Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2026-02-12 Meeting participants: YvetteRuiz, read.ai meeting notes, Yvette’s Notetaker (Otter.ai), JanieceGarcia, Steven, Pranav Narahari, Amber Lin, Samuel Roberts, Uttam Kumaran
WEBVTT
1 00:00:19.660 ⇒ 00:00:21.080 YvetteRuiz: It could be.
2 00:01:54.970 ⇒ 00:01:56.100 JanieceGarcia: Oh, it is working.
3 00:01:58.270 ⇒ 00:01:59.220 YvetteRuiz: Hello.
4 00:01:59.990 ⇒ 00:02:00.860 JanieceGarcia: Blur!
5 00:02:03.760 ⇒ 00:02:05.479 JanieceGarcia: I replied back to the email.
6 00:02:05.620 ⇒ 00:02:06.139 JanieceGarcia: Oh, yeah.
7 00:02:06.140 ⇒ 00:02:06.959 YvetteRuiz: I hear you.
8 00:02:07.600 ⇒ 00:02:09.240 JanieceGarcia: No? You can’t hear me?
9 00:02:09.800 ⇒ 00:02:15.089 YvetteRuiz: Are you talking? I’m assuming you’re talking.
10 00:02:15.770 ⇒ 00:02:16.970 YvetteRuiz: Is it me?
11 00:02:17.190 ⇒ 00:02:18.030 YvetteRuiz: Can you hear me?
12 00:02:18.030 ⇒ 00:02:20.390 JanieceGarcia: So, I can hear you, can you hear me?
13 00:02:20.840 ⇒ 00:02:21.590 JanieceGarcia: No?
14 00:02:22.080 ⇒ 00:02:22.550 JanieceGarcia: What?
15 00:02:24.110 ⇒ 00:02:25.529 Steven: I can hear you, Denise.
16 00:02:26.090 ⇒ 00:02:29.219 YvetteRuiz: Oh, it was me. I didn’t have my volume up.
17 00:02:32.560 ⇒ 00:02:34.560 YvetteRuiz: It’s been a day, sorry.
18 00:02:34.670 ⇒ 00:02:37.620 YvetteRuiz: How was the pizza meet… how was the meeting, Steven?
19 00:02:37.950 ⇒ 00:02:40.059 Steven: It’s good. Yeah, all went well.
20 00:02:40.170 ⇒ 00:02:42.409 YvetteRuiz: Did we get complaints on the food?
21 00:02:42.480 ⇒ 00:02:43.260 Steven: No.
22 00:02:43.360 ⇒ 00:02:47.720 YvetteRuiz: Okay, good. Except there were two Supreme with anchovies on them.
23 00:02:48.440 ⇒ 00:02:51.539 YvetteRuiz: Did I put… oh shit, sorry, I don’t know what was on…
24 00:02:51.540 ⇒ 00:02:57.520 Steven: People didn’t… people didn’t like… or I think they ended up beating it, but those… those were definitely the two that were left over.
25 00:02:57.520 ⇒ 00:02:59.150 YvetteRuiz: Oh, okay.
26 00:02:59.380 ⇒ 00:03:14.760 YvetteRuiz: I goofed, because I had… I never closed the order. I was gonna actually order Schlossky’s, a different… and I didn’t close the order, and then I was like, shoot, and I was like, oh wow, it’s gonna have to be pizza.
27 00:03:14.760 ⇒ 00:03:16.090 Steven: It’s fine. Pizza’s good.
28 00:03:16.370 ⇒ 00:03:17.000 YvetteRuiz: It’s good.
29 00:03:17.990 ⇒ 00:03:20.560 Steven: Just no anchovies next time.
30 00:03:22.880 ⇒ 00:03:26.230 Samuel Roberts: What’s wrong with anchovies? I missed the beginning of that.
31 00:03:28.290 ⇒ 00:03:32.010 YvetteRuiz: People didn’t like… people don’t like anchovies in their… on their pizza.
32 00:03:32.010 ⇒ 00:03:37.440 Samuel Roberts: Ugh, I love anchovies on my pizza, but I know I’m only the only one I’m ever around who does want that.
33 00:03:38.180 ⇒ 00:03:39.800 Steven: Shouldn’t been here for lunch today.
34 00:03:42.720 ⇒ 00:03:43.950 YvetteRuiz: Hi, guys!
35 00:03:44.610 ⇒ 00:03:45.900 Amber Lin: Hello!
36 00:03:48.940 ⇒ 00:03:53.859 Amber Lin: Hey, before we kick off, I want to introduce Pranav.
37 00:03:54.160 ⇒ 00:04:03.600 Amber Lin: He’s going to be starting to take on these meetings and handle some of the communication on our team. So, Panav, would you want to introduce yourself?
38 00:04:03.600 ⇒ 00:04:19.369 Pranav Narahari: Yeah, totally. Thanks, Amber. Nice to meet everyone here. Yeah, just as Amber said, kind of gonna join these next few meetings. I know we kind of meet on, like, a pretty regular cadence, like Monday, Thursday, and so, yeah, Amber’s gonna show me the ropes a little bit, and then,
39 00:04:19.579 ⇒ 00:04:23.019 Pranav Narahari: I’ll be… you’ll be seeing a lot more of me in the next couple weeks, so… excited.
40 00:04:24.560 ⇒ 00:04:27.199 YvetteRuiz: Awesome! Well, hello, Prinav.
41 00:04:27.660 ⇒ 00:04:30.170 Amber Lin: And Pranav, you’re also based in Texas, right?
42 00:04:30.560 ⇒ 00:04:33.250 Pranav Narahari: I am. Yeah, I’m actually in Austin, Texas right now.
43 00:04:33.790 ⇒ 00:04:35.660 YvetteRuiz: Oh, okay, cool!
44 00:04:35.660 ⇒ 00:04:38.380 Pranav Narahari: Yeah. Yeah, I should have mentioned that too, yeah, I forgot,
45 00:04:38.380 ⇒ 00:04:41.890 YvetteRuiz: We’re from San Antonio.
46 00:04:41.890 ⇒ 00:04:42.429 JanieceGarcia: Thank you.
47 00:04:42.430 ⇒ 00:04:44.840 YvetteRuiz: We travel. We’re here today in Austin.
48 00:04:45.220 ⇒ 00:04:47.299 Pranav Narahari: Gotcha, gotcha, gotcha.
49 00:04:47.710 ⇒ 00:04:48.480 JanieceGarcia: Welcome!
50 00:04:48.950 ⇒ 00:04:49.620 YvetteRuiz: Right.
51 00:04:49.620 ⇒ 00:04:50.290 Pranav Narahari: Thank you.
52 00:04:52.870 ⇒ 00:05:08.490 Amber Lin: Oh, let’s get started. I know Matt’s not going to be here today, and I know we also have a meeting tomorrow with all the different trainers, so, I won’t cover that too much in depth, so that we don’t,
53 00:05:08.490 ⇒ 00:05:13.660 Amber Lin: Do it twice, but let me pull up my presentation, and we can get started.
54 00:05:20.430 ⇒ 00:05:21.170 Amber Lin: Alright.
55 00:05:21.530 ⇒ 00:05:27.110 Amber Lin: So, I think usage continues to be pretty good. This screenshot is old, but…
56 00:05:27.500 ⇒ 00:05:30.839 Amber Lin: Here is the report for this week.
57 00:05:31.350 ⇒ 00:05:42.919 Amber Lin: So you can see mechanical has still up there. Make this bigger for y’all. And then, for example, under Mechanical.
58 00:05:43.070 ⇒ 00:05:52.370 Amber Lin: We have Clarissa’s doing really well, Heaven’s doing really well, and then Susie, they’re all, up there in usage, and I think we see…
59 00:05:52.530 ⇒ 00:06:07.670 Amber Lin: an overall increase, and not just heavy users using it 60 times per week, and other people not using it at all, so I think the whole team is overall using it more. I think same with past.
60 00:06:08.070 ⇒ 00:06:13.470 Amber Lin: Right here, I think Sonya’s still using it a lot, and Eloy is also using it a lot.
61 00:06:13.580 ⇒ 00:06:28.219 Amber Lin: So, that’s good. I think next thing I want to point out is we have a few people, I think some of them are new, so we’d love to get their email so that we get to classify them into their departments.
62 00:06:31.500 ⇒ 00:06:39.200 YvetteRuiz: Will do, Amber. I know, I got an email from Shannon, I think, and she put the request. I was actually messing with that this morning.
63 00:06:39.200 ⇒ 00:06:45.100 Amber Lin: Awesome, sounds good. Yeah, let me know once you did that, and we’ll check if the updates go through.
64 00:06:45.870 ⇒ 00:06:46.350 JanieceGarcia: And then…
65 00:06:46.350 ⇒ 00:06:53.000 YvetteRuiz: Hey, Janiece, real quick, Janiece, like, when our new hires start, basically, can we just automatically do that?
66 00:06:53.240 ⇒ 00:06:54.350 JanieceGarcia: If you.
67 00:06:54.350 ⇒ 00:06:54.800 YvetteRuiz: bonus.
68 00:06:54.800 ⇒ 00:07:05.300 JanieceGarcia: show me, Yvette? Because I’m not sure how to get them access, but you and I can go over that, so that way it doesn’t even have to be part of your thing, it’ll just be part of my basic week check-off.
69 00:07:05.860 ⇒ 00:07:08.110 YvetteRuiz: Because I want to incorporate that
70 00:07:08.330 ⇒ 00:07:11.989 YvetteRuiz: Because I was looking on… I was doing onboarding training with Haley yesterday.
71 00:07:12.170 ⇒ 00:07:15.660 YvetteRuiz: And I want to be sure that Andy is part of that.
72 00:07:15.660 ⇒ 00:07:18.440 JanieceGarcia: Yep. No, I was thinking about that, too. You and I…
73 00:07:18.870 ⇒ 00:07:19.989 JanieceGarcia: We’re thinking the same.
74 00:07:20.350 ⇒ 00:07:20.810 YvetteRuiz: Got you.
75 00:07:20.810 ⇒ 00:07:22.479 JanieceGarcia: It’s already on my notes, anyway.
76 00:07:22.780 ⇒ 00:07:23.680 YvetteRuiz: Perfect.
77 00:07:23.950 ⇒ 00:07:29.199 JanieceGarcia: While we’re on that, though, can we also… because I know Eloy…
78 00:07:29.470 ⇒ 00:07:37.819 JanieceGarcia: not part of PEST, so the team, the admin team, the scheduling team, making sure that they’re Under one?
79 00:07:38.130 ⇒ 00:07:43.940 JanieceGarcia: Because, like, Kevin’s still under mechanical, Eloy’s under pest, I don’t know where…
80 00:07:44.480 ⇒ 00:07:55.000 YvetteRuiz: So, what’s the best way to do that, Amber? So, because they used to be in those departments, but we just never changed, I mean, we just never let Amber know, or whoever, so how do we go in there and…
81 00:07:55.000 ⇒ 00:07:55.390 Amber Lin: Yeah.
82 00:07:55.390 ⇒ 00:07:56.859 YvetteRuiz: Can we edit it ourselves?
83 00:07:56.860 ⇒ 00:08:04.330 Amber Lin: Yeah, totally. So it’s the same sheet as where you would add their emails, I think, because these… we were missing emails for these three folks.
84 00:08:04.930 ⇒ 00:08:22.450 Amber Lin: there were some new people that we didn’t add emails for, so if there’s new people, just feel free to add their names in, and I can quickly… I’ll make this a drop-down so that you can just select the right department that they’re in. And then we can go back, you said…
85 00:08:24.130 ⇒ 00:08:30.909 JanieceGarcia: So, like, if we have ones that were coming over from a different team.
86 00:08:30.910 ⇒ 00:08:36.549 Amber Lin: So, for example, here you would say, what is Eloy on right now?
87 00:08:36.729 ⇒ 00:08:40.869 JanieceGarcia: He’s Customer Service Scheduling Team, is what the team’s called.
88 00:08:45.349 ⇒ 00:08:48.869 JanieceGarcia: And there’s… and there’s, 19 of them.
89 00:08:48.979 ⇒ 00:08:50.889 JanieceGarcia: So, I need to go through and update.
90 00:08:50.890 ⇒ 00:08:52.540 Amber Lin: Gotcha, okay, so then we.
91 00:08:52.540 ⇒ 00:08:53.599 JanieceGarcia: And they’re all admins.
92 00:08:53.600 ⇒ 00:08:57.050 Amber Lin: Okay, so we’ve changed that as well.
93 00:08:57.180 ⇒ 00:09:01.479 JanieceGarcia: So once you come here, you can take a look… I can do that.
94 00:09:01.480 ⇒ 00:09:04.799 Amber Lin: Who’s new, who we need to change.
95 00:09:04.990 ⇒ 00:09:06.689 Amber Lin: And then, I think that will…
96 00:09:07.520 ⇒ 00:09:08.849 Amber Lin: I’ll make it easier.
97 00:09:08.850 ⇒ 00:09:09.430 JanieceGarcia: Okay.
98 00:09:10.000 ⇒ 00:09:10.770 Amber Lin: Cool.
99 00:09:15.370 ⇒ 00:09:16.190 Amber Lin: Alright.
100 00:09:17.740 ⇒ 00:09:21.980 Amber Lin: So there’s that, I’ll… I can send you the link to that.
101 00:09:24.250 ⇒ 00:09:25.880 JanieceGarcia: That’s in our.
102 00:09:26.140 ⇒ 00:09:27.819 Amber Lin: Yeah, that’s in the spreadsheet hub.
103 00:09:27.820 ⇒ 00:09:30.320 JanieceGarcia: Spreadsheet. Okay. Where everything is.
104 00:09:30.650 ⇒ 00:09:32.490 JanieceGarcia: Where I added the emails the last time.
105 00:09:32.680 ⇒ 00:09:33.890 Amber Lin: Okay.
106 00:09:36.600 ⇒ 00:09:37.390 Amber Lin: Cool.
107 00:09:39.370 ⇒ 00:09:52.149 Amber Lin: Sam, do you want to talk a bit about the transcript updates? We had some progress there, and then we also got the requirements of what we want to do, so would love if you can share a little bit.
108 00:09:52.150 ⇒ 00:10:01.690 Samuel Roberts: Yeah, so, there’s not a ton to show off, unfortunately, because, it’s not as visual as it might be, but the… I’m…
109 00:10:02.090 ⇒ 00:10:08.630 Samuel Roberts: Long story short, I’m not able to search the API for filtering by inbound versus outbound, because I think last time we saw that there was some…
110 00:10:08.800 ⇒ 00:10:27.239 Samuel Roberts: differences there. What I am able to do is request a bunch of transcripts, and then look the metadata on them, rather. So it’s gonna be a… it’s a little more of a process than just saying, give me the transcripts for these dates. I was, with that, able to find information about queues, channels, the inbound,
111 00:10:27.310 ⇒ 00:10:38.320 Samuel Roberts: So, I started to kind of compile a list of that, but it looks like these ones that I, you know, was fetching were old. I think these are 2021, 2022, so there’s a lot in there, so I gotta…
112 00:10:38.360 ⇒ 00:10:50.669 Samuel Roberts: dial that in a little bit, but I was able to get cues like reception, Respass Austin, Handyman, commercial pass window, so these seem to make sense to me. I didn’t know, is there a master list of these?
113 00:10:50.670 ⇒ 00:10:54.179 YvetteRuiz: Did you… did you need me to send you that? That would be fantastic.
114 00:10:54.180 ⇒ 00:10:55.140 Samuel Roberts: That would be great.
115 00:10:55.140 ⇒ 00:10:56.860 YvetteRuiz: I can go ahead and provide that to you.
116 00:10:56.860 ⇒ 00:11:01.209 Samuel Roberts: Okay, yeah, I don’t… I was hoping that I’d be able to search by those fields, like I said, but…
117 00:11:01.210 ⇒ 00:11:06.080 YvetteRuiz: Now, I’m gonna chat the data team right now and tell them to give you all our cues.
118 00:11:06.450 ⇒ 00:11:12.310 Samuel Roberts: Yeah, perfect. Cause that, yeah, that’ll help me filter out whatever other things might be in there.
119 00:11:12.920 ⇒ 00:11:33.469 Samuel Roberts: But yeah, I mean, it’s… it’s a little bit of a process. I left it running last night when I got offline and came in this morning to see a bunch of transcripts downloaded. And that’s just a small section of them, because I don’t want to do the whole… the big backfill until I get the okay from Tim, because there’s rate limits and stuff, so I want to make sure we run that at the right time, even if we do it in stages.
120 00:11:33.480 ⇒ 00:11:39.280 Samuel Roberts: But it’s… it is coming in, it looks like this data is better than what I was getting before, at least it’s not just…
121 00:11:39.440 ⇒ 00:11:41.030 Samuel Roberts: random,
122 00:11:41.360 ⇒ 00:11:47.480 Samuel Roberts: outbound calls, that might not be what we want. So I’m still trying to figure out the best way to
123 00:11:49.100 ⇒ 00:12:00.049 Samuel Roberts: get them… get the right ones, because right now it’s a kind of a big process to just hit the API and download a ton of them and sort, but that might be the best path forward. I’m waiting to hear back from
124 00:12:00.620 ⇒ 00:12:19.529 Samuel Roberts: Benjamin, I think, at 8x8, I’ve emailed him a couple times. I haven’t heard back yet. He was pretty helpful last time, but I don’t know what the limits are of what the API can really do, because the docs say one thing, he kind of acknowledged that that wasn’t accurate last time, and he knows that there’s problems there, so I think this is kind of the best path forward now that I’ve at least.
125 00:12:20.010 ⇒ 00:12:26.639 YvetteRuiz: I’ll give a… I’ll, I’ll reach out to our, rep for 8x8 and give them a poke, so we can.
126 00:12:26.640 ⇒ 00:12:36.109 Samuel Roberts: Okay, yeah. Yeah, even if they can just poke him to get back to me, it doesn’t need to be a whole, you know, whole little meeting together. Sure. Looking for a little bit more direction from him.
127 00:12:36.110 ⇒ 00:12:36.450 YvetteRuiz: Okay.
128 00:12:36.450 ⇒ 00:12:36.910 Samuel Roberts: Exactly, so…
129 00:12:36.910 ⇒ 00:12:37.699 YvetteRuiz: I can do that.
130 00:12:37.840 ⇒ 00:12:38.430 Samuel Roberts: Thanks.
131 00:12:39.780 ⇒ 00:12:47.659 Samuel Roberts: So yeah, hopefully next week I’ll have a little bit more, to actually show. I can do a little more analysis on the ones I’ve downloaded now that I got the metadata, so I know what I’m looking at, at least, so…
132 00:12:47.970 ⇒ 00:12:48.900 Amber Lin: Yeah.
133 00:12:49.130 ⇒ 00:13:05.309 Amber Lin: Sounds good. And Yvette, I got the initial goals we have for the transcripts, so I think the first two goals of, looking at what they’re missing out and looking at their qualitative skills, we should be able to do based on the text.
134 00:13:05.370 ⇒ 00:13:07.590 Amber Lin: And then…
135 00:13:07.630 ⇒ 00:13:25.189 Amber Lin: And then, I think for the performance and the measurement of how many calls total, and then what’s the booking rate, I think we’ll need to look into that a little bit more, and perhaps we’ll also need to connect it to Evolve to see if a call gets booked.
136 00:13:25.220 ⇒ 00:13:40.239 Amber Lin: But so far, I want to confirm, like, is this the initial goals you want? And for… if something gets booked, will it be very clear in the transcript, or do you have to go into Evolve and see, hey, was this call booked?
137 00:13:42.250 ⇒ 00:13:47.860 YvetteRuiz: Yes, so what you listed out was, yes, everything that we talked about on Monday,
138 00:13:49.010 ⇒ 00:14:07.710 YvetteRuiz: In the call, you should be able to gather that information, Amber, because it’s clearly going to go through all the steps, asking for the information, confirming the date and the time and everything, so you should be able to. I mean, we could cross-reference to it, but I think you’ll get it from the call.
139 00:14:07.830 ⇒ 00:14:13.699 Amber Lin: Awesome. Okay, that’s great to hear, because that will make our… make it faster, because we don’t need to connect to Evolve.
140 00:14:13.700 ⇒ 00:14:14.540 Samuel Roberts: Definitely.
141 00:14:15.450 ⇒ 00:14:17.230 Amber Lin: Cool. Yeah.
142 00:14:17.520 ⇒ 00:14:30.660 Amber Lin: Gonna touch on the zip code stuff a bit. We’re in the process of reconciliating each of the spreadsheets. We started from Lawn, which had the most missing.
143 00:14:30.660 ⇒ 00:14:43.060 Amber Lin: We were able to resolve that, and I believe most of it is matching, except for some managers who don’t have assignments. And then we are currently going through the home improvement sheet to check
144 00:14:43.110 ⇒ 00:15:00.860 Amber Lin: was missing in our assignments. And after that, we’ll go through the pest technicians, and then later on the inspectors, because those have a bit… those are mostly in our zip code database, so we’re just working in order of
145 00:15:01.210 ⇒ 00:15:18.729 Amber Lin: severity. So, I would love to see if the home… sorry, the mechanical team, which is what we did last week, and if the lawn team were able to go ahead and test and see if their questions got answered.
146 00:15:19.410 ⇒ 00:15:36.910 JanieceGarcia: I will say, on the mechanical side, when I spoke to Tara last Thursday, she was starting to test it. We have seen some… she’s sent in a couple, tickets that I’ve seen. It hasn’t been as much when it comes back to who, or what, or how long, or…
147 00:15:36.910 ⇒ 00:15:45.390 JanieceGarcia: what their skills are. So I know the mechanical side’s gotten better. We’ve… we’ve been working with Casey. You can see those transactions in the ticketing system.
148 00:15:45.810 ⇒ 00:15:53.719 Amber Lin: Okay, that will be… that’s… that’s awesome. And so, I think, Juan, we’re able to tell them this week to start testing as well.
149 00:15:53.720 ⇒ 00:15:55.890 JanieceGarcia: Perfect. Okay, I’ll let Patricia know.
150 00:15:55.890 ⇒ 00:15:57.120 Amber Lin: Yeah, great.
151 00:15:57.430 ⇒ 00:16:13.910 Amber Lin: So this is kind of how we do our checks. So we have for each person, the name or their area, and then we go ahead and check if they exist in our database. So we run through them one by one.
152 00:16:16.450 ⇒ 00:16:28.259 Amber Lin: Okay. The last part is about the central doc updates. As you know, we’re trying to look at each of the department’s central docs and trying to reorganize and improve
153 00:16:28.260 ⇒ 00:16:42.760 Amber Lin: So that we have more accurate answers, we have less conflicting answers, especially when asked the same question. So this week, we were able to, one, do a category of the questions asked.
154 00:16:42.920 ⇒ 00:16:48.359 Amber Lin: And do a check of duplicate or conflicting content.
155 00:16:48.540 ⇒ 00:16:53.809 Amber Lin: Pranav, do you want to share a little bit on the question categorization that you did?
156 00:16:55.340 ⇒ 00:17:00.600 Pranav Narahari: Yeah, sure. Let me pull that up real quick, so I don’t miss something.
157 00:17:01.910 ⇒ 00:17:15.910 Amber Lin: Yeah, this is what, Yvette, this is what you wanted to look at, and what we wanted for each of the trainers to see. Oh, for our department, what are they asking about? Because we’ve been only looking at triage tickets, which is what they’re getting wrong.
158 00:17:15.910 ⇒ 00:17:22.039 Amber Lin: But that doesn’t tell us what the CSRs need. That just tells us what we need to improve.
159 00:17:22.550 ⇒ 00:17:23.770 YvetteRuiz: Gary, yes, thank you.
160 00:17:23.770 ⇒ 00:17:25.200 Amber Lin: Yeah, so once we…
161 00:17:25.200 ⇒ 00:17:29.640 YvetteRuiz: Just really quick, Sam, Brian is going to be emailing you all the cues here in a minute.
162 00:17:29.640 ⇒ 00:17:30.760 Samuel Roberts: Okay, great.
163 00:17:30.760 ⇒ 00:17:31.580 YvetteRuiz: Yep,
164 00:17:31.580 ⇒ 00:17:32.100 Samuel Roberts: Thank you.
165 00:17:32.620 ⇒ 00:17:48.330 Amber Lin: Yeah, I think once we fix or confirm, like, the categories of different questions we’re gonna ask, we will eventually put it in the dashboard as well, so that people can go in every week and click around on their own.
166 00:17:49.270 ⇒ 00:17:57.180 Pranav Narahari: Yep. Amber, do you have that, table? I have it up right here, too, but it might be worth sending it over as well, maybe after the meeting.
167 00:17:57.280 ⇒ 00:18:02.770 Amber Lin: Yeah, totally. Do you want to share a screen, and then we can coordinate to send it later?
168 00:18:03.030 ⇒ 00:18:06.470 Pranav Narahari: Perfect, yeah, let me… Let me do that.
169 00:18:21.640 ⇒ 00:18:22.410 Pranav Narahari: Okay.
170 00:18:25.730 ⇒ 00:18:39.609 Pranav Narahari: So, we have 10 different categories here that we found based on just, not from just last week’s data, but from… I think it’s the previous, like, couple months, I believe. And so…
171 00:18:39.970 ⇒ 00:18:47.820 Pranav Narahari: Yeah, these are 10 categories that we felt that were very distinct, but also just, like, was able to encompass the entire dataset.
172 00:18:47.980 ⇒ 00:19:06.740 Pranav Narahari: So, I’d love to get y’all’s feedback on this to see if, like, some of these categories you feel like are not super useful, and maybe you’d rather predefine certain categories. But I thought the data was pretty interesting here. We also were able to look at unique occurrences versus just total, and so…
173 00:19:06.800 ⇒ 00:19:16.409 Pranav Narahari: What we can also do with, like, a future iteration of this is just, like, assess, like, okay, what type of questions are being most frequently asked as well within the specific category.
174 00:19:16.510 ⇒ 00:19:19.670 Pranav Narahari: But, yeah, I think…
175 00:19:19.890 ⇒ 00:19:31.790 Pranav Narahari: probably you guys just getting able to look at these individual categories, and we can discuss this now, too, if you’d like, or after the call via Slack, whatever works, but
176 00:19:32.160 ⇒ 00:19:34.469 Pranav Narahari: Yeah, I thought this was pretty, pretty interesting.
177 00:19:35.180 ⇒ 00:19:35.830 JanieceGarcia: This is…
178 00:19:35.830 ⇒ 00:19:36.520 YvetteRuiz: And…
179 00:19:37.000 ⇒ 00:19:52.370 YvetteRuiz: No, for sure. I mean, yeah, we kind of figured that that top one is going to be every… the zip codes, the inspector, that is the primary one that everyone’s asking questions on, is who covers what area for what zip… what service.
180 00:19:52.500 ⇒ 00:20:04.930 YvetteRuiz: Staff and personal… what personnel inquiry? So, I’m kind of curious about that. So, it looks like a lot of people… I mean, that’s number two, so people are actually asking questions like.
181 00:20:06.030 ⇒ 00:20:12.469 YvetteRuiz: you know, what department? I mean, is that what that’s kind of defining? Like, where do they,
182 00:20:12.880 ⇒ 00:20:15.890 YvetteRuiz: What’s their position, or is that what that is?
183 00:20:16.990 ⇒ 00:20:17.750 Pranav Narahari: I believe so.
184 00:20:17.750 ⇒ 00:20:21.260 Amber Lin: Sample questions we can look at for each category?
185 00:20:21.730 ⇒ 00:20:26.010 Pranav Narahari: I don’t have that set up right now. I do think that’s super useful, though, so, like…
186 00:20:26.160 ⇒ 00:20:37.379 Pranav Narahari: What I’ll also do is, like, rank these, so the second category isn’t necessarily, like, it’s actually probably, like, one of the least frequent ones out of these. Oh, okay, okay, gotcha.
187 00:20:37.950 ⇒ 00:20:41.899 YvetteRuiz: Oh, I’m sorry, I’m looking at the count now. Never mind, okay.
188 00:20:42.730 ⇒ 00:20:46.019 YvetteRuiz: I thought they were 1, 2, 3, okay, so there it is.
189 00:20:46.020 ⇒ 00:20:46.560 JanieceGarcia: And…
190 00:20:46.560 ⇒ 00:20:47.900 YvetteRuiz: 2,000, sir.
191 00:20:47.900 ⇒ 00:20:48.610 JanieceGarcia: I’ve seen…
192 00:20:48.610 ⇒ 00:20:49.720 YvetteRuiz: It’s an offering.
193 00:20:49.720 ⇒ 00:20:58.749 JanieceGarcia: Some of them, Yvette, and I’ve actually sent it back to say, like, why is the CSR asking this? Because it’s not something…
194 00:20:59.330 ⇒ 00:21:02.879 JanieceGarcia: I’ve seen weird questions like that, but I’ve sent it back to the manager.
195 00:21:03.250 ⇒ 00:21:16.970 YvetteRuiz: But I think that’s interesting because, you know, last week, or whenever we met with the trainers, that was kind of the question that came up, if you guys remember, you know, them having to go to Paylocity, the spreadsheet not being kept up to date.
196 00:21:16.970 ⇒ 00:21:29.739 YvetteRuiz: what was the best way for them to find out, because they do ask that. I mean, obviously, I didn’t look at the number, but I just want to make sure, like, are we giving them the right information? What’s populating?
197 00:21:29.740 ⇒ 00:21:42.109 JanieceGarcia: I’m wondering if, too, the staff and personnel inquiries, if that’s, like, who is whose manager, because if that’s the case as well, that would definitely make sense, because you don’t see it as often, but you definitely do see it.
198 00:21:43.280 ⇒ 00:21:46.219 JanieceGarcia: The pricing, cost, and fees…
199 00:21:47.100 ⇒ 00:21:47.930 YvetteRuiz: Serious.
200 00:21:48.600 ⇒ 00:21:54.910 YvetteRuiz: So, interesting. Service availability and offer… what does that say? I’m sorry, it’s very little. Offering?
201 00:21:55.120 ⇒ 00:21:56.069 Pranav Narahari: Offering. Offering.
202 00:21:57.330 ⇒ 00:22:01.899 YvetteRuiz: Well, I’m curious on that. So, service availability…
203 00:22:02.160 ⇒ 00:22:05.909 JanieceGarcia: Is that gonna be, like, in this zip code, do you do this service?
204 00:22:06.690 ⇒ 00:22:08.360 Pranav Narahari: Yeah, I believe so.
205 00:22:08.470 ⇒ 00:22:11.809 Pranav Narahari: I did see quite a few queries about that, so…
206 00:22:12.630 ⇒ 00:22:24.290 Pranav Narahari: I also… I feel like what would be super useful for maybe, like, a follow-up to this is just, like, a description on each category, and then what Amber… like, some sample questions. What I’ll do is just, like.
207 00:22:24.500 ⇒ 00:22:29.659 Pranav Narahari: I’ll pull the most common questions, like the ones that were asked the most for each category.
208 00:22:30.540 ⇒ 00:22:32.869 Pranav Narahari: I think that’ll really help us with this conversation.
209 00:22:33.530 ⇒ 00:22:34.110 JanieceGarcia: Okay.
210 00:22:34.330 ⇒ 00:22:38.000 YvetteRuiz: And customer scenario.
211 00:22:38.590 ⇒ 00:22:44.890 YvetteRuiz: Yeah, you were thinking that, I wonder, customer scenarios and situational? Is that… right?
212 00:22:44.890 ⇒ 00:22:50.059 JanieceGarcia: Yep, that’s what it is. I’m very curious to see what some of these are.
213 00:22:50.350 ⇒ 00:22:51.080 YvetteRuiz: I think you’ll.
214 00:22:51.080 ⇒ 00:22:56.240 Amber Lin: Customer scenarios is probably like, oh, this customer has a certain thing, or this customer.
215 00:22:56.240 ⇒ 00:22:57.570 YvetteRuiz: How do I handle it?
216 00:22:57.570 ⇒ 00:23:07.229 Amber Lin: Yeah, it has a very specific situation, or, like, it rained, or it has a pet, it has a pet in some places, like, I think those, like, specific scenarios is…
217 00:23:07.730 ⇒ 00:23:08.330 YvetteRuiz: Yeah.
218 00:23:08.330 ⇒ 00:23:09.210 Amber Lin: That is…
219 00:23:09.540 ⇒ 00:23:23.610 YvetteRuiz: I did a sit-along with a CSR last week, and they do use it for that. I mean, I think they had someone that was asking about lizards, and they’re like, do we do that type of service? But yeah.
220 00:23:24.860 ⇒ 00:23:29.380 JanieceGarcia: And I’ve seen the one, because we’ve had that come up recently, is…
221 00:23:29.680 ⇒ 00:23:35.069 JanieceGarcia: Customers calling in because they have a lot of cats around their home. How do we handle…
222 00:23:37.850 ⇒ 00:23:40.020 YvetteRuiz: Okay, so yeah, this is gonna be some interesting…
223 00:23:40.810 ⇒ 00:23:42.060 JanieceGarcia: Cool. Interesting data.
224 00:23:42.060 ⇒ 00:23:44.459 YvetteRuiz: Service definitions and coverage.
225 00:23:46.240 ⇒ 00:23:50.760 YvetteRuiz: See, look, that’s that service definition and coverage.
226 00:23:50.920 ⇒ 00:23:58.100 YvetteRuiz: I know that’s a big one for our new hires, because they don’t know, like, what’s covered,
227 00:23:58.970 ⇒ 00:24:03.499 YvetteRuiz: what does it mean? What is that… I’m assuming the definition is, like, the codes and stuff, I mean…
228 00:24:03.520 ⇒ 00:24:09.349 JanieceGarcia: I think one… 2, 3, and…
229 00:24:09.860 ⇒ 00:24:13.550 JanieceGarcia: Well, I mean, all of them, I mean, they really are, but… Gonna be huge.
230 00:24:13.550 ⇒ 00:24:13.930 YvetteRuiz: Yeah.
231 00:24:13.930 ⇒ 00:24:15.800 JanieceGarcia: For… for new hires.
232 00:24:16.170 ⇒ 00:24:17.640 YvetteRuiz: But then taking it back to the.
233 00:24:18.590 ⇒ 00:24:19.770 JanieceGarcia: Go ahead, Yvette, sorry.
234 00:24:19.770 ⇒ 00:24:31.380 YvetteRuiz: No, I was just gonna say, taking it back to the trainers, and then looking at the documentation that we have, is like, how easy is that being provided to our CSRs? Yep. That’s gonna be interesting to look at.
235 00:24:31.380 ⇒ 00:24:31.820 Amber Lin: Yeah.
236 00:24:31.820 ⇒ 00:24:35.300 JanieceGarcia: And I’m actually surprised that service procedures and how-to
237 00:24:35.890 ⇒ 00:24:42.530 JanieceGarcia: And policies with cancellations and account management, that they’re not higher than… I’m really surprised.
238 00:24:42.810 ⇒ 00:24:54.700 Amber Lin: Yeah, I think really it’s because people kind of know how to schedule a specific thing, the only difference really is, like, oh, the pricing is different, or you select a different time frame.
239 00:24:54.700 ⇒ 00:25:17.169 Amber Lin: And that’s why I think, Janice, when we were creating the new central docs, we asked the trainers, don’t you think this is the same thing? They were like, no, this is different. So this is… maybe this is proof to them that, hey, maybe it is pretty similar. Your guys are not really asking about, this specific thing. So, this is why we did the categorization as a first step.
240 00:25:17.170 ⇒ 00:25:24.849 Amber Lin: Because that helps us look at, oh, we’re gonna look at pricing first, or we’re gonna look at service definitions first.
241 00:25:25.240 ⇒ 00:25:25.580 YvetteRuiz: Yep.
242 00:25:25.580 ⇒ 00:25:35.589 Amber Lin: luckily, those are easier things to change, because they’re just, something equals this. They’re very concrete answers. So that will be our first step.
243 00:25:35.830 ⇒ 00:25:45.489 JanieceGarcia: I’m also thinking, too, like, this goes back to… and this is more of, you know, us on trainers, but are we focusing on the right thing in the central dock?
244 00:25:46.930 ⇒ 00:25:49.190 YvetteRuiz: Yeah, yeah, no, yeah, yeah, that’s what I was…
245 00:25:49.480 ⇒ 00:25:53.270 YvetteRuiz: That’s gonna be very interesting, if we take it back and seeing, okay, what…
246 00:25:53.790 ⇒ 00:26:12.319 YvetteRuiz: what is this providing to our agents right here? And then you have, like, the top 3 right there. I mean, we can attack that really quick and say, okay, we’re asking this a lot, you know, what’s the information and how is it being provided to us? I’m just kind of curious how all that’s kind of laying out for the cleanup purposes and making sure that Andy is…
247 00:26:13.380 ⇒ 00:26:15.949 YvetteRuiz: Yeah, they’re right. Yes, yes.
248 00:26:16.410 ⇒ 00:26:17.390 YvetteRuiz: Listen.
249 00:26:19.080 ⇒ 00:26:20.180 YvetteRuiz: Okay.
250 00:26:21.010 ⇒ 00:26:36.150 Amber Lin: Yeah, and then I’ll share the next task that we were doing. So we were looking at the duplicate on conflicting information in the… specific in the mechanical central doc to start. So let me share…
251 00:26:36.670 ⇒ 00:26:45.030 Amber Lin: what we found… So… For example.
252 00:26:45.180 ⇒ 00:27:01.220 Amber Lin: Let’s take the timeframes as an example. So, we have these different timeframes. They are worded slightly differently, and they’re very similar across the different services.
253 00:27:01.340 ⇒ 00:27:03.080 Amber Lin: So,
254 00:27:03.630 ⇒ 00:27:19.809 Amber Lin: when something gets changed in one section, but not changed in the other, I think that’s where, errors come up, because they really look so similar that it will be hard for Andy to distinguish, like, oh, this is completely different things.
255 00:27:20.700 ⇒ 00:27:23.710 Amber Lin: And so… .
256 00:27:24.950 ⇒ 00:27:31.030 JanieceGarcia: But I feel like we should have… we’re supposed to, I thought, across the board, and Yvette, I could be wrong.
257 00:27:31.030 ⇒ 00:27:31.980 YvetteRuiz: but…
258 00:27:32.360 ⇒ 00:27:37.050 JanieceGarcia: We should have the same timeframes across the board, through all.
259 00:27:37.850 ⇒ 00:27:42.320 YvetteRuiz: We should, but because some of the, like, water quality…
260 00:27:42.520 ⇒ 00:27:51.230 YvetteRuiz: They only have so many technicians covering all the branches, so they’ve got to adjust. I mean, this is going to be one of Bobby’s pain points forever, because we…
261 00:27:51.390 ⇒ 00:28:01.970 YvetteRuiz: it’s just a bad customer experience sometimes when you’re giving them big old windows or different time windows versus standardized time windows. But yeah, to Amber’s point, yeah, it can get…
262 00:28:02.500 ⇒ 00:28:03.170 JanieceGarcia: Nothing.
263 00:28:03.360 ⇒ 00:28:04.530 YvetteRuiz: Yeah, yeah.
264 00:28:04.530 ⇒ 00:28:20.899 Amber Lin: Yeah, and I think if we were to combine it together, then we can say, for water quality, it is different, so it might even be easier for people to remember that, hey, this is the only one that’s different, so that they don’t give the wrong timeframes to the customers.
265 00:28:20.900 ⇒ 00:28:21.520 YvetteRuiz: Yeah.
266 00:28:22.350 ⇒ 00:28:22.870 Amber Lin: Yeah.
267 00:28:23.650 ⇒ 00:28:28.610 Amber Lin: Let’s see.
268 00:28:34.970 ⇒ 00:28:35.760 YvetteRuiz: Quicker.
269 00:28:35.760 ⇒ 00:28:41.999 Amber Lin: Yeah, and also some stuff on the fees, which we saw was a very frequently asked question.
270 00:28:42.060 ⇒ 00:28:52.250 Amber Lin: There’s some different… the fees are really scattered all across the different central docs, because it’s mentioned under…
271 00:28:52.250 ⇒ 00:29:02.729 Amber Lin: almost every section, because there’s some mentioned in examples, some mentioned on its own, some mentioned as part of the scheduling specifications, so…
272 00:29:03.040 ⇒ 00:29:14.539 Amber Lin: I think, really, that’s why Mechanical had so many triage tickets on, hey, it’s giving me the wrong fees, is because we really have it in all sections.
273 00:29:14.540 ⇒ 00:29:15.000 YvetteRuiz: ball.
274 00:29:15.000 ⇒ 00:29:15.779 Amber Lin: nearly impossible.
275 00:29:15.780 ⇒ 00:29:31.680 YvetteRuiz: I think we can clean that up very easy. I mean, obviously, that’s… there’s reason behind it. Every market’s gonna be different, you know what I mean? But I think if we kind of just say, okay, HVAC, I mean, whatever repair, San Antonio Corpus, you know, what is that? I think we can clean that up a lot.
276 00:29:31.680 ⇒ 00:29:34.649 Amber Lin: Yeah, I think that’s one of the things we’re kind of
277 00:29:34.770 ⇒ 00:29:53.210 Amber Lin: going to start with, because it’s the most straightforward one, of… we want to have a one dedicated section for pricing, so that when things change, the trainers know where to update it, instead of, just adding it as a new line, a dog, and then it conflicting with other spaces.
278 00:29:53.600 ⇒ 00:30:01.579 YvetteRuiz: Yeah, and I think it’s because they’ve used training materials somewhere using it, and they use that as… but yeah, we’ve got to clean all that up, but I can see…
279 00:30:01.580 ⇒ 00:30:13.089 Amber Lin: We have it kind of like a table, and then you can have different, you can have it under, see, different zip codes are this price, this is that price, so it’ll be a lot cleaner that way.
280 00:30:14.770 ⇒ 00:30:15.670 Amber Lin: Cool.
281 00:30:15.850 ⇒ 00:30:22.939 Amber Lin: So, I think tomorrow, I’m waiting… I know Mustafa’s still working on…
282 00:30:23.020 ⇒ 00:30:36.799 Amber Lin: an attempt to edit the central doc based on the recommendations, so hopefully we can share that tomorrow when we meet with the trainers. And Pranav, if we can have the sample questions as well for tomorrow.
283 00:30:36.800 ⇒ 00:30:47.180 Amber Lin: Even better if it’s by department, so that we can show the trainers, hey, this is what people are looking for, and this is where your dog is at. So that’s my plan for tomorrow.
284 00:30:47.800 ⇒ 00:31:04.139 YvetteRuiz: Okay, Amber, you’re going to share, that… what we just… you just shared with us, the… the pricing and all that, because I think that’s going to be super helpful for the trainers, because that’s, again, easy cleanup that we can, like, the pricing, the… the timeframes and things like that. You’re going to share that tomorrow?
285 00:31:04.140 ⇒ 00:31:04.820 Amber Lin: Yeah, totally.
286 00:31:04.820 ⇒ 00:31:05.780 YvetteRuiz: Okay, okay.
287 00:31:07.120 ⇒ 00:31:09.370 Amber Lin: Awesome. So that’s all from me today.
288 00:31:10.040 ⇒ 00:31:19.379 JanieceGarcia: Can I ask, too, Amber, how can I get the trainers added for triage tickets to be able to assist, since the managers are the ones that are all in.
289 00:31:21.610 ⇒ 00:31:27.310 Amber Lin: Yeah, if you send me their emails, I would be able to add them.
290 00:31:35.320 ⇒ 00:31:35.760 YvetteRuiz: Hmm.
291 00:31:37.500 ⇒ 00:31:38.940 Amber Lin: I’ll note that down.
292 00:31:41.310 ⇒ 00:31:51.700 YvetteRuiz: I’m really excited about the transcripts. Sam, I already emailed you the information on the queues, so you should get that. Oh, you’re muted!
293 00:31:52.600 ⇒ 00:31:59.319 Samuel Roberts: Yeah, I just saw the email, I opened it up, requested assets, got it immediately, so yeah, I saw them all. That’ll be helpful for filtering out.
294 00:31:59.860 ⇒ 00:32:09.719 YvetteRuiz: Perfect. Just let me know if you need anything. I’ll… right after this call, I’ll send an email to 8x8, just so we can, you know, kind of nudge them on that aspect of it, but I’m really…
295 00:32:10.080 ⇒ 00:32:16.619 YvetteRuiz: I am excited about getting those transcripts. I’ve been wanting to for a minute. Yeah.
296 00:32:17.640 ⇒ 00:32:19.469 YvetteRuiz: Me too? Okay.
297 00:32:19.470 ⇒ 00:32:21.219 Amber Lin: Alright, that’s all.
298 00:32:21.680 ⇒ 00:32:26.690 YvetteRuiz: Alrighty. Well, thank you guys. I hope you guys have a good rest of your day. Amber, we’ll see you tomorrow.
299 00:32:26.690 ⇒ 00:32:29.190 JanieceGarcia: See ya. Thanks, everyone. Have a good one.