Meeting Title: Brainforge x Insomnia: FDA-Rewards Campaign Insights Date: 2025-11-18 Meeting participants: Amber Lin, Uttam Kumaran, Matt
WEBVTT
1 00:00:48.080 ⇒ 00:00:49.450 Amber Lin: Hi there!
2 00:00:50.820 ⇒ 00:00:52.010 Uttam Kumaran: Hello, friend.
3 00:00:53.310 ⇒ 00:00:54.370 Uttam Kumaran: That’s true.
4 00:00:54.370 ⇒ 00:00:56.150 Amber Lin: Very odd to me.
5 00:00:56.560 ⇒ 00:00:57.909 Uttam Kumaran: Oh, thank you!
6 00:00:58.140 ⇒ 00:00:59.659 Uttam Kumaran: My dad bought it for me.
7 00:00:59.960 ⇒ 00:01:01.080 Amber Lin: Oh, wow.
8 00:01:01.290 ⇒ 00:01:03.059 Uttam Kumaran: He likes Patagonia a lot.
9 00:01:03.340 ⇒ 00:01:13.410 Amber Lin: Oh, okay, that makes sense. I remember you were on a call with Emily, and you said your dad likes J.Crew, and then I also heard on another call that you hate J.Crew.
10 00:01:15.180 ⇒ 00:01:20.550 Uttam Kumaran: I used to not like J. Crew. I think it’s, like, Like, NPC clothing?
11 00:01:21.830 ⇒ 00:01:28.120 Uttam Kumaran: But some of their stuff got better. Yeah.
12 00:01:28.120 ⇒ 00:01:28.810 Amber Lin: Cool.
13 00:01:29.700 ⇒ 00:01:36.099 Amber Lin: I know we changed the time of the meeting. Do you know if Matt is joining? Does he have time?
14 00:01:36.100 ⇒ 00:01:39.780 Uttam Kumaran: Yeah, he’s the one that wanted me to move it to now, so he should.
15 00:01:39.780 ⇒ 00:01:41.070 Amber Lin: Oh, okay.
16 00:01:51.430 ⇒ 00:01:52.410 Uttam Kumaran: Sorry, say that again?
17 00:01:52.840 ⇒ 00:01:58.609 Amber Lin: I have a few questions listed out. I looked at the punch data a bit.
18 00:01:59.020 ⇒ 00:02:05.300 Amber Lin: And we have some FDA data, we have some
19 00:02:05.460 ⇒ 00:02:09.069 Amber Lin: Punch data, we don’t have all of it.
20 00:02:09.970 ⇒ 00:02:10.699 Amber Lin: But…
21 00:02:11.039 ⇒ 00:02:19.579 Amber Lin: I think it’s… for the first call, mostly just gonna be more strategic. Like, Robert gave me most of these questions.
22 00:02:19.690 ⇒ 00:02:27.800 Amber Lin: and wanted to see how FDA ties into Lifecycle. I looked at Punch a bit, and I also want to ask him how
23 00:02:27.980 ⇒ 00:02:39.010 Amber Lin: rewards ties into lifecycle, and I… because I know Matt is new to rewards, probably… he probably got handed over a few months ago.
24 00:02:39.110 ⇒ 00:02:44.199 Amber Lin: So I want to see where he’s at with rewards, or if he knows what to do, even.
25 00:02:44.530 ⇒ 00:02:45.760 Amber Lin: Oh, hi.
26 00:02:46.260 ⇒ 00:02:46.610 Uttam Kumaran: Hey, Matt.
27 00:02:46.610 ⇒ 00:02:47.630 Amber Lin: Hi, Matt!
28 00:02:47.630 ⇒ 00:02:48.879 Matt: They don’t.
29 00:02:50.030 ⇒ 00:02:51.659 Uttam Kumaran: Hey, good, how are you?
30 00:02:51.980 ⇒ 00:02:54.409 Amber Lin: Hi there! Nice meeting room!
31 00:02:54.410 ⇒ 00:02:57.079 Uttam Kumaran: A very colorful office, yeah, wow.
32 00:03:04.090 ⇒ 00:03:06.610 Amber Lin: Cool. I don’t think I… any of them…
33 00:03:06.610 ⇒ 00:03:06.980 Matt: I’m nervous.
34 00:03:06.980 ⇒ 00:03:07.710 Amber Lin: format.
35 00:03:08.630 ⇒ 00:03:09.320 Matt: True.
36 00:03:11.150 ⇒ 00:03:13.990 Uttam Kumaran: It’s… it’s sort of cutting in and out, Matt.
37 00:03:18.400 ⇒ 00:03:18.930 Uttam Kumaran: if…
38 00:03:19.980 ⇒ 00:03:22.000 Amber Lin: Yeah, it’s a bit choppy for me as well.
39 00:03:24.870 ⇒ 00:03:27.390 Amber Lin: Should we try no video? Would that make it better?
40 00:03:32.900 ⇒ 00:03:35.670 Matt: Yeah, let’s… I’m gonna turn mine off.
41 00:03:35.990 ⇒ 00:03:40.589 Amber Lin: Cool, yeah, now your audio seems better. Yeah, I can stop my video, too.
42 00:03:41.670 ⇒ 00:03:42.850 Amber Lin: Is it better now?
43 00:03:44.000 ⇒ 00:03:44.630 Uttam Kumaran: Yeah, I can hear you.
44 00:03:44.630 ⇒ 00:03:45.669 Matt: Okay, cool.
45 00:03:45.670 ⇒ 00:03:46.470 Amber Lin: Cool.
46 00:03:46.730 ⇒ 00:03:47.350 Matt: Yep.
47 00:03:47.930 ⇒ 00:03:49.289 Uttam Kumaran: Yeah, it’s great to meet!
48 00:03:49.490 ⇒ 00:03:53.580 Uttam Kumaran: Sounds good. Yeah, I, is that, is that the, is that the actual office?
49 00:03:53.980 ⇒ 00:03:57.510 Matt: Yes, so I’m at the headquarters here in Philadelphia, yep.
50 00:03:57.530 ⇒ 00:03:58.280 Amber Lin: Great.
51 00:03:58.490 ⇒ 00:04:01.890 Uttam Kumaran: Nice, very colorful, palm-themed office, that’s awesome.
52 00:04:03.100 ⇒ 00:04:03.450 Matt: Yes.
53 00:04:03.450 ⇒ 00:04:05.630 Amber Lin: How long have you been with the company?
54 00:04:06.390 ⇒ 00:04:07.439 Amber Lin: We are all over.
55 00:04:07.440 ⇒ 00:04:07.950 Matt: I agree.
56 00:04:09.240 ⇒ 00:04:10.690 Matt: You’re all under? Nice.
57 00:04:10.950 ⇒ 00:04:15.320 Matt: Yeah, I’ve been in Insomnia for…
58 00:04:15.690 ⇒ 00:04:19.370 Matt: A little over a year and a half now, I joined in May of 2024.
59 00:04:19.519 ⇒ 00:04:20.829 Amber Lin: Wow.
60 00:04:20.829 ⇒ 00:04:23.590 Matt: So, that’s not actually…
61 00:04:23.790 ⇒ 00:04:29.490 Matt: pretty long for this company, to be honest, so… how many of the more, tenured people on our team.
62 00:04:30.290 ⇒ 00:04:36.259 Uttam Kumaran: Nice. Yeah, I’m here in Austin, and Amber’s in LA. But I… I used to live in,
63 00:04:36.370 ⇒ 00:04:41.880 Uttam Kumaran: I used to live in New York, and my sister went to Swarthmore, outside of Philly, so… spent some time in Philly, too.
64 00:04:41.880 ⇒ 00:04:42.960 Matt: Oh, okay, yeah.
65 00:04:43.300 ⇒ 00:04:45.550 Matt: Nice, nice, yeah, I remember that.
66 00:04:46.210 ⇒ 00:04:55.179 Uttam Kumaran: Great. Well, yeah, I mean, I’m pumped to chat. I think Amber has a bunch of stuff, so hopefully this will be a pretty dense meeting. Maybe,
67 00:04:55.750 ⇒ 00:05:02.630 Uttam Kumaran: maybe, like, we can jump right into things, and I think, Matt, like, this is considered a first time meeting, I think you can feel free to give feedback on, like.
68 00:05:02.740 ⇒ 00:05:21.249 Uttam Kumaran: on if what you’re seeing or what kind of our questions are too basic, but we’re probably some of the questions, just getting a lay of the land on, like, your thinking about, you know, the channel strategy here, and… and I think we’ve produced some good stuff, to sort of chew on and get some good follow-up. So yeah, maybe, Amber, I can let you…
69 00:05:21.420 ⇒ 00:05:22.510 Uttam Kumaran: Sort of drive.
70 00:05:23.080 ⇒ 00:05:42.480 Amber Lin: Yeah, cool. Let me share screen so we have something visual to follow. So I know that, Matt, you mainly handle FDA, and I know, after Kelsey went on maternity leave, you also took over rewards, so I mostly have questions on these two areas, if that’s good with you.
71 00:05:42.870 ⇒ 00:05:50.930 Matt: Yeah, just for context, I, neither of those are stuff that I, like, did before Kelsey went on leave.
72 00:05:50.930 ⇒ 00:05:52.529 Amber Lin: Mmm, oh, really? Okay.
73 00:05:52.530 ⇒ 00:05:57.410 Matt: Kessie was doing both of those, and I picked it up since she’s, she went on maternity leave, yeah.
74 00:05:57.410 ⇒ 00:05:58.639 Amber Lin: Where in Westside?
75 00:05:59.330 ⇒ 00:06:03.319 Matt: She… it was early September, I believe.
76 00:06:03.320 ⇒ 00:06:08.410 Amber Lin: Oh, wow, that’s… that’s been a very short period of time. How do you feel handling this?
77 00:06:08.530 ⇒ 00:06:13.570 Matt: Yeah, it’s been a little, been a little busy and stressful, but we’re doing it, so…
78 00:06:13.570 ⇒ 00:06:18.400 Amber Lin: Well, okay, I hope that we can help, because we’re also…
79 00:06:18.400 ⇒ 00:06:18.840 Matt: heaven.
80 00:06:18.840 ⇒ 00:06:30.790 Amber Lin: helping Birdie with her campaigns, and I bet there’s quite a bit of similarities, since it’s all marketing-related, so hopefully we can give you some insights and give you some action plans as well, to make it easier.
81 00:06:31.330 ⇒ 00:06:32.060 Matt: Sounds good.
82 00:06:32.060 ⇒ 00:06:49.059 Amber Lin: Yeah, so let’s start by FDA. I know right now we’re in Uber Eats, and then we’re in DoorDash. I think we’re also in Grubhub, so how do you, handle the different channels?
83 00:06:50.790 ⇒ 00:06:58.710 Matt: So, yeah, so we… we don’t do any sort of promotions or sponsor listings in Grubhub, it’s just Uber and DoorDash.
84 00:06:58.830 ⇒ 00:07:07.110 Matt: And for some historical context here, like, we didn’t really used to spend As far as I know.
85 00:07:07.590 ⇒ 00:07:19.240 Matt: And also, Kelsey… Kelsey will tell you this too, like, she’s not an FDA expert. She was given this when our head of digital, or when our director of digital left the company back in, like, April.
86 00:07:19.240 ⇒ 00:07:19.590 Amber Lin: Oh.
87 00:07:19.590 ⇒ 00:07:23.409 Matt: So, it’s not like we have anyone that really specializes in this.
88 00:07:25.060 ⇒ 00:07:31.310 Matt: So we didn’t really used to have a, like, really… built-out strategy for FDA.
89 00:07:31.630 ⇒ 00:07:33.120 Matt: Motions and sponsor listings.
90 00:07:33.560 ⇒ 00:07:41.539 Matt: we just got it going on, and we gotta start spending in it a little bit towards the end of 2024. And then…
91 00:07:42.410 ⇒ 00:07:53.870 Matt: We have a, like, a decent spend plan, and we saw, like, pretty good returns in, like, the first, kind of few months of this year, and then…
92 00:07:54.270 ⇒ 00:08:10.040 Matt: I believe because we were seeing good returns from somewhere, and this is kind of a question that we don’t really know the answer to, like, I think it was our former CMO, but, we were given… Kelsey, at least, was given direction to kind of just ramp up spending.
93 00:08:10.250 ⇒ 00:08:16.409 Matt: So… She was spending a lot more on promotions and sponsored listings, which…
94 00:08:16.760 ⇒ 00:08:21.940 Matt: In return, was definitely getting us good revenues, but the…
95 00:08:22.440 ⇒ 00:08:24.790 Matt: We were experiencing diminishing returns, for sure.
96 00:08:25.450 ⇒ 00:08:27.960 Matt: And it got to the point where we were spending…
97 00:08:28.600 ⇒ 00:08:34.580 Matt: you know, a lot of money on FDA every single month, to a point that no one is comfortable with.
98 00:08:34.750 ⇒ 00:08:40.840 Matt: So ever since I took over in September, we’ve been trying to kind of just reduce it.
99 00:08:41.010 ⇒ 00:08:46.259 Matt: And it kind of just… not just, like, see what happens, but just.
100 00:08:46.260 ⇒ 00:08:46.720 Amber Lin: doing everything.
101 00:08:46.720 ⇒ 00:09:00.109 Matt: we can to kind of reduce it. So, we had it, and I mean, I don’t know if you guys have been in the documents, but, like, for a little… for September and October, for a bit, we were running a special, like, DoorDash
102 00:09:00.150 ⇒ 00:09:07.679 Matt: Co-funded opportunity, so that was requiring us to spend kind of more money, than we were hoping to spend.
103 00:09:08.170 ⇒ 00:09:15.940 Matt: So, like, once that kind of got out of the way, we’ve been able to reduce spend a bit more, and, we’re actually in the process of doing…
104 00:09:16.430 ⇒ 00:09:19.140 Matt: An incrementality test on our own.
105 00:09:19.470 ⇒ 00:09:36.600 Matt: So, for promotions, when it comes to FDAs, we originally started with 4 stores, and we just didn’t assign them any promotions through, the food delivery apps, and, we expanded it to 30 extra random stores.
106 00:09:36.830 ⇒ 00:09:44.370 Matt: So now there’s, 34 stores that are not receiving, promotions.
107 00:09:45.110 ⇒ 00:09:54.429 Matt: through, delivery apps. So, I actually have a meeting later today to talk about the, progress of that. It’s been running for about a week and a half now.
108 00:09:54.640 ⇒ 00:09:58.420 Matt: So whereas Sarah is starting to see some early results with…
109 00:09:59.860 ⇒ 00:10:01.929 Amber Lin: What, what is, what are… That’s interesting.
110 00:10:01.930 ⇒ 00:10:17.810 Uttam Kumaran: what are the sort of spend goal, like, guidance math that you have? I know, I… I mean, I’ve certainly seen how high the spend is on FDA, but, like, what is… what is sort of the guidance you’re going towards? And that’s something, Amber, that we can also start to look at, like,
111 00:10:17.900 ⇒ 00:10:22.399 Uttam Kumaran: You know, where the spend kind of plateaus, and maybe even help there.
112 00:10:22.930 ⇒ 00:10:28.419 Matt: So, here’s… here’s the issue that we have all been dealing with. There is no guidance at all.
113 00:10:28.690 ⇒ 00:10:45.910 Matt: So it was really, our CMO was like, ramp up, spend, ramp up, spend, and then we were like, FDA’s doing good for the business, and then it got to a point where, like, our leadership team, especially our CEO, was like, wow, we are spending so much money on FDA, like, we know.
114 00:10:45.910 ⇒ 00:10:46.300 Uttam Kumaran: Yeah.
115 00:10:46.300 ⇒ 00:10:47.020 Matt: this time.
116 00:10:47.170 ⇒ 00:10:53.160 Matt: So, right now, the guidance is literally reduce it. So, but we can’t…
117 00:10:53.850 ⇒ 00:11:06.500 Matt: we are, obviously, because we’ve become dependent on it this year, we don’t want to just cut it out and then see a massive drop-off in revenue. So that’s why we were doing this incrementality test, to kind of see what the…
118 00:11:06.750 ⇒ 00:11:25.459 Matt: kind of like what the ROI is, effectively. Great. And then once we get to that, I think we’re going to be able to kind of develop an actual plan of what we want to do, like, if we want to run promotions Mondays through Thursdays, something like that, and if we want to keep it between.
119 00:11:25.460 ⇒ 00:11:27.070 Amber Lin: You know, like…
120 00:11:27.260 ⇒ 00:11:35.589 Matt: $10,000 a day or something like that. I don’t know, we’re… that’s all going to kind of hopefully be developed based off the results of the experiments out of the study.
121 00:11:36.030 ⇒ 00:11:46.299 Amber Lin: Gotcha. Is there any other experiments that you want to run, or are we just experimenting with yes or no promotions?
122 00:11:46.680 ⇒ 00:11:49.780 Matt: There’s plenty of experience.
123 00:11:49.780 ⇒ 00:11:50.390 Amber Lin: Oh, okay.
124 00:11:50.390 ⇒ 00:11:58.250 Matt: Personally, because we’ve only… We also only do, like, one… Type of a promotion.
125 00:11:58.250 ⇒ 00:11:58.610 Amber Lin: Oh, boy.
126 00:11:58.610 ⇒ 00:12:01.029 Matt: is, like, a spendex get y.
127 00:12:01.280 ⇒ 00:12:01.709 Amber Lin: I don’t know.
128 00:12:01.710 ⇒ 00:12:11.270 Matt: So it’s like, the one that works best for us, and if you look at it, we’ve been running it a lot recently, is the SPEN15 Safe 3.
129 00:12:12.620 ⇒ 00:12:17.900 Matt: So that’s very, very… That’s… that’s basically as, like, our best return rate.
130 00:12:18.070 ⇒ 00:12:31.899 Matt: For any of the promotions that we run. But I would love to experiment with some of the different types of offers that we can do. I know there’s, like, buy one, get ones, there’s, like, a spend certain amount and, like, get a product, which we did kind of.
131 00:12:32.640 ⇒ 00:12:40.320 Matt: test at the beginning of, like, 2 weeks ago, and it just didn’t do well. And then also, we want to…
132 00:12:41.170 ⇒ 00:12:44.660 Matt: I do a little bit of testing with sponsored listings as well.
133 00:12:44.700 ⇒ 00:12:59.350 Matt: So, I think our plan is to do, basically, an identical incrementality test with sponsored listings. And then also, we do have the functionality with sponsored listings to target new customers only.
134 00:12:59.380 ⇒ 00:13:05.230 Matt: So we want to see, also, how that could potentially affect, acquisition.
135 00:13:07.870 ⇒ 00:13:17.619 Amber Lin: Yeah, that’s awesome, because I was… we were just talking about, the different segmentations with Bertie and Amrita, so we’re talking about
136 00:13:17.830 ⇒ 00:13:25.690 Amber Lin: from the first to second purchase, what does it look like? And how do we make people make their second purchase faster?
137 00:13:26.130 ⇒ 00:13:41.880 Amber Lin: And if you would like, I can also send you over the insights that I sent to them, but that’s more of online and then in-app purchases, because I haven’t… we haven’t looked at the FDA purchase data yet, but there might be something interesting there as well.
138 00:13:41.880 ⇒ 00:13:42.490 Matt: Yep.
139 00:13:42.790 ⇒ 00:13:43.620 Matt: True.
140 00:13:43.620 ⇒ 00:13:57.379 Amber Lin: Yeah. I know you mentioned you only run the get… spend X, get Y. Has that always been the case, or has there historically been different campaign types that you’ve been running?
141 00:13:57.710 ⇒ 00:14:03.360 Matt: Like, 90% of the time, that’s been what we do. Oh, okay. Historically, like.
142 00:14:03.880 ⇒ 00:14:15.500 Matt: there… there might have been, every now and then, whenever I look back at, the sheet that was kept in 2024, there might be a, like, a free…
143 00:14:15.600 ⇒ 00:14:26.949 Matt: chocolate chunk offer or something, or, like, a percent off ice cream that they tried, but, 9 times out of 10, it has been, spend next to Y for the outside of promotion, at least.
144 00:14:26.950 ⇒ 00:14:29.559 Amber Lin: Oh, cool, okay.
145 00:14:29.960 ⇒ 00:14:31.649 Matt: It applies to, like, limited time.
146 00:14:31.650 ⇒ 00:14:32.780 Amber Lin: offers, right?
147 00:14:32.910 ⇒ 00:14:33.520 Amber Lin: Sponsor.
148 00:14:33.520 ⇒ 00:14:33.909 Matt: I’m sorry?
149 00:14:33.910 ⇒ 00:14:36.319 Amber Lin: Do you also run…
150 00:14:36.320 ⇒ 00:14:40.010 Matt: Yeah. Go ahead. Most of those things in addition to promotions.
151 00:14:40.300 ⇒ 00:14:42.100 Matt: On both DoorDash and Uberg.
152 00:14:47.680 ⇒ 00:14:54.819 Matt: So, sponsor those things, I mentioned that we were trying to just lower spend in general. So I’ve…
153 00:14:54.940 ⇒ 00:15:02.189 Matt: From when I took over FDA to now, we’ve been able to reduce spend on sponsored listings by about 30-40%.
154 00:15:02.340 ⇒ 00:15:02.770 Amber Lin: Mmm.
155 00:15:02.770 ⇒ 00:15:07.990 Matt: without seeing, like, a massive drop-off in return. So…
156 00:15:08.300 ⇒ 00:15:13.970 Matt: Promotion is kind of the next thing that we’re trying to tackle, which is why we’re doing that incrementality test at the moment.
157 00:15:14.570 ⇒ 00:15:20.810 Uttam Kumaran: Matt, where are you… are you doing your reporting, kind of, out of the, like, existing…
158 00:15:21.030 ⇒ 00:15:26.570 Uttam Kumaran: Sort of reporting that our team supports, or are you kind of producing stuff on your own, or kind of, like, where are you looking at numbers?
159 00:15:26.940 ⇒ 00:15:39.380 Matt: It’s kind of a bit of everywhere, to be honest. It’s basically, I do really, really rely on the numbers that you guys pull for, like, the actuals.
160 00:15:39.650 ⇒ 00:15:52.290 Matt: And then, based off of that, I will do reporting. I will, like, break it out by sponsor listing, or, by campaign type, or, like, by weeks. So I actually, every…
161 00:15:52.290 ⇒ 00:16:00.600 Matt: every morning on Mondays, I do reports based off of the previous week, so I do, like, rely on your actual data for that.
162 00:16:00.630 ⇒ 00:16:04.760 Matt: And… yeah, when it comes to,
163 00:16:05.290 ⇒ 00:16:11.579 Matt: kind of a spend, area when it comes to how we’re reviewing,
164 00:16:11.820 ⇒ 00:16:18.049 Matt: like, the impact of, like, reducing spend on some cases, I am actually not
165 00:16:18.050 ⇒ 00:16:33.179 Matt: looking at your reporting for that, and looking at our internal reporting that we have that is looking at gross sales, and we can separate gross sales out by channel, so I’m breaking it out by gross sales and growth rates and transactions by.
166 00:16:33.180 ⇒ 00:16:33.690 Amber Lin: Correct.
167 00:16:33.690 ⇒ 00:16:48.750 Matt: And, we’re able to look at that simultaneously with, kind of the numbers that you guys have, and be like, okay, we started reducing promotion spend here, but then looking at gross sales, there was no impact. So stuff like that.
168 00:16:50.010 ⇒ 00:16:51.999 Amber Lin: Cool, okay.
169 00:16:52.610 ⇒ 00:17:11.779 Amber Lin: I think the last question I have on FDA is, how do you, or how do you think the company thinks about, how the FDA strategy fits into the customer journey? Because you talked about, as well, if people make their first purchase, maybe we need to target them more, and, like, how do you think it fits, and do people
170 00:17:12.400 ⇒ 00:17:27.300 Amber Lin: discover insomnia through FDA, or do they just go to, say, Uber after they made an insomnia purchase, and they’re like, I don’t want to go to a store, I just want to order an Uber Eats? So how do you think it fits in to everything?
171 00:17:27.300 ⇒ 00:17:36.479 Matt: Yeah, so… basically… The people that are on FDA stay on FDA.
172 00:17:36.480 ⇒ 00:17:36.930 Amber Lin: Mmm.
173 00:17:36.930 ⇒ 00:17:39.270 Matt: they are, it’s not many customers on FDAs.
174 00:17:40.140 ⇒ 00:17:40.300 Amber Lin: Oh.
175 00:17:40.460 ⇒ 00:17:54.720 Matt: I have the goal that we need to figure out a strategy around, which we don’t have one right now, is to get people that are buying from us on FDAs into actually buying from us, like, on our app or website.
176 00:17:54.740 ⇒ 00:17:58.640 Amber Lin: Like, getting them to become a rewards member, so that’s… that’s…
177 00:17:59.270 ⇒ 00:18:09.079 Matt: Next steps, once we figure out the spending problem, is to figure out the strategy on how to convert FDA customers into Insomnia Rewards customers.
178 00:18:09.080 ⇒ 00:18:09.640 Amber Lin: Huh.
179 00:18:10.900 ⇒ 00:18:20.779 Matt: That’s the goal, basically, one, a lot of… and this is kind of straight up across a lot of our platforms, a lot of the people that
180 00:18:20.880 ⇒ 00:18:23.270 Matt: Buy from us, either our…
181 00:18:23.580 ⇒ 00:18:41.620 Matt: like, they buy once, and then they often just, like, don’t buy again, so they are. There are a lot of good people, but then people that do, like, currently buy from us, about, like, 60% of our customers on, like, Uber Eats and DoorDash are, like, the past members, so, like.
182 00:18:41.720 ⇒ 00:18:48.410 Matt: for Uber, it’s, like, they’re Uber One members, and then DoorDash are, like, DashPass members. So…
183 00:18:48.720 ⇒ 00:18:57.899 Matt: I think one of the reasons… one of the main reasons why we can’t really convert them into our own customers is because they’re already doing, like, free delivery through that.
184 00:18:57.900 ⇒ 00:18:58.719 Amber Lin: I see.
185 00:18:58.720 ⇒ 00:18:59.670 Matt: So…
186 00:18:59.790 ⇒ 00:19:06.250 Matt: One of the… one of the ideas that I have that we need to toy with is, like, can we…
187 00:19:06.690 ⇒ 00:19:12.150 Matt: for any Insomnia Awards member, just constantly offer free delivery, so that we can then.
188 00:19:12.150 ⇒ 00:19:12.730 Amber Lin: Mmm.
189 00:19:12.730 ⇒ 00:19:14.239 Matt: like, hey, I know we talked to the person.
190 00:19:14.240 ⇒ 00:19:14.930 Uttam Kumaran: about.
191 00:19:15.250 ⇒ 00:19:15.840 Matt: Sorry, you talked about.
192 00:19:15.840 ⇒ 00:19:19.229 Uttam Kumaran: We talked… we talked with Bertie just about this, Amber, actually.
193 00:19:19.730 ⇒ 00:19:35.859 Uttam Kumaran: was, like, the rewards and, like, free delivery, and yeah, I forgot exactly what we talked about, but we should link this back to our conversation there. Because, yeah, like, we were talking to her about, yeah, like, how have you thought about free delivery as something constant for reward members.
194 00:19:36.000 ⇒ 00:19:40.579 Uttam Kumaran: And, like, has there been an analysis on that? So, yeah, that makes sense.
195 00:19:41.120 ⇒ 00:19:50.209 Matt: Yeah, yeah, that… I think that’s probably the best way to go and switch over, because we do naturally increase our prices on the food delivery app.
196 00:19:50.210 ⇒ 00:19:50.909 Uttam Kumaran: Of course.
197 00:19:51.110 ⇒ 00:20:09.529 Matt: So, you know, if they’re ordering from us on our app, and they’re already getting for delivery, they are spending less than they would be on an Uber or a DoorDash. So, that’s… that’s kind of the long-term goal. Of course, right now, the short term is getting the spend to a comfortable amount that we’re, happy with.
198 00:20:11.240 ⇒ 00:20:12.110 Amber Lin: I see.
199 00:20:13.160 ⇒ 00:20:17.150 Uttam Kumaran: Yeah, I also wonder, Amber, and I don’t know, Matt, if you’ve done this, but have you looked at, like, cross…
200 00:20:17.420 ⇒ 00:20:26.199 Uttam Kumaran: sort of platform, like, I don’t think we’re getting any information from Uber or DoorDash on who those customers are, right? And, like, how they’re moving over.
201 00:20:28.160 ⇒ 00:20:36.759 Matt: We kinda have… So, I mean, have you heard the name Robert Cantor before?
202 00:20:37.070 ⇒ 00:20:37.710 Uttam Kumaran: Yeah.
203 00:20:38.040 ⇒ 00:20:45.250 Matt: Yeah, so I think he kind of has some data on, like, customer acquisition through these platforms.
204 00:20:46.220 ⇒ 00:20:56.959 Matt: And kind of, like, tracking them, so I think he’s the person to go to, but from my understanding is that people that are ordering us from FDAs aren’t going to us anywhere else.
205 00:20:57.150 ⇒ 00:20:58.909 Matt: And kind of, like, vice versa.
206 00:21:01.650 ⇒ 00:21:02.670 Amber Lin: Let’s see.
207 00:21:04.000 ⇒ 00:21:07.519 Amber Lin: Yeah, because I originally wanted to look at if…
208 00:21:07.690 ⇒ 00:21:18.159 Amber Lin: their first purchase was indeed on FDA or somewhere else, but I wasn’t able to see that because, like, the only thing I could link people together was their emails.
209 00:21:18.240 ⇒ 00:21:29.390 Amber Lin: So I don’t know if they hop in between, if it… if it even matters to them, if they hop between, so that could be interesting to look in if we were able to put that together.
210 00:21:29.890 ⇒ 00:21:36.319 Matt: Yeah, and we tried to do a test in the summer for, I think it was 4 weeks,
211 00:21:36.480 ⇒ 00:21:48.430 Matt: where if a store… I think we used one district, so it must have been, like, something like Texas or something, maybe even, like, Dallas area, that if, like, a store got…
212 00:21:48.690 ⇒ 00:21:51.130 Matt: an order from an FDA,
213 00:21:51.810 ⇒ 00:22:08.119 Matt: could they then, like, place a cookie in there with, like, a sticker, and be like, you know, you want more free treats like this? Like, sign up for us, like, on our awards. And then hopefully, like, that would be, like, a way to convert people, but I don’t think…
214 00:22:08.580 ⇒ 00:22:12.219 Matt: I think the test was kind of rushed, and we didn’t really have a good way to track.
215 00:22:12.220 ⇒ 00:22:13.670 Amber Lin: impact. I see.
216 00:22:14.060 ⇒ 00:22:14.820 Matt: Yeah.
217 00:22:17.190 ⇒ 00:22:22.739 Amber Lin: Yeah, sounds good. I mean, that’s my questions for now on FDA,
218 00:22:23.600 ⇒ 00:22:40.779 Amber Lin: if there’s some… like, is there something we can immediately help you on? Either give you data, or help with the measurement, or help with analysis that will really help, make an impact right now? Because I know you’re working on the spend, and that seems like something we can help with.
219 00:22:41.150 ⇒ 00:22:42.759 Matt: Yeah,
220 00:22:43.870 ⇒ 00:22:50.580 Matt: So, I mean, I might have more of an ask for you once we are digging deeper into this test, but,
221 00:22:50.700 ⇒ 00:22:57.370 Matt: Something if… so… I mean, do you, do you guys, I know for the, for the, like.
222 00:22:57.970 ⇒ 00:23:07.450 Matt: actuals that you pull, that’s just a web speaker, right? But are you guys, like, in the systems, able to, like, download reports and, like, manipulate data in it that way, too?
223 00:23:08.550 ⇒ 00:23:21.129 Uttam Kumaran: Yeah, we can do that. Yeah, exactly. So, we just support, through scraping, like, whatever needs for the daily stuff, but we’re able to do, like, a lot more, like, ad hoc analysis on, like, a much larger data set.
224 00:23:21.250 ⇒ 00:23:28.490 Uttam Kumaran: Which is, like, kind of, like, what ends up in some of the slides that we’re sending. So totally, if you have those, or if you’re like, hey.
225 00:23:28.780 ⇒ 00:23:32.600 Uttam Kumaran: Like, let’s use these datasets to go, like, yeah, that’s exactly what we would do.
226 00:23:33.050 ⇒ 00:23:39.680 Matt: So, something that we’re trying to get an understanding of is…
227 00:23:40.550 ⇒ 00:23:48.500 Matt: If there’s a way to see how our spend is being, like, split into, like, by store.
228 00:23:49.220 ⇒ 00:23:49.680 Amber Lin: Like, yeah.
229 00:23:49.680 ⇒ 00:24:05.610 Matt: DoorDash, we set a budget. Uber, we kind of just give an offer, and it, like, fluctuates, because the only way that you can set a budget for a promotional offer on Uber is by, like, setting a…
230 00:24:05.770 ⇒ 00:24:22.039 Matt: weekly budget by store, so it’s, like, different, so we never really do that, and I can’t remember if Kelsey said there was a reason why, I don’t know if we were, like, worried about it hitting that cap and then not maximizing up the possible
231 00:24:22.260 ⇒ 00:24:28.750 Matt: Revenue or whatever, but we are looking to get an understanding of
232 00:24:29.130 ⇒ 00:24:34.910 Matt: How is the spend, like, breaking down onto, like, a per-store level basis?
233 00:24:35.340 ⇒ 00:24:38.229 Matt: So if that is…
234 00:24:38.390 ⇒ 00:24:54.120 Matt: if there’s a way to download reports, and I mean, there’s a way to download reports. I don’t know if they go into that granular detail or not. And if there is, being able to organize that, because we would love to see…
235 00:24:54.500 ⇒ 00:24:55.720 Matt: That…
236 00:24:55.950 ⇒ 00:25:07.229 Matt: kind of in coordination where, like, with these test stores, right? Because if you broke it down, theoretically, these stores that are part of the incrementality test should not be receiving any spend associated with them.
237 00:25:07.520 ⇒ 00:25:14.129 Matt: And then we can look at that relative to, like, gross sales and, impact on that.
238 00:25:14.300 ⇒ 00:25:18.130 Matt: Particular store’s, results.
239 00:25:20.410 ⇒ 00:25:21.130 Amber Lin: Gotcha, okay.
240 00:25:21.130 ⇒ 00:25:35.529 Matt: So, is that something that you guys would want me to kind of just download the reports and then send to you and see if you guys are able to find that? Or are you guys, like, in the platforms and you’re gonna… you would want to, like, try and download the reports yourself?
241 00:25:35.530 ⇒ 00:25:37.269 Uttam Kumaran: If you can download them.
242 00:25:37.270 ⇒ 00:25:39.960 Matt: Yeah. And either just indicate where you got them.
243 00:25:39.960 ⇒ 00:25:51.070 Uttam Kumaran: I mean, ideally, both, like, we’ll… but as long as you… if you can… if you can download them and send them to us, that’s perfect, and then we can take it from there if you just indicate where you got them from. That’s perfect. Yeah, that’s a great use case.
244 00:25:51.410 ⇒ 00:25:52.480 Matt: Okay, cool.
245 00:25:54.100 ⇒ 00:25:54.860 Amber Lin: Awesome.
246 00:25:55.180 ⇒ 00:26:12.719 Amber Lin: Okay, that’s… I think that would be the next step on FDA. I know we’re a little bit short of time, and I did take a look at the punch data. I went in and went on the platform and saw, the campaigns, and then I saw the different types of offers and rewards.
247 00:26:12.840 ⇒ 00:26:15.050 Amber Lin: Let’s see…
248 00:26:15.940 ⇒ 00:26:35.039 Amber Lin: That’s my first question, because I was also in Braze quite a bit, and there’s also campaigns in Punch. Are those the same as Braze, or do you… does someone import the campaigns you create here, and then import the Punch campaigns into Braze? Like, how does that work?
249 00:26:35.300 ⇒ 00:26:42.929 Matt: So, campaigns and Braze,
250 00:26:44.070 ⇒ 00:26:51.390 Matt: So, it’s two different functions. So, if campaigns and Braves are…
251 00:26:51.630 ⇒ 00:26:55.360 Matt: like, communication campaigns, right? So they’re… it’s…
252 00:26:55.480 ⇒ 00:27:11.759 Matt: a way that we’re communicating to the customer, whether it’s through a content card, or through a push message, or a SMS, or an email, that’s, like, what campaigns are referring to in Braze, whereas campaigns and punch, that basically means, like, offer.
253 00:27:12.700 ⇒ 00:27:15.999 Amber Lin: Oh, okay. So, when I set up the campaign punch.
254 00:27:16.010 ⇒ 00:27:32.320 Matt: That is what is distinguishing, like, for this past week, we did our Thankful Thursday campaign. The campaign and punch is me saying, okay, people that spent $5 in a transaction from Thursday morning to Saturday morning at 3 AM.
255 00:27:32.770 ⇒ 00:27:35.959 Matt: Like these people a two free classic cookie reward.
256 00:27:36.650 ⇒ 00:27:43.960 Amber Lin: That’s what campaign refers to in punch. Okay. Whereas Braze, the campaign is just, oh, gotcha. Okay.
257 00:27:43.960 ⇒ 00:27:45.550 Matt: It’s two different things, yeah.
258 00:27:45.550 ⇒ 00:27:51.650 Amber Lin: And all communications about these campaigns are sent through the app, right?
259 00:27:51.650 ⇒ 00:27:58.339 Matt: So, communications, that are being, like, targeted to customers.
260 00:27:58.730 ⇒ 00:28:00.020 Matt: That’s off their brains.
261 00:28:00.530 ⇒ 00:28:06.119 Matt: And if it’s, like, a content card, or…
262 00:28:06.220 ⇒ 00:28:09.300 Matt: So content cards, like, show on our website or our ads.
263 00:28:10.140 ⇒ 00:28:17.149 Matt: But if it’s, if it’s push notifications, that is technically, like, through our app, but Braze is, like, the engine sending it.
264 00:28:17.580 ⇒ 00:28:22.550 Matt: And then, yeah, email and then SMS, that’s just done directly through Braze.
265 00:28:22.850 ⇒ 00:28:24.610 Matt: Crunch…
266 00:28:24.610 ⇒ 00:28:34.259 Amber Lin: Yeah, go ahead. So if we had, say, this Thankful Thursday, we would have all the different communications through those channels about.
267 00:28:34.260 ⇒ 00:28:40.399 Matt: Oh, that, if you clicked in that and punch, that won’t tell you anything about communications, because communications are.
268 00:28:40.400 ⇒ 00:28:40.790 Amber Lin: No.
269 00:28:40.790 ⇒ 00:28:41.430 Matt: Grace.
270 00:28:42.590 ⇒ 00:28:53.940 Matt: So, that, if you clicked on that campaign, what it is gonna tell you, it’s gonna tell you the, different statistics that, are related to…
271 00:28:54.810 ⇒ 00:28:58.310 Matt: Just, like, how that offer performed.
272 00:29:00.430 ⇒ 00:29:06.569 Matt: And I haven’t performed in all, like, the customers that it was, sent to, so it would,
273 00:29:07.320 ⇒ 00:29:09.000 Matt: I’m trying to pull it up here.
274 00:29:09.320 ⇒ 00:29:09.890 Amber Lin: Yeah, I…
275 00:29:09.890 ⇒ 00:29:11.670 Matt: It’ll show you… I can show you.
276 00:29:11.670 ⇒ 00:29:12.030 Amber Lin: too.
277 00:29:12.030 ⇒ 00:29:23.550 Matt: It would show you net sales, and campaign sales, and AOV, and the redeem rate, and, like, the discount, and all that stuff. It… that’s, completely different stuff than what Braze is showing, yeah.
278 00:29:23.550 ⇒ 00:29:24.410 Amber Lin: Yeah.
279 00:29:24.930 ⇒ 00:29:25.880 Amber Lin: Okay.
280 00:29:28.680 ⇒ 00:29:29.850 Amber Lin: Cool, okay.
281 00:29:30.520 ⇒ 00:29:35.159 Amber Lin: Let me see… oh, I know we’re at time.
282 00:29:36.110 ⇒ 00:29:40.449 Amber Lin: Do you have anything after this? I have one last question on the website.
283 00:29:41.140 ⇒ 00:29:44.950 Matt: Yeah, you can ask your question until I get kicked out of my meeting room here.
284 00:29:45.290 ⇒ 00:29:52.449 Amber Lin: Okay, we were just talking with, Bertie and Marita about things we can
285 00:29:52.490 ⇒ 00:30:12.460 Amber Lin: experiments we can implement, and especially if we want to have the website show different things to… a new customer sees this, and a returning customer sees something different, because we want them to maybe get more boxes and get bigger boxes. Do you think that’s possible? Like, what type of segmentation
286 00:30:12.480 ⇒ 00:30:15.300 Amber Lin: Is possible on the website.
287 00:30:16.430 ⇒ 00:30:20.159 Matt: So, when you say show things on the website, are your friends, like, the hero bands?
288 00:30:20.260 ⇒ 00:30:27.599 Amber Lin: I believe Birdie O’Shael mentioned different parts of the website have different
289 00:30:28.050 ⇒ 00:30:33.530 Amber Lin: There’s different things you can do, but say the offers,
290 00:30:34.300 ⇒ 00:30:40.019 Amber Lin: let’s say, I guess the… for example, the made-for-you section.
291 00:30:40.020 ⇒ 00:30:55.009 Matt: For that? No, we cannot target for that. We cannot target anything that’s, like, a menu section. That is just designated based off of, what store that you have selected has in stock.
292 00:30:55.110 ⇒ 00:30:56.230 Amber Lin: Huh.
293 00:30:56.250 ⇒ 00:31:07.309 Matt: So that would change, like, you’re in LA, your prices are gonna be more expensive than, what is out of season in, Texas. But…
294 00:31:08.210 ⇒ 00:31:11.230 Matt: So that’s the only, like, kind of dynamic thing.
295 00:31:11.230 ⇒ 00:31:11.619 Amber Lin: It’s just been.
296 00:31:11.620 ⇒ 00:31:16.750 Matt: on location. And then…
297 00:31:18.730 ⇒ 00:31:26.279 Matt: So, the hero banners, though, like the, content cards, now that is something that we could possibly, change, like, the targeting.
298 00:31:26.430 ⇒ 00:31:38.909 Matt: We could possibly change that based off of, like, rewards, or, whatever… whatever functionality Birdie has for targeting an email, we could basically do it for our content card.
299 00:31:38.910 ⇒ 00:31:40.280 Amber Lin: Oh, awesome, okay.
300 00:31:40.690 ⇒ 00:31:47.369 Amber Lin: And Content Car is the one that scrolls on top, right?
301 00:31:48.100 ⇒ 00:31:50.540 Matt: Correct? Yeah. Okay. It’s the,
302 00:31:50.720 ⇒ 00:31:56.119 Matt: It’s the Carousel era banners there on the top, but then it’s also, like, the ribbon banner that’s, like, a.
303 00:31:56.120 ⇒ 00:31:56.600 Amber Lin: It’s something.
304 00:31:56.600 ⇒ 00:31:57.380 Matt: Yeah.
305 00:31:57.380 ⇒ 00:32:07.369 Amber Lin: Awesome. Okay, that’s very helpful, because I’m… I’m calling Birdie in a few hours, and I… I need to… I want to give her some recommendations on what we can do, so this is very helpful.
306 00:32:07.580 ⇒ 00:32:11.870 Matt: And just, just so you know, I… I handle the content cards.
307 00:32:11.870 ⇒ 00:32:12.699 Amber Lin: Mmm, okay.
308 00:32:12.700 ⇒ 00:32:19.080 Matt: And bird does everything else in Braze. So that’s, like, the only part of Breeze that I really touch, but…
309 00:32:19.440 ⇒ 00:32:20.160 Amber Lin: I see.
310 00:32:22.010 ⇒ 00:32:33.730 Amber Lin: Thank you so much for your time, this is really, really helpful. We’ll follow up on the FDA data, so if you can send us the stuff you download, we can follow up on that.
311 00:32:33.730 ⇒ 00:32:37.289 Matt: I’ll probably do it this afternoon, because I’m looking to dig into that myself, too.
312 00:32:37.290 ⇒ 00:32:37.870 Amber Lin: Cool.
313 00:32:37.870 ⇒ 00:32:38.430 Matt: Yup.
314 00:32:39.080 ⇒ 00:32:41.070 Matt: Alright, thank you very much. Thank you so much.
315 00:32:41.320 ⇒ 00:32:41.860 Matt: Have a good one.
316 00:32:41.860 ⇒ 00:32:42.330 Uttam Kumaran: Thank you!
317 00:32:42.330 ⇒ 00:32:43.450 Amber Lin: Bye!