Meeting Title: ABC Home and Commercial Weekly Sync Date: 2026-05-08 Meeting participants: Pranav Narahari, JanieceGarcia, YvetteRuiz, Uttam Kumaran
WEBVTT
1 00:00:06.630 ⇒ 00:00:07.290 Pranav Narahari: Hey, Janine.
2 00:00:07.720 ⇒ 00:00:10.329 Pranav Narahari: Sorry, I don’t know what that was. That was weird.
3 00:00:10.710 ⇒ 00:00:15.009 JanieceGarcia: No, you’re good, no worries. Well, I was in it, and then it was, like, it all of a sudden just…
4 00:00:16.670 ⇒ 00:00:21.170 JanieceGarcia: closed, and then so I was trying to get back in, and it wouldn’t let me.
5 00:00:21.550 ⇒ 00:00:23.790 Pranav Narahari: Weird, yeah, I’ve never experienced that before.
6 00:00:25.770 ⇒ 00:00:31.059 Pranav Narahari: If that happens again, I’ll probably just update the link in here. Maybe it’s just… something happened to the link.
7 00:00:32.159 ⇒ 00:00:34.269 JanieceGarcia: Maybe. I don’t know.
8 00:00:38.110 ⇒ 00:00:42.469 Pranav Narahari: Here, I’ll just put the link in the channel.
9 00:00:46.290 ⇒ 00:00:47.430 Pranav Narahari: There we go.
10 00:00:47.660 ⇒ 00:00:48.620 Pranav Narahari: Okay, cool.
11 00:00:49.340 ⇒ 00:00:51.400 Pranav Narahari: How’s your afternoon been? It’s been busy?
12 00:00:52.430 ⇒ 00:00:56.410 JanieceGarcia: Very busy, yes, but all good, all good.
13 00:00:57.030 ⇒ 00:00:59.049 Pranav Narahari: Cool, I’ll try to bring the energy today.
14 00:01:01.450 ⇒ 00:01:08.559 Pranav Narahari: We’ll, it should be pretty dark. There’s a few exciting things, actually, of this week, but,
15 00:01:09.310 ⇒ 00:01:15.319 Pranav Narahari: you know, we can… I don’t want to hold you guys for too long, so we can cut the discussion short if we need to.
16 00:01:17.040 ⇒ 00:01:18.600 JanieceGarcia: Okay. No worries.
17 00:01:19.700 ⇒ 00:01:23.940 JanieceGarcia: I know Yvette was trying to leave, I think, by 3.
18 00:01:23.940 ⇒ 00:01:24.560 Pranav Narahari: Okay.
19 00:01:29.180 ⇒ 00:01:33.100 JanieceGarcia: And she just shut her door. I’m sitting right next to her now, so…
20 00:01:33.460 ⇒ 00:01:34.750 Pranav Narahari: Okay, nice.
21 00:01:38.240 ⇒ 00:01:40.650 Pranav Narahari: Yeah, we can probably breeze through, like…
22 00:01:41.270 ⇒ 00:01:46.089 Pranav Narahari: usage. It seems like since we have, like, those weekly meetings now, where we’re.
23 00:01:46.850 ⇒ 00:01:48.569 Pranav Narahari: Talking specifically with the trainers, like.
24 00:01:48.940 ⇒ 00:01:52.479 Pranav Narahari: We’re going into depth, and even further depth than we usually do there.
25 00:01:52.620 ⇒ 00:01:55.570 Pranav Narahari: So, I’ll give Yvette a quick recap on that.
26 00:01:55.860 ⇒ 00:01:59.950 Pranav Narahari: And then kind of get into more… more interesting stuff.
27 00:02:01.430 ⇒ 00:02:02.890 JanieceGarcia: Cool beans. Awesome.
28 00:02:05.070 ⇒ 00:02:06.090 YvetteRuiz: Hello?
29 00:02:06.520 ⇒ 00:02:07.280 Pranav Narahari: Hey, Yves!
30 00:02:10.000 ⇒ 00:02:12.899 YvetteRuiz: I… I don’t know if Steven’s gonna join us today.
31 00:02:13.150 ⇒ 00:02:18.820 YvetteRuiz: Okay. I told him that I did want to schedule a meeting between me, you, and Hampernov, I just needed to…
32 00:02:19.230 ⇒ 00:02:25.500 YvetteRuiz: finalize some things with you, so… He’s aware of next week, we’ll pin down a date.
33 00:02:25.610 ⇒ 00:02:31.320 Pranav Narahari: Cool, and I know you’re gonna be wrapping things up kind of soon, right? In, like, 25 minutes?
34 00:02:31.320 ⇒ 00:02:34.200 YvetteRuiz: It’s my mom’s birthday, and I need to try to…
35 00:02:34.940 ⇒ 00:02:43.270 YvetteRuiz: I have… I’m just last minute everything. I had to babysit my grandson yesterday, I wasn’t able to do what I needed to do, so anyhow, sorry.
36 00:02:43.270 ⇒ 00:02:45.769 Pranav Narahari: No, no, let’s just make the best use of your time.
37 00:02:47.450 ⇒ 00:03:03.270 Pranav Narahari: Yeah, so, you know, usage, we… Janice and I have talked with, like, the trainers all week, kind of just talking about where things were good, where things were bad. Overall, you know, I’m looking at the usage right now. It says we’re down about 200, which is, like, 20%.
38 00:03:04.300 ⇒ 00:03:17.830 Pranav Narahari: you know, not… we kind of had a lot of commentary with the trainers themselves about, like, why that could be the case. Janice, is there anything you want to… because I don’t want to spend too much time on that, but is there anything kind of you want to talk to about that before we move on?
39 00:03:18.900 ⇒ 00:03:24.490 JanieceGarcia: About the usage? Well, I know, like, today we did with Tiffany and Brenda, their usage is up.
40 00:03:24.890 ⇒ 00:03:25.350 Pranav Narahari: Yep.
41 00:03:25.350 ⇒ 00:03:33.329 JanieceGarcia: Dispatch was still seeing some down, but they were still… everybody is still pretty much using it. The…
42 00:03:34.030 ⇒ 00:03:43.550 JanieceGarcia: tickets, and there were some issues in regards to Lanier, and with what Ashley, was working on, and a mix-up.
43 00:03:43.550 ⇒ 00:03:57.290 JanieceGarcia: But we got through all of that. So everything, even Ashley had stated yesterday, the accuracy was better. Tara even had made a comment yesterday while in Austin that, accuracy was better and Andy seemed faster.
44 00:03:57.300 ⇒ 00:04:00.119 JanieceGarcia: So, hoping the…
45 00:04:00.120 ⇒ 00:04:05.300 YvetteRuiz: I just… sorry, before I… I just wanted to say, I was testing Andy yesterday, and I was, like, at…
46 00:04:06.060 ⇒ 00:04:15.290 YvetteRuiz: I mean, just the time… I was, I was, like, within 2 seconds at the moment, like, a second, like, I was getting some good time, but that was just me, but anyhow, sorry to interrupt you.
47 00:04:15.290 ⇒ 00:04:23.339 JanieceGarcia: No, no, no, you’re fine, and that’s… and I don’t know if, Pranav, you guys have been working on that, because I don’t even realize the time…
48 00:04:23.440 ⇒ 00:04:28.600 JanieceGarcia: Speed, to be honest, I’m gonna be honest about that. I… I don’t, but I’ll sit there and I…
49 00:04:28.780 ⇒ 00:04:42.450 JanieceGarcia: feed questions just as much. And I know dispatch was one that we were really struggling with in regards to getting them to adapt to being able to use Andy. So to hear that from Ashley, and after Ashley and,
50 00:04:42.450 ⇒ 00:04:50.650 JanieceGarcia: Rayanne had met with Pranav yesterday, and all of their tickets were clear. Everything that they have now is new, but like I was telling you, Yvette, already, that
51 00:04:50.890 ⇒ 00:05:07.570 JanieceGarcia: it’s sitting there. It’s not on them. It’s not on the trainers. It’s, they’ve already done what they need to. They’ve already put in their templates, and they’ve had that, so that’s why I was saying we should see them, as of 2 o’clock, be batched for the next day, because those are things that need to change.
52 00:05:07.570 ⇒ 00:05:08.579 Pranav Narahari: Yeah, and
53 00:05:08.970 ⇒ 00:05:26.019 Pranav Narahari: On that point, too, like, things may show as to-do on linear, and if, you know, if you click on the ticket, it’ll show that there is actually some progress. The more accurate measure will be those memos that go out, because that’s where we’re tracking things based on state. So you’ll see certain labels as, like.
54 00:05:26.020 ⇒ 00:05:32.209 Pranav Narahari: State new, some as, you know, state batched, state, completed, things of that nature.
55 00:05:32.210 ⇒ 00:05:49.779 Pranav Narahari: And so, that’s what we kind of use to, like, really assess, hey, where are kind of tickets in the pipeline? But Yvette, also, we don’t want it to be confusing when people go into linear and they see, hey, these things have been in to-do all day, are people touching them?
56 00:05:49.840 ⇒ 00:05:53.020 Pranav Narahari: Casey and I, well, I’m basically just, like.
57 00:05:53.310 ⇒ 00:06:03.099 Pranav Narahari: designing the solution so that even the statuses reflect exactly where they’re at as well. Okay. We’ll figure something that makes the most sense there.
58 00:06:03.100 ⇒ 00:06:16.160 Pranav Narahari: the process is working exactly right, just visually, you know, maybe it looks kind of weird that it still just says to-do instead of, you know, a different status. So, nothing… everything’s working as planned, it’s just, that one tweak we’ll make.
59 00:06:17.060 ⇒ 00:06:20.710 YvetteRuiz: Yeah, no, for sure, and I… and just because, again.
60 00:06:20.770 ⇒ 00:06:34.979 YvetteRuiz: and everybody knows, when I’m, like, in it, everybody can feel it, because I’m kind of asking all kinds of questions, so when I got the last… when I sent that email about the 100 tickets, I got with everybody, and I said, this is unacceptable. And again, I can’t see
61 00:06:34.980 ⇒ 00:06:46.420 YvetteRuiz: where the delay is, so I’m happy that we’re getting that in there, you know what I mean? Because I’m just like, there’s… we just can’t operate like that. I mean, if you’re… if we’re gonna get agent feedback, and it’s gonna be like that, like, we’ve got to start moving.
62 00:06:46.690 ⇒ 00:06:48.600 Pranav Narahari: Totally. Yeah, that makes sense.
63 00:06:48.810 ⇒ 00:06:51.339 Pranav Narahari: And yeah, on the…
64 00:06:51.810 ⇒ 00:06:53.649 Uttam Kumaran: Hello! How are you? Hey, what’s up?
65 00:06:54.070 ⇒ 00:06:54.640 YvetteRuiz: bet.
66 00:06:55.180 ⇒ 00:07:03.109 Pranav Narahari: Yeah, we have 20 minutes, so this, Uten, we’re gonna cut this meeting a little bit short, because, Yvette’s, you’re gonna see your mom, right?
67 00:07:03.110 ⇒ 00:07:04.570 YvetteRuiz: Yeah, yeah, it is.
68 00:07:05.140 ⇒ 00:07:16.170 Pranav Narahari: I also just wanted to talk a little bit, Yvette, about, actually, maybe, Janiece, this is more so kind of conversation that me and you have had, is just, like, there is kind of a lack of feedback
69 00:07:16.170 ⇒ 00:07:28.610 Pranav Narahari: on Indy, it’s probably, like, 10% of usage is actually getting a thumbs up or a thumbs down, and that’s something that we wanted to fix. So, we talked about how do we have, like, some type of automated solution so that
70 00:07:28.610 ⇒ 00:07:39.649 Pranav Narahari: every week, the trainers can at least see what has not been given feedback, and if they have the time, then they can go in and give that manual feedback themselves. Like, just taking the one second to give things a thumbs up.
71 00:07:39.650 ⇒ 00:07:51.580 Pranav Narahari: So those… that full process has been automated, and it’s in the Google Drive. So, Janiece, if you want to take a look, if you go into our shared folder, ABC Home and Commercial.
72 00:07:51.720 ⇒ 00:07:56.000 Pranav Narahari: And then, you’ll see a new folder in there, no feedback reports.
73 00:07:58.520 ⇒ 00:08:03.200 YvetteRuiz: Pranav, in that, I’m so sorry, did I hear that? For the trainers to provide
74 00:08:03.500 ⇒ 00:08:06.029 YvetteRuiz: Thumbs up, thumbs down, and feedback. Is that correct?
75 00:08:06.030 ⇒ 00:08:06.620 Pranav Narahari: God.
76 00:08:06.620 ⇒ 00:08:12.660 YvetteRuiz: That’s… That’s… that’s good. But we’re not… we’re not doing it on our end, is that what we’re seeing?
77 00:08:13.110 ⇒ 00:08:21.200 Pranav Narahari: Yeah, so what we’re seeing is that we’re getting a certain level of thumbs up, thumbs down, but it’s around, like, that 10-20% per week.
78 00:08:21.750 ⇒ 00:08:28.009 Pranav Narahari: And so right now, with our usage, I think, since it’s being split across departments, we’re only getting around, like, 1,000 queries per week.
79 00:08:28.370 ⇒ 00:08:47.530 Pranav Narahari: the remaining, I think people can still be, like, spending the 10-15 minutes per week to just bang out the entire… like, feedback for every single, for every single message. At some point, we’ll get to, like, 10,000 per week, and we’re not gonna… we’re not gonna recommend that you guys give feedback for every single thing, but I think for right now, it would be… it’d be super helpful.
80 00:08:48.400 ⇒ 00:08:50.780 JanieceGarcia: What is the folder called again for Nav?
81 00:08:50.940 ⇒ 00:08:53.290 Pranav Narahari: It’s called No Feedback Reports.
82 00:08:58.010 ⇒ 00:08:59.410 Pranav Narahari: Let me make sure…
83 00:09:00.890 ⇒ 00:09:02.660 JanieceGarcia: Like, I’m not pulling it…
84 00:09:02.860 ⇒ 00:09:03.730 Pranav Narahari: Nope.
85 00:09:04.200 ⇒ 00:09:06.119 Pranav Narahari: Let me reshare it with you.
86 00:09:09.270 ⇒ 00:09:10.640 JanieceGarcia: I’ll star it.
87 00:09:19.330 ⇒ 00:09:22.040 Pranav Narahari: And I’ll also just link it in the chat as well.
88 00:09:28.470 ⇒ 00:09:29.150 JanieceGarcia: No, I haven’t.
89 00:09:29.710 ⇒ 00:09:30.929 Pranav Narahari: Okay, perfect.
90 00:09:33.810 ⇒ 00:09:42.300 Pranav Narahari: Okay, cool. That’s pretty self-explanatory, if,
91 00:09:42.700 ⇒ 00:09:55.150 Pranav Narahari: if you guys have any questions on that, let me know, but I think the trainer should be able to see, like, there’s a… there’s one column for thumbs up feedback, or thumbs feedback for thumbs up, thumbs down, and then if it’s thumbs down, that detailed feedback is required.
92 00:09:58.380 ⇒ 00:10:10.499 Pranav Narahari: Yeah, and then, Yvette, we were talking for the last couple weeks about transcripts. I’m jumping really fast, just because we, you know, we have not that much time. But let me… let me share my screen real quick.
93 00:10:10.860 ⇒ 00:10:13.559 Pranav Narahari: And show you what we found with that.
94 00:10:13.900 ⇒ 00:10:16.700 Pranav Narahari: That initial, just, pull of cancellations.
95 00:10:22.210 ⇒ 00:10:40.730 Pranav Narahari: So, we generated this one report based on just one queue. You know, we can pull from queue, we can pull from tags, we just kind of wanted to show you a POC of the end-to-end process, and so we chose the window queue,
96 00:10:41.130 ⇒ 00:10:46.580 Pranav Narahari: And then, this is what we noticed for this time interval. We’re able to pull all this information here, which I think…
97 00:10:46.580 ⇒ 00:10:47.250 YvetteRuiz: Wow.
98 00:10:47.450 ⇒ 00:10:53.170 Pranav Narahari: exactly what we’re… what Yvette sounds most interesting to you, right? So…
99 00:10:53.280 ⇒ 00:11:08.580 Pranav Narahari: these things are, like you said, billing is already, like, tagging certain things as cancellation, maybe not super accurately. This, we’re doing specific checks to see if things are in the transcript defined, or defining the call as canceled or not canceled.
100 00:11:08.710 ⇒ 00:11:27.669 Pranav Narahari: And so, what we did for this, like, proof of concept, 410 transcripts, we analyzed all of them, and then we found that 25 of them were cancellations. And then we, did a further classification across, and then we found that there’s 15 different types of cancellations, that we found.
101 00:11:27.980 ⇒ 00:11:44.350 Pranav Narahari: And so, that’s all based off of those, individual buckets that, to me, Yvette. So, we gave this AI system, hey, you can put these transcripts into these various buckets, and then let us know what the count is for each one of those.
102 00:11:44.590 ⇒ 00:11:47.149 Pranav Narahari: And so this is what we found.
103 00:11:47.960 ⇒ 00:11:49.120 Pranav Narahari: Some of them…
104 00:11:49.360 ⇒ 00:12:00.239 Pranav Narahari: because of the construction property condition, that was one bucket in there. Financial hardship, medical reasons, etc. All this stuff here.
105 00:12:00.240 ⇒ 00:12:01.580 YvetteRuiz: Oh, wow, okay.
106 00:12:02.690 ⇒ 00:12:16.889 YvetteRuiz: So, I’m sorry, I just want to make sure that I understand. So the reasons codes that I shared with you, that’s… you put those in there, and what AI did is just based off those buckets, they just plugged them in. Is that correct? Is that what I’m understanding?
107 00:12:17.130 ⇒ 00:12:17.740 Pranav Narahari: Yeah.
108 00:12:17.890 ⇒ 00:12:18.580 YvetteRuiz: Okay.
109 00:12:19.180 ⇒ 00:12:26.370 Pranav Narahari: Yeah. And then, I’ll send this over to you as well, because if you really want to read through this entire thing,
110 00:12:26.600 ⇒ 00:12:38.230 Pranav Narahari: we have, you know, links to each one of the transcripts here, and then if you scroll down, it’ll give a little bit of a reason why, for why I defined each transcript as a specific type of cancellation.
111 00:12:38.370 ⇒ 00:12:42.599 Pranav Narahari: And so… Yeah, kind of give this a read, because.
112 00:12:42.600 ⇒ 00:12:43.580 YvetteRuiz: Yes, I do.
113 00:12:44.200 ⇒ 00:12:57.619 Pranav Narahari: of, like, which of these buckets are actually still of concern to you guys. I know you mentioned to me that, like, there’s way too many buckets here, we probably want to condense this further. And on our end, we built out the entire system end-to-end.
114 00:12:57.820 ⇒ 00:13:10.880 Pranav Narahari: in a way where, like, if you want to update the buckets, if you want to let us know to, like, look from different type of queues, different type of tags from 8x8, it’s really straightforward on our end. So yeah, I will… I will send.
115 00:13:10.880 ⇒ 00:13:24.549 YvetteRuiz: Wow, that looks pretty… that looks pretty slick, but yes, that’s what I was gonna ask. If you can send it over, I would love to do a deep dive, and you already know, Pranav, I have a meeting with Bobby and everybody, all the DMs, on Wednesday. It’d be very cool to show them some of this stuff, too, but I wanted to look at it and kind of…
116 00:13:24.680 ⇒ 00:13:30.859 YvetteRuiz: And I’ll reach out to you if I have, actually, other questions as well, but that looks pretty slick.
117 00:13:30.860 ⇒ 00:13:38.320 Pranav Narahari: Cool. Yeah, yeah, I think, this is obviously super information-dense, just because we wanted to show you the justification for everything.
118 00:13:38.470 ⇒ 00:13:39.039 YvetteRuiz: Yeah, thank you.
119 00:13:39.130 ⇒ 00:13:51.890 Pranav Narahari: I guess it’ll be just, like, a one-pager showing you probably just, like, this high-level stuff, like the summary, key reasons why, things of that nature, and just, like, yeah, there’s largest cancellation buckets, things like that.
120 00:13:52.180 ⇒ 00:13:52.660 JanieceGarcia: Yep.
121 00:13:53.890 ⇒ 00:13:58.280 Pranav Narahari: But yeah, and then… Let’s keep cruising.
122 00:13:59.710 ⇒ 00:14:03.999 Uttam Kumaran: Can you share this to the email thread, I think, also, Pranav? I’m sure,
123 00:14:04.680 ⇒ 00:14:06.970 Uttam Kumaran: Steven would love to see this, too.
124 00:14:07.550 ⇒ 00:14:09.799 YvetteRuiz: He’ll be in our office on Wednesday.
125 00:14:09.800 ⇒ 00:14:10.730 Uttam Kumaran: Okay, okay, okay, cool.
126 00:14:13.330 ⇒ 00:14:14.600 Pranav Narahari: Perfect. Yeah.
127 00:14:16.440 ⇒ 00:14:36.130 Pranav Narahari: Yeah, and so one other thing that they were talking about earlier this week was just transcripts in general. Specifically, you know, what is the most pressing? Hold time, cancellations, but then after that, transcripts are where a lot of the… a lot of the answers are in terms of how do we direct CSRs.
128 00:14:36.130 ⇒ 00:14:38.950 Pranav Narahari: And so that needs to be an ongoing effort.
129 00:14:38.950 ⇒ 00:14:44.299 Pranav Narahari: And so… Let me just share my screen again.
130 00:14:44.940 ⇒ 00:14:45.990 Pranav Narahari: I don’t… have you been…
131 00:14:45.990 ⇒ 00:14:55.050 YvetteRuiz: I’m just gonna keep on going to that, Pranav, it is. I had my forecasting meeting this morning, and, you know, I’m looking at staffing, headcount and all, and…
132 00:14:55.490 ⇒ 00:15:05.039 YvetteRuiz: Again, I always go… you can always see, data always points you somewhere, right? And so, pest is the biggest department, and that’s one of the biggest struggles that we have, and…
133 00:15:05.040 ⇒ 00:15:05.880 Pranav Narahari: you know.
134 00:15:05.880 ⇒ 00:15:07.810 YvetteRuiz: The data team is telling me that you’re…
135 00:15:07.910 ⇒ 00:15:20.900 YvetteRuiz: you’re one person heavy, and, you know, the manager’s telling me something different, so then I’m like, you know, we’re looking at it, it’s like, you’ve got their time is between holds and offline time. And then, when you look at their offline time.
136 00:15:21.100 ⇒ 00:15:30.570 YvetteRuiz: for the month of April, they had theirself offline 69 hours, and the reason was customer issue. And I’m, like, dying here, trying to figure out, I’m just like.
137 00:15:31.060 ⇒ 00:15:36.700 YvetteRuiz: what are… what are they… what is… what is that call like? What is it? You know? So, yes, anyhow.
138 00:15:37.210 ⇒ 00:15:42.230 Pranav Narahari: No, I mean, to kind of… I know that struggle, too, like, even internally, too, like…
139 00:15:42.300 ⇒ 00:15:49.589 Pranav Narahari: for me, like, I kind of want to just know, like, what the blockers are, you know? And it’s like, if you just see the tag, hey.
140 00:15:49.620 ⇒ 00:16:05.579 Pranav Narahari: blocked currently because of some customer issue, it’s like, okay, what is that issue? So then I can help figure that out for you. I mean, that’s why, like, I understand this problem pretty well. It’s all… all the gold is in that… in those transcripts.
141 00:16:05.940 ⇒ 00:16:14.500 Pranav Narahari: And so, what we talked about at some point was, like, okay, how do we analyze all the transcripts across all the departments at the same time?
142 00:16:14.660 ⇒ 00:16:34.160 Pranav Narahari: I think the better approach is what we just did with cancellations, is like, let’s take a sub-subsection, and then just show you the insight from that subsection, like cancellations, and then let’s move on to the next one. What that will do is, like, it’ll drive more actionable insights, I think.
143 00:16:34.160 ⇒ 00:16:44.060 Pranav Narahari: We’ll be able to really get into the nitty-gritty. We still kind of analyze 410 transcripts, because, you know, you guys are going through, what is it, like, 3,000 calls a day?
144 00:16:44.060 ⇒ 00:17:00.299 Pranav Narahari: And so it’s still gonna be a large number of transcripts, it’s not just gonna be, like, you know, a couple dozen or something like that, but this makes it more manageable, and I think even on y’all’s end, too, like, for you and for Janice to kind of give instruction to the trainers about where things can be improved.
145 00:17:00.610 ⇒ 00:17:08.699 Pranav Narahari: It’s gonna be a lot more, easy to… easy to pick up on what the insights are from our analysis.
146 00:17:08.700 ⇒ 00:17:17.649 YvetteRuiz: Yeah, now, I mean, because you’ll have the story. I mean, now it’s putting action behind what are we going to do with it, right? So, no, agree 100%.
147 00:17:18.069 ⇒ 00:17:24.679 Pranav Narahari: So, basically, everything that I just talked about is what this is, encapsulating. So, how.
148 00:17:24.680 ⇒ 00:17:27.440 YvetteRuiz: This is what you send me, right? Or is this different, Pranav?
149 00:17:27.710 ⇒ 00:17:29.250 Pranav Narahari: Exact same thing that I sent you.
150 00:17:29.250 ⇒ 00:17:35.569 YvetteRuiz: Okay, alright, because I had already taken a read through it, I just haven’t had a chance to connect with you. And I did, okay, yeah.
151 00:17:35.740 ⇒ 00:17:55.230 Pranav Narahari: Awesome. So, yeah, analyzing transcripts over and over again, kind of just, like, seeing the different themes that we’re noticing, grouping things into buckets, that’s what this is about. So, like, basically what we just did for, cancellations, and then what we’re gonna add on top of that is just, how do we have this on an automated schedule? So, like, how do we send a report every week?
152 00:17:55.410 ⇒ 00:18:08.589 Pranav Narahari: So, that’s kind of what this is about. I’ll scoop back up to up here, which is, I don’t know if we fully got to talk about this event. I know we were kind of running short on our last meeting, either Tuesday or Wednesday.
153 00:18:08.600 ⇒ 00:18:17.969 Pranav Narahari: But this is a dashboard where you can get the context of every single transcript and the usage, the CRM,
154 00:18:18.310 ⇒ 00:18:30.870 Pranav Narahari: whatever else is in 8x8, and then you can ask questions about, you know, what are cancellations looking like? Where has hold time increased the most, in the past, you know, in the past week?
155 00:18:30.970 ⇒ 00:18:42.730 Pranav Narahari: this is kind of your interface to have access across all the data that the customer service division has. It sounds like to me, from a lot of the conversations that we had, that…
156 00:18:43.060 ⇒ 00:18:58.590 Pranav Narahari: a lot of, like, maybe the frustration is that you kind of… you hit, like, a roadblock, which is, like, I can’t see any further, you know? I can see that there is an issue, but I can’t see… I can’t then go in and diagnose that issue. Like, I’m completely out of the loop.
157 00:18:58.740 ⇒ 00:18:59.430 Pranav Narahari: Right?
158 00:18:59.430 ⇒ 00:19:14.270 YvetteRuiz: Yeah, no, I… and we talked about that. That’s… that’s exactly what I just shared the example with you today, like, okay, you have 69, and in the month of April, I had 69 hours… 69 hours of… of people that were offline, and their customer issues. It’s like.
159 00:19:14.910 ⇒ 00:19:23.559 YvetteRuiz: Okay, the customer issues, what specifically are they struggling with? Like, we have Andy, what… I mean, what are they looking for?
160 00:19:23.860 ⇒ 00:19:24.940 Pranav Narahari: Yes, and…
161 00:19:25.690 ⇒ 00:19:39.679 Pranav Narahari: Transcripts, transcripts, transcripts, right? It’s like… and, that’s what this… I call this CSR pulse. It’s basically just, how can you just get a pulse on everything that’s happening, customer service, related?
162 00:19:39.940 ⇒ 00:19:42.010 Pranav Narahari: And so…
163 00:19:42.010 ⇒ 00:19:44.890 YvetteRuiz: That’s very cool. I know that when we talked about it, that’s…
164 00:19:45.620 ⇒ 00:19:48.819 YvetteRuiz: yeah, I mean, when you can click into and get that granular.
165 00:19:49.100 ⇒ 00:19:49.730 Pranav Narahari: Yeah.
166 00:19:50.150 ⇒ 00:19:51.190 YvetteRuiz: That’s value.
167 00:19:51.560 ⇒ 00:20:06.539 Pranav Narahari: Yeah, and this one right here, Janice, we talked about it a little bit earlier today. I said I was gonna talk to you a little bit about it more in this call. It’s how do we further automate and further just broaden what this ANDI feedback looks like?
168 00:20:06.800 ⇒ 00:20:18.509 Pranav Narahari: As of right now, the only way that we have, like, a structured feedback loop is via the triage tickets. Yep. So, user gives a thumbs up, thumbs down, they give their…
169 00:20:18.750 ⇒ 00:20:34.549 Pranav Narahari: they’re… they give optional, kind of, reason why, and then we built out a more intricate process in the last couple weeks about, okay, where does that exist in the central… where does that go into the central deck? Do we check to see if there’s duplicates or conflicting information?
170 00:20:34.600 ⇒ 00:20:40.309 Pranav Narahari: And so a lot of that is automated, but what can… what is missing, I still feel, is that
171 00:20:40.500 ⇒ 00:20:53.179 Pranav Narahari: in just my one-on-one conversations with you, our conversations with trainers, in emails, even Yvette this week, too, you sent me a document, which was, hey, can we get this added into Andy?
172 00:20:53.180 ⇒ 00:21:04.649 Pranav Narahari: we’re supporting all of that ad hoc, and that’s becoming more and more frequent. Let’s build a process around that so we can automate that as well, so we can start working on these other projects.
173 00:21:04.970 ⇒ 00:21:06.220 Pranav Narahari: And then…
174 00:21:06.440 ⇒ 00:21:26.390 Pranav Narahari: One other big thing too, Janiece, is, like, which I’ve noticed as we’ve rolled out this new triage system, is that there’s, like, hundreds of tickets that just get assigned to you, and it requires you to manually just read through every single one, and I think we can build a really good solution that’s extremely accurate, which can then do the assigning for you.
175 00:21:26.410 ⇒ 00:21:29.850 Pranav Narahari: I really don’t think it’s super important in the…
176 00:21:30.320 ⇒ 00:21:40.039 Pranav Narahari: going forward, that you… yeah, for you to be assigning these tickets to the trainers, I think that would be a super easy win, and then save you a lot of time every day.
177 00:21:42.440 ⇒ 00:21:43.160 JanieceGarcia: Yes, sir.
178 00:21:43.160 ⇒ 00:21:44.930 YvetteRuiz: Yep, no, I agree with that.
179 00:21:45.210 ⇒ 00:21:59.450 YvetteRuiz: Especially if it can get automated, like, you know, okay, this is dispatch, this is lawn, this is it. But the thing is, the final sign-off and everything would… that’s where the importance comes in, of having… making sure the right players sign off on all that.
180 00:21:59.450 ⇒ 00:22:13.439 JanieceGarcia: Right, and then how would the database stuff work? Because right now it is, you know, just me or you guys entering in the zip codes, because those would be automatically sent to them as well.
181 00:22:14.130 ⇒ 00:22:23.339 JanieceGarcia: And that would be, a turnaround time that would be delayed, because they would have to send it back to me, and then I would update the database, and then go through.
182 00:22:23.820 ⇒ 00:22:37.479 Pranav Narahari: I think we may have talked about this before, but if not, like, I think that part can also be automated, because, it’s really simple for us to send… you’ll be able to assess, hey, is this the right information that should be added to the database?
183 00:22:37.480 ⇒ 00:22:45.700 Pranav Narahari: Once you give that sign-off, which you’ll do for all tickets, we can then go into the database automatically, and then
184 00:22:45.700 ⇒ 00:22:47.809 Pranav Narahari: Add that information in.
185 00:22:47.820 ⇒ 00:22:55.789 Pranav Narahari: It won’t require you to, like, you know, click a new person, add that information, add the notes. We can automate that whole process as well.
186 00:22:56.410 ⇒ 00:23:11.160 Pranav Narahari: So, in thinking about this Andy feedback, like, section, really automating a lot more of what we currently have, and then also finding these other ways of, like, where we’re getting feedback to Andy, and then automating those as well.
187 00:23:11.670 ⇒ 00:23:12.240 JanieceGarcia: Nice.
188 00:23:12.710 ⇒ 00:23:13.320 Pranav Narahari: Yeah.
189 00:23:14.650 ⇒ 00:23:26.259 Pranav Narahari: And then, yeah, lastly, this, like, scorecard, Yvette, we talked a little bit about this, I think, a few weeks ago at this point, but it ties in really well with CSR Pulse.
190 00:23:26.380 ⇒ 00:23:41.410 Pranav Narahari: Basically how I see this is that it can give Janice Yu and Yvette Yu, like, a snapshot of everybody’s kind of performance, on the CSR side. You guys will define, like, what metrics are the things that we want to be looking at.
191 00:23:41.410 ⇒ 00:23:49.490 Pranav Narahari: And then, we can give you, like, the supporting transcript information, for all that, for their…
192 00:23:49.620 ⇒ 00:24:03.939 Pranav Narahari: basically, their performance. We can talk a little bit more about, like, what that scorecard looks like exactly, but I think once we’ve finished CSR Pulse, we will have all the data integrations needed to build out that scorecard.
193 00:24:05.950 ⇒ 00:24:07.110 YvetteRuiz: Yep, okay.
194 00:24:07.590 ⇒ 00:24:08.520 Pranav Narahari: Cool.
195 00:24:09.400 ⇒ 00:24:13.199 Pranav Narahari: That was probably the fastest meeting. I don’t think I could have…
196 00:24:13.200 ⇒ 00:24:16.460 YvetteRuiz: I’m sorry, Bernal. I’m sorry.
197 00:24:16.460 ⇒ 00:24:22.060 Pranav Narahari: I’m glad we actually got to go through everything, and we still have a couple more minutes left, but… yeah, I kind of just want to pause there.
198 00:24:22.840 ⇒ 00:24:31.170 YvetteRuiz: Yeah, I mean, that’s all great stuff. I mean, again, the more granular we can get, and we already know, I mean, we can talk until we’re blue in the face, the transcripts are… is…
199 00:24:31.270 ⇒ 00:24:42.979 YvetteRuiz: where we’re gonna get the insights there. I’m very excited to get that… that other day, the date on the cancellation, so if you can send that to me, I’ll read through it. And then, of course, next week,
200 00:24:43.010 ⇒ 00:24:56.350 YvetteRuiz: I’m gonna just kinda… I highlighted some things, I’ll forward it to you based off of that sheet that you shared with me, where we just discussed, just additional questions that I have, and then whatever day we can pin down with Steven next week, we can go ahead and get that done.
201 00:24:56.640 ⇒ 00:25:03.560 Pranav Narahari: Perfect. Yeah, yeah, that conversation with Steven, just let me know. Do you want me to schedule that, or did you wanna…
202 00:25:03.560 ⇒ 00:25:21.140 YvetteRuiz: I’m gonna… I told him that… I already have updated him on all our conversations, and kind of where we’re at and everything. Like, we have our meeting next Wednesday, all of us, regarding cancellations, so this kind of is all perfect timing, and I just need to know what day he’s gonna be available, next week.
203 00:25:21.140 ⇒ 00:25:22.730 Pranav Narahari: Gotcha. Okay, sounds good.
204 00:25:24.100 ⇒ 00:25:29.280 YvetteRuiz: Yeah, but I think other than that, thank you so much, and I appreciate you working with my time today.
205 00:25:29.450 ⇒ 00:25:31.160 Pranav Narahari: No worries, no worries.
206 00:25:31.740 ⇒ 00:25:32.340 Pranav Narahari: Cool.
207 00:25:32.640 ⇒ 00:25:35.890 YvetteRuiz: Alright, well, Janiece, anything on your end?
208 00:25:35.890 ⇒ 00:25:42.360 JanieceGarcia: Nope, he answered my questions, and then I told you about what he was talking about with the linear tickets, so… Yeah. Yep.
209 00:25:42.720 ⇒ 00:25:43.270 YvetteRuiz: Okay.
210 00:25:43.580 ⇒ 00:25:46.689 YvetteRuiz: Alright, well, it’s good to see you, Tim, as well.
211 00:25:46.940 ⇒ 00:25:47.610 YvetteRuiz: 50.
212 00:25:47.870 ⇒ 00:25:54.259 Uttam Kumaran: Great to see you both, appreciate it. Pranav, that readout looks really good. I’m excited for the feedback, Yvette, on Wednesday.
213 00:25:54.480 ⇒ 00:26:06.529 YvetteRuiz: Yeah, yeah, no, for sure. Yep, cancellations, hot topic. I mean, we’ve got a lot of stuff going on. I mean, it’s been kind of a conversation, but there’s just a lot of dollars out there. There’s a lot of everything going on.
214 00:26:07.650 ⇒ 00:26:13.800 YvetteRuiz: Definitely. Alrighty, guys. Well, I hope you guys have an excellent weekend, and I will be in touch!
215 00:26:14.010 ⇒ 00:26:14.760 Uttam Kumaran: Okay, perfect.
216 00:26:14.760 ⇒ 00:26:15.370 JanieceGarcia: Thank you.
217 00:26:15.370 ⇒ 00:26:16.450 YvetteRuiz: Thanks, guys. Bye.
218 00:26:16.450 ⇒ 00:26:17.100 Uttam Kumaran: Bye.