Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2025-05-15 Meeting participants: Uttam Kumaran, Amber Lin, Steven, Janiecegarcia, Yvetteruiz, Mattburns, Scott_Harmon
WEBVTT
1 00:00:23.510 ⇒ 00:00:24.280 Uttam Kumaran: Hello!
2 00:00:24.570 ⇒ 00:00:25.550 YvetteRuiz: Hi.
3 00:00:26.090 ⇒ 00:00:26.630 Uttam Kumaran: How are you?
4 00:00:26.630 ⇒ 00:00:28.869 YvetteRuiz: How are you? I’m good. How are you doing.
5 00:00:28.870 ⇒ 00:00:36.069 Uttam Kumaran: Good I was. Gonna I think Amber will be here in a sec. But amber is actually gonna be in town early next week.
6 00:00:36.270 ⇒ 00:00:36.760 YvetteRuiz: Oh!
7 00:00:36.760 ⇒ 00:00:37.370 YvetteRuiz: Oh!
8 00:00:37.370 ⇒ 00:00:50.910 Uttam Kumaran: So she’s gonna be in town Monday and Tuesday I was. Gonna I know she’s I’m I’m gonna be out of. I’m gonna be leaving for a brief vacation on on after Wednesday, but she will be here the rest of the week.
9 00:00:51.020 ⇒ 00:01:04.090 Uttam Kumaran: If you guys are, I don’t know. I don’t. I know sometimes you. You may be in town on Tuesday, but if Tuesday doesn’t work, then maybe she can stop by. When you guys are typically there on on Thursday. But just wanted to give her an opportunity to come. Say, Hi.
10 00:01:04.620 ⇒ 00:01:10.811 YvetteRuiz: That’s very cord. We were just asking her that earlier this week when we met. When was she gonna come down? There she is.
11 00:01:11.690 ⇒ 00:01:15.510 YvetteRuiz: Hi Amber. We just you just told us the news.
12 00:01:17.870 ⇒ 00:01:18.709 YvetteRuiz: You’re gonna be coming.
13 00:01:18.710 ⇒ 00:01:19.460 Amber Lin: Excited.
14 00:01:20.905 ⇒ 00:01:22.350 YvetteRuiz: Yeah.
15 00:01:22.350 ⇒ 00:01:25.379 Amber Lin: You’re in Austin on Thursday, right?
16 00:01:25.620 ⇒ 00:01:32.000 YvetteRuiz: Yeah, and if it’s I’m there on Thursdays. But if I need to move it, I certainly can.
17 00:01:32.370 ⇒ 00:01:34.058 Amber Lin: No, I’m there the whole week.
18 00:01:34.340 ⇒ 00:01:35.350 YvetteRuiz: Okay. I’ll.
19 00:01:35.350 ⇒ 00:01:36.750 Amber Lin: Go in Thursday.
20 00:01:36.880 ⇒ 00:01:40.449 YvetteRuiz: Okay, cool. Then I’m excited to meet you in person.
21 00:01:42.230 ⇒ 00:01:44.240 YvetteRuiz: Hi, Matt, How’s Hi, Steven.
22 00:01:44.240 ⇒ 00:01:44.930 Steven: Hey, guys.
23 00:01:44.930 ⇒ 00:01:45.730 Uttam Kumaran: Hey, guys.
24 00:01:45.730 ⇒ 00:01:48.600 Amber Lin: Hi, different. Background, today.
25 00:01:50.830 ⇒ 00:01:54.330 Steven: Yeah, Steven, you look like you’re in like the White House or something.
26 00:01:54.330 ⇒ 00:01:56.369 Steven: This is our new office building. Actually.
27 00:01:57.560 ⇒ 00:01:58.640 Uttam Kumaran: Okay.
28 00:01:58.640 ⇒ 00:02:00.520 Steven: Yeah, it’s fancy.
29 00:02:00.520 ⇒ 00:02:02.150 Uttam Kumaran: Nice. Wow!
30 00:02:03.450 ⇒ 00:02:05.869 YvetteRuiz: So, Udem, where are you going on vacation?
31 00:02:06.170 ⇒ 00:02:13.559 Uttam Kumaran: I am going to Argentina. One of our former employees is getting married.
32 00:02:13.670 ⇒ 00:02:41.620 Uttam Kumaran: and he asked me to go, and I said, This is like when he was working here, and I was like, you don’t have to have like you don’t have to do me, you know, and he was like. No, no, I really want you to come, so we’ll be going. The wedding’s on Saturday, but we’ll be going. Me and my girlfriend will be going. We’re leaving Tuesday evening, and in Argentina there’s an area called Mendoza, which is like wine country famous for like Malbec. If anyone drinks wine, and so we’re spending 3 days there, and then one.
33 00:02:41.620 ⇒ 00:02:42.230 YvetteRuiz: Ow.
34 00:02:42.230 ⇒ 00:02:47.526 Uttam Kumaran: Back in the city, so it should be nice. I haven’t taken time off in
35 00:02:49.010 ⇒ 00:03:09.099 Uttam Kumaran: maybe the entire to the company, so this will be nice. And we have some great people on staff. Now that will handle stuff. And of course it’ll be a phone phone call away. But yeah, it should be really really nice. But yes, I was Matt and Steven. Amber will be in town next week, so hopefully she’ll be able to stop by and say Hi! To everyone.
36 00:03:09.290 ⇒ 00:03:10.320 Steven: Yeah. Awesome.
37 00:03:11.720 ⇒ 00:03:18.970 Uttam Kumaran: I I unfortunately, I’ll be on vacation next week, or I’m taking time off, too. So okay, great.
38 00:03:18.970 ⇒ 00:03:22.954 Amber Lin: I’ll actually be in Mexico City next week. So, looking forward to it, no.
39 00:03:23.310 ⇒ 00:03:24.250 YvetteRuiz: Nice.
40 00:03:24.250 ⇒ 00:03:32.373 Amber Lin: Everybody’s on vacation. Technically, it’s also a vacation, because I’m in Austin the whole week, and I am using Utam’s house.
41 00:03:32.760 ⇒ 00:03:50.220 Uttam Kumaran: Yeah, we’re doing a, we’re doing a data event on Monday. We. We rented a house with one of our like a data company that we do some work with here. So we’re inviting some like data leaders. And amber does photography on the side. So I was like.
42 00:03:50.440 ⇒ 00:03:59.920 Uttam Kumaran: I would love for you to just come. Take photos that only have to get a photographer. And then, yeah, I I was like, Oh, so we should just go should go. Say Hi to ABC, it’s like
43 00:04:00.140 ⇒ 00:04:03.390 Uttam Kumaran: it’s 10 min from where I am right now. Amber. So yeah.
44 00:04:03.390 ⇒ 00:04:03.940 Steven: Cool.
45 00:04:04.760 ⇒ 00:04:06.135 YvetteRuiz: Lots of good stuff.
46 00:04:06.480 ⇒ 00:04:18.799 Amber Lin: Yeah, exciting today’s meeting gonna be pretty fast. I do wanna share a few updates. And then we can also plan for what we wanted to do next week as well.
47 00:04:18.910 ⇒ 00:04:20.509 Amber Lin: So let me share.
48 00:04:20.510 ⇒ 00:04:21.240 YvetteRuiz: Yes.
49 00:04:26.980 ⇒ 00:04:27.720 Amber Lin: Who knows?
50 00:04:29.170 ⇒ 00:04:42.719 Amber Lin: Oh, I forgot to change the dates. So here are the few things I want to talk about. We, me, Yvette and Janice discussed these items on Monday, but I know they were so busy with a conference. So it’s more of a
51 00:04:43.260 ⇒ 00:04:51.000 Amber Lin: we’re getting ready to implement these next week, so very excited to have some new progress as well.
52 00:04:51.550 ⇒ 00:05:04.849 Amber Lin: 1st up is the overflow agents we discussed with event engineers on, how do we want to improve this time based on learnings of rolling out Andy to everybody last time. So
53 00:05:05.250 ⇒ 00:05:19.390 Amber Lin: we already have the grants from Tim to be able to roll it out to everybody. I believe Yvette has the Admin role to add everyone. So we created what we did was create a new
54 00:05:19.520 ⇒ 00:05:33.049 Amber Lin: procedure for rolling out. Andy of okay, want to make sure that they know feedback is really important. Want to make sure that we have mechanisms in place to check in with everybody, and ultimately to make sure that this group
55 00:05:33.270 ⇒ 00:05:37.350 Amber Lin: will also get the full benefit of Andy, because we know that.
56 00:05:37.620 ⇒ 00:05:48.109 Amber Lin: comparing to our regular pet Csr. Agents, this overflow agents will get the most benefit out of using Andy. So we really want to make sure that this goes very successfully.
57 00:05:51.180 ⇒ 00:06:09.409 Amber Lin: Next one I will be in office. I will be in Austin next week. So when we talked on Monday, we were like, Okay, let’s do some office hours where this, where the reps can drop in to talk about their concerns with Andy, and that will really help boost their usage. Since I’m gonna go
58 00:06:09.500 ⇒ 00:06:22.489 Amber Lin: to Austin next week. I would love to just spend a half day or full day there and then the reps can come. Talk to me, and I can help them walk through, Andy. Let them know how things go.
59 00:06:23.630 ⇒ 00:06:24.639 Steven: I love that.
60 00:06:24.970 ⇒ 00:06:31.609 Amber Lin: Yeah, I don’t know how many of the reps is actually in Austin are all of them in Austin, where they kind of dispersed.
61 00:06:32.520 ⇒ 00:06:49.278 YvetteRuiz: The majority of them are, I mean. But then we have others in other locations, San Antonio, but we can definitely coordinate the ones that are there? Time? You know, like I was mentioning to you on Monday, we just need to plan with Brian. So then that way we block them out. So not we don’t have everyone off the phones.
62 00:06:49.920 ⇒ 00:06:56.350 Amber Lin: Okay, yeah, sounds good. And if I’m there in person I’ll just walk around. I’ll see who’s free, and I’ll go talk to them.
63 00:06:57.330 ⇒ 00:06:58.300 YvetteRuiz: Perfect.
64 00:06:58.300 ⇒ 00:07:00.470 Amber Lin: Yeah, very excited for that.
65 00:07:01.770 ⇒ 00:07:17.060 Amber Lin: So we also talked about another part of boosting usage is having that daily reminder in place. And I’ve asked Annie, our data analyst to create dashboards where we can see who’s not using it as well.
66 00:07:17.412 ⇒ 00:07:31.869 Amber Lin: So that’s in progress. Next Monday, when we review the dashboard event. I can show you what’s on the dashboard, but we can also explore possibilities of sending that leaderboard to the Csrs every morning so that they can see. Oh.
67 00:07:32.819 ⇒ 00:07:42.939 Amber Lin: I’m at usage 0, or okay. I see this person next to me is on top of the leaderboard so hopefully that will spark some more usage, as well.
68 00:07:43.170 ⇒ 00:07:43.720 YvetteRuiz: Yeah.
69 00:07:44.250 ⇒ 00:07:48.600 MattBurns: Hey vette, in terms of the in terms of the overflow agents.
70 00:07:49.250 ⇒ 00:07:55.620 MattBurns: Do you have a plan just to have you introduced it to them, really, or do they? Are they familiar with it at all? The overflow pest agents.
71 00:07:55.870 ⇒ 00:08:10.849 YvetteRuiz: So only a few of them and a lot of them got the information from the company meeting, but a handful of them already know. So they’re they’re excited to start testing this because of the, you know, the constant questions that they have so.
72 00:08:11.150 ⇒ 00:08:12.439 MattBurns: And so when I talk.
73 00:08:12.660 ⇒ 00:08:18.810 MattBurns: I was just saying, in one respect, you know, for overflow, since they don’t handle pest all the time.
74 00:08:18.910 ⇒ 00:08:22.929 MattBurns: you would think Andy would be in one way even more helpful
75 00:08:23.612 ⇒ 00:08:32.549 MattBurns: cause again. They just don’t take that many calls, so that might be something to concentrate on when Amber’s there next week to.
76 00:08:33.380 ⇒ 00:08:46.050 YvetteRuiz: Yeah, no, for sure. And that’s kind of what I was sharing with amber on Monday said, I think we’re going to get a lot of more value and usage from those overflow agents because of that very reason. So yeah, we will. We’ll we’ll put the focus there, Matt.
77 00:08:46.050 ⇒ 00:08:46.839 MattBurns: Good deal.
78 00:08:51.110 ⇒ 00:09:20.300 Amber Lin: Awesome. So I don’t know if you guys had a chance to test Trainer bot that I sent via the email. But kind of want to demonstrate how it works. So you guys can have more confidence where you’re actually testing it out. So taking the example of pet poisoning right? And 1st of all, this is the current section we have in the Central Doc, not too fleshed out. But this is such. A. This is such an important topic, because
79 00:09:20.390 ⇒ 00:09:39.870 Amber Lin: if a pet is poison, it is an emergency. As Janice has added here, so we want to make sure that the agents know exactly what to do, because when a customer calls and they’re overwhelmed, you get overwhelmed as well. So, having that step 1, 2, 3 will really help them process those emotions and make sure we don’t miss anything.
80 00:09:41.050 ⇒ 00:09:52.859 Amber Lin: So we asked the bot. Okay, we gotta gotta have an outline for creating that. And the bot gives us out gives us a structure, and we give it more details to fill it out.
81 00:09:53.530 ⇒ 00:10:17.440 Amber Lin: So the trainer ideally just blurbs a whatever is on their mind. You don’t need to make it pretty. You just need to give whatever information that you know. And then the bot will say, Okay, what other areas are there? It asks more, follow up questions and based on those questions, it fleshes out that structure. So now we know, okay, what else do I need to add?
82 00:10:17.810 ⇒ 00:10:36.740 Amber Lin: And we give it more information. And ultimately it creates a pretty full document of what this is, what is for, what the scope is to make sure that? Okay? Does it need to cover more? Does it? Is it too small of a document? Can we combine it with other documents?
83 00:10:36.870 ⇒ 00:10:49.969 Amber Lin: And then it has the process of step 1, 2, 3, 4, 5, so that the agents can follow exactly, and they can know exactly what to do and not to get overwhelmed in those very emotional moments.
84 00:10:50.320 ⇒ 00:10:54.139 YvetteRuiz: Yeah, yeah, this will vary cool. So you took
85 00:10:54.470 ⇒ 00:10:59.139 YvetteRuiz: the the one we had, for example, that pet poisoning. And that’s how you. The questions were
86 00:10:59.763 ⇒ 00:11:04.080 YvetteRuiz: based off of that. And then that’s the the last one is the doc that it created.
87 00:11:04.612 ⇒ 00:11:29.599 Amber Lin: It creates a doc. Currently, we still have to copy copy this and then paste it in the Central Doc and do some manual tweaks here and there. We wanna make sure that every single detail is correct. And in the future we can investigate how to make this an automatic update, but I wanted to get this out to the trainers as soon as possible, to make sure every update we make from now will be more complete.
88 00:11:30.610 ⇒ 00:11:37.598 YvetteRuiz: That’s pretty cool. We have not. I haven’t tried it. And we just got back today. So that’s the plan here. In the next couple of days.
89 00:11:37.840 ⇒ 00:11:38.580 Amber Lin: Yeah. And
90 00:11:39.300 ⇒ 00:11:49.660 Amber Lin: when I’m in person we’ll just test it out together. I think we can run through a lot of those errors that we wanted to fix. And we can just we can just go at it and make it a lot better.
91 00:11:51.380 ⇒ 00:11:57.269 YvetteRuiz: Denise. We could probably test it out with the timeframe windows of the thing that just came up right now. That would be a good one.
92 00:11:58.740 ⇒ 00:11:59.360 Amber Lin: Yeah.
93 00:11:59.750 ⇒ 00:12:10.200 Amber Lin: exciting. Keep me posted. And this is one last update that I want to talk about. So, so we talked about last time. Okay, we have all these documents
94 00:12:10.500 ⇒ 00:12:14.950 Amber Lin: in the Csr pest folder. I looked at it. I think there’s
95 00:12:15.130 ⇒ 00:12:22.500 Amber Lin: around a hundred 79 ish documents. And so I went ahead and
96 00:12:23.110 ⇒ 00:12:37.539 Amber Lin: made a directory of all the documents, is in there in a Google sheet. And that is also shared with you guys and think, moving forward from the next step from this would be to look at each of these documents and say, Okay, is this in the central Doc?
97 00:12:37.780 ⇒ 00:12:44.547 Amber Lin: If yes. Can we retire this current document to make sure that all the Csrs are
98 00:12:45.070 ⇒ 00:12:49.969 Amber Lin: looking at one single source of truth. So I think from this
99 00:12:50.090 ⇒ 00:12:56.180 Amber Lin: especially, we can start to initiate our document archive initiative.
100 00:12:56.590 ⇒ 00:12:57.420 Amber Lin: Okay, that’s a little.
101 00:12:57.420 ⇒ 00:13:05.800 YvetteRuiz: Perfect. I, yeah, that’s gonna be a big step getting rid of all that other data and having them just trust Andy and get rid of everything else that’s hanging out there.
102 00:13:06.530 ⇒ 00:13:07.329 Uttam Kumaran: Amber, is it.
103 00:13:07.330 ⇒ 00:13:07.865 Amber Lin: Yeah.
104 00:13:08.400 ⇒ 00:13:12.520 Uttam Kumaran: Is there an order in which we’re tackling these.
105 00:13:13.200 ⇒ 00:13:16.630 Amber Lin: I would love to get some more insight from you and Janice as well.
106 00:13:17.890 ⇒ 00:13:18.610 Uttam Kumaran: Yeah, when I.
107 00:13:18.610 ⇒ 00:13:20.600 Amber Lin: Other column we can mark mark.
108 00:13:20.600 ⇒ 00:13:25.960 Uttam Kumaran: There’s gonna be some that are like, probably very unused. I would honestly leave those for the end.
109 00:13:26.310 ⇒ 00:13:29.230 Uttam Kumaran: like I would start with the ones that are used
110 00:13:29.620 ⇒ 00:13:56.070 Uttam Kumaran: most commonly because it’ll force to make sure that the bot is is able to support those the other ones that, like haven’t been updated in a while, or are like very short like, that’s fine. So I I’m I’m not. You know you. You may be able to even ask Tim to give you like the logs on these docs. And who’s asking? I don’t know. Maybe he might have that, so you can sort of rank this in some way.
111 00:13:56.180 ⇒ 00:13:59.640 Uttam Kumaran: but that’d be my feedback given. There’s more than a hundred.
112 00:14:00.820 ⇒ 00:14:11.140 YvetteRuiz: Cause, Janice, I think what we talked about, I mean that is, I mean, you have some going to Andy asking it, and then you still have some that are referring back to the Csr knowledge, I mean the folder right.
113 00:14:11.140 ⇒ 00:14:11.760 JanieceGarcia: Yes.
114 00:14:11.870 ⇒ 00:14:12.430 JanieceGarcia: Correct.
115 00:14:12.430 ⇒ 00:14:16.089 YvetteRuiz: But to your I don’t know which one specifically
116 00:14:16.300 ⇒ 00:14:18.959 YvetteRuiz: do. They keep pulling or searching and stuff?
117 00:14:19.080 ⇒ 00:14:31.970 YvetteRuiz: But the goal is to get rid of that, because I know that everything that we’re updating that goes to the central docs. These old files are not getting updated. So I’m trying to get rid of these. So then, that way they can trust the one.
118 00:14:32.848 ⇒ 00:14:35.560 JanieceGarcia: Central, Doc and Andy.
119 00:14:36.720 ⇒ 00:14:38.339 Uttam Kumaran: Tim may have that I don’t know. I think it’s worth.
120 00:14:38.340 ⇒ 00:14:40.550 YvetteRuiz: Okay, yeah, we can. We can ask him, yeah.
121 00:14:40.550 ⇒ 00:14:40.930 Uttam Kumaran: Yeah.
122 00:14:44.780 ⇒ 00:14:49.530 Amber Lin: Okay, sounds good. I’ll put it on my to do list. And we can definitely figure this out together.
123 00:14:52.290 ⇒ 00:14:55.620 Amber Lin: I think that’s pretty much it last thing is.
124 00:14:55.850 ⇒ 00:15:08.850 Amber Lin: for last time we talked about this knowledge based structure, and a next step from that would be to identify what’s missing, not only in individual documents.
125 00:15:09.140 ⇒ 00:15:15.307 Amber Lin: but also within the whole knowledge base. To see. Okay, what else do we need to fill in?
126 00:15:15.880 ⇒ 00:15:38.720 Amber Lin: This initiative also ties into formatting the Central Doc nicely. So if essentially, especially if the Csrs are going to give up the Google Google drive and start looking in the central doc, we want to make sure that it’s easy on eyes easy to navigate. So those are 2 things that I will also like to start doing next week as well.
127 00:15:40.300 ⇒ 00:15:41.700 YvetteRuiz: Makes sense. Okay.
128 00:15:43.850 ⇒ 00:15:46.160 Amber Lin: That’s everything. Thank you. All.
129 00:15:47.070 ⇒ 00:15:49.020 Amber Lin: Opening the floor up for questions.
130 00:15:50.720 ⇒ 00:15:58.090 Uttam Kumaran: Yeah, I guess. My question was just gonna be, you know, I I sort of look at the the dashboard just to check adoption.
131 00:15:58.485 ⇒ 00:16:14.434 Uttam Kumaran: And I know for me, I think I I see the progress that we’re making on the trainer Bot, and I think a lot of that’s going in for me. I think there’s still the biggest thing I want to see is that the numbers of exchanges are going up. It’s actually nice. I am seeing that
132 00:16:14.930 ⇒ 00:16:22.679 Uttam Kumaran: A lot of people that I talked to at when I came in are using it a lot more now. Even some folks that I don’t think we’re super
133 00:16:22.910 ⇒ 00:16:48.080 Uttam Kumaran: immediately super interested in using it. So that’s actually really great. So I think for me, the number one focus again would just be I think, amber when you’re here to really dissect like what? What’s stopping folks? Of course not. Every call is gonna necessitate a use of it right? I think it’s for us to understand what percent is that? And of course, for the overflow folks, I think I wanna make sure that they have access to this as well.
134 00:16:48.190 ⇒ 00:16:50.069 Uttam Kumaran: because I think I wanna
135 00:16:50.350 ⇒ 00:16:56.560 Uttam Kumaran: share that. They’re they’re they’re leveraging it as well. But the trainer stuff, I think looks really really great.
136 00:16:57.590 ⇒ 00:17:05.820 Steven: Do we have now? I I probably have it somewhere in an email. But, Yvette, do you have access? Are you checking the dashboard to kind of see usage. Do you have access to that now?
137 00:17:06.359 ⇒ 00:17:06.959 YvetteRuiz: Excuse me.
138 00:17:07.290 ⇒ 00:17:15.500 Steven: Can you send that to me? Got it somewhere, but send it. Send it to me. I know we had talked about that. But yeah, just seeing I’d like to be able to track and see the usage rate as well.
139 00:17:15.930 ⇒ 00:17:27.566 YvetteRuiz: Yeah. And that was one thing that we were talking about is getting a a way to run those reports. So then that way, we can post them regularly. With our agents. So yeah.
140 00:17:27.890 ⇒ 00:17:40.640 Steven: Yeah, that’d be great. That would be ideal if we get a weekly, for Matt would enjoy that as well. Weekly report that just says, Hey, you know, very basic breakdown usage, and even I don’t know. It’d be kind of interesting to see like times of day if it’s used more.
141 00:17:40.640 ⇒ 00:18:05.739 Uttam Kumaran: That’s in. Yeah, that’s that’s that’s in there. And we also have all the phone data, right? So we have a lot of the data that Brian and David are seeing. So the dashboard, you know, starts at the top overview of just like the calls that are coming in and I’ll even just I’ll just flash it up. Since that’s what I’ve sort of been looking at. But basically like what I, what I tend to look at is, I’m just looking at last. 14 days.
142 00:18:06.127 ⇒ 00:18:31.149 Uttam Kumaran: and just want to see comparison. So I look at okay, how many calls were made? I kind of get a sense for? Okay, it looks like we’re, I mean, of course, the team is taking more calls ideally. What I want to see anecdotally is that things are getting handled faster. I don’t know. This isn’t like that big. So I’m not not entirely sure. And then you can actually see of of the calls. How many reps are taking
143 00:18:31.420 ⇒ 00:18:45.190 Uttam Kumaran: a bot assisted calls right? And so we want to see at least a couple per day, and then that kind of grows over time, and then you can also see who was using the bot and made this, and then in this period stop using it.
144 00:18:45.340 ⇒ 00:18:50.750 Uttam Kumaran: and then at the bottom, here you’ll you’ll be able to see how many are using the oh, by the ways
145 00:18:50.960 ⇒ 00:19:14.439 Uttam Kumaran: are they? Thumbs up versus thumbs down. How many are escalating? Meaning like the the bot didn’t answer, or it had the wrong information. And then you can see that our escalation times are are pretty flat and I think it has a lot. And if anything you you see in here where you’re like, we want to take this one step deeper, you should just shout, it’ll. It’s pretty quick for us to make those changes.
146 00:19:17.360 ⇒ 00:19:22.730 Steven: To get to run a report and just shoot out a weekly report end of the week or beginning of the week.
147 00:19:23.510 ⇒ 00:19:29.120 Uttam Kumaran: Yeah, I think, Amber, you can just schedule this and maybe fire it to this crew, or if you want to.
148 00:19:29.230 ⇒ 00:19:32.130 Uttam Kumaran: if you wanna download it to Pdf, and like.
149 00:19:32.360 ⇒ 00:19:37.610 Uttam Kumaran: even add a couple of what you’re noticing like. Circle a couple of things to highlight.
150 00:19:37.610 ⇒ 00:19:39.020 Uttam Kumaran: Great! I feel like that’s a really that’s a.
151 00:19:39.020 ⇒ 00:19:39.380 YvetteRuiz: I see.
152 00:19:39.380 ⇒ 00:19:40.709 Uttam Kumaran: Easy thing that we can do.
153 00:19:40.710 ⇒ 00:20:02.780 Amber Lin: Yeah. And currently, me and Yvette have a weekly meeting on Monday. We kind of go through. We kind of cycle through. One is for planning one week, for planning, one week for Kpi reviews. I think I can definitely send out a dashboard like quick video or quick. Pdf, every month every week.
154 00:20:03.129 ⇒ 00:20:07.739 Amber Lin: To get everybody caught up on. Okay, what’s happening? What do we need to do? I think that’s a great idea.
155 00:20:08.100 ⇒ 00:20:12.270 Scott_Harmon: One thing you can do that would probably be fun and
156 00:20:12.570 ⇒ 00:20:17.900 Scott_Harmon: take reduce your time. Amber is, you could probably have Andy write his own report.
157 00:20:18.235 ⇒ 00:20:18.849 Steven: There you go!
158 00:20:19.480 ⇒ 00:20:25.130 Scott_Harmon: That’d be very slick, I mean, you know, every Friday just send out, hey? From Andy.
159 00:20:25.390 ⇒ 00:20:33.819 Scott_Harmon: you know. Here’s what I saw. Oh, these people are using me, you know. Here were the key highlights, you know, cause Andy can write that. Obviously we could
160 00:20:34.200 ⇒ 00:20:43.959 Scott_Harmon: set that to fire every Friday and and send out his own. Not that you not that you wouldn’t do a great job, but it would pretty pretty easy to have him do it himself. I think.
161 00:20:44.390 ⇒ 00:20:55.579 Steven: I do love that idea of continue to use Andy like, because again, people need to get more use like, Hey, Andy can do a lot of things and answer a lot of things that have Andy as again kind of like a person and and fun, and how you have a little fun with it.
162 00:20:55.840 ⇒ 00:21:05.630 Scott_Harmon: Yeah, I think I think the more that just get the persona people used to hearing from it. And you know, like I said, it can generate his own, his own status report every Friday.
163 00:21:06.880 ⇒ 00:21:11.770 Scott_Harmon: and does. Have we tried it with voice mode? I assume it works just fine with Tom just.
164 00:21:12.300 ⇒ 00:21:15.422 Uttam Kumaran: We? We have it hooked up on our end.
165 00:21:16.210 ⇒ 00:21:19.939 Uttam Kumaran: well, we have. We sort of it’s just sort of sitting there. Of course.
166 00:21:19.940 ⇒ 00:21:25.050 Scott_Harmon: I don’t know if it makes any sense for the for the Csrs to do voice mode, but
167 00:21:25.150 ⇒ 00:21:31.529 Scott_Harmon: I’ve been using chatgpt voice mode the last 3 weeks, and it’s just completely addicting like it.
168 00:21:31.830 ⇒ 00:21:32.360 Scott_Harmon: It’s.
169 00:21:32.360 ⇒ 00:21:39.870 Uttam Kumaran: I use it all the time. Yeah, cause drive, and I need to. Still, I’m like, Oh, if I can get some work done like I can.
170 00:21:39.870 ⇒ 00:21:45.860 Scott_Harmon: I do it in my car all the time I do it when I’m so it it should just.
171 00:21:45.860 ⇒ 00:21:52.619 Steven: What are some examples I haven’t really used for, like, what are some examples of, what do you use it for while yeah, while you’re driving? Or just.
172 00:21:52.620 ⇒ 00:21:56.190 Scott_Harmon: Just everything that I would type like. You know.
173 00:21:57.000 ⇒ 00:22:02.079 Scott_Harmon: I have a sports nut. So some I heard something about some new
174 00:22:03.060 ⇒ 00:22:23.680 Scott_Harmon: nil portal thing for the longhorns. Sorry, Steven, and it and I asked about it. And it just came back and said, Yeah, this player went to Blah. Blah is a 5 star rated blah blah. He was recruited by Ohio State and Georgia, and finally picked Texas. And and then I asked, You know, do you know how much I do. We know how much nil money he got paid and came back, gave me the nil.
175 00:22:24.090 ⇒ 00:22:27.570 Scott_Harmon: You know what the nl thing was, and you just talk to it?
176 00:22:28.150 ⇒ 00:22:29.530 Scott_Harmon: Yeah, it’s.
177 00:22:29.530 ⇒ 00:22:42.719 Uttam Kumaran: Yeah, I I you know, for for me, it’s like typing the medium of typing. It limits what you can do right? And actually, that’s why even Steven, the little app that I sent you, called Whisper.
178 00:22:42.720 ⇒ 00:22:43.180 Steven: Yeah.
179 00:22:43.180 ⇒ 00:22:50.159 Uttam Kumaran: For me. I do a lot of writing, and our whole company is remote. So I try to use it for emails. But we can talk. And
180 00:22:50.260 ⇒ 00:23:20.060 Uttam Kumaran: the way you talk is a lot different the way you type. And if you want to, and for me using it with AI helps, because I may just have a general sense of an idea. For example, we’re we’re trying new sales channels. And so I just want I just want some insight into like, how should I be thinking about this problem? What are some benchmarks? I could go into chat and type. But let’s I want to go. I just want to go do that on a walk, or I’m like in between something, and typing doesn’t make the most sense. That’s where I find it to be really, really helpful.
181 00:23:20.800 ⇒ 00:23:24.279 Steven: Yeah, I need to start using this for jotting down thoughts
182 00:23:25.080 ⇒ 00:23:35.859 Steven: do, because I’ll be driving. We’re very serious on our. We can’t pick our phone up and drive. But and then I’ll be like, Oh, I need to remember to do this when I get home, and then I get home. And
183 00:23:35.860 ⇒ 00:23:36.440 Steven: you should.
184 00:23:36.440 ⇒ 00:23:38.300 Steven: Yeah, I just need to.
185 00:23:41.470 ⇒ 00:23:42.949 Scott_Harmon: Really good, really good.
186 00:23:42.950 ⇒ 00:23:43.550 YvetteRuiz: Yeah.
187 00:23:44.100 ⇒ 00:23:44.480 YvetteRuiz: Yeah.
188 00:23:44.480 ⇒ 00:23:46.564 YvetteRuiz: Steven’s fading away.
189 00:23:47.530 ⇒ 00:23:48.020 Amber Lin: Okay.
190 00:23:48.020 ⇒ 00:23:48.750 MattBurns: Well, we’re
191 00:23:49.270 ⇒ 00:24:03.040 MattBurns: we’re working on the infrastructure and it properties, even as we speak. I I told Tim today he told me I think it was he had to spend about $15,000 on
192 00:24:03.710 ⇒ 00:24:07.284 MattBurns: stuff for the new building. I said, Yeah, let’s get it done. Please hurry.
193 00:24:10.330 ⇒ 00:24:15.899 YvetteRuiz: Yeah, but that’s an interesting voice. Piece of it, for sure.
194 00:24:15.900 ⇒ 00:24:33.569 Uttam Kumaran: I would try it out. And you know, I think maybe as part of emails. Well, if yeah, I think you know, Steven expressed interest. So try to think about ways I’m using it, and things that actually stick versus like little gimmicky things. And I use the speech to text all all day, every day, because.
195 00:24:33.570 ⇒ 00:24:37.489 Scott_Harmon: Does Andy have it enabled? I just don’t know if it’s available in Andy.
196 00:24:37.490 ⇒ 00:24:39.680 Uttam Kumaran: I don’t think you can speak.
197 00:24:39.870 ⇒ 00:24:41.000 YvetteRuiz: I don’t think you okay.
198 00:24:41.000 ⇒ 00:24:50.290 Uttam Kumaran: At the moment. But I use it where, like, I’m writing an email. And I just need it’s like, Okay, it’s gonna take me 20 min to write this. I just like need to get it just
199 00:24:50.290 ⇒ 00:24:50.690 Uttam Kumaran: proven
200 00:24:51.004 ⇒ 00:25:00.439 Uttam Kumaran: or even if something you want to. DM somebody on your company. And you’re like I could write these 10 sentences. But let me just say it really quickly, and
201 00:25:00.770 ⇒ 00:25:10.119 Uttam Kumaran: you know we’re not like it’s not like minutes matter over here, but I would like some minutes back, and it’s it’s just nice to do to use it in that way.
202 00:25:13.670 ⇒ 00:25:15.019 Scott_Harmon: Well, I think it looks like
203 00:25:15.170 ⇒ 00:25:21.660 Scott_Harmon: great progress. Amber. You know how excited I get about the trainer. Bot. So I’m really excited to get Janice and Yvette
204 00:25:22.090 ⇒ 00:25:26.170 Scott_Harmon: to get spend quality time with it, and really get that ready to productize
205 00:25:26.820 ⇒ 00:25:28.800 Scott_Harmon: and get it rolled out, because that’s
206 00:25:28.930 ⇒ 00:25:35.920 Scott_Harmon: the last key piece, I think, into making this thing really take off. So I agree with you.
207 00:25:35.920 ⇒ 00:25:38.490 Scott_Harmon: really excited about the design that you came up. It looks.
208 00:25:38.840 ⇒ 00:25:39.440 JanieceGarcia: I am too.
209 00:25:39.440 ⇒ 00:25:40.440 Scott_Harmon: Really solid.
210 00:25:41.460 ⇒ 00:25:55.729 Uttam Kumaran: Yeah, I think the the biggest thing to kind of plant in our mind is every meeting I’m gonna sort of. I really wanna align us towards the adoption. So I wanna see that people that weren’t using it last few weeks are using it. Now, you know we have. We only now probably had a full
211 00:25:55.840 ⇒ 00:26:05.329 Uttam Kumaran: 3 weeks or so of like call data, and more than just like one or 2 of us on the call using it. So the data is rolling in, and we can start to look at that. So
212 00:26:05.700 ⇒ 00:26:16.229 Uttam Kumaran: I want to see that people, everybody’s at least everybody in the list uses it once a day while they’re making calls, and then we’ll go to see, like, okay, out of the 40 to 60 calls a day.
213 00:26:16.340 ⇒ 00:26:38.720 Uttam Kumaran: How much of these are are becoming bot assisted. And then also for us to look at those questions, I think once we hit that sort of okay, maybe 10 or 20% of them are using it. Maybe, then I want the feedback to be, what questions are they asking? And then, is there an impact to training that we need to do? Is there some impact to the product or the service like that is really, I think, the
214 00:26:38.870 ⇒ 00:26:50.860 Uttam Kumaran: the last mile? I think some of that we can even do today. I think it’s just just a matter of getting into the data. But that’s that’s even what I’m excited. That hopefully, hopefully, we can enable Brian and David to do with this data.
215 00:26:50.860 ⇒ 00:26:53.339 Scott_Harmon: Just scope wise. I think it’s nothing we
216 00:26:53.620 ⇒ 00:26:57.139 Scott_Harmon: talked about here in this initial scope, but we’re kind of getting
217 00:26:57.560 ⇒ 00:27:00.220 Scott_Harmon: pretty close to what I would call full production.
218 00:27:01.205 ⇒ 00:27:05.610 Scott_Harmon: One of the things we ruled out of the initial scope is
219 00:27:05.710 ⇒ 00:27:08.880 Scott_Harmon: any customer facing version of Andy where you’re
220 00:27:09.120 ⇒ 00:27:12.480 Scott_Harmon: people. Via your website could just directly talk to Andy.
221 00:27:13.124 ⇒ 00:27:17.960 Scott_Harmon: I guess I’d I’d like y’all to think about that. I mean, there are pros and cons
222 00:27:18.230 ⇒ 00:27:22.040 Scott_Harmon: to it. Obviously lots of customer support people have.
223 00:27:22.730 ⇒ 00:27:29.099 Scott_Harmon: you know, AI, directly customer facing, we just haven’t. Really. We want to stay focused on Csrs to
224 00:27:29.810 ⇒ 00:27:31.180 Scott_Harmon: hit the mission.
225 00:27:31.470 ⇒ 00:27:36.530 Scott_Harmon: But 1 1 thing to think about going forward is.
226 00:27:36.530 ⇒ 00:27:41.659 MattBurns: We did actually talk internally a little bit, Scott, about that. I mean, we’re
227 00:27:41.960 ⇒ 00:27:49.150 MattBurns: we’re investigating a couple different platforms, particularly for after hours service that
228 00:27:49.270 ⇒ 00:27:52.019 MattBurns: we think can certainly be handled
229 00:27:52.450 ⇒ 00:27:58.140 MattBurns: via a bot as opposed to, you know, a live operator. And so we’re
230 00:27:58.280 ⇒ 00:28:04.570 MattBurns: looking at that stuff. And yeah, it’s come up, you know. Could could Andy ultimately do that? Particularly
231 00:28:06.180 ⇒ 00:28:23.370 MattBurns: as it gains more knowledge of what we do? I mean, it’s it’s it’s going to be more refined specifically to answer the the questions properly, because it’s that’s what it’s built about is, you know, just specifically our knowledge base and our data. But
232 00:28:23.650 ⇒ 00:28:31.899 MattBurns: and also, like you, said Utam, it’d be interesting. And and maybe, Janice, you and Yvette already have a feel for what type of questions.
233 00:28:32.040 ⇒ 00:28:40.820 MattBurns: you know. Is it more about service specs, or scheduling, or other services? You know. What? What are we? What is Andy really helping with?
234 00:28:41.210 ⇒ 00:28:43.470 MattBurns: Because, as you determine
235 00:28:43.620 ⇒ 00:28:49.119 MattBurns: where that is, that’s where you can really beef up that area of of the platform. So.
236 00:28:49.120 ⇒ 00:28:49.510 JanieceGarcia: Right.
237 00:28:49.780 ⇒ 00:28:51.880 YvetteRuiz: Yeah, I mean, I think it’s been. It’s
238 00:28:52.140 ⇒ 00:28:57.349 YvetteRuiz: it’s already helped us find a gap, you know. Bridge some gaps, you know. Reward points, you know.
239 00:28:57.350 ⇒ 00:28:57.759 MattBurns: You know.
240 00:28:57.760 ⇒ 00:29:12.979 YvetteRuiz: There’s things that weren’t clear with reward points, right? And we know that we want to incorporate that. You know questions about service, you know, how do we handle those things? So? And those are right now what’s coming in the most? Because as we get busier, it’s like, Oh, how do we
241 00:29:13.350 ⇒ 00:29:16.809 YvetteRuiz: treat for that? Or who do I schedule those types of questions and stuff so.
242 00:29:16.810 ⇒ 00:29:25.039 MattBurns: Well, and don’t forget to the kind of the reminder of oh, by the way, and just having that front and center, because that translates into real dollars for us.
243 00:29:25.300 ⇒ 00:29:32.270 YvetteRuiz: Yeah, absolutely. And you saw, I mean, I put the post up. The pest division had the highest increase. And oh, by the way, this
244 00:29:32.270 ⇒ 00:29:35.851 YvetteRuiz: this past month? Yeah, that. That. So.
245 00:29:36.370 ⇒ 00:29:37.870 Uttam Kumaran: Great. I love that.
246 00:29:37.870 ⇒ 00:29:39.780 Amber Lin: Exciting to hear.
247 00:29:40.280 ⇒ 00:29:40.850 Uttam Kumaran: Yeah.
248 00:29:40.850 ⇒ 00:29:49.200 Uttam Kumaran: And actually, you know, we were, we were nervous because Andy doesn’t. It’s like, kind of forward with, like the amount of times that it’ll tell you that. And people are like no, no, that’s great, because otherwise I’ll forget. Like.
249 00:29:49.990 ⇒ 00:29:55.519 Uttam Kumaran: get to the call end of the call, and then they sort of don’t recall like I could. Just I could do this. And so yeah, it’s great.
250 00:29:56.850 ⇒ 00:29:58.615 MattBurns: Yeah, it’d almost be interesting if
251 00:29:59.080 ⇒ 00:30:14.180 MattBurns: if Andy could pick up on the fact that you know the call is wrapping up, you know, because the Customers or the Csr is going. Is there anything else I can do for you today? And as soon as Andy hears that it’s like boom. Give me a reminder to do, because you’re right, Utah, I think sometimes
252 00:30:14.550 ⇒ 00:30:17.709 MattBurns: they it just goes out of their mind. They’re not thinking of it. So.
253 00:30:18.170 ⇒ 00:30:23.299 Uttam Kumaran: Yeah, one thing, Amber that could be helpful is, and this is something you should. You should kick to. The team is
254 00:30:23.630 ⇒ 00:30:35.629 Uttam Kumaran: once a once a session starts with Andy. If they notice that there’s a question, and there’s maybe like no response, maybe part, there should just be some auto response that at least just sends like
255 00:30:36.310 ⇒ 00:30:42.399 Uttam Kumaran: just sends. And oh, by the way, just 30 seconds, if there’s if it doesn’t hear anything from the Csr.
256 00:30:43.080 ⇒ 00:30:50.550 Uttam Kumaran: So like, gets closer to what Matt is saying, which is like. Just put it out there, no matter what right. If the Csr is talking to. It gets the answer they need.
257 00:30:50.870 ⇒ 00:30:58.109 Uttam Kumaran: I don’t know whether it’s 15 seconds or 30 seconds, or maybe Andy, just like says, Hey, I’m still here. But here’s here’s an all, by the way, just to remind you.
258 00:30:58.470 ⇒ 00:31:00.260 Uttam Kumaran: and like that’s all that’s like.
259 00:31:00.260 ⇒ 00:31:00.730 YvetteRuiz: Cool.
260 00:31:00.730 ⇒ 00:31:02.959 Uttam Kumaran: Low hanging fruit for our guys to do.
261 00:31:03.800 ⇒ 00:31:20.459 YvetteRuiz: So question. I know that I noticed that, and I hadn’t asked this, but I noticed that we put the the bull’s eye target on there. The thought behind that was to populate quickly. The oh, by the ways! But nothing’s generating right now off there! What is that still kind of pending, or
262 00:31:21.320 ⇒ 00:31:25.969 YvetteRuiz: do what? What is what are we waiting on on that end? Amber.
263 00:31:26.310 ⇒ 00:31:29.529 Amber Lin: Oh, I thought it was. I thought it was generating. Let.
264 00:31:29.530 ⇒ 00:31:35.310 YvetteRuiz: No, it just says success, but not but no contact no content return.
265 00:31:35.310 ⇒ 00:31:53.620 Amber Lin: Oh, okay, let me check with that. I believe it’s probably because maybe we change the window offer we or we set a specific time window for okay, we want this window offer for the next 2 weeks, and it probably the time period passed. So let me check with the team, and that should be a pretty easy fix.
266 00:31:56.340 ⇒ 00:32:06.809 YvetteRuiz: I think that’s the other helpful thing, you know. That’s what we talked about. Steven, you know. Is there something quick, button that we can. They can press that populates. Oh, by the ways and the buttons! There! It’s just not
267 00:32:06.910 ⇒ 00:32:08.477 YvetteRuiz: generating the offers.
268 00:32:09.000 ⇒ 00:32:10.069 Steven: Gotcha cool.
269 00:32:12.760 ⇒ 00:32:13.640 YvetteRuiz: Okay.
270 00:32:14.140 ⇒ 00:32:16.000 MattBurns: Well, good good stuff, guys.
271 00:32:16.290 ⇒ 00:32:17.480 Scott_Harmon: Good stuff, amber.
272 00:32:17.760 ⇒ 00:32:18.409 Uttam Kumaran: Thank you so much.
273 00:32:19.540 ⇒ 00:32:23.159 YvetteRuiz: Ever excited to see you, who knows great time, be safe.
274 00:32:23.160 ⇒ 00:32:24.413 Uttam Kumaran: Thank you.
275 00:32:25.040 ⇒ 00:32:26.749 YvetteRuiz: I appreciate it.
276 00:32:27.110 ⇒ 00:32:27.830 Steven: Bye.
277 00:32:27.830 ⇒ 00:32:28.640 Uttam Kumaran: Bye.