Meeting Title: Brainforge x ABC Home and Commercial: Weekly Project Check Date: 2025-04-04 Meeting participants: Amber Lin, Steven, Janiecegarcia, Yvetteruiz, Scott_Harmon
WEBVTT
1 00:00:18.880 ⇒ 00:00:20.569 JanieceGarcia: Good morning, Scott!
2 00:00:20.570 ⇒ 00:00:22.110 Scott_Harmon: Hey, Denise, how are you?
3 00:00:22.110 ⇒ 00:00:23.090 JanieceGarcia: Good! How are you?
4 00:00:23.500 ⇒ 00:00:24.540 Scott_Harmon: Not too bad
5 00:00:29.950 ⇒ 00:00:31.619 Scott_Harmon: getting ready for a big weekend
6 00:00:33.180 ⇒ 00:00:40.349 JanieceGarcia: Supposed to be really ugly. Here, I’m like, why is it gonna rain, this weekend? So we don’t have anything planned? What about you
7 00:00:40.470 ⇒ 00:00:42.539 JanieceGarcia: cause. Austin’s supposed to be ugly, too.
8 00:00:43.926 ⇒ 00:00:45.700 Scott_Harmon: Yeah. Yeah.
9 00:00:46.530 ⇒ 00:00:49.400 Scott_Harmon: West of Austin. Out in the hill country
10 00:00:50.010 ⇒ 00:00:54.810 Scott_Harmon: Yeah, I think we’re gonna have kind of a rainy weekend. So I know
11 00:00:54.810 ⇒ 00:00:56.939 Amber Lin: Does it rain often in Texas?
12 00:00:56.940 ⇒ 00:00:59.729 Scott_Harmon: It hasn’t rained at all in like a year or so.
13 00:00:59.730 ⇒ 00:01:00.890 Amber Lin: Wow!
14 00:01:00.890 ⇒ 00:01:02.979 Scott_Harmon: Biggest drought in history. And like.
15 00:01:03.730 ⇒ 00:01:09.270 Scott_Harmon: it rained last week. And I was outside I’m, like what is this stuff falling from the sky like.
16 00:01:10.650 ⇒ 00:01:18.189 Scott_Harmon: well, I feel the same. I’m in California. It rained it drizzled a bit. Yesterday, I was like, I feel like the city’s getting drowned.
17 00:01:18.610 ⇒ 00:01:21.700 Scott_Harmon: But Southern California. You live in Southern California.
18 00:01:21.700 ⇒ 00:01:23.859 Amber Lin: Yeah, I live. I live in Los Angeles.
19 00:01:24.400 ⇒ 00:01:34.649 Scott_Harmon: Yeah, Southern California. Los Angeles is a desert. Technically. So it’s not supposed to rain in California. We’re we’re not supposed to be a desert here, but I think
20 00:01:34.650 ⇒ 00:01:35.596 Scott_Harmon: we’ve been.
21 00:01:36.070 ⇒ 00:01:36.570 Scott_Harmon: Oh, my God!
22 00:01:39.920 ⇒ 00:01:41.999 JanieceGarcia: We’ve definitely been
23 00:01:43.300 ⇒ 00:01:48.050 Amber Lin: Is Yvette or Steven or Matt joining today
24 00:01:48.050 ⇒ 00:01:49.959 JanieceGarcia: As far as I know. Yes.
25 00:01:51.310 ⇒ 00:01:53.499 Amber Lin: And we’ll wait a little bit for them to come.
26 00:01:55.080 ⇒ 00:01:58.430 Amber Lin: We have some pretty good updates this week. I’m very excited
27 00:01:58.430 ⇒ 00:02:00.690 Scott_Harmon: Great. Can’t wait to hear how things are going
28 00:02:00.690 ⇒ 00:02:06.602 Amber Lin: Yeah, we had. Janice was meeting earlier, and I was like, Oh, this week is gonna this is gonna be a good meeting
29 00:02:07.410 ⇒ 00:02:08.919 JanieceGarcia: This is, I’m excited.
30 00:02:10.220 ⇒ 00:02:12.149 JanieceGarcia: Gonna be a great meeting
31 00:02:15.600 ⇒ 00:02:19.319 Scott_Harmon: Denise, are you in Austin or San Antonio? I always you’re you’re in
32 00:02:19.320 ⇒ 00:02:20.829 JanieceGarcia: I’m in San Antonio
33 00:02:20.830 ⇒ 00:02:27.559 Scott_Harmon: I thought, Okay, and where? What side of the city is ABC’s office on like? Where? Where is it?
34 00:02:27.560 ⇒ 00:02:33.879 JanieceGarcia: ABC. Is northeast 35 right after the Forum
35 00:02:34.430 ⇒ 00:02:35.610 Scott_Harmon: Gotcha. Okay. Gotcha
36 00:02:35.610 ⇒ 00:02:39.199 JanieceGarcia: But I live in like New Braunfels, Marion area
37 00:02:39.200 ⇒ 00:02:41.580 Scott_Harmon: Okay. So you just drive. Do you have to drive down 35,
38 00:02:43.270 ⇒ 00:02:46.719 Scott_Harmon: It’s kind of nice, though. You’re like halfway between Austin and San Antonio, like
39 00:02:47.120 ⇒ 00:02:53.049 JanieceGarcia: I am, I am. I’m originally from Austin. I grew up in South Austin
40 00:02:53.320 ⇒ 00:02:54.800 Scott_Harmon: Oh, you did! Where’d you go to high school?
41 00:02:55.340 ⇒ 00:03:00.440 JanieceGarcia: I actually graduated from Aikens High School. I was the 1st graduating class
42 00:03:00.810 ⇒ 00:03:01.670 Scott_Harmon: Oh, okay.
43 00:03:01.920 ⇒ 00:03:03.970 JanieceGarcia: From Aiken, so
44 00:03:06.280 ⇒ 00:03:09.940 Scott_Harmon: It’s funny you’re either a South Austin person or a North Austin person.
45 00:03:10.080 ⇒ 00:03:10.955 JanieceGarcia: Yeah.
46 00:03:12.210 ⇒ 00:03:16.860 Scott_Harmon: We were North Austin, like, I have no idea what’s really south of
47 00:03:17.330 ⇒ 00:03:19.352 Amber Lin: The lake, or 2 90 like
48 00:03:20.080 ⇒ 00:03:23.120 JanieceGarcia: It’s all. It’s all a mystery to me. That’s funny.
49 00:03:23.310 ⇒ 00:03:32.269 Amber Lin: Austin is so like Austin itself is such a big city. I’ve I’ve haven’t been yet, but I really do want to go
50 00:03:32.890 ⇒ 00:03:37.390 Scott_Harmon: You know what’s weird is? Austin is actually not a big city. It’s teeny, really. Yes.
51 00:03:37.390 ⇒ 00:03:41.580 Scott_Harmon: San Antonio is like San Antonio is 3 times bigger than Austin. It’s
52 00:03:41.820 ⇒ 00:03:42.370 Amber Lin: It’s
53 00:03:42.370 ⇒ 00:03:47.430 Scott_Harmon: Yeah. Austin punches way above its weight. Everybody thinks it’s big. It’s a teeny city. It’s only
54 00:03:47.970 ⇒ 00:03:53.400 Scott_Harmon: the the metropolitan areas just over a million. It’s not very big at all. It’s teeny.
55 00:03:53.400 ⇒ 00:03:54.930 Scott_Harmon: Wow, yeah.
56 00:03:54.930 ⇒ 00:03:55.560 JanieceGarcia: Hmm.
57 00:03:56.140 ⇒ 00:04:00.149 Scott_Harmon: It’s just because in tech, it’s gotten to be such a major
58 00:04:00.150 ⇒ 00:04:06.479 Amber Lin: Yeah, it has a big presence on the Internet, and I only know things off the Internet. So I think it’s big
59 00:04:07.050 ⇒ 00:04:11.079 Scott_Harmon: And a lot of for some reason it’s become like a hot place, for, like.
60 00:04:12.850 ⇒ 00:04:19.194 Scott_Harmon: you know, Joe Rogan, and, like all these kind of famous people move their act to Austin. I don’t really know why, but
61 00:04:20.120 ⇒ 00:04:22.429 Scott_Harmon: It’s just become trendy, I guess so.
62 00:04:22.940 ⇒ 00:04:24.430 JanieceGarcia: It definitely has.
63 00:04:24.600 ⇒ 00:04:27.789 JanieceGarcia: But that’s why I’m like I could not ever live there again.
64 00:04:28.310 ⇒ 00:04:34.339 Scott_Harmon: San Antonio is a much bigger city, and it’s just it’s very chill. I’m a huge fan of San Antonio
65 00:04:34.640 ⇒ 00:04:35.280 JanieceGarcia: Yes.
66 00:04:36.650 ⇒ 00:04:37.429 Amber Lin: I love it.
67 00:04:39.670 ⇒ 00:04:41.644 YvetteRuiz: Probably gonna pop in late
68 00:04:42.040 ⇒ 00:04:43.190 Scott_Harmon: There you are no problem
69 00:04:43.190 ⇒ 00:04:44.249 YvetteRuiz: Hi, Scott!
70 00:04:44.780 ⇒ 00:04:45.567 YvetteRuiz: How are you?
71 00:04:45.830 ⇒ 00:04:47.129 YvetteRuiz: I’m well! How are you
72 00:04:47.130 ⇒ 00:04:49.009 Scott_Harmon: We were just deciding if Austin is.
73 00:04:49.220 ⇒ 00:04:52.110 Scott_Harmon: or San Antonio, or better, which city we’re in
74 00:04:52.990 ⇒ 00:04:54.070 YvetteRuiz: San Antonio.
75 00:04:55.280 ⇒ 00:04:57.140 Scott_Harmon: I might vote for San Antonio, and I’ll
76 00:04:57.698 ⇒ 00:05:02.161 YvetteRuiz: Yeah. My vote has San Antonio
77 00:05:02.720 ⇒ 00:05:04.890 Amber Lin: Well, your vote’s gonna decide where I go.
78 00:05:05.810 ⇒ 00:05:08.570 YvetteRuiz: There you go!
79 00:05:09.490 ⇒ 00:05:10.589 Amber Lin: All 3 of us.
80 00:05:10.907 ⇒ 00:05:13.690 JanieceGarcia: All 3 of us, San Antonio. So there you go.
81 00:05:14.210 ⇒ 00:05:15.249 YvetteRuiz: From this point, now
82 00:05:16.626 ⇒ 00:05:20.600 Amber Lin: We would love. Welcome you to San Antonio amber Yay.
83 00:05:21.490 ⇒ 00:05:24.480 YvetteRuiz: That means that I’m not welcome in the Austin office.
84 00:05:28.850 ⇒ 00:05:34.770 Amber Lin: Alright is, would Stephen and Matt be joining today, or are they pretty busy
85 00:05:35.001 ⇒ 00:05:37.778 YvetteRuiz: He! He’s on a call, he said. He’ll probably join in late
86 00:05:38.010 ⇒ 00:05:39.169 Amber Lin: Okay, awesome. Yeah.
87 00:05:41.160 ⇒ 00:05:42.560 Scott_Harmon: Is Utam joining in
88 00:05:42.770 ⇒ 00:05:49.060 Amber Lin: Utah’s at another call right now. He’ll hop in after, say, 2015, 20.
89 00:05:49.290 ⇒ 00:05:51.960 Amber Lin: Yeah. So they’ll probably be here at the same time.
90 00:05:51.960 ⇒ 00:05:52.690 Scott_Harmon: Okay.
91 00:05:53.150 ⇒ 00:05:56.900 Amber Lin: Okay, let me present my screen.
92 00:06:01.010 ⇒ 00:06:05.950 Amber Lin: So here’s this week we had a pretty good week.
93 00:06:06.150 ⇒ 00:06:21.319 Amber Lin: So we’re gonna go over a few main things. The main thing we focused on was to get the bot rolled out to the 5 Csrs and improve the bot based on their feedback. And then we also have some data team updates. And then
94 00:06:22.040 ⇒ 00:06:27.630 Amber Lin: we also, I also put here the announcement slides that I made so that Janice can
95 00:06:28.190 ⇒ 00:06:35.939 Amber Lin: video. And then Steven. They can use it to announce to the company. So we’ll look at that as well.
96 00:06:36.710 ⇒ 00:06:54.920 Amber Lin: So 1st of all, so let’s just look at how we’re doing on the right is a screenshot of the last 4 weeks, right? So we can see when you look at the trends here, 2, 3 main things. We want to look at right. So 1st of all, the total exchanges have went up. So that means
97 00:06:55.170 ⇒ 00:07:16.529 Amber Lin: more. People are using it, which makes sense because we rolled out to more people. The average quality score is going up. So that’s that’s good. And the error rates is going down significantly, and we’ll go into. Why, that’s happening in a second. But we’re on a good trend here. We know everything’s heading to the better direction.
98 00:07:17.490 ⇒ 00:07:25.629 Amber Lin: So take this. So for this week, March 31, st April 4, th
99 00:07:25.810 ⇒ 00:07:46.280 Amber Lin: we did a lot of improvements on the bot me. Our team at Janice worked very closely to look at all the errors to check all the hard questions and make sure we have them correctly documented, and make sure the bot knows how to answer. And that’s why we see a significant downtrend in the error rates.
100 00:07:46.290 ⇒ 00:07:57.609 Amber Lin: Actually, we did a lot better than 23%, which I’ll show in a quick second. So these are all the answers the bot got wrong, and for all of them
101 00:07:57.710 ⇒ 00:08:02.339 Amber Lin: we based on the feedback, we made changes to
102 00:08:02.620 ⇒ 00:08:06.299 Amber Lin: the central dog. You can see we made 48 changes.
103 00:08:06.672 ⇒ 00:08:32.749 Amber Lin: So the bot is a lot smarter. Now it knows how to downsell. So to when the customer wants to go, we’re gonna say, Okay, here’s something that you should. Still, you should still use. We try to avoid using the word save and much more of these tiny little details that you kind of. Only remember when you start using the bot, or try and try and look at a specific answers
104 00:08:32.750 ⇒ 00:08:37.349 Scott_Harmon: So I I sorry to interrupt you, Amber, but just just
105 00:08:37.580 ⇒ 00:08:49.859 Scott_Harmon: huge props for you and the team. And then Denise, this this exact thing. This actual getting in and digging through and making the answers be accurate, is is
106 00:08:49.990 ⇒ 00:08:54.520 Amber Lin: The absolute magic for making AI work in a business. Yeah? So
107 00:08:54.620 ⇒ 00:09:00.580 Scott_Harmon: 75% of the companies that are trying. AI don’t ever get to this point. They don’t.
108 00:09:00.980 ⇒ 00:09:06.269 Scott_Harmon: They get infatuated with it. They play around with it, but they don’t kind of roll up their sleeves
109 00:09:06.460 ⇒ 00:09:14.180 Scott_Harmon: and get it to be accurate and usable enough to deploy, and those rollouts fail.
110 00:09:14.310 ⇒ 00:09:18.929 Scott_Harmon: and and so it takes. It takes a lot of
111 00:09:19.290 ⇒ 00:09:21.930 Scott_Harmon: just getting in and doing this last
112 00:09:22.340 ⇒ 00:09:26.700 Scott_Harmon: 10% to make it really be successful. So I just want to commend the work.
113 00:09:26.990 ⇒ 00:09:35.269 Scott_Harmon: you know. I know it’s taken hours, I’m sure, on your end, Janice and and Amber to get to get it done. But it is the magic piece, right? Like.
114 00:09:35.400 ⇒ 00:09:38.140 Scott_Harmon: it’s what what makes the AI
115 00:09:38.500 ⇒ 00:09:41.439 Scott_Harmon: kind of work in reality. So I just want to really
116 00:09:41.550 ⇒ 00:09:43.679 Scott_Harmon: commend you guys for your hard work.
117 00:09:44.240 ⇒ 00:09:45.400 YvetteRuiz: Absolutely.
118 00:09:45.820 ⇒ 00:09:46.780 Amber Lin: Yeah.
119 00:09:47.320 ⇒ 00:10:15.359 Amber Lin: If Janice haven’t been testing with all these hard questions, we won’t have the answers, and if she didn’t join our stand ups and meetings, and told us continuously, Hey, this didn’t go that. Well, that didn’t go that. Well, we would never have had made these improvements, because we rely on Janice, essentially, to tell us what’s not right, because we don’t know, because we’re we’re not in the business. So it’s huge things with Janice to making this possible even
120 00:10:16.060 ⇒ 00:10:16.560 YvetteRuiz: Yeah.
121 00:10:16.560 ⇒ 00:10:17.339 JanieceGarcia: Thank you. Guys.
122 00:10:17.340 ⇒ 00:10:18.600 YvetteRuiz: It’s a team team
123 00:10:18.600 ⇒ 00:10:19.360 JanieceGarcia: Work.
124 00:10:19.360 ⇒ 00:10:19.929 YvetteRuiz: Do you remember?
125 00:10:19.930 ⇒ 00:10:20.430 JanieceGarcia: Right.
126 00:10:20.890 ⇒ 00:10:22.679 Scott_Harmon: Teamwork makes the dream work right?
127 00:10:22.680 ⇒ 00:10:25.160 JanieceGarcia: Exactly. That’s my favorite saying. I love
128 00:10:27.550 ⇒ 00:10:57.320 Amber Lin: Yeah. And next slide is actually, very, very exciting. So we rolled out to the 5 Csrs this week. They’re all 5 of them are using it right now, as you can see on the left here. It’s who’s using it the past week. This is data from the past 7 days, so we can see we have someone that’s using it. Very frequently we have joy. And of course Janice is using it a lot, and then we have the other
129 00:10:57.700 ⇒ 00:11:22.599 Amber Lin: Csrs, and then we have our internal managers also, testing it as well. They started fully using this on Wednesday, so, as you can see on the right. Wednesday is day one, and yesterday was day 2, and our error rates improved so much between these 2 days, because me and Janice, our team at Janice were able to go into and tackle all those questions.
130 00:11:22.600 ⇒ 00:11:32.660 Amber Lin: and so we can see that they use them more on the second day, and then we can see the quality score improved and the error rate got better
131 00:11:32.660 ⇒ 00:11:33.500 YvetteRuiz: Nice
132 00:11:33.670 ⇒ 00:11:34.010 Amber Lin: So.
133 00:11:34.010 ⇒ 00:11:36.460 YvetteRuiz: I see Yusuf is not using it.
134 00:11:39.130 ⇒ 00:11:41.298 YvetteRuiz: Yeah, I’m gonna follow up on that one
135 00:11:42.500 ⇒ 00:11:43.833 Scott_Harmon: -Oh! -Oh.
136 00:11:44.500 ⇒ 00:11:47.389 Amber Lin: -Oh! I mean, this is what this board is for
137 00:11:47.390 ⇒ 00:11:55.430 YvetteRuiz: Yeah. Well, I mean the I want this to be the go to place. You know what I mean, and I know uses
138 00:11:56.340 ⇒ 00:12:00.650 YvetteRuiz: performance. So this would be a very helpful him thing
139 00:12:00.650 ⇒ 00:12:01.100 Scott_Harmon: Brand.
140 00:12:01.100 ⇒ 00:12:02.852 YvetteRuiz: So I’ll follow up
141 00:12:03.290 ⇒ 00:12:05.950 Amber Lin: Yeah, this is great. And then
142 00:12:05.950 ⇒ 00:12:06.290 YvetteRuiz: Oh, we can
143 00:12:06.290 ⇒ 00:12:12.339 Amber Lin: Also tell Joy, hey, you’re doing great. You’re helping us a lot. So
144 00:12:13.110 ⇒ 00:12:41.069 Amber Lin: onto the so just to check on our progress. So last week we presented this action plan that we want to do all the top 4. We’re we’re done. These ones in yellow is sort of a daily thing that we’re currently doing. And the next few is conducting the feedback survey which I know Janice is gonna check with them today. So these are just some things we want to do. So we’re on really good track for what we decided on what the action plan is. And also
145 00:12:41.510 ⇒ 00:12:47.079 Amber Lin: we want to keep in mind what our goals is for the rollout. So
146 00:12:47.540 ⇒ 00:12:53.610 Amber Lin: we agreed that we want to make sure that things are aligned, that they use it. So
147 00:12:54.090 ⇒ 00:13:16.350 Amber Lin: the what’s considered a success for this rollout. We’re also on really good progress, because we’re logging all the gaps and we’re updating it. And we are also working on the the part where we track the business metrics. I’ll show you guys in a second. And lastly, after we do the surveys, we’ll know if they found it helpful.
148 00:13:16.760 ⇒ 00:13:17.780 Amber Lin: So I think we’re
149 00:13:17.780 ⇒ 00:13:18.120 YvetteRuiz: So.
150 00:13:18.120 ⇒ 00:13:21.339 Amber Lin: On pretty good track for what we decide on for this week.
151 00:13:22.250 ⇒ 00:13:32.030 Amber Lin: So next part is some very exciting updates for the data team. We were able to meet with David and Brian this week.
152 00:13:32.320 ⇒ 00:13:37.229 Amber Lin: and from that meeting, then we got the call data
153 00:13:37.410 ⇒ 00:14:03.580 Amber Lin: which allows us to track the key Kpis, because ultimately we want the bot to perform well. But ultimately the company needs benefit from that. So now we have the data from last month, I believe so. Actually, from February to 24 to April 6, th we have the call data. How long things took average wait time.
154 00:14:04.086 ⇒ 00:14:13.780 Amber Lin: Average busy time handling time, etc. So we have all those data, and we’re working on connecting the call data to the bot usage data. So once
155 00:14:13.780 ⇒ 00:14:16.669 Scott_Harmon: From 8 by. Does that come from 8 by 8 event
156 00:14:17.040 ⇒ 00:14:29.160 YvetteRuiz: Yes, that’s correct. Yeah. So we’re, I’m we’re trying to grant amber the team access to the Api. So then that way, they can automatically connect there. But right now, the data team just shared the information with them. I believe
157 00:14:29.160 ⇒ 00:14:29.780 Amber Lin: Yeah.
158 00:14:30.680 ⇒ 00:14:35.920 Scott_Harmon: So the goal here amber is to be able to correlate usage of the bot
159 00:14:36.948 ⇒ 00:14:43.070 Scott_Harmon: with some of these big metrics that we track to see. Hey? Is it influencing
160 00:14:43.270 ⇒ 00:14:50.739 Scott_Harmon: call times, or is it influencing? Wait times like. So that’s the goal is to see if we can see these correlations start to show up
161 00:14:50.740 ⇒ 00:14:52.100 Amber Lin: Yeah, exactly.
162 00:14:52.100 ⇒ 00:14:53.489 Scott_Harmon: Got it. Got it
163 00:14:53.490 ⇒ 00:15:15.709 Amber Lin: Yeah, because that’s how we measure if we’re impacting the business. So this is the crucial crucial part for our next sprint. And actually, we are going to meet with David and Brian Weekly and Brian, I asked if he wants. It’s okay with joining our stand up. So Brian might also start to join
164 00:15:15.730 ⇒ 00:15:24.840 Amber Lin: at least every other, maybe every other day, to our standards until we get this on track. So this is very important to us. We ask you
165 00:15:24.840 ⇒ 00:15:52.359 YvetteRuiz: Amber. Do we have access to cause? I I mean, I’ve been looking at I I’m in the dashboard every day, you know. Looking at it, and just following up on a couple of things on my end. But that data is. And again, I don’t know if I have the most updated if it changes, because again, I forgot who it was. Casey, send me all these links. I just accepted it. And do I mean, do we have these Kpis as well in the real, that I’m
166 00:15:52.720 ⇒ 00:15:53.813 YvetteRuiz: is there
167 00:15:54.360 ⇒ 00:15:58.879 Amber Lin: Oh, this one! We sent it to Utam to check 1st
168 00:15:59.290 ⇒ 00:15:59.980 YvetteRuiz: Okay.
169 00:15:59.980 ⇒ 00:16:09.040 Amber Lin: We wanna make sure that everything is right before we send it to you guys, just in case it doesn’t like basic questions or problems doesn’t come up, we want.
170 00:16:09.950 ⇒ 00:16:11.929 Amber Lin: assure first, st before I send it
171 00:16:14.170 ⇒ 00:16:25.318 YvetteRuiz: Okay, I just wanna make sure that I cause I’m looking at it. And sometimes I’m like, Okay, and I know that it’s constantly being worked on. I just don’t know if I have the most up to date information sometimes
172 00:16:25.590 ⇒ 00:16:26.050 Amber Lin: Sounds good
173 00:16:26.050 ⇒ 00:16:32.165 YvetteRuiz: And maybe I don’t. I mean, maybe I’m not supposed to look at it until we have these Friday meetings. You gotta tell me
174 00:16:32.420 ⇒ 00:16:36.090 Amber Lin: Just look at it. If it’s not updated, just message me, I’ll
175 00:16:36.090 ⇒ 00:16:36.430 YvetteRuiz: Good.
176 00:16:36.430 ⇒ 00:16:49.349 Amber Lin: Message the team like, if you want something there, message me and I’ll ask the team. Hey? Can you do this? If they’re awake they will help you sometimes. They’re just not in that. They’re on the other side of the world.
177 00:16:49.850 ⇒ 00:16:52.839 Amber Lin: They are they have finished their day
178 00:16:52.840 ⇒ 00:16:53.555 YvetteRuiz: Understood,
179 00:16:54.690 ⇒ 00:17:14.669 Amber Lin: So we have a new data team member, and she’s not here because I know this is this meeting is more for the executives to get a better idea of it. But Yvette has already met Annie. Annie will be working with David and Brian to work on the dashboard to work on more of the data stuff because Miguel and Casey are
180 00:17:14.810 ⇒ 00:17:27.540 Amber Lin: amazing AI engineers. And if we ask them to do a lot of data, it’s sort of diverse their attention. So we want the experts on their expert topics. And that’s why we have the team.
181 00:17:28.470 ⇒ 00:17:29.005 Amber Lin: Yeah.
182 00:17:30.715 ⇒ 00:17:47.189 Amber Lin: That is mostly the updates for our current progress right now. We are at the end of the 1st week for April, so we are halfway through our 1st group, testing with the 5 Csrs think next
183 00:17:47.430 ⇒ 00:18:00.029 Amber Lin: next week we’ll prepare for our full deployment. So to make sure we have the training videos, we have it announced. We fix most a lot of the bugs. We got feedback from.
184 00:18:00.340 ⇒ 00:18:06.250 Amber Lin: So next week’s gonna be a lot of preparing for the full deployment. And also
185 00:18:06.540 ⇒ 00:18:13.950 Amber Lin: maybe after next week, when we have more time freed up. Well, we’re starting to we already this week.
186 00:18:13.960 ⇒ 00:18:39.079 Amber Lin: Our team looked at how we want to deploy, or how do we want to make the trainer assistant agent the one that updates the documents and creates them. So we’re discussing in the team about what kind of tools are available. How can we do this? What’s the best way that delivers the most result in the shortest time? So we’re already discussing that because we see it coming. So that’s our current progress
187 00:18:39.230 ⇒ 00:18:41.009 Amber Lin: on a bigger scale.
188 00:18:41.530 ⇒ 00:18:43.179 YvetteRuiz: Awesome. That’s great.
189 00:18:43.480 ⇒ 00:18:58.839 YvetteRuiz: Yeah. And I’m glad that you mentioned the video, because that’s our company meetings next week. So that’s I was, I was going to email, you guys, to see where we were at with that but I know we’re working on a lot of projects. I’ll put in a presentation for our Csrs
190 00:18:59.620 ⇒ 00:19:04.560 Amber Lin: Yeah, we have the, do you mean the training videos or the announcement video.
191 00:19:04.560 ⇒ 00:19:20.625 YvetteRuiz: So. No, no, the announcements, not. I know that I know just the announcement. So we wanna announce this to our all, our all our employees and let them know what we’re working on. And now we feel like everything we discussed last week pretty much
192 00:19:21.140 ⇒ 00:19:35.819 Amber Lin: Fantastic. I’ll show you guys the slides we made in a second. So for each slide. Oh, anyways, before we do that, we got the colors from you guys. And we made new logos with the
193 00:19:36.350 ⇒ 00:19:38.301 YvetteRuiz: Oh, how cute with the color! Palettes
194 00:19:38.580 ⇒ 00:19:45.799 Amber Lin: Yeah. So we’ll finalize this and we’ll send it out to you guys to do the little contest
195 00:19:45.800 ⇒ 00:19:47.829 YvetteRuiz: Awesome. That’s so cute
196 00:19:48.970 ⇒ 00:19:49.676 JanieceGarcia: They’re adorable.
197 00:19:50.590 ⇒ 00:19:53.659 Amber Lin: So cute. I already have my favorites, but I
198 00:19:54.550 ⇒ 00:19:56.330 YvetteRuiz: I am.
199 00:19:57.390 ⇒ 00:19:58.480 Scott_Harmon: Love, those
200 00:19:59.240 ⇒ 00:20:02.470 Amber Lin: Yeah, they’re they’re very, very adorable.
201 00:20:04.040 ⇒ 00:20:12.550 Amber Lin: Last one. So the announcement slides. Actually, I will go zoom out of presentation mode.
202 00:20:12.800 ⇒ 00:20:18.009 Amber Lin: So we have these slides, and for each of them
203 00:20:18.697 ⇒ 00:20:22.179 Amber Lin: I have created a script of either
204 00:20:22.540 ⇒ 00:20:28.330 Amber Lin: friendly and professional or just very lively cause. I’m not sure what the tone is of how you
205 00:20:29.950 ⇒ 00:20:30.820 Amber Lin: cause they might
206 00:20:31.410 ⇒ 00:20:36.989 Amber Lin: casual. So I have 2 versions for each slide that Janice can sort of read off of
207 00:20:36.990 ⇒ 00:20:37.540 YvetteRuiz: Thank you.
208 00:20:37.540 ⇒ 00:20:38.270 Amber Lin: So
209 00:20:39.570 ⇒ 00:20:47.539 Amber Lin: we present Andy, we’re gonna keep this simple. I haven’t updated that because I I really like this logo. But it’s not the regular
210 00:20:47.540 ⇒ 00:20:50.077 YvetteRuiz: Yeah, that was my fame, too.
211 00:20:50.500 ⇒ 00:20:57.429 Amber Lin: I know I really like that one. So I kept it there. So we’re gonna say, the day in the life of Csr.
212 00:20:57.830 ⇒ 00:21:01.820 Amber Lin: gonna keep the text mostly in the talking. So
213 00:21:02.060 ⇒ 00:21:05.809 Amber Lin: we want to tell them that we feel you. So we know your frustration.
214 00:21:06.770 ⇒ 00:21:09.550 Amber Lin: And then we’re gonna say, okay, what’s gonna
215 00:21:09.960 ⇒ 00:21:15.830 Amber Lin: what’s gonna happen for you? What what is Andy gonna do for you? Essentially.
216 00:21:16.520 ⇒ 00:21:20.570 Amber Lin: some common F and q’s very important, very important.
217 00:21:21.924 ⇒ 00:21:23.939 YvetteRuiz: Right? Everything that. Yeah.
218 00:21:23.940 ⇒ 00:21:29.039 Amber Lin: Yeah, cause I, there’s a lot of people don’t really know anything about
219 00:21:29.040 ⇒ 00:21:29.670 YvetteRuiz: Sure.
220 00:21:29.670 ⇒ 00:21:32.379 Amber Lin: AI. So it’s important that we kind of address there.
221 00:21:34.072 ⇒ 00:21:57.689 Amber Lin: So those 3 slides are for the Csrs. We have. I have one for the trainers, because this will impact, they will benefit their lives as well. So we have shorter trainings. They don’t. This. The reps don’t always ask you. So you have no time and then you can focus on coaching, etc. so hopefully, this will get some buy in for the manage managers as well.
222 00:21:57.850 ⇒ 00:22:01.560 Amber Lin: because they will be the ones checking in uses.
223 00:22:02.050 ⇒ 00:22:14.269 Amber Lin: And lastly, how this gonna benefit the company. So for any success, that’s curious. They care about this, and also for the managers. This is probably their kpis, so
224 00:22:15.840 ⇒ 00:22:26.250 Amber Lin: that they know about this. And just a quick timeline and a quick call to action. So that’s that’s what I plan for. These slides.
225 00:22:26.250 ⇒ 00:22:30.879 Scott_Harmon: I love them. Just a small catch. It’s Andy with an I not with a Y, so in the title it’s
226 00:22:33.320 ⇒ 00:22:36.619 Amber Lin: No, we need to update the Google Chat and
227 00:22:36.620 ⇒ 00:22:38.880 JanieceGarcia: We need to update the actual bot
228 00:22:39.330 ⇒ 00:22:39.880 Scott_Harmon: Yeah.
229 00:22:40.410 ⇒ 00:22:41.310 Amber Lin: Yeah.
230 00:22:41.420 ⇒ 00:22:51.710 Amber Lin: I like it with the I because Janice told me it was Andy for AI. I was like, oh, that’s why it ends. I
231 00:22:51.710 ⇒ 00:22:52.330 Scott_Harmon: Right.
232 00:22:52.330 ⇒ 00:22:53.270 Amber Lin: Yeah.
233 00:22:53.700 ⇒ 00:22:54.570 JanieceGarcia: Okay.
234 00:22:55.110 ⇒ 00:22:58.699 Amber Lin: We’ll update that I like Andy with an eye much better.
235 00:23:00.080 ⇒ 00:23:02.780 Amber Lin: Anyways, that’s our update for today.
236 00:23:02.780 ⇒ 00:23:25.720 YvetteRuiz: Wow, that’s a lot of great stuff. I mean, yeah, I mean, that’s making a whole lot of progress. And I know it does take a lot of work. For sure. So super thankful for that. I know I met with Matt yesterday. And I know is not here. So we’re waiting for the price after we met Scott. From you guys. Regarding
237 00:23:25.720 ⇒ 00:23:28.402 Scott_Harmon: I’ve met with Utah on it once, I think.
238 00:23:29.010 ⇒ 00:23:34.000 Scott_Harmon: I I think he wanted to get it out on Monday. He’s he’s just a little backlog. So
239 00:23:34.240 ⇒ 00:23:43.950 Scott_Harmon: that’s kind of on us. We’ll we’ll get something out hopefully by the end of the day. I it’s gonna be what we discussed last week. It’s gonna be those 3 basic tiers. We’re trying to keep it simple.
240 00:23:44.080 ⇒ 00:23:45.899 Scott_Harmon: We’ll do it quarterly.
241 00:23:46.060 ⇒ 00:23:49.500 Scott_Harmon: A lot of it’s going to have to do with with.
242 00:23:49.950 ⇒ 00:23:53.919 Scott_Harmon: Okay, how did we do this quarter? How do we do against the benchmarks?
243 00:23:54.130 ⇒ 00:24:03.530 Scott_Harmon: We’ll put some bonuses in there, like, for example, on upsells. Or, oh, by the way, is like we’ll you know they’ll if if Andy’s real successful in helping on that, we’ll make a little more.
244 00:24:03.630 ⇒ 00:24:08.720 Scott_Harmon: But I think I think we’ll get it across and hopefully get something negotiated pretty quickly.
245 00:24:09.040 ⇒ 00:24:09.780 YvetteRuiz: Okay. Good.
246 00:24:10.150 ⇒ 00:24:10.710 Scott_Harmon: Make sure.
247 00:24:10.710 ⇒ 00:24:32.010 YvetteRuiz: One of the things we were talking about was, Oh, by the way, and and I don’t know what we’ve. I know there’s a lot of work that’s already. But what have we incorporated already with? Oh, by the way, so like the suggestion stuff. Because when I type in some of the things there’s still things like I just happen to be had a meeting with someone who’s struggling with. Oh, by the way, this morning, and he gave me his feedback, which is
248 00:24:32.010 ⇒ 00:24:57.739 YvetteRuiz: 100%. Everything that I have been mentioning. It’s like, I just don’t know when to offer it. Event. I’m not looking at those things. And I’m like, okay. So I kind of showed him the Andy Andy piece of it. And I was like, you know, this is what we’re currently working on. He was like, yeah, that would be super helpful. But when I try to type an example, there’s nothing really populating there. So I’m just like we’re still building that. So I just wanna I know that’s a key metric that we’re looking at. So what does that look like with
249 00:24:57.740 ⇒ 00:25:04.230 YvetteRuiz: it. Those suggestions, I mean, is that something that we need to meet to discuss? I mean, how is that? What? What does that look like
250 00:25:05.280 ⇒ 00:25:14.809 Amber Lin: So that last time we had a discussion on the oh, by the ways we were talking about the structure of the answers, because we, weren’t we?
251 00:25:14.900 ⇒ 00:25:43.239 Amber Lin: At 1 point we had it oh, by the way, for every answer, but then it sort of complicated the length, and it was very hard to read because there was a lot of stuff. So right now, we’re sort of deciding on where to put it. Essentially. So, if you have more specific instructions that will really help us to know where to put it, because if we put all by the way, the answers will be longer, and a lot of in a lot of situations they may want might not want a longer answer
252 00:25:43.240 ⇒ 00:25:46.070 YvetteRuiz: Yeah, I let me just double click on that
253 00:25:46.340 ⇒ 00:25:51.870 Scott_Harmon: Yvette, if you could send us an email with the
254 00:25:52.230 ⇒ 00:26:00.610 Scott_Harmon: 5 or 6 or 7, you know power situations where? Oh, by the ways, makes sense, you know.
255 00:26:00.840 ⇒ 00:26:06.229 Scott_Harmon: you should really push them here when when this here here and here, and
256 00:26:06.560 ⇒ 00:26:11.919 Scott_Harmon: because the agent needs to know, like to Amber’s point, the assistant can’t just
257 00:26:12.040 ⇒ 00:26:16.499 Scott_Harmon: throw them up there on every call, because sometimes they just don’t make sense. They make the answer too long.
258 00:26:16.680 ⇒ 00:26:18.370 Scott_Harmon: They just don’t kind of fit.
259 00:26:18.650 ⇒ 00:26:22.400 Scott_Harmon: And so it’s the same coaching you’d give a Csr
260 00:26:23.320 ⇒ 00:26:25.510 Scott_Harmon: as to when to use them.
261 00:26:26.435 ⇒ 00:26:38.649 Scott_Harmon: We need to give that same training and to the to the AI agent. So it knows, because right now it does, it does it right. It’s just gonna suggest things will be clumsy. It’ll be like, Oh, awkward like.
262 00:26:39.210 ⇒ 00:26:43.279 Scott_Harmon: And and so that’s a missing piece.
263 00:26:43.780 ⇒ 00:26:45.660 Scott_Harmon: Yeah, we need from you
264 00:26:45.660 ⇒ 00:27:10.440 YvetteRuiz: Completely understand. And I and that’s I thought we were taught. We that was originally. What we had talked about is, how are we going to work then. I know I’ve had this conversation with Janice. I also had it with Uta Moon. We met a couple of weeks ago with Brian initially, because I’m like, okay, we could do this one or 2 ways, you know, who want to do testing right? So like, let’s say, for instance, let’s just focus on one primary thing that oh, oh, by the way, that I want to hit each and every time, could we? Just?
265 00:27:10.450 ⇒ 00:27:22.040 YvetteRuiz: And I’ll I’ll create the email and stuff. So like, let’s say, I want to go in there. And I want to promote tree work. Right? We’re in season. We’re in lawn or composting. Okay, let’s come up with that spiel. And how would that kind of
266 00:27:22.580 ⇒ 00:27:36.529 YvetteRuiz: offer and see how many offers we build just based off that one specific offer, so doing it, testing that way. And then we could start incorporating other other things as season the seasons come up, or something like that. But I
267 00:27:36.530 ⇒ 00:27:36.950 Scott_Harmon: Yeah, I can.
268 00:27:37.290 ⇒ 00:27:43.009 Scott_Harmon: I think I think, for now that’s a great way to get started. If we if we have to kind of do
269 00:27:43.430 ⇒ 00:27:46.520 Scott_Harmon: not one at a time, but like like
270 00:27:47.180 ⇒ 00:27:59.830 Scott_Harmon: like do like. Eventually, we want the agent to be smart enough to just know based on this training. Oh, it’s time it’s this season. It’s this kind of client. But if we need to kind of make it a little more manual where you’re telling it.
271 00:28:00.230 ⇒ 00:28:03.520 Scott_Harmon: you know, literally, hey, for the next 4 weeks.
272 00:28:04.320 ⇒ 00:28:09.359 Scott_Harmon: Push this to these, you know. That’s fine, right? It doesn’t take much work. You can do it
273 00:28:09.360 ⇒ 00:28:18.220 YvetteRuiz: I just thought that would be a starting point, just to kind of get it going right? Like, okay, let’s test it. Let’s see how comfortable an agent, and we’ll start working on the other things because you’re right, Scott.
274 00:28:18.220 ⇒ 00:28:18.750 Scott_Harmon: I like
275 00:28:18.750 ⇒ 00:28:19.210 YvetteRuiz: Of what?
276 00:28:19.210 ⇒ 00:28:27.490 Scott_Harmon: Get one to work right? Get like, oh, we saw like, okay, that with whatever the tree thing gosh, you know, we got a 15%,
277 00:28:27.830 ⇒ 00:28:32.570 Scott_Harmon: you know. Take up rate on that. Now test it with that one.
278 00:28:33.790 ⇒ 00:28:48.169 Scott_Harmon: Then we can figure out, okay, let’s add 2 or 3 more, and then eventually it’ll just be automatic, the through the training agent. You’ll just add one. And you know this will all just kind of work automatically 6 months from now. But let’s get one to work
279 00:28:48.170 ⇒ 00:28:48.700 YvetteRuiz: Okay.
280 00:28:49.850 ⇒ 00:28:56.180 Scott_Harmon: And and and then I think it’ll just be kind of just, you know steamroll from there
281 00:28:56.510 ⇒ 00:28:57.050 YvetteRuiz: Yeah.
282 00:28:57.730 ⇒ 00:28:59.200 Amber Lin: That’s a great suggestion.
283 00:28:59.420 ⇒ 00:29:07.300 Amber Lin: And I think to start with, one will take a lot less work. And then we can actually measure the results better with just one
284 00:29:07.690 ⇒ 00:29:22.386 YvetteRuiz: Yeah, yeah, okay. Well, we’ll I’ll I’ll get that together. We’ll we’ll start working on that one. I’ll see. You know who’s gonna be the lucky department that we’re gonna focus on to drive that push those oh, by the ways for
285 00:29:23.758 ⇒ 00:29:27.669 YvetteRuiz: and then ultimately to Amber, that Kpi
286 00:29:27.790 ⇒ 00:29:50.969 YvetteRuiz: will be on the dashboard as well. Right? Is that something that we’re going to be able to go in there and see? Cause I did provide you guys and David, and then we’ll also continue, because that’s 1 thing that I would like to see. I mean, I see it in reports. But to be able to see this from a dashboard, especially if Matt. And then we’re looking at it. Okay, here we’re oh, by the ways last month. Here’s you know. I mean just to kind of see that that’s going to be available to us right on the dashboard.
287 00:29:51.200 ⇒ 00:29:58.360 Amber Lin: As long as 8 by 8. Has that data, or Brian Brian and David has that data. Put it on there. Hi, Steve.
288 00:29:58.360 ⇒ 00:29:58.750 Scott_Harmon: No, that
289 00:29:58.750 ⇒ 00:30:00.019 YvetteRuiz: That’s a yeah. Go ahead.
290 00:30:00.020 ⇒ 00:30:06.679 Scott_Harmon: We’ll be able to show on our dashboard how many times our agent offered the oh, right away
291 00:30:06.680 ⇒ 00:30:07.750 YvetteRuiz: Okay. Okay.
292 00:30:07.750 ⇒ 00:30:14.640 Scott_Harmon: Right. So that’ll be on the dashboard. I’m sure. Amber, I don’t know like it’s a separate
293 00:30:15.210 ⇒ 00:30:18.199 Scott_Harmon: graph, like I don’t know, but obviously, we have the data.
294 00:30:20.600 ⇒ 00:30:24.399 JanieceGarcia: Cause. That would be the upselling piece, right? So that’s what
295 00:30:25.270 ⇒ 00:30:31.669 Scott_Harmon: I don’t know if 8 by 8 I don’t know if 8 by 8 keeps track of, I doubt it does right. So
296 00:30:31.670 ⇒ 00:30:35.099 YvetteRuiz: Now we’re working on trying to build key phrases.
297 00:30:36.480 ⇒ 00:30:38.890 YvetteRuiz: but right now it doesn’t. We keep it manually? We keep
298 00:30:38.890 ⇒ 00:30:45.640 Scott_Harmon: I don’t know where you’d have a record of 8 by 8 sales like like that’s got to be in some other system somewhere.
299 00:30:45.640 ⇒ 00:30:51.239 YvetteRuiz: Yeah, we we have it. Our data team has it. We, we have that data that we pull every month
300 00:30:51.240 ⇒ 00:30:56.749 Scott_Harmon: Would be some additional data work, I think, Amber, if we if we needed to.
301 00:30:57.520 ⇒ 00:31:00.189 Scott_Harmon: you know, on our dashboard show.
302 00:31:00.750 ⇒ 00:31:04.530 Scott_Harmon: Okay? The the agent pitched or suggested a
303 00:31:05.060 ⇒ 00:31:10.019 Scott_Harmon: Oh, by the way, this many times, and here’s how many.
304 00:31:10.390 ⇒ 00:31:14.719 Scott_Harmon: Oh, by the way, sales happened that that’s gonna be a little tricky to. You know, we’re gonna need
305 00:31:14.720 ⇒ 00:31:15.430 YvetteRuiz: Inputs
306 00:31:15.430 ⇒ 00:31:17.919 Scott_Harmon: 2 different data sources to do that. So
307 00:31:18.420 ⇒ 00:31:22.040 Scott_Harmon: I suggest we start with our side, which is.
308 00:31:22.680 ⇒ 00:31:29.909 Scott_Harmon: get 1. 0, by the way, to work and get it on the dashboard right? We we really know if it worked or not. But at least we’ll know. Hey.
309 00:31:30.520 ⇒ 00:31:34.870 Scott_Harmon: you know it, suggested this one, you know, 22 times, you know, like
310 00:31:35.860 ⇒ 00:31:37.599 Scott_Harmon: And and let’s start from there.
311 00:31:38.810 ⇒ 00:31:39.929 Scott_Harmon: Hi, Steven.
312 00:31:42.290 ⇒ 00:31:43.209 Steven: How’s it going
313 00:31:43.580 ⇒ 00:31:46.605 Scott_Harmon: Good good. We’re just just
314 00:31:47.470 ⇒ 00:31:50.590 Scott_Harmon: just wrapping up. I don’t know if you you share the slides, but
315 00:31:51.220 ⇒ 00:31:57.460 Scott_Harmon: really tremendous progress from the just kind of the rollout and statistics phase, and the biggest just to highlight for me
316 00:31:57.620 ⇒ 00:32:07.790 Scott_Harmon: was that error rate is coming down real nicely on the answers, which is kind of my biggest focus, because there’s some real hard work from both. You know, Janice and Brain forge people
317 00:32:07.960 ⇒ 00:32:09.470 Scott_Harmon: really coming down.
318 00:32:09.610 ⇒ 00:32:13.830 Scott_Harmon: And so it looks like we’re starting to get it cleaned up and ready for prime time, and
319 00:32:14.340 ⇒ 00:32:18.830 Steven: But y’all’s error rate is lower than our Csr error rate at this point
320 00:32:22.060 ⇒ 00:32:23.530 Steven: on who you’re talking to.
321 00:32:24.800 ⇒ 00:32:27.619 Steven: Not Janice, not Janice. Janice didn’t have an error rate
322 00:32:28.460 ⇒ 00:32:30.700 JanieceGarcia: I wouldn’t say that, Steven.
323 00:32:32.060 ⇒ 00:32:33.280 JanieceGarcia: I make mistakes
324 00:32:33.910 ⇒ 00:32:38.457 Scott_Harmon: And then on the business front. We’re a little bit late on our side, getting you a proposal
325 00:32:38.840 ⇒ 00:32:44.920 Scott_Harmon: for our Q. 2, our second, you know, for, and I’ll get with you, Tom. He’s just been swamped. No.
326 00:32:44.920 ⇒ 00:32:45.540 Steven: Cool.
327 00:32:45.540 ⇒ 00:32:47.699 Scott_Harmon: No excuses. We’re just late. We’ll get something to you
328 00:32:47.700 ⇒ 00:32:51.596 Steven: Cool. Okay, yeah, that’ll be good. Yeah. And I’ll catch up with you. Vette on the
329 00:32:52.170 ⇒ 00:32:53.520 Steven: how everything else is going
330 00:32:53.520 ⇒ 00:33:06.489 YvetteRuiz: She and the presentation. But the presentation that was my main thing is because company meetings next week, so we can go ahead and get that out there. Amber did put all those slides together, so I’ll let you know. I’ll just kind of how we want to do that
331 00:33:06.490 ⇒ 00:33:08.589 Scott_Harmon: And most importantly, Stephen, you gotta vote
332 00:33:08.920 ⇒ 00:33:12.769 Scott_Harmon: for the can. You put up the the contesting again? Amber. Yeah.
333 00:33:13.570 ⇒ 00:33:14.250 YvetteRuiz: Palettes.
334 00:33:14.250 ⇒ 00:33:14.514 Amber Lin: Sure.
335 00:33:14.780 ⇒ 00:33:17.129 Scott_Harmon: You gotta vote for your favorite
336 00:33:19.605 ⇒ 00:33:22.280 Amber Lin: We all have our personal favorite.
337 00:33:23.660 ⇒ 00:33:33.989 Amber Lin: We have a personal favorites, but we gotta gotta let people vote. Gotta have democracy. So this is a contest
338 00:33:33.990 ⇒ 00:33:37.220 Steven: Were the ones I created. So I got a little bias here.
339 00:33:37.490 ⇒ 00:33:38.010 Steven: I mean
340 00:33:38.424 ⇒ 00:33:41.322 YvetteRuiz: Unfortunately, they’re the 2 worst ones. But
341 00:33:42.900 ⇒ 00:33:44.648 Scott_Harmon: I think they’re all 4 good.
342 00:33:44.940 ⇒ 00:33:46.880 YvetteRuiz: It’s all really good
343 00:33:47.270 ⇒ 00:33:50.505 Amber Lin: Is this one? But that’s not ABC colors
344 00:33:51.210 ⇒ 00:33:56.779 Steven: I was. Gonna say, I like that one. But yeah, I like it. I like that color the Andy Van Eater, the number 2, I guess
345 00:33:56.780 ⇒ 00:33:58.270 Amber Lin: Yeah, I like that one, too.
346 00:33:58.270 ⇒ 00:34:02.330 Scott_Harmon: I do like that one. Yeah, okay.
347 00:34:02.330 ⇒ 00:34:05.899 Steven: Yeah, number one and number 3 were ones that I created on Jim. And I
348 00:34:08.918 ⇒ 00:34:12.351 YvetteRuiz: I like them, Steven.
349 00:34:13.210 ⇒ 00:34:14.230 Amber Lin: Like number 2,
350 00:34:14.239 ⇒ 00:34:16.739 YvetteRuiz: That’ll be winter, Andy
351 00:34:17.489 ⇒ 00:34:20.499 Scott_Harmon: Oh, that’s right. You need to have them be seasonal and stuff
352 00:34:20.500 ⇒ 00:34:21.670 YvetteRuiz: Yeah, there, you go.
353 00:34:21.670 ⇒ 00:34:24.270 Steven: Be fun. Yeah, I mean, put little Christmas flair to
354 00:34:24.810 ⇒ 00:34:25.190 Amber Lin: Wanted to
355 00:34:25.620 ⇒ 00:34:28.680 YvetteRuiz: Well, we’re promoting Christmas lights right?
356 00:34:29.190 ⇒ 00:34:31.770 Steven: We should change, we should change up. Yeah, he he does need that
357 00:34:32.290 ⇒ 00:34:33.710 Scott_Harmon: Coming. Up. Yeah. Yeah.
358 00:34:33.719 ⇒ 00:34:34.169 Amber Lin: Oh, yeah.
359 00:34:34.170 ⇒ 00:34:34.710 Steven: Yeah.
360 00:34:34.719 ⇒ 00:34:38.269 Amber Lin: Yeah, like we can have every season. We’ll have a different hat like same hat
361 00:34:39.020 ⇒ 00:34:40.630 Steven: Can you put some Easter bunny ears on
362 00:34:40.639 ⇒ 00:34:42.529 Scott_Harmon: That’ll be some Easter money. Yeah.
363 00:34:42.949 ⇒ 00:34:44.130 YvetteRuiz: Oh, yup!
364 00:34:45.350 ⇒ 00:34:52.070 Amber Lin: Yeah. And Steven, just really quick. This is the. These are the slides, and I have scripts for Janice
365 00:34:52.239 ⇒ 00:35:00.949 Amber Lin: to record. I don’t know if you guys want to record this or just present this at the company meeting, but we have all everything, and I’ll send this to you guys later
366 00:35:01.950 ⇒ 00:35:03.594 YvetteRuiz: Yeah, that’s what I’ll talk to Steven about.
367 00:35:03.800 ⇒ 00:35:06.170 Steven: Yeah, yeah, we’ll chat about that awesome
368 00:35:06.380 ⇒ 00:35:07.359 Amber Lin: Good stuff.
369 00:35:07.810 ⇒ 00:35:36.360 YvetteRuiz: But no, thank you so much, guys for you know, Amber, you know, working with the entire team. Janice. I agree with Scott, and it’s kind of what we we’ve been saying. It’s like it’s only going to be as good as the work that we put into it. So you know, we were determined to carve out the time to for that with with Janice, the trainer, with our, our with Shannon, and then also with the data team as well. So I really really appreciate the patience and just working with us.
370 00:35:36.600 ⇒ 00:35:37.460 YvetteRuiz: But yeah.
371 00:35:37.790 ⇒ 00:35:42.840 Scott_Harmon: Yeah and amber. This thing has just been running like a well oiled machine since you came on board. So
372 00:35:42.840 ⇒ 00:35:43.800 Amber Lin: I’m so happy
373 00:35:45.460 ⇒ 00:35:46.240 Steven: Were like.
374 00:35:46.240 ⇒ 00:35:50.190 YvetteRuiz: Yeah, we don’t even need a we don’t miss at all
375 00:35:51.010 ⇒ 00:35:52.349 Scott_Harmon: Wondering if you could organize some stuff
376 00:35:52.350 ⇒ 00:35:52.740 Amber Lin: Thank you.
377 00:35:53.660 ⇒ 00:35:54.769 Scott_Harmon: But we can talk about that
378 00:35:54.770 ⇒ 00:35:55.460 YvetteRuiz: Yeah.
379 00:35:56.960 ⇒ 00:36:13.840 YvetteRuiz: Now, her emails are really good. I love the little videos that she put together. They’re quick. They’re easy, you know, when I read, you know, when you I asked the question you send it right over. It was easy for me to follow, so that stuff is super helpful. The communication is very clear. So I’m super thankful for that, because I get to get caught up
380 00:36:14.090 ⇒ 00:36:16.421 YvetteRuiz: on the stuff that I’m not able to attend
381 00:36:17.770 ⇒ 00:36:21.763 Scott_Harmon: Everybody. Well, have a good weekend hopefully. Get a little bit of rain here, and
382 00:36:22.600 ⇒ 00:36:23.240 JanieceGarcia: We will
383 00:36:23.550 ⇒ 00:36:25.470 Scott_Harmon: Should be an exciting week next week.
384 00:36:26.350 ⇒ 00:36:27.440 YvetteRuiz: Yup perfect!
385 00:36:27.440 ⇒ 00:36:28.690 JanieceGarcia: Thank you.
386 00:36:28.690 ⇒ 00:36:29.370 YvetteRuiz: You guys
387 00:36:29.490 ⇒ 00:36:29.880 Amber Lin: Bye.