Meeting Title: ABC Project Review Date: 2025-09-30 Meeting participants: read.ai meeting notes, Uttam Kumaran, MattBurns, Amber Lin, Steven
WEBVTT
1 00:01:17.390 ⇒ 00:01:20.789 MattBurns: Well, that’s part… Open. She is, yeah.
2 00:01:23.340 ⇒ 00:01:24.120 Uttam Kumaran: Hey, Matt.
3 00:01:24.560 ⇒ 00:01:25.999 MattBurns: Good morning, how are you?
4 00:01:26.400 ⇒ 00:01:27.770 Uttam Kumaran: Good, how are you?
5 00:01:27.770 ⇒ 00:01:29.160 MattBurns: Good, good.
6 00:01:29.840 ⇒ 00:01:32.300 MattBurns: Waiting for Steven, I guess.
7 00:01:33.050 ⇒ 00:01:35.920 MattBurns: How was your, weekend.
8 00:01:36.430 ⇒ 00:01:50.449 Uttam Kumaran: Weekend was good. I was actually in Maryland, visiting some friends. A friend of mine just had a little kiddo 6 months ago, so we were just visiting them. They live in Moncton, Maryland. I don’t know if you’ve ever been.
9 00:01:51.650 ⇒ 00:02:01.069 Uttam Kumaran: to Maryland, but it’s, like, probably 40 minutes north of Baltimore, kind of in the woods, so it’s really, really nice.
10 00:02:01.560 ⇒ 00:02:04.680 MattBurns: Yeah, so, relaxing. Yeah.
11 00:02:05.020 ⇒ 00:02:05.380 Uttam Kumaran: Yeah.
12 00:02:05.380 ⇒ 00:02:09.969 MattBurns: Well, I was in Colorado, so it’s always relaxing in Colorado for me.
13 00:02:09.970 ⇒ 00:02:11.960 Uttam Kumaran: Where do you go, in Colorado?
14 00:02:12.870 ⇒ 00:02:19.230 MattBurns: Estes Park. We have a house there, so we go there quite a bit. And in the fall.
15 00:02:21.090 ⇒ 00:02:26.870 Uttam Kumaran: Estes Park is right next to the National Park, Rocky Mountain National Park, so we do a lot of hiking, and…
16 00:02:27.160 ⇒ 00:02:30.659 MattBurns: You get a lot of beautiful aspen, the color changes, and…
17 00:02:31.070 ⇒ 00:02:38.449 MattBurns: The elk are in the rut, so the elk are everywhere. So that’s pretty cool, so…
18 00:02:38.450 ⇒ 00:02:39.400 Uttam Kumaran: Wow, okay.
19 00:02:39.400 ⇒ 00:02:41.069 MattBurns: Yeah, good getaway, so…
20 00:02:41.510 ⇒ 00:02:42.190 Uttam Kumaran: Yeah.
21 00:02:42.190 ⇒ 00:02:43.809 MattBurns: like that, so…
22 00:02:44.790 ⇒ 00:02:49.880 Uttam Kumaran: Yeah, we’re planning on going to, to Boulder next month for a few days, so…
23 00:02:50.160 ⇒ 00:02:50.890 MattBurns: Yep.
24 00:02:50.890 ⇒ 00:02:51.390 Uttam Kumaran: Yeah.
25 00:02:51.390 ⇒ 00:02:57.300 MattBurns: And we’re only about, 45 minutes northwest of Boulder.
26 00:02:58.000 ⇒ 00:02:59.190 Uttam Kumaran: Hmm, amazing.
27 00:02:59.190 ⇒ 00:03:07.270 MattBurns: Yeah, Boulder’s beautiful. And a quick trip from the airport, you know, that’s… That’s pretty easy, so…
28 00:03:07.310 ⇒ 00:03:09.909 Uttam Kumaran: What made you pick, Estes Park?
29 00:03:10.270 ⇒ 00:03:23.939 MattBurns: Well, we had been going there, for years, and my wife’s brother, there’s a big YMCA camp in Estes Park, and he had been sending his family to the Y camp there, and he said, hey, if… because every summer we would
30 00:03:24.220 ⇒ 00:03:34.659 MattBurns: kind of pick a vacation house that we would do at the time, VRBO, or Vrbo, as they call it now. And we did it in Esses Park a few years, and said, you know what, let’s just…
31 00:03:34.880 ⇒ 00:03:37.059 MattBurns: We’re in a position we can buy something and…
32 00:03:38.700 ⇒ 00:03:41.380 MattBurns: get out of this Texas heat, particularly when I…
33 00:03:41.560 ⇒ 00:03:42.430 Uttam Kumaran: Yes.
34 00:03:42.430 ⇒ 00:03:46.210 MattBurns: When I retire, it’s gonna be like, we’ll spend a lot of time up there, so…
35 00:03:46.610 ⇒ 00:03:53.170 Uttam Kumaran: Great. Yeah, same thing, we’re just trying to get some fresh air, and I grew up in California, so I really miss…
36 00:03:53.400 ⇒ 00:03:56.439 Uttam Kumaran: Being able to, like, drive up to Sequoia, or…
37 00:03:57.140 ⇒ 00:04:00.500 Uttam Kumaran: Just to somewhere where it’s quiet and there’s a lot of nature.
38 00:04:00.720 ⇒ 00:04:06.079 Uttam Kumaran: So that’s really nice. And yeah, everything seems like a really quick drive from Denver, you know, so…
39 00:04:06.080 ⇒ 00:04:08.040 MattBurns: Yep. Yeah, no, Denver’s…
40 00:04:08.220 ⇒ 00:04:11.839 MattBurns: And that airport, since it’s not downtown, is easy to get in and out of, and…
41 00:04:12.210 ⇒ 00:04:13.120 Uttam Kumaran: Yeah.
42 00:04:13.350 ⇒ 00:04:17.200 MattBurns: So, all good. Well, let me text Steven here and see if he’s…
43 00:04:17.209 ⇒ 00:04:17.929 Uttam Kumaran: Sure.
44 00:04:18.240 ⇒ 00:04:19.750 MattBurns: Coming on board here.
45 00:04:36.720 ⇒ 00:04:38.170 MattBurns: Amber, how are you doing?
46 00:04:40.820 ⇒ 00:04:41.790 Amber Lin: Hi there!
47 00:04:42.260 ⇒ 00:04:43.730 Amber Lin: Doing good!
48 00:04:44.040 ⇒ 00:04:48.720 MattBurns: Feeling very energetic this morning, so very excited for this meeting.
49 00:04:49.310 ⇒ 00:04:49.950 MattBurns: Good.
50 00:04:50.450 ⇒ 00:04:52.800 MattBurns: Are you… and you’re in California most of the time?
51 00:04:53.000 ⇒ 00:04:57.279 Amber Lin: Yeah, I’m in California, I’ve been here for 2 years.
52 00:04:57.280 ⇒ 00:04:58.140 MattBurns: Which city?
53 00:04:58.540 ⇒ 00:05:03.759 Amber Lin: California, Los Angeles, and I’m in Culver City. Have you visited before?
54 00:05:04.570 ⇒ 00:05:06.709 MattBurns: I’ve been to LA before, but not… not…
55 00:05:07.060 ⇒ 00:05:16.380 Amber Lin: I see. I see. I’m mostly still in LA, because before I came here, people still say LA, and I don’t really know how big it was.
56 00:05:16.380 ⇒ 00:05:29.269 Amber Lin: But there’s LA City, and there’s LA County, and LA County is huge, but LA City is smaller, so it depends on what LA people say, and it’s hard.
57 00:05:29.600 ⇒ 00:05:30.700 MattBurns: No, for sure.
58 00:05:31.230 ⇒ 00:05:33.450 Amber Lin: Alright, well, Steven’s here, so… Hi, Steven.
59 00:05:33.540 ⇒ 00:05:36.989 Steven: I had someone in my office distracting me, and…
60 00:05:38.900 ⇒ 00:05:40.560 MattBurns: It happens, it happens.
61 00:05:40.930 ⇒ 00:05:41.340 MattBurns: Yep.
62 00:05:41.340 ⇒ 00:05:42.140 Uttam Kumaran: Awesome.
63 00:05:42.490 ⇒ 00:05:45.449 MattBurns: Well, good. Udom, I’ll defer to you if you want to kind of get.
64 00:05:45.450 ⇒ 00:05:52.460 Uttam Kumaran: Yeah, and so let me just, let me pull something up on my side, and we can get going.
65 00:05:53.000 ⇒ 00:05:53.780 MattBurns: Okay.
66 00:05:56.950 ⇒ 00:05:58.390 Uttam Kumaran: One second…
67 00:06:00.890 ⇒ 00:06:16.700 Uttam Kumaran: Great, so kind of weird… one thing we’re trying to do on our delivery side is to do, more formal monthly sort of project reviews with all of our clients. You know, I think it’s… it’s really great talking to folks like you, because you’ve seen
68 00:06:16.770 ⇒ 00:06:28.670 Uttam Kumaran: We… again, we’ve only been in business a little over 2 years, and things are changing for us, so it’s awesome to be able to pass a lot of that benefit on, and one of the things we’re trying to do is
69 00:06:28.680 ⇒ 00:06:45.300 Uttam Kumaran: to have a higher-level conversation with all of our clients on a monthly basis that sits a little bit above, sort of, the week-to-week, or, like, the sprint-to-sprint changes, which, you know, we’re already doing on a weekly basis. So really the theme of this meeting, we wanted to just talk through
70 00:06:45.610 ⇒ 00:06:52.249 Uttam Kumaran: And I wanted to hear from y’all about how you’re thinking about the project, sort of from not the level of
71 00:06:52.250 ⇒ 00:07:01.179 Uttam Kumaran: oh, we need access to one thing, or there’s a new feature coming up, but more about, okay, what has the true impact been today? We also have a little bit of a…
72 00:07:01.180 ⇒ 00:07:13.029 Uttam Kumaran: proposal on, you know, from our lens of how the project’s been going, on some things we’d like to propose, to consider, to expand it a bit further,
73 00:07:13.190 ⇒ 00:07:29.289 Uttam Kumaran: But that’s generally the theme of today’s conversation. Would also love to get any feedback you have from anyone on the team, from the way we communicate. Anything we could do to be better is really, really important for us to hear. So yeah, that’s generally the
74 00:07:29.400 ⇒ 00:07:39.789 Uttam Kumaran: what today is about. So, I just wanted to flash this up, you know, and this is really what I think we’ve… we’ve strived to, you know, to deliver
75 00:07:40.150 ⇒ 00:07:55.539 Uttam Kumaran: Since we’ve, you know, began our partnership. I think we… what we saw when we initially came in is, you know, a system with tons of documents, tons of pain for CSRs, and now we… I think we’ve… we’ve solved a lot of those
76 00:07:55.540 ⇒ 00:08:04.240 Uttam Kumaran: problems through the technology that has been made available. Which, you know, I think from our initial scope, I feel pretty good. I think.
77 00:08:04.240 ⇒ 00:08:05.910 MattBurns: You know, as we…
78 00:08:05.910 ⇒ 00:08:24.880 Uttam Kumaran: naturally improve the system, more people are using it, and we’re naturally expanding usage. But I guess I wanted to sort of pause here and kind of see if, you know, we’re looking at the data and we’re talking to folks, but would love to kind of hear from your lens within the company and, like, how you guys think about the impact,
79 00:08:25.260 ⇒ 00:08:30.939 Uttam Kumaran: And, like, just generally hear about, you know, how progress has been…
80 00:08:31.400 ⇒ 00:08:45.479 Uttam Kumaran: you know, so far in terms of, like, impact. And again, a couple things that I remember from our original conversation we talked about is, one, one of the things that really stood out to me was how long it takes to train CSRs, and how long they typically stay with you, right? And I know we haven’t…
81 00:08:45.660 ⇒ 00:09:04.919 Uttam Kumaran: gone through a two-year cycle and seen impacts, but for me, it’s important to know that we’re able to train those people faster, or they’re able to ramp up faster, and ideally, they churn less, right? And so their ROI for y’all is a lot higher on the people, and so that’s just one thing that popped into mind, but… but yeah, I would love to…
82 00:09:05.090 ⇒ 00:09:07.370 Uttam Kumaran: To hear from… from your side, yeah.
83 00:09:07.370 ⇒ 00:09:09.829 MattBurns: Why don’t you start, and I’ll… I’ll…
84 00:09:09.830 ⇒ 00:09:11.560 Steven: Chime in on your thoughts after that.
85 00:09:12.190 ⇒ 00:09:26.720 Steven: Yeah, I mean, obviously, Yvette and Janiece have the most day-to-day knowledge, on that. As far as the training, I agree, I think that’s one of the biggest pieces that it can help with. You know, I don’t know that we’ve had it long enough to know for sure. I know
86 00:09:26.720 ⇒ 00:09:33.179 Steven: We’ve seen in the data that new people tend to use it more. I don’t remember who it was, someone on the last one, one of the newer
87 00:09:33.480 ⇒ 00:09:39.000 Steven: There were employees that use it a lot, so that’s obviously a good sign. I’d have to talk to Janiece and see…
88 00:09:39.040 ⇒ 00:09:41.190 Uttam Kumaran: Their feedback after using it.
89 00:09:41.300 ⇒ 00:09:57.500 Steven: for a month or two on the new employees, but yeah, I think that’s one of the pieces that’s probably assisted the most so far. I know we’re obviously looking forward to the voice piece, which I think is a… Yvette is very excited about. That’s kind of integral to get some of the transcriptions and some of the voice piece in there, but as far as
90 00:09:57.600 ⇒ 00:10:10.090 Steven: Now, I’d say the biggest benefit has been for the trainees, and then some of the cross, you know, the pest people, or the mechanical people that are trying to do pest stuff. It’s just helpful to have a resource there. I’ll be very curious what events
91 00:10:10.180 ⇒ 00:10:19.930 Steven: finding is, I think they had their kind of sit-down, more deep-dive meeting with some users. Matt, do you remember when… was that going to be by the end of this week, or middle of this week?
92 00:10:20.380 ⇒ 00:10:27.440 MattBurns: Yeah, what they’re… I guess they’ve assigned… Yvette’s kind of assigned some of the supervisors to actually sit with
93 00:10:27.730 ⇒ 00:10:38.470 MattBurns: you know, some of the new people and some of the… some of their people to, you know, help them, utilize Andy, more efficiently and better, and…
94 00:10:38.570 ⇒ 00:10:48.109 MattBurns: You know, bigger picture, I know that Yvette and Janiece like working with you, Amber. They feel like everybody’s on the same page.
95 00:10:49.220 ⇒ 00:10:49.800 Uttam Kumaran: Great.
96 00:10:49.800 ⇒ 00:10:55.980 MattBurns: And like Steven said, And you said, Utam, you know, at the big… you know.
97 00:10:56.390 ⇒ 00:10:59.460 MattBurns: Getting somebody up to speed quicker is a big deal.
98 00:10:59.840 ⇒ 00:11:08.699 MattBurns: Reducing the stress that they feel when they get calls that they’re not… maybe it’s not in their wheelhouse, it’s a pest…
99 00:11:08.880 ⇒ 00:11:11.219 MattBurns: Covering a lawn, or vice versa.
100 00:11:11.640 ⇒ 00:11:13.030 MattBurns: That’s helped.
101 00:11:13.140 ⇒ 00:11:19.180 MattBurns: you know… The feeling is that the accuracy.
102 00:11:19.850 ⇒ 00:11:24.880 MattBurns: Can be better, particularly with the kind of the zip code, Assign.
103 00:11:26.060 ⇒ 00:11:28.639 MattBurns: Inspectors and those kind of things, and…
104 00:11:29.130 ⇒ 00:11:33.939 MattBurns: You know, just enhancing that, you know, putting all the documents in one spot.
105 00:11:34.280 ⇒ 00:11:39.040 MattBurns: Was probably a really good thing, too, because…
106 00:11:39.470 ⇒ 00:11:52.560 MattBurns: now there’s just one central database, and it’s like, I don’t have to go looking all over the place, or you’ve got one CSR using an outdated document that was from 8 years ago on a piece of paper that they have taped up to their desk.
107 00:11:52.980 ⇒ 00:12:04.510 MattBurns: So, I think all those things are… our wins, you know, I do think… continuing
108 00:12:05.060 ⇒ 00:12:09.939 MattBurns: to demonstrate to our CSRs, particularly some of the older ones that
109 00:12:10.370 ⇒ 00:12:12.960 MattBurns: maybe aren’t relying on Andy as much.
110 00:12:13.100 ⇒ 00:12:16.170 MattBurns: that… It would help them also?
111 00:12:16.350 ⇒ 00:12:22.839 MattBurns: Not having to escalate the calls. You know, I don’t think we really have any data yet on
112 00:12:23.760 ⇒ 00:12:31.109 MattBurns: Has it helped, or does it really help with, oh, by the way, because we’re putting such a big emphasis on that internally anyway.
113 00:12:31.220 ⇒ 00:12:31.730 MattBurns: But…
114 00:12:31.730 ⇒ 00:12:32.140 Uttam Kumaran: Yeah.
115 00:12:32.140 ⇒ 00:12:35.650 MattBurns: But no, I think the way…
116 00:12:37.400 ⇒ 00:12:42.230 MattBurns: The progress we’ve made in the last 8 months, I think, is quite good.
117 00:12:42.650 ⇒ 00:12:44.370 MattBurns: Maybe we haven’t…
118 00:12:44.750 ⇒ 00:12:51.419 MattBurns: Amber hit the marks of utilization yet, but the trend seems to be in the right direction, I guess?
119 00:12:53.080 ⇒ 00:12:59.910 Steven: Yeah, and that’s what I want to, you know, that’s why Yvette kind of did that, because I had gone into her and talked… I went and talked to a CSR.
120 00:12:59.910 ⇒ 00:13:00.460 MattBurns: Yeah.
121 00:13:00.460 ⇒ 00:13:15.339 Steven: She actually had lead line, because it was one of the, Lauren Anderson, I think at one point, she was one of the higher users, and so I was just asking her, trying to get some feedback, and she… she said, she was like, yeah, I love using it, and so I was kind of trying to ask, I was like, okay, we’re…
122 00:13:15.480 ⇒ 00:13:22.680 Steven: is it a valuable tool? Is it just nice to use, or is it, like, really helpful? No, it is very helpful. And I was like, okay, if you didn’t have
123 00:13:22.880 ⇒ 00:13:26.670 Steven: Andy, would it be harder to get the info? She’s like.
124 00:13:26.670 ⇒ 00:13:27.860 Uttam Kumaran: It’s a great question.
125 00:13:27.860 ⇒ 00:13:32.620 Steven: Maybe a little bit, but her use cases, because I went and told Yvette that, I was like, you know.
126 00:13:32.680 ⇒ 00:13:47.860 Steven: there’s got to be value more than just, like, yeah, I like it, it’s gotta be useful, versus the old looking at the sheet, and she was kind of like, it is helpful, how much more helpful is it? You know, I don’t know, because sometimes it is easier just to look at the sheet, so I had asked Yvette about that, and she’s like, well.
127 00:13:47.920 ⇒ 00:13:59.540 Steven: Lauren using it just for, really only for the inspector zip code list, she doesn’t use it for some of the other things. That’s why she wanted to do kind of same questions to some CSRs, like, okay, how are you using it, why are you using it, and
128 00:13:59.820 ⇒ 00:14:05.240 Steven: if you are… because we don’t want to force people to use it just because we’re telling them to use it. It’s got to be useful and valuable.
129 00:14:05.240 ⇒ 00:14:13.669 Uttam Kumaran: No, the way you said it is a great question. It’s what they teach, like, product folks to say, is like, if it wasn’t here, how painful would it be, basically?
130 00:14:13.840 ⇒ 00:14:30.599 Steven: So, when she did say getting all that kind of into one central doc was very helpful, she wasn’t looking all over, but again, she was kind of one specific use case, just the zip code list. She said she used it a lot for that, but yeah, I was like, if it’s away, would it take you much more time? She was like, maybe a little more time.
131 00:14:31.150 ⇒ 00:14:34.219 Steven: And her only complaint was that she still does have some…
132 00:14:34.310 ⇒ 00:14:49.510 Steven: that it doesn’t populate, and sometime I know that’s on our end, that we didn’t have the sheet correct, but I was like, you know, once that gets totally corrected, would you prefer to use this? And overall, she was like, yeah, I prefer to use this, assuming it always answers correctly, and she’s like, that’s the only time
133 00:14:49.510 ⇒ 00:14:54.459 Steven: it is sometimes a hassle. If I ask it and it doesn’t know it, then I go to the sheet, and it has the answer.
134 00:14:54.540 ⇒ 00:15:03.390 Steven: And… but overall, yeah, but that’s why you might want to deep dive in a little bit more, some of the CSRs that use it beyond just the zip code list to kind of ask those same questions.
135 00:15:04.990 ⇒ 00:15:09.870 Uttam Kumaran: Yeah, it makes a lot of sense. I think there’s… there’s kind of two pieces there. One,
136 00:15:09.920 ⇒ 00:15:29.520 Uttam Kumaran: you know, a lot of the folks that we’ve onboarded, they’ve had a lot of institutional knowledge, right? And so, part of, I think, the benefit here is you can bring on people that are either less skilled or even have less institutional knowledge, and they can still perform those same tasks without having to know the exact sheet to go to.
137 00:15:29.570 ⇒ 00:15:47.759 Uttam Kumaran: who the person is, like, those, like, institutional ways of finding the problem, they no longer have to leverage that. So, I think one thing, if it is definitely helpful for her, imagine someone who starts in her role tomorrow with no background in it, right? So I think that’s…
138 00:15:48.040 ⇒ 00:15:58.900 Uttam Kumaran: that’s definitely one thing. The second piece is, for the trainers, it was obvious that they had no sort of support infrastructure around them on how to deliver feedback, and how to…
139 00:15:58.940 ⇒ 00:16:11.709 Uttam Kumaran: beyond just, like, we have structured monthly sessions where we train, and then people kind of go off. There is nothing more dynamic, meaning you can’t build almost, like, a little bit of a personalized training for every
140 00:16:11.710 ⇒ 00:16:25.740 Uttam Kumaran: individual CSR. And one of the things that we’ll talk about today is, with the transcripts, how do we build something so that we can accelerate the feedback that can get given back to a CSR, and therefore they improve, versus
141 00:16:25.930 ⇒ 00:16:34.529 Uttam Kumaran: okay, we’re just running through our typical training, and I think we fixed a lot of the issues, that is, the data isn’t there. So I’m now less…
142 00:16:34.790 ⇒ 00:16:47.169 Uttam Kumaran: concerned with, like, okay, the information isn’t there. We now have a process of remedying those types of problems. Now, I think I want to see how we can impact the trainers so they can deliver feedback faster.
143 00:16:47.170 ⇒ 00:16:57.159 Uttam Kumaran: that are directed from an actual call, right? Almost like you’re reviewing game tape. Very similarly, how can we make it easy for the trainers to identify an opportunity where
144 00:16:57.390 ⇒ 00:17:06.289 Uttam Kumaran: Andy could have been used, and oh, by the way, could have been shared, or again, like, one thing we’ll talk about is, like, identifying all the calls where someone gets put on hold.
145 00:17:06.390 ⇒ 00:17:13.520 Uttam Kumaran: accelerating the fact that feedback can get to those CSRs, so that you can… you can move them up even faster.
146 00:17:13.530 ⇒ 00:17:31.999 Uttam Kumaran: So it’s… I also think, like, look, we talked at the beginning that not every call is going to require Andy. Ideally, they’re able to answer them without, you know, looking through knowledge base, right? So we’re not looking for 100% of calls are used, but for the calls that
147 00:17:32.030 ⇒ 00:17:48.939 Uttam Kumaran: there is struggle, we want to make sure that Andy is the first spot they go to, for those. And then, also, again, like, I think there’s still probably a portion of calls where Andy is not getting used, or it’s being used, but it’s just still not supportive enough.
148 00:17:49.030 ⇒ 00:18:03.720 Uttam Kumaran: But again, I think the one thing, the joy, what we found is that ANDI for the trainers is a different use case than ANDI for the CSRs, right? And so, I think one thing we’ll kind of talk about today is, like, how to accelerate the training process.
149 00:18:03.720 ⇒ 00:18:20.620 Uttam Kumaran: And ultimately, what that should help with is now, when you promote someone to become a trainer, you bring on a trainer, they also don’t need to be, like, the world’s best trainer, right? Because they have this set of support system for them to identify transcripts where there could have been improvements, identify CSRs that need feedback.
150 00:18:20.650 ⇒ 00:18:30.009 Uttam Kumaran: And that way, again, I know that trainers, I’m sure, are more expensive, and there’s fewer of them, and so how can you support a growing
151 00:18:30.210 ⇒ 00:18:35.549 Uttam Kumaran: CSR staff with the same or limited amount of trainers, you know?
152 00:18:35.830 ⇒ 00:18:53.950 Uttam Kumaran: I guess that’s also a question, Matt, like, how do you see, like, how has the trend been in the business around, like, the customer support system, like, as an expense? Has it grown fairly linearly with the growth of the business, or how do you see, like, what are the goals for that
153 00:18:54.090 ⇒ 00:19:07.830 Uttam Kumaran: that sort of cost center. Like, I think we… you know, I know we… initially, we talked about the impact of overflow, the impacts of, okay, there’s just people churn in this side of the business, but sort of curious to hear, like, how you think about this
154 00:19:07.950 ⇒ 00:19:11.330 Uttam Kumaran: sort of line item, on the P&L overall.
155 00:19:11.800 ⇒ 00:19:16.249 MattBurns: Yeah, good question. I mean, the… our turnover trend
156 00:19:16.500 ⇒ 00:19:20.130 MattBurns: Is way down in the last couple years on this.
157 00:19:20.300 ⇒ 00:19:24.040 MattBurns: work. Now, some of that has to do with… A lot of the…
158 00:19:24.170 ⇒ 00:19:28.080 MattBurns: profile we’ve done in terms of hiring. We’ve hired.
159 00:19:28.080 ⇒ 00:19:28.560 Uttam Kumaran: Great.
160 00:19:28.560 ⇒ 00:19:30.349 MattBurns: Will it fit the profile?
161 00:19:30.810 ⇒ 00:19:35.610 MattBurns: We’ve created an advancement plan, I think, that rewards people.
162 00:19:36.050 ⇒ 00:19:45.460 MattBurns: for, added responsibility and effectiveness and those kind of things, so that helps. So…
163 00:19:45.920 ⇒ 00:19:49.599 MattBurns: And it’s interesting, in our mechanical
164 00:19:49.790 ⇒ 00:19:54.059 MattBurns: side of the business, it takes a little more…
165 00:19:54.230 ⇒ 00:20:00.849 MattBurns: support, i.e. the costs are higher for, the CSR or office
166 00:20:00.950 ⇒ 00:20:11.289 MattBurns: office expense line for personnel in the office, because there’s more projects to do. There’s a dispatch team, as well as.
167 00:20:11.740 ⇒ 00:20:16.720 MattBurns: CSR team, so… But… the hope, and I haven’t…
168 00:20:17.220 ⇒ 00:20:23.029 MattBurns: really verified it yet. And again, all that… what we’ve been doing, too, over the last few years is
169 00:20:23.250 ⇒ 00:20:26.740 MattBurns: We’ve actually been… In one way.
170 00:20:27.370 ⇒ 00:20:36.059 MattBurns: putting a little bit more… I don’t want to call it pressure, but we’ve been measuring and verifying what the CSRs
171 00:20:36.300 ⇒ 00:20:38.710 MattBurns: are doing on a regular basis. In other words.
172 00:20:38.710 ⇒ 00:20:39.330 Uttam Kumaran: Yeah.
173 00:20:39.430 ⇒ 00:20:56.269 MattBurns: Yvette really is good, and her team is also saying, oh, I’ve got some downtime now, so hey, make some outgoing calls, or I’m overloaded… Great. I’m overloaded in pests, so hey, some of you non-pest people that are.
174 00:20:56.700 ⇒ 00:20:57.210 Uttam Kumaran: Yes.
175 00:20:57.210 ⇒ 00:21:03.099 MattBurns: the helping pest, now you can cover, so… Definitely doing more with less.
176 00:21:03.320 ⇒ 00:21:04.250 MattBurns: is…
177 00:21:05.250 ⇒ 00:21:12.580 MattBurns: a big win, and how much Andy’s playing into that, I think partially, I can’t measure it, like I said.
178 00:21:12.580 ⇒ 00:21:12.970 Uttam Kumaran: Sure.
179 00:21:12.970 ⇒ 00:21:19.170 MattBurns: I know turnover’s down, and labor in this area is also down a bit, so…
180 00:21:19.500 ⇒ 00:21:24.280 MattBurns: Those are all things. Again, that allows us
181 00:21:24.400 ⇒ 00:21:27.410 MattBurns: in one respect, to pay for Andy.
182 00:21:27.680 ⇒ 00:21:32.510 MattBurns: Okay, because again, my overall cost in that area is down, I’ve got more… more room to pay.
183 00:21:32.510 ⇒ 00:21:32.900 Uttam Kumaran: Yes.
184 00:21:32.900 ⇒ 00:21:45.179 MattBurns: So, I think all good so far on that. It’s, like I said, some of it’s tough to… to really say. Yeah. You know, but… but we’re feeling… I think we’re feeling good about our CSRs, we’re feeling good about their performance.
185 00:21:45.310 ⇒ 00:21:47.840 MattBurns: And we’re feeling good about the lower turnover, so…
186 00:21:48.830 ⇒ 00:22:05.440 Uttam Kumaran: Yeah, I think similar… I think it’s similar in our business, which is a people of business, is we look at utilization, right? So we want to make sure that when some people are slammed, the other folks can sub in, versus some people being slammed, some people being at, like, 50%, right? So I do think Andy sort of probably helps to smooth.
187 00:22:05.440 ⇒ 00:22:05.839 MattBurns: No question.
188 00:22:05.840 ⇒ 00:22:06.409 Uttam Kumaran: that out a bit.
189 00:22:06.770 ⇒ 00:22:24.899 MattBurns: When we were… we used to be much more siloed, you would only have lawn CSRs. Well, there’s no way the lawn CSRs can handle the lawn calls in springtime. You could triple your staff in lawn, and you couldn’t handle all the calls. Well, now we can handle them a lot better, because I’ve got all the overflow.
190 00:22:25.070 ⇒ 00:22:31.310 MattBurns: So, that’s where we’re really feeling, probably, if I had to say what’s the most impact
191 00:22:31.690 ⇒ 00:22:35.709 MattBurns: of Andy is that now I have all these other CSRs
192 00:22:35.830 ⇒ 00:22:44.920 MattBurns: who can help, and they can use Andy as their knowledge source, since they’re just not that familiar with some of the… some of the nuances of the calls.
193 00:22:45.060 ⇒ 00:22:46.090 MattBurns: So…
194 00:22:46.720 ⇒ 00:22:48.100 Uttam Kumaran: And again, Yvette.
195 00:22:48.520 ⇒ 00:23:00.670 MattBurns: continues to give me good feedback on that. She feels like the progress is being made with coverage and accuracy, not having to call up, call the supervisor, put the customer on hold, so…
196 00:23:00.990 ⇒ 00:23:01.550 Uttam Kumaran: Yeah.
197 00:23:02.370 ⇒ 00:23:08.089 MattBurns: All that’s… you know, how tangible it is, but I know it’s benefiting us, for sure.
198 00:23:08.410 ⇒ 00:23:19.819 Uttam Kumaran: Yeah, and so I think, again, like, as the year goes by, I think one thing we want to see is that, like, the hires, the additional hires you are making are for a particular reason, not because, okay, we just…
199 00:23:19.910 ⇒ 00:23:39.699 Uttam Kumaran: we just have to add people. Ideally, you should see that smoothing out in utilization, but you should also see that, like, again, if people are able to do more with the hour, right, or do more with their time. So that nicely, hopefully, should cover some of the increase. Okay, so that’s… that’s really great to hear. I mean, a couple things that we wanted to…
200 00:23:39.980 ⇒ 00:23:47.340 Uttam Kumaran: talk about is a little bit of, like, where we see Andy now, and, like, what we think about, like, a future system. And I want to talk
201 00:23:47.460 ⇒ 00:23:49.270 Uttam Kumaran: One today, a little bit about
202 00:23:49.290 ⇒ 00:24:04.139 Uttam Kumaran: continuing to assist customer service, but I also would love to probe and see, like, where else in the business you think there’s similar opportunity. Now that you’ve seen sort of what our team is capable of, and the types of ways that we work.
203 00:24:04.150 ⇒ 00:24:15.689 Uttam Kumaran: I’m sure there are other opportunities as well. So, in terms of Andy, one of the things that we have right now, the product is very simply just the chatbot, right, in terms of what the user sees. So…
204 00:24:15.840 ⇒ 00:24:31.709 Uttam Kumaran: what is that limit? Really, people can’t get a view into how it’s being paired with the transcripts, meaning what calls are using ANDI versus not, what calls should be using ANDI versus not, right? And these are questions the trainers and Yvette’s team should be asking.
205 00:24:31.730 ⇒ 00:24:42.270 Uttam Kumaran: Also, we’re very limited in how we can display information to the CSRs. All we can do is answer in, like, a short block of text, meaning we can’t pull up, like.
206 00:24:42.270 ⇒ 00:24:52.490 Uttam Kumaran: oh, here’s the actual link to the document. That way, they can look above and below where the answer is, in case they need it. We can’t, and so, very simply, that’s something like this.
207 00:24:52.490 ⇒ 00:24:54.840 Uttam Kumaran: Where we can’t say, great.
208 00:24:54.850 ⇒ 00:25:05.550 Uttam Kumaran: we ask a question about a document, it actually pulls up where in the document. This technology exists today, it is something that we do, but given a very simple chat window, right, and this is…
209 00:25:05.600 ⇒ 00:25:07.290 Uttam Kumaran: This is something a bit custom.
210 00:25:07.370 ⇒ 00:25:18.870 Uttam Kumaran: given just the Google Chat window, we can’t do something like this, right? The minimum we can do is link out to the document, but you can see the power in saying, like, here’s actually in the document where this is coming from.
211 00:25:18.950 ⇒ 00:25:30.450 Uttam Kumaran: That way, of course, if they’re getting a question about a topic, probably they want to still know the areas below and above it and near it, right? So, that’s, that’s one point that we found.
212 00:25:30.670 ⇒ 00:25:49.569 Uttam Kumaran: Other thing is, we’ve done a lot of great work on centralizing this knowledge, and it could definitely get leveraged for other use cases. Something like company-wide knowledge search. I’m sure that there are other folks in the company that can start actually using the back end of ANDI for other questions around company policy.
213 00:25:49.570 ⇒ 00:26:04.930 Uttam Kumaran: And I’m sure more that I’m not really able to opine about. So, I think the back-end system that we develop can actually get leveraged for a lot more than just assisting CSRs. Anytime folks have a question that is asking a question over documents.
214 00:26:05.110 ⇒ 00:26:11.869 Uttam Kumaran: This is a system that now can leverage that. And then the last piece is something that’s more proactive.
215 00:26:12.190 ⇒ 00:26:30.020 Uttam Kumaran: Of course, like, right now, in everything in AI, I always tell people that humans are the limiting factor. And so one thing that we want to try to build more of is opportunities for AI to actually alert that there is an opportunity. For example, you can say a transcript gets finished at the end of a call.
216 00:26:30.020 ⇒ 00:26:38.739 Uttam Kumaran: There is an opportunity where an oh, by the way, should have been used, but it wasn’t. Instead of waiting for a trainer two weeks from now to
217 00:26:38.750 ⇒ 00:26:51.289 Uttam Kumaran: go through the data, identify that call. Instead, at that moment, an alert can get sent, which is, again, another AI that looks at the transcript, looks at the Andy logs, and says, hey, here are the improvements.
218 00:26:51.420 ⇒ 00:27:07.950 Uttam Kumaran: what I look at that as is shortening the length between feedback loops, right? If you can imagine, I’m sure the trainers are very limited now, and then they can only give… right now, really, feedback and training is happening on a very fixed basis, with very limited personalization, meaning broad feedback.
219 00:27:07.950 ⇒ 00:27:21.519 Uttam Kumaran: some of that probably sticks with some people, some of that doesn’t, because maybe they’re good in that area. And so this is where a system like this can very easily be adapted to start to give more personalized feedback, but also
220 00:27:21.520 ⇒ 00:27:32.049 Uttam Kumaran: do that much more frequently. We’re gonna start… we start… we’re now able to, since we talked to 8x8, we can get the transcripts pretty much at the end of every call.
221 00:27:32.050 ⇒ 00:27:43.920 Uttam Kumaran: And so, this is a great opportunity for us to think about some more proactive alerting, either on calls where there’s, like, real… there’s, like, compliance issues, or calls where there are really
222 00:27:43.940 ⇒ 00:27:50.389 Uttam Kumaran: easy opportunities to just give a quick feedback, hey, I reviewed the transcript from your call, I saw that there was this opportunity.
223 00:27:50.530 ⇒ 00:27:51.530 Uttam Kumaran: And…
224 00:27:51.640 ⇒ 00:27:57.540 Uttam Kumaran: Again, if you think about giving feedback every month, that means you just have 12 points of feedback a year.
225 00:27:57.720 ⇒ 00:28:13.260 Uttam Kumaran: That’s very, very hard, and, you know, we’re all sort of folks that have built up people in our companies. Giving feedback fast and often is so important, and so I think of that as a clear way of
226 00:28:13.260 ⇒ 00:28:24.060 Uttam Kumaran: even if those folks don’t use ANDI, I think this is a way for us to affect their overall performance by enabling the trainers themselves. And so…
227 00:28:24.060 ⇒ 00:28:27.529 MattBurns: I like that thought, Udom, because you’re right.
228 00:28:28.820 ⇒ 00:28:33.530 MattBurns: If it’s reviewed by a trainer, There’s a lag time.
229 00:28:33.920 ⇒ 00:28:36.940 MattBurns: There’s a capacity, they can’t get to everything, and…
230 00:28:37.110 ⇒ 00:28:43.130 MattBurns: That’s interesting what you just said, even if they don’t… utilize Andy.
231 00:28:43.530 ⇒ 00:28:46.530 MattBurns: If the review by the AI bot says.
232 00:28:46.850 ⇒ 00:28:51.419 MattBurns: here are some things you could have done differently, and here’s why. They’re still going to benefit.
233 00:28:51.420 ⇒ 00:28:52.380 Uttam Kumaran: Spot on.
234 00:28:52.760 ⇒ 00:28:55.089 MattBurns: Yeah, but that’s interesting. Okay.
235 00:28:55.610 ⇒ 00:28:59.010 Uttam Kumaran: And again, it’s a use case where the system is still leveraged, right?
236 00:28:59.350 ⇒ 00:29:17.760 Uttam Kumaran: But it’s just… it’s not exactly the chat window as the way in, right? And again, we’ve considered these two users, where initially we were focused on CSRs, but the trainers are actually a huge linchpin in this whole system working. And so, the system’s serving them, it may not just be the chat window.
237 00:29:17.760 ⇒ 00:29:26.710 Uttam Kumaran: it may have to be a set of alerts and some, you know, another sort of way of serving them, because they are a clear driver into the CSR improvement.
238 00:29:26.800 ⇒ 00:29:31.310 Uttam Kumaran: You know, and so.
239 00:29:31.550 ⇒ 00:29:33.160 Uttam Kumaran: One of the things that we…
240 00:29:33.270 ⇒ 00:29:45.269 Uttam Kumaran: we wanted to propose, and again, I… maybe I’ll just… I’ll highlight this, and then I would also love to hear a little… I know we’re… I know we’re also getting close to time, so I just want to make sure if you guys are able to do another, like, 5 or 10 minutes.
241 00:29:45.270 ⇒ 00:29:46.310 MattBurns: Sure, sure.
242 00:29:46.310 ⇒ 00:29:47.139 Steven: I’m good, yeah.
243 00:29:47.650 ⇒ 00:29:51.640 Uttam Kumaran: So, one of the things we kind of wanted to propose was
244 00:29:51.690 ⇒ 00:30:04.570 Uttam Kumaran: and we’ve shared a little bit of, is a little bit of a standalone system. This is something that we want to not only replicate the exact sort of chat interface for,
245 00:30:04.590 ⇒ 00:30:14.199 Uttam Kumaran: that Andy’s using, but also definitely be more improved. Doing things like identifying the areas in the transcript that can be,
246 00:30:14.310 ⇒ 00:30:31.679 Uttam Kumaran: an opportunity for feedback, identifying how often, oh, by the ways, cancellations, putting on hold happens within transcripts. Additionally, making something that can be used for cross-department search, so anyone in any department can start to use
247 00:30:31.790 ⇒ 00:30:41.560 Uttam Kumaran: like, generally just search over all the knowledge that exists. Again, hopefully making the backend system we’ve built open to a bunch of the other use cases.
248 00:30:41.560 ⇒ 00:30:55.909 Uttam Kumaran: And then also start to deliver kind of things that we mentioned, which is, like, a little bit of a nicer UI where you can easily copy thumbs up, thumbs down. You can also link out to, view documents inline, so…
249 00:30:56.060 ⇒ 00:31:10.319 Uttam Kumaran: We just think that some of these features are going to give the CSRs a much better experience, and then also the trainers themselves can go in and look at a single area to see the transcripts and see what opportunities they could use to improve.
250 00:31:11.900 ⇒ 00:31:25.529 Uttam Kumaran: So, kind of, like, what we’ve… we’ve laid out a little bit of a project plan around that. It’s roughly another, you know, 120, 130 hours of work, in order to build sort of a standalone system
251 00:31:25.530 ⇒ 00:31:33.850 Uttam Kumaran: That again, would basically replicate this exact same Andy experience, so the chat window, but would give you almost like a…
252 00:31:34.240 ⇒ 00:31:51.089 Uttam Kumaran: like, a pop-up display of several other improvements to make. And then the last piece I kind of wanted to mention is just to think about, where else in the company any of the work that we’ve done today can be leveraged, because I really do think that
253 00:31:51.120 ⇒ 00:32:08.050 Uttam Kumaran: the CSRs are probably just one area where people are asking questions over documents or structured data, you know? And so, I think this could be a shoe-in into building a little bit of a platform within ABC to sort of assist with tasks that rhyme with
254 00:32:08.080 ⇒ 00:32:14.609 Uttam Kumaran: a lot of what we’ve done. So maybe I’ll pause there, and just kind of, like, get initial feedback.
255 00:32:14.670 ⇒ 00:32:17.160 Uttam Kumaran: From, from both of y’all.
256 00:32:17.590 ⇒ 00:32:18.810 MattBurns: Steven, go ahead.
257 00:32:18.810 ⇒ 00:32:21.929 Steven: I think on the… the standalone.
258 00:32:22.570 ⇒ 00:32:36.570 Steven: So how would that… how would they access that? It would be a website they’ve got open on their screen. I mean, obviously, we don’t want… the chat is nice, because it’s within our workspace, they’re in there all the time. You know, I assume this would be a pretty easy way to access it as well.
259 00:32:38.120 ⇒ 00:32:43.719 Uttam Kumaran: Yeah, so it would be something standalone, but it would be just on the ABC domain, basically, so it would just be…
260 00:32:43.770 ⇒ 00:32:47.120 Uttam Kumaran: Like, you could say it’s, like, platform…
261 00:32:47.120 ⇒ 00:33:07.080 Uttam Kumaran: So, it would just be something else for them to access. You’re right in that it is very… the reason we went with the chatbot is because it’s very easy to use, so this would have to be a significantly more effective interface for it to be, like, a replacement. Like, we don’t want to just put the chatbot behind a website and then
262 00:33:07.080 ⇒ 00:33:16.889 Uttam Kumaran: call it a day. In fact, it should be… at minimum, the chatbot can still be there, so it’s very easy, but this should have other helpful improvements to…
263 00:33:16.890 ⇒ 00:33:34.680 Uttam Kumaran: like, isolating documents, doing… again, pulling up helpful things more proactively. Additionally, right now, the trainers don’t have an interface at all to interact with any of this data. The dashboard that we develop is sort of what they’re using, but a lot of their work is
264 00:33:35.080 ⇒ 00:33:39.660 Uttam Kumaran: Is not with, not within, sort of, the bounds of the chatbot today.
265 00:33:40.020 ⇒ 00:33:54.830 Steven: Yep. Yeah, I agree. I think it… how would it… what would it look like? What would the user interface look like? And that’d be more to Yvette and Janiece. I see the value in it, so they’re in it all the time, so that’d be more of what I want to talk to them and see what their thoughts are. I do think the chatbot would still need a…
266 00:33:54.830 ⇒ 00:34:06.490 Steven: stay within Google Chat, and then for others that either utilize it more, like I said, I see the value in it, but we want to make sure, as I said, between Yvette and Janiece. As far as the other places to use it, I still think
267 00:34:07.210 ⇒ 00:34:11.860 Steven: From the get-go, one of the ways we wanted to utilize it was more…
268 00:34:12.100 ⇒ 00:34:14.370 Steven: the CSRs are, I don’t know, 100…
269 00:34:14.480 ⇒ 00:34:19.730 Steven: Ish of our employees, so 10% of our total workforce, you know, what’s the other 90%?
270 00:34:19.830 ⇒ 00:34:38.640 Steven: technicians… I would still think, as far as from the knowledge database standpoint, if we can get to a way, we’ve talked about utilizing the HR side, the handbook, people can ask questions about their PTO rate, and I mean, I was just… the other day, working with someone on a HR issue, and we were trying to search the
271 00:34:38.900 ⇒ 00:34:46.890 Steven: employee handbook, and couldn’t find… and I knew, I was like, man, if Andy had this, he’d probably be able to answer this. So, now, how do we get there? Obviously, there’s…
272 00:34:47.320 ⇒ 00:34:55.779 Steven: working through Paylocity, which is our HR software, I know can sometimes be a struggle, I don’t know how easy they are.
273 00:34:55.980 ⇒ 00:35:06.029 Steven: releasing information, and I know HR would have something to say about that, too, but yeah, ultimately, I would love… because we also have an ABC NPS, it has some of our, like, our truck inspection forms, our…
274 00:35:07.900 ⇒ 00:35:20.790 Steven: vehicle, if you have an accident policy, if you have an injury policy, I mean, I would love to get to the point where you’re in the field, I have an auto accident, man, I haven’t had this in 3 years, hopefully you never have that, but if you do, what do I… we have all the time, no one knows what to do.
275 00:35:20.790 ⇒ 00:35:21.280 Uttam Kumaran: Yes.
276 00:35:21.280 ⇒ 00:35:35.509 Steven: they don’t know what to do, so they go to ABCMPS to be able to ask, what should I do? That’s an easy use case. Again, HR, handbook policies, who do I email or talk to for this, or help desk ticket, if they ask it a question, and
277 00:35:35.670 ⇒ 00:35:44.970 Steven: you know, Andy knows, well, that should be a help desk ticket to IT. It can populate and say, here’s the link to go send it. So, ideally, that would be a perfect world. Now, how do we get there?
278 00:35:45.220 ⇒ 00:36:01.119 Steven: when do we get there? You know, I don’t know if we’re ready for that yet, but yeah, ultimately, I would love to have a one… one source, whether Andy can answer everything, or at least push people in the right direction to be able to answer those questions, which a lot of… we know, 70-80% of them can be answered
279 00:36:01.420 ⇒ 00:36:04.709 Steven: by Andy, probably, because it’s in a handbook somewhere, or… but that…
280 00:36:04.710 ⇒ 00:36:05.980 Uttam Kumaran: Yeah. Also…
281 00:36:06.490 ⇒ 00:36:14.310 Steven: requires us to be organized and have all that info in an area that Andy can access it, which is sometimes an issue.
282 00:36:15.710 ⇒ 00:36:16.320 Uttam Kumaran: Yeah.
283 00:36:16.330 ⇒ 00:36:19.730 MattBurns: steven, who are we using for the…
284 00:36:20.500 ⇒ 00:36:23.239 MattBurns: call monitoring, right? Is it call source, or…
285 00:36:23.240 ⇒ 00:36:23.790 Steven: Yeah.
286 00:36:24.840 ⇒ 00:36:31.999 MattBurns: to get with Yvette on that and say, because what you said, Utam, Intrigues me a bit, where…
287 00:36:33.820 ⇒ 00:36:39.200 MattBurns: Using the trainer bot side of things to
288 00:36:39.620 ⇒ 00:36:45.070 MattBurns: really give immediate feedback to the CSR, because I don’t know…
289 00:36:45.740 ⇒ 00:36:49.000 MattBurns: How well that’s happening under call source.
290 00:36:49.430 ⇒ 00:36:58.939 Steven: I was thinking the exact same thing under… they basically… they do our QA side, listening to phone calls, like, AI, with the transcripts, AI should be able to do that.
291 00:36:58.940 ⇒ 00:36:59.990 MattBurns: Because what.
292 00:36:59.990 ⇒ 00:37:01.450 Steven: You know, obviously.
293 00:37:01.880 ⇒ 00:37:08.470 MattBurns: Utam, you know owning the business, it’s an economic decision, too.
294 00:37:08.470 ⇒ 00:37:09.220 Uttam Kumaran: Totally.
295 00:37:09.560 ⇒ 00:37:11.299 MattBurns: if I can… if I can…
296 00:37:11.960 ⇒ 00:37:20.849 MattBurns: get a real feel for… well, I… and I’m not saying this is the case, I have to get… find out more with Yvette and her team, but if I could say.
297 00:37:21.280 ⇒ 00:37:26.960 MattBurns: You know what, if I’ve got this, I don’t know that I need call source anymore. That’s a…
298 00:37:27.470 ⇒ 00:37:42.889 MattBurns: that’s a… not an additional expense, it’s a replacement of an expense, maybe it’s a saving of an expense, I don’t know, or a reduction in some case. So, that’s the first thing I’m going to do, is explore that, because I really like
299 00:37:44.400 ⇒ 00:37:48.579 MattBurns: And, and, and again, how… How we get that feedback.
300 00:37:48.730 ⇒ 00:37:52.839 MattBurns: that training feedback to the CSR,
301 00:37:53.000 ⇒ 00:37:55.059 MattBurns: Because if they’re taking phone calls.
302 00:37:55.490 ⇒ 00:37:59.010 Uttam Kumaran: one after another. When do they get the feedback? How can they… Correct.
303 00:37:59.010 ⇒ 00:38:09.050 MattBurns: So we can work through that, but yeah, if this could be a better, efficient utilization of that call monitoring, that would be…
304 00:38:09.500 ⇒ 00:38:17.770 MattBurns: a win, for sure. And like Steven said, we’ve talked before about our technicians
305 00:38:17.890 ⇒ 00:38:23.579 MattBurns: you know, like you said, the auto accident is perfect. If at some point they could… they have an auto accident.
306 00:38:24.270 ⇒ 00:38:27.089 MattBurns: they almost ask Andy, what do I do?
307 00:38:27.390 ⇒ 00:38:28.070 MattBurns: You know.
308 00:38:28.070 ⇒ 00:38:28.440 Uttam Kumaran: Yeah.
309 00:38:28.440 ⇒ 00:38:38.649 MattBurns: resource of, okay, here’s what you do. So I don’t… those are… those are all things that, we’ve, in brief, talked about, which goes around our communication
310 00:38:38.820 ⇒ 00:38:45.260 MattBurns: five-year plan as to how do we… how do we get better communication throughout the company.
311 00:38:45.770 ⇒ 00:38:51.890 MattBurns: And knowledge bases and resources and all that is a big part of that, so…
312 00:38:52.710 ⇒ 00:38:56.389 Uttam Kumaran: Yeah, I think on the… I totally hear you. If somehow, I think.
313 00:38:56.690 ⇒ 00:39:00.359 Uttam Kumaran: like, I know part of the call source is monitoring for…
314 00:39:00.370 ⇒ 00:39:15.139 Uttam Kumaran: like, credit cards and compliance things as well, so… so I know that that’s going on. But certainly, if this can augment that, it’s a huge win. But totally, this is not only just for that, but of course for the rapid feedback, right? So I think
315 00:39:15.140 ⇒ 00:39:29.000 Uttam Kumaran: it’s helpful to know that if this… if we can hit two birds with one stone there, I think this would definitely pay for itself there. I think second, yeah, I think it’s really helpful for me to hear about those other opportunities. I think it’s up to the
316 00:39:29.070 ⇒ 00:39:32.369 Uttam Kumaran: You know, y’all to… to think about what is the…
317 00:39:32.380 ⇒ 00:39:50.630 Uttam Kumaran: you know, what is the… what would the ROI be today if you had a system that could do that? I don’t think we are very far from, you know, Steven, making sure that we run the same sort of knowledge-gathering exercise around a certain basket of policies, putting it behind Andy, and even putting that behind a cell phone number. Like.
318 00:39:50.760 ⇒ 00:40:09.960 Uttam Kumaran: I don’t think we’re, like, months away from that. I think we’re, like, probably, like, weeks away from that. So I think it’s helpful to know, where we can assist there, and, you know, how you guys can leverage us to continue to push for more efficiency in the business. Of course, on our side, it’s just making sure that I can allocate
319 00:40:09.960 ⇒ 00:40:23.309 Uttam Kumaran: you know, our engineering time to do that. We’re still, of course, maintaining and updating the current state of Andy, so… but I don’t… again, like, I think it’s… some of those annoyances or things, it’s hard to…
320 00:40:23.480 ⇒ 00:40:25.890 Uttam Kumaran: Put a direct through line to the money.
321 00:40:25.980 ⇒ 00:40:36.950 Uttam Kumaran: But, like, you know, if you can get some type of estimate on, like, here’s the amount of calls that happen, here’s the amount of times that because someone doesn’t get a clear answer, they have to take someone else’s time.
322 00:40:36.950 ⇒ 00:40:49.859 Uttam Kumaran: Or they’re… or a customer somehow downstream, a customer is affected, and what is the ROI? You know, I’m… I’m certain that, you know, we can work to make sure that this is a… the amount of money you spend with us is a…
323 00:40:49.910 ⇒ 00:40:53.470 Uttam Kumaran: is a net win, for sure. Yeah.
324 00:40:53.990 ⇒ 00:40:54.600 MattBurns: Okay.
325 00:40:55.330 ⇒ 00:40:56.140 MattBurns: Good.
326 00:40:56.860 ⇒ 00:40:59.180 MattBurns: Well, I’ll chat… let me chat with Yvette first about
327 00:40:59.630 ⇒ 00:41:04.390 MattBurns: Call source, because that may be kind of an immediate thing we can say, well, yeah, let’s…
328 00:41:04.800 ⇒ 00:41:07.919 Steven: Let’s move forward on the trainer side of it to…
329 00:41:08.370 ⇒ 00:41:09.160 MattBurns: You know…
330 00:41:09.460 ⇒ 00:41:13.649 MattBurns: think about that, and then Steven, maybe you and Yvette and I can have a call on that, just to…
331 00:41:13.990 ⇒ 00:41:22.719 MattBurns: include Denise, if we want to, to say, alright, what are their thoughts? Because I know they’re working so intimately with Amber that we definitely got to get
332 00:41:22.920 ⇒ 00:41:25.909 MattBurns: Some good feedback from them on this, so…
333 00:41:25.910 ⇒ 00:41:26.500 Steven: Yep.
334 00:41:27.490 ⇒ 00:41:28.110 Steven: Good.
335 00:41:28.110 ⇒ 00:41:29.070 Uttam Kumaran: Yeah, and again, you know…
336 00:41:29.420 ⇒ 00:41:33.399 Steven: You know this probably already, but I think one of the biggest benefits we’ve enjoyed
337 00:41:33.590 ⇒ 00:41:44.849 Steven: working with y’all is, I know there’s other AI companies… I mean, AI’s moving so fast, you don’t know who’s gonna come out on top yet, but what we’ve appreciated about y’all is… is just working with y’all. The people you have are great, and the…
338 00:41:45.090 ⇒ 00:41:57.490 Steven: the, customization from… I mean, just the time… I know y’all have dedicated a lot of time and effort from Amber and Sam and everyone else. We just enjoy working with y’all, and yeah, I think you know it already, but you’ve got a great, great team that…
339 00:41:57.490 ⇒ 00:41:58.280 Uttam Kumaran: No, I appreciate it.
340 00:41:58.310 ⇒ 00:41:59.670 Steven: That provides value.
341 00:42:00.320 ⇒ 00:42:03.190 Uttam Kumaran: Yeah, and that’s why I want to make sure, and you guys know since the beginning.
342 00:42:03.290 ⇒ 00:42:21.189 Uttam Kumaran: we want to make sure that every dollar you spend with us is 5 out, you know, on the other side. So, as close as we can align to that, even if there is an area where, like, hey, we don’t… we don’t know exactly, but we want to set up something that’s more of we hit some certain milestones to get there, I’m totally open to things like that.
343 00:42:21.190 ⇒ 00:42:34.060 Uttam Kumaran: I just know that the system we’ve set up and the amount of time we’ve been with y’all, there are definitely other applications. It’s up to, kind of, us to find those points of innovation, and I will tell you that other companies
344 00:42:34.100 ⇒ 00:42:51.750 Uttam Kumaran: there’s not, like, a left… look left or right, because this is all brand new. And so, it’s really tough, because you can’t go look at other companies and see, like, what’s happened, because you guys are on the forefront of this. And so, I think additionally, like, we… I don’t care whether it’s OpenAI or Claw, like.
345 00:42:51.770 ⇒ 00:43:08.209 Uttam Kumaran: I care about getting the outcomes for y’all, and so hopefully we can bear the brunt of, like, finding the best technologies, but again, like, you’re gonna get a software vendor that’s gonna sell you on their solution today, and they’re gonna leave you to dry with a tool. And I just think…
346 00:43:08.290 ⇒ 00:43:27.359 Uttam Kumaran: especially how fast AI is moving, it’s not gonna work out that way for some of these tools. It’s… what you guys… the reason we’ve been able to find success is because of the customization. We haven’t just implemented one, like, turn on a platform here. It takes a lot of knowledge and strategic work, so…
347 00:43:27.730 ⇒ 00:43:31.479 Uttam Kumaran: Yeah, I think it could be really interesting to talk through that.
348 00:43:31.910 ⇒ 00:43:39.049 MattBurns: Yeah, and we’ve kind of followed that thinking, Utam, with… Just our…
349 00:43:39.180 ⇒ 00:43:42.979 MattBurns: Regular proprietary software and our,
350 00:43:43.130 ⇒ 00:43:47.630 MattBurns: our sales software. We generally deal with smaller.
351 00:43:48.150 ⇒ 00:43:51.819 MattBurns: Custom vendors who can really work with us on things, like…
352 00:43:52.020 ⇒ 00:44:04.209 MattBurns: I don’t want to go to Salesforce for things, because they’re not going to listen to me and say, hey, here’s a change maybe we could make. They’re going to go, well, the platform is a platform, we’re enhancing it all the time, but maybe not…
353 00:44:04.690 ⇒ 00:44:07.760 MattBurns: But… You know, you don’t get a vote, so…
354 00:44:08.370 ⇒ 00:44:15.730 MattBurns: I like the fact that it’s more of a mutual development here that benefits us directly, so, yeah.
355 00:44:15.730 ⇒ 00:44:21.230 Uttam Kumaran: It’s also software’s changing, right? Like, we’re… the whole software now is interacting through Google Chat.
356 00:44:21.270 ⇒ 00:44:35.700 Uttam Kumaran: It’s not a typical thing, right? And that’s where we’re also innovating in that. Our solutions are not… our immediate vote is not build a new platform, right? The reason we’re coming to the table with that is because, okay, there’s, like, a slew of features that I think we can unlock.
357 00:44:35.730 ⇒ 00:44:44.350 Uttam Kumaran: But it’s not our first vote. In fact, even in our company, I’m, like, less tools. I don’t want more UIs. Things should be centralized to where we work.
358 00:44:44.380 ⇒ 00:44:55.959 Uttam Kumaran: And so, in the world of AI now, that’s the direction to head, is less tools, more flexibility, and again, like, we use a lot of AI to develop the things for our customers, so…
359 00:44:56.140 ⇒ 00:45:04.379 Uttam Kumaran: our speed is enhanced that way as well. And yeah, again, like, a lot of the work we’re doing on the knowledge engineering side.
360 00:45:04.760 ⇒ 00:45:18.229 Uttam Kumaran: I think you’ll find that starts to rhyme with other problems, Steven, as you’re seeing. So that’s where it’s like, however we can start to mold all the great work, you know, and it’s just a couple tweaks to get it to support there, would love to do that, you know.
361 00:45:19.890 ⇒ 00:45:20.500 Steven: Boom.
362 00:45:21.270 ⇒ 00:45:21.880 MattBurns: Great.
363 00:45:22.910 ⇒ 00:45:24.030 MattBurns: Well, I’ll… we’ll…
364 00:45:24.510 ⇒ 00:45:29.610 MattBurns: Steven and I’ll get with Yvette and just give you some feedback, as to what we think, but…
365 00:45:29.610 ⇒ 00:45:30.400 Uttam Kumaran: Cool.
366 00:45:30.430 ⇒ 00:45:34.539 MattBurns: We’re happy with things. We think, again, we want to continue to make progress.
367 00:45:34.740 ⇒ 00:45:37.999 MattBurns: Sounds like we’re all working in the same direction on it, so…
368 00:45:38.880 ⇒ 00:45:39.500 Uttam Kumaran: Correct.
369 00:45:39.650 ⇒ 00:45:40.700 MattBurns: Perfect.
370 00:45:40.900 ⇒ 00:45:41.260 MattBurns: Okay.
371 00:45:41.260 ⇒ 00:45:41.940 Uttam Kumaran: Okay.
372 00:45:42.400 ⇒ 00:45:43.520 Uttam Kumaran: Thank you, not so much.
373 00:45:43.880 ⇒ 00:45:44.390 Steven: Thank you, y’all.
374 00:45:44.390 ⇒ 00:45:47.030 Uttam Kumaran: Yeah. Thank you, talk soon. Bye.
375 00:45:47.360 ⇒ 00:45:48.070 Amber Lin: Hi!