Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2025-06-26 Meeting participants: Steven, Amber Lin, Janiecegarcia, Yvetteruiz, Matt Burns
WEBVTT
1 00:00:26.360 ⇒ 00:00:27.110 YvetteRuiz: Nope.
2 00:00:33.180 ⇒ 00:00:34.000 Matt Burns: Hey, Brett.
3 00:00:34.520 ⇒ 00:00:38.250 YvetteRuiz: Hi, Matt, how’s your day going.
4 00:00:39.270 ⇒ 00:00:40.509 Matt Burns: Good so far. How about you?
5 00:00:40.510 ⇒ 00:00:47.655 YvetteRuiz: That’s good good getting caught up, that’s for sure. It’s so quiet over here
6 00:00:48.140 ⇒ 00:00:50.608 YvetteRuiz: at this the new office so.
7 00:00:51.020 ⇒ 00:00:52.430 Matt Burns: You’re in the new building today.
8 00:00:52.430 ⇒ 00:00:58.036 YvetteRuiz: Yeah, yes, the cleaning people were here today. So it’s it looks really nice.
9 00:00:58.760 ⇒ 00:00:59.870 Matt Burns: That’s great!
10 00:01:00.400 ⇒ 00:01:00.930 YvetteRuiz: Hi.
11 00:01:01.460 ⇒ 00:01:02.146 Matt Burns: Hi! There!
12 00:01:02.490 ⇒ 00:01:05.370 Amber Lin: Hi, Hi, Matt! Nice to see you again.
13 00:01:05.570 ⇒ 00:01:07.739 YvetteRuiz: You too, ma’am. How are you doing today.
14 00:01:08.483 ⇒ 00:01:12.200 Amber Lin: I’m good. Oh, Yvette, congratulations!
15 00:01:12.480 ⇒ 00:01:15.063 YvetteRuiz: Oh, thank you, Miss Amber.
16 00:01:16.270 ⇒ 00:01:23.400 Amber Lin: I don’t. Okay, Denise, have. Shannon delivered her surprise. I don’t want to spoil it.
17 00:01:23.710 ⇒ 00:01:24.260 JanieceGarcia: No.
18 00:01:25.040 ⇒ 00:01:27.446 Amber Lin: Oh, okay.
19 00:01:32.400 ⇒ 00:01:33.169 JanieceGarcia: If I get to see.
20 00:01:33.170 ⇒ 00:01:33.500 YvetteRuiz: I’m.
21 00:01:33.500 ⇒ 00:01:34.100 JanieceGarcia: Tomorrow. Then you can.
22 00:01:34.646 ⇒ 00:01:41.203 YvetteRuiz: You’ll see me tomorrow. Yeah, yeah. Well, I’m now. I’m excited.
23 00:01:41.750 ⇒ 00:01:42.960 Matt Burns: He should be.
24 00:01:42.960 ⇒ 00:01:43.514 YvetteRuiz: Exactly.
25 00:01:44.070 ⇒ 00:01:45.059 JanieceGarcia: We are too.
26 00:01:45.550 ⇒ 00:01:54.820 YvetteRuiz: I know if the girls are just super super excited. They were sending a lot of happy thoughts and blessings out there, so I was so thankful for all you guys.
27 00:01:55.120 ⇒ 00:01:59.220 JanieceGarcia: And then all his, all his little pictures I’m like, Oh, my gosh!
28 00:02:00.021 ⇒ 00:02:03.228 YvetteRuiz: Yeah, sweet boy.
29 00:02:05.350 ⇒ 00:02:10.850 YvetteRuiz: Alrighty guys. So who Steven’s on a call right now. So I know he’s gonna jump in here in just a second.
30 00:02:11.325 ⇒ 00:02:14.160 Amber Lin: Okay, yeah. Let me check with them real, quick.
31 00:02:14.160 ⇒ 00:02:16.819 YvetteRuiz: Yeah, he just yelled. He’s jumping on.
32 00:02:18.280 ⇒ 00:02:18.960 JanieceGarcia: Hey!
33 00:02:20.430 ⇒ 00:02:22.249 YvetteRuiz: How’s Austin Janice.
34 00:02:23.840 ⇒ 00:02:24.335 JanieceGarcia: Good.
35 00:02:24.830 ⇒ 00:02:25.160 YvetteRuiz: Good.
36 00:02:25.160 ⇒ 00:02:26.729 JanieceGarcia: Great Great Great Great Great
37 00:02:28.550 ⇒ 00:02:28.950 YvetteRuiz: Channel.
38 00:02:28.950 ⇒ 00:02:31.990 JanieceGarcia: Work with the new hires so, and
39 00:02:31.990 ⇒ 00:02:36.140 JanieceGarcia: and then I had an interview for with Brianna today.
40 00:02:36.140 ⇒ 00:02:37.310 YvetteRuiz: Oh, okay. Okay.
41 00:02:37.540 ⇒ 00:02:39.619 JanieceGarcia: So I’ll definitely.
42 00:02:39.860 ⇒ 00:02:41.509 YvetteRuiz: Loop me in. Okay. Yep.
43 00:02:42.109 ⇒ 00:02:43.309 YvetteRuiz: Sounds good.
44 00:02:43.910 ⇒ 00:02:45.200 Amber Lin: Oh, there’s Steven.
45 00:02:45.200 ⇒ 00:02:46.310 JanieceGarcia: But it’s busy, busy.
46 00:02:50.240 ⇒ 00:02:51.160 Amber Lin: Hi.
47 00:02:51.330 ⇒ 00:02:52.810 Steven: Hi! How’s it going.
48 00:02:52.910 ⇒ 00:02:53.640 YvetteRuiz: Even.
49 00:02:54.770 ⇒ 00:03:01.309 Amber Lin: Pretty good, very excited. We’re all talking about if it’s grandson right now.
50 00:03:01.310 ⇒ 00:03:02.560 YvetteRuiz: Yeah.
51 00:03:03.960 ⇒ 00:03:05.710 Steven: The talk of the town over here.
52 00:03:05.940 ⇒ 00:03:06.170 JanieceGarcia: Yeah.
53 00:03:06.170 ⇒ 00:03:08.879 Matt Burns: Yeah. Steven and I were just talking about him.
54 00:03:09.150 ⇒ 00:03:11.740 Matt Burns: It should be should be the talk of the town.
55 00:03:11.740 ⇒ 00:03:17.170 YvetteRuiz: Yeah, big news is Udem joining us today.
56 00:03:17.340 ⇒ 00:03:34.149 Amber Lin: I don’t know. We should probably just get started without him. He just flew into New York to meet with Robert, our other CEO. So they’re talking about Q. 3. Planning in person. So I’m not sure if you will be able to join.
57 00:03:35.120 ⇒ 00:03:35.475 YvetteRuiz: Okay.
58 00:03:36.190 ⇒ 00:03:38.969 Matt Burns: Okay, well, we’ll get started. Then, yeah.
59 00:03:38.970 ⇒ 00:03:50.699 Amber Lin: Yeah, there’s a recording, and I’ll send it to him. A meeting with him after or if not, then tomorrow. So let me share my screen and we’ll get started.
60 00:04:07.380 ⇒ 00:04:27.799 Amber Lin: So I want to start again with Andy’s usage. I think it’s a great point for us to look at it together and know where we’re at. So this chart has the usage for each 2 week period. So 14 days since we started. So, starting from March.
61 00:04:27.980 ⇒ 00:04:52.630 Amber Lin: we can see that the session times are growing steadily, and I’m comparing it to our current goal, our stage one goal of 2,000 usage per month, and we can see we are steadily growing towards that goal. However, the adoption is still behind our goals, and the growth is not as fast as we want them to be.
62 00:04:54.040 ⇒ 00:05:03.439 Amber Lin: But it is good news that it is steadily growing, and later on, in this presentation I want to talk about the roadmap. I set out to
63 00:05:03.610 ⇒ 00:05:11.299 Amber Lin: not only grow usage, but really target those items that why we started this project in the 1st place.
64 00:05:11.300 ⇒ 00:05:36.160 Amber Lin: So I’m very excited to present that roadmap to you guys. I’ve been thinking about it day and night. Yesterday I was going to sleep, and my partner was like, Why are you still working? I’m thinking about thinking about a product. I’m thinking about how I can actually deliver more than just A AI Chatbot, because I’m not selling a product. And ultimately.
65 00:05:36.160 ⇒ 00:05:40.990 Amber Lin: I really want to align it to what ABC needs.
66 00:05:41.990 ⇒ 00:05:43.046 YvetteRuiz: Well, thank you. Amber.
67 00:05:43.310 ⇒ 00:05:44.150 Amber Lin: So.
68 00:05:44.767 ⇒ 00:06:02.800 Amber Lin: And so I think this slide would also be helpful. I updated it since last week to include not only the top users from the far from the past 14 days. But also the users that might need a little bit more nudging. So the users with the last less than 5 sessions
69 00:06:02.800 ⇒ 00:06:17.409 Amber Lin: within the last 14 days. So you guys all have access to this slide. And I think this could be a good starting point to encourage those who have used it a lot and help those to see if any of these people need extra help.
70 00:06:18.080 ⇒ 00:06:42.119 YvetteRuiz: Yeah, the good. The the thing I wanted to point out here, Matt and Steven, is that we have our overflow agents, and I brought this up to you on Monday. Amber is the high. There’s a few of them in here that are high usage, and our managers, their managers, are pushing that, you know, every time they have questions they are directing them there. The key thing that we want them to do is make sure that they’re providing feedback. But we do have the overflow agent showing there which is
71 00:06:42.120 ⇒ 00:06:48.306 YvetteRuiz: where we felt that they would, you know, get more value, a lot more value from using Andy.
72 00:06:51.280 ⇒ 00:07:02.719 Amber Lin: Totally. And I met with the Qa. Agents this week, and we’ll start also incorporating that when we listen into calls to see. Okay, maybe that’s a question that
73 00:07:02.850 ⇒ 00:07:09.549 Amber Lin: we should have asked Andy, instead of escalating to a supervisor and waiting for that to get answered.
74 00:07:10.370 ⇒ 00:07:14.532 YvetteRuiz: I talked to Cynthia. She gave me a brief update of y’all’s meeting yesterday.
75 00:07:16.360 ⇒ 00:07:17.253 YvetteRuiz: Thank you.
76 00:07:18.610 ⇒ 00:07:36.800 Amber Lin: So now I want to talk about the roadmap I see for the next few months, and how we should how I think we should approach really delivering value for folks at ABC.
77 00:07:38.180 ⇒ 00:07:56.149 Amber Lin: So overall from my understanding, you guys came to us because you wanted to grow profitability, and then by focusing on the customer service performance, making it more operationally efficient and boosting any retention
78 00:07:56.250 ⇒ 00:08:16.999 Amber Lin: or and boosting conversions and customer service is really the key lever for protecting the revenue and controlling the cost that we need. You guys are a connection to all the other areas in the business. And this is really a great launch pad for broader optimization.
79 00:08:17.650 ⇒ 00:08:18.790 Amber Lin: And
80 00:08:19.080 ⇒ 00:08:25.669 Amber Lin: I, in my view, as I’ve worked with you guys for the past few months, I sort of understood better
81 00:08:25.790 ⇒ 00:08:48.490 Amber Lin: what values we’re trying to bring, and it could be to cut delays in the customer service and reduce the churn to unlock the upsells and enable people’s self service, and to speed up training of the agents and help improve the support system we have, so that overall we can have better quality
82 00:08:49.380 ⇒ 00:08:55.069 Amber Lin: of customer service overall which will in turn benefit our profitability.
83 00:08:55.750 ⇒ 00:09:05.060 Amber Lin: And the reason I know we eventually arrived at a contract that is based on the AI usage, and we
84 00:09:05.350 ⇒ 00:09:28.469 Amber Lin: aim for that, because we believe it directly supports these outcomes. The usage is the means, but these are really the outcomes, that matter. And when I designed this roadmap. I kept that in mind of usage is a numeric goal that we can reach because a lot of these things are qualitative. But ultimately I,
85 00:09:28.720 ⇒ 00:09:37.739 Amber Lin: no matter how the usage goal goes. I want to deliver these values, these benefits for ABC.
86 00:09:39.050 ⇒ 00:09:42.870 Matt Burns: Yeah, amber. Yeah. And I touched a little bit.
87 00:09:42.870 ⇒ 00:09:43.280 Amber Lin: Because.
88 00:09:43.280 ⇒ 00:09:44.710 Matt Burns: Reporting on
89 00:09:45.840 ⇒ 00:09:53.759 Matt Burns: retention. You know, we used to have a separate retention team that actually Cynthia headed that up. But we kind of morph that into the
90 00:09:55.284 ⇒ 00:09:59.030 Matt Burns: Customer Service Group, our agents and
91 00:09:59.480 ⇒ 00:10:08.580 Matt Burns: we talked a little this morning on. When we get a cancellation request, how can we address that
92 00:10:09.350 ⇒ 00:10:20.910 Matt Burns: effectively. And what can Andy really do to provide all the different options or strategies or
93 00:10:21.320 ⇒ 00:10:31.430 Matt Burns: just alternatives to a flat out cancellation? So you know, we talk about selling business the
94 00:10:32.070 ⇒ 00:10:38.550 Matt Burns: the other way that we can sell businesses to reduce cancellation. Certainly. And you know, that’s
95 00:10:39.590 ⇒ 00:10:42.299 Matt Burns: kind of always been an issue
96 00:10:42.430 ⇒ 00:10:47.210 Matt Burns: really, in the service industry when you have maintenance contracts is okay, how do we?
97 00:10:48.690 ⇒ 00:10:58.779 Matt Burns: How do we reduce what’s going out, you know, on the back side? We can sell a lot of business. But the other way is to retain a lot of business. So just
98 00:10:59.010 ⇒ 00:11:08.539 Matt Burns: maybe really looking at ways, Andy can really prompt the agent when they, when they hear that word cancel.
99 00:11:09.810 ⇒ 00:11:12.750 Matt Burns: what can we really do to put right in front of the
100 00:11:12.920 ⇒ 00:11:15.490 Matt Burns: agent immediately to say, well, here’s some.
101 00:11:15.800 ⇒ 00:11:19.410 Matt Burns: Here’s some ways. So again, that’s a a tangible
102 00:11:19.730 ⇒ 00:11:23.449 Matt Burns: way that we can achieve. The the business goal
103 00:11:25.000 ⇒ 00:11:28.279 Matt Burns: is exactly that, you know. Reduce the churn.
104 00:11:28.460 ⇒ 00:11:33.080 Matt Burns: But specifically when that word cancellation is mentioned.
105 00:11:33.470 ⇒ 00:11:37.019 Matt Burns: you know. Kind of kind of really having Andy kick in at that point.
106 00:11:38.710 ⇒ 00:11:59.399 Amber Lin: Yeah, totally. And I remember I kept thinking about it after you that brought it up to brought it up to me, and we discussed. How we want to improve the cancellation flows and based on our discussion, I’ve actually started working on improving the documentation. And I want to show you guys in a bit of
107 00:11:59.400 ⇒ 00:12:09.380 Amber Lin: I wanted to have clear steps and decision trees and to have scripts set in for each of those decision.
108 00:12:09.640 ⇒ 00:12:09.990 Matt Burns: Does it?
109 00:12:09.990 ⇒ 00:12:15.979 Amber Lin: Points, so that it’s not only easier for the Csrs just to feed it out.
110 00:12:16.270 ⇒ 00:12:31.250 Amber Lin: and it’s easier for Andy to provide a recommendation when it identifies that situation, because right now we don’t have enough documentation to support andy telling the Csrs what to do, and.
111 00:12:31.250 ⇒ 00:12:31.620 Matt Burns: Right.
112 00:12:31.620 ⇒ 00:12:47.649 Amber Lin: That’s why in the roadmap, that’s my number one priority of making sure that we do have all we need to provide that support, and then is to making sure that the Csr is actually take advantage of that support. We have.
113 00:12:48.040 ⇒ 00:12:48.610 Matt Burns: Yep.
114 00:12:49.930 ⇒ 00:12:52.110 Amber Lin: Thank you, and so
115 00:12:53.010 ⇒ 00:13:07.579 Amber Lin: want to quickly pass through the current state and talk about the ideal state. And I want to show you guys what what I have in mind. So right now, we don’t have much usage for Andy. Because
116 00:13:08.420 ⇒ 00:13:35.240 Amber Lin: trust is inconsistent, because documentation is not full, and because it’s not that accurate. And so the Csrs don’t have a compelling reason to use Andy because they’re worried that they might not be able to perform as well. And so that’s the number one blocker to achieving better better performance. And that’s what I want to tackle 1st off.
117 00:13:35.700 ⇒ 00:14:03.880 Amber Lin: And what I want to achieve is that people 1st of all, that we ensure everything is accurate and sufficient to help the Csrs. And then to make sure everybody is realizing these low hanging through these benefits that they could achieve in cutting their response, time making their responses more accurate. And then I want to enable our trainers to
118 00:14:04.280 ⇒ 00:14:25.579 Amber Lin: self. Serve with these AI tools as well to help them improve the documentation to help them look at Csr’s performance, especially if we start to integrate the 8 by 8 transcripts. I want to enable ABC and the people working at ABC to use the new tools. That is AI to
119 00:14:25.680 ⇒ 00:14:38.650 Amber Lin: help boost their efficiency. So it’s not because we want to use AI because it’s this new flashy thing is because there’s actually a lot of immediate benefits that we can see from using a new tool.
120 00:14:42.840 ⇒ 00:15:06.650 Amber Lin: And so for the short term I want to keep things very tangible. And so for the short term, I want to go from a usage of around currently about 400 per month to get to 2,000 sessions per month by the say, by the end of August. So within 2 months.
121 00:15:07.270 ⇒ 00:15:19.989 Amber Lin: and how I see we’ll be able to achieve that is really, 1st of all, providing the foundations that everything is covered, and everything is accurate and trustworthy.
122 00:15:20.680 ⇒ 00:15:23.530 Amber Lin: And so that means that we
123 00:15:23.660 ⇒ 00:15:37.200 Amber Lin: go through the Central Doc and, if possible, rewrite everything to cover all the corners and provide detailed steps and procedures so that both humans and AI can follow.
124 00:15:37.560 ⇒ 00:15:55.419 Amber Lin: I also want to incorporate all the outstanding spreadsheets so that the Csrs don’t have to jump between the different spreadsheets, and they can directly ask it through Andy. And lastly, in order to give people the confidence and able to
125 00:15:55.420 ⇒ 00:16:12.390 Amber Lin: trust in AI. I want to give them a list of questions that the trainers can say, hey, I’ve tested this out. I confirmed this. You don’t need to ask me this. You need to go ask Andy. Because I this is, I can. I’m 100% sure that this can be answered.
126 00:16:12.520 ⇒ 00:16:25.269 Amber Lin: And so that’s the 1st month of what I really want to target is to make sure that we have the foundations to build trust. And then the second month is really shaping the behaviors around usage
127 00:16:25.820 ⇒ 00:16:27.030 Amber Lin: by
128 00:16:27.370 ⇒ 00:16:42.390 Amber Lin: adding friction to alternative paths and reducing friction and adding incentives to using Andy. And that will that will mean both Ui improvements, and with the help of our trainers
129 00:16:43.420 ⇒ 00:16:46.650 Amber Lin: I’m going to pause here. I know I said a lot of words.
130 00:16:51.510 ⇒ 00:16:53.069 Matt Burns: Good makes sense to me. Yep.
131 00:16:53.070 ⇒ 00:16:54.070 Amber Lin: Okay. Awesome.
132 00:16:54.070 ⇒ 00:17:18.759 YvetteRuiz: Yeah. And I like that. You put the numbers together, amber, you know, setting the goals like, where do we want to go from here, you know, to this over here, cause it’s measurable. And you know and I’m right there with you. I mean, we’ve talked again, just making sure that the Trust is there. We need to make sure that that information is accurate. So then, that way, the Csrs trust that. But then also working with the trainers and making sure that they’re
133 00:17:18.810 ⇒ 00:17:27.860 YvetteRuiz: buying into this, they’re up making sure that our the feedback that we’re getting is updated. So then that way the things get stay up to date.
134 00:17:28.569 ⇒ 00:17:48.189 Amber Lin: Yeah, awesome. Yeah, I think that’s why pushed for a contract based on usage. Because that is really the only thing that we can measure numerically, and everything else is sort of qualitative and sort of doesn’t. There’s no direct measure to see how they can improve.
135 00:17:48.190 ⇒ 00:17:56.040 YvetteRuiz: Yeah, just a quick question. So, Matt, I did want to just kind of in in Stephen, we talked about so
136 00:17:56.140 ⇒ 00:18:10.159 YvetteRuiz: 8 by 8. We already got the Apis to them. So the integration of that, the the transcripts that we’re talking about is we’re still waiting on that piece. And I’ll follow up with Tim again. But
137 00:18:10.380 ⇒ 00:18:25.180 YvetteRuiz: the goal, I mean what would would be great. And you guys, I think you guys would agree is being able to get access to the transcripts of the phone calls. So then that way, we can get the actual data of what are we hearing? Where are we missing this? And are we using
138 00:18:25.630 ⇒ 00:18:37.870 YvetteRuiz: Andy? Cause that’s gonna really tell us a whole lot by getting those transcripts? I know he’s he was going to do some checking and get some final approval. I’m not sure from who, Matt. But I just want to kind of let you know that we’re still on! Hold
139 00:18:38.470 ⇒ 00:18:40.690 YvetteRuiz: on that from Tim.
140 00:18:41.380 ⇒ 00:18:46.290 Matt Burns: Well, I obviously we can hear the recordings so.
141 00:18:47.190 ⇒ 00:18:50.940 Matt Burns: We’re just talking about getting transcripts of those same recordings.
142 00:18:52.800 ⇒ 00:18:54.260 YvetteRuiz: That is correct. Yes.
143 00:18:54.550 ⇒ 00:18:55.150 Matt Burns: Okay.
144 00:18:55.700 ⇒ 00:18:56.330 YvetteRuiz: Yep.
145 00:18:56.930 ⇒ 00:18:57.340 Matt Burns: Well.
146 00:18:57.340 ⇒ 00:19:01.540 YvetteRuiz: And amber. Correct me if I’m wrong. He has. You guys have not gotten that from Tim.
147 00:19:02.240 ⇒ 00:19:07.580 Amber Lin: No, I asked him earlier this week around Tuesday. I haven’t heard back yet.
148 00:19:09.743 ⇒ 00:19:11.509 Matt Burns: Yeah, give him a bump
149 00:19:12.017 ⇒ 00:19:15.229 Matt Burns: event, and you can copy me on there to
150 00:19:15.890 ⇒ 00:19:18.740 Matt Burns: give him an extra extra ball. Yeah.
151 00:19:20.480 ⇒ 00:19:23.910 Amber Lin: Okay, awesome. That would be really helpful.
152 00:19:27.680 ⇒ 00:19:48.740 Amber Lin: let’s see. Okay, we have 10 min left. I’ll just. I’ll send you guys this deck. But essentially this is. This is a few initiatives that I want to take on, and this goes into my project. Management plans of how I put in tickets for each sprint and have different projects. So
153 00:19:48.740 ⇒ 00:20:00.069 Amber Lin: I separated into 4 initiatives, so making sure that the central, the our knowledge base, covers everything and it’s accurate. The second and 3rd one is
154 00:20:00.220 ⇒ 00:20:01.180 Amber Lin: really
155 00:20:01.440 ⇒ 00:20:13.160 Amber Lin: having the push and pull to drive the usage behaviors. And lastly, it’s about enabling our trainers and having
156 00:20:13.320 ⇒ 00:20:19.670 Amber Lin: making us ready, so that we can easily replicate what we did to other departments and other divisions.
157 00:20:21.260 ⇒ 00:20:21.700 Matt Burns: Yep.
158 00:20:21.700 ⇒ 00:20:37.800 Amber Lin: And I. This is a visual representation of what we talked about earlier. And I’ll just skip over so I can talk about a few updates and what I what I want to do for next sprint, which is the next immediate 2 weeks.
159 00:20:43.090 ⇒ 00:20:48.760 Amber Lin: And so this sprint? We focused a lot about
160 00:20:49.665 ⇒ 00:21:14.780 Amber Lin: so updating the knowledge base and my engineers was focused on developing a trainer bot, so enabling us to directly make updates from a Google chat to the Google Doc. So my trainers were. My engineers were working on that, and I was working with Janice and Shannon on the different spreadsheets at Central Doc and the Csr feedbacks.
161 00:21:15.130 ⇒ 00:21:39.999 Amber Lin: And down here, just a quick note on the data side, we built a pipeline to automatically get data from the 8 by 8 Apis, we’re adding another analyst engineer to help us model that data so that we can have, we can put it into our visualization tools. And we’re still waiting on Tim as we talked about.
162 00:21:43.100 ⇒ 00:21:44.490 Amber Lin: And so
163 00:21:44.820 ⇒ 00:21:58.789 Amber Lin: here are a few updates that I want to share. So for the past 2 weeks we’ve been really working on the feedback sheet that has all the feedbacks that the Csr. Submitted through Andy
164 00:21:59.340 ⇒ 00:22:16.389 Amber Lin: and together me and Janice and Shannon went through all of the outstanding feedbacks, and we assigned owners and actions to every single one of them. And this week we said, Okay, we will take all of the service availability questions or service information questions about, okay.
165 00:22:16.390 ⇒ 00:22:29.899 Amber Lin: what is this thing? Do we cover this? Do we cover that? How? What is the price of this. So these are all simple updates that we can make. And we set a goal that we want to update at least all of this.
166 00:22:30.130 ⇒ 00:22:33.439 Amber Lin: All of these questions by next sprint.
167 00:22:35.660 ⇒ 00:22:36.190 YvetteRuiz: Awesome.
168 00:22:38.200 ⇒ 00:22:45.979 Amber Lin: And this one is what I what I wanted to talk about earlier when Matt and Yvette, you guys mentioned the cancellations so.
169 00:22:45.980 ⇒ 00:22:46.300 Matt Burns: Yeah.
170 00:22:46.300 ⇒ 00:22:56.750 Amber Lin: After my conversation with Yvet, I went ahead and looked at the current documentation. We have for cancellations, similarly for escalations and call lists.
171 00:22:57.440 ⇒ 00:23:23.249 Amber Lin: and what I want to do is really having the different steps of okay. Step One. We want to identify a reason. Okay, here’s a script that the Csr should use to extract those reasons and based on the people’s answers, we can say, Okay, go to A, go to B, go to C, and they jump to that section and see. Okay, the next line, I should say, I say, okay, thanks for letting us know you’re moving. And then.
172 00:23:23.770 ⇒ 00:23:33.199 Amber Lin: having more scripts to identify what we can really do. And because we have a set steps and set scripts in place.
173 00:23:35.020 ⇒ 00:23:37.660 Amber Lin: we can really say that. Okay.
174 00:23:38.010 ⇒ 00:23:54.730 Amber Lin: we had all of this in place. Why didn’t you follow it? Or Andy will prompt the Csrs to say, Hey, you need to say this script. You need to get these informations you cannot jump directly to letting them cancel.
175 00:23:54.910 ⇒ 00:23:58.780 Amber Lin: So this is what I wanted to say earlier.
176 00:23:59.070 ⇒ 00:24:00.010 YvetteRuiz: Yeah, good
177 00:24:00.913 ⇒ 00:24:15.640 YvetteRuiz: like that. You know what I mean, because it’s it’s a b, and it’s just really taking into those points. Once they ask, you know what I mean? Once they ask the question, it’s just, it’s it’s much easier. So I’d like to give this a test. Is this one? Is this piece already in, or this is just a sample right now.
178 00:24:15.916 ⇒ 00:24:24.479 Amber Lin: This is a sample because I I made it. I don’t. Wanna I cause I don’t know everything that goes into it. So I wanna confirm before we add it in.
179 00:24:25.340 ⇒ 00:24:25.900 YvetteRuiz: Yeah.
180 00:24:26.110 ⇒ 00:24:51.869 YvetteRuiz: Well, yeah, okay, I’ll I’ll visit with Janice. I’m gonna go. I want to go through the original list. I mean the Re, the initial reasons. And I was talking to Matt that I want to re-look at all our reasons that we have currently and kind of just kind of align it with what we have and evolve and what those reasons are. So we can really get to the actual reasoning. And then I have actual scripts for Csrs that that way we get good data.
181 00:24:55.620 ⇒ 00:25:00.583 Amber Lin: Awesome. And another big initiative that we took on this.
182 00:25:01.500 ⇒ 00:25:27.169 Amber Lin: this cycle is looking at the inspector sheet and trying to consolidate all the different tabs in the inspector sheet into one master sheet, so that there’s no questions as to okay, who does this person cover? Is it commercial? Is it residential, but it says it has a special situation here. So we really want to drill it down to okay, zip code.
183 00:25:27.170 ⇒ 00:25:33.449 Amber Lin: each zip code and each service, and to have that level granularity
184 00:25:34.170 ⇒ 00:25:38.500 Amber Lin: so quick. This is still a work in progress, but
185 00:25:39.030 ⇒ 00:25:45.249 Amber Lin: we have each department under that we have residential and commercial
186 00:25:45.280 ⇒ 00:25:55.609 Amber Lin: same for ken free. Well, chem free, we have it divided by each service. It has same for lawn, same for home improvement.
187 00:25:55.610 ⇒ 00:26:19.290 Amber Lin: So really have. Originally we have all of these different spreadsheets. And it was really hard to incorporate into AI because there was a lot of specific specific instructions such as, Okay, this doesn’t do. It doesn’t do this in this specific place, but it does do that. So, consolidating all those requirements into
188 00:26:19.520 ⇒ 00:26:30.650 Amber Lin: one Master Spreadsheet. And even if the Csrs. Don’t use this. This will be really helpful for Annie to give an accurate answer on who the inspector is.
189 00:26:32.080 ⇒ 00:26:59.319 YvetteRuiz: Yeah, this is huge. This is what I announced in our Tuesday meeting, Matt and Steven. So we pretty much took, you know, Julie Sheet that talks about the service by what? We’ll click, by what we do like, what cities do we service, what zip codes, what service do we do there? And then we took our inspector list, or who the inspectors are, what skill sets do they have who covers what? And then we’re really gonna go to the next level. Right, Matt, because a lot of times someone will call and say, Okay, do you do
190 00:26:59.320 ⇒ 00:27:23.510 YvetteRuiz: ceiling fans installation. So really getting deep into like who? Yes, we do. Who? Who’s going to be the inspector that goes out there? And do we cover that city? And I think that the way Ambert and the team went in there and and drilled it down to one specific sheet that we can just gather that data that Andy just pulls it from. It’s it’s just huge. Because I mean, that’s gonna help us right away. When when a new customer calls us getting the answer that we need.
191 00:27:27.440 ⇒ 00:27:39.059 Amber Lin: Yeah. And currently, this has the inspectors. My goal is to combine the inspector sheet the technicians. So the service sheet along with
192 00:27:39.080 ⇒ 00:28:08.910 Amber Lin: the service areas sheet. So when we look for a single zip code and say, we want to look at this zip code and this specific service, we can quickly say, Okay, number one, yes, we do service or no, we don’t service. And then number 2, okay, we want a estimate who can do SMS or we want to service who can do service. So it really having the same format for each one of them helps. AI give a quick answer that covers everything.
193 00:28:08.910 ⇒ 00:28:15.899 Amber Lin: And that makes that helps the Csrs not have to jump around between different sources.
194 00:28:17.770 ⇒ 00:28:18.480 Amber Lin: Yeah.
195 00:28:19.821 ⇒ 00:28:24.730 Amber Lin: And lastly, just quickly. For next sprint.
196 00:28:24.770 ⇒ 00:28:48.670 Amber Lin: I really want to make sure that all the serviceability items are updated, and we can notify the Csrs that their feedback has been adopted. I want to also build up a trusted answer bank, so that we can say, Okay, all of these questions, and these answers are confirmed to be accurate. So whenever they get asked again, we can say, Hey, here’s a trusted answer.
197 00:28:48.680 ⇒ 00:29:03.950 Amber Lin: and the main focus for next is, I really want to go into the Central Doc and look at those top priority items, such as the cancellations, such as internal procedures relating to
198 00:29:04.400 ⇒ 00:29:08.989 Amber Lin: billing relating to account management.
199 00:29:09.380 ⇒ 00:29:26.910 Amber Lin: and the most common questions that come up. So we have 2 sprints in the upcoming months. So each one is 2 weeks, and I want to split the central dock into 2 halves. And I want to want to do the 1st priority ones next 2 weeks.
200 00:29:31.520 ⇒ 00:29:38.929 Amber Lin: Yeah, that’s all I have for this week I’ve took off the whole 30 min.
201 00:29:39.560 ⇒ 00:29:41.450 YvetteRuiz: That’s a lot.
202 00:29:41.450 ⇒ 00:29:43.400 Steven: Yeah, a lot of info you’re working on.
203 00:29:43.400 ⇒ 00:29:46.959 Matt Burns: Yeah. Looks like a good plan, though. I like the way you laid it out.
204 00:29:47.220 ⇒ 00:29:47.900 Matt Burns: Yeah.
205 00:29:48.110 ⇒ 00:29:48.690 YvetteRuiz: Hmm.
206 00:29:48.860 ⇒ 00:29:49.880 Amber Lin: Awesome.
207 00:29:50.550 ⇒ 00:30:08.329 YvetteRuiz: I’m really excited about the cancellation piece of it. I mean, I think that’s gonna be a huge win. If we can really help get the Csr’s. Those questions, I mean, you know. And what can they? What what questions should they ask, and what save tactics can we offer? You know, I mean, especially with the move. I mean, that was just
208 00:30:08.680 ⇒ 00:30:20.660 YvetteRuiz: are you staying in the same? What area are you saying? We have multiple locations a lot of times. They’re moving to Austin, or they’re moving out of Austin into the Little. You know the little out, the little outsk outskirt areas there. So.
209 00:30:20.660 ⇒ 00:30:23.450 Matt Burns: Well, and that’s just just such a direct
210 00:30:24.020 ⇒ 00:30:26.159 Matt Burns: savings of dollars to us. Yeah.
211 00:30:27.000 ⇒ 00:30:27.550 YvetteRuiz: Yep.
212 00:30:28.220 ⇒ 00:30:34.490 YvetteRuiz: yep, yep. And then, Inspector Sheet, I just I can’t wait till that one’s that one. So that’s another big win for us. That’s like.
213 00:30:35.250 ⇒ 00:30:42.859 Matt Burns: Do they spend a good bit of time on that guy sometimes trying to figure that out. Is that shorten the phone call? I guess for sure. Right? Yeah.
214 00:30:42.860 ⇒ 00:30:44.580 JanieceGarcia: It really, wouldn’t I?
215 00:30:44.860 ⇒ 00:30:57.829 JanieceGarcia: And even with looking at the feedback, a lot of the feedback right now that we’re getting is they’re asking who covers this area for inspections. A lot of the questions that’s on there is, it has to do with the inspections.
216 00:30:58.010 ⇒ 00:31:01.250 Matt Burns: Okay? Well, again, that’s a that’s a win for sure.
217 00:31:02.010 ⇒ 00:31:13.590 YvetteRuiz: Because not knowing that, Matt, when we go like to Waco, when we open these cities, those are just quick, quick, you know. If you want to know? Like, do I go to Waco? Do I go to the Zip code? It’s just gonna populate that for you versus turning
218 00:31:14.470 ⇒ 00:31:18.079 YvetteRuiz: or wait sitting there waiting, trying to figure out, or just saying, no, we don’t.
219 00:31:18.080 ⇒ 00:31:18.620 JanieceGarcia: Yeah.
220 00:31:19.180 ⇒ 00:31:19.970 Matt Burns: Right.
221 00:31:20.820 ⇒ 00:31:24.679 Matt Burns: How just real quick on the the prompting
222 00:31:25.071 ⇒ 00:31:32.380 Matt Burns: with the Oh, by the way, does that? Are we gonna get some traction. There. You think 2 guys a little bit of the
223 00:31:32.620 ⇒ 00:31:36.940 Matt Burns: where it’s the, you know, kind of the the button, or whatever to say.
224 00:31:36.940 ⇒ 00:31:52.719 Amber Lin: Oh, yeah, totally I’m not sure if oh, Matt, you weren’t here the last few times to talked about. Oh, by the way, button. So since last time we talked, we so the button now
225 00:31:53.318 ⇒ 00:32:22.749 Amber Lin: outputs 3. 0, by the way, suggestions based on a spreadsheet that you guys can update any offers you have. So whenever it changes, you can update it in a spreadsheet and assign it, say high priority meeting, high priority meeting priority and low priority, and Andy will look at those rankings and take the top 3 most related and most helpful impactful offers and output that in the button response.
226 00:32:22.760 ⇒ 00:32:37.349 Amber Lin: So right now it’s tailored to what you guys want to push. So Janice knows how to add them already. So whenever it’s seasonal or where there’s a new offer. We can assign a high priority, and it will always pop up.
227 00:32:38.520 ⇒ 00:32:39.170 Matt Burns: Right.
228 00:32:40.010 ⇒ 00:32:49.937 YvetteRuiz: So we’re gonna change it, Matt. Once we’re since we know we wanna focus on tree. Right now, we’re gonna put that one. I mean. I’m sorry not tree trash in San Antonio. We’ll put that one up there.
229 00:32:51.110 ⇒ 00:32:52.030 Matt Burns: Good, good.
230 00:32:52.030 ⇒ 00:32:55.970 Steven: More trash business. We don’t have any trash trucks. They break down every day, but.
231 00:32:56.378 ⇒ 00:33:02.091 YvetteRuiz: Or whatever you want to put out. There, Steve, we’ll put it out there.
232 00:33:02.710 ⇒ 00:33:03.520 YvetteRuiz: Hmm!
233 00:33:03.520 ⇒ 00:33:07.510 Matt Burns: Don’t tell me that good.
234 00:33:07.510 ⇒ 00:33:08.040 YvetteRuiz: He’s
235 00:33:08.900 ⇒ 00:33:25.930 YvetteRuiz: alrighty. Well, I’m again. Thank you so much. Amber for everything. Janice and Shannon. You guys have invested a lot of time. I’ll finalize the stuff on the Central Doc. I know that’s pending me, and then I’ll give Tim a nudge on the transcripts, because I know that’s another big, helpful tool that’s really gonna help us.
236 00:33:26.260 ⇒ 00:33:26.760 Matt Burns: That’s it.
237 00:33:26.950 ⇒ 00:33:27.470 JanieceGarcia: Thank you.
238 00:33:28.210 ⇒ 00:33:28.890 Matt Burns: Thanks guys.
239 00:33:28.890 ⇒ 00:33:29.830 YvetteRuiz: Alright guys.
240 00:33:29.830 ⇒ 00:33:30.709 Steven: Oh, my God!
241 00:33:30.710 ⇒ 00:33:31.300 JanieceGarcia: I.
242 00:33:31.640 ⇒ 00:33:32.110 Amber Lin: Thank you.
243 00:33:32.110 ⇒ 00:33:32.870 YvetteRuiz: Bye.
244 00:33:33.150 ⇒ 00:33:34.060 Amber Lin: Bye.