Meeting Title: ABC | Biz KPI Review Date: 2025-05-01 Meeting participants: Annie Yu, Amber Lin, Janiecegarcia, Yvetteruiz
WEBVTT
1 00:00:49.040 ⇒ 00:00:50.400 Amber Lin: Hi! There!
2 00:00:50.820 ⇒ 00:00:51.880 YvetteRuiz: Hi.
3 00:00:52.140 ⇒ 00:00:53.220 Annie Yu: Hello!
4 00:00:53.510 ⇒ 00:00:55.919 Amber Lin: Hi, different. Background, today.
5 00:00:55.920 ⇒ 00:00:57.730 YvetteRuiz: Don’t do that.
6 00:00:58.150 ⇒ 00:00:58.770 JanieceGarcia: And.
7 00:00:59.320 ⇒ 00:01:00.140 YvetteRuiz: Hmm!
8 00:01:01.200 ⇒ 00:01:03.079 YvetteRuiz: Why is this not working.
9 00:01:04.209 ⇒ 00:01:04.949 JanieceGarcia: -Oh.
10 00:01:07.580 ⇒ 00:01:08.350 Amber Lin: You participate.
11 00:01:08.350 ⇒ 00:01:11.250 JanieceGarcia: I don’t think she meant to do that. She might be asking me to come over there.
12 00:01:15.960 ⇒ 00:01:21.528 JanieceGarcia: How are y’all? No, I’m in Austin today, and I was in a routing meeting, and I was like, Oh, my gosh! I gotta get to my 11 o’clock.
13 00:01:21.720 ⇒ 00:01:24.670 Amber Lin: Oh, I see! I see!
14 00:01:24.990 ⇒ 00:01:27.740 JanieceGarcia: So I just didn’t move where I was.
15 00:01:29.550 ⇒ 00:01:32.309 Amber Lin: Oh, well, Denise, your brains are so cool.
16 00:01:32.520 ⇒ 00:01:33.330 JanieceGarcia: My, what.
17 00:01:33.540 ⇒ 00:01:34.660 Amber Lin: Your rings.
18 00:01:35.640 ⇒ 00:01:36.570 JanieceGarcia: Oh, thank you!
19 00:01:39.060 ⇒ 00:01:40.719 Amber Lin: Yeah, lovely, but.
20 00:01:40.720 ⇒ 00:01:42.010 YvetteRuiz: Hello! How are you?
21 00:01:42.010 ⇒ 00:01:43.160 Amber Lin: Room together.
22 00:01:43.380 ⇒ 00:01:44.020 YvetteRuiz: Huh!
23 00:01:44.180 ⇒ 00:01:46.120 Amber Lin: Are you guys in the same room together.
24 00:01:46.680 ⇒ 00:01:48.839 JanieceGarcia: No, we were in 2 different meetings.
25 00:01:48.840 ⇒ 00:01:49.500 Amber Lin: Oh, yeah.
26 00:01:51.330 ⇒ 00:01:54.770 YvetteRuiz: Yeah, are you guys.
27 00:01:54.770 ⇒ 00:01:55.857 Amber Lin: It’d be good.
28 00:01:56.220 ⇒ 00:01:57.200 YvetteRuiz: Good.
29 00:01:57.200 ⇒ 00:02:03.580 Amber Lin: Day. So it’s already like my week is sort of trickling off ready.
30 00:02:03.580 ⇒ 00:02:09.580 Amber Lin: So I’m very happy about that. The last 3 days has been very, very long.
31 00:02:10.052 ⇒ 00:02:17.139 YvetteRuiz: It’s coming down, coming down. I owe you a couple of responses, and I was.
32 00:02:17.140 ⇒ 00:02:20.510 Amber Lin: That’s okay, like, okay.
33 00:02:20.740 ⇒ 00:02:26.860 Amber Lin: yeah, I just send my emails just as a placeholder. So you just feel free to look at them anytime.
34 00:02:27.040 ⇒ 00:02:30.389 YvetteRuiz: Yeah, no, I have them. I have them placed. And just that I have to.
35 00:02:30.800 ⇒ 00:02:46.116 YvetteRuiz: I need a moment to process stuff. So like I, there’s so much stuff that I’m I’m most. I’m dealing with a lot. So like, I’m like, Okay, wait, I have a plan. I need to process all this, and so I just need that moment to set aside so I can get back to you
36 00:02:46.390 ⇒ 00:02:49.479 YvetteRuiz: fully. Let me send.
37 00:02:49.730 ⇒ 00:03:01.209 Amber Lin: I made sort of a agenda for today. So I just remember what stuff we needed to talk about. So some dashboard stuff
38 00:03:03.230 ⇒ 00:03:09.279 Amber Lin: Annie will help us go over. And then there’s a few other things
39 00:03:10.090 ⇒ 00:03:25.170 Amber Lin: that I kind of wanna talk about. So there’s the Api key that I kind of want to check about with how Tim is going, because we we need that to get more data essentially. And then other items that
40 00:03:25.200 ⇒ 00:03:53.429 Amber Lin: I just wanted to remind that you probably already saw my email of Csr office hours, or maybe having a slack channel. And how do we do it for the overflow agents and like standards for the trainer updates, we don’t have to talk about it in this meeting. Maybe we can talk about it after the dashboard stuff, and if Annie is busy and you can just hop off, and then we can go over a little bit of things. If you have some time. If not, we can just do things basic.
41 00:03:54.070 ⇒ 00:03:59.030 YvetteRuiz: Yeah, we, we actually have a 1130 stop a hard stop today. So
42 00:03:59.030 ⇒ 00:03:59.739 YvetteRuiz: I want to focus on
43 00:03:59.740 ⇒ 00:04:05.100 YvetteRuiz: on the Kpis. And then, like, I said, I know that I have action items to get back to you on so
44 00:04:05.220 ⇒ 00:04:08.220 YvetteRuiz: we can. I can reach out. We can meet on that.
45 00:04:08.220 ⇒ 00:04:14.410 Amber Lin: Yeah, totally okay. I’ll let you share share screen, and then we’ll start going through things.
46 00:04:14.410 ⇒ 00:04:15.300 Annie Yu: Hey?
47 00:04:15.850 ⇒ 00:04:19.399 Annie Yu: Oh, not that one. Okay, here.
48 00:04:19.579 ⇒ 00:04:23.840 Annie Yu: So do does everyone have link to this dashboard?
49 00:04:25.240 ⇒ 00:04:26.109 YvetteRuiz: I do. Yes,
50 00:04:26.400 ⇒ 00:04:44.180 Annie Yu: Okay, cool. So right now, we are still manually uploading data. And Brian had to manually provide them. So today we’re just filtering on April 1st to April 24.th that’s what we have so far. And
51 00:04:44.460 ⇒ 00:04:58.949 Annie Yu: I think all starting with the high level. So for this dashboard we have kind of think of them into 3 sections, one’s call kpi. This includes all the calls, whether or not the bot was used.
52 00:04:59.070 ⇒ 00:05:08.240 Annie Yu: and then the second section is bot assisted call. So it’s self-evident. So the calls where bot was used at the time.
53 00:05:08.400 ⇒ 00:05:17.870 Annie Yu: and this 3rd one is more like technical performance for the bot itself. So this can include any usage within or outside of a call.
54 00:05:18.100 ⇒ 00:05:23.550 Annie Yu: Okay? And then and just know that these metrics
55 00:05:23.980 ⇒ 00:05:38.449 Annie Yu: we would love to explore more metrics as we go, or I guess, as we do have that Api integration. But right now we are focusing on the most, I think, straightforward one. And this
56 00:05:38.600 ⇒ 00:05:44.560 Annie Yu: will be, and should be evolving, and so welcome any feedback, or if there’s any
57 00:05:44.840 ⇒ 00:05:53.199 Annie Yu: kpi, we should prioritize on getting that would be also really helpful to know. So
58 00:05:54.040 ⇒ 00:06:08.689 Annie Yu: from the top line we have just these summary tiles where we can see the total cost average handling time. And this is in minutes also average queue. Wait time. And I saw that it’s usually pretty low. So I
59 00:06:08.830 ⇒ 00:06:19.399 Annie Yu: kind of adjusted in seconds, and then the call, call back rate this one. I’m now calculating it based on cause. We have a type.
60 00:06:19.920 ⇒ 00:06:27.909 Annie Yu: a type data field. So I’m just using the course that are labeled as Callback.
61 00:06:28.060 ⇒ 00:06:41.260 Annie Yu: divided by all the costs. But I think I’m not sure if this 1% is realistic, or that’s the right way to calculate. So we can also like talk about that later. And then.
62 00:06:41.410 ⇒ 00:06:42.360 YvetteRuiz: Down below.
63 00:06:42.360 ⇒ 00:06:47.480 YvetteRuiz: Sorry you said. I’m sorry you said total amount calls divided by what.
64 00:06:48.010 ⇒ 00:06:54.909 Annie Yu: Amount of costs that were labeled as callback, divided by all the calls.
65 00:06:55.980 ⇒ 00:06:56.660 YvetteRuiz: Gotcha.
66 00:06:56.660 ⇒ 00:07:04.570 Amber Lin: Yeah, I just don’t think maybe not. Every of the callback calls were actually labeled as Callback, because called.
67 00:07:05.430 ⇒ 00:07:20.019 Amber Lin: we wouldn’t know if it’s a call back. And one thing I want to point out here is that this is this should be all the calls, as you can see. Further down here by queue name, you’ll see that any. So you can zoom in a little bit. This is.
68 00:07:20.110 ⇒ 00:07:37.430 Amber Lin: there’s different departments. So there’s reception takes up a really big part of what we really want to look at, for our department is residential. Pest and then there’s a few smaller pest things of residential pest. Cb, but
69 00:07:37.600 ⇒ 00:07:50.899 Amber Lin: that’s what we will want to focus on, for when we look at the different metrics, because I I would imagine that receptions call time or handling time would be a lot shorter.
70 00:07:50.900 ⇒ 00:08:02.778 YvetteRuiz: It’s very. It’s much shorter. Yeah. So reception is just where everything starts and they disturb, they distribute the calls to the other team. So really, we just wanna look at the the queue, the trade services.
71 00:08:03.100 ⇒ 00:08:13.180 Annie Yu: Oh, yeah, so good. Call amber. Then that’s maybe filter on. Let’s use this queue name as a filter. And I saw there are.
72 00:08:13.500 ⇒ 00:08:14.370 Annie Yu: wait.
73 00:08:15.890 ⇒ 00:08:23.069 Annie Yu: Okay, queue name. And then pest! I saw there are a few, so should we just select all of them, for now.
74 00:08:23.830 ⇒ 00:08:26.220 YvetteRuiz: Yeah, you can go ahead and select all of them, for now it’s.
75 00:08:26.220 ⇒ 00:08:29.410 JanieceGarcia: Maybe unselect in house press, though Yvette.
76 00:08:29.410 ⇒ 00:08:34.650 YvetteRuiz: Yeah, so well, right now go back to the past, please. I’m sorry.
77 00:08:35.799 ⇒ 00:08:36.959 Annie Yu: We test.
78 00:08:36.960 ⇒ 00:08:44.290 YvetteRuiz: Oh, my phone’s my laptop’s gonna die. So Kim free passed Kimphrey. Cb.
79 00:08:45.108 ⇒ 00:08:55.079 YvetteRuiz: res pest backline. No, so I only want Kim free pest, kim, free pest callback, residential pest, residential callback.
80 00:08:55.490 ⇒ 00:08:55.930 Annie Yu: Hmm.
81 00:08:55.930 ⇒ 00:08:56.440 YvetteRuiz: Loved.
82 00:08:58.030 ⇒ 00:08:58.700 JanieceGarcia: There you go!
83 00:08:58.700 ⇒ 00:09:06.490 YvetteRuiz: Yep, that should be it, since that’s the only queues that we’re working with right now.
84 00:09:06.910 ⇒ 00:09:07.700 Annie Yu: Okay.
85 00:09:09.830 ⇒ 00:09:11.000 Annie Yu: Much.
86 00:09:11.630 ⇒ 00:09:14.209 Annie Yu: Nice so here we have the app.
87 00:09:14.210 ⇒ 00:09:14.830 YvetteRuiz: Changing.
88 00:09:14.830 ⇒ 00:09:28.604 Annie Yu: Billing time over time, and Max handling overtime, and my thinking is always compared these 2 together. So if we see a spike here that might be just because one really long call that’s causing it.
89 00:09:29.520 ⇒ 00:09:38.729 Annie Yu: and then, yeah, this is pretty straightforward now, so we can see the count of calls by queue name, and then there’s offer action.
90 00:09:39.210 ⇒ 00:09:51.050 Annie Yu: And this one I also see that it’s we can see, like accept it here, accept it and offer timeout. So my thinking is in the future, we can think of how to really categorize them. If.
91 00:09:51.050 ⇒ 00:09:51.730 YvetteRuiz: Yeah.
92 00:09:51.730 ⇒ 00:10:00.030 Annie Yu: Because I feel like this is way too long. And then it doesn’t really make sense to have that in one category. But we can refine this.
93 00:10:00.460 ⇒ 00:10:04.329 Annie Yu: So that’s that’s the high level of the call. Kpis.
94 00:10:04.640 ⇒ 00:10:05.020 YvetteRuiz: We can.
95 00:10:05.020 ⇒ 00:10:07.660 Annie Yu: Move on to the bot assisted calls.
96 00:10:08.070 ⇒ 00:10:09.419 Annie Yu: So here we
97 00:10:09.560 ⇒ 00:10:16.399 Annie Yu: do have very similar kpis, but we also have some extra ones from the bot itself.
98 00:10:17.180 ⇒ 00:10:21.390 Annie Yu: so we can see it. Within this period we have this
99 00:10:21.620 ⇒ 00:10:27.880 Annie Yu: number of calls that were bot assisted, and then this is in.
100 00:10:27.880 ⇒ 00:10:39.300 YvetteRuiz: So once again, going to bot assisted. That means that somehow, somewhere, we’re tying that phone number that the Csr had and then with the bot at that same time.
101 00:10:39.910 ⇒ 00:11:09.820 Annie Yu: Yes, and that was that took us a while to figure out just because our bot exchange records is not based on a phone start or end time is is that one timestamp when people hit enter for their question. So we had to kind of get the start time and then end time of a call, and then the participant of that call, and then tie those in. So when this person within this timeframe use the bot, call, then no.
102 00:11:10.380 ⇒ 00:11:14.510 YvetteRuiz: Chat that will fall under that call sheet. Okay?
103 00:11:15.070 ⇒ 00:11:35.109 Annie Yu: And then the bot was designed to use. Oh, by the way, so we filter on those calls. So 20% of these calls used. Oh, by the way, and doesn’t mean that they accepted accepted necessarily. And then average execution time of the bot
104 00:11:35.550 ⇒ 00:11:45.349 Annie Yu: thumbs up rate. And this sums up rate. Just note that it doesn’t mean 79% of these calls has thumbs up, but more so like
105 00:11:45.520 ⇒ 00:11:53.769 Annie Yu: over the people who click either thumbs up or down. 79% of the people click on the thumbs up
106 00:11:54.810 ⇒ 00:12:02.140 Annie Yu: average handling time. I think this one is also where we are.
107 00:12:02.640 ⇒ 00:12:11.529 Annie Yu: We’ve been talking about like is average handling time. The the best way for us to track for the bot assisted calls.
108 00:12:13.070 ⇒ 00:12:20.169 Annie Yu: The thought there is. If we could get like average holding time that would probably more directly indicate
109 00:12:22.760 ⇒ 00:12:27.600 Annie Yu: if the the bot actually helps that searching time or not.
110 00:12:27.780 ⇒ 00:12:28.420 YvetteRuiz: Yeah.
111 00:12:28.420 ⇒ 00:12:29.200 Annie Yu: Yeah.
112 00:12:29.540 ⇒ 00:12:44.870 YvetteRuiz: Yeah, no, you’re. You’re totally right about that right? Cause. It’s it’s how much, how much hold time, because we do see that we do see that that becomes excessive. I do. We also do want to look at ht in time. You know what I mean like, what has that done
113 00:12:45.420 ⇒ 00:13:00.340 YvetteRuiz: compared to past years. Now that we have the AI bot, you know what I mean. One is waited, you know, whole time for sure, but then the average handle time. Are we more efficient with our phone calls because we have those answers right there? And is that Ht, going down.
114 00:13:00.960 ⇒ 00:13:14.650 Annie Yu: That’s a good call. Yeah. So that’s actually a good point. Yeah. So we can compare you mean, like all the call Ahc, for all the calls, and maybe compared to last year or last quarter in that sense. Okay.
115 00:13:14.650 ⇒ 00:13:18.200 YvetteRuiz: Yeah, because I could go in there. And I know, like, for instance.
116 00:13:18.350 ⇒ 00:13:24.729 YvetteRuiz: I had like right now, I just use right now I had these new hires that came in. I hired them right
117 00:13:24.730 ⇒ 00:13:49.530 YvetteRuiz: normally. That’s what drives up. My, T, because they’re the ones that have a little bit more struggles with. So if I know, like last year I had, this was the Ht. I know the reasons kind of, but then I compare them to this year when I brought them up. Is there a difference there? So that that’s really telling me a story right there, along with your to your point. I, the whole time is something that we do want to incorporate as well.
118 00:13:49.930 ⇒ 00:13:51.090 Amber Lin: Yeah, I’m not got it go
119 00:13:51.090 ⇒ 00:14:19.600 Amber Lin: much sense. And I was talking with Annie yesterday when we looked at is the problem with average handling time is that we don’t know what’s making it long or what’s making it short. We don’t know who is making the call. Maybe the people who are using the bot right now is either really experienced, like Janice, or either very unexperienced, like joy, and both of them on these spectrums we expect them to have longer times, because either the problem is more complex or they just don’t have enough knowledge.
120 00:14:19.760 ⇒ 00:14:21.010 Amber Lin: And so
121 00:14:21.190 ⇒ 00:14:34.999 Amber Lin: having those factors into, why is the handling time 8 min here versus any. If you scroll up for the pest that we selected, it’s right now. It’s at 4.5 min, but that’s all of the pest calls. So we.
122 00:14:35.000 ⇒ 00:14:35.660 YvetteRuiz: Great.
123 00:14:35.660 ⇒ 00:14:38.899 Amber Lin: I think something we could also do is to look at
124 00:14:39.670 ⇒ 00:14:52.396 Amber Lin: what are we? What type of calls that we’re actually having? And what does it mean when, as you talked about too like maybe comparing to last year at the same time with the same or same
125 00:14:52.990 ⇒ 00:14:57.369 Amber Lin: people, composition like call type composition would be a better comparison.
126 00:14:59.050 ⇒ 00:15:15.789 YvetteRuiz: Yeah, no, for sure. Amber and and I and I like what the direction to join on the type of call, because there is a level that we we track with. Deposition codes right? And in pest, particularly, we
127 00:15:15.980 ⇒ 00:15:20.230 YvetteRuiz: when Brian shares the data with me on some of the deposition calls like.
128 00:15:20.660 ⇒ 00:15:27.950 YvetteRuiz: how much time did an agent spend on a status? Right? So one of them is called customer escalations.
129 00:15:27.950 ⇒ 00:15:52.749 YvetteRuiz: and we see that like a lot a lot. And so for me, one of the things that I go in there and I dial down is like, Okay, we’re gonna have to start listening to those phone calls. We’re gonna have to start figuring out like, what exactly, are those customer escalation phone calls like, what is it? Is it truly a customer escalation call? Or is it something that the the agents not fully trained on? But yeah, that’s the type of information that’s gonna be super
130 00:15:52.750 ⇒ 00:15:57.729 YvetteRuiz: impactful. To have on on to be able to see some of those
131 00:15:58.350 ⇒ 00:16:00.979 YvetteRuiz: type of cause. I’m sorry I lost my train of thought. There.
132 00:16:01.990 ⇒ 00:16:02.540 JanieceGarcia: Yep.
133 00:16:04.400 ⇒ 00:16:16.150 Amber Lin: That’s awesome. I will think about all these things that we can do, and once we have the Apis, I think it’ll be it’ll be great to also work with Brian to have these 2. Do you guys have dashboards right now
134 00:16:16.340 ⇒ 00:16:20.980 Amber Lin: for internal? Or is it just a spreadsheet for all these. All this stuff.
135 00:16:22.020 ⇒ 00:16:25.270 YvetteRuiz: We have a. We have spreadsheets, and we have dashboards.
136 00:16:25.270 ⇒ 00:16:26.150 Amber Lin: Okay. Then.
137 00:16:26.150 ⇒ 00:16:29.070 YvetteRuiz: Through through 8 by 8. Yes.
138 00:16:29.070 ⇒ 00:16:29.780 Amber Lin: Sounds good.
139 00:16:30.040 ⇒ 00:16:52.510 YvetteRuiz: Yeah. But there’s some stuff. Just so you guys know that we’re still kind, we’re we’re still building it wasn’t until recently that our data team was given access. It’s like we’re trying to give access to you guys on a lot of the stuff through 8 by 8. So right now. I mean, we’re asking all the questions right now that you guys are asking is like, can this dashboard provide me?
140 00:16:52.510 ⇒ 00:17:07.050 YvetteRuiz: You know, the type of calls that are coming in everything that we’re talking about right now. So that’s kind of what we’re learning. So you’re absolutely right, Amber, once you guys are able to get the Api and automatically integrate with that. Then there’s so much more other data that we can start pulling out of there.
141 00:17:09.010 ⇒ 00:17:10.890 Amber Lin: Totally. Do you think
142 00:17:11.332 ⇒ 00:17:21.970 Amber Lin: Brian is a good person to invite to these calls as well? Is he sort of helping on the data team. Or should we invite David instead? Because this is very high level stuff.
143 00:17:22.400 ⇒ 00:17:23.630 YvetteRuiz: Yeah, I think
144 00:17:23.760 ⇒ 00:17:47.759 YvetteRuiz: I think it’s gonna be David that needs to be part of this, because he’s the one that I work with directly. Every time, like when, when I’m asking the questions as far as like, Hey, here’s the data that I want to see, you know, here’s the deposition codes that we want to get because right now, we’re working a lot with speech analytics. Because of this very reason, I want to know the exact phone calls that come in. So yeah, it’s gonna be David.
145 00:17:48.290 ⇒ 00:17:52.740 Amber Lin: Okay, I’ll coordinate with him to invite him to these as well.
146 00:17:53.180 ⇒ 00:17:56.230 YvetteRuiz: And I’m make him aware when I talk to him.
147 00:17:56.230 ⇒ 00:17:57.240 Amber Lin: Sounds good.
148 00:17:59.050 ⇒ 00:18:03.070 Annie Yu: No, I think that makes sense. So we can be
149 00:18:03.190 ⇒ 00:18:08.300 Annie Yu: kind of aligned on how to categorize the type of data. And then
150 00:18:08.570 ⇒ 00:18:13.989 Annie Yu: what metrics to to calculate, and the formulation, and all that. So I think that’s great.
151 00:18:14.250 ⇒ 00:18:39.018 YvetteRuiz: Yeah, I, yeah, this is what this is the very reason that I wanted to have this meeting, because I wanna make sure. You know, I know we’re investing the time. We’re getting all this data, which is great. But what is specifically because this is what is gonna is gonna drive the behaviors, right? Or or I’m gonna be able to go back to my bosses and say, Okay, look, since we in in Incorporated Andy, these are the behaviors. This is the stuff that we’re finding.
152 00:18:45.870 ⇒ 00:18:46.810 Annie Yu: Okay. I’ll.
153 00:18:46.810 ⇒ 00:18:47.370 YvetteRuiz: Yeah.
154 00:18:47.370 ⇒ 00:18:58.579 Annie Yu: Quickly go through this part, too. So very similarly handling time, Max, handling time. But here we also have the average execution time, because we wanna make sure it’s not
155 00:18:58.710 ⇒ 00:19:02.260 Annie Yu: because of a long execution time that’s dragging a call.
156 00:19:02.450 ⇒ 00:19:11.949 Annie Yu: And then we also have count by queue name. Then, on the right hand side. We have that breakdown on, whether the call.
157 00:19:12.110 ⇒ 00:19:14.150 Annie Yu: So think of it like
158 00:19:14.860 ⇒ 00:19:21.899 Annie Yu: you always start with this one and then go into okay within these calls. How many of them used?
159 00:19:22.130 ⇒ 00:19:27.270 Annie Yu: Oh, by the way, so there is that.
160 00:19:27.270 ⇒ 00:19:41.480 Amber Lin: Any quick question. I’m a little bit confused on the used. Oh, by the ways, what does the what does the colors mean like if it’s 20% on residential pest, and then 40% on chem free pest, does it mean like
161 00:19:42.170 ⇒ 00:19:48.409 Amber Lin: total? All these add like, should does? Is it kind of more like a pie chart type.
162 00:19:48.785 ⇒ 00:19:59.679 Annie Yu: Kind of yes. So I would say, you always compare this one column to this one column. So we say, okay, there’s 83%, 83 calls from
163 00:19:59.940 ⇒ 00:20:04.959 Annie Yu: residential pest. And within those 83 calls, we have 22,
164 00:20:05.510 ⇒ 00:20:12.480 Annie Yu: 22.9% that were used, that used. Oh, by the way, and then 77% without.
165 00:20:12.480 ⇒ 00:20:18.349 Amber Lin: So this is not what the oh, by the ways were about, but more so like what?
166 00:20:18.850 ⇒ 00:20:19.759 Amber Lin: What type.
167 00:20:19.760 ⇒ 00:20:20.260 JanieceGarcia: Cold.
168 00:20:20.260 ⇒ 00:20:21.690 Amber Lin: As it was used in.
169 00:20:21.690 ⇒ 00:20:25.100 Annie Yu: Yeah, the distribution of oh, by the way, usage.
170 00:20:25.400 ⇒ 00:20:33.430 Amber Lin: Oh, okay, so it’s not like we had this many. Oh, by the ways about window cleaning or this many. Oh, by the way, it’s about.
171 00:20:35.740 ⇒ 00:20:44.880 Annie Yu: I think the queue kind of indicates that right? So that’s within this queue name. And then we can see how many
172 00:20:45.290 ⇒ 00:20:54.899 Annie Yu: that used, and you were saying like window. So if we are filter on a window queue, then we can also see the breakdown. There.
173 00:20:54.900 ⇒ 00:20:55.230 YvetteRuiz: On!
174 00:20:55.230 ⇒ 00:20:56.999 YvetteRuiz: Oh, oh, no, no, no.
175 00:20:57.660 ⇒ 00:20:59.790 Amber Lin: I think what I mean is that
176 00:21:00.480 ⇒ 00:21:04.019 Amber Lin: what your queue name? Right? Your queue name is kind of
177 00:21:04.660 ⇒ 00:21:11.329 Amber Lin: how we how do we divide it by queue name. So is that the type of Csr like what department they’re working in.
178 00:21:11.330 ⇒ 00:21:31.430 YvetteRuiz: Yes, so the queue defines. Where? Where? What department the Csr is working in. So right now we only pulled up residential pest. So in residential pest. The queues, all the Csrs and residential pest are under residential pest, Kim free, and then the callbacks for both Kim, free and residential pest.
179 00:21:32.510 ⇒ 00:21:33.060 Amber Lin: Okay.
180 00:21:34.300 ⇒ 00:21:39.500 Amber Lin: Sounds good. So I think, Annie, for the oh, by the ways it’s
181 00:21:39.760 ⇒ 00:21:53.199 Amber Lin: so. Our! Oh, by the ways offer a variety of different things, some a lot of times, not even in pass, because the goal of Oh, by the ways is to drive more upsells across the company.
182 00:21:53.620 ⇒ 00:22:01.040 Amber Lin: And I think right now, this queue name one is really helpful to see, hey? For
183 00:22:01.380 ⇒ 00:22:07.506 Amber Lin: maybe we we can have like one overall percentage of
184 00:22:08.810 ⇒ 00:22:19.320 Amber Lin: stuff that used. Oh, by the ways! And then we can have another one of what the oh, by the waves were actually about because it could be about trees. It could be about lawns.
185 00:22:19.320 ⇒ 00:22:20.180 YvetteRuiz: Yeah, could be that.
186 00:22:20.180 ⇒ 00:22:21.229 Amber Lin: But still about.
187 00:22:21.230 ⇒ 00:22:22.400 YvetteRuiz: Very good.
188 00:22:22.400 ⇒ 00:22:23.060 Amber Lin: Yeah.
189 00:22:23.060 ⇒ 00:22:32.379 Annie Yu: So, okay, I’m thinking how feasible that is given. So I think my question is, is there like a certain pattern?
190 00:22:32.750 ⇒ 00:22:36.719 Annie Yu: Maybe, let’s say, like 3 questions that would target
191 00:22:38.118 ⇒ 00:22:47.520 Annie Yu: window that we can extract. So for to calculate this, we have to say, go back to the message output and say.
192 00:22:48.080 ⇒ 00:23:02.180 Annie Yu: If this sentence contains that’s a win, like some some type of keywords, and then categorize those calls into, I think the window that you were talking about. Does that make sense?
193 00:23:04.220 ⇒ 00:23:04.860 Annie Yu: So you’re talking.
194 00:23:04.860 ⇒ 00:23:06.340 YvetteRuiz: About on the phone call.
195 00:23:07.033 ⇒ 00:23:14.240 Amber Lin: I think what Annie means. Is that so? Andy suggests a Oh, by the way, it’s a line of text, and
196 00:23:14.700 ⇒ 00:23:38.470 Amber Lin: we have to categorize that line of text into some category right? And how we do that is, we see if that sentence of Oh, by the way, contains, say, window cleaning, and if it contains window cleaning, then we categorize it in the dashboard as window cleaning. Oh, by the way, count, plus one so.
197 00:23:38.470 ⇒ 00:23:39.220 YvetteRuiz: Right that
198 00:23:39.700 ⇒ 00:24:05.879 YvetteRuiz: right now we’re only doing past right? So I think everything that’s coming up under past is simply just. Oh, by the way, mosquito, from what I see, which is the only oh, by the way, and and probably rodent. So how would we go in there? Because that’s that was the ultimate goal is to make sure it doesn’t matter what queue you’re in. You’re gonna get an oh, by the way, offer, that’s kind of where amber we came up with the
199 00:24:05.990 ⇒ 00:24:11.429 YvetteRuiz: with the bullseye, you know. Just kind of the selection piece of it. Because
200 00:24:11.720 ⇒ 00:24:20.209 YvetteRuiz: how? How are we going to track that you know what I mean, how that’s the struggle that our Csrs have today is like, how do I go in there and incorporate that?
201 00:24:20.420 ⇒ 00:24:24.750 YvetteRuiz: You know what I mean, but right now the only time it’s popping up as oh, by the way, is.
202 00:24:24.950 ⇒ 00:24:28.450 YvetteRuiz: it’s just tied to the division. It seems like.
203 00:24:31.830 ⇒ 00:24:32.680 YvetteRuiz: So? How.
204 00:24:32.680 ⇒ 00:24:33.060 JanieceGarcia: Would.
205 00:24:33.500 ⇒ 00:24:57.360 JanieceGarcia: You, you would literally have to, because yes, I know Yvette definitely wants to go in. It doesn’t matter what queue we’re in like I myself. I’m available to take calls for all of them, but I should be offering an Oh, by the way, on every single call, so am I doing that, and that gets her. The number of oh, by the way, is actually being used breaking it down like you’re saying Amber and going in and trying to figure out each.
206 00:24:57.480 ⇒ 00:25:08.640 JanieceGarcia: you know. Well, what? Oh, by the way, was offered, or what did it go to? What division did it go to to do that, we would have to open it up to where it’s not just the mosquito that Andy is offering.
207 00:25:08.640 ⇒ 00:25:08.980 Amber Lin: Yes.
208 00:25:08.980 ⇒ 00:25:10.730 JanieceGarcia: Would be all of them, and.
209 00:25:10.730 ⇒ 00:25:11.420 Amber Lin: We’re not.
210 00:25:11.420 ⇒ 00:25:12.909 JanieceGarcia: You know we’re not doing that yet.
211 00:25:13.380 ⇒ 00:25:19.199 Amber Lin: Okay. So one of the things the 1st thing I hear is to make sure that we
212 00:25:19.902 ⇒ 00:25:37.899 Amber Lin: that we are opened up to more offers. I did try to do that. I just think there’s something in the prompting that’s stopping it from happening. But I do think we have a list of all the services that we script from the website. And essentially, we’ll do. Oh, by the way, is we just suggest, hey, we have this other service line right?
213 00:25:39.600 ⇒ 00:25:46.649 JanieceGarcia: Yes, and you guys, and I’ll make sure again. We just had new. Oh, by the ways that are actually out there. But I will.
214 00:25:46.650 ⇒ 00:25:47.020 Amber Lin: Okay.
215 00:25:47.020 ⇒ 00:25:47.900 JanieceGarcia: To make sure that.
216 00:25:47.900 ⇒ 00:25:51.730 Amber Lin: Awesome. I would love to have those 2
217 00:25:52.320 ⇒ 00:26:01.580 JanieceGarcia: But that’s gonna be ultimately, if you know, if Yvet says yes, go ahead and and open that up to to all of them, because I know she wanted to focus on the one@firstst
218 00:26:02.240 ⇒ 00:26:03.590 Amber Lin: Yvette, what do you think.
219 00:26:03.940 ⇒ 00:26:11.269 YvetteRuiz: Yeah, I mean that. That’s fine. I’m just kinda curious how that’s all gonna be laid out. Because that’s a lot of stuff.
220 00:26:11.770 ⇒ 00:26:16.459 YvetteRuiz: Yeah, kind of why me and Steven made the decision to go to the
221 00:26:17.560 ⇒ 00:26:21.070 YvetteRuiz: the button. That kind of just shows, you know.
222 00:26:21.070 ⇒ 00:26:21.390 YvetteRuiz: Yeah.
223 00:26:21.390 ⇒ 00:26:22.170 YvetteRuiz: And.
224 00:26:23.520 ⇒ 00:26:41.650 Amber Lin: Totally, and I think, for the button future down the line of we can also start tracking, hey? Did the Csrs actually use it? So we could even have feedback mechanism of once you click the button to suggest that. Oh, by the way, it has another feedback button. Of what did I use it or no.
225 00:26:41.650 ⇒ 00:27:04.839 YvetteRuiz: The other thing, too. And and again, I’m just my mind just goes in all these different directions because we’re talking about integrating more with 8 by 8, right? So like, if we’re integrated with that, you know the speech, the key phrases kind of what Annie was saying. Could we go in there and pull that based off the call. Looks like you guys are already doing that to some extent with the
226 00:27:05.580 ⇒ 00:27:06.970 YvetteRuiz: with the
227 00:27:07.400 ⇒ 00:27:21.679 YvetteRuiz: what was that one that’s tied to it the usage, if they use Andy, if they ask the question. So you would think somehow, somewhere, that we can connect that with the phrase when they’re on the phone call. If they use the if they use the oh, by the way.
228 00:27:21.680 ⇒ 00:27:22.350 Amber Lin: Does that make?
229 00:27:23.190 ⇒ 00:27:27.030 Amber Lin: Oh, so we will filter the transcripts of
230 00:27:27.580 ⇒ 00:27:49.860 Amber Lin: I see that’s really cool, like internally, to give you an update. We’re we’re doing that internally as well. We’re developing internal agents that has ideas of all of the stuff we’re doing has our slack messages, our Zoom transfers, our project management tickets and our github codes, so we can just right now I can ask the bot, hey, what
231 00:27:50.170 ⇒ 00:27:54.920 Amber Lin: did I say? This last meeting? And then kind of goes through the transcript and checks.
232 00:27:55.140 ⇒ 00:27:56.870 Amber Lin: Hey, this is what happened.
233 00:27:57.110 ⇒ 00:28:07.280 Amber Lin: And like. So what I just wanted to say, we know how that can be done, and we are already doing that before, so we’ll see if we can do it with 8 by 8 transcripts.
234 00:28:07.800 ⇒ 00:28:29.481 YvetteRuiz: Oh, okay, okay, yeah. That’s yeah. Cause again, what? 8 by it has all the transcripts in there. But if there was, and somehow, some way you can go in there. I mean that that’ll be perfect, even if we had to go in there and train our agents, you know, to use key phrases, but I would think like anything that says, Oh, by the way, would you like tree? I think we would be able to connect? I’m just. I’m I’m I guess the question is, can we? I would assume so. But.
235 00:28:30.061 ⇒ 00:28:51.480 Amber Lin: Totally, we’ll do some more exploration. Once we have the api set up and we’ll Brian and David, I, think we’re almost at the end of the dashboard. The last part you already know, this is pretty much the same as the other dashboard, just more of a condensed view, and then we also have the you username like usage
236 00:28:52.330 ⇒ 00:28:53.370 Amber Lin: tracking.
237 00:28:54.720 ⇒ 00:28:56.390 JanieceGarcia: Wow! He’s always ahead of me now.
238 00:28:56.390 ⇒ 00:28:59.579 YvetteRuiz: No, I was like looking at joys of a joys. Yeah.
239 00:28:59.870 ⇒ 00:29:01.700 YvetteRuiz: she’s rocking it. I love it.
240 00:29:02.480 ⇒ 00:29:03.230 Amber Lin: Yeah.
241 00:29:05.105 ⇒ 00:29:08.429 YvetteRuiz: Up there. Amy’s moving up.
242 00:29:09.130 ⇒ 00:29:23.630 Amber Lin: Ideally, I want to find a way to track updates in a central doc as well. I’m asking my engineers of How is, where is the best way to do that. So we’ll come back with some updates. I know we want to lead upward for that as well.
243 00:29:24.130 ⇒ 00:29:33.159 YvetteRuiz: Yep, yes. So going back to cause I thought Tim had already released and given everything that you guys needed
244 00:29:33.750 ⇒ 00:29:34.569 YvetteRuiz: is that.
245 00:29:36.434 ⇒ 00:29:57.549 Amber Lin: Last time I know last Thursday we talked. There’s a additional Api key that needs authorization. From him. We did get the original key we were able to get some of the data in. But for that particular key it was not enough information. It was very high level stuff, and not enough for us to do the detailed exploration that we do right now.
246 00:29:58.190 ⇒ 00:30:04.960 YvetteRuiz: So the information that Utam sends you that he send us. That’s not what you need. You need something more.
247 00:30:05.947 ⇒ 00:30:11.079 Amber Lin: Tim should already know. It’s just he needs to authorize one
248 00:30:12.140 ⇒ 00:30:19.889 Amber Lin: Api keys. He already sent it in our slack channel that he’s gonna do that. I just don’t know when he will be able to get back to us.
249 00:30:20.240 ⇒ 00:30:23.844 YvetteRuiz: Yeah, I mean, I’m here with Tim. Right now, I’ll just go give him an edge
250 00:30:24.070 ⇒ 00:30:29.900 Amber Lin: Awesome that all I needed. Yeah, it’s 1130. If you guys have to hop.
251 00:30:29.900 ⇒ 00:30:39.659 YvetteRuiz: Yeah, well, thank you so much for doing that. I mean I I my hope was not to cut the call short. But this was super helpful going through this exercise. I do feel
252 00:30:39.660 ⇒ 00:31:01.840 YvetteRuiz: bringing David into the conversation. Once we get 100% in integrated to evolve, then we can really start connecting the dots. You know what I mean, like, okay, what is it that we want to see? So then that way, we’re all speaking the same language. We all understand the whole process of what we’re we’re trying to. I mean the whole, the the key kpis that we’re trying to get information from. But I really.
253 00:31:02.010 ⇒ 00:31:18.860 YvetteRuiz: I really can get excited about these phrases, because I know, I feel like we can really get more information from the phone calls that can tie to Andy the usage of Andy and some of the other things, especially the oh, by the way, piece of it, that one’s so huge.
254 00:31:18.860 ⇒ 00:31:19.460 JanieceGarcia: Yes.
255 00:31:20.120 ⇒ 00:31:22.749 Amber Lin: Cool. I’m excited about it, too.
256 00:31:23.590 ⇒ 00:31:28.349 Amber Lin: Alrighty! I’ll let you guys go. Thank you for coming to the meeting. I’ll see you tomorrow.
257 00:31:28.800 ⇒ 00:31:29.160 JanieceGarcia: Same thing.
258 00:31:29.160 ⇒ 00:31:29.880 Annie Yu: Thanks, Annie.
259 00:31:29.900 ⇒ 00:31:30.879 Annie Yu: Thank you, Andy.
260 00:31:30.880 ⇒ 00:31:31.200 Annie Yu: One.
261 00:31:31.200 ⇒ 00:31:32.920 Amber Lin: Bye, i.