Meeting Title: Mixpanel Training Date: 2025-11-19 Meeting participants: Kristina, Mat Schwarz, Danny Valdez, Ryon, Henry Zhao, Casie Aviles, Judd, Judd K
WEBVTT
1 00:00:54.650 ⇒ 00:00:55.970 Kristina: Hi, Matthias!
2 00:00:56.690 ⇒ 00:00:57.500 Mat Schwarz: Boom.
3 00:01:02.090 ⇒ 00:01:03.140 Kristina: Hi, Donnie!
4 00:02:17.240 ⇒ 00:02:18.850 Mat Schwarz: You are muted.
5 00:02:21.090 ⇒ 00:02:25.890 Henry Zhao: I’m not talking yet. Ryan… What else are we missing?
6 00:02:26.320 ⇒ 00:02:29.119 Henry Zhao: Okay, Joseph and Judd.
7 00:02:32.250 ⇒ 00:02:39.789 Ryon: I’m gonna keep my camera off, Henry, because they’re doing a fire alarm test in my building, so I just don’t want you guys to hear all the nasty sounds.
8 00:02:39.790 ⇒ 00:02:43.090 Henry Zhao: In the meantime, I’m gonna make sure that you all have access to Mixpanel.
9 00:02:43.420 ⇒ 00:02:51.179 Henry Zhao: I’ll do that first. And then you guys can pull it up. You’re gonna get an invite in your email, so you can pull it up and follow along if you, learn by doing.
10 00:02:53.510 ⇒ 00:02:56.999 Henry Zhao: Okay. Christina, can you send me your email in the… in this chat?
11 00:02:58.150 ⇒ 00:03:02.620 Kristina: Of course, I think Ryan has already given me the access to, to it.
12 00:03:02.620 ⇒ 00:03:06.330 Henry Zhao: Yeah, let me check who has access. Settings… project settings…
13 00:03:06.330 ⇒ 00:03:07.399 Kristina: There we go.
14 00:03:11.130 ⇒ 00:03:12.429 Henry Zhao: Okay, I’ll check that.
15 00:03:19.430 ⇒ 00:03:23.950 Henry Zhao: Alright, so Danny is in here, Judd is in here, Christina’s in here.
16 00:03:25.960 ⇒ 00:03:28.040 Henry Zhao: Casey, do you have access, right? Yeah, I do.
17 00:03:29.050 ⇒ 00:03:37.370 Henry Zhao: I think I’m just missing Joseph. Oh, no, Joseph is in here, too. Okay, great! So I’ll give you some time… guys, some time to pull it up so we can go through this together,
18 00:03:37.730 ⇒ 00:03:39.230 Henry Zhao: I’m gonna share my screen.
19 00:03:42.230 ⇒ 00:03:43.500 Henry Zhao: Sorry, one second.
20 00:03:44.520 ⇒ 00:03:47.289 Henry Zhao: Been in back-to-back meetings, so a lot going on.
21 00:03:55.390 ⇒ 00:03:56.670 Henry Zhao: You guys see my screen?
22 00:03:58.020 ⇒ 00:03:58.720 Kristina: Yes.
23 00:03:59.700 ⇒ 00:04:04.500 Henry Zhao: I’m gonna just give Joseph a little more time to get in. I’m gonna just ping him and see if he’s joining.
24 00:04:11.530 ⇒ 00:04:22.660 Henry Zhao: So before we get started, I just want to get a quick, show of hands, or just, you can mention in the chat, how many of you have already worked with Mixpanel before, or is this something that’s brand new for you guys? Or have you guys worked with Amplitude?
25 00:04:22.830 ⇒ 00:04:24.899 Henry Zhao: Ga4, anything similar?
26 00:04:26.780 ⇒ 00:04:31.549 Danny Valdez: I’ve done a little bit of mixed panel work, but not a ton.
27 00:04:31.550 ⇒ 00:04:32.200 Henry Zhao: Okay.
28 00:04:34.470 ⇒ 00:04:36.690 Henry Zhao: Judd, have you worked a lot with Mixpanel before?
29 00:04:38.170 ⇒ 00:04:45.409 Judd: No, I’m kind of, like, I’ve played around with it, tried to understand it, but I’m, like, trying to figure it out at the same time, so yeah, not really anything.
30 00:04:45.630 ⇒ 00:04:51.640 Henry Zhao: Okay, so today I’m gonna just basically talk about Mixpanel with the context of how this…
31 00:04:51.760 ⇒ 00:04:57.220 Henry Zhao: applies to Eden, okay? So I think the main things here are attribution.
32 00:04:57.300 ⇒ 00:05:15.970 Henry Zhao: Right? So we have UTM data in Mixpanel, and you will be able to look at the behavior of people that have come in from your UTMs. So, Christina, you would basically filter by your UTMs, Judd, you would filter by email, and then you can look at what is the behavior of the people that came in to answer questions like, am I bringing in valuable users?
33 00:05:16.510 ⇒ 00:05:29.719 Henry Zhao: Am I bringing in the right users, right? So let’s say that you bring in a lot of people from a certain campaign, and they go to the intake page, so you’re getting a lot of different, awareness and a lot of initial demand, but then people are dropping off.
34 00:05:29.910 ⇒ 00:05:34.460 Henry Zhao: That might be a sign that you are not bringing in the right users, because it’s not what they expected.
35 00:05:34.730 ⇒ 00:05:35.780 Henry Zhao: Does that make sense?
36 00:05:36.400 ⇒ 00:05:48.530 Henry Zhao: or maybe people drop off when you get to the pricing page. That maybe tells us that either the pricing is too high, or you’re advertising to the wrong demographic, right? Let’s just assume that we have a really expensive product.
37 00:05:48.780 ⇒ 00:05:55.160 Henry Zhao: And you’re advertising to people that want to spend less. These are the insights that you’re going to be able to get from Mixpanel.
38 00:05:56.150 ⇒ 00:06:11.870 Henry Zhao: beyond what we already have in our Tableau dashboards. And you’ll be able to get it really quickly, because Mixpanel is set up to be very easy to self-serve, and be able to answer your questions in a quick way, right? So the way Mixpanel works is through events and user profiles, okay?
39 00:06:12.340 ⇒ 00:06:19.459 Henry Zhao: So you’ll see here on this left side of the screen that we have data, and these three are kind of the resources you’re going to use to understand the data in Mixpanel.
40 00:06:19.680 ⇒ 00:06:34.229 Henry Zhao: So events are anything that happens, right? So anytime somebody loads an intake page, anytime someone clicks on an email, anytime someone gets identified, so, like, when you log in, and you give your email address, now we know who you are, right? We know you’re Christina.
41 00:06:34.510 ⇒ 00:06:40.320 Henry Zhao: We know your Judd, etc, from your email or from your account. Okay, so all of these are events.
42 00:06:40.630 ⇒ 00:06:46.810 Henry Zhao: Once you are identified, you are also a user, right? So think of users as profiles. So…
43 00:06:47.280 ⇒ 00:07:02.759 Henry Zhao: if this were Facebook, right, events would be, like, your activity log, and users would be your user profile. Kind of think of it like that. So in users, we have people that are unidentified, so maybe we just have their email, or they’re very anonymous, or they’re a customer, so we have all types of information on them.
44 00:07:04.460 ⇒ 00:07:16.499 Henry Zhao: So we’re gonna go through events first, right? So events, if you go to this events tab, you can open up here and see all the different events we have, right? So we have order completed, which in this case is submitting your
45 00:07:16.790 ⇒ 00:07:20.510 Henry Zhao: payment information on the intake, so that BASC creates an order for you.
46 00:07:20.870 ⇒ 00:07:29.629 Henry Zhao: loaded a page is basically a page impression, right? So anytime you load a page, this event will have the URL information, the UTM that shows where you came from.
47 00:07:30.070 ⇒ 00:07:43.239 Henry Zhao: It’ll have the title of the page, and multiple other information about the page that the user landed on. Signed up is obviously signed up for an account, intake started, checkout started.
48 00:07:43.420 ⇒ 00:07:49.599 Henry Zhao: Don’t worry about experimented view if you’re not working on experiments. Product added…
49 00:07:49.730 ⇒ 00:08:00.769 Henry Zhao: And then some of these are custom events. So if you go to, like, this Rank Your Cardio Health, this is… you can see over here a custom event. That means somebody, which in this case is Rob Wiley, set up an event.
50 00:08:00.960 ⇒ 00:08:17.690 Henry Zhao: that basically could be, like, a manipulation, or transformation, or a combination of events that we are sending from the platform. And we’re sending these events from platforms like Customer I.O, Webflow, the intake forms.
51 00:08:17.970 ⇒ 00:08:23.929 Henry Zhao: Using a tool called Segment. So we set up these events to be tracked, and then you can come in and set up custom events.
52 00:08:24.620 ⇒ 00:08:30.419 Henry Zhao: So let’s say that, ryan wants to know people that started an intake, right?
53 00:08:30.530 ⇒ 00:08:35.569 Henry Zhao: So he might create a custom event that has all of the initial pages of intake forms.
54 00:08:35.780 ⇒ 00:08:41.659 Henry Zhao: And then that would be an intake started event, so we can combine all the intakes into one intake-started event.
55 00:08:41.780 ⇒ 00:08:49.780 Henry Zhao: Instead of pulling all 20 intake forms, separately. Okay? So you can go in and see all of these, these different things.
56 00:08:51.350 ⇒ 00:08:59.889 Henry Zhao: Users is, similar, so here is all the user profiles we have. You can click on a specific one to look at their information.
57 00:08:59.990 ⇒ 00:09:04.330 Henry Zhao: So this distinct ID is, like, an anonymous ID, so you can… so you don’t…
58 00:09:04.560 ⇒ 00:09:09.319 Henry Zhao: So when you pass data, you can keep it anonymized and remain HIPAA compliant.
59 00:09:09.950 ⇒ 00:09:19.209 Henry Zhao: And then here you can, in the activity feed, see all of their activity, right? So, on November 16th, they updated treatment, they ordered… updated an order, they clicked on an email.
60 00:09:19.320 ⇒ 00:09:20.960 Henry Zhao: And then an order was shipped.
61 00:09:21.300 ⇒ 00:09:27.240 Henry Zhao: So Judd can come in and figure out if this email click directly led to this order being shipped.
62 00:09:27.560 ⇒ 00:09:36.799 Henry Zhao: So, Judd, it depends on what your window is, right? So, if you say that this person’s order shipped was 2 days later, I maybe don’t want to attribute it to the email click.
63 00:09:37.230 ⇒ 00:09:54.379 Henry Zhao: And I said ownership, but I mean, like, if they place an order, right? If they place the order two days later, do we want to credit that to the email? You can also come in here and look at the UTMs, if there are any, to see if the UTM is labeled as, email, to see where they came from. Maybe they came from a Google result.
64 00:09:55.280 ⇒ 00:09:59.850 Henry Zhao: But also, maybe they googled it because of that email, right? So this is, on you guys to decide.
65 00:10:00.300 ⇒ 00:10:17.999 Henry Zhao: And then Lexicon is kind of like an encyclopedia, right? So this lexicon is a guide where you can look at all of the events, the event properties that are set up, right? So each event has different properties. So, like, if you place an order, you might want to know about that order. What was the cost? Was it a 6-month treatment, or a 3-month treatment, or a 1-month treatment?
66 00:10:18.160 ⇒ 00:10:32.539 Henry Zhao: If they filled out an intake, you might want to know their weight or their BMI, that’s something we might want to be adding. So these would all go under event properties, and then profile properties are properties of the user. So you might have their country, their device name, their first name.
67 00:10:32.710 ⇒ 00:10:40.809 Henry Zhao: What device… are they using Apple or Android? What was their first UTM? So, like, how did they find out about Eden?
68 00:10:41.020 ⇒ 00:11:00.660 Henry Zhao: Things like that. And then here you have custom events to figure out how the custom events were set up. So you might see this intake started custom event and say, okay, here’s the definition. So the definition is, anyone that has a CTA product intake clicked where the current URL contains start online in the URL.
69 00:11:00.700 ⇒ 00:11:06.959 Henry Zhao: or experiment viewed, where current URL contains start online. So this is how the intake started event was defined.
70 00:11:07.510 ⇒ 00:11:13.450 Henry Zhao: Any questions up till this point? I know I’ve gone through this pretty fast, but that’s because this is kind of the basics. Any questions?
71 00:11:15.260 ⇒ 00:11:16.040 Danny Valdez: Nope.
72 00:11:17.010 ⇒ 00:11:22.449 Henry Zhao: So you can set up your own custom events, by… I’ll show you that in a sec.
73 00:11:23.260 ⇒ 00:11:30.300 Henry Zhao: And then you can also put in lookup tables, so this is like a VLOOKUP in Excel, right? So, lookup tables helps you to…
74 00:11:30.370 ⇒ 00:11:43.880 Henry Zhao: combine actual data that you have, so maybe you want to combine all GLP-1 into the category GLP-1. So you might have a table where you would, like, in Excel or in Google Sheets, list all the products that are GLP-1,
75 00:11:43.880 ⇒ 00:11:54.709 Henry Zhao: And then have a column called Category, and put GLP1. Then you can upload it here. Then you can map in Mixpanel the product to your category. So then you can use that category as an event property.
76 00:11:54.810 ⇒ 00:11:59.609 Henry Zhao: That way, instead of, like, grouping all the GLP-1s together manually, you can use lookup tables.
77 00:12:00.780 ⇒ 00:12:09.270 Henry Zhao: And then the other things are pretty self-explanatory. You can have metrics that you care about, you can have behaviors that you care about, they’re all along the same lines.
78 00:12:09.890 ⇒ 00:12:18.709 Henry Zhao: And then you can also set up cohorts. So cohorts are just groups of people, right? So you can say, maybe I want to create a cohort of people who have started
79 00:12:18.920 ⇒ 00:12:20.899 Henry Zhao: intake in… in…
80 00:12:21.010 ⇒ 00:12:35.899 Henry Zhao: but didn’t complete order, right? So maybe you want to create this cohort because you, Judd, you want to, analyze their behavior and see how you can retarget them to finish the intake, right? So you would go to, intake started.
81 00:12:37.520 ⇒ 00:12:40.730 Henry Zhao: Total events greater than or equal to 1, so that means they’ve started the intake.
82 00:12:40.940 ⇒ 00:12:45.039 Henry Zhao: Let’s say we want to just care about the people in the last 60 days, so you can set that up here.
83 00:12:45.550 ⇒ 00:12:48.680 Henry Zhao: And then you also want order completed.
84 00:12:49.080 ⇒ 00:12:50.789 Henry Zhao: to be zero.
85 00:12:52.120 ⇒ 00:12:53.060 Henry Zhao: Okay?
86 00:12:53.390 ⇒ 00:13:00.140 Henry Zhao: In the last, did not do order completed, and then here I’d probably want to do ever.
87 00:13:01.420 ⇒ 00:13:03.329 Henry Zhao: Because maybe I’m trying to get,
88 00:13:04.140 ⇒ 00:13:07.910 Henry Zhao: all people that I’ve never created in order, so I’m just gonna put whatever year right here.
89 00:13:11.080 ⇒ 00:13:17.819 Henry Zhao: So here you can see already there’s 48,000 people that have started an intake form in the last 60 days and didn’t complete an order.
90 00:13:18.430 ⇒ 00:13:20.179 Henry Zhao: So once you create the cohort.
91 00:13:21.130 ⇒ 00:13:23.650 Henry Zhao: You can actually see who these people are.
92 00:13:23.930 ⇒ 00:13:30.159 Henry Zhao: And you can start analyzing them, or even exporting a list of them, and Judd, you can fix… put this into CustomerIO.
93 00:13:30.410 ⇒ 00:13:32.260 Henry Zhao: And work directly that way.
94 00:13:33.120 ⇒ 00:13:45.319 Henry Zhao: So now let’s focus on some reporting. So… so to do some reporting, you’re gonna go to the Reporting tab here. So you can see Insights, Funnels, Flows, Retention. So we’re gonna start with the most basic report, which is insights.
95 00:13:45.450 ⇒ 00:13:51.560 Henry Zhao: So Insights allows you to look at events over time, and it also allows you to compare different groups of people.
96 00:13:51.780 ⇒ 00:13:56.119 Henry Zhao: Right, so let’s use a metric, let’s say, order completed.
97 00:13:56.640 ⇒ 00:14:04.269 Henry Zhao: So here I get to see order completed over the last 30 days, broken down by day. You could also do 6 months, and then look at it by month.
98 00:14:04.520 ⇒ 00:14:20.690 Henry Zhao: You can still look at it by week, whatever you do want to look at it by. So this just gives you event volumes based on what you’ve set it up as, but here you can filter, and you can also look at breakdowns. So maybe I want to look at breakdown of UTM source, or medium, it’s better.
99 00:14:20.730 ⇒ 00:14:26.070 Henry Zhao: So if I break it down by medium, I can see out of the orders completed in the past 6 months.
100 00:14:26.160 ⇒ 00:14:33.370 Henry Zhao: where are they coming from, right? So here you can see the highest one is not set, which should improve once we have our edge layer data set.
101 00:14:33.700 ⇒ 00:14:43.470 Henry Zhao: Then comes CPC, so that’s our next highest source of order completes, then email action, and then search CPC, so on and so forth.
102 00:14:44.610 ⇒ 00:14:49.720 Henry Zhao: You can also filter. So let’s filter by, let’s say, only,
103 00:14:50.790 ⇒ 00:14:53.249 Henry Zhao: Region, so let’s filter by region.
104 00:14:53.950 ⇒ 00:15:04.069 Henry Zhao: Let’s say I only want people in Arizona, California, and Colorado. Let’s say that I’m responsible for those regions. You can see the behavior for only that region.
105 00:15:05.280 ⇒ 00:15:06.140 Henry Zhao: Okay?
106 00:15:06.600 ⇒ 00:15:10.670 Henry Zhao: You can also look at this by column chart, bar chart, pie chart, etc.
107 00:15:10.910 ⇒ 00:15:12.689 Henry Zhao: This should be pretty self-explanatory.
108 00:15:14.590 ⇒ 00:15:28.550 Henry Zhao: Okay, the next type of reporting you can do is a funnel, okay? So, funnel allows you to look at people that did action A, what percentage of them then did action B? So, we can go to, for example, intake started.
109 00:15:29.730 ⇒ 00:15:45.870 Henry Zhao: And I want to know, out of the people that did intake started, how many of them completed an order, right? So here you see I have a metric where A is intake started, and then B is order completed, and you have a window, right? So I want them to finish this within how many days? So for now, I’m going to do one day.
110 00:15:46.420 ⇒ 00:15:49.100 Henry Zhao: I’m gonna get rid of this region filter, I’m gonna get rid of the breakdown.
111 00:15:49.540 ⇒ 00:15:57.479 Henry Zhao: And here you’ll be able to see the intake started, took order completed, okay? This you can also plot over time to look at conversion rates, right?
112 00:15:57.640 ⇒ 00:16:00.690 Henry Zhao: So here you can look at how conversion rate trends over time.
113 00:16:00.820 ⇒ 00:16:09.029 Henry Zhao: And it’ll label it as conversion rate of intake started through order completed, all steps. And you can add other steps, too. So after order completed, you can do, like, order shipped.
114 00:16:10.960 ⇒ 00:16:15.899 Henry Zhao: To continue to see Kind of, how that funnel… how that funnel looks.
115 00:16:18.750 ⇒ 00:16:37.989 Henry Zhao: Okay? Then you have, this, flow chart, right? So you can see what steps people are taking, right? So, Ryan, you gave an example of you wanted to look at intake forms and where people are dropping off. One way you can do that here, is go to intake started, okay?
116 00:16:38.160 ⇒ 00:16:44.000 Henry Zhao: And then here you’ll see all the loaded up pages that people are going through, and here you’ll be able to figure out
117 00:16:44.440 ⇒ 00:16:55.900 Henry Zhao: what are the actual ways I should be filtering the loaded up pages to understand the actual intake flow, okay? So I’m gonna filter by a specific intake. So I’m going to look at…
118 00:16:57.360 ⇒ 00:17:04.859 Henry Zhao: I’m gonna look at this one specifically, okay? So let’s say I want to investigate the steps people are doing for NAD+, for this specific intake.
119 00:17:05.119 ⇒ 00:17:11.699 Henry Zhao: I would go ahead and take this URL, so I would copy, probably start online visit, HNR1N, and filter by that, okay?
120 00:17:12.310 ⇒ 00:17:15.480 Henry Zhao: So, for the filter, I’m gonna do,
121 00:17:16.319 ⇒ 00:17:20.879 Henry Zhao: UTM… no, it’s, URL. So, current URL.
122 00:17:22.599 ⇒ 00:17:25.940 Henry Zhao: I’m going to type in this, okay? I’m gonna do add.
123 00:17:27.540 ⇒ 00:17:30.000 Henry Zhao: And I don’t want it to be is, I want it to be contains.
124 00:17:32.900 ⇒ 00:17:35.859 Henry Zhao: Wait, actually, I need to… sorry, I’m gonna need to filter it on the intake started.
125 00:17:36.320 ⇒ 00:17:39.090 Henry Zhao: So, same thing, but I need to do it here.
126 00:17:46.990 ⇒ 00:17:49.160 Henry Zhao: What am I doing wrong, Ryan? Do you know?
127 00:17:50.310 ⇒ 00:17:53.380 Ryon: Switch to contains, under the NCA.
128 00:17:53.380 ⇒ 00:17:53.869 Henry Zhao: Oh, yeah, yeah.
129 00:17:53.870 ⇒ 00:18:09.380 Ryon: That URL here as well, and then this, URL isn’t gonna have anything, this particular intake is just a test intake, so grab, like, the GLP-1 intake, because this one’s gonna be… it’s a fake intake. H… this one that ends with this slug, HNR1N, this is a fake intake I created for Zarn and I. Just grab something else.
130 00:18:09.380 ⇒ 00:18:11.429 Henry Zhao: Can you give me one that’s high volume, so we can look at it?
131 00:18:11.430 ⇒ 00:18:17.989 Ryon: Zz, try ZZ, 71Z, or try just Z, just hit Z.
132 00:18:18.820 ⇒ 00:18:34.959 Henry Zhao: Yeah, let’s just try Z, let’s pretend that that’s one intake, okay? There you go. So pretend that’s one intake, and you’re gonna look at intake started, and then… so 82% of people move on to the next page, then 78% go on to the third page, etc. So, Ryan, you actually probably don’t even need your funnel page, you could probably just look at it this way.
133 00:18:35.100 ⇒ 00:18:54.320 Henry Zhao: Because I would assume most people are not going back, so this’ll already give you a good enough example of how many people are ending where in the intake form. So, if the intake has, like, 12 steps, I can just do 12 steps here, and you’ll be able to see where the drop-off is already without needing to set up, like, a different funnel report for each intake form. Does that make sense?
134 00:18:55.130 ⇒ 00:19:08.890 Ryon: It does. I guess I’m just kind of skeptical of this data, Henry, because it seems rather optimistic, and very, these drop-off rates are really good. And I’m trying to wonder.
135 00:19:08.890 ⇒ 00:19:10.199 Henry Zhao: I don’t expect him to be.
136 00:19:11.020 ⇒ 00:19:16.360 Henry Zhao: I would expect it to be. Not a lot of people are gonna be like, oh my god, you’re asking me this question, I’m gone.
137 00:19:16.960 ⇒ 00:19:26.649 Henry Zhao: I feel like you drop off usually when it’s time to pay, or when you have to submit an ID, or you have to fill out, like, 17 different pieces of information, or get your iPhone. All that stuff, I think, gets higher drop-off.
138 00:19:26.690 ⇒ 00:19:37.669 Henry Zhao: And it makes sense that the first drop-off is the highest, because those are, like I said, the people that maybe weren’t expecting to have to fill out an intake, etc. But once you’ve gotten to step 2, I expect the drop-off not to be that high for the following steps, okay?
139 00:19:37.790 ⇒ 00:19:44.649 Henry Zhao: But either way, you can expand this by property, so here you were… you would expand by title to see what I was showing you.
140 00:19:45.020 ⇒ 00:19:51.229 Henry Zhao: So yeah, so you would want to actually filter out your form by 36964 Intro 2.
141 00:19:51.380 ⇒ 00:19:54.379 Henry Zhao: Is that actually the next title for this intake form?
142 00:19:55.100 ⇒ 00:19:56.679 Ryon: That’s the first…
143 00:19:56.730 ⇒ 00:20:16.349 Ryon: screen that you would see, basically. So I assume that the event is going to fire and take started, and then it’s gonna load screen intro 2, so that’s basically the first screen, and then the second one here is gonna, yeah, this is the medical necessity screen that’s screening their GLP1, and then this is the BMI calculator, that’s the BMI consent.
144 00:20:16.350 ⇒ 00:20:20.629 Ryon: Yep. This, yeah, starting with… yeah, this all generally makes sense.
145 00:20:20.630 ⇒ 00:20:21.699 Henry Zhao: procedure, yeah.
146 00:20:22.250 ⇒ 00:20:29.229 Henry Zhao: One thing you might want to filter out is the experiments viewed, because sometimes that will interrupt your flow.
147 00:20:29.420 ⇒ 00:20:32.689 Henry Zhao: So you can do that just by filtering that out.
148 00:20:34.650 ⇒ 00:20:36.440 Henry Zhao: Okay.
149 00:20:36.440 ⇒ 00:20:44.239 Danny Valdez: Hey, Henry, where did I see you change the allotted, like, how many steps deep you can go in this flow? You went from something to 12?
150 00:20:44.240 ⇒ 00:20:44.750 Henry Zhao: Yeah, so under.
151 00:20:44.750 ⇒ 00:20:45.220 Danny Valdez: It takes time.
152 00:20:45.220 ⇒ 00:20:48.230 Henry Zhao: You can look at how many steps before and how many steps after.
153 00:20:48.230 ⇒ 00:20:48.880 Danny Valdez: Okay.
154 00:20:50.300 ⇒ 00:21:01.800 Henry Zhao: So, if you have, like, let’s say you have two intake pages that take you to the same order completed screen, you can even look at that before, right? So you can go to the, thank you page and look at the steps before it. So you can do before, you can do after.
155 00:21:03.300 ⇒ 00:21:13.629 Henry Zhao: And then you can filter this event, too, so you can also do first-time filters, so this is the filter, but you saw earlier there’s also first-time filter. So, here you can also only count a user’s first time ever doing this event.
156 00:21:17.270 ⇒ 00:21:18.080 Henry Zhao: Okay?
157 00:21:18.190 ⇒ 00:21:26.429 Henry Zhao: And then this last one, I think, is not super relevant to, Eden, but maybe you guys can think of a use case, but this is, like, a retention…
158 00:21:26.830 ⇒ 00:21:36.120 Henry Zhao: chart, where you can say, like, out of the people that intake started and then order completed, how many of them were retained? So I think this is more important for maybe Judd, if you want to look at email.
159 00:21:36.450 ⇒ 00:21:39.440 Henry Zhao: First ever email received, so…
160 00:21:40.210 ⇒ 00:21:47.509 Henry Zhao: Oh, actually… so you wouldn’t… Judd, so actually we got rid of email sent, because there was too much volume coming in, and we were getting overages.
161 00:21:47.710 ⇒ 00:21:49.559 Henry Zhao: So I ignore that.
162 00:21:51.250 ⇒ 00:22:03.029 Henry Zhao: But what you can do, Judd, is you can filter by email campaign, and look at the different behaviors of people by campaign. So you can see if certain campaigns have a better adoption rate, or if certain ones have better, have more drop-off.
163 00:22:06.080 ⇒ 00:22:20.569 Henry Zhao: Okay, that’s pretty much all I wanted to cover today, so I’ll just, leave the last few minutes for questions. A few things we’re gonna set up next are session replay, so that will literally let you watch videos of people filling out the intake form, which actually, I probably wanna… we probably wanna figure out if that’s even.
164 00:22:20.570 ⇒ 00:22:33.789 Ryon: HIPAA compliant, because I don’t know if we should be allowing people to see people fill in their weights. So, Henry, we already have session replay, for pretty much everything via VWO, but this is good to have as well. We’ll just have multiple session replays, so…
165 00:22:34.160 ⇒ 00:22:39.290 Henry Zhao: Yeah, Adam wanted us to implement it, so we can implement it and see what we get, because we already get session replaced.
166 00:22:39.450 ⇒ 00:22:41.179 Henry Zhao: And Casey will be helping us set that up.
167 00:22:42.070 ⇒ 00:23:04.760 Ryon: The big next step for me with this, Henry, is just gonna be, like, getting the edge layer data, but, like, specifically, I want to add… I’m adding a bunch of events to Segment, which are gonna send to this, that I built out over the… over the weekend, and then I also want to get into the personas, like I had mentioned, the cohorts, and setting those up. So, you know, Danny and I are talking about what that looks like, you know, what the different cohorts might be, and then how we might be able to, like.
168 00:23:04.760 ⇒ 00:23:11.900 Ryon: say you’re part of this cohort or that cohort, but, yeah, like, that’s gonna be the next big step for me, is measuring conversion rate by product.
169 00:23:11.900 ⇒ 00:23:15.899 Ryon: By channel, and then by, cohort, basically.
170 00:23:22.880 ⇒ 00:23:23.750 Henry Zhao: All right.
171 00:23:23.750 ⇒ 00:23:29.449 Judd K: I have a quick question. I mean, this is kind of a much broader question, just about, like.
172 00:23:29.930 ⇒ 00:23:38.940 Judd K: How do we… Attribute UTMs here versus in GA versus in…
173 00:23:39.090 ⇒ 00:23:41.799 Judd K: Tableau, and are those all consistent?
174 00:23:42.460 ⇒ 00:23:44.930 Henry Zhao: Right now, they’re consistent, because right now they all come from Basque.
175 00:23:45.060 ⇒ 00:23:51.370 Henry Zhao: But we’re gonna eventually supplement that with the edge layer data that we talked about at the last training.
176 00:23:51.490 ⇒ 00:24:02.029 Henry Zhao: That will only have data as of November, so… and it will not… it will be like a supplement, not a replacement. So basically, we’re going to first look at edge layer data. If there’s no edge layer data, we replace it with the BASC data.
177 00:24:03.040 ⇒ 00:24:07.140 Judd K: And then, secondly, like, if someone comes in on a…
178 00:24:08.050 ⇒ 00:24:16.979 Judd K: paid campaign, and then they get halfway through the… or most of the way through the intake, and then they drop off, and then they get an email, and they click through that, and they get back.
179 00:24:17.350 ⇒ 00:24:23.419 Judd K: And they… complete the intake with their credit card and everything like that? Are we…
180 00:24:24.260 ⇒ 00:24:28.860 Judd K: Are we overriding the UTM, the first UTM that they came in through?
181 00:24:29.130 ⇒ 00:24:32.499 Henry Zhao: Yeah, so that’s a great question. I’m gonna show you in the database.
182 00:24:36.410 ⇒ 00:24:46.739 Henry Zhao: So basically, we’re gonna have a table in this attribution, I guess, called order… so we’re gonna have one called orderCompleted.
183 00:24:46.770 ⇒ 00:25:02.860 Henry Zhao: and one called Thank You Page, right? So order completed is, what was the last UTM that we saw from you before you submitted your payment information? And temp thank you page… this thank you page table is gonna say, what are the… what was the last UTM we saw from you before you reached the thank you page?
184 00:25:03.380 ⇒ 00:25:14.899 Henry Zhao: So, for example, this person, it’s anonymized, right? Finally got to the thank you page, and this is what brought them awareness. So they found out about Eden on IG through this campaign, but…
185 00:25:15.620 ⇒ 00:25:21.160 Henry Zhao: Why is this null? Well, don’t worry about that. So, let’s take a look at this one. So this one,
186 00:25:21.380 ⇒ 00:25:27.200 Henry Zhao: So yeah, this person got awareness from Gabriela Martinez, and also finished the thank you page from that source.
187 00:25:27.440 ⇒ 00:25:29.770 Henry Zhao: Whereas…
188 00:25:30.440 ⇒ 00:25:37.349 Henry Zhao: Let’s see, there’s another one. So this one got awareness from Facebook, but then finished their thank you page through a Google search.
189 00:25:37.520 ⇒ 00:25:40.120 Henry Zhao: So we do have that distinction once we have the edge layer data.
190 00:25:46.050 ⇒ 00:25:49.149 Henry Zhao: But remember, this will only have data as of November.
191 00:25:49.950 ⇒ 00:26:03.589 Ryon: Will this, be complementary to the data that’s similar to this, Henry, in the order summary table? You guys have, like, similar columns in there, that’s like, you know, last year TM or first UTM. Is this gonna be in addition to that, or what’s powering that now?
192 00:26:03.810 ⇒ 00:26:06.439 Henry Zhao: That’s also from Basque, so everything that’s UTM right now is from Basque.
193 00:26:07.370 ⇒ 00:26:08.580 Ryon: And I believe…
194 00:26:08.580 ⇒ 00:26:12.009 Henry Zhao: GA4 is probably directly from GA, right? Whatever you send them in the GTM.
195 00:26:12.010 ⇒ 00:26:19.400 Ryon: I mean, GA4 is using the data attribution model, and I, like I said this morning, like, I don’t think it’s the be-all, end-all, but it’s nice to have, so, you know, whatever.
196 00:26:22.310 ⇒ 00:26:28.339 Henry Zhao: Yeah, right now everything’s from BASC, but BASC obviously has its limitations, so we want to supplement it with the edge layer data.
197 00:26:29.960 ⇒ 00:26:35.290 Henry Zhao: And that’s where we’ll hopefully see more conversions from emails, from Christina’s links, etc.
198 00:26:35.460 ⇒ 00:26:37.259 Henry Zhao: And for Matt from Catalysts.
199 00:26:42.080 ⇒ 00:26:46.559 Henry Zhao: Cool, these are all great questions. If there… are there any other questions? If not, we will…
200 00:26:46.720 ⇒ 00:26:48.230 Henry Zhao: I’ll give you guys 4 minutes back.
201 00:26:49.530 ⇒ 00:26:51.829 Henry Zhao: Well, oh, I see one, yeah.
202 00:26:51.830 ⇒ 00:27:01.820 Kristina: Yes, so if you could just show me, because I’ve been trying to do it, and then once we set that up, would I always be able to go do that? Would I always be able to just go there, see the numbers, or do I need to.
203 00:27:01.820 ⇒ 00:27:02.280 Henry Zhao: Yeah.
204 00:27:02.280 ⇒ 00:27:04.200 Kristina: up, and then every time.
205 00:27:04.200 ⇒ 00:27:06.830 Henry Zhao: You can save it, yeah, so I will show you. So go to Insights.
206 00:27:07.180 ⇒ 00:27:14.409 Henry Zhao: Should I report for this UTM? Okay, so maybe you just say, like, I just want to see how many people started an intake from that UTM, let’s say.
207 00:27:14.550 ⇒ 00:27:19.099 Kristina: I’m only able to see, and I don’t know if this is my screen, but only your,
208 00:27:19.560 ⇒ 00:27:23.019 Kristina: Link, like, I’m not seeing the rest of your screen.
209 00:27:23.020 ⇒ 00:27:26.219 Danny Valdez: I only see, like, your URL bar as well, it was weird.
210 00:27:26.590 ⇒ 00:27:29.810 Henry Zhao: Oh, interesting. I’m the… Reshare.
211 00:27:31.240 ⇒ 00:27:36.810 Henry Zhao: Share. Desktop 1… Can you see it? Yeah, that’s good now. Okay.
212 00:27:38.480 ⇒ 00:27:44.700 Henry Zhao: Okay, the first things… some of this might take a little bit of playing around, But let’s see…
213 00:27:45.190 ⇒ 00:27:50.970 Henry Zhao: So let’s filter by… Source first. Okay.
214 00:27:53.180 ⇒ 00:27:57.820 Henry Zhao: Let’s filter out by source first, PR campaigns.
215 00:27:59.470 ⇒ 00:28:09.969 Henry Zhao: And if there’s no data, that means we are probably… it’s probably not in the right event. Okay, so let’s just do loaded a page. I don’t know how many people loaded a page from these sources and campaigns.
216 00:28:10.830 ⇒ 00:28:12.289 Henry Zhao: So I’m gonna do medium…
217 00:28:16.420 ⇒ 00:28:17.739 Henry Zhao: UTM Medium.
218 00:28:18.700 ⇒ 00:28:20.050 Henry Zhao: Earned media.
219 00:28:22.600 ⇒ 00:28:27.949 Henry Zhao: So, that’s your data, Christina. So, this is your traffic coming in from those UTMs on each day.
220 00:28:28.160 ⇒ 00:28:32.770 Henry Zhao: For the last 30 days, you can also make this 30… 3 months.
221 00:28:33.420 ⇒ 00:28:34.790 Henry Zhao: And then look at it by day.
222 00:28:35.580 ⇒ 00:28:41.250 Henry Zhao: And then if you want to save it, you would just go to Save, give it a name. So, traffic from…
223 00:28:41.460 ⇒ 00:28:42.850 Henry Zhao: PR campaigns.
224 00:28:46.220 ⇒ 00:28:51.490 Henry Zhao: Actually, no, you would… you can just type it here, so… Traffic from PR campaigns.
225 00:28:51.700 ⇒ 00:28:54.729 Henry Zhao: And it should autosave. And then you can,
226 00:28:54.960 ⇒ 00:29:05.310 Henry Zhao: refresh data here, you can copy the URL and share it with other people. You can also add it to a dashboard, that’s what you would do by save. So you would save it to a specific dashboard. So you might want to say, like, Christina’s dashboard.
227 00:29:05.530 ⇒ 00:29:08.029 Henry Zhao: I’ll create this for you, and you can put stuff in there.
228 00:29:08.260 ⇒ 00:29:12.039 Henry Zhao: And now you can go to this dashboard and see stuff whenever you would like.
229 00:29:13.000 ⇒ 00:29:14.390 Kristina: Beautiful, thank you so much.
230 00:29:14.700 ⇒ 00:29:17.459 Henry Zhao: So if you’re expecting more than this, this might be a good conversation to have.
231 00:29:17.730 ⇒ 00:29:20.710 Henry Zhao: have, like, are people… are your PR…
232 00:29:20.930 ⇒ 00:29:27.689 Henry Zhao: articles actually sending out the right link, right? If they’re sending it wrong, or they don’t have your ETMs, traffic might be really low like this.
233 00:29:27.920 ⇒ 00:29:31.919 Kristina: So, the problem is we only started with a link, like, a month ago.
234 00:29:32.370 ⇒ 00:29:33.860 Kristina: Okay.
235 00:29:33.860 ⇒ 00:29:37.179 Henry Zhao: tracking what we’re seeing here, right? The first one I see is October 16th.
236 00:29:37.320 ⇒ 00:29:45.389 Kristina: Yes, so we have… I have over 5,000 placements for the brand, but only able to track them from October.
237 00:29:46.270 ⇒ 00:29:47.010 Henry Zhao: Yeah.
238 00:29:47.430 ⇒ 00:30:06.010 Henry Zhao: And then you can break down by, if you have campaigns in here, this is why I said, like, you guys can then set up your campaign, content, term, that’s where you can then break it down here, right? So let’s say that for your 5,000 placements, you put in the term what the placement was, then you can do the breakdown and see it here, right? You could see, like, oh, this article, that article, etc.
239 00:30:06.780 ⇒ 00:30:08.539 Kristina: Okay, cool. Thank you so much.
240 00:30:08.870 ⇒ 00:30:19.590 Henry Zhao: So you might want to tell… if your placements are able to do this, Christine, you might want to tell them to add, like, a UTM campaign, or term, or content, whatever it may be, to, like, identify that it’s them. But if you don’t care about that, that’s fine also.
241 00:30:20.640 ⇒ 00:30:32.750 Kristina: It will be applicable for some, but in most cases, they only allow me to use a vanity link that, Ryan created for me, which has… which then redirects to PR campaigns and earned media.
242 00:30:33.200 ⇒ 00:30:34.610 Henry Zhao: Gotcha. Okay.
243 00:30:35.800 ⇒ 00:30:37.120 Kristina: Okay, thank you.
244 00:30:37.950 ⇒ 00:30:38.909 Henry Zhao: Yeah, no problem.
245 00:30:39.570 ⇒ 00:30:40.930 Henry Zhao: Any other questions?
246 00:30:42.730 ⇒ 00:30:45.730 Henry Zhao: Alright, if you guys have questions that come up later, just feel free to ping me.
247 00:30:47.990 ⇒ 00:30:49.130 Henry Zhao: Alright, thanks guys.
248 00:30:49.390 ⇒ 00:30:51.119 Judd K: Thank you very much, I appreciate it.