Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2025-11-13 Meeting participants: JanieceGarcia, T F, read.ai meeting notes, YvetteRuiz, MattBurns, Uttam Kumaran, Amber Lin
WEBVTT
1 00:01:40.060 ⇒ 00:01:41.570 YvetteRuiz: Hello!
2 00:01:42.620 ⇒ 00:01:44.100 JanieceGarcia: Hello, Yvette!
3 00:01:44.620 ⇒ 00:01:45.629 YvetteRuiz: How are you?
4 00:01:46.830 ⇒ 00:01:47.630 JanieceGarcia: sore.
5 00:01:48.060 ⇒ 00:01:51.310 YvetteRuiz: Oh, so haha, did you work out today?
6 00:01:51.640 ⇒ 00:01:53.919 JanieceGarcia: I did this morning, I am, like, barely.
7 00:01:53.920 ⇒ 00:01:54.610 YvetteRuiz: Every movie.
8 00:01:54.910 ⇒ 00:02:00.650 JanieceGarcia: I did, I’m like, I’ve got to keep it going, and Eric and Brenda are laughing at me.
9 00:02:01.090 ⇒ 00:02:02.040 JanieceGarcia: Good.
10 00:02:02.040 ⇒ 00:02:07.739 YvetteRuiz: Creeping around. Oh my goodness. But good for you.
11 00:02:08.280 ⇒ 00:02:09.719 YvetteRuiz: Muscles are growing.
12 00:02:11.030 ⇒ 00:02:12.020 YvetteRuiz: True.
13 00:02:12.020 ⇒ 00:02:13.569 Uttam Kumaran: Hi, Matt. Hi, Unum.
14 00:02:13.570 ⇒ 00:02:14.160 MattBurns: Good!
15 00:02:14.450 ⇒ 00:02:16.219 YvetteRuiz: Bye.
16 00:02:18.260 ⇒ 00:02:19.150 YvetteRuiz: Whew!
17 00:02:21.600 ⇒ 00:02:22.270 Uttam Kumaran: Huh?
18 00:02:22.840 ⇒ 00:02:23.850 YvetteRuiz: Is Amber here?
19 00:02:24.570 ⇒ 00:02:26.360 Uttam Kumaran: I don’t think she’s here yet.
20 00:02:26.540 ⇒ 00:02:27.270 YvetteRuiz: Oh, okay.
21 00:02:33.170 ⇒ 00:02:35.850 JanieceGarcia: I’m working on all of our tickets.
22 00:02:36.030 ⇒ 00:02:39.290 JanieceGarcia: Since I haven’t touched them since last week.
23 00:02:40.980 ⇒ 00:02:43.059 JanieceGarcia: So I’m catching up on all of those right now.
24 00:02:43.060 ⇒ 00:02:44.389 YvetteRuiz: Do we have a lot?
25 00:02:46.380 ⇒ 00:02:48.170 YvetteRuiz: We do.
26 00:02:49.420 ⇒ 00:03:03.769 JanieceGarcia: But some of them, and I sent to Casey, just so you know, Utam, I did send to Casey, because it seems like since he redid the… as I’m testing, since he redid everything for the inspectors for me.
27 00:03:05.100 ⇒ 00:03:07.609 JanieceGarcia: Now the skills and zip sheet isn’t working.
28 00:03:08.030 ⇒ 00:03:08.710 Uttam Kumaran: Okay.
29 00:03:10.680 ⇒ 00:03:12.750 JanieceGarcia: So, I’m sending him screenshots, though.
30 00:03:13.040 ⇒ 00:03:16.930 Uttam Kumaran: Yeah, yeah, just send in screenshots, and you can send it, yeah, if you’re sending it in Slack, that’s fine.
31 00:03:18.420 ⇒ 00:03:29.710 JanieceGarcia: I’m like, it’s weird because it was just fine. And so I thought it was maybe the way that the agent was actually asking the question, and I’m like, nope, it’s not coming up for me either.
32 00:03:32.390 ⇒ 00:03:34.990 Uttam Kumaran: Yeah, I… he’s the expert.
33 00:03:35.370 ⇒ 00:03:36.830 Uttam Kumaran: Yup.
34 00:03:39.710 ⇒ 00:03:40.540 JanieceGarcia: Hi, Amber.
35 00:03:43.520 ⇒ 00:03:44.220 Amber Lin: Hey there!
36 00:03:45.000 ⇒ 00:03:46.140 MattBurns: Hi, Amber.
37 00:03:46.950 ⇒ 00:03:47.650 YvetteRuiz: New.
38 00:03:47.800 ⇒ 00:03:52.419 YvetteRuiz: Steven will not be joining us today, so this is the group right here.
39 00:03:52.790 ⇒ 00:03:54.050 Amber Lin: Okay, awesome.
40 00:03:54.050 ⇒ 00:03:54.570 YvetteRuiz: zip.
41 00:03:55.870 ⇒ 00:03:59.370 Amber Lin: Alright, let me pull up my slides.
42 00:04:12.990 ⇒ 00:04:13.800 Amber Lin: Okay.
43 00:04:15.820 ⇒ 00:04:20.769 Amber Lin: So starting off, want to look at the usage again.
44 00:04:20.910 ⇒ 00:04:24.980 Amber Lin: I think this week we did slightly better.
45 00:04:25.190 ⇒ 00:04:26.590 Amber Lin: Then…
46 00:04:26.880 ⇒ 00:04:35.880 Amber Lin: before, but we’re still a bit far away from our goal. We’re almost at the middle of November, and we are
47 00:04:36.110 ⇒ 00:04:39.190 Amber Lin: Short of what we want to achieve, which is…
48 00:04:39.430 ⇒ 00:04:49.560 Amber Lin: $2,000 per month, and we’re a little bit short of that. And I know we’ve been trying daily reminders. If I know you guys have been talking to the trainers.
49 00:04:49.700 ⇒ 00:04:57.610 Amber Lin: And we’ve been… having our working sessions, so I was wondering why the usage has been…
50 00:04:57.740 ⇒ 00:05:00.149 Amber Lin: Logging, and then going down.
51 00:05:02.660 ⇒ 00:05:07.989 Uttam Kumaran: Yeah, well, I guess, Amber, like, how is… like, how is… I guess that’s more of a question for us to answer, like, how is…
52 00:05:08.310 ⇒ 00:05:12.400 Uttam Kumaran: How have the daily reminders been working? Like, what has been the… Yeah.
53 00:05:12.400 ⇒ 00:05:22.890 Amber Lin: I just checked it, and then this is the group of people that we’ve been testing it with, so I used the list that Eva gave me, and…
54 00:05:23.450 ⇒ 00:05:32.900 Amber Lin: We some… we’ve seen some increases, some declines, which… it… it makes it hard to conclude that the daily reminders has been
55 00:05:33.120 ⇒ 00:05:38.280 Amber Lin: Positively impacting or making a real difference, and this is
56 00:05:38.820 ⇒ 00:05:51.070 Amber Lin: not just random. And also, I don’t know how much people are actively engaging with the daily reminders, because I looked in the group of our testers, and
57 00:05:51.330 ⇒ 00:05:54.600 Amber Lin: It doesn’t seem like everybody checks it every day.
58 00:05:56.710 ⇒ 00:06:01.290 Uttam Kumaran: But I guess, like, I guess what is, like… so right now, it’s sending a group DM to everybody?
59 00:06:03.100 ⇒ 00:06:08.299 Uttam Kumaran: And, okay. And then, but what is the, like, how do you know it’s, like,
60 00:06:08.880 ⇒ 00:06:10.509 Uttam Kumaran: People are seeing it or not.
61 00:06:10.990 ⇒ 00:06:26.439 Amber Lin: People usually put a reminder after they’ve seen it, and we’ve just seen only a few people. We’ve seen responses from Dorothy, and we’ve seen responses from, I think Melissa.
62 00:06:26.670 ⇒ 00:06:33.430 Amber Lin: But we’ve not seen re… we’ve not seen any… everyone engaged with this message.
63 00:06:34.300 ⇒ 00:06:34.950 Uttam Kumaran: Okay.
64 00:06:35.630 ⇒ 00:06:41.409 Uttam Kumaran: I guess my only feedback is, like, I still feel like there’s no risk to rolling it out wider.
65 00:06:41.940 ⇒ 00:06:49.950 Uttam Kumaran: So, I would say, like, as long as we’re not seeing, like, any issues with it, I feel like we should roll it out. I also think maybe you should even consider
66 00:06:50.130 ⇒ 00:06:54.539 Uttam Kumaran: like, multiple times per day. Like, I guess it’s just not really, like…
67 00:06:55.410 ⇒ 00:07:01.850 Uttam Kumaran: given just, like, how short the message is, I don’t see, like, what the risk is there, so, like, maybe that’s another test you can run.
68 00:07:01.970 ⇒ 00:07:09.049 Uttam Kumaran: You know, because, again, people are just seeing in the morning, but folks are taking calls all day, so…
69 00:07:09.050 ⇒ 00:07:09.980 Amber Lin: Okay.
70 00:07:10.250 ⇒ 00:07:23.309 Amber Lin: And I also, when we roll out, I do want to send individual messages instead of group DMs, but we’ll need a way to check how… if people have seen those. Like, we’ll need to find a way to measure that.
71 00:07:23.310 ⇒ 00:07:41.380 YvetteRuiz: Yeah, that’s… that’s exactly what I was going to say. One, I… that was going to be my… my ask today, is I want to open it up to everybody, because personally, people reaching out to me have really liked me, they’ve enjoyed this. Second, I do want a true way to measure it, not just a thumbs up. I want to be able to go in there, because, I mean.
72 00:07:41.380 ⇒ 00:07:45.220 JanieceGarcia: I don’t always give it a thumbs up, and I read it every day. Yeah, me too.
73 00:07:45.220 ⇒ 00:07:47.620 YvetteRuiz: Okay, so…
74 00:07:47.620 ⇒ 00:07:53.979 Amber Lin: Okay, we’ll look into what data we can get, if people have opened it or read it, if we can get data on that.
75 00:07:54.320 ⇒ 00:07:54.930 YvetteRuiz: Yeah.
76 00:07:55.070 ⇒ 00:08:01.770 YvetteRuiz: Because I think, I mean, even, like, today’s message, I mean, I think somebody came in here and said, I really like that. Yesterday, I got.
77 00:08:01.770 ⇒ 00:08:02.310 Amber Lin: Hmm.
78 00:08:02.310 ⇒ 00:08:07.329 YvetteRuiz: at the… at our managers, they’re like, I really like the message that was sent out regarding the… the, you know…
79 00:08:07.330 ⇒ 00:08:08.000 Amber Lin: Awesome.
80 00:08:08.000 ⇒ 00:08:10.130 YvetteRuiz: I’m ready to go… I want to push it out to everybody.
81 00:08:10.130 ⇒ 00:08:23.079 Amber Lin: Okay, that’s exciting, and especially if we include, say, facts about each department, I think it’ll make it easier if you want to have overflow training in the future, because they’ve been exposed, and they’ve seen what’s happening.
82 00:08:23.860 ⇒ 00:08:35.009 YvetteRuiz: Yeah. Matt, can you read those? I mean, what… can you see that… what… what those messages are? I mean, what are your… what’s your kind of feedback on that? I was gonna, send a snippet to you earlier.
83 00:08:35.620 ⇒ 00:08:42.770 MattBurns: Yeah, let me read through them, so I can read them out for sure. So, I mean…
84 00:08:44.220 ⇒ 00:08:45.600 MattBurns: But I do think…
85 00:08:46.480 ⇒ 00:08:51.330 MattBurns: a better way to acknowledge that they’ve read them. I mean, not everyone’s going to give it a thumbs up, so…
86 00:08:51.850 ⇒ 00:08:55.960 Uttam Kumaran: Yeah, maybe, Amber, you can ask AI team, like, if they can see people, like.
87 00:08:55.960 ⇒ 00:09:00.500 Amber Lin: Usually, you can get an event that says, like, someone’s a message.
88 00:09:02.250 ⇒ 00:09:13.270 Uttam Kumaran: Yeah, I just don’t see a risk in scaling it up. I also just think that, like, if anecdotally people are finding it helpful, then I would rather… it’s sort of like we do with other experiments, it’s just you kind of want to…
89 00:09:13.420 ⇒ 00:09:31.319 Uttam Kumaran: you just want to kind of see different cohorts of activities. So, I think it’s, like, safe enough at this point, and we’ve sort of nailed what we’re sending. I guess, Amber, how did we arrive on, like, what messages are getting sent for the next 30, 60 days? Are those being generated, or, like, how are we arriving at those messages?
90 00:09:31.620 ⇒ 00:09:46.060 Amber Lin: Yeah, so the messages are in a few components. So, if you see here, there’s example, did you know that, and remember, right? Those are three sections. The first one, we take it from, past…
91 00:09:46.120 ⇒ 00:09:57.529 Amber Lin: questions that have been asked to Andy. Did you know, is the second section, we take it from the central doc, from the different central docs, who say, this is a piece of information that might be interesting.
92 00:09:57.530 ⇒ 00:10:06.129 Amber Lin: And remember, it’s just a, like, a core value, encouragement for every day. And we can change that, and I’ll probably…
93 00:10:06.130 ⇒ 00:10:18.959 Amber Lin: either do it on our Monday meeting, or I’ll send over the doc, and I want to go through it together, because if some holidays are coming up, or if you have special offers, I do want the reminders to reflect that.
94 00:10:19.300 ⇒ 00:10:19.940 Uttam Kumaran: Okay.
95 00:10:20.180 ⇒ 00:10:26.799 YvetteRuiz: You know, I wonder if we could put, like, a fun spin to it, and this kind of thought, like, maybe put an Easter egg in there, and then…
96 00:10:26.800 ⇒ 00:10:27.520 Amber Lin: Oh, okay.
97 00:10:27.520 ⇒ 00:10:33.510 YvetteRuiz: Kind of asks the question, and whoever gets it gets some… a prize or something, I don’t know.
98 00:10:33.510 ⇒ 00:10:33.910 Amber Lin: Yeah.
99 00:10:33.920 ⇒ 00:10:36.160 YvetteRuiz: Make it fun.
100 00:10:36.160 ⇒ 00:10:46.769 Amber Lin: That would be fun, and then we can totally plan that in, and you can check a week ahead, say, oh, this week we’re gonna have Easter eggs, and everybody get ready, and then I can put that into our weekly message as well.
101 00:10:47.050 ⇒ 00:10:48.390 YvetteRuiz: Yeah, okay, I think that’s.
102 00:10:48.390 ⇒ 00:10:53.909 Amber Lin: Cool. Yeah, I’ll grab time with you guys, or we’ll just use Monday’s meeting, and we’ll confirm that.
103 00:10:55.270 ⇒ 00:10:56.360 YvetteRuiz: Great.
104 00:10:56.360 ⇒ 00:10:57.170 Amber Lin: Awesome.
105 00:10:58.280 ⇒ 00:10:58.900 Uttam Kumaran: Okay.
106 00:11:00.170 ⇒ 00:11:00.860 Amber Lin: Yeah, okay.
107 00:11:00.860 ⇒ 00:11:06.819 MattBurns: Do our, do our lower participation users… go back to the original slide, Amber, yeah.
108 00:11:10.270 ⇒ 00:11:23.560 Uttam Kumaran: Yeah, I guess I’m sorry to interrupt. Maybe one question also, Amber, is, like, I wonder… I want to… it would be great to see, like, for the last two, three months, who is consistently low. Like, I wonder, do you think we can pull up that data even in this meeting, just to see…
109 00:11:23.560 ⇒ 00:11:27.800 Amber Lin: Yeah, I think so, let me… Let me grab that.
110 00:11:28.020 ⇒ 00:11:30.509 Amber Lin: But let me share…
111 00:11:30.510 ⇒ 00:11:33.780 Uttam Kumaran: I’d like to… because I’d like to rule out… I guess, like…
112 00:11:33.780 ⇒ 00:11:34.170 Amber Lin: huh?
113 00:11:34.170 ⇒ 00:11:45.769 Uttam Kumaran: I want to spend time on the people that are using it the best, but they’re ideal, right? So for… I think it would be helpful for us to look at who is consistently, like, in the bottom, and start to arrive at some…
114 00:11:45.790 ⇒ 00:11:54.740 Uttam Kumaran: Okay, like, what do those people need? Or at least, again, we could do another round of talking to those people, especially now that we have a few months of data.
115 00:11:55.340 ⇒ 00:11:57.309 Amber Lin: Totally. Here, I…
116 00:11:57.310 ⇒ 00:11:59.199 Uttam Kumaran: Sorry, Matt, I interrupted, I don’t know if that was…
117 00:11:59.200 ⇒ 00:12:02.010 MattBurns: No, that’s where I was going.
118 00:12:02.010 ⇒ 00:12:04.290 Amber Lin: All good. So I grabbed that…
119 00:12:04.390 ⇒ 00:12:11.590 Amber Lin: data. Let me make this screen a little bit larger. And so this I did from August 1st to.
120 00:12:11.620 ⇒ 00:12:12.850 MattBurns: November.
121 00:12:12.850 ⇒ 00:12:15.650 Amber Lin: So that gives us a few ranges.
122 00:12:15.940 ⇒ 00:12:27.790 Amber Lin: And… I would say that we can look at… Usages, say, under… Under 10, so…
123 00:12:28.050 ⇒ 00:12:30.250 Amber Lin: And look at this list here.
124 00:12:30.840 ⇒ 00:12:34.089 Amber Lin: Any of these names that stand out to you?
125 00:12:34.450 ⇒ 00:12:37.909 YvetteRuiz: So, Yusuf is no longer here. Whoa, hang on, let me see.
126 00:12:39.040 ⇒ 00:12:53.009 YvetteRuiz: Cass is a supervisor, Jimmy is a branch manager, Dottie’s in the welcome team. I don’t know, Shanice will speak a little bit more to that. Monica Lopez is no longer with us. Oso is in Home Improvement.
127 00:12:53.240 ⇒ 00:13:01.779 YvetteRuiz: Justin McSpadden, your teams. Some of these people that are down there are admin, admin roles, a little bit higher admin, not phone people.
128 00:13:01.780 ⇒ 00:13:04.310 Amber Lin: Okay, so if we go up a little bit…
129 00:13:04.450 ⇒ 00:13:07.280 Amber Lin: Would that be more of the CSRs?
130 00:13:07.280 ⇒ 00:13:14.290 YvetteRuiz: Yes, and then you have some dispatchers there, which, again, their usage is going to be a little bit lower than a regular CSR.
131 00:13:14.860 ⇒ 00:13:16.200 YvetteRuiz: But…
132 00:13:16.860 ⇒ 00:13:21.169 Uttam Kumaran: Any chance you can show… you can add month to, like, the, to the…
133 00:13:21.410 ⇒ 00:13:23.860 Uttam Kumaran: Basically, we see total exchanges by month, by person.
134 00:13:23.860 ⇒ 00:13:25.039 YvetteRuiz: You, you know.
135 00:13:25.040 ⇒ 00:13:26.119 Amber Lin: Should be…
136 00:13:26.120 ⇒ 00:13:34.520 YvetteRuiz: could… is there any way to title, and I don’t want to make the… make it harder than it is, but, like, you know, you have the names there, and if you could put, like, CSR admin or something.
137 00:13:34.520 ⇒ 00:13:36.330 Uttam Kumaran: Yeah, we can add roles for sure.
138 00:13:36.330 ⇒ 00:13:37.330 YvetteRuiz: role. Yeah.
139 00:13:37.840 ⇒ 00:13:43.669 Uttam Kumaran: So go… go all the way to the right, and let’s just look at, like, September versus October, because those are complete months.
140 00:13:43.670 ⇒ 00:13:44.580 MattBurns: Hmm.
141 00:13:44.580 ⇒ 00:13:46.630 Amber Lin: Let me scroll down…
142 00:13:47.880 ⇒ 00:13:50.839 Uttam Kumaran: Okay, so all these folks, I assume, are just
143 00:13:51.620 ⇒ 00:13:59.700 Uttam Kumaran: Yeah, I mean, what we are seeing, though, is some people were at zero in September, and they’re using it a little bit in October.
144 00:13:59.700 ⇒ 00:14:04.479 JanieceGarcia: We have to remember, too, I don’t think we started adding the other teams, though, until October.
145 00:14:04.480 ⇒ 00:14:09.210 Amber Lin: Like, my team didn’t even get added until October.
146 00:14:09.210 ⇒ 00:14:13.430 YvetteRuiz: And then you have commercial in here that they’re just rolling in there.
147 00:14:14.200 ⇒ 00:14:18.210 YvetteRuiz: We’ve gone in phases, remember. Good point, Janiece.
148 00:14:18.210 ⇒ 00:14:20.950 Uttam Kumaran: Maybe, Amber, keep scrolling up here.
149 00:14:24.250 ⇒ 00:14:27.300 Uttam Kumaran: Until we just get to a batch of, like, maybe just the bottom of people.
150 00:14:27.300 ⇒ 00:14:27.840 JanieceGarcia: Stop.
151 00:14:27.840 ⇒ 00:14:31.009 Uttam Kumaran: We’re in both months, so, like, this is probably, like, a good sense.
152 00:14:31.190 ⇒ 00:14:35.240 JanieceGarcia: That’s what I would look at, is, like, pest and lead line.
153 00:14:35.390 ⇒ 00:14:39.479 JanieceGarcia: Because home and lawn weren’t even.
154 00:14:41.030 ⇒ 00:14:41.750 Uttam Kumaran: Presley, like…
155 00:14:41.750 ⇒ 00:14:42.710 JanieceGarcia: Mechanical.
156 00:14:42.740 ⇒ 00:14:50.250 Uttam Kumaran: you know, good… I would say, like, Amber, probably the number to calculate here is, like, if everyone were to use it
157 00:14:50.410 ⇒ 00:14:53.910 Uttam Kumaran: once a day, like, what… what’s Sections B.
158 00:14:53.910 ⇒ 00:14:57.920 Amber Lin: And what we’re seeing here in some cases is, yeah, some people are using it.
159 00:14:58.430 ⇒ 00:15:07.550 Uttam Kumaran: kind of consistently… I guess maybe you can actually… maybe, Amber, remove from this graph the exchange’s feedback, and we can just have, like, the number of exchanges.
160 00:15:08.020 ⇒ 00:15:13.050 Uttam Kumaran: So, what we’re just looking at here is…
161 00:15:14.880 ⇒ 00:15:19.759 Uttam Kumaran: Yeah, just for September and October, you’re seeing that, yeah, some people who are
162 00:15:22.420 ⇒ 00:15:25.109 Uttam Kumaran: Yeah, they’re hovering in this, like, one a day…
163 00:15:26.700 ⇒ 00:15:35.390 Uttam Kumaran: Range or so, but there is still some people that are… they used it less, like… Luis… Brianna…
164 00:15:36.010 ⇒ 00:15:40.619 YvetteRuiz: So, Luis isn’t at, like, a landscape coordinator. Again, he’s backup.
165 00:15:40.620 ⇒ 00:15:41.350 JanieceGarcia: I asked for.
166 00:15:41.350 ⇒ 00:15:44.629 YvetteRuiz: Brianna is a dispatcher.
167 00:15:44.630 ⇒ 00:15:47.070 Uttam Kumaran: Okay, so one thing, probably, Amber, we can do is…
168 00:15:47.480 ⇒ 00:15:50.899 Uttam Kumaran: Yeah, it rolls into here, that way we can… Filter out the…
169 00:15:50.900 ⇒ 00:15:51.240 Amber Lin: Okay.
170 00:15:51.280 ⇒ 00:15:54.020 Uttam Kumaran: We’re okay with them not using it.
171 00:15:54.020 ⇒ 00:15:54.950 MattBurns: Right?
172 00:15:54.950 ⇒ 00:16:03.380 YvetteRuiz: Yeah, because, I mean, a lot of your bigger users, their CSRs, I mean, I could already kind of just breed, like, Deandra, Denise…
173 00:16:04.070 ⇒ 00:16:14.370 Uttam Kumaran: Kelsey, Dylan… So, can you sort Amber, by the biggest… the biggest net delta decrease? So, if you sort by, like, yeah, that guy…
174 00:16:15.130 ⇒ 00:16:17.070 Uttam Kumaran: Is there anybody out?
175 00:16:17.360 ⇒ 00:16:18.730 JanieceGarcia: There you go.
176 00:16:20.210 ⇒ 00:16:21.940 Uttam Kumaran: See, like, who.
177 00:16:22.660 ⇒ 00:16:23.899 YvetteRuiz: Your top ones.
178 00:16:24.370 ⇒ 00:16:28.500 Uttam Kumaran: Well, like, you could just see who decreased… the most…
179 00:16:29.240 ⇒ 00:16:38.529 Amber Lin: Or what, like, what… which… what is it showing us? I think this is, like, overall decrease from August. I can do… I’m gonna do…
180 00:16:39.160 ⇒ 00:16:42.450 JanieceGarcia: But even, I mean, even looking at that, you can see, like.
181 00:16:42.660 ⇒ 00:16:44.380 Amber Lin: Daniel, for example.
182 00:16:45.620 ⇒ 00:16:55.480 JanieceGarcia: She… well, I think welcome we just put in there. That’s not a good one to use. But Tiffany, who was in PES, she’s in all of them, and she went down each month.
183 00:16:58.400 ⇒ 00:16:59.100 Amber Lin: Look at that.
184 00:16:59.100 ⇒ 00:17:02.660 Uttam Kumaran: I want to look at September versus, like, October, basically.
185 00:17:02.880 ⇒ 00:17:03.780 Amber Lin: Okay.
186 00:17:04.180 ⇒ 00:17:05.420 Uttam Kumaran: by person.
187 00:17:18.109 ⇒ 00:17:20.950 Uttam Kumaran: I guess we should just remove… I said don’t.
188 00:17:20.950 ⇒ 00:17:21.599 Amber Lin: Let me…
189 00:17:21.609 ⇒ 00:17:23.069 Uttam Kumaran: I don’t really care about the phone calls.
190 00:17:24.710 ⇒ 00:17:29.969 Amber Lin: Here, let’s just do October, and then you can compare with the previous period, which is…
191 00:17:30.160 ⇒ 00:17:35.680 Amber Lin: Which is September. So you can see that… in here…
192 00:17:36.070 ⇒ 00:17:40.429 Uttam Kumaran: Well, I don’t think… at the previous period, I think it’s still… it’s… oh, it’s September, okay.
193 00:17:40.660 ⇒ 00:17:41.260 Amber Lin: Yeah.
194 00:17:42.460 ⇒ 00:17:47.340 Amber Lin: So… I guess we can just look.
195 00:17:48.140 ⇒ 00:17:50.340 Amber Lin: Don’t need to pivot, then.
196 00:17:53.120 ⇒ 00:17:56.930 Amber Lin: So, in that case, you can see… Who…
197 00:17:57.390 ⇒ 00:17:57.940 YvetteRuiz: Mrs.
198 00:17:57.940 ⇒ 00:17:58.480 Amber Lin: grease.
199 00:17:58.480 ⇒ 00:18:00.699 MattBurns: Decrease. Let me say decrease.
200 00:18:01.170 ⇒ 00:18:01.989 JanieceGarcia: Oh, whoa.
201 00:18:01.990 ⇒ 00:18:02.480 Amber Lin: Here we go.
202 00:18:02.480 ⇒ 00:18:03.489 YvetteRuiz: knows a lot.
203 00:18:04.780 ⇒ 00:18:08.050 Amber Lin: Those were our two highest… Yeah.
204 00:18:09.000 ⇒ 00:18:10.390 YvetteRuiz: Scott and Brian?
205 00:18:11.140 ⇒ 00:18:15.520 JanieceGarcia: Scott and Brian were always on here. I mean, Brian’s still… I know Brian’s still asking questions.
206 00:18:16.080 ⇒ 00:18:19.229 JanieceGarcia: I don’t see Scott coming in as much,
207 00:18:19.400 ⇒ 00:18:23.980 JanieceGarcia: Because of the feedback. Brian’s really great at giving the feedback.
208 00:18:26.120 ⇒ 00:18:32.930 YvetteRuiz: And so, how… how many of this, Janiece, would be tied to… I know that there was a lag in some of the…
209 00:18:33.860 ⇒ 00:18:34.899 YvetteRuiz: Some of the inspectors…
210 00:18:35.070 ⇒ 00:18:44.609 YvetteRuiz: Yeah, that was kind of the feedback that we got, and I know that continued to be… that’s what they searched, and they were not getting… I mean, we still didn’t have it updated correctly.
211 00:18:44.880 ⇒ 00:18:49.699 JanieceGarcia: Right, and I mean… Yes and no.
212 00:18:50.360 ⇒ 00:19:01.379 JanieceGarcia: I would say it’s definitely still a bigger piece as to why, but it’s needing to let them know, like, hey, know the inspectors. And I think it’d be great, truthfully, on one of the,
213 00:19:02.130 ⇒ 00:19:06.840 JanieceGarcia: Updates to let them know the inspector zip codes are in there. Now.
214 00:19:06.940 ⇒ 00:19:10.429 JanieceGarcia: They can ask, and they are giving the right information.
215 00:19:10.610 ⇒ 00:19:16.039 JanieceGarcia: Because I know, like, for Brian, for example, That’s all he does.
216 00:19:16.920 ⇒ 00:19:25.220 JanieceGarcia: And so I’ve already been in communication with him, letting him know, hey, that is updated, but it’d be good just to send it out to everyone.
217 00:19:26.050 ⇒ 00:19:32.249 YvetteRuiz: Yeah, and that’s how we want to add it. We want to add everyone. Not everyone was on that update, we were just… That update yesterday.
218 00:19:32.250 ⇒ 00:19:32.940 JanieceGarcia: Yep.
219 00:19:35.620 ⇒ 00:19:42.849 JanieceGarcia: But the… I mean, the tickets, too, that are coming in now, it’s more of the home, lawn, commercial.
220 00:19:43.060 ⇒ 00:19:46.919 JanieceGarcia: So it’s getting them set and them going, but…
221 00:19:47.210 ⇒ 00:19:52.770 JanieceGarcia: there’s… that shouldn’t be a reason. Like, Brianna, for example, that Brianna, that’s the best Brianna.
222 00:19:52.770 ⇒ 00:19:58.019 YvetteRuiz: Oh, that’s Pest, Brianna, that’s not… not mechanical, yeah. I think Pess did a big dive.
223 00:19:58.020 ⇒ 00:19:58.500 JanieceGarcia: a huge.
224 00:19:58.500 ⇒ 00:19:59.930 YvetteRuiz: went down. Dived.
225 00:20:00.140 ⇒ 00:20:00.920 JanieceGarcia: Huge dive.
226 00:20:01.420 ⇒ 00:20:03.710 JanieceGarcia: It’s like they’re reverting backwards, too.
227 00:20:04.260 ⇒ 00:20:09.170 JanieceGarcia: sheets. And maybe we go ahead and we lock those sheets off, Yvette.
228 00:20:10.600 ⇒ 00:20:12.769 YvetteRuiz: We were… that was supposed to be the plan, we were supposed to kind of.
229 00:20:12.770 ⇒ 00:20:13.110 Uttam Kumaran: Yeah.
230 00:20:13.110 ⇒ 00:20:14.530 YvetteRuiz: lock them out.
231 00:20:14.530 ⇒ 00:20:16.600 JanieceGarcia: So, if we do that, then…
232 00:20:16.900 ⇒ 00:20:21.360 JanieceGarcia: You know, the only ones that they would have access to is the inspectors and the pests and zills.
233 00:20:21.630 ⇒ 00:20:25.110 JanieceGarcia: Skills, but everything else would be… Locked out.
234 00:20:25.110 ⇒ 00:20:39.860 Uttam Kumaran: I think one thing that would be helpful, Janiece, is even just to confirm that, is to go ask them, like, was… is there a reason you’re using the sheet… you’re using the old docs versus Andy? And if the answer is, like, not great, then there’s… I don’t think there’s a reason not to, right?
235 00:20:42.380 ⇒ 00:20:43.560 Uttam Kumaran: But, yeah.
236 00:20:46.000 ⇒ 00:20:50.859 YvetteRuiz: We… well, we just met with the trainers. We know the answer to that. What was Kenny’s response?
237 00:20:51.070 ⇒ 00:20:56.879 JanieceGarcia: Kenny’s response, hers was, She’s used to the sheets, and that’s how…
238 00:20:57.060 ⇒ 00:20:58.220 YvetteRuiz: It’s a habit.
239 00:20:58.640 ⇒ 00:21:08.980 YvetteRuiz: And she’s the trainer who’s supposed to be leading that team, so that’s, again, why we’re going to meet with them next week. Again, that’s why we’re going to meet with them on a regular basis, because
240 00:21:08.980 ⇒ 00:21:19.570 YvetteRuiz: We need the trainers to be the ones driving this, and you can’t really… the other ones are just in the phase of building that, like, home, slowly there,
241 00:21:19.570 ⇒ 00:21:20.050 JanieceGarcia: on.
242 00:21:20.050 ⇒ 00:21:22.830 YvetteRuiz: mechanical should be lawns, the next one, and then commercial, but…
243 00:21:22.970 ⇒ 00:21:26.779 MattBurns: In the… in the example of Kenny, Yvette.
244 00:21:27.320 ⇒ 00:21:32.439 MattBurns: Is it quicker or easier to use the sheet? Is that why she’s doing it, or what?
245 00:21:32.440 ⇒ 00:21:47.050 JanieceGarcia: We tested it, Matt. We actually tested it, and we’ve tested it even on some of the work sessions that we have with Amber and the group, because they’ll say that. They’ll tell me, oh, no, it’s just faster, I know exactly where it is. Okay, well, let’s test it on a call.
246 00:21:47.210 ⇒ 00:21:58.740 JanieceGarcia: And let’s do that. So we’ll pull… we’ll listen to a call. I will ask Andy, with sharing my screen, we’ll ask Andy. They’ll go in and look, and it’s like, okay, which one was faster? And it’ll be Andy.
247 00:21:59.150 ⇒ 00:22:00.570 MattBurns: Oh, that’s happening.
248 00:22:00.570 ⇒ 00:22:18.600 JanieceGarcia: think… they think it’s… it’s taking longer to ask Andy, because they’re sitting there and they know that he’s thinking about it, but it’s still less than 8 seconds. I mean, it’s quick, and you can’t go and look through all those sheets… Right. …and try to find what you need in less than 8 seconds.
249 00:22:18.760 ⇒ 00:22:22.439 JanieceGarcia: Unless you’re a huge bee typer.
250 00:22:22.440 ⇒ 00:22:33.939 YvetteRuiz: Yeah, and then now even more, because I know one was my thing, too, is that processing piece. Now that we added it, now that there’s… now there’s no guessing. You know that it’s getting the answers there for you, because
251 00:22:34.060 ⇒ 00:22:39.249 YvetteRuiz: before, it was kind of like, is it gonna do anything? But now it’s showing that now.
252 00:22:39.250 ⇒ 00:22:40.310 JanieceGarcia: Exactly.
253 00:22:40.520 ⇒ 00:22:43.929 JanieceGarcia: So, it’s a habit. It’s really getting them out of that habit.
254 00:22:44.170 ⇒ 00:22:49.260 MattBurns: Yeah, well, and… Particularly since she’s a trainer, That, that’s…
255 00:22:49.750 ⇒ 00:22:53.409 MattBurns: You gotta break that habit, that’s a requirement now. This is where you go.
256 00:22:53.500 ⇒ 00:23:07.849 YvetteRuiz: Yeah, and Matt, that’s why we started, so that was the next steps, is every trainer, we’re meeting with them now on a week. I didn’t get to do the first one, Janiece met with them the first, but next week is the follow-up, where we’re going to set expectations.
257 00:23:07.850 ⇒ 00:23:16.179 YvetteRuiz: And we’re probably gonna tie in… now that I’m thinking of it, I’ll probably even tie KPIs, some type of KPIs to that. KPI to it.
258 00:23:16.460 ⇒ 00:23:28.139 MattBurns: Yeah, because it’s one thing if it truly is quicker and more convenient to have a sheet, but if it’s… if it’s not, ultimately, Andy’s going to be more accurate, because you’re updating it. The sheet’s not going to be, so…
259 00:23:28.350 ⇒ 00:23:29.710 MattBurns: Yeah, no, I…
260 00:23:30.200 ⇒ 00:23:45.430 Uttam Kumaran: And so another phase… another phase thing we can do is you can test that, like, if we want to selectively remove people, and you want to kind of slow roll that, like, we can… we should do that. But I totally agree, like, for the trainers, I don’t know, I feel like…
261 00:23:46.240 ⇒ 00:23:55.250 Uttam Kumaran: it’s… and it’s just gonna make this prop… this better, the more they give feedback, and we… we’re gonna… we’re continuing to attack, like, the speed issue.
262 00:23:55.250 ⇒ 00:24:07.080 Uttam Kumaran: So, if it’s already kind of at that point, it’s just gonna get better. So… and it’s not like those sheets are going… they’re not, like, we don’t have to, like, get rid of them, but I really feel like if there’s… if we’re at the point
263 00:24:07.080 ⇒ 00:24:13.379 Uttam Kumaran: where the quality and the speed is… is better, then there’s no reason.
264 00:24:14.060 ⇒ 00:24:16.380 Uttam Kumaran: So I think… yeah, go ahead.
265 00:24:16.720 ⇒ 00:24:18.900 JanieceGarcia: And it even was, and I don’t…
266 00:24:19.510 ⇒ 00:24:28.239 JanieceGarcia: My thing, too, is, okay, are we going from one sheet to another? Because it’s central dock, even. She was like, oh, well, I’ll just go to the central dock and look at it. Like, no, no, no, no.
267 00:24:28.610 ⇒ 00:24:29.170 YvetteRuiz: Excuse me.
268 00:24:29.170 ⇒ 00:24:35.570 JanieceGarcia: Well, it’s confusing, and exactly, because you… you should use that for training purposes, not actual.
269 00:24:35.570 ⇒ 00:24:36.130 Uttam Kumaran: Yes.
270 00:24:36.130 ⇒ 00:24:51.400 YvetteRuiz: But also, exactly, and that’s kind of the point right there, and the reason why I wanted to bring in the trainers is because it is their responsibility to build this. This is their responsibility. The only… that is their… that’s their responsibility, and that’s why I wanted to start having this, because
271 00:24:51.400 ⇒ 00:25:03.570 YvetteRuiz: Look, the whole goal is to use Andy to get them the answers, not go tap you on the shoulder, not go do that, so your responsibility is to build this, and if it’s not working, you’re to provide us the feedback.
272 00:25:04.270 ⇒ 00:25:04.840 JanieceGarcia: Right.
273 00:25:04.940 ⇒ 00:25:06.730 MattBurns: Exactly. Yep.
274 00:25:07.200 ⇒ 00:25:09.070 MattBurns: For sure. Okay, good.
275 00:25:09.690 ⇒ 00:25:11.330 Uttam Kumaran: Yeah, I mean, I guess, like.
276 00:25:11.600 ⇒ 00:25:16.040 Uttam Kumaran: we definitely don’t want to step on people’s toes, but, like, I don’t know, I feel like that’s…
277 00:25:16.260 ⇒ 00:25:19.619 Uttam Kumaran: That is, I think, something that we should do, is just consider
278 00:25:20.210 ⇒ 00:25:23.509 Uttam Kumaran: like, removing that access, and, you know, I also think…
279 00:25:23.510 ⇒ 00:25:24.340 JanieceGarcia: Fine with it.
280 00:25:24.340 ⇒ 00:25:25.160 Uttam Kumaran: Okay.
281 00:25:26.070 ⇒ 00:25:26.930 Uttam Kumaran: Yeah.
282 00:25:27.080 ⇒ 00:25:28.609 Uttam Kumaran: I feel like that’s…
283 00:25:29.480 ⇒ 00:25:34.419 Uttam Kumaran: it sort of leaves no other option. We could always roll that back, of course, but…
284 00:25:34.890 ⇒ 00:25:40.479 Uttam Kumaran: I don’t know. I feel like, especially for the trainers, It’s only gonna get…
285 00:25:40.480 ⇒ 00:25:44.799 YvetteRuiz: We already dipped our toe in. Now it’s time to go in there and start, really.
286 00:25:44.800 ⇒ 00:25:45.659 JanieceGarcia: Really doing that.
287 00:25:45.660 ⇒ 00:25:46.640 YvetteRuiz: But yeah.
288 00:25:46.640 ⇒ 00:25:51.100 JanieceGarcia: And it’s not like we haven’t been transparent with them this whole time, because we’ve been telling them.
289 00:25:51.470 ⇒ 00:25:53.020 JanieceGarcia: They’re gonna go away.
290 00:25:53.020 ⇒ 00:25:54.240 Uttam Kumaran: Yeah, yeah.
291 00:25:54.750 ⇒ 00:26:03.410 Uttam Kumaran: And then the other thing, Andrew, I think it would be really helpful for us to add a couple of properties to each of the users here, which is their role or the department, and then that way we.
292 00:26:03.410 ⇒ 00:26:03.820 YvetteRuiz: Yes.
293 00:26:03.820 ⇒ 00:26:12.960 Uttam Kumaran: segment a little bit at a higher level versus looking at every single person. Now, before, we were only looking at, like, 20, 30 people. Now there’s quite a bit of people to go through one-on-one.
294 00:26:12.960 ⇒ 00:26:19.750 Amber Lin: Yeah, that was… that was what I realized when I was trying to find the data for specific users. We have so many right now.
295 00:26:19.750 ⇒ 00:26:23.719 Uttam Kumaran: Yeah, so I think we can move towards, like,
296 00:26:24.350 ⇒ 00:26:29.090 Uttam Kumaran: Move towards, like, reporting out weekly now on, like, the department or the role level.
297 00:26:29.280 ⇒ 00:26:39.549 Amber Lin: Awesome, yeah. I’ll work with the engineers on that, because I also really want to say your department had this usage. That would be really nice to do.
298 00:26:39.550 ⇒ 00:26:41.699 JanieceGarcia: And I would, like, I mean, I know…
299 00:26:42.020 ⇒ 00:26:50.079 JanieceGarcia: my department even is, you know, the routers are heavy on admin, but they do still have to make phone calls. I would like to see their
300 00:26:50.780 ⇒ 00:26:51.300 JanieceGarcia: 30.
301 00:26:51.300 ⇒ 00:26:51.960 Amber Lin: Well, often…
302 00:26:51.960 ⇒ 00:26:59.619 YvetteRuiz: But even, like, I’m also thinking of it, I mean, at some point, and again, we’ll work with the trainers on this, I do feel like…
303 00:27:00.200 ⇒ 00:27:05.639 YvetteRuiz: you know, again, just thinking through this, they… I still want any admin person to have.
304 00:27:05.640 ⇒ 00:27:06.360 JanieceGarcia: Anybody.
305 00:27:06.360 ⇒ 00:27:19.989 YvetteRuiz: I still point them there, because there’s still overflow at a level. So, making sure that we build the habit with them, even if it’s just testing or asking questions here and there, just to kind of keep their knowledge up.
306 00:27:20.930 ⇒ 00:27:21.610 Amber Lin: True.
307 00:27:21.610 ⇒ 00:27:22.100 JanieceGarcia: Very true.
308 00:27:22.100 ⇒ 00:27:29.530 Amber Lin: Yeah. Question on overflow agents. So, if a person is in multiple departments, how do we count that?
309 00:27:33.800 ⇒ 00:27:35.240 YvetteRuiz: As far as the role?
310 00:27:35.820 ⇒ 00:27:47.959 Amber Lin: Yeah, if they’re in multi… if they’re overflowing to different departments, and they’re taking calls for both, do I count… where do I count their calls? Do I count them as to their overflow department?
311 00:27:48.240 ⇒ 00:27:51.359 Amber Lin: Do I just count them towards their regular department?
312 00:27:51.360 ⇒ 00:27:52.270 YvetteRuiz: primary.
313 00:27:52.270 ⇒ 00:27:53.270 JanieceGarcia: primaries, I would say.
314 00:27:53.270 ⇒ 00:27:53.929 Amber Lin: Cool, okay.
315 00:27:56.280 ⇒ 00:28:03.680 YvetteRuiz: Yeah, because, I mean, if we needed to, we can get really granular, because we know the count of overflow calls that they take. Awesome.
316 00:28:04.090 ⇒ 00:28:09.809 Amber Lin: So last time we talked about follow-up questions, and we’ve been… we’ve been
317 00:28:10.410 ⇒ 00:28:16.379 Amber Lin: changed Andy a little bit so he can’t ask follow-up questions now. I just want to make sure that
318 00:28:16.620 ⇒ 00:28:28.760 Amber Lin: Because we know what attributes we have, but I’m not sure what responses to give based on which attribute. Do we have that ready in the central doc, or do you guys want to review that together?
319 00:28:28.760 ⇒ 00:28:34.469 JanieceGarcia: Casey actually sent me that in one of my tickets.
320 00:28:34.680 ⇒ 00:28:37.950 JanieceGarcia: So, I will have that ready for him.
321 00:28:37.950 ⇒ 00:28:38.720 Amber Lin: Okay.
322 00:28:39.080 ⇒ 00:28:39.899 Amber Lin: Okay, that would be…
323 00:28:39.900 ⇒ 00:28:43.539 JanieceGarcia: I’ll probably have that ready for him by tomorrow morning, at the latest.
324 00:28:43.540 ⇒ 00:28:44.260 Amber Lin: Yeah, so that’s what I.
325 00:28:44.260 ⇒ 00:28:49.220 JanieceGarcia: We’ll have that, like, next week we can test if we can roll this out, and then have a few use cases.
326 00:28:49.670 ⇒ 00:28:54.259 JanieceGarcia: For sure. Because he listed everything and just wants me to move it around.
327 00:28:54.260 ⇒ 00:28:59.540 Amber Lin: Okay. So, yep. Yeah. And then, we also talked about
328 00:28:59.830 ⇒ 00:29:06.899 Amber Lin: Gathering all the services under each department, so we can have more accurate, do we service this or not?
329 00:29:06.900 ⇒ 00:29:13.120 JanieceGarcia: Do we have that yet, or do you want me to schedule, like, a call with all the trainers, and we can grab that?
330 00:29:13.380 ⇒ 00:29:18.240 JanieceGarcia: That’s the list… that’s part of the list that I’ve already sent you, right? The one…
331 00:29:18.240 ⇒ 00:29:19.790 Amber Lin: Yeah.
332 00:29:19.930 ⇒ 00:29:33.219 Amber Lin: I think we just want it to be slightly more granular, since when we first created it, it was more of, like, coverage, but right now, we do want to list every single thing. Do we have every single thing in there?
333 00:29:33.220 ⇒ 00:29:43.340 YvetteRuiz: No, we still… I mean, we do… all I need to do is have all the managers send it to me. You’re… you’re talking about the list, the… the service and skills, so if it says electric.
334 00:29:43.340 ⇒ 00:29:43.850 Amber Lin: Yes.
335 00:29:43.850 ⇒ 00:29:46.059 YvetteRuiz: What are all the electric services? Yeah, so…
336 00:29:46.890 ⇒ 00:29:51.700 YvetteRuiz: We’ll get that to you, Amber. I’m just waiting for the handyman, the home improvement, I’m sorry, department.
337 00:29:51.700 ⇒ 00:29:52.710 Amber Lin: Sounds good.
338 00:29:52.870 ⇒ 00:30:01.700 Amber Lin: Okay, that is all the updates I have here, and then we’re just… we’re continuing to work on the triage issues, and then I believe Lutamo
339 00:30:02.170 ⇒ 00:30:07.030 Amber Lin: Polish up the disc… Plan, and send that over to you guys.
340 00:30:07.030 ⇒ 00:30:17.180 Uttam Kumaran: Yeah, maybe I can also just spend a moment… Matt, I know we’ve been kind of working internally on sort of the discovery plan since our meeting, and maybe I’ll just
341 00:30:17.410 ⇒ 00:30:21.640 Uttam Kumaran: I was basically going to summarize this document,
342 00:30:22.610 ⇒ 00:30:31.489 Uttam Kumaran: in an email, but I would love to get your perspective, and then I’m happy to send it out to the whole group that we met with. But…
343 00:30:31.790 ⇒ 00:30:33.670 Uttam Kumaran: Roughly,
344 00:30:34.090 ⇒ 00:30:48.479 Uttam Kumaran: I mean, I thought that was a great meeting. I think we generally agreed on, sort of, three core areas, which was awareness, conversion, retention, like, we say res… retention, and then a little bit morbid, but, like, resurrection. Basically, we’ve talked to all these folks.
345 00:30:48.480 ⇒ 00:30:49.060 MattBurns: Yep.
346 00:30:49.060 ⇒ 00:31:03.269 Uttam Kumaran: to get them back in the funnel. And so, I think it was great, like, coming into that conversation, I didn’t know exactly where to focus on, and I think these are gray areas with, like, pretty clear ways in where we’ve supported a bunch of folks to not only get
347 00:31:03.280 ⇒ 00:31:12.129 Uttam Kumaran: a lot of the visibility that I think we’re lacking, but basically get a plan on, like, once we know the metrics there, what we can do. So…
348 00:31:12.270 ⇒ 00:31:19.739 Uttam Kumaran: I think, you know, we did a little bit of a mapping on, like, what is the current state based on our conversation, but I think
349 00:31:19.770 ⇒ 00:31:37.620 Uttam Kumaran: Overall, we want to look at, okay, the profile of where we attract new leads, segmenting those by channel, and so channel analysis is something that we do for a lot of folks. We want to understand not only how they’re coming in, but what is their conversion rate across which channels.
350 00:31:37.620 ⇒ 00:31:41.070 Uttam Kumaran: And then we want to also look at
351 00:31:41.230 ⇒ 00:31:56.109 Uttam Kumaran: all the past data we have on past clients, and look at, sort of, what we’re calling, like, win-back campaigns, or look at the life cycle. And so, for us, it’s… that… that is sort of, like.
352 00:31:56.150 ⇒ 00:32:03.879 Uttam Kumaran: these are the kind of the questions that we’re trying to answer, and we would do that basically in a month-long discovery sprint, and so…
353 00:32:03.940 ⇒ 00:32:13.189 Uttam Kumaran: This is everything from, like, how are we effectively acquiring leads across channels, so we have all our common channels. We want to look at lead to bookings, so looking at
354 00:32:13.260 ⇒ 00:32:30.280 Uttam Kumaran: the conversion rates across those channels from… from lead to someone getting booked. We also want to look at all the cancellation types, we want to look at the average lifetime of an ABC customer, and then look at, like.
355 00:32:30.370 ⇒ 00:32:34.720 Uttam Kumaran: We talked about, hey, we have our reward program, our loyalty program, like, what’s…
356 00:32:34.720 ⇒ 00:32:35.290 MattBurns: Stop.
357 00:32:35.290 ⇒ 00:32:53.329 Uttam Kumaran: Has that been effective? What’s been the impact of that? Similarly, like, a win-back campaign, this is something that we run for a lot of folks, and I mean, we’re… we do this even at Brainforge, which is like, hey, we talk to all these folks, and for one reason or another, it wasn’t the right time, but that doesn’t mean you shouldn’t ever keep knocking on the door, right? And so.
358 00:32:53.370 ⇒ 00:32:58.000 Uttam Kumaran: One thing what we would want to do is say, okay, have we ever run one of these?
359 00:32:58.070 ⇒ 00:33:17.660 Uttam Kumaran: how would we design a new one, and, like, what is the opportunity? You know, if we were to get X percent of those folks that we contacted decided to… to convert, like, okay, what is the true opportunity of that? And then really, we would… we kind of also wanted to do a sort of a knowledge transfer, you know, on…
360 00:33:17.660 ⇒ 00:33:31.259 Uttam Kumaran: what’s going on in marketing. I think, really, what I would hope to deliver is that even without us, if you were to have a conversation like that meeting, there is a concise document on, like, here’s everything that’s happened in marketing, and here’s…
361 00:33:31.260 ⇒ 00:33:41.170 Uttam Kumaran: here’s sort of, like, what the opportunity is. And so, I think sort of at the end of that, we would want to sort of get together with… for, like, some type of
362 00:33:41.250 ⇒ 00:33:50.070 Uttam Kumaran: basically some type of deck that we can present to that same crew. We identify, like, here’s all the measured KPIs, and here are some tests that we can run.
363 00:33:50.260 ⇒ 00:33:54.670 Uttam Kumaran: And then we want to also produce this, like, knowledge transfer
364 00:33:55.010 ⇒ 00:34:07.059 Uttam Kumaran: memo, for less. So, like, making sure we… we… we’ve codified what all the stuff that he’s run, current systems, vendors, and so you’re never else… you’re never going to a meeting with, like.
365 00:34:07.340 ⇒ 00:34:21.019 Uttam Kumaran: we have no clue where everything is, at least there’s something there, and that’s, like, a great thing that can last. And then, we would also totally tell you, like, hey, where are there gaps, and just, like, you don’t have the right tools in place. And so.
366 00:34:21.179 ⇒ 00:34:38.059 Uttam Kumaran: for us, like, we typically do that. We would try to just timebox that to, like, a month, and basically say, like, let’s see how far we can… we can drive. I think, of course, some channels will be more manual and more people versus others, but
367 00:34:38.250 ⇒ 00:34:53.139 Uttam Kumaran: I’m sure in just a few conversations, we’ll at least, for us, it’s understanding where are 80% of the customers coming from now, and, like, what are the places that we’ve tried, and then we sort of build a little bit of, like, that bell curve of, like.
368 00:34:53.199 ⇒ 00:35:05.430 Uttam Kumaran: okay, what are… what are things we… on either end that there is opportunity? And then we come to the say, hey, you have a great conversion rate here, there’s probably not much more else you can do on this channel. Or, hey, there’s… there’s actually…
369 00:35:05.560 ⇒ 00:35:14.359 Uttam Kumaran: we’ve seen that although people have not been coming through a channel, the conversion rates are high, and we should drive more there. And so those are the exact things that we’ve come with, and we’ve come with those
370 00:35:14.860 ⇒ 00:35:33.000 Uttam Kumaran: opportunities. And so I can certainly send you, like, a little bit of a price, and this is… this type of discovery work is stuff we do all the time. All of our new clients, and this is actually what we did, you know, our first sort of month or two with… on the Andy project, anyways, was meeting with everybody, so…
371 00:35:33.580 ⇒ 00:35:39.299 Uttam Kumaran: We have a little bit of a process there, but, like, what do you think of, kind of, like, hearing… hearing that, or…
372 00:35:39.660 ⇒ 00:35:47.900 Uttam Kumaran: Is there… yeah, I guess I just want to get your feedback, and I also would love to send this to Steven as well, to hear what he thinks, but…
373 00:35:48.470 ⇒ 00:35:55.940 MattBurns: Yeah, why don’t you do that? Send it to Steven and me first, we’ll talk about it, see if we think we’re on the right track, and then we can get it to Bobby and Bo as well, and…
374 00:35:55.940 ⇒ 00:35:56.580 Uttam Kumaran: Okay.
375 00:35:56.580 ⇒ 00:36:02.579 MattBurns: That’d be a good… good way to proceed. I can chat with Steven about it, maybe as early as tomorrow.
376 00:36:02.580 ⇒ 00:36:12.769 Uttam Kumaran: Okay, yeah, I’m definitely, you know, given your… you guys know what Bobby’s focused on, and sort of where he wants, so the last thing I want to do here is sort of
377 00:36:13.000 ⇒ 00:36:21.349 Uttam Kumaran: boil the ocean. And so, for me, it’s really… it was really helpful in that conversation for him to really push on, be like, what exactly are we getting? And so…
378 00:36:21.750 ⇒ 00:36:28.480 Uttam Kumaran: that’s what I wanna… I wanna deliver, and… and I think a big part of this, you know, is that, like.
379 00:36:28.770 ⇒ 00:36:36.730 Uttam Kumaran: have us go, like, ask all these questions and get you, like, that memo of, like, what’s the current state of things. Especially given if… given…
380 00:36:37.030 ⇒ 00:36:39.559 Uttam Kumaran: if… as Les is leaving.
381 00:36:39.560 ⇒ 00:36:41.170 MattBurns: Let’s just transition now.
382 00:36:41.180 ⇒ 00:36:41.770 Uttam Kumaran: You know?
383 00:36:42.260 ⇒ 00:36:45.679 Uttam Kumaran: I think there’s a perfect opportunity for us to help there, and then
384 00:36:45.780 ⇒ 00:36:52.079 Uttam Kumaran: again, for us, we propose tests, and we size those opportunities. Like, hey, there’s a huge opportunity here if we are to
385 00:36:52.380 ⇒ 00:36:57.980 Uttam Kumaran: capture and move this number here, here’s the… here is the… the revenue opportunity. That’s exactly how we…
386 00:36:58.290 ⇒ 00:37:14.239 Uttam Kumaran: That’s exactly how we price, and Amber is doing this for a couple of other customers, where this is what we do, we just run these tests, and so we run a test, and we… we help execute that, and then we basically show the data on what’s been happening. And, like, that’s where we want
387 00:37:14.460 ⇒ 00:37:20.400 Uttam Kumaran: all of our clients to get to it is this, like, experimentation. Like, similarly on ABC, right, we’re taking a subset of users.
388 00:37:20.500 ⇒ 00:37:26.810 Uttam Kumaran: We’re now… we… and the whole way, we’ve kind of done this in an experiment-think fashion as we learn, versus, like.
389 00:37:26.830 ⇒ 00:37:44.469 Uttam Kumaran: let’s go try a bunch of stuff and… and never know what actually worked. So that’s, like, kind of, like, what we’d be going for. So I’ll send this over to you and Steven over email. Does this doc format work? Okay, perfect. You can leave comments or give me a ring, and…
390 00:37:45.960 ⇒ 00:37:56.069 MattBurns: Yeah, he and I’ll chat about it. If we have questions, we’ll get with you, and then just kind of give you some feedback, and if we think we’re in the right area, right direction.
391 00:37:56.310 ⇒ 00:37:57.380 Uttam Kumaran: Okay.
392 00:37:58.450 ⇒ 00:37:59.900 MattBurns: Perfect. Perfect.
393 00:38:00.010 ⇒ 00:38:01.010 MattBurns: Okay.
394 00:38:01.310 ⇒ 00:38:02.150 MattBurns: Great.
395 00:38:03.460 ⇒ 00:38:04.529 YvetteRuiz: Alrighty, guys!
396 00:38:04.840 ⇒ 00:38:05.780 MattBurns: Hey, guys.
397 00:38:05.910 ⇒ 00:38:06.910 MattBurns: Thank you.
398 00:38:06.910 ⇒ 00:38:07.510 JanieceGarcia: Awesome.
399 00:38:07.510 ⇒ 00:38:08.880 YvetteRuiz: Thank you.
400 00:38:09.110 ⇒ 00:38:09.670 Uttam Kumaran: Thanks, everyone.
401 00:38:09.670 ⇒ 00:38:10.229 MattBurns: I gotcha.
402 00:38:10.230 ⇒ 00:38:10.870 Uttam Kumaran: Bye.