Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2026-01-22 Meeting participants: JanieceGarcia, read.ai meeting notes, Yvette’s Notetaker (Otter.ai), Matt’s Notetaker (Otter.ai), MattBurns, Uttam Kumaran, Amber Lin, Steven, YvetteRuiz
WEBVTT
1 00:00:46.810 ⇒ 00:00:47.670 MattBurns: Hi, Matt!
2 00:00:47.750 ⇒ 00:00:48.810 JanieceGarcia: Hi, Utam.
3 00:00:49.620 ⇒ 00:00:50.510 MattBurns: Hi, guys.
4 00:00:52.040 ⇒ 00:00:53.140 Uttam Kumaran: Hello!
5 00:00:55.040 ⇒ 00:00:58.290 Uttam Kumaran: Denise, I missed you in San Antonio yesterday.
6 00:00:58.990 ⇒ 00:01:00.659 JanieceGarcia: You were in San Antonio?
7 00:01:00.660 ⇒ 00:01:08.619 Uttam Kumaran: We were, steven showed me your office, or, like, we came into the little area where you guys usually… I saw Yvette yesterday.
8 00:01:09.090 ⇒ 00:01:14.140 JanieceGarcia: Nice! Yeah, my office is where all the CSRs are, it’s… it’s right there. It’s usually open.
9 00:01:14.410 ⇒ 00:01:17.649 Uttam Kumaran: No, we saw it was open, but we didn’t see it in there.
10 00:01:17.650 ⇒ 00:01:18.760 JanieceGarcia: College Station.
11 00:01:18.760 ⇒ 00:01:19.830 Uttam Kumaran: Oh, okay.
12 00:01:19.830 ⇒ 00:01:21.950 JanieceGarcia: I drove to College Station yesterday.
13 00:01:22.570 ⇒ 00:01:22.890 Uttam Kumaran: Great.
14 00:01:22.890 ⇒ 00:01:25.470 MattBurns: What were you doing at College Station? I didn’t know that.
15 00:01:25.700 ⇒ 00:01:31.030 JanieceGarcia: Since Lizzie is under me, I go and visit her, once a month.
16 00:01:31.530 ⇒ 00:01:32.210 MattBurns: Okay.
17 00:01:32.700 ⇒ 00:01:33.380 JanieceGarcia: So…
18 00:01:33.700 ⇒ 00:01:41.430 MattBurns: Well, I’m sorry, Utam, I wasn’t able to jump on to the meeting yesterday. I had other stuff lined up, but Steven and Beau both said it went well, so that’s good.
19 00:01:41.780 ⇒ 00:01:54.159 Uttam Kumaran: Yeah, no problem. I’ll be summarizing. There’s a lot of follow-ups I need to buy, like, a half hour to write down how it went, but yeah, we… we had, like, a pretty hardcore, like, 4… 3 or 4 hours of, like…
20 00:01:54.390 ⇒ 00:02:01.669 Uttam Kumaran: back-to-back-to-back content, so it was really, really good. There’s a lot of takeaways, and we have a little bit of the plan for the next two weeks, you know, sorted out.
21 00:02:01.670 ⇒ 00:02:06.980 MattBurns: Yeah, I know Bobby’s got a tough schedule the next two weeks, but he really wants to,
22 00:02:07.200 ⇒ 00:02:09.739 MattBurns: find some time, so I’m sure we’ll be able to, hopefully.
23 00:02:10.419 ⇒ 00:02:12.159 MattBurns: Kind of do the final presentation would be good.
24 00:02:12.160 ⇒ 00:02:14.919 Uttam Kumaran: The office looks great in San Antonio, by the way, it’s like…
25 00:02:14.920 ⇒ 00:02:15.979 MattBurns: Yeah, lots of…
26 00:02:15.980 ⇒ 00:02:17.849 Uttam Kumaran: I mean, one, it’s kind of a maze?
27 00:02:17.850 ⇒ 00:02:18.530 MattBurns: It’s amazing.
28 00:02:18.790 ⇒ 00:02:20.109 Uttam Kumaran: It is, but…
29 00:02:20.110 ⇒ 00:02:20.539 MattBurns: Flat ass.
30 00:02:20.540 ⇒ 00:02:22.430 Uttam Kumaran: It’s, it’s very nice.
31 00:02:22.940 ⇒ 00:02:29.249 MattBurns: Well, we were down there Tuesday night, we did our thinking meeting, we… Bobby and I came up from Corpus.
32 00:02:29.910 ⇒ 00:02:34.500 MattBurns: And, yeah, I had a good group there, that went well, and
33 00:02:34.630 ⇒ 00:02:39.620 MattBurns: But yeah, trying to navigate through the office, challenge.
34 00:02:39.740 ⇒ 00:02:44.800 Uttam Kumaran: Yeah, yeah. It’s a maze! Yeah.
35 00:02:45.440 ⇒ 00:02:51.079 MattBurns: Hey, Amber, can I ask really quick before we start? Who am I talking to on the Brainforge?
36 00:02:51.850 ⇒ 00:02:54.379 JanieceGarcia: Chat. Is it you, or is it Casey?
37 00:02:54.780 ⇒ 00:03:06.809 Amber Lin: Sometimes it’s me, sometimes it’s Casey. We only have one account, so I think sometimes when Casey sends a message, he’ll say it’s Casey, but when he forgets, like, you won’t be able to tell, you can always ask.
38 00:03:06.810 ⇒ 00:03:14.820 JanieceGarcia: Okay, I think it’s… oh, I guess it is Casey now, yep. Because our… Y’all don’t wanna hear this.
39 00:03:16.140 ⇒ 00:03:17.769 JanieceGarcia: the inspectors.
40 00:03:19.270 ⇒ 00:03:24.900 JanieceGarcia: My one agent that nothing she does really is
41 00:03:25.260 ⇒ 00:03:28.709 JanieceGarcia: Well, everything she does is just the inspector side.
42 00:03:29.130 ⇒ 00:03:31.859 Amber Lin: And she’s getting…
43 00:03:32.130 ⇒ 00:03:36.480 JanieceGarcia: Not the right information, or it’s showing an error message, so…
44 00:03:36.700 ⇒ 00:03:42.299 JanieceGarcia: I told her to screenshot it, send it to me. She’s sending, triage tickets, because she’s doing.
45 00:03:42.300 ⇒ 00:03:42.680 Amber Lin: doing something.
46 00:03:42.680 ⇒ 00:03:46.309 JanieceGarcia: down. So, fixing to go through them, I’m just gonna send them to Casey.
47 00:03:46.590 ⇒ 00:03:58.480 Amber Lin: Cool, yeah, sounds good. We just put our, new database live, so I think some of the changes may not have been updated. We wanted to test it with you, but it was taking a while, so…
48 00:03:58.480 ⇒ 00:04:10.549 Amber Lin: wanted to see… wanted to just push it live and then see what comes in. So, I think we’ll book time with you either today or tomorrow, and then we’ll go through that. And I’ll grab Casey to be on that meeting as well.
49 00:04:10.940 ⇒ 00:04:14.509 JanieceGarcia: Okay, probably tomorrow, because I am in Austin, and I’ll be traveling back.
50 00:04:14.950 ⇒ 00:04:15.959 Amber Lin: Hmm, okay.
51 00:04:16.610 ⇒ 00:04:20.940 Amber Lin: Cool, sounds good. I’ll find time with you, maybe earlier tomorrow.
52 00:04:25.350 ⇒ 00:04:27.180 Amber Lin: Awesome.
53 00:04:41.000 ⇒ 00:04:41.740 Amber Lin: Alright.
54 00:04:42.020 ⇒ 00:04:48.150 Amber Lin: So… This week, usage has improved significantly, especially for pests.
55 00:04:48.440 ⇒ 00:04:54.350 Amber Lin: Overall, our usage has continued to be pretty good, and I think
56 00:04:54.510 ⇒ 00:05:04.589 Amber Lin: the pest department, commercial department are all catching up, so I think the trainers are doing a really good job of promoting usage in their departments.
57 00:05:06.600 ⇒ 00:05:11.350 Amber Lin: And then… This is the zip code update we were just talking about.
58 00:05:11.380 ⇒ 00:05:18.429 Amber Lin: We reached out for feedback, but I think you guys were a bit busy, and then we didn’t get around to
59 00:05:18.450 ⇒ 00:05:32.089 Amber Lin: Looking through it together, so we… we’re going to test it live for, for now, and then we’ll see what feedback comes in to see, okay, what… maybe what aspects we have missed.
60 00:05:32.210 ⇒ 00:05:45.300 Amber Lin: But it should be identical to what we had before, because we had all the entries and the information from the same source. This is mostly just a different user interface to present everything, so…
61 00:05:45.300 ⇒ 00:05:49.869 JanieceGarcia: I think one is I still need some details for mechanical.
62 00:05:49.870 ⇒ 00:05:58.890 Amber Lin: I followed what… followed up with them again. And two, Janice, I would love to work through this with you, especially, you mentioned that the new tickets just came in.
63 00:06:04.440 ⇒ 00:06:05.380 Amber Lin: Cool.
64 00:06:05.750 ⇒ 00:06:13.000 JanieceGarcia: And I will say, I haven’t, had to input anything, but I’ve looked through it, and from what I can see, I think it’s…
65 00:06:13.820 ⇒ 00:06:14.700 JanieceGarcia: Great.
66 00:06:15.000 ⇒ 00:06:23.369 Uttam Kumaran: Yeah, Amber, do you want to open up the app? Because I’m going to show Matt and Steven, like, because they’re… they’re probably familiar a little bit with the before.
67 00:06:25.350 ⇒ 00:06:39.950 Uttam Kumaran: But, yeah, we just want to sh… if you don’t mind just pulling up the application. So basically, we… you know, I think originally the solution for a lot of this was really just, like, the inspector spreadsheet. And so, the second version of this, we brought that
68 00:06:40.050 ⇒ 00:06:54.040 Uttam Kumaran: database into something that the AI can interact with, and this final iteration is actually allowing a much simpler way for folks to update. And so we actually just built a little application that helps to facilitate
69 00:06:54.300 ⇒ 00:06:58.840 Uttam Kumaran: the creation and update of records there.
70 00:06:59.710 ⇒ 00:07:02.520 Uttam Kumaran: So yeah, maybe, Amber, you can… you can share that.
71 00:07:07.010 ⇒ 00:07:13.360 Amber Lin: I’m trying to find it, because there’s a set of passwords, and… accounts that…
72 00:07:13.720 ⇒ 00:07:15.850 Amber Lin: Great, I have it right here.
73 00:07:24.880 ⇒ 00:07:29.640 JanieceGarcia: Can I say I favorited… So if you need me to go in there, I can.
74 00:07:30.710 ⇒ 00:07:34.040 Amber Lin: One second… Oh, okay, cool.
75 00:07:41.810 ⇒ 00:07:42.630 Amber Lin: I haven’t.
76 00:07:45.660 ⇒ 00:07:48.830 Amber Lin: So this is a…
77 00:07:48.960 ⇒ 00:07:59.620 Amber Lin: Main improvement on the user interface of not only being able to see things in a more organized way, be able to filter
78 00:07:59.650 ⇒ 00:08:09.209 Amber Lin: By department, search by person’s name, their role, brand, zip code, or even different services and service types.
79 00:08:09.250 ⇒ 00:08:18.199 Amber Lin: So this is a lot easier for us, who don’t work directly with databases to navigate and know
80 00:08:18.200 ⇒ 00:08:33.639 Amber Lin: what’s in there, because in the past, I had to ask Casey, hey, can you go through the database and look up what we have? And then I have to wait for him to look up, he has to tell me, this is what I saw, and then I have to tell it to Denise. But right now, with this.
81 00:08:33.789 ⇒ 00:08:47.310 Amber Lin: we can search and filter for the people that we are trying to find, and so it saves a lot of back and forth. It’s a lot more straightforward, to view. That’s the first part. And second is,
82 00:08:47.840 ⇒ 00:08:49.539 Amber Lin: If we want to…
83 00:08:49.640 ⇒ 00:08:58.080 Amber Lin: add any people. We can add them here, we can update their areas, edit where they…
84 00:08:59.110 ⇒ 00:09:04.829 Amber Lin: If there’s, new people to be added, there’s different…
85 00:09:05.320 ⇒ 00:09:25.100 Amber Lin: roles and services, so if there is a certain change in service names, or we added a new service, such as Trashman, it’ll be a lot easier to add it into a database and immediately see it take effect, instead of, having to go through the whole
86 00:09:25.620 ⇒ 00:09:30.900 Amber Lin: Find this in the database, and then locate it, and write code to update it,
87 00:09:31.170 ⇒ 00:09:41.029 Amber Lin: So this is a lot more friendly for the… for the average user that, like me, who don’t, who don’t write the code for these databases.
88 00:09:41.710 ⇒ 00:09:56.560 Amber Lin: And so, this is also easier for needs for your vet, and in the future, for the service managers to update who’s new. So, if they want to add a new person, and say, hey, I want to assign this person to a certain zip code.
89 00:09:56.560 ⇒ 00:10:06.039 Amber Lin: They can just do that. So they don’t have to report it to Janice, Janice has to do it, and then, so there… it removes a lot of lead time in the updates.
90 00:10:08.180 ⇒ 00:10:18.420 Amber Lin: Let’s see… so… I want to show you an example of how we used it to make
91 00:10:18.730 ⇒ 00:10:22.679 Amber Lin: One of the, one of the triage tickets that came in.
92 00:10:23.110 ⇒ 00:10:24.200 Amber Lin: So…
93 00:10:28.080 ⇒ 00:10:29.560 Amber Lin: So, for example.
94 00:10:29.570 ⇒ 00:10:39.879 Amber Lin: This one came from Patricia. So she was asking about, okay, she asked about a certain service, and it didn’t come up, and she said.
95 00:10:39.880 ⇒ 00:10:52.110 Amber Lin: And Greg does do this service. So, we were able to go into the database, we found… we typed in Greg, and this, this person came up, and we were able to assign him to…
96 00:10:52.960 ⇒ 00:10:59.450 Amber Lin: to the ZIP, and make sure that, I think with this database, we can also have the specific
97 00:10:59.660 ⇒ 00:11:09.499 Amber Lin: His specific skills, and then also the notes for this specific technician, not just for every technician for this service.
98 00:11:09.890 ⇒ 00:11:15.310 Amber Lin: And then another example, so… We wanted to…
99 00:11:16.410 ⇒ 00:11:19.420 Amber Lin: So is Travis Hall from Residential to commercial.
100 00:11:19.560 ⇒ 00:11:26.090 Amber Lin: And in this case, we were able to find him, And these assignments.
101 00:11:26.230 ⇒ 00:11:32.549 Amber Lin: Update him… update all these assignments to residential, and then…
102 00:11:32.910 ⇒ 00:11:38.549 Amber Lin: And then again, when we searched for residential termite taxi didn’t show up anymore.
103 00:11:38.810 ⇒ 00:11:51.619 Amber Lin: So, I think, Janice, I would love to take time to go through a few triage tickets together, so we can see, if you feel comfortable doing this on your own moving forward.
104 00:11:53.850 ⇒ 00:11:58.400 JanieceGarcia: We definitely can, and I actually have somebody that I need to remove, so…
105 00:11:58.400 ⇒ 00:12:06.200 Amber Lin: Great, fantastic. If you have a few examples, we’ll use that. If not, we’ll just find something in the triage tickets.
106 00:12:07.900 ⇒ 00:12:08.769 JanieceGarcia: I have a couple.
107 00:12:09.370 ⇒ 00:12:09.890 Amber Lin: Great.
108 00:12:10.640 ⇒ 00:12:12.040 JanieceGarcia: I’ll just add them to myself.
109 00:12:13.980 ⇒ 00:12:21.889 Amber Lin: So that’s the main updates we have on Andy’s side. In the background, we’re doing the migration to
110 00:12:22.000 ⇒ 00:12:28.309 Amber Lin: the ABC hosted platform, so that’s still going on. We’ve been able to migrate
111 00:12:29.150 ⇒ 00:12:46.789 Amber Lin: the cancellations and templates, so we’re refining how they come up, in the responses, because we had more things that we’re able to use and change in the new ABC-based platform, so we’ll come back soon with updates on that.
112 00:12:49.720 ⇒ 00:12:56.319 Amber Lin: Any more questions on the Andes side, that we want to talk about and discuss?
113 00:12:56.850 ⇒ 00:12:57.340 Uttam Kumaran: Yo.
114 00:12:57.790 ⇒ 00:12:58.710 Amber Lin: Yeah, go ahead.
115 00:12:58.710 ⇒ 00:13:11.509 Uttam Kumaran: Maybe one piece, so today we sort of were able to unlock, being able to land Evolve data, into our BigQuery, Google, like, BigQuery, data warehouse.
116 00:13:11.510 ⇒ 00:13:31.270 Uttam Kumaran: So, I know, Amber, you started working with that today. In addition, now that that’s sort of unlocked, I’m gonna also follow up on getting the 8x8 transcript data loaded there, and then I know we have some follow-up items on, understanding and analyzing some of what’s in the transcript, and so that will sort of come up next. So that’s…
117 00:13:31.270 ⇒ 00:13:42.919 Uttam Kumaran: part of that got unlocked by some of the discovery work that we’re doing, and so we’ll be pushing on that, so I know Yvette was excited about that, so I’ll be, you know, following up there.
118 00:13:44.620 ⇒ 00:13:49.559 JanieceGarcia: Yeah, I feel like that’s probably the only update I had related to Andy on that side.
119 00:13:49.890 ⇒ 00:13:52.630 Amber Lin: Yeah, I was gonna say that was part of the discovery.
120 00:13:52.630 ⇒ 00:13:53.649 Uttam Kumaran: Oh, okay, sorry, go ahead.
121 00:13:53.650 ⇒ 00:14:09.750 Amber Lin: I had this over here. I was like, we can’t use it for discovery, but I think we can also use a lot of it for Andy. I looked at the tables there. I didn’t have too much time, because we only got it today or yesterday, but for example.
122 00:14:10.120 ⇒ 00:14:17.660 Amber Lin: You can see the… I looked at rewards points to… to start with, because I remember in cancellations, this is one of the…
123 00:14:17.790 ⇒ 00:14:23.109 Amber Lin: say, the last save tactics that we use for everybody. And when you look here.
124 00:14:23.970 ⇒ 00:14:39.420 Amber Lin: this is the range of rewards points, and this is how many customers there… there are. And there’s about 10K customers who have 5,000 rewards points or more. 10K reward points, and there’s,
125 00:14:39.960 ⇒ 00:14:43.199 Amber Lin: 90K customers who have
126 00:14:43.590 ⇒ 00:15:02.799 Amber Lin: 1,000 to 5,000 reward points. I don’t know how rewards points and monetary value translate, but I think that sounds like a lot of reward points, and if they let it accumulate to 10K, I really don’t know if they even know about rewards points, or…
127 00:15:03.120 ⇒ 00:15:21.899 MattBurns: Well, they probably don’t. They probably don’t. They’re worth roughly… it’s a 5% discount. On average, it’s a little more, a little less, but I do think that’s one… I know the sales guys use it. They’ll do a 360 on an account when they go out there, because they’ll use that as a closing tool.
128 00:15:21.990 ⇒ 00:15:26.120 MattBurns: Also a way to try and get, bundles.
129 00:15:26.460 ⇒ 00:15:31.199 MattBurns: Like, if we’re called out for pests, they’ll sell the pest, but then they go, hey, do you realize you have
130 00:15:31.520 ⇒ 00:15:39.880 MattBurns: You know, some reward points from either previous business or something else they do, and then try and use it as a closing tool or to get a bundle, so…
131 00:15:41.090 ⇒ 00:15:48.339 MattBurns: The statement, I know at one point, if somebody got a statement, it would show it. Yvette, do you know any other places they would see it?
132 00:15:48.650 ⇒ 00:15:55.559 MattBurns: On the portal, yeah, the portal, and then the statement, if it’s still printing there.
133 00:15:55.650 ⇒ 00:15:59.480 YvetteRuiz: And I think those were the other… those were the two others.
134 00:15:59.720 ⇒ 00:16:03.299 MattBurns: Yeah, but there’s no… and again, that could be…
135 00:16:03.510 ⇒ 00:16:13.749 MattBurns: Yvette, do they… are they using it much for, oh, by the way, if somebody says… you know, that’s a good lead-in, because you could say, oh, by the way, Mrs. Jones, you have
136 00:16:13.870 ⇒ 00:16:14.860 MattBurns: you know.
137 00:16:14.980 ⇒ 00:16:24.580 MattBurns: 8,000 points, which is worth about this, and we’re running a special on this, or maybe it’s double reward points for this offer. You know, we could come up with a lot of different things.
138 00:16:25.030 ⇒ 00:16:25.550 YvetteRuiz: Yeah.
139 00:16:25.550 ⇒ 00:16:26.280 MattBurns: Yeah.
140 00:16:26.380 ⇒ 00:16:35.120 YvetteRuiz: Do you do it for the, by the way, piece? I know, we did something… we just kicked off something on the mechanical side, for that.
141 00:16:35.120 ⇒ 00:16:47.029 YvetteRuiz: But that was kind of what we wanted to kind of talk… one of the recommendations we did on ownership thinking when we were sitting at our table is kind of revamping that whole piece, because you’re absolutely right, Matt, we can do a whole lot of different
142 00:16:48.400 ⇒ 00:16:52.960 YvetteRuiz: Promotions through that, or kind of making it… putting more exposure to it.
143 00:16:53.610 ⇒ 00:17:07.799 MattBurns: Yeah, and obviously, it’s a cost to us, if somebody uses that to pay their bill, which it’s very difficult to tell somebody, oh, you can’t do that, so we learned that real early. We’ll let them pay their bill with it, but if we could make it
144 00:17:07.990 ⇒ 00:17:12.740 MattBurns: more attractive, Particularly to buy a contracted service…
145 00:17:13.500 ⇒ 00:17:19.209 YvetteRuiz: I think for moving would be an excellent one. I think, like, if you’re moving to, like, someone’s moving, you’re moving into a new home.
146 00:17:19.210 ⇒ 00:17:19.710 MattBurns: Hi, Transfer.
147 00:17:20.180 ⇒ 00:17:21.149 MattBurns: Good points, yeah.
148 00:17:21.380 ⇒ 00:17:24.300 YvetteRuiz: Yeah, doing something like that.
149 00:17:24.430 ⇒ 00:17:26.950 YvetteRuiz: Or even, like, if they wanted to add
150 00:17:27.119 ⇒ 00:17:29.750 YvetteRuiz: an attribute or something, I don’t, you know…
151 00:17:29.950 ⇒ 00:17:31.320 MattBurns: Yeah, yep, yep.
152 00:17:32.930 ⇒ 00:17:33.450 Amber Lin: Yeah.
153 00:17:33.450 ⇒ 00:17:46.819 Uttam Kumaran: Yeah, I actually talked to Julie about this, and we were thinking about the exact same reason. There’s probably one… there’s probably, like, kind of two campaigns. One is, like, we just want to make this information more available to sales folks, so they can quickly understand, like.
154 00:17:46.860 ⇒ 00:18:02.369 Uttam Kumaran: Yeah, when they’re trying to go after a customer, that, hey, what is their reward points situation? And second, there’s probably a campaign that we can run just around, like, hey, you have these reward points, you should go ahead and engage with us again to schedule your next service, and you can leverage these.
155 00:18:02.560 ⇒ 00:18:05.769 Uttam Kumaran: Yeah, so this is great. I’m glad we finally have this.
156 00:18:09.380 ⇒ 00:18:19.160 Amber Lin: Yeah, and then there’s… this is another data from the same table. We’re able to look at when was their last service date.
157 00:18:19.350 ⇒ 00:18:37.129 Amber Lin: On a higher level, so we can tell that, okay, about 70% of people had their last service about a year ago. I would love to break it down by what service they had, to see what type of service we can go after for renewal.
158 00:18:37.200 ⇒ 00:18:52.600 Amber Lin: Like, for example, maybe appliances… appliance services have bigger durations in between, but say, if it was a lawn mowing service, then it… it might be a lot easier to get that customer back on track, because
159 00:18:52.600 ⇒ 00:19:03.050 Amber Lin: I would say, if they haven’t had their service for lawn mowing for a year, we… they’re considered maybe churned, or they’re inactive, so…
160 00:19:03.050 ⇒ 00:19:10.610 MattBurns: My guess, Amber… Amber, my guess is a lot of those are… never probably had a repetitive service.
161 00:19:10.830 ⇒ 00:19:11.300 Amber Lin: Mmm.
162 00:19:11.300 ⇒ 00:19:14.859 MattBurns: One time, because, again, appliance repair is going to be a one-time.
163 00:19:15.140 ⇒ 00:19:25.080 MattBurns: By and large, just like a plumbing service call, or an air conditioning service call. Those kind of things don’t, by nature, have repeat services.
164 00:19:25.640 ⇒ 00:19:31.150 MattBurns: That’s probably why they haven’t had anything… they never bought a service that had
165 00:19:31.370 ⇒ 00:19:35.970 MattBurns: Any kind of repeating component to it, but… General.
166 00:19:39.890 ⇒ 00:19:57.320 Amber Lin: Cool. This is just… I think this is the tip of the iceberg. I only looked at a few fields, so I think next time we meet, I’ll be able to have a lot more insights, and especially on, if a customer had these different contracts, how do they behave?
167 00:19:57.410 ⇒ 00:20:02.730 Amber Lin: So a lot more of information on, different customers.
168 00:20:06.860 ⇒ 00:20:08.600 Amber Lin: Cool.
169 00:20:08.950 ⇒ 00:20:17.979 Amber Lin: Yvette, since you’re here, I think the last piece I wanted to ask about is on the cancellations. I know last time we talked about this.
170 00:20:18.030 ⇒ 00:20:33.260 Amber Lin: And we were… we’re right… even right now, we’re discussing the different save tactics, such as reward points. So I wanted to see if the trainers had a chance to add in additional save tactics… tactics, additional scripts.
171 00:20:33.450 ⇒ 00:20:36.170 Amber Lin: Do you know if they had a chance to look at it yet?
172 00:20:36.170 ⇒ 00:20:53.660 YvetteRuiz: Yes, so I received a couple of them. Some of them are still meeting with their DMs, which they… several of them came up with a few of them, so I’ll be sharing that with you. I gave them until tomorrow to get me all the information, so yes, we… Awesome. We were… we all were here… they were here
173 00:20:53.850 ⇒ 00:20:58.990 YvetteRuiz: Tuesday. When are they here, Janiece? She’s… Tuesday. Whenever we’re here, we all kind of…
174 00:20:59.130 ⇒ 00:21:03.310 YvetteRuiz: work through all this. We had a working session on this.
175 00:21:03.310 ⇒ 00:21:04.089 Amber Lin: Awesome. Okay.
176 00:21:04.530 ⇒ 00:21:08.660 Amber Lin: That’s all from me,
177 00:21:09.200 ⇒ 00:21:14.149 Amber Lin: Let’s see… any other questions or things we want to discuss right here?
178 00:21:14.380 ⇒ 00:21:24.569 Uttam Kumaran: Yeah, Yvette, we just mentioned before we hopped on that we’re gonna… now that we have access to Google BigQuery and we’re able to land data, we’re gonna start to move the transcript data in there as well, so…
179 00:21:25.580 ⇒ 00:21:29.090 YvetteRuiz: Yay! Excited about that.
180 00:21:29.090 ⇒ 00:21:31.080 Uttam Kumaran: Yeah, so that should be great.
181 00:21:31.300 ⇒ 00:21:45.579 YvetteRuiz: Yeah. The other thing is, like I told Utam, tomorrow we’ll test out the zip code and get back all the feedback, because we have… we’ve not been able to kind of dedicate the time to go in there and do that, so… Oh, yeah. We’ll look at that.
182 00:21:45.780 ⇒ 00:21:55.869 Amber Lin: But we’re… I was just saying with Janiece, tomorrow, I might book a time with her, and Casey, and to go over some of the triage tickets, and make sure we know how to do that.
183 00:21:56.460 ⇒ 00:22:04.860 YvetteRuiz: Yeah. I just got off the phone with Patricia. I know you guys have a meeting with her tomorrow, because she had some just concerns on some of the… and I just guess I’ll ask… I told her that I…
184 00:22:04.860 ⇒ 00:22:16.849 YvetteRuiz: I was going to jump into this meeting. But, you know, one of her questions is… because they’re really… they’re all pushing Andy. I mean, I think they’ve… they’ve done all… a great job of continuing to work on it, and…
185 00:22:16.850 ⇒ 00:22:41.760 YvetteRuiz: But she was saying that a lot of the agents, they’re asking the same question, because that’s part of their daily update. They’re being asked, hey, ask Andy this question, what is it telling you? But they’re getting multiple different answers. Like, one would get the right answer, and then one would go in there and says, hey, you’re going to have to talk to your supervisor about that. So, they’re not sure why that’s happening, but I know she said, she said, are you meeting with her tomorrow, Amber, or is it someone else meeting with her?
186 00:22:41.760 ⇒ 00:22:44.360 YvetteRuiz: Because she told me she was… she has a meeting with somebody.
187 00:22:45.120 ⇒ 00:22:48.209 Amber Lin: It should be with Casey. Yeah, let me check.
188 00:22:48.210 ⇒ 00:22:48.920 YvetteRuiz: Okay, I see.
189 00:22:49.460 ⇒ 00:22:52.500 YvetteRuiz: But I’m not sure why would that be triggering that.
190 00:22:52.500 ⇒ 00:22:56.589 Amber Lin: Yeah, she has a meeting with Casey tomorrow.
191 00:22:56.910 ⇒ 00:22:59.030 Amber Lin: I believe mine.
192 00:22:59.030 ⇒ 00:23:00.500 JanieceGarcia: Let me make sure…
193 00:23:01.740 ⇒ 00:23:02.440 Amber Lin: Hmm.
194 00:23:03.840 ⇒ 00:23:07.059 Amber Lin: I actually don’t see that on this…
195 00:23:07.290 ⇒ 00:23:13.650 Amber Lin: Calendar, so let me confirm with… .
196 00:23:13.650 ⇒ 00:23:16.790 YvetteRuiz: She had it in the feedback, too. I just haven’t looked at it.
197 00:23:16.790 ⇒ 00:23:17.300 JanieceGarcia: Yep.
198 00:23:17.490 ⇒ 00:23:28.110 JanieceGarcia: And this is probably what she’s talking about, like, but all 3 of them… Got the same… response?
199 00:23:28.410 ⇒ 00:23:29.760 JanieceGarcia: But it’s different.
200 00:23:35.210 ⇒ 00:23:39.549 JanieceGarcia: Like, it’s the same response, but it’s in a different format, which is odd.
201 00:23:41.450 ⇒ 00:23:44.149 JanieceGarcia: But this does go back to zip codes, too.
202 00:23:48.760 ⇒ 00:23:49.170 Amber Lin: Yeah.
203 00:23:49.170 ⇒ 00:24:07.729 YvetteRuiz: So, I can follow up with her, Amber. I’ll just… I’ll ask her right now, like, who are you meeting with tomorrow, for sure? But I just wanted to bring it up, because I’m just a little bit concerned of… because the example she gave me, she was like, hey, look, Jenny asked this question, Samantha asked the same question, and it told her, go ask your supervisor for it, or it gave it something totally different. I said, well.
204 00:24:08.770 ⇒ 00:24:13.609 YvetteRuiz: I was like, I’m not sure what could be triggering that, but I’ll follow up on that.
205 00:24:13.610 ⇒ 00:24:17.290 Amber Lin: I have a hypothesis, but let… let me… let us meet with her.
206 00:24:17.590 ⇒ 00:24:17.930 YvetteRuiz: Okay.
207 00:24:17.930 ⇒ 00:24:27.770 Amber Lin: If it’s a zip code issue, or we have it, we know we can resolve that. If it’s a central dock issue, we’ll audit the central doc to see if there’s certain wordings in there.
208 00:24:28.230 ⇒ 00:24:28.910 YvetteRuiz: Okay.
209 00:24:29.230 ⇒ 00:24:30.250 YvetteRuiz: Perfect.
210 00:24:31.660 ⇒ 00:24:33.310 Uttam Kumaran: Okay, great.
211 00:24:33.780 ⇒ 00:24:40.179 Uttam Kumaran: All right, well, thank you, everyone. Yeah, I think next week we’ll… I’ll follow up, Steven, on your email about, sort of, scheduling things for next week.
212 00:24:40.430 ⇒ 00:24:41.410 Steven: Cool. Okay.
213 00:24:42.190 ⇒ 00:24:43.700 Uttam Kumaran: Okay, perfect.
214 00:24:43.870 ⇒ 00:24:44.720 YvetteRuiz: Alrighty.
215 00:24:45.310 ⇒ 00:24:45.860 YvetteRuiz: Hi, guys.
216 00:24:45.860 ⇒ 00:24:47.330 Uttam Kumaran: Thanks so much. Bye.