Meeting Title: Marketing Analytics Eden Data Sync Date: 2026-03-05 Meeting participants: Ryon, Zoran Selinger, Mitesh Patel
WEBVTT
1 00:00:00.000 ⇒ 00:00:03.510 Ryon: This report here… is sort of…
2 00:00:05.910 ⇒ 00:00:09.459 Ryon: It’s kind of telling me a different story.
3 00:00:09.630 ⇒ 00:00:11.710 Ryon: Well…
4 00:00:11.980 ⇒ 00:00:12.690 Zoran Selinger: Yeah, so…
5 00:00:12.690 ⇒ 00:00:13.780 Ryon: And different.
6 00:00:13.980 ⇒ 00:00:16.440 Zoran Selinger: This is all old-time data.
7 00:00:16.560 ⇒ 00:00:29.689 Zoran Selinger: Right. Okay, so that’s why, like, Meta will be a little bit light still, and TikTok will be light in this report, because we only started, what, 35, 40 days?
8 00:00:31.150 ⇒ 00:00:34.969 Zoran Selinger: since we have meta, yeah, since we have meta.
9 00:00:35.220 ⇒ 00:00:36.460 Zoran Selinger: So…
10 00:00:38.860 ⇒ 00:00:39.230 Ryon: So…
11 00:00:39.230 ⇒ 00:00:41.170 Zoran Selinger: Look a little bit light here.
12 00:00:41.350 ⇒ 00:00:45.009 Ryon: This report here is looking at things that seems like the campaign level.
13 00:00:45.510 ⇒ 00:00:46.120 Ryon: Right?
14 00:00:46.120 ⇒ 00:00:55.070 Zoran Selinger: I mean, yeah, so every combination of source medium will be in there. That’s the breakdown.
15 00:00:55.070 ⇒ 00:00:57.629 Ryon: So then this one here, times 2, what is that referring to?
16 00:00:57.630 ⇒ 00:01:03.749 Zoran Selinger: That means… that means someone visited via Google CPC two times before converted.
17 00:01:03.750 ⇒ 00:01:04.840 Ryon: Okay.
18 00:01:04.980 ⇒ 00:01:11.890 Ryon: So, when I see things like this right here, this means this was a stacked campaign.
19 00:01:12.580 ⇒ 00:01:17.070 Ryon: Right? So, in other words, they went to Google first, then they went to a Basque email.
20 00:01:17.170 ⇒ 00:01:19.479 Ryon: Basically. Is that what I’m understanding here?
21 00:01:19.750 ⇒ 00:01:25.589 Zoran Selinger: Yeah, exactly, yeah. So these are all the… all the touchpoints they had.
22 00:01:25.960 ⇒ 00:01:31.120 Zoran Selinger: obviously, it’s not gonna be all the touchpoints. Sometimes they will,
23 00:01:31.720 ⇒ 00:01:38.170 Zoran Selinger: So, like I said, if there is an existing session, we do not record, again.
24 00:01:39.210 ⇒ 00:01:51.279 Zoran Selinger: So only if there is no current session. So, yeah, we do not see every single interaction, but we mostly see every campaign interaction here.
25 00:01:51.780 ⇒ 00:01:53.799 Ryon: Anything that is tagged, yeah.
26 00:01:54.180 ⇒ 00:01:58.099 Ryon: Okay, so… Sorry, for whatever reason, my eyes are itching right now.
27 00:02:03.240 ⇒ 00:02:14.250 Ryon: When I look at this here, basically what I’m sort of seeing is that the concept, or the idea that we have, that we are predominantly a multi-channel
28 00:02:15.720 ⇒ 00:02:17.000 Ryon: like, company.
29 00:02:17.220 ⇒ 00:02:19.859 Ryon: Is… not really true.
30 00:02:20.220 ⇒ 00:02:27.999 Ryon: The first multi-channel set of conversion pathways that I see is right here.
31 00:02:28.110 ⇒ 00:02:29.470 Ryon: Row 15.
32 00:02:29.610 ⇒ 00:02:36.420 Ryon: Most other pathways, it would seem, are people going back to the same channel
33 00:02:36.970 ⇒ 00:02:47.140 Ryon: a lot of times. Like, CPC here, CPC twice, affiliate once, email once, CPC again three times, email twice, CPC four times…
34 00:02:47.140 ⇒ 00:02:58.379 Zoran Selinger: Yeah, yeah, you see a lot of that. So, row 22, for example, you have Catalyst starting, then ending on Google CPC.
35 00:03:07.870 ⇒ 00:03:08.700 Ryon: Alright.
36 00:03:11.920 ⇒ 00:03:19.340 Ryon: So… I guess I’m trying to reconcile this with Northbean.
37 00:03:19.790 ⇒ 00:03:26.340 Ryon: Right. When I look at this here, it sort of tracks, right, the conversion, the channels.
38 00:03:29.450 ⇒ 00:03:33.180 Ryon: That are driving the most revenue are generally single
39 00:03:33.740 ⇒ 00:03:37.639 Ryon: Conversion channels, meaning, like, the person does not leave that channel.
40 00:03:37.840 ⇒ 00:03:48.640 Zoran Selinger: some of the other channels, if I come down here to, like, Facebook… Yeah, because… and you see from the path length and days to conversion, most cases, it’s going to be the same day.
41 00:03:48.890 ⇒ 00:03:50.190 Zoran Selinger: conversion.
42 00:03:50.480 ⇒ 00:03:52.450 Zoran Selinger: Okay. In most cases, yeah.
43 00:03:52.450 ⇒ 00:03:57.120 Ryon: So the first thing I ask is, where’s Facebook in this? Can I filter this by Facebook?
44 00:03:57.670 ⇒ 00:04:05.900 Zoran Selinger: Yeah, if you click on those three dots, do you have any filtering options there?
45 00:04:06.670 ⇒ 00:04:07.940 Zoran Selinger: You don’t.
46 00:04:11.330 ⇒ 00:04:15.800 Zoran Selinger: Okay, so let me… let me just take… take a note of that.
47 00:04:17.089 ⇒ 00:04:19.149 Ryon: Okay, so I’m just gonna look at…
48 00:04:19.789 ⇒ 00:04:22.719 Ryon: It alphabetically real quick cameo, which…
49 00:04:22.939 ⇒ 00:04:24.939 Ryon: I can only go down to 50.
50 00:04:25.289 ⇒ 00:04:26.789 Ryon: Can I download this?
51 00:04:26.959 ⇒ 00:04:28.309 Ryon: Let me download this.
52 00:04:33.429 ⇒ 00:04:34.999 Ryon: possibilities.
53 00:04:36.850 ⇒ 00:04:38.080 Zoran Selinger: Oh, Mitesh is here.
54 00:04:38.410 ⇒ 00:04:41.919 Ryon: Yeah, I’m going… I actually pinged in my house, I was hoping he would arrive.
55 00:04:41.920 ⇒ 00:04:42.730 Zoran Selinger: varied dish.
56 00:04:42.730 ⇒ 00:04:43.240 Mitesh Patel: Hello!
57 00:04:43.240 ⇒ 00:04:45.460 Ryon: So, here’s what I’m getting at here.
58 00:04:45.590 ⇒ 00:04:51.100 Ryon: I’m trying to make sure that what we see in Northbeam is the same as what we see here in this report, which…
59 00:04:51.100 ⇒ 00:05:05.790 Ryon: Broadly, at a high level, it kind of seems like it is, right? Even though this report is definitely more, like, detailed, like, it gets down to a lower level than what I… what Northbean does. But if I was to look for… I think it’s just Facebook.
60 00:05:07.110 ⇒ 00:05:08.210 Zoran Selinger: No, it’s Meta.
61 00:05:08.210 ⇒ 00:05:08.640 Ryon: Mera?
62 00:05:08.640 ⇒ 00:05:10.860 Zoran Selinger: Paid… Meta Paid Social.
63 00:05:12.670 ⇒ 00:05:25.980 Ryon: So… Alright, so here is… the number of conversions that came through Meta Paid Social alone. 19.
64 00:05:26.100 ⇒ 00:05:31.410 Ryon: And revenue of $4,260. Now, this is using edge layer data, I assume?
65 00:05:31.640 ⇒ 00:05:33.349 Zoran Selinger: This is old age, this is just…
66 00:05:33.350 ⇒ 00:05:33.760 Ryon: knowledge.
67 00:05:33.800 ⇒ 00:05:35.110 Zoran Selinger: Each table, yeah.
68 00:05:35.110 ⇒ 00:05:38.999 Ryon: But, aside from this, right, I see…
69 00:05:39.300 ⇒ 00:05:58.370 Ryon: a total of 19 or so conversions that went through some other channel and also Meta, right? So, proportionally, if I round this up to 20, and round this up to 20, that basically means 50% of all of the users who eventually end up converting
70 00:05:58.370 ⇒ 00:06:13.939 Ryon: are coming through some other channel in addition to Facebook. So unlike all the other channels that drive the most revenue for us, Facebook is a multi-channel approach, right? Am I correct? Is that statement correct? Is that how I’m reading this correctly?
71 00:06:15.780 ⇒ 00:06:16.819 Zoran Selinger: Yeah, right?
72 00:06:16.820 ⇒ 00:06:21.360 Mitesh Patel: But this is… Meta has direct conversions, and it has.
73 00:06:21.360 ⇒ 00:06:22.150 Ryon: No, no, okay.
74 00:06:22.150 ⇒ 00:06:24.920 Mitesh Patel: Contribution, conversions as well.
75 00:06:24.920 ⇒ 00:06:25.900 Ryon: Right, I just…
76 00:06:25.900 ⇒ 00:06:26.600 Mitesh Patel: don’t.
77 00:06:26.800 ⇒ 00:06:33.180 Ryon: That I want to make sure, because this also seems to say that,
78 00:06:33.790 ⇒ 00:06:52.789 Ryon: you know, Meta is not getting a lot of credit for the things that it gets credit for, so I want to make sure we’re… I’m looking at this data correctly and I’m reconciling it correctly. The only thing I’m a little confused by is the number of conversions. Of course, the way these things are counting is different, right? The way they’re counting is different, but,
79 00:06:55.820 ⇒ 00:07:05.770 Ryon: I guess what I’m saying is, like, we should be very careful in thinking of Meta as anything other than a support platform, because it’s.
80 00:07:05.770 ⇒ 00:07:07.099 Mitesh Patel: It does both.
81 00:07:07.320 ⇒ 00:07:12.580 Mitesh Patel: It needs to have direct contribution, and it has indirect contribution.
82 00:07:14.100 ⇒ 00:07:20.360 Zoran Selinger: Yeah, Ron, you might remember that I pushed back on last click a lot.
83 00:07:21.090 ⇒ 00:07:30.450 Zoran Selinger: And the way Mitesh counter-argued is because I was treating Meta as top
84 00:07:30.700 ⇒ 00:07:42.509 Zoran Selinger: top of the funnel channel, and they see it more as, basically, every level of the funnel should be served at some point by Meta.
85 00:07:44.160 ⇒ 00:07:48.320 Ryon: So… I guess what I’m getting at here, and this… this is…
86 00:07:49.460 ⇒ 00:07:58.129 Ryon: I know you guys know this, this is all obvious statements, we’re all in alignment. What I’m trying to get at here is, if I just look at the last 30 days, not year-to-date.
87 00:07:59.400 ⇒ 00:08:04.530 Ryon: for me, I’m trying to decide, when do I raise a red flag here?
88 00:08:04.670 ⇒ 00:08:18.889 Ryon: I can see from the data here, which, this is good data, I can see this data, that 50% of all conversions for Meta are coming through other channels. Makes sense, everyone agrees, not at all a problem.
89 00:08:19.240 ⇒ 00:08:27.100 Ryon: But I can also see here that we’ve spent 65,000, and we haven’t seen anything really from it overall
90 00:08:27.230 ⇒ 00:08:39.129 Ryon: That would compel me to believe that this channel is acquiring the healthy traffic. So, to use our, and I ask, when do we raise a red flag here? Like, if I’m… if I’m the…
91 00:08:39.299 ⇒ 00:08:51.590 Ryon: big guy here, and I’m trying to say, like, this a problem is not a problem. Like, I’m looking at Northbeam, I’m looking at this data now from Omni here, which is good data, and I guess I’m a little worried or concerned that we’re not seeing the results we need to be seeing.
92 00:08:52.250 ⇒ 00:09:10.159 Mitesh Patel: Yeah, Ryan, you don’t need to worry about raising that red flag. I think that’s what the channel owners are gonna be accountable to, okay? That’s something, Josh and I are managing, and if, in this case, Ghost doesn’t deliver.
93 00:09:10.290 ⇒ 00:09:14.200 Mitesh Patel: You know, we’re gonna give them another, another month, they don’t deliver, they’ll be out.
94 00:09:14.380 ⇒ 00:09:15.860 Mitesh Patel: And… I understand, but…
95 00:09:15.860 ⇒ 00:09:23.809 Ryon: they’re coming to me for insights, which is why I’m asking to make sure that I understand the data correctly. So what are the insights they’re looking at?
96 00:09:23.810 ⇒ 00:09:25.469 Zoran Selinger: Yeah, Doc, I’m really curious.
97 00:09:25.470 ⇒ 00:09:45.760 Ryon: The agencies are coming to me asking for, hey, can you help us get access to platforms, or can you help us understand the data a little better? In particular, okay, Odyssey is asking me to build a dashboard, so I asked Zoron to build a channel dashboard that we can copy and, you know, create for every single one of the channels inside of Mixpanel, so they can compare the Northbeam data against the Mixpanel data.
98 00:09:45.760 ⇒ 00:09:47.180 Ryon: Right? So…
99 00:09:47.650 ⇒ 00:09:59.279 Ryon: at some point, people are gonna come knocking on my door and try and understand what they’re looking at, or if there is more to the North Beam data, okay? So I guess what I’m trying to get at here is.
100 00:09:59.280 ⇒ 00:10:09.370 Ryon: A, I need to be prepared and understand the data enough to give them, like, a broad answer. My opinion of them so far is they all have… they both have north of a 90% bounce rate.
101 00:10:09.430 ⇒ 00:10:17.800 Ryon: Like, 100%, that’s just crap traffic, and it’s not doing anything, okay? But also, I see a lot of this…
102 00:10:18.360 ⇒ 00:10:26.020 Ryon: all over the place, kind of thing, so I don’t really want to be, you know, too harsh and saying, like, well, you know, you guys aren’t doing nothing. Like, they’re doing something.
103 00:10:26.020 ⇒ 00:10:39.130 Ryon: But it’s not a great, you know, thing. And I can also see over here. So, I’m just trying to get alignment so that we’re all on the same page. I don’t really care who holds them accountable, I don’t really care whose job it is to say that they’re working or not working, I’m just trying to make sure that I understand the data well enough to say, like, yep.
104 00:10:39.130 ⇒ 00:10:53.470 Mitesh Patel: Right, so understand the data with… and explain the data, how it works, point them to the documentation, connect them with Eric, and you guys join as well, to help them understand the data and how it’s being measured.
105 00:10:53.920 ⇒ 00:11:02.449 Mitesh Patel: That’s what they’re asking you for, right? Help them with that, or point them to the North Beam documentation, like we did when I was on the call with them.
106 00:11:02.790 ⇒ 00:11:07.159 Mitesh Patel: I don’t think… I think the discussion about red flags is separate from that.
107 00:11:09.300 ⇒ 00:11:09.970 Ryon: Okay.
108 00:11:11.370 ⇒ 00:11:17.949 Zoran Selinger: I think, Ryan, what you said last week about just coming to them and supporting them with
109 00:11:18.280 ⇒ 00:11:20.040 Zoran Selinger: Forget about,
110 00:11:20.370 ⇒ 00:11:36.730 Zoran Selinger: report on top of a report on top of a report. Let’s give them events, let’s give them audiences that we can give them… something that they can actually use in campaigns, because it looks like they do… they… they don’t, like, the…
111 00:11:37.180 ⇒ 00:11:42.430 Zoran Selinger: The audiences that they are targeting, the traffic that they are getting, both conversion.
112 00:11:42.490 ⇒ 00:12:02.260 Zoran Selinger: conversion events show, and the engagement that Ron looked at, it really does look like, it’s bad traffic for now. So, what Ron said last week is right. We just, let’s give them as much support as we need, but not in this, okay, can you give me a report here or there? No.
113 00:12:03.070 ⇒ 00:12:12.549 Zoran Selinger: Let’s give them converters, converter audiences, let’s give them, like, specific engagements on the website audiences, stuff like that.
114 00:12:12.940 ⇒ 00:12:17.490 Zoran Selinger: So they can actually, so they can actually.
115 00:12:18.710 ⇒ 00:12:19.100 Mitesh Patel: Rice.
116 00:12:19.100 ⇒ 00:12:22.959 Zoran Selinger: Start getting existing audiences, stop getting existing audiences.
117 00:12:23.170 ⇒ 00:12:27.210 Mitesh Patel: Ryan, earlier you mentioned, when we were looking at this,
118 00:12:27.380 ⇒ 00:12:30.699 Mitesh Patel: You said this is different from…
119 00:12:31.080 ⇒ 00:12:37.219 Mitesh Patel: the GA4’s conversion path report, because this was more at the campaign level, not channel level.
120 00:12:37.220 ⇒ 00:12:50.939 Zoran Selinger: Yeah, so that GA Google Analytics report, you could actually do exactly the same thing. You could have chosen a different dimension. So this is UTM source and UTM medium.
121 00:12:51.080 ⇒ 00:12:53.090 Zoran Selinger: Combination.
122 00:12:53.380 ⇒ 00:12:54.340 Zoran Selinger: Obviously so.
123 00:12:54.340 ⇒ 00:12:54.979 Ryon: I do make clothes.
124 00:12:54.980 ⇒ 00:12:57.030 Zoran Selinger: channels instead of this.
125 00:12:57.030 ⇒ 00:13:07.190 Ryon: I understood your request, Mitesh, to only want the channel itself, which would be UTM Source, which I think that’s probably pretty easy to update, you just wouldn’t see something like search CPC, and you know, you’d aggregate…
126 00:13:07.190 ⇒ 00:13:11.870 Mitesh Patel: Well, no, you can’t aggregate it, because I can’t, you know, I actually need,
127 00:13:12.040 ⇒ 00:13:19.470 Mitesh Patel: like, Google Organic versus Google Search CPC versus Google Shopping are all different.
128 00:13:19.870 ⇒ 00:13:22.949 Zoran Selinger: Yeah, and you’ll see that this way. You’ll see it that way.
129 00:13:23.090 ⇒ 00:13:33.890 Ryon: You’ll see it that way, so if this is good enough for you, that’s fine. I understood your request to want to aggregate things by channel, which campaign doesn’t matter at that point, but this is breaking it down to medium and.
130 00:13:33.890 ⇒ 00:13:37.979 Mitesh Patel: So, okay, help me understand this data a little better then, Zoran.
131 00:13:39.080 ⇒ 00:13:46.099 Mitesh Patel: First one says, a customer saw a search ad and clicked on it and converted.
132 00:13:46.590 ⇒ 00:13:52.879 Mitesh Patel: Yes. The second one says they clicked on two search ads before converting.
133 00:13:52.880 ⇒ 00:13:53.710 Zoran Selinger: Exactly.
134 00:13:53.710 ⇒ 00:13:56.720 Mitesh Patel: Over how much time, what’s the attribution window here?
135 00:13:56.720 ⇒ 00:14:05.479 Zoran Selinger: So here, I mean, that table doesn’t show you that. So it’s across lifetime. We… we… this is across lifetime.
136 00:14:06.300 ⇒ 00:14:10.160 Zoran Selinger: And you see, like, the aggregate, you see the path length.
137 00:14:10.550 ⇒ 00:14:17.930 Zoran Selinger: You see the days to conversion. So this is all aggregate, this is all the edge… edge data that we have at the moment.
138 00:14:17.930 ⇒ 00:14:18.530 Mitesh Patel: Okay.
139 00:14:19.240 ⇒ 00:14:19.820 Zoran Selinger: Oh.
140 00:14:20.480 ⇒ 00:14:21.300 Zoran Selinger: I already noticed.
141 00:14:22.030 ⇒ 00:14:26.129 Zoran Selinger: I’ll need to add some… some filters here, like…
142 00:14:26.780 ⇒ 00:14:30.940 Zoran Selinger: date selection and all that. So this is basically just version 1.
143 00:14:32.010 ⇒ 00:14:32.780 Mitesh Patel: Okay.
144 00:14:36.210 ⇒ 00:14:44.319 Zoran Selinger: That’s basically exact replica of what was in, what was in Google Analytics.
145 00:14:44.480 ⇒ 00:14:54.209 Zoran Selinger: I also like this report. The data that I see here, especially for pet length and days to conversion, is very typical.
146 00:14:54.340 ⇒ 00:14:57.399 Zoran Selinger: Nothing really surprises me there too much.
147 00:14:59.760 ⇒ 00:15:07.529 Zoran Selinger: And if you scroll down, Orion, you’ll see, like, top starting, top first touch, and top last touch.
148 00:15:08.060 ⇒ 00:15:09.510 Zoran Selinger: As well.
149 00:15:09.870 ⇒ 00:15:17.420 Zoran Selinger: So we are still, obviously, we are very, very, Google Ads, dominated,
150 00:15:17.420 ⇒ 00:15:18.160 Mitesh Patel: Yeah, of course.
151 00:15:18.400 ⇒ 00:15:37.870 Zoran Selinger: Makes, so far, at least. And yeah, I do expect these… these charts to change over the last, you know, so last month, plus, you know, next 6 months, as we introduce more… more channels. Yeah. Especially if we manage to…
152 00:15:37.870 ⇒ 00:15:40.479 Zoran Selinger: To get them enough support to actually
153 00:15:41.410 ⇒ 00:15:44.200 Zoran Selinger: work for, like, TikTok and Meta.
154 00:15:44.430 ⇒ 00:15:47.410 Zoran Selinger: I’d like to see a little bit more diverse,
155 00:15:47.780 ⇒ 00:15:55.340 Mitesh Patel: Hold on, let me… I have a bunch of questions. I just want… just for me to understand this better, you got conversions, unique con… or users, and revenue.
156 00:15:55.670 ⇒ 00:15:57.140 Mitesh Patel: I’m gonna share.
157 00:15:57.140 ⇒ 00:15:59.059 Ryon: Why don’t you share your screen, Mitesh, to make it easier?
158 00:15:59.510 ⇒ 00:16:00.200 Mitesh Patel: Yeah.
159 00:16:11.690 ⇒ 00:16:16.309 Mitesh Patel: how can we have 36 convert… see my row 38 here?
160 00:16:17.120 ⇒ 00:16:20.599 Mitesh Patel: How can we have 36 conversions with zero revenue?
161 00:16:23.440 ⇒ 00:16:24.400 Zoran Selinger: Okay, D.
162 00:16:31.190 ⇒ 00:16:33.160 Mitesh Patel: 28 conversions, 149.
163 00:16:33.190 ⇒ 00:16:34.780 Zoran Selinger: And 140…
164 00:16:35.120 ⇒ 00:16:43.290 Mitesh Patel: Yeah, some of this, you know, I just want to make sure the data, like, I wanna… we should, like, just make sure the data matches, right?
165 00:16:43.400 ⇒ 00:16:47.730 Mitesh Patel: So, like, when you… export this…
166 00:17:16.430 ⇒ 00:17:17.609 Zoran Selinger: 100.
167 00:17:24.109 ⇒ 00:17:29.899 Zoran Selinger: Yeah, numbers do look a little bit… I wasn’t looking at the numbers that much.
168 00:17:31.890 ⇒ 00:17:35.640 Zoran Selinger: I’ll have a look exactly what’s happening with revenue.
169 00:17:44.610 ⇒ 00:17:47.490 Mitesh Patel: Yeah, $50 AOV doesn’t make sense.
170 00:17:47.490 ⇒ 00:17:51.120 Zoran Selinger: Is that, yeah, what do we see across others?
171 00:17:57.150 ⇒ 00:18:00.119 Mitesh Patel: I think we just need to validate the data a bit, man.
172 00:18:00.930 ⇒ 00:18:02.410 Zoran Selinger: Yeah, yeah.
173 00:18:02.610 ⇒ 00:18:15.890 Mitesh Patel: And by the way, I really love this, right? So, I don’t want to just say… because this… but this is where my head goes right away. We’ve got to validate the, you know, the data, right? And Ryan knows this, and Ryan’s like, oh, look at this. I’m like, oh, that doesn’t make sense to me.
174 00:18:15.890 ⇒ 00:18:24.620 Zoran Selinger: No, that’s… I mean, that’s great. This is, like I said, this is version-wide. I wanted to see if you’re… if you’re really interested in this.
175 00:18:26.500 ⇒ 00:18:27.730 Zoran Selinger: I see that you are.
176 00:18:27.730 ⇒ 00:18:32.769 Mitesh Patel: Show me stuff like this, because I’ll be really interested in it, and then ask you for more and more.
177 00:18:34.340 ⇒ 00:18:40.150 Zoran Selinger: Let’s do… I mean, I already, I already put tickets in for next week.
178 00:18:41.550 ⇒ 00:18:56.129 Zoran Selinger: you mentioned you want to see Norvium data here, or something similar, so I’m going to explore that, as well. That’s already… and obviously, we are very happy that you like Omni. We do… we like Omni very much.
179 00:18:56.320 ⇒ 00:18:58.920 Zoran Selinger: So, absolutely not a problem.
180 00:18:59.080 ⇒ 00:19:03.859 Zoran Selinger: I will happily work in Omni.
181 00:19:04.830 ⇒ 00:19:13.770 Zoran Selinger: On that note, Ryan, you said… vendor-specific dashboards in MixPanel.
182 00:19:13.930 ⇒ 00:19:16.779 Zoran Selinger: Why MixPanel and not Omni?
183 00:19:17.350 ⇒ 00:19:24.210 Ryon: Great transition. So, I was chatting with Mitesh… I was chatting with Mitesh this morning. At a high level.
184 00:19:26.010 ⇒ 00:19:32.570 Ryon: you know, we have a lot of expectations for Mixpanel, but it turns out that maybe it’s just better to put the data into Omni.
185 00:19:32.570 ⇒ 00:19:54.189 Ryon: So I was kind of messaging you about that, before this call. And I know that at the moment, MixedPanel’s data kind of sits off on an island. It’s not even in the data warehouse anywhere, right? It just… it’s piped from segment on the client side through to, Mixpanel directly, and doesn’t ever touch the data warehouse. So, my question is, A,
186 00:19:54.610 ⇒ 00:20:07.379 Ryon: Does Omni have everything we need to replicate what we see in Mixpanel? And you and I can chat about that more if we need to. I think the short answer is it has most of it. It doesn’t have all of it, right?
187 00:20:08.330 ⇒ 00:20:14.400 Zoran Selinger: I mean, we don’t have, like, product analytics data without MixedPanel, at least.
188 00:20:14.900 ⇒ 00:20:17.110 Zoran Selinger: from what I understand, we have…
189 00:20:17.570 ⇒ 00:20:27.239 Zoran Selinger: a little bit of data from Google Analytics, but it’s not… it’s probably not full-fledged as Mixpanel.
190 00:20:27.240 ⇒ 00:20:32.649 Ryon: Well, can we just send the data that Mixpanel is getting through to the data warehouse, and then pull it into Omni that way?
191 00:20:33.460 ⇒ 00:20:45.759 Zoran Selinger: I mean, theoretically, yes, but what I said in the message is we are essentially trying to recreate the functionality of a tool, like Mixpanel.
192 00:20:46.190 ⇒ 00:20:49.199 Zoran Selinger: Which is obviously a problem.
193 00:20:49.200 ⇒ 00:20:54.070 Ryon: So, at a high level, is Omni a good replacement for Mixpanel, or can it serve as a replacement.
194 00:20:54.070 ⇒ 00:21:04.079 Zoran Selinger: No, that… I mean, those… those two tools are completely different. So Omni, Omni is just, essentially a layer of,
195 00:21:04.270 ⇒ 00:21:19.210 Zoran Selinger: dbt project of your… it’s a tool that sees everything that’s in your data warehouse, and also that is managed with dbt. That’s the strength of… so, anything that we have in the warehouse.
196 00:21:19.360 ⇒ 00:21:24.940 Zoran Selinger: This tool can replace essentially everything there.
197 00:21:25.260 ⇒ 00:21:25.870 Ryon: So, tell me.
198 00:21:25.870 ⇒ 00:21:30.899 Zoran Selinger: data, but we need the data in our warehouse, and Mixpanel provides that.
199 00:21:32.020 ⇒ 00:21:32.540 Ryon: Okay.
200 00:21:32.740 ⇒ 00:21:34.030 Zoran Selinger: I think we do.
201 00:21:34.030 ⇒ 00:21:41.500 Ryon: Hold on, hold on, so you’re saying that no Mixpanel cannot be replaced by Omni, even if we put the data into the data warehouse?
202 00:21:44.020 ⇒ 00:22:00.339 Zoran Selinger: If we have a system that will give us the same data Mixpanel does, then we don’t have to use Mixpanel. But Mixpanel is a product analytics tool that we see pages and events that we use everywhere.
203 00:22:00.470 ⇒ 00:22:04.499 Zoran Selinger: From it, and it’s gonna be really hard to replace it.
204 00:22:04.890 ⇒ 00:22:08.890 Zoran Selinger: Really, really hard. I mean…
205 00:22:08.890 ⇒ 00:22:13.260 Mitesh Patel: Hold on a second, let me… and I asked this question, Zora, and I want to understand…
206 00:22:13.600 ⇒ 00:22:18.530 Mitesh Patel: What data is in Mixpanel that we cannot get into BigQuery?
207 00:22:20.160 ⇒ 00:22:22.930 Zoran Selinger: But… So this is, like
208 00:22:23.850 ⇒ 00:22:30.220 Zoran Selinger: Typical product analytics data. So this is, what are people doing on our website?
209 00:22:32.320 ⇒ 00:22:42.619 Zoran Selinger: So if you… if we are to avoid MixedPanel, we would need to have super elaborate setups in Google Tag Manager, for example.
210 00:22:43.370 ⇒ 00:22:44.140 Zoran Selinger: Bye.
211 00:22:44.820 ⇒ 00:22:52.750 Zoran Selinger: super… so, because what… what… so this is the difference between implicit and explicit tracking, right?
212 00:22:53.210 ⇒ 00:23:09.279 Zoran Selinger: when you put a mixed panel tag in there, or, you know, amplitude or whatever, it just tracks essentially every click that happens, every interaction with your product. We don’t have to explicitly,
213 00:23:09.410 ⇒ 00:23:11.320 Zoran Selinger: Define what’s being tracked.
214 00:23:11.610 ⇒ 00:23:16.769 Mitesh Patel: And then we add more data, so Mixpanel does every click tracking as.
215 00:23:16.770 ⇒ 00:23:17.820 Zoran Selinger: Everything.
216 00:23:18.270 ⇒ 00:23:35.480 Mitesh Patel: It does that on its own, and then we complement that data through, like, from data, other data sources through Segment, and we add that to Mixpanel, and the reports we get on Mixpanel combine its own data, its own analytics data, with these additional source data. Okay.
217 00:23:35.480 ⇒ 00:23:47.120 Zoran Selinger: Yeah, that’s just an additional feature of, you know, this MixedPanel product analytics tool that they have. The point is to track every interaction on the website.
218 00:23:47.900 ⇒ 00:23:55.659 Zoran Selinger: To recreate this, we need to do what an implicit tracking does with explicit tracking.
219 00:23:56.000 ⇒ 00:24:05.760 Zoran Selinger: And Google Tag Manager is an explicit tracking tool where we essentially have to define everything very specifically.
220 00:24:08.050 ⇒ 00:24:11.940 Zoran Selinger: So… It’s gonna be real… I mean…
221 00:24:12.100 ⇒ 00:24:19.419 Zoran Selinger: No, it’s not viable to go through the route of… Replacing a product analytics tool.
222 00:24:19.920 ⇒ 00:24:21.190 Mitesh Patel: Okay, got it.
223 00:24:25.120 ⇒ 00:24:28.959 Ryon: Okay, it’s not a replacement then. It’s not a replacement, then.
224 00:24:28.960 ⇒ 00:24:34.290 Zoran Selinger: Yeah, but we don’t, for example, we don’t have to use, mixed panel
225 00:24:34.670 ⇒ 00:24:48.000 Zoran Selinger: for, like, a CDP. We don’t have to use… we don’t have to import any data into Mixpanel. We can import MixedPanel data into our warehouse, and then use Omni for everything.
226 00:24:48.840 ⇒ 00:24:49.430 Ryon: Okay.
227 00:24:49.810 ⇒ 00:24:54.230 Ryon: So… Wait, hold on. You just said…
228 00:24:54.230 ⇒ 00:25:01.480 Zoran Selinger: So our MixPanel… so our MixedPanel account at that point is just doing product analytics.
229 00:25:01.680 ⇒ 00:25:02.220 Ryon: Yeah.
230 00:25:02.220 ⇒ 00:25:06.919 Zoran Selinger: data gathering. And that’s it. That’s all it does. It just collects the.
231 00:25:07.030 ⇒ 00:25:08.540 Ryon: website events.
232 00:25:08.920 ⇒ 00:25:18.359 Zoran Selinger: and gives us… gives us those events into… pushes it into BigQuery. And then we can do everything else in Omni.
233 00:25:18.940 ⇒ 00:25:24.749 Zoran Selinger: we don’t have to, we don’t have to import anything into Mixpanel, necessarily.
234 00:25:24.880 ⇒ 00:25:28.120 Zoran Selinger: If you’re gonna do all the analysis in Omni.
235 00:25:28.550 ⇒ 00:25:31.999 Ryon: That’s what I’m aiming for here. If we could build out a…
236 00:25:32.000 ⇒ 00:25:33.770 Zoran Selinger: That’s great. I agree.
237 00:25:33.770 ⇒ 00:25:39.869 Ryon: Build out a world where we don’t need Mixpanel. Can Omni support all of the reports
238 00:25:40.110 ⇒ 00:25:45.449 Ryon: that we have in Mixpanel if it had the same data. That’s what I’m asking.
239 00:25:45.740 ⇒ 00:25:47.579 Mitesh Patel: Other than analytics, it sounds like.
240 00:25:47.830 ⇒ 00:26:01.210 Mitesh Patel: Because Mixpanel has its own analytics data, kind of like Mixpanel and GA4 have their own analytics data. But we’re importing a bunch of other data into Mixpanel simply for reporting.
241 00:26:02.020 ⇒ 00:26:03.469 Mitesh Patel: Can’t we just report that?
242 00:26:04.010 ⇒ 00:26:05.270 Ryon: Good, good, good.
243 00:26:05.270 ⇒ 00:26:12.810 Mitesh Patel: that data that we’re importing into Mixpanel can be imported into Omni instead for reporting and dashboarding.
244 00:26:14.530 ⇒ 00:26:20.849 Zoran Selinger: Technically speaking, Omni is our data warehouse, so It’s already there.
245 00:26:21.340 ⇒ 00:26:28.430 Zoran Selinger: Omni is… is a layer on our data warehouse. So it has everything that we…
246 00:26:28.430 ⇒ 00:26:28.870 Mitesh Patel: having to…
247 00:26:28.870 ⇒ 00:26:29.909 Zoran Selinger: data warehouse.
248 00:26:32.110 ⇒ 00:26:33.420 Zoran Selinger: Sorry, second?
249 00:26:33.970 ⇒ 00:26:36.760 Mitesh Patel: Alright, hold on, I’m gonna share my screen here again.
250 00:26:38.380 ⇒ 00:26:39.890 Zoran Selinger: Awesome. Thank you.
251 00:26:44.290 ⇒ 00:26:45.230 Zoran Selinger: kids…
252 00:26:46.520 ⇒ 00:26:55.019 Mitesh Patel: So here is, some, you know, Ryan CVR board.
253 00:26:55.130 ⇒ 00:26:56.130 Mitesh Patel: Right?
254 00:26:57.360 ⇒ 00:27:04.200 Mitesh Patel: I don’t know if this is based on imported data to Mixpanel, or Mixpanel’s analytics data.
255 00:27:04.320 ⇒ 00:27:06.869 Mitesh Patel: Let’s say it’s based on imported data.
256 00:27:08.730 ⇒ 00:27:14.450 Mitesh Patel: We can import that data, into Omni instead of Mixpanel, and do similar.
257 00:27:14.450 ⇒ 00:27:14.970 Zoran Selinger: Yes.
258 00:27:14.970 ⇒ 00:27:15.630 Mitesh Patel: Correct?
259 00:27:15.630 ⇒ 00:27:17.239 Zoran Selinger: Yes, exactly.
260 00:27:19.000 ⇒ 00:27:21.179 Mitesh Patel: Ryan, where’s your drop-off report data?
261 00:27:22.240 ⇒ 00:27:26.269 Ryon: Yeah, you’re not gonna see that here, it’s in the product… here, let me show you. I’ll make it public.
262 00:27:28.350 ⇒ 00:27:33.290 Zoran Selinger: Because if this is imported data, it’s coming from…
263 00:27:33.930 ⇒ 00:27:35.420 Ryon: our warehouse.
264 00:27:35.890 ⇒ 00:27:38.840 Zoran Selinger: Which Omni already has access to. So, yeah.
265 00:27:38.840 ⇒ 00:27:39.310 Mitesh Patel: Okay. Okay.
266 00:27:39.420 ⇒ 00:27:42.540 Zoran Selinger: Support can very likely be,
267 00:27:42.660 ⇒ 00:27:48.119 Zoran Selinger: I just saw a lot, like, a line chart, so probably even identically recreated.
268 00:27:48.120 ⇒ 00:27:52.010 Ryon: Go to the dashboard that I just dropped the link in the chat for, Natash.
269 00:27:52.820 ⇒ 00:28:02.239 Zoran Selinger: So, on your question about can, Omni do everything Mixpanel can, the only reservations I have
270 00:28:02.490 ⇒ 00:28:07.309 Zoran Selinger: I would need to check is how does funnel reporting look in Omni?
271 00:28:07.920 ⇒ 00:28:09.490 Zoran Selinger: got it.
272 00:28:09.490 ⇒ 00:28:11.589 Mitesh Patel: Hey, Ryan, you dropped the Omni.
273 00:28:12.070 ⇒ 00:28:13.680 Mitesh Patel: You dropped the Omni app.
274 00:28:13.680 ⇒ 00:28:17.059 Ryon: Hold on, hold on, hold on, I hit copy, it didn’t copy the right URL.
275 00:28:18.550 ⇒ 00:28:19.520 Ryon: That URL.
276 00:28:32.120 ⇒ 00:28:36.260 Mitesh Patel: Alright, so the other thing that I like about Mixpanel is.
277 00:28:36.260 ⇒ 00:28:36.810 Zoran Selinger: Huh?
278 00:28:36.810 ⇒ 00:28:47.169 Mitesh Patel: you know, I can… like, it’s got, like, this calendar stuff, it’s got different filters already built in. I think that’ll take some… a long time for us to replicate in Omni, though, right?
279 00:28:48.610 ⇒ 00:28:50.139 Zoran Selinger: I’m not sure.
280 00:28:50.360 ⇒ 00:28:51.349 Zoran Selinger: I’m not sure.
281 00:28:51.660 ⇒ 00:28:52.610 Mitesh Patel: Okay.
282 00:28:54.070 ⇒ 00:28:57.049 Mitesh Patel: See, what I’m trying to do, and maybe we can’t do it, is…
283 00:28:58.010 ⇒ 00:29:01.959 Mitesh Patel: Let me add… let me tell you what conversation I just had with Northbeam.
284 00:29:02.580 ⇒ 00:29:08.699 Mitesh Patel: I was looking at this data in Northbeam, right?
285 00:29:08.880 ⇒ 00:29:22.930 Mitesh Patel: And I’m like, I was talking with Adam Palma about, I want a dashboard, right? I want to… I’m gonna dedicate a screen, and it’s my near-real-time dashboard for the entire business unit.
286 00:29:23.280 ⇒ 00:29:31.629 Mitesh Patel: It’s gonna have a section on marketing, it’s gonna have a section on ops, and it’s gonna have a section on, care, right?
287 00:29:31.940 ⇒ 00:29:42.689 Mitesh Patel: We can collect all the data from different places and stick it in a spreadsheet or whatever. And I was like, Adam, build me this dashboard.
288 00:29:43.860 ⇒ 00:29:45.100 Mitesh Patel: Now…
289 00:29:45.880 ⇒ 00:29:58.789 Mitesh Patel: He’s like, you know, all this is in North Beam Guy, like, why do you want me to, you know, spend in revenue by channel overall? I’m like, look, if I just set today as the date, which I did here, and hourly, I can see, hour by hour.
290 00:29:59.030 ⇒ 00:30:04.579 Mitesh Patel: what our spend and revenue is, and number of conversions, right? So it’s just my real-time dashboard.
291 00:30:04.920 ⇒ 00:30:11.190 Mitesh Patel: But, in the plan that we’re on, you know, this thing gets updated only 4 times a day.
292 00:30:11.190 ⇒ 00:30:12.040 Zoran Selinger: hours.
293 00:30:12.040 ⇒ 00:30:26.649 Mitesh Patel: Yeah, every 6 hours. Yeah. So, we… I didn’t know, like, it was planned and based, I should have guessed, but I, you know, I sent an email, Adam sent an email on my behalf, saying, we want this updated hourly, so Mitesh can get his data, and I don’t have to build it from scratch.
294 00:30:27.040 ⇒ 00:30:45.250 Mitesh Patel: And they’re like, well, that’s gonna be an upgrade, you have to go to the professional plan, blah blah blah, and, you know, like, 12… you can get 12 updates every 2 hours. I’m like, can I get 10 updates during the day, and only 2 updates at night? No, it doesn’t work like that. You know. So we would be, instead of $2,500 a month.
295 00:30:45.250 ⇒ 00:30:47.179 Mitesh Patel: It would go to $5,000 a month.
296 00:30:47.510 ⇒ 00:30:48.120 Zoran Selinger: Yeah.
297 00:30:48.120 ⇒ 00:30:52.749 Mitesh Patel: And I’m like, well… if I have all this data in Omni.
298 00:30:53.880 ⇒ 00:30:59.930 Mitesh Patel: Or, you know… so now I need… so I’m trying to, like, I need North Beam, and I need to expand North Beam.
299 00:31:00.230 ⇒ 00:31:06.229 Mitesh Patel: We need Mixpanel, and we need to expand Mixpanel, fix the data in it, whatever, whatever, right?
300 00:31:06.560 ⇒ 00:31:31.009 Mitesh Patel: And now we got Omni, too. And so we got 3 tools, and more… yeah, the tools, the cost is one thing, right? But then we got different people, different resources, and availability, like, you and Greg are all about Omni right now, right? I know Greg is supposed to be helping us with Mixpanel, but he doesn’t have the bandwidth to do it, so now we gotta hire this other guy to work on Mixpanel, and it just…
301 00:31:31.360 ⇒ 00:31:37.360 Mitesh Patel: the… The tools and the resources required are becoming too thin, right?
302 00:31:38.320 ⇒ 00:31:45.119 Mitesh Patel: like, I don’t want 3 tools if we can do a 2 tools, and two sets of resources and expertise, rather than three sets.
303 00:31:45.320 ⇒ 00:31:59.250 Mitesh Patel: Right? Sure. So that’s why, you know, I started exploring with Ryan this morning, like, can this replace Mixpanel? But no, it can’t. Can this replace North Beam? I don’t think so, but I don’t know.
304 00:32:00.800 ⇒ 00:32:06.780 Mitesh Patel: Can I stay on this current plan with Northbeam and get the hourly dashboard I’m looking for in Omni?
305 00:32:09.650 ⇒ 00:32:10.410 Zoran Selinger: No.
306 00:32:10.670 ⇒ 00:32:13.710 Zoran Selinger: I don’t… no, I don’t think you can.
307 00:32:13.880 ⇒ 00:32:15.210 Zoran Selinger: Yeah.
308 00:32:15.550 ⇒ 00:32:18.250 Mitesh Patel: Because Omni doesn’t have the connectors that…
309 00:32:18.520 ⇒ 00:32:21.260 Mitesh Patel: Northbeam has to the platforms, right?
310 00:32:21.930 ⇒ 00:32:24.910 Mitesh Patel: to, like, Google Ads and Meta and so on.
311 00:32:25.220 ⇒ 00:32:38.300 Zoran Selinger: Listen, we can build. You can build absolutely no problem, like, we have Polytomic, we can pull data every minute, if you want, into the warehouse. That’s not a problem. Problem is…
312 00:32:38.620 ⇒ 00:32:45.029 Zoran Selinger: Norvim is not our connector, it’s not replacing polyatomic. Norvim does attribution.
313 00:32:45.370 ⇒ 00:32:46.190 Zoran Selinger: Right?
314 00:32:46.780 ⇒ 00:32:52.549 Zoran Selinger: That’s what’s the differentiator here. It’s not a problem to pull… it’s no problem to pull data.
315 00:32:52.710 ⇒ 00:32:53.340 Zoran Selinger: Probably…
316 00:32:53.340 ⇒ 00:32:54.610 Mitesh Patel: No, no, no, look.
317 00:32:54.610 ⇒ 00:32:55.920 Zoran Selinger: solve attribution.
318 00:32:55.920 ⇒ 00:33:09.899 Mitesh Patel: Northbeam is for attribution, and I know we can’t… I don’t want us to build our own attribution, I get it. Robert last year was proposing we build our own attribution. I’m like, no, let Northbeam do that, because I know, like, building an attribution system is…
319 00:33:10.160 ⇒ 00:33:14.250 Mitesh Patel: It’s bigger than a bread box, and they have dozens of engineers working on it. We don’t.
320 00:33:14.580 ⇒ 00:33:15.240 Mitesh Patel: Right?
321 00:33:15.410 ⇒ 00:33:20.559 Mitesh Patel: But, regardless of that, I don’t want it to be an attribution thing.
322 00:33:20.840 ⇒ 00:33:24.440 Mitesh Patel: What I’m trying to use Northbeam for is my dashboard.
323 00:33:25.270 ⇒ 00:33:30.030 Mitesh Patel: Right? And what I need in the dashboard is not necessarily attribution.
324 00:33:30.940 ⇒ 00:33:35.029 Mitesh Patel: Right? Well, I do need attribution, shit, because of the revenue.
325 00:33:35.030 ⇒ 00:33:35.360 Zoran Selinger: That’s…
326 00:33:35.360 ⇒ 00:33:36.910 Mitesh Patel: Based on attribution, not the spend.
327 00:33:36.910 ⇒ 00:33:38.520 Zoran Selinger: Yes. Yes.
328 00:33:40.110 ⇒ 00:33:57.519 Zoran Selinger: So the… you still, like, I see no redundancy in NordBeam, unfortunately. I don’t like that I’m saying that, but I think that’s the case. NordBeam we need for attribution, mixed panel we need for actual product tracking, and then…
329 00:33:57.520 ⇒ 00:34:04.350 Zoran Selinger: Omni can be a layer of reporting for all of that. It just… the data just needs to be in
330 00:34:04.760 ⇒ 00:34:07.370 Zoran Selinger: In the warehouse.
331 00:34:07.920 ⇒ 00:34:18.270 Zoran Selinger: Unfortunately, for Norman specifically, yeah, we will only get a fresh data point every 6 hours.
332 00:34:19.409 ⇒ 00:34:22.819 Mitesh Patel: Yeah, unless if I pay them $5,000, then I can get it every 2 hours.
333 00:34:24.510 ⇒ 00:34:25.139 Zoran Selinger: Yeah.
334 00:34:25.780 ⇒ 00:34:31.940 Zoran Selinger: Which, again, I wouldn’t call, I wouldn’t call every 2 hours near real time.
335 00:34:31.949 ⇒ 00:34:34.089 Mitesh Patel: I would want it at least hourly, yeah, I know.
336 00:34:34.090 ⇒ 00:34:36.780 Zoran Selinger: Yeah, I know, I know, I think it’s…
337 00:34:36.900 ⇒ 00:34:41.789 Zoran Selinger: I think for $5,000, every 2 hours is a little bit steep.
338 00:34:42.940 ⇒ 00:34:49.460 Mitesh Patel: I told them, I said, look, I can have 12 updates a day, because that’s what the 5,000 gives us.
339 00:34:49.880 ⇒ 00:35:00.129 Mitesh Patel: But I want 10 during the day, like, between 6 a.m. and 8pm or something, and only 2 at night, because I don’t care about updates at night. I’m not looking at it.
340 00:35:00.130 ⇒ 00:35:01.749 Zoran Selinger: Yeah, yeah, I understand.
341 00:35:01.750 ⇒ 00:35:04.449 Mitesh Patel: And they’re like, oh, we don’t do that, but I’ll ask the product team.
342 00:35:05.500 ⇒ 00:35:11.529 Mitesh Patel: I said, or I will pay you $5,000, but you give me the hourly updates for free.
343 00:35:13.220 ⇒ 00:35:13.880 Zoran Selinger: I doubt it.
344 00:35:13.880 ⇒ 00:35:15.919 Mitesh Patel: He’s gonna say yes to that, but we’ll see.
345 00:35:18.050 ⇒ 00:35:24.460 Ryon: Okay, so it sounds like then, from this conversation, any one of these platforms has unique
346 00:35:24.860 ⇒ 00:35:28.230 Ryon: Features and functions that justifies the existence of all three.
347 00:35:29.150 ⇒ 00:35:37.830 Ryon: There’s two things that I need, then, to talk to you about. I know we’re over time here, Zaran. The second one I’m gonna say you and I can talk about tomorrow. Adam…
348 00:35:38.240 ⇒ 00:35:55.279 Ryon: McBride has been asking me to build a more comprehensive drop-off report that combines client-side events with server-side events, right? So, some of the stuff in regards to orders, order drop-off, order information, etc, combined with the stuff on the client-side, like the drop-off data around each of the funnels.
349 00:35:55.280 ⇒ 00:35:59.370 Ryon: Very hard to do right now, since we have no way to bridge the gap between the two.
350 00:35:59.370 ⇒ 00:36:03.709 Ryon: I’m gonna look and see what’s out there and what we can and can’t do, but we’ll talk about that later.
351 00:36:03.710 ⇒ 00:36:21.259 Ryon: The second thing is around the stuff that Josh Yoon had sent. Do you have bandwidth in the coming weeks, or week, to start working on trying to solve through the problems he’s identified? Because there’s a few things I think you and I need to be meeting with on a regular basis about
352 00:36:21.630 ⇒ 00:36:35.120 Ryon: MixPanel, and, like, as an example, we seem to have stopped sending, BASC last UTMs in October when we started sending the edge layer data. But the orders
353 00:36:35.230 ⇒ 00:36:36.240 Ryon: data.
354 00:36:36.540 ⇒ 00:36:44.890 Ryon: the order data, which comes from the webhooks, it looks like, depends on the last chance, or last touch UTMs from BASC. It does not depend on the
355 00:36:46.200 ⇒ 00:36:51.139 Ryon: edge layer data, so I can’t even see new orders, unless I look at things
356 00:36:52.080 ⇒ 00:36:56.700 Ryon: just zoomed in to the webhook data alone, like, by order ID.
357 00:36:56.970 ⇒ 00:36:58.800 Zoran Selinger: So…
358 00:36:58.800 ⇒ 00:37:05.459 Ryon: can we look at scoping how we get all of that deployed, updated? Because MixPanel right now is…
359 00:37:05.620 ⇒ 00:37:09.929 Ryon: radically underutilized, I think, and we need to expand and build on it even more.
360 00:37:11.450 ⇒ 00:37:16.530 Zoran Selinger: I mean, I think it is a very important tool, and we should…
361 00:37:17.270 ⇒ 00:37:22.050 Zoran Selinger: We should make sure the data in there is… is correct and sufficient.
362 00:37:24.430 ⇒ 00:37:25.120 Ryon: Okay.
363 00:37:25.120 ⇒ 00:37:28.279 Zoran Selinger: I see that as very important, yeah.
364 00:37:28.280 ⇒ 00:37:31.549 Ryon: Have you read through the audit that Josh sent at all?
365 00:37:31.550 ⇒ 00:37:36.659 Zoran Selinger: No, haven’t had a chance, but I can prepare for next week, if you… if you want.
366 00:37:36.880 ⇒ 00:37:46.489 Ryon: Yeah, let’s chat about it in the sprint planning meeting on Monday, but I’d like to try and get a lot of the key things that we need to get done
367 00:37:46.530 ⇒ 00:37:56.960 Ryon: in Mixpanel done so that we can be reporting on things better. Let’s just start with the channel level report that I had mentioned, and then we’ll talk about the drop-off report. I’m gonna take a stab at that manually myself.
368 00:37:57.090 ⇒ 00:38:06.829 Ryon: And figure out what I can and can’t do there. Fortunately for you, also, I have compl… I have completed a version of the…
369 00:38:07.770 ⇒ 00:38:16.709 Ryon: projections by product, and I’ll talk to you about that later, but I’m gonna be presenting this to Matt and to Mitesh, kind of talking through what that looks like.
370 00:38:16.740 ⇒ 00:38:31.720 Ryon: The concern I have around this is I’m leaning heavy on the North Beam data for this, and I still have some misgivings about some of the data that I’m seeing. So, if February seems okay, like, February seems okay, but January and December, like…
371 00:38:31.720 ⇒ 00:38:40.329 Ryon: Not sure how much I trust those, for certain channels. And then other channels, like, seem like they’re completely misrepresenting the data altogether, so we gotta work through that.
372 00:38:40.330 ⇒ 00:38:45.420 Zoran Selinger: I mean, you will remember that we had that tracking issue with the lack of.
373 00:38:46.200 ⇒ 00:38:46.980 Ryon: In January.
374 00:38:46.980 ⇒ 00:38:55.699 Zoran Selinger: product, yeah, in January, and yeah, that was from the 1st to the 13th, if I remember correctly. So that will throw off things significantly there.
375 00:38:55.700 ⇒ 00:38:58.470 Ryon: Yeah, January is, broadly speaking, from what I can tell.
376 00:38:58.950 ⇒ 00:39:06.579 Ryon: like, just not usable. December, I don’t know. Some channels… and some of them, like, it’s more difficult to kind of project out, because
377 00:39:06.580 ⇒ 00:39:26.010 Ryon: we don’t have any data yet for these channels, like Facebook or TikTok. Like, they just started. So, like, how am I supposed to look at your struggle data? We gotta sort of set benchmarks there. But anyways, okay, we’ll talk more about that, but I think at the very least, let’s get started with that channel dashboard, something we can just copy-paste for all channels, and then use the UTM sources and give them some additional data, and then share it publicly with people so that they have something to look at.
378 00:39:26.110 ⇒ 00:39:32.500 Ryon: And then talk about expanding next week, and building off that. Bitesh, does that work for you? Is this a good action plan?
379 00:39:34.210 ⇒ 00:39:35.750 Mitesh Patel: Yeah, that works.
380 00:39:35.750 ⇒ 00:39:49.500 Ryon: Okay, and then I don’t want to leave Omni off to the side, because there’s a lot of really good data in here, and I feel like it can be the answer to a lot of problems, but I’m just sort of getting used to it myself, so…
381 00:39:50.730 ⇒ 00:39:51.530 Ryon: We’ll see.
382 00:39:51.930 ⇒ 00:39:54.659 Ryon: Yeah. We’ll see. Okay.
383 00:39:54.660 ⇒ 00:39:59.749 Zoran Selinger: I think the key for… the key for Omni is… is really self-service.
384 00:40:00.340 ⇒ 00:40:04.420 Zoran Selinger: And it’s… the AI feature is doing well.
385 00:40:05.740 ⇒ 00:40:19.460 Zoran Selinger: But it’ll take time for us to get used to it. Obviously, as we create useful reports for you, you’re gonna be in there more, and you’re gonna start asking questions and actually self-serve.
386 00:40:20.370 ⇒ 00:40:24.430 Zoran Selinger: Over time. So, we’ll get there. It’s a really good tool.
387 00:40:24.820 ⇒ 00:40:26.140 Ryon: Okay, cool.
388 00:40:26.550 ⇒ 00:40:40.899 Ryon: All right, thank you, for staying over, Zahn, I appreciate it. Sure. And I will record the Everflow meeting later, make sure I ask all the necessary questions. My hope is they’ve got a native integration with Basque, so it should be pretty easy for me to spin it up, really quickly. But, I’ll let you know.
389 00:40:41.600 ⇒ 00:40:42.440 Zoran Selinger: Okay.
390 00:40:42.690 ⇒ 00:40:44.719 Ryon: Awesome. Thank you guys.
391 00:40:44.720 ⇒ 00:40:45.889 Mitesh Patel: Thank you. Bye.