.790 ⇒ 00:06:59.960 Amber Lin: So that’s good. That’s mostly about the integrating the different call data. There’s another thing. If you can see on my screen that they brought up last Friday’s meeting about the oh, by the way, upsells
45 00:07:00.670 ⇒ 00:07:05.639 Amber Lin: right. I don’t know if you had a chance to look at the ticket, so essentially, we want to give.
46 00:07:06.080 ⇒ 00:07:15.200 Amber Lin: We have the upsells that the bot suggests right. And so we kind of want to 1st of all identify
47 00:07:18.720 ⇒ 00:07:42.590 Amber Lin: so can you try to do an exploration to see using maybe sequel? Can you see what type, what responses contains? The oh, by the ways is essentially they spell it out like, Oh, by the way, they just spell it out like that. So it’d be pretty easy to identify. I think it’s probably it contains
48 00:07:42.720 ⇒ 00:07:45.110 Amber Lin: query, and then.
49 00:07:45.110 ⇒ 00:08:02.319 Annie Yu: I think the the thing now is, I’m looking at their data, and I don’t think there is a column like you, said Category here for trees or termites. There’s queue name, which I think means more like a department. They’re like pest
50 00:08:02.470 ⇒ 00:08:05.447 Annie Yu: campfree past long.
51 00:08:06.790 ⇒ 00:08:09.599 Amber Lin: Do you mean your data or our data?
52 00:08:09.600 ⇒ 00:08:10.809 Annie Yu: There are data.
53 00:08:10.810 ⇒ 00:08:14.180 Amber Lin: Oh, this is for our data. So this is for the Bots data.
54 00:08:16.460 ⇒ 00:08:22.059 Annie Yu: And so is there, cause I’m also not seeing a category.
55 00:08:22.060 ⇒ 00:08:33.529 Amber Lin: No, there’s not, but it will probably have to create one. So essentially could you see if you could create one, or if you can can you ask
56 00:08:34.109 ⇒ 00:08:38.390 Amber Lin: can you see who you can ask or delegate it to.
57 00:08:40.690 ⇒ 00:08:50.799 Annie Yu: Okay, let me let me think through this. So when you say, Oh, by the way, cause I thought the oh, by the way, data is the one of the files that they share.
58 00:08:52.400 ⇒ 00:09:00.399 Amber Lin: Yes, but here’s the thing. Oh, by the way, is it upsell? Right? So the agents that
59 00:09:00.520 ⇒ 00:09:11.899 Amber Lin: customer service representative do upsells in their calls, and what we want to do for our bot is our our bot also suggests these things.
60 00:09:11.980 ⇒ 00:09:35.460 Amber Lin: So sometimes, when you see the bot response would also contain. Oh, by the way, have you seen this? So this is essentially a text. This is a string of things that the bot would have suggested, and what we want to do is we want to single out in our responses of what we did to say. Hey? We suggested all these upsells.
61 00:09:35.510 ⇒ 00:09:44.700 Amber Lin: and then eventually one. We want to correlate the bots data to their call data to say, Hey, we suggested all these upsells.
62 00:09:45.270 ⇒ 00:09:59.769 Amber Lin: and this apparently led to more revenue because your reps were able to sell more things. So that was the goal. But we need to identify if we suggested upsells in our bot responses.
63 00:09:59.770 ⇒ 00:10:00.890 Annie Yu: Okay, got it?
64 00:10:00.890 ⇒ 00:10:06.700 Amber Lin: Yeah, if say, how frequently per category do we?
65 00:10:07.000 ⇒ 00:10:08.270 Amber Lin: Do we mention it?
66 00:10:08.460 ⇒ 00:10:12.919 Annie Yu: Okay, okay? And this is all within our bot data. Right?
67 00:10:12.920 ⇒ 00:10:19.820 Amber Lin: Yeah, this should be all in the responses right now. There’s no category. But we do have all the outputs
68 00:10:20.556 ⇒ 00:10:31.810 Amber Lin: from the from the bot responses. So maybe doing a query query on that, and maybe say, if it contains this, and it’s this category, etc.
69 00:10:32.116 ⇒ 00:10:43.759 Annie Yu: If I have to do the category I would love like a list of names. I guess maybe that’s something we can get from them because I there’s no way I can know, like what would be the
70 00:10:44.240 ⇒ 00:10:51.499 Annie Yu: words or the category. And I I’m betting they probably already have a way to categorize their.
71 00:10:52.753 ⇒ 00:10:53.940 Annie Yu: Their data.
72 00:10:54.230 ⇒ 00:10:55.860 Amber Lin: You get well, we got
73 00:10:59.700 ⇒ 00:11:10.589 Amber Lin: yeah, I will do that. I was gonna email them later, anyway. So I’m gonna do that today. But if today you have some time we can look at just to create a sequel. Query.
74 00:11:10.590 ⇒ 00:11:17.939 Annie Yu: Yeah, yeah, I can definitely start with, like the frequency or percentage that we are trying to get for sure.
75 00:11:18.090 ⇒ 00:11:20.309 Amber Lin: Okay, sounds good. That would be great.
76 00:11:21.250 ⇒ 00:11:24.370 Amber Lin: Oh, great! I will do that.
77 00:11:28.390 ⇒ 00:11:30.416 Annie Yu: But then
78 00:11:31.570 ⇒ 00:11:41.820 Annie Yu: Another follow up question on that I’m not sure. And I I don’t have the right answer now, but I’m I’m just throwing it out there. I’m not sure if I
79 00:11:41.930 ⇒ 00:11:45.659 Annie Yu: have the data to show people accept
80 00:11:46.070 ⇒ 00:11:49.330 Annie Yu: this. Oh, by the way, with our bot data.
81 00:11:49.540 ⇒ 00:11:51.190 Amber Lin: And that would be by that.
82 00:11:51.190 ⇒ 00:11:53.440 Annie Yu: I can see how often we use it.
83 00:11:54.350 ⇒ 00:12:01.370 Annie Yu: But I can’t show within that pool how many people accept this upsell.
84 00:12:02.150 ⇒ 00:12:08.099 Amber Lin: Oh, I see. I think that will be further down the line, because there’s nothing we can do without their call data.
85 00:12:08.100 ⇒ 00:12:09.310 Annie Yu: Okay. Okay.
86 00:12:09.310 ⇒ 00:12:27.259 Amber Lin: Yeah, so don’t worry. Maybe that’s a question that we can discuss with Brian. Okay, what do you think we should do? And that’s probably also related to the joining data, because we kind of need to join the different calls and the different timings of things. So we’ll ask him. He knows more about data than we do of the call data.
87 00:12:27.440 ⇒ 00:12:28.260 Amber Lin: Okay?
88 00:12:29.620 ⇒ 00:12:34.929 Amber Lin: Alright, I’ll clean up these tickets a little bit. But essentially, you know what we want to do today, right?
89 00:12:34.930 ⇒ 00:12:38.140 Annie Yu: So today’s focus would be.
90 00:12:38.140 ⇒ 00:12:38.770 Amber Lin: Okay.
91 00:12:38.770 ⇒ 00:12:41.339 Annie Yu: Really that? Oh, by the way, and then
92 00:12:44.310 ⇒ 00:12:46.750 Annie Yu: And then the the meeting with him later.
93 00:12:46.750 ⇒ 00:12:54.060 Amber Lin: Yeah, meeting with him. We’ll talk about 8 by 8, and we’ll talk about joining the call data and then for you, probably
94 00:12:54.400 ⇒ 00:12:59.460 Amber Lin: figuring out the Oh, by the ways! And then getting Brian to do more of the modeling work.
95 00:13:00.400 ⇒ 00:13:09.429 Annie Yu: Okay? And and just one more question on this spot. Oh, by the way, do we expect like a visualization? Or it could be just some notes.
96 00:13:10.063 ⇒ 00:13:20.039 Amber Lin: Eventually. Yeah, we want a visualization. But we can’t really do that until we get the data. So I have it in the, have it in a task. So eventually, we wanna make a
97 00:13:20.870 ⇒ 00:13:21.730 Amber Lin: Oh.
98 00:13:22.010 ⇒ 00:13:22.590 Annie Yu: Okay, so our.
99 00:13:22.590 ⇒ 00:13:25.170 Amber Lin: Clean out of tickets, but one of them should be a dashboard.
100 00:13:25.170 ⇒ 00:13:29.340 Annie Yu: Yeah. Okay. Okay. So for today’s output, it doesn’t have to be in a dashboard.
101 00:13:29.340 ⇒ 00:13:31.349 Amber Lin: No, no, I don’t think we can do that yet right.
102 00:13:31.350 ⇒ 00:13:32.410 Annie Yu: Okay, cool.
103 00:13:32.790 ⇒ 00:13:38.049 Annie Yu: Yeah, that’s that’s great to know. I feel like I still like I’m taking
104 00:13:38.380 ⇒ 00:13:54.500 Annie Yu: like, it still takes me some time to really bring data to real. And then eventually, because I think real is pretty stringent on the data type. So when I if I want to see like a duration time. I have to
105 00:13:54.660 ⇒ 00:13:56.770 Annie Yu: get all the.
106 00:13:57.110 ⇒ 00:14:09.759 Annie Yu: I guess, all the day hour, seconds to second level, and then to second, and then divided them by like 60 when I want to see minutes and all that.
107 00:14:09.760 ⇒ 00:14:23.030 Amber Lin: Oh, I see, I see we can. Just. We can also spit the feedback to the, to the real vendors, cause they’re still a small enough team. If you say this is a problem, they’ll probably change it for you.
108 00:14:23.360 ⇒ 00:14:29.339 Annie Yu: Yeah, I I will get more familiar with it before I like make any suggestion, because.
109 00:14:29.340 ⇒ 00:14:29.940 Amber Lin: Okay.
110 00:14:29.940 ⇒ 00:14:33.379 Annie Yu: Could be a reason. They’re doing it this way. But yeah.
111 00:14:33.380 ⇒ 00:14:37.540 Amber Lin: Okay. But we can always ask, because sometimes they just probably haven’t thought of it.
112 00:14:37.660 ⇒ 00:14:39.440 Annie Yu: Yeah, yeah.
113 00:14:39.440 ⇒ 00:14:42.829 Amber Lin: Sounds good. I need to take a party break before I.
114 00:14:43.670 ⇒ 00:14:45.350 Annie Yu: Go ahead.
115 00:14:46.030 ⇒ 00:14:47.269 Annie Yu: I’ll see you soon.
116 00:14:47.270 ⇒ 00:14:48.560 Amber Lin: Okay. I’ll talk to you soon.