Meeting Title: ABC x Brainforge | Data Meeting Date: 2025-04-08 Meeting participants: Annie Yu, Brian Gonzales, Amber Lin
WEBVTT
1 00:00:39.470 ⇒ 00:00:40.960 Annie Yu: Hello, Brian!
2 00:00:41.590 ⇒ 00:00:42.257 Brian Gonzales: How’s it going.
3 00:00:43.890 ⇒ 00:00:45.700 Annie Yu: Good! How are you?
4 00:00:45.900 ⇒ 00:00:47.390 Brian Gonzales: Doing, fantastic.
5 00:00:47.390 ⇒ 00:00:53.999 Annie Yu: I am. I’ve been trying like not to look at us stock market.
6 00:00:55.130 ⇒ 00:00:59.100 Annie Yu: So today I haven’t. So it’s good.
7 00:01:00.950 ⇒ 00:01:02.249 Brian Gonzales: Can be a bumpy ride.
8 00:01:04.780 ⇒ 00:01:11.319 Brian Gonzales: That’s the time to like. Everybody’s like like I was like, I’m gonna wait till everything drops. Maybe I’ll start buying stuff for for once. So.
9 00:01:12.830 ⇒ 00:01:13.775 Annie Yu: Yeah.
10 00:01:14.720 ⇒ 00:01:20.399 Brian Gonzales: No, I sold all my crypto like a year ago. I was like, yeah, I saw it top out. I was like no getting rid of all this, so.
11 00:01:21.040 ⇒ 00:01:21.500 Annie Yu: Cool.
12 00:01:21.500 ⇒ 00:01:22.300 Brian Gonzales: That would be awesome.
13 00:01:23.680 ⇒ 00:01:25.120 Annie Yu: Hello, Amber!
14 00:01:25.520 ⇒ 00:01:26.800 Amber Lin: Hi.
15 00:01:28.430 ⇒ 00:01:29.469 Brian Gonzales: Hey? Where’s it going.
16 00:01:30.400 ⇒ 00:01:31.400 Amber Lin: Doing good.
17 00:01:31.660 ⇒ 00:01:34.040 Amber Lin: I’ll send a
18 00:01:34.490 ⇒ 00:01:41.260 Amber Lin: quick meeting agenda that I just typed out. It looks awful, but so that we can remember
19 00:01:41.520 ⇒ 00:01:44.169 Amber Lin: what we want to talk about.
20 00:01:44.650 ⇒ 00:02:04.789 Amber Lin: I’ll let Annie lead, because Annie Look knows a lot more about the data stuff. I only know about the operational stuff and how things kind of work, so I’ll let Annie and you lead the conversation here, and I will take notes, and I’ll I’ll take note of anything I need to ask from management or stuff.
21 00:02:05.450 ⇒ 00:02:10.190 Annie Yu: Okay, and I think, can I actually try to like
22 00:02:11.250 ⇒ 00:02:31.400 Annie Yu: Clarify again, like how Brian and I or us will be working together? That that question is more so around like data modeling stuff. Because right now, doing exploration, I sometimes have to do obviously joins, which I’m fine. But I’m just thinking, like down the road
23 00:02:31.600 ⇒ 00:02:39.360 Annie Yu: for future more like integration. Once the Kpi is connected, all that will Brian kind of
24 00:02:39.540 ⇒ 00:02:46.970 Annie Yu: be in our github or Dbt, and then to do the data modeling is that the expectation.
25 00:02:48.716 ⇒ 00:02:54.419 Amber Lin: Brian, I’ll let you answer that question. How much would you be able to get involved.
26 00:02:54.980 ⇒ 00:02:58.639 Brian Gonzales: I can do anything you all need me to do, really, I mean, if it’s to do it.
27 00:02:58.640 ⇒ 00:03:03.129 Brian Gonzales: If it’s due, it’s to do with data like I’ll jump in. I’m pretty much the
28 00:03:03.720 ⇒ 00:03:16.279 Brian Gonzales: like the like. I I’ve mentioned in the other meetings. I’m more of the I like to progressively learn. So whatever I don’t know, like, if if it’s something that I can do, yeah, it’s I can do it. That’s no problem.
29 00:03:16.510 ⇒ 00:03:23.300 Brian Gonzales: So and if I can’t, we’ll get David like to help me. Kind of, you know.
30 00:03:23.460 ⇒ 00:03:27.930 Brian Gonzales: Push it out to somebody else or something like that. But for the most part. Yeah, I’ll head up on this. That’s no problem.
31 00:03:29.530 ⇒ 00:03:38.569 Annie Yu: Cool, cool. Okay? Then, I think, amber correct me if I’m wrong. So today’s meeting, we’re trying to figure out how we can
32 00:03:39.417 ⇒ 00:03:45.550 Annie Yu: really connect our data from our bot side to ABC’s
33 00:03:46.680 ⇒ 00:03:51.489 Annie Yu: average handling. Wait, what’s that call? You’re you’re like
34 00:03:51.810 ⇒ 00:03:59.459 Annie Yu: phone interaction data. And that includes data around ht as well as oh, by the way.
35 00:04:00.150 ⇒ 00:04:10.859 Amber Lin: Is. Yeah, essentially so for each of the bot conversation. It help it. It has. Essentially, there’s another. There’s the agent that’s having a call right. So we want to
36 00:04:11.200 ⇒ 00:04:19.839 Amber Lin: connect that call to this conversation that the agents having with the bot, so that eventually down the line we can see
37 00:04:20.130 ⇒ 00:04:26.829 Amber Lin: what? What, specifically have we benefited that singular call? So we just want to connect those 2 together.
38 00:04:27.940 ⇒ 00:04:41.660 Annie Yu: Okay, then I can pull up my screen, and I just have something very scrappy. I’m not sure, Brian, have you seen our bot table yet?
39 00:04:42.780 ⇒ 00:04:45.249 Brian Gonzales: Maybe I’ve seen a lot of different things. So.
40 00:04:45.818 ⇒ 00:04:48.050 Annie Yu: You show me? I’ll tell you for sure.
41 00:04:48.050 ⇒ 00:05:06.589 Annie Yu: Okay? And also just one clarifying question. I know that you guys send over 2 files and one includes. Oh, by the way, and one includes is more like on the queue level. So which one, when we say 8 by 8. Which one? Are we referring to.
42 00:05:06.860 ⇒ 00:05:09.770 Brian Gonzales: So both of those are from 8 by 8.
43 00:05:10.140 ⇒ 00:05:16.120 Brian Gonzales: So the one with the call volume and ht, that’s just like, because they’re like 2 different reports.
44 00:05:16.869 ⇒ 00:05:23.059 Brian Gonzales: If you’ve ever worked with 8 by 8 they have a very. They have a very ugly way of like putting out data.
45 00:05:25.190 ⇒ 00:05:49.850 Brian Gonzales: So with the other data that the 1st set of data with the Ht, and the call volume that’s just gonna be like, just exactly that. The oh, by the way, references disposition codes that they use so. And those can be a little tricky, too, because in some cases you’re gonna see 2 names on dispositions, but we can when we start getting into the weeds.
46 00:05:50.549 ⇒ 00:06:00.270 Brian Gonzales: I can kind of show you what I do manually, and maybe we can train the bot to kind of do the same. So but again, we’ll we’ll cross that bridge when we get a little that deeper, so.
47 00:06:00.690 ⇒ 00:06:21.750 Annie Yu: Okay, cool, cool. So I’m I’m just gonna share what I found. But then, if I’m de rolling, let me know if we’re not focusing on the right thing. So here I’m I’m really just doing some exploration across the spreadsheets you share as well as our bot conversation locks.
48 00:06:26.390 ⇒ 00:06:35.069 Annie Yu: So I’m I’m this is just a quick flash up of our bot conversation. Actually, this one. So
49 00:06:36.000 ⇒ 00:06:54.660 Annie Yu: this will be it. And then we don’t have to get too too much into it. But I think for me, I’m just trying to figure out the aggregated level of each data. So with our bot, our primary key will be each record. Id, and then
50 00:06:55.020 ⇒ 00:07:12.039 Annie Yu: we don’t have any data column. But within this record, id, we actually have that timestamp when it happens. And then there’s also conversation. Id. That means one conversation can have multiple record. Id.
51 00:07:12.659 ⇒ 00:07:17.240 Annie Yu: and there’s also like a username in this one.
52 00:07:17.610 ⇒ 00:07:21.510 Annie Yu: And then coming back to your data.
53 00:07:21.760 ⇒ 00:07:29.009 Annie Yu: I’m actually gonna start with this 1. 0, but the one with Oh, by the way, I think this is one row per
54 00:07:30.161 ⇒ 00:07:34.720 Annie Yu: not bad one row per. What’s that
55 00:07:35.070 ⇒ 00:07:41.420 Annie Yu: interaction that we will call and here we also have timestamp
56 00:07:41.680 ⇒ 00:07:51.699 Annie Yu: as well as username and conversation. Id. I think one question I have is, I was seeing kind of
57 00:07:52.580 ⇒ 00:07:54.840 Annie Yu: I forgot the percentage. But
58 00:07:55.010 ⇒ 00:08:01.529 Annie Yu: there are a few lines with null values in username. We’re actually not username, but
59 00:08:01.790 ⇒ 00:08:10.160 Annie Yu: participant in this table. Essentially, I think that means the username in our bot table. And is that.
60 00:08:11.330 ⇒ 00:08:18.329 Annie Yu: yeah, I think one question I have here is this, why are there many node values? But we can get to that later.
61 00:08:18.900 ⇒ 00:08:20.760 Annie Yu: And then coming back to this.
62 00:08:21.010 ⇒ 00:08:35.430 Annie Yu: and I’m just naming them how I understand it. But this is the one without. Oh, by the way, so here I think this table is a bit different. Because I I think here’s my note.
63 00:08:36.429 ⇒ 00:08:38.049 Annie Yu: To really
64 00:08:38.720 ⇒ 00:08:55.919 Annie Yu: distinct each record we will have to use start time and time as well as Qid to identify the difference. And then here the granularity would be, I’ve and and keep me honest. But each role represents
65 00:08:56.110 ⇒ 00:08:59.590 Annie Yu: 1 h of each queue activity.
66 00:09:00.630 ⇒ 00:09:03.110 Annie Yu: So with that understanding.
67 00:09:03.310 ⇒ 00:09:20.590 Annie Yu: I’m just thinking through. Okay in my head how I join them all together. And with this one, let’s start with this one aggregated level, I would say we we could left join them to that full interaction with the Oh, by the way, info
68 00:09:20.750 ⇒ 00:09:27.549 Annie Yu: on both queue name as well as the time creation of this table
69 00:09:28.040 ⇒ 00:09:31.379 Annie Yu: and wait. Actually sorry the time
70 00:09:31.540 ⇒ 00:09:56.419 Annie Yu: for this table. We will use that start time and end time. So we have that 1 h increment for each record. And then we can bring oh, by the way, data. So we essentially get the time creation within each hour. That’s how I would join them. But I’m also not sure if there’s a better way but then the more important question would be joining our bot
71 00:09:56.750 ⇒ 00:09:58.679 Annie Yu: data into
72 00:09:58.900 ⇒ 00:10:13.429 Annie Yu: your data, which I don’t think, and also keep me honest. I don’t think we can do that directly with bot table to the A. Ht. Table, but then we can do like a fuzzy join where
73 00:10:14.290 ⇒ 00:10:20.760 Annie Yu: our we can get that. We can strip that bot created timestamp here.
74 00:10:20.900 ⇒ 00:10:29.180 Annie Yu: and then it could be we wanted to after a phone time creation. And then we can get
75 00:10:29.380 ⇒ 00:10:33.729 Annie Yu: we can filter on. If this record happens after this
76 00:10:34.120 ⇒ 00:10:40.230 Annie Yu: phone call, like, within 5 min, we would account this
77 00:10:40.820 ⇒ 00:10:48.300 Annie Yu: bot record into that phone call, if that makes sense at all. But I think this is a very fuzzy drawing which
78 00:10:48.960 ⇒ 00:10:59.720 Annie Yu: I don’t know how accurate that will be. And this is just like throwing out some ideas. But I’m not sure if you have any like better data or better ways to do it.
79 00:11:00.180 ⇒ 00:11:06.739 Brian Gonzales: So I’m looking at the 2 reports real quick, so I may have something that you can use rather than.
80 00:11:07.010 ⇒ 00:11:08.339 Annie Yu: The timestamp.
81 00:11:08.340 ⇒ 00:11:14.309 Brian Gonzales: Exactly. Yeah. Cause that’s gonna give you a very broad range of of day of a data set. So
82 00:11:15.180 ⇒ 00:11:18.390 Brian Gonzales: give me one second. I just wanna pull something here.
83 00:11:18.560 ⇒ 00:11:26.659 Annie Yu: And that would be great cause I I know that you had to do manual work to get those spreadsheets to us. So I’m I’m betting you probably have
84 00:11:27.150 ⇒ 00:11:34.229 Annie Yu: more the squinchable id level we can’t use.
85 00:11:35.010 ⇒ 00:11:42.970 Brian Gonzales: Yeah, it’s 1 of the things that I always, while I’m loading this that I was always pushing for is unique. Ids like employee ids.
86 00:11:43.420 ⇒ 00:11:46.119 Brian Gonzales: Because any like I I’ve worked for
87 00:11:46.240 ⇒ 00:11:48.880 Brian Gonzales: this is a smaller company. But I’ve worked for like
88 00:11:49.170 ⇒ 00:11:59.060 Brian Gonzales: data that’s been like tens of thousands of people. It’s like, no, you need unique employee ids. Otherwise, that’s the only way you can connect a lot of these through databases.
89 00:11:59.601 ⇒ 00:12:06.669 Brian Gonzales: Okay, yeah, it looks like we can do that. So what you would probably want to use is hold on one second. Let me make sure.
90 00:12:07.450 ⇒ 00:12:11.469 Brian Gonzales: like you say I want to keep myself honest. Let me make sure I’m looking at the right thing.
91 00:12:14.000 ⇒ 00:12:16.559 Brian Gonzales: thinking you can use the transaction. Id.
92 00:12:19.280 ⇒ 00:12:23.120 Annie Yu: Transaction ids, and that oh, by the way.
93 00:12:34.930 ⇒ 00:12:36.250 Brian Gonzales: No, okay.
94 00:12:36.810 ⇒ 00:12:42.239 Brian Gonzales: So this this particular report that I’m giving you is more of an overall report.
95 00:12:43.000 ⇒ 00:12:49.290 Brian Gonzales: If we’re doing it for this specific thing right here, like to to kind of.
96 00:12:51.070 ⇒ 00:12:56.650 Brian Gonzales: I guess, like, yeah, because we’re trying to quantify like the time that it takes like like
97 00:12:56.930 ⇒ 00:13:02.330 Brian Gonzales: like Amber said, like, we want to see like, what does it look like in that conversation
98 00:13:02.510 ⇒ 00:13:06.890 Brian Gonzales: to understand like how efficient we can make that agent with the bot.
99 00:13:07.150 ⇒ 00:13:13.999 Brian Gonzales: So what I’m thinking is cause this right here that I gave you is more of an overview of the
100 00:13:14.280 ⇒ 00:13:20.270 Brian Gonzales: the calls. What I can do is I can pull a report. That’s more of a granular look.
101 00:13:21.174 ⇒ 00:13:35.289 Brian Gonzales: Per employee, and I believe, if I’m not mistaken, that should hand the transaction. Id, so that would be more of what you need. So this is why I I told David I was like, we just need to kind of give them what we think they need.
102 00:13:35.430 ⇒ 00:13:38.870 Brian Gonzales: Figure out what they’re using it for. And then we can kind of like, say, Okay, cool.
103 00:13:39.010 ⇒ 00:13:40.109 Brian Gonzales: We can do that.
104 00:13:40.606 ⇒ 00:13:43.989 Brian Gonzales: Give me one second and I will pull something.
105 00:13:44.250 ⇒ 00:13:48.580 Brian Gonzales: We were running. What dates march.
106 00:13:52.350 ⇒ 00:13:56.260 Brian Gonzales: So I’m thinking we do. Maybe interactions.
107 00:14:00.950 ⇒ 00:14:02.780 Annie Yu: I, okay.
108 00:14:02.780 ⇒ 00:14:10.110 Brian Gonzales: So. So what I’ll do is on that. I’ll I’ll take that. It won’t take me long. It just it. I don’t want to take too much of the time here, because, you know
109 00:14:10.720 ⇒ 00:14:23.139 Brian Gonzales: I’ll pull something here for you specifically for what you’re looking for, so I think I can find it for you, because then what I’ll do is I’ll literally just create a report to make sure that those transaction ids
110 00:14:24.131 ⇒ 00:14:27.570 Brian Gonzales: are in that particular report per individual.
111 00:14:32.360 ⇒ 00:14:39.289 Annie Yu: I? Okay? And then I think that would be helpful. But also I’m wrapping my head around
112 00:14:39.430 ⇒ 00:14:48.320 Annie Yu: even with transaction. Id, which I think I believe one of the spreadsheet you gave me already has transaction. Id.
113 00:14:48.740 ⇒ 00:14:49.859 Brian Gonzales: That’s the oh, by the way, one.
114 00:14:49.860 ⇒ 00:15:16.039 Annie Yu: Yeah. But then, even with that transaction, id, I, I don’t have a way to join that into our bot table. So this is a pretty small table, so we can probably look at it. So these one are like generated by our bot and then execution time, input output. And these are. The performance of the
115 00:15:16.720 ⇒ 00:15:27.919 Annie Yu: I’m actually looking at the wrong one, I think. Yeah, this is the one we want to join them together. So similarly record id conversation id.
116 00:15:28.470 ⇒ 00:15:33.160 Annie Yu: But then here we only have that username, as the I would say, like.
117 00:15:33.780 ⇒ 00:15:37.980 Annie Yu: how we can identify who’s using this.
118 00:15:38.260 ⇒ 00:15:45.750 Annie Yu: So I’m trying to see if there’s any way we can get to that username meaning.
119 00:15:45.750 ⇒ 00:15:46.190 Brian Gonzales: So.
120 00:15:46.190 ⇒ 00:15:50.370 Annie Yu: The participant or agent name in in your data.
121 00:15:51.580 ⇒ 00:15:55.500 Brian Gonzales: That normally is the participant, that that’s for that one
122 00:15:57.270 ⇒ 00:15:58.695 Annie Yu: But then, yeah,
123 00:15:59.480 ⇒ 00:16:02.779 Brian Gonzales: So what I was gonna say is, okay. So let me
124 00:16:03.250 ⇒ 00:16:08.425 Brian Gonzales: let me level set with you real quick. The cause. I don’t recognize the username
125 00:16:10.156 ⇒ 00:16:16.450 Brian Gonzales: like, the the header there. So I’m trying to figure out was that part of the the data that I sent you.
126 00:16:16.670 ⇒ 00:16:18.969 Annie Yu: Oh, no! This is from our bot!
127 00:16:19.570 ⇒ 00:16:20.330 Brian Gonzales: Okay.
128 00:16:20.330 ⇒ 00:16:27.390 Annie Yu: And I, yeah, I I’m pretty comfortable to say that that would be the participant.
129 00:16:27.810 ⇒ 00:16:29.469 Brian Gonzales: Yeah, I’m pretty sure it is.
130 00:16:29.470 ⇒ 00:16:30.520 Annie Yu: Yeah, yeah.
131 00:16:30.520 ⇒ 00:16:31.220 Brian Gonzales: And.
132 00:16:31.790 ⇒ 00:16:35.260 Annie Yu: So you’re saying the participant sometimes comes out blank.
133 00:16:36.390 ⇒ 00:16:42.309 Annie Yu: Yes, I think within the march data we have.
134 00:16:43.590 ⇒ 00:16:49.219 Brian Gonzales: And which specific report were you pulling that data from? Was that the oh, by the way, one that we were discussing.
135 00:16:49.220 ⇒ 00:16:54.489 Annie Yu: By the way, one, I think, only that one has participant, at least from those 2 spreadsheets.
136 00:16:54.490 ⇒ 00:17:02.959 Brian Gonzales: Yeah, that. Yeah. That’s why I was getting at like that other one is more of a high overview of what transpired that particular month.
137 00:17:03.832 ⇒ 00:17:05.360 Brian Gonzales: This other one is
138 00:17:08.050 ⇒ 00:17:13.529 Brian Gonzales: this is exactly yeah. This is like agent over agent. So what I’m thinking is.
139 00:17:15.109 ⇒ 00:17:19.069 Brian Gonzales: I’m just looking at a a couple of the data sets real fast just to see.
140 00:17:20.349 ⇒ 00:17:25.380 Brian Gonzales: So it only looks like the ones that are blank, or they didn’t accept them.
141 00:17:26.369 ⇒ 00:17:43.499 Brian Gonzales: They rather abandon, because 8 by 8 has like a tick for everything that it does. So if you forward it to an another queue that’s considered like a line item at that point. So what I’m thinking is what we may have to do, is
142 00:17:45.449 ⇒ 00:17:57.229 Brian Gonzales: it it? Again? This is just some thinking out loud is maybe because just looking at the data right here, a lot of the same stuff that are the stuff that you would need is in the handled portion of it.
143 00:17:57.579 ⇒ 00:18:01.689 Brian Gonzales: and that’s from the the column outcome.
144 00:18:03.079 ⇒ 00:18:12.229 Brian Gonzales: So I’m thinking, maybe like we can kind of look at it deeper and say, like, let’s look at specifically anything that was handled.
145 00:18:13.857 ⇒ 00:18:20.779 Brian Gonzales: Transfer to queue like I may be able just to kind of like, maybe kind of
146 00:18:21.309 ⇒ 00:18:27.269 Brian Gonzales: clean that up a bit in that sense. So if that would help in that specific instance right here.
147 00:18:28.659 ⇒ 00:18:30.289 Brian Gonzales: So what are your thoughts on that.
148 00:18:39.150 ⇒ 00:18:44.412 Annie Yu: I think my question then, is still around
149 00:18:49.550 ⇒ 00:18:57.790 Annie Yu: around like the level of detail we want to get. So I I think now, my challenge is, I can’t really
150 00:18:58.130 ⇒ 00:19:03.599 Annie Yu: get to like a record level to join them together. But if we only want to see
151 00:19:04.770 ⇒ 00:19:10.200 Annie Yu: everything by user, not username participant level. I think we
152 00:19:10.620 ⇒ 00:19:12.250 Annie Yu: we already have what we have.
153 00:19:14.920 ⇒ 00:19:16.640 Annie Yu: So yeah.
154 00:19:17.260 ⇒ 00:19:28.130 Brian Gonzales: Okay? Then, I mean, I just want because you had asked about the nulls. Like, if if do we wanna like? Do we care that they’re there? Does it cause any issues with anything? Or can we just assume that those are just
155 00:19:28.890 ⇒ 00:19:30.699 Brian Gonzales: like blank transactions.
156 00:19:31.610 ⇒ 00:19:39.850 Annie Yu: I think that’s an a question for you. I I mean, I think we have enough data without Noel.
157 00:19:40.785 ⇒ 00:19:41.400 Annie Yu: This.
158 00:19:41.680 ⇒ 00:19:45.830 Annie Yu: These are the the records.
159 00:19:46.000 ⇒ 00:19:47.330 Brian Gonzales: So.
160 00:19:47.700 ⇒ 00:19:52.419 Annie Yu: There’s 79, 60, 36, with No.
161 00:19:53.190 ⇒ 00:19:56.989 Annie Yu: Alex, which I that’s just a question like, is that expected.
162 00:19:58.080 ⇒ 00:20:04.120 Brian Gonzales: Yeah. So that’s yeah. That’s like, what like, less than a percent like couple of percentage.
163 00:20:04.280 ⇒ 00:20:06.230 Brian Gonzales: So take a step with it. Yeah.
164 00:20:06.230 ⇒ 00:20:12.369 Annie Yu: Yeah. Yeah. So if that’s the case, that means we, these kind of 79, 36,
165 00:20:12.770 ⇒ 00:20:18.079 Annie Yu: we won’t be able to join them with our bot.
166 00:20:20.070 ⇒ 00:20:31.659 Brian Gonzales: So we can’t use the like, I say, cause if I what what data set are we using? Are we specifically using the oh, by the way, here are we. Are we using the Ht. One as well.
167 00:20:34.830 ⇒ 00:20:37.679 Brian Gonzales: In this particular data set for the conversation logs.
168 00:20:38.210 ⇒ 00:20:46.260 Brian Gonzales: When you. I’m sorry, let me rephrase a bit. You you’re saying you want to tie the data that we have with your conversation. Bot correct.
169 00:20:46.560 ⇒ 00:20:47.200 Annie Yu: Yes.
170 00:20:47.420 ⇒ 00:20:53.149 Brian Gonzales: Okay, so what data set are we using to tie to this bot?
171 00:20:53.290 ⇒ 00:20:56.430 Brian Gonzales: Both of those? Are we using both of those sheets I sent you.
172 00:20:57.170 ⇒ 00:21:07.280 Annie Yu: That’s what I did with my exploration. But now I’m thinking, maybe we can just start with, I think maybe just the oh, by the way. And then
173 00:21:08.530 ⇒ 00:21:19.899 Annie Yu: w because in oh, by the way, there’s also the duration time like handling time. Right? Would that also can get to that metric.
174 00:21:21.730 ⇒ 00:21:29.999 Brian Gonzales: Yeah, absolutely. That’s that’s what I was getting at like. I wanted to cause that one will have that. That particular transactions, duration
175 00:21:30.528 ⇒ 00:21:34.580 Brian Gonzales: are, it is in this case it would be handling time.
176 00:21:35.578 ⇒ 00:21:37.891 Brian Gonzales: Because duration could include
177 00:21:39.520 ⇒ 00:21:47.819 Brian Gonzales: There’s a lot of variables within duration. But handling time is specifically built for what we need it for for a hand like average handle time.
178 00:21:48.580 ⇒ 00:21:54.809 Brian Gonzales: So I would prefer if we use that particular data set like the actual handling time, because
179 00:21:55.400 ⇒ 00:22:12.609 Brian Gonzales: I think the duration takes into account of that calls lifespan, like even the transfers through the queues when the handling time actually quantifies the agent interaction with that which is what we need it for. Specifically, we want to see, like.
180 00:22:13.140 ⇒ 00:22:13.510 Annie Yu: And.
181 00:22:13.510 ⇒ 00:22:17.571 Brian Gonzales: I? Wanna exactly. Yeah. Cause I wanna have a delta for that.
182 00:22:18.320 ⇒ 00:22:40.259 Brian Gonzales: like, if I have like. You know, David is is doing a call here in in March. This is what his handle time was. He used the bot we his handle. Time has been cut to here, and he’s gotten X amount of oh, by the ways on top of that. So I think that’s where we kind of can start and I can get you more
183 00:22:40.930 ⇒ 00:22:45.200 Brian Gonzales: data if it doesn’t have enough in that interactions.
184 00:22:45.610 ⇒ 00:22:49.430 Brian Gonzales: But we can start there to kind of see, like what we can figure out from that.
185 00:22:50.230 ⇒ 00:22:56.850 Annie Yu: Okay, that makes sense. So it’s focused on the handling. I think it’s called handling duration in the in this one, right?
186 00:22:56.850 ⇒ 00:22:58.089 Brian Gonzales: Correct. Yes.
187 00:22:58.090 ⇒ 00:22:58.976 Annie Yu: And okay.
188 00:23:00.010 ⇒ 00:23:10.850 Annie Yu: okay, now, I feel better about it. I think we can really just join on username. And I’ll do like a left join so anchor on oh, by the way, and then left join our bot table.
189 00:23:11.590 ⇒ 00:23:18.419 Brian Gonzales: Okay. But okay, let’s let’s let’s pause for a sec. Because I wanna make sure that we we get all of this, too, because it’s a lot I I
190 00:23:18.660 ⇒ 00:23:23.639 Brian Gonzales: I feel for you on this cause like I had. I came into this like I was just like, Oh, my God! This is a lot of
191 00:23:24.176 ⇒ 00:23:28.419 Brian Gonzales: so Annie, with the because one thing I wanna make sure is that
192 00:23:29.740 ⇒ 00:23:49.359 Brian Gonzales: because we still need to tie the the conversation to, maybe this is another conversation we’re going to, and if it is, you know, I apologize. But the con actual conversation that that particular agent is using on that. Bot! How do we tie that together, I’m thinking again goes back to the whole. Let’s use the transaction. Id
193 00:23:50.076 ⇒ 00:23:52.013 Brian Gonzales: that would be the way to go.
194 00:23:52.750 ⇒ 00:23:53.590 Brian Gonzales: I.
195 00:23:53.590 ⇒ 00:23:54.155 Annie Yu: But
196 00:23:55.440 ⇒ 00:23:56.000 Brian Gonzales: Go ahead!
197 00:23:56.000 ⇒ 00:24:07.619 Annie Yu: But I think with what we have now, just because you do have a very specific time, transact time creation in your table. But then in our bot. It’s the time when each
198 00:24:08.873 ⇒ 00:24:15.080 Annie Yu: chat happens, so there’s no way there would be very. There will be aligned.
199 00:24:16.480 ⇒ 00:24:18.329 Annie Yu: So that’s where I’m challenged.
200 00:24:18.800 ⇒ 00:24:24.143 Brian Gonzales: Yeah, no, no, that’s what I’m getting at. Because when you look at that specific
201 00:24:25.970 ⇒ 00:24:39.091 Brian Gonzales: like when you’re looking at that particular report, it gives you a range like it happened between one and 2, which is like ridiculous. That’s like, Wow, that’s like 60 min that I that this bot could have been used.
202 00:24:39.850 ⇒ 00:24:41.729 Brian Gonzales: so that’s why I’m thinking
203 00:24:42.370 ⇒ 00:24:58.580 Brian Gonzales: of giving you another report with a more granular look of they got on that, because it’s it’ll have timestamps similar to what you have here. Because I use that for for, like more of a deep dive on my end whenever I’m trying to figure out like
204 00:24:59.140 ⇒ 00:25:16.139 Brian Gonzales: what that agent was doing during these times. So I can like, I can break down their day minute over minute. Essentially. So that’s why, I’m thinking I can give you that same data, that same data set. Because now I know what you’re using it for, like, okay, cool, I’ll give you that data set for the same March.
205 00:25:16.660 ⇒ 00:25:23.700 Brian Gonzales: and then I think, if you start there, that may give you a better look of what you’re trying to accomplish there with tying those conversations.
206 00:25:24.990 ⇒ 00:25:41.319 Brian Gonzales: So, and I believe it has the transaction ids, too. So again it might be where I don’t have to send you that other one and this one. I can just send you one Csv file with everything you need along with the Oh, by the ways, and then we can start there and then kind of level set there.
207 00:25:42.250 ⇒ 00:25:45.877 Annie Yu: Okay, sounds good. And and just so, you know,
208 00:25:46.810 ⇒ 00:25:57.179 Annie Yu: I’m not trained data engineer. So I might have like more questions as I do it, or when it, when things get like too large or too complicated, I might just like.
209 00:25:57.480 ⇒ 00:26:01.971 Annie Yu: be like, Brian, can you like? Can you handle it?
210 00:26:02.380 ⇒ 00:26:09.549 Brian Gonzales: Yeah, whatever you need. I’m here, like, I say, anything to make our jobs easier. So I’ve yeah, that’s no problem. I have no issues with that.
211 00:26:10.890 ⇒ 00:26:11.980 Annie Yu: Cool
212 00:26:13.300 ⇒ 00:26:17.879 Brian Gonzales: I’m not a data engineer either, though, but people think I am. I’m like I. It’s just that I’ve been doing it for so long.
213 00:26:17.880 ⇒ 00:26:18.940 Brian Gonzales: We’re good.
214 00:26:22.240 ⇒ 00:26:25.992 Annie Yu: Cool. Cool. Let me just go through. Okay.
215 00:26:26.720 ⇒ 00:26:30.969 Brian Gonzales: I’m curious. I got a question about this. This particular data set that you’re using
216 00:26:31.080 ⇒ 00:26:34.109 Brian Gonzales: is this similar to how like access database works
217 00:26:34.410 ⇒ 00:26:46.170 Brian Gonzales: cause you were talking about fuzzy queries and stuff like that, I know, like power query. It’s like it’s almost like you’re talking power. Query language a little bit. Is that what this is? It’s like power, query, or or very similar to it.
218 00:26:47.360 ⇒ 00:26:50.659 Annie Yu: I don’t wait. What do you mean? Can you repeat that.
219 00:26:50.660 ⇒ 00:27:05.299 Brian Gonzales: So you would, you would cause. I just noticed some of the language you were using like fuzzy connections. I was like, that’s power query that you’re talking about. And and I and I ask because I’m just curious to see like cause. When I start to interface with this, I’m just trying to like.
220 00:27:06.010 ⇒ 00:27:10.140 Brian Gonzales: I always like to kind of visualize like the future of what we’re gonna be doing
221 00:27:10.280 ⇒ 00:27:23.140 Brian Gonzales: is this something that I’m gonna be using a lot of I mean, if everything goes between the big bosses, you know. But on our level, is this an interface that I would be that I would have available to me is what I’m asking.
222 00:27:23.140 ⇒ 00:27:24.509 Annie Yu: Oh! This one, this one!
223 00:27:24.510 ⇒ 00:27:24.900 Brian Gonzales: Yes.
224 00:27:24.930 ⇒ 00:27:40.130 Annie Yu: Like, I. I really want to get you access to this. And but I think we also have our github. And really, what happens on the engineering side should be happening in Github
225 00:27:41.470 ⇒ 00:27:45.719 Annie Yu: here. Like I, I could manually just updated that
226 00:27:45.830 ⇒ 00:27:50.809 Annie Yu: I manual up updated the the data you you sent to me.
227 00:27:51.680 ⇒ 00:28:00.179 Annie Yu: Play around with it. But yes, I think I really will want to get you access to both Snowflake and Github, if if that’s
228 00:28:00.710 ⇒ 00:28:07.010 Annie Yu: possible, with with. I think Amber will have more visibility on on this, but I think that’s the plan.
229 00:28:07.010 ⇒ 00:28:32.963 Amber Lin: Yeah, totally. We will access to everything I’ve been paying with him about giving the Grants. I think he just wants to clean them a little bit of tech depths before he sends you this because it’s a little messy right now. So you wanna make sure that when we give it to you. It’s a little bit cleaner, but I’m getting. I’m asking him. Apparently he hasn’t gave it to me yet, but maybe you can access it through the
230 00:28:33.470 ⇒ 00:28:45.650 Amber Lin: maybe not directly view the snowflake, but when you set up the rail you can still do the you can still pull the credits and have access to data, but probably not just just not able to view.
231 00:28:45.650 ⇒ 00:28:47.130 Brian Gonzales: It’s granular. Yeah.
232 00:28:47.490 ⇒ 00:28:48.140 Amber Lin: Yeah.
233 00:28:48.930 ⇒ 00:28:51.839 Brian Gonzales: No, that’s no problem, and I like messy amber. So it’s all good.
234 00:28:51.840 ⇒ 00:29:00.859 Amber Lin: Okay, sounds good. I wanted. Were you able to set up the real dashboard on local.
235 00:29:02.000 ⇒ 00:29:03.328 Brian Gonzales: So I was gonna
236 00:29:04.520 ⇒ 00:29:17.879 Brian Gonzales: I was gonna ask you about that. So what I noticed on that before I shift gears on that. Did you have anything before we move forward on that one, Annie like? Is, is that all you needed from me on on that particular data set. I’m assuming we’ll need more. But.
237 00:29:18.590 ⇒ 00:29:36.290 Annie Yu: I I think I can take that as of now, and I’ll let you know if I still have problem, and also just to your point. No, we’re not using power query. And when I say Fuzzy, I just means that it’s not like accurate, as like joining on a customer. Id or transaction. Id, I just
238 00:29:36.290 ⇒ 00:29:38.890 Annie Yu: I just mean Fuzzy. It’s.
239 00:29:38.890 ⇒ 00:29:43.469 Brian Gonzales: No, no, that that’s actually a term in power. Query. That’s why I was like, Oh, okay, we’re doing.
240 00:29:43.470 ⇒ 00:29:47.143 Annie Yu: This park great, which I I have never learned that word, but.
241 00:29:47.450 ⇒ 00:29:53.660 Brian Gonzales: Yeah, it’s it’s in there. That’s funny. That’s why I was like, Oh, that’s interesting. So okay, so but
242 00:29:54.100 ⇒ 00:30:00.500 Brian Gonzales: back to to Amber, like, I said, because I’ve very. You’ll learn this about me, Amber, as you work with me more and more. I like to kind of like
243 00:30:00.850 ⇒ 00:30:22.629 Brian Gonzales: compartmentalize, compart compartmentalize can’t even talk to everything. So anyway. So back to your your thing. So one thing that I I did feedback on it because I saw that you have the setup here, but I think you’re missing one step on there, which is, I don’t know what that interface is. So when I look at the video, I’m like.
244 00:30:22.950 ⇒ 00:30:24.480 Brian Gonzales: I have real
245 00:30:25.801 ⇒ 00:30:36.689 Brian Gonzales: like I, I have like the I guess the web interface. But then I saw like you said, Yeah, go down to the left here, and I was like, Wait, I don’t have that ui on the left, and I’m thinking that’s part of your drive.
246 00:30:37.250 ⇒ 00:30:58.870 Amber Lin: I see. I think you’re looking at cursor, have you? Have? Have you tried cursor yet? It’s essentially an AI powered Vs code? I think it will also work in Vs code. It’s just for me. Sometimes I don’t even know what command to run, so I use. I use cursor. Let me share my screen.
247 00:30:58.990 ⇒ 00:31:01.790 Amber Lin: you know, if you.
248 00:31:01.960 ⇒ 00:31:09.379 Amber Lin: if you want to try it out, it’s AI powered coding platform that does a lot for you, but I know.
249 00:31:09.380 ⇒ 00:31:09.930 Brian Gonzales: Oh, you got.
250 00:31:09.930 ⇒ 00:31:24.919 Amber Lin: So much more technical. So this has a very big margin of benefit for me. I don’t know how much it will benefit you guys. So this is essentially like Vs code. The ui is like Vs code.
251 00:31:25.150 ⇒ 00:31:29.279 Amber Lin: So you have the terminal. There you have the same thing.
252 00:31:29.590 ⇒ 00:31:35.650 Amber Lin: the files. And then on the right, oh, on the right. It’s just
253 00:31:35.760 ⇒ 00:31:38.979 Amber Lin: the AI panel where you chat, and then
254 00:31:39.140 ⇒ 00:31:42.629 Amber Lin: they’ll write the code and you just approve it line by line.
255 00:31:43.030 ⇒ 00:31:51.390 Brian Gonzales: Got it. But what I’m saying is like cause cause you had said like, do you have instructions on how to install like the developer desktop app
256 00:31:52.015 ⇒ 00:31:55.844 Brian Gonzales: and the links you were using on there. I was like, Oh, I don’t have those links.
257 00:31:56.530 ⇒ 00:32:04.329 Amber Lin: I see I don’t think they have. They don’t have an app. I think you just install, so let me go to.
258 00:32:04.330 ⇒ 00:32:08.440 Brian Gonzales: That’s the interface I was talking. So that’s slack right there, that’s what that is.
259 00:32:08.720 ⇒ 00:32:10.360 Amber Lin: So.
260 00:32:11.390 ⇒ 00:32:13.190 Annie Yu: Green sidebar.
261 00:32:13.190 ⇒ 00:32:14.910 Brian Gonzales: Exactly. I don’t have that.
262 00:32:15.560 ⇒ 00:32:21.320 Amber Lin: Oh, I see, this is a this is not Google. This is a different browser.
263 00:32:21.320 ⇒ 00:32:28.400 Brian Gonzales: So that’s why I was getting at like I was like, you’re using that I was like, but I don’t have that. And I was like. So your instructions show me that.
264 00:32:28.590 ⇒ 00:32:29.960 Brian Gonzales: So all right.
265 00:32:30.120 ⇒ 00:32:32.159 Amber Lin: You can have it.
266 00:32:32.670 ⇒ 00:32:36.129 Brian Gonzales: I mean, whatever y’all want to give. But I was just like, Okay, I was like.
267 00:32:36.580 ⇒ 00:32:39.912 Brian Gonzales: it sounds like, I’ll I’ll get some clarification. So it’s all good.
268 00:32:40.190 ⇒ 00:32:43.549 Amber Lin: I like it, cause I have so many tabs.
269 00:32:44.470 ⇒ 00:32:49.520 Amber Lin: Don’t want to have the open open all the time. So this one these are all closed, so is.
270 00:32:49.520 ⇒ 00:32:50.060 Brian Gonzales: Thank you.
271 00:32:50.060 ⇒ 00:33:06.999 Amber Lin: Combination of bookmarks and tabs. It’s just a lot easier to manage. And I have different spaces. So this is my work, my other stuff. This is my photography. So it’s all goes in one place, and I just find it a lot more manageable.
272 00:33:07.390 ⇒ 00:33:10.439 Brian Gonzales: Got it. So you’re multiple talented, got it, I understand now.
273 00:33:12.405 ⇒ 00:33:21.390 Brian Gonzales: So so the the link is what I need then, specifically, for the particular like, yeah, if you just provide like, maybe in the summary.
274 00:33:21.390 ⇒ 00:33:28.910 Amber Lin: Let me just copy and paste that I will just send it to you in our slack right now.
275 00:33:29.190 ⇒ 00:33:30.030 Amber Lin: Do you mean that.
276 00:33:30.030 ⇒ 00:33:48.789 Brian Gonzales: What I’m saying is, your your tutorial was great. It was just that one spot I was like, okay, let’s pause there real quick, and let’s cause, if you can add like cause when you’re clicking like how to do it, you’re showing me on on that particular interface, and it’s like, I don’t have that interface. So it’s like, maybe if she provides like, Hey, here’s the link.
277 00:33:48.880 ⇒ 00:34:00.360 Brian Gonzales: But provided in the summary a. Again, this is just like my feedback to you like for that instructions. If you could put like the link for that particular like how to install the real developer.
278 00:34:01.376 ⇒ 00:34:06.310 Brian Gonzales: That would be like top tier at that point, like, yeah, that would be pretty okay.
279 00:34:07.790 ⇒ 00:34:12.590 Amber Lin: Let me call, send you that 1st
280 00:34:12.989 ⇒ 00:34:19.620 Amber Lin: how to install, and then I mean also give you the info link.
281 00:34:21.030 ⇒ 00:34:23.860 Amber Lin: so that we’ll probably use that, too.
282 00:34:29.870 ⇒ 00:34:35.789 Amber Lin: When you say interface. You meant you meant the 2 web pages. I showed you right, not my browser or cursor.
283 00:34:35.790 ⇒ 00:34:42.880 Brian Gonzales: Well, I mean, it’s the it’s the green bar. So I I call it. Yeah, yeah, the green bar on there. I could. I could be like, very.
284 00:34:43.190 ⇒ 00:34:44.040 Brian Gonzales: yeah.
285 00:34:44.040 ⇒ 00:34:52.520 Amber Lin: I see it makes sense, because if I’m watching something new, I’ll think that the Green bar is part of the real dashboard, and I’ll be really confused.
286 00:34:52.670 ⇒ 00:34:58.529 Brian Gonzales: That’s why I was thinking I was like, Okay, maybe I didn’t set this up correctly and cause I’ve never seen that. And I was like.
287 00:34:59.130 ⇒ 00:35:03.450 Brian Gonzales: and I was like, no, those are like bookmarks, almost.
288 00:35:03.740 ⇒ 00:35:04.029 Amber Lin: Yeah.
289 00:35:04.030 ⇒ 00:35:07.850 Brian Gonzales: So I was like, let me just ask her to clarify that. So that’s all good.
290 00:35:07.850 ⇒ 00:35:20.270 Amber Lin: For clarifying great. So that’s the dashboard part looking at my meeting agenda. Oh, the api integration! Do you know anything about the 8? By Apis?
291 00:35:20.750 ⇒ 00:35:24.690 Brian Gonzales: I just know that it exists.
292 00:35:25.230 ⇒ 00:35:25.950 Brian Gonzales: Okay.
293 00:35:25.950 ⇒ 00:35:29.300 Brian Gonzales: what? What are you needing? What are you needing to know, or what do you? What do you? What do you need? And.
294 00:35:29.300 ⇒ 00:35:42.640 Amber Lin: I see. So we’re trying to figure out how to eventually automate the data process, because right now, you’re manually exporting. And we’re manually importing. So we want to eventually pull that through the Api. So it will be a lot faster.
295 00:35:44.170 ⇒ 00:35:47.889 Brian Gonzales: So let me pull something real quick because I looked it up.
296 00:35:49.040 ⇒ 00:35:58.409 Brian Gonzales: So remember one of the the It guys says, Yeah, it doesn’t have Api, and I literally found it in like seconds. So I was like, no, it’s it’s got a lot of these dashboards they they have Apis.
297 00:36:01.530 ⇒ 00:36:02.999 Brian Gonzales: Let me see.
298 00:36:06.810 ⇒ 00:36:10.759 Brian Gonzales: Oh, I think I know where I found it. I know exactly where it actually.
299 00:36:23.666 ⇒ 00:36:28.070 Annie Yu: Amber. Just one quick note. I I think we do have to
300 00:36:28.674 ⇒ 00:36:37.140 Annie Yu: get Brian access to Snowflake just because I think on Github. We only have the file.
301 00:36:37.210 ⇒ 00:36:37.800 Amber Lin: We’re.
302 00:36:37.800 ⇒ 00:36:45.320 Annie Yu: For real and and go ahead and explore the data. If he ha wants to explore the data that will be done in Snowflake.
303 00:36:45.320 ⇒ 00:37:05.199 Amber Lin: I think so, but probably the M. Poll will do that because it’s a line of credit. It’s a credit that lets you access snowflake. Eventually. I do want Utam. I’m I’m asking Utam to do that, so we can view Snowflake directly. But right now the M. Pull should solve the problem of
304 00:37:05.370 ⇒ 00:37:07.829 Amber Lin: seeing it in the real dashboard, for now.
305 00:37:09.240 ⇒ 00:37:11.870 Annie Yu: Okay. But then how would he?
306 00:37:12.190 ⇒ 00:37:18.390 Annie Yu: No. Which table to use if he wants to build that source in real.
307 00:37:20.190 ⇒ 00:37:23.790 Amber Lin: That’s we’ll get that access. We’re working on that.
308 00:37:24.200 ⇒ 00:37:24.940 Annie Yu: Okay.
309 00:37:28.820 ⇒ 00:37:31.644 Annie Yu: don’t sweat it in. Amber’s got it. She got it figured out.
310 00:37:31.970 ⇒ 00:37:35.080 Amber Lin: Oh, I don’t need to get on Utam’s ass.
311 00:37:35.570 ⇒ 00:37:43.829 Brian Gonzales: No, it’s I know. I know it’s you’re the project manager. I’ve I’ve done your. I’ve been in that position so I was like, no, I know I know what she’s doing. Yes, I’ve been in that position. I’m not.
312 00:37:44.100 ⇒ 00:37:44.920 Amber Lin: Boy.
313 00:37:45.300 ⇒ 00:37:50.269 Brian Gonzales: So I did that for the previous Comp. Company I was with, I fell into that role.
314 00:37:50.440 ⇒ 00:37:50.820 Amber Lin: Oh!
315 00:37:50.820 ⇒ 00:37:57.090 Brian Gonzales: I was like, what guys, this is like, cause I’m a very logical person. So
316 00:37:57.360 ⇒ 00:38:04.280 Brian Gonzales: you have to have a lot of patience, and you can’t look at things in black and white when you’re a project manager.
317 00:38:04.540 ⇒ 00:38:13.780 Brian Gonzales: and Annie can probably attest to this. We’re very black and white like if and I always say, like, we’re the data people are like if and and people.
318 00:38:13.900 ⇒ 00:38:25.850 Brian Gonzales: I don’t know if you understand what that means. But it’s a it’s a language. If an and statement it’s like, if it’s this, then it’s this, if it’s not that, then it’s this, that’s how we are like. So we’re all crazy, so I mean at least I am.
319 00:38:25.850 ⇒ 00:38:29.110 Amber Lin: If you’re not done, then what are you gonna do or.
320 00:38:29.110 ⇒ 00:38:37.479 Brian Gonzales: Exactly. Yeah. So that’s what I mean. But for you you have to be a little bit more flexible. Me? Not so much so. But I but I feel your pain so.
321 00:38:41.700 ⇒ 00:38:42.165 Amber Lin: Hmm
322 00:38:42.630 ⇒ 00:38:53.360 Brian Gonzales: But but yeah, so what I’ll do is I’ll work on the Api stuff for you. I’ll find out what you guys need in order to connect to it, cause I know what you need. It’s a specific.
323 00:38:53.360 ⇒ 00:39:01.920 Amber Lin: Email back from Tim. Yvette, emailed Tim. And he said, There’s we don’t have anyone in 8 by 8. We just
324 00:39:02.310 ⇒ 00:39:08.730 Amber Lin: there’s we just have 8 by 8 on his own. And we and he sent me a link
325 00:39:08.930 ⇒ 00:39:13.740 Amber Lin: from 8 8 by eight’s website about the Api. So we’re gonna have to figure it out from scratch.
326 00:39:14.320 ⇒ 00:39:16.079 Brian Gonzales: Yeah, I was gonna tell you.
327 00:39:16.430 ⇒ 00:39:29.570 Brian Gonzales: that’s right. And that’s wrong, because that’s what I hate. When he always says, like, Yeah, we don’t have it. I go. No, they do have it. You just don’t have a connection to it like very again, black and white for me is like, no, no, it’s there. You just have to know how to connect to it.
328 00:39:30.182 ⇒ 00:39:34.650 Brian Gonzales: I haven’t done that in a in a long time. I had to. Yeah. So. No, it’s
329 00:39:34.890 ⇒ 00:39:39.660 Brian Gonzales: but I’ll send you what I can just to see if it can help you guys out a little bit. But
330 00:39:40.120 ⇒ 00:39:45.109 Brian Gonzales: I’ll see what I can do, cause I know it’s it’s a it’s a it’s an address that you have to tie to is what it is.
331 00:39:45.350 ⇒ 00:39:47.870 Brian Gonzales: But you need somebody on that end to
332 00:39:48.110 ⇒ 00:39:52.049 Brian Gonzales: cause. When I asked Udman, I can’t say ever say his name right, Udman.
333 00:39:52.360 ⇒ 00:40:02.699 Brian Gonzales: I was like dude that. Do you have somebody to do that, because that takes a little skill to do that. It’s now we got it. I was like, Okay, dude, don’t just letting me know like it’s a little. It’s a little lengthy process, but.
334 00:40:02.700 ⇒ 00:40:06.439 Amber Lin: Oh, you mean, said that the Api will be easy.
335 00:40:06.850 ⇒ 00:40:11.709 Brian Gonzales: Well, I know how. Yeah, but I that’s like, Oh, that’s cool. And I’m like thinking to myself.
336 00:40:11.710 ⇒ 00:40:12.543 Amber Lin: Oh no!
337 00:40:12.960 ⇒ 00:40:21.109 Brian Gonzales: Not easy at all like anybody that knows Apis like it’s it’s a pain. It could be a real pain sometimes it’s easy. But no, no, I get it so.
338 00:40:21.110 ⇒ 00:40:36.750 Amber Lin: I see. Okay, that’ll be great. I’ll hear back from you a little bit on how that’s done, and then, if we need help. We also have after we figure that out our internal team, we can connect it to our AI or connected to our process.
339 00:40:37.220 ⇒ 00:40:41.949 Brian Gonzales: Cool. So for now we’ll just do the manual like upload Csvs. For now.
340 00:40:41.950 ⇒ 00:40:47.340 Amber Lin: Like. They don’t need it real time, for now they can survive.
341 00:40:47.990 ⇒ 00:40:52.499 Brian Gonzales: Yeah. But eventually I I know the real time stuff is we’ll we’ll kind of
342 00:40:53.000 ⇒ 00:40:58.450 Brian Gonzales: benefit. That’ll that’ll be super beneficial, because, like, I say, cause I want to build dashboards like real time dashboards.
343 00:40:58.450 ⇒ 00:41:00.099 Brian Gonzales: Yeah. So.
344 00:41:01.560 ⇒ 00:41:08.069 Brian Gonzales: but okay, I think we’re I’m at a good spot. I don’t know about you guys if y’all need anything else on my end. So.
345 00:41:08.070 ⇒ 00:41:10.270 Amber Lin: Feel pretty good about it, Annie. Anything from you.
346 00:41:11.210 ⇒ 00:41:16.979 Annie Yu: No, that’s pretty much it. And I I know that, Brian, you will send me another.
347 00:41:17.320 ⇒ 00:41:22.020 Brian Gonzales: Yeah, probably a little bit more detailed. That we can. I want to get you one
348 00:41:22.410 ⇒ 00:41:25.349 Brian Gonzales: good file to say, like, Okay, this is
349 00:41:25.480 ⇒ 00:41:35.219 Brian Gonzales: what you’re looking for for the bot. So I’ll try to get you that same data, but probably a little bit more columns on it, just to give you more what you need.
350 00:41:35.651 ⇒ 00:41:41.079 Brian Gonzales: And then once I send you that, because again, it’s going to be in that raw data form.
351 00:41:41.670 ⇒ 00:41:45.510 Brian Gonzales: reach out to me like, if you need me to kind of cause
352 00:41:46.000 ⇒ 00:41:58.539 Brian Gonzales: some of the handle time has to be calculated in a certain way, and it like you say, if you’re not a data person with Kpis cause you don’t necessarily gotta be a data person. You. If I’ve just no kpis with call Center kpis.
353 00:41:59.045 ⇒ 00:42:09.909 Brian Gonzales: I’d be happy to work with you. And just hey, this is how it works just to give you a basic breakdown. So that way we can teach the bot like this is how you calculate it, and then boom! Cause
354 00:42:10.020 ⇒ 00:42:18.879 Brian Gonzales: I can teach somebody to do to do that stuff. But I can’t talk to a bot like you can like or program the bot. So I think that’s where we’ll come hand in hand.
355 00:42:19.390 ⇒ 00:42:20.170 Brian Gonzales: So.
356 00:42:20.510 ⇒ 00:42:37.890 Annie Yu: Okay, okay? And and just for that data, can you? Also, I know that we already have time creation. If there is a column that means like time, like ending like end time. That will be. I think that will be helpful, too, for that timestamp. Join thing.
357 00:42:39.750 ⇒ 00:42:41.009 Brian Gonzales: See what I can find on it.
358 00:42:43.150 ⇒ 00:42:43.950 Brian Gonzales: Cool.
359 00:42:44.220 ⇒ 00:42:56.450 Amber Lin: Yeah. And I’ll let you guys schedule whatever meetings you need. I feel like, I can. I can sit out and just get the updates don’t have to be in meetings the whole day. So if.
360 00:42:56.450 ⇒ 00:42:59.713 Brian Gonzales: It’s a slacker amber such a I’m kidding.
361 00:43:00.190 ⇒ 00:43:03.106 Amber Lin: I will avoid all the work that I can.
362 00:43:03.430 ⇒ 00:43:09.834 Brian Gonzales: Hey, that’s something. Yeah, I avoid like people all I can. So I could do work. So I’m the opposite. So.
363 00:43:11.342 ⇒ 00:43:16.050 Annie Yu: Do you guys have a stand up? Is that is that correct?
364 00:43:16.362 ⇒ 00:43:20.030 Annie Yu: The stand up is mostly my meeting with Janice. Okay, okay, okay.
365 00:43:20.030 ⇒ 00:43:29.050 Amber Lin: The bot testing. And I like Miguel in case you don’t really need to join that now anymore. But if we need any updates, we’ll send it in slack. We’ll figure that out.
366 00:43:29.200 ⇒ 00:43:30.920 Annie Yu: Alright! Alright! Thank you.
367 00:43:30.920 ⇒ 00:43:32.540 Amber Lin: Thank you guys. And thank you.
368 00:43:32.540 ⇒ 00:43:33.470 Brian Gonzales: Appreciate you.
369 00:43:34.430 ⇒ 00:43:35.390 Brian Gonzales: Bye, guys.
370 00:43:35.390 ⇒ 00:43:36.460 Amber Lin: Bye.