Name: Mixpanel & Google Analytics: A Live Walkthrough & Comparison
Uploaded: 2026-02-25T05:46:06.992Z
Duration: 59 min 44 s
Description: Mixpanel & Google Analytics: A Live Walkthrough & Comparison

Transcript for "Mixpanel & Google Analytics: A Live Walkthrough & Comparison": Hi everybody. Thank you for joining us today. I'm Celine and I will be leading the webinar today. We'll just wait a little bit for everyone to join and then we'll kick off. Okay. All right. I think we have most of the folks in. Thanks for joining us today. Reintroducing myself, I am Xin Lin, and I will be leading the webinar today. I'm gonna move on to the next slide. Okay. So just like a quick introduction, my name is Sin Lin. I'm a solutions engineer at Mixpanel based in the Singapore office, And I've been with Mixpanel for around seven years now. Across these, like, seven years, there has been a lot of customers and trends that I'm seeing in terms of moving from, like, GA four to Mixpanel, comparing GA four against Mixpanel as well, and trying to understand, like, what these differences are. These kinds of concerns are quite universal and are set across all the different industries like ecommerce, our fintech, as well as media and gaming. And before we dive in, I'll also quickly set the context for today's session. So what we have seen is that mostly teams begin their analytics journey with web analytics where you'll be able to understand your traffic acquisition as well as top line metrics. But as product scale and questions start to change, teams want to understand beyond the user visits and more around the user behavior as well. And that includes drop offs, how do we drive growth, how do we drive retention. So that's where, you know, today's session comes in, and we'll be focusing on how GA and Mixpanel are built to answer different types of questions and how that impacts your day to day job score, your day to day types of decisions making across the different roles. So let's take a quick look at what we have for today. Right. Okay. So we'll start off with a short introduction to level set on how analytics has evolved and why this comparison between the two tools matters today. We will also spend a few minutes breaking down the core differences between Google Analytics and Mixpanel, how they are built, feature wise as well as non feature wise, and then the types of questions that each tool is designed to answer. Following that, we'll go into the personas. So your product teams, growth teams, data teams, marketing teams to really understand how each team actually measures their KPIs and which tool fits each of these needs better. So the bulk of our session today will actually be a live ecommerce demo. We'll take three, you know, typical product and growth questions, and then we'll answer them side by side in GA four as well as mixed panel, so you can see the practical difference. During the demo, feel free to like use the q and a tab. Post your questions in the q and a panel as we go as well. We'll be covering that after the demo and, you know, don't forget to also respond to the polls that are in the post tab right now. Okay? Alright. So we'll move on to the introduction. We'll start off with the history of GA four. Alright? So GA four over here, there are, like, four main milestones that you want to look at. You may or may not know that GA4 actually started off, like, as a company called Agin, where they actually pioneer traffic tracking by creating UTM text and UTM parameters to create the campaign sources, campaign names, campaign creatives that we know very well today. In twenty o five, Google acquired Urgent, rebranded the technology as Urgent from Google, alright, and eventually launching it as the free Google Analytics service in twenty o seven. So from twenty o seven to, like, you know, around 2017. Right? That's when we have, like, classic GA universal analytics. That's essentially where we have a lot of focus we have seen on, like, web traffic, web app usage, and analytics is usually driven on, like, session based tracking using cookies as well as pixels in order to group and understand user interactions within specific sessions, within specific time frames via user IDs. So in 2020, that's where there was, like, this shift from, like, you know, universal analytics to GA four, where we basically shifted the model from, like, sessions to event based because each user interaction as a page view, a form filler, an onboarding creator, add to cart, all of these, we want to understand them as discrete events for better analysis. And we also wanna focus a little bit more on cross platform data because we're now, like, deeply in, the mobile app era where, you know, users tend to interact with brands on multiple platforms as well. So today, there is, like, some kind of unified tracking with GA four to try and unify web as well as ad reporting into a single stream. Alright? I'll also talk a little bit around, like, where GA starts to, like, break for, like, product and growth teams and how this kind of sets the stage for the types of problems and questions that Mixpanel solves for. Okay. Alright. So over here, you can see that we have this, like, high level difference between, like, the typical types of analytics tools that we see today. We have our legacy solutions like GA over here where, you know, we do struggle a little bit with, like, cross platform. Another, like, you know, big problem that we see with customers coming from GA is the ability to actually identify and figure out who our users are because as part of, like, our monetization motion, we always want to figure out who dropped off, how can we figure out who these users are, and then we target them. With GA anonymization is the default, so it makes this process fairly fragmented. Secondly, there is also like a lot of sampling that happens as well as delay in terms of data ingestion. So it also doesn't really do a lot in terms of building the data charts. Now we have like reporting and BI next, where basically anything under the sun can be like put in as like a data point for us to do our analysis. That includes all sorts of different types of entities and reports like sales of data, warehouse related data, inventory, operational data as well. So it doesn't really focus so much on, like, what users are doing. And, you know, it also requires a pretty high level of complexity where every type of question that you ask requires some fluency in SQL in order to get your answers out. Alright. So that's where mixed panel came comes in. Alright. We are purpose built for our product analytics for product analytics and journey insights with our event based tracking model as well as native support for cross platform behaviors. Now I'll talk a little bit more around how mixed panel differs. So I'm going to split this part into two bits. Right? Going to the next piece on the key differences. Alright. We'll do, like, two high level comparisons. First one being on the infrastructure side, and second one being on, you know, the analytics capabilities and performance side. Okay. So from infrastructure aspect, we can see over here, right, that, you know, GA four does not do real time analysis. You know, when you send in your data, there is a little there is a delay in your reports from anywhere from twenty four to forty eight hours as well. There is also, like, difficulty when it comes to scaling. Basically, once you reach more than 10,000,000 events in any type of report, sampling immediately gets turned on. And also in terms of tracking and having your data trust, the default way to send data to GA four is to use Google Tag Manager, but it is inconsistent along with the sampling as well as the latency in your analysis. It's very difficult for you to reconcile this if whatever types of back end data that you are tracking for your true transactions and your true user convergence itself. Right? The next one will also be on, you know, our integration ecosystem where, you know, GA does have, like, you know, a fairly close ecosystem and mostly supports products within the Google ecosystem itself. Whereas for Mixpanel, it is going to be real time analysis. We ask scale to ingest and analyze, you know, beyond billions of events on a daily basis as well. There are multiple ways for you to ingest your data in with trust as we allow you to import data through SDKs, APIs, as well as your warehouse data warehouse as your single source of truth. And we do also integrate with, like, an ecosystem of different partners from, like, ad data, from attribution to user engagement for your user campaigns as well. Now let's talk a little bit more on the analytics capabilities and where it differs. So Mixpanel as like, you know, a company that is purely focused on digital analytics, we outpaced UEFA by offering a suite of AI driven session replace, click maps, integrated feature management that are not natively found inside GA four. Beyond that, you know, we do have the concept of tracking your events as well as your users. So you use that to empower teams to run your experiments and also manage your data governance in one place, and that eliminates the need for any types of third party add ons. Now for companies who want to move past, like, more fragmented and, you know, basic reporting and into a more real time actionable cycle of, like, analysis, testing, as well as optimization, that's where Mixpanel will work well for you. Okay? Right. So we'll move into the next part on the personas as well as the key types of questions. We'll be covering three main types of personas today. The first one being that of the product managers. Right? So in terms of a a user feed level, I would say this is like a five out of five in terms of user feed. For product managers, we often see a lot of focus and prioritization in terms of what features should I be shipping. Which one should I continue development on? Which one should I pause my, you know, further development on as well? So if you want to go in and understand which features do well, where can we get our adoption, and how can we understand our users, Mixpanel has like a a suite of capabilities that can help us understand that. You can see that a lot of problems are faced in terms of tracking full user behavior across platforms, and that's where we have conversion analysis, session replay, as well as real time alerts to let you know if adoption has suddenly went above or below any types of thresholds. Okay? Now the next type of persona that we'll also go into is that of the gross marketer. So gross marketers will want to focus a lot on, you know, the ease of measuring the campaign success. How can we tie that to user engagement? How can we tie that to ROI as well? So Mixpanel is able to bring in your campaign data for and also run attribution related analysis on your conversions, on your page views, on your ad cards, any other key user milestones as well. Alright? In terms of user engagement and improving LTV, we also want to focus a lot on figuring out who are the important users that we'll need to deep dive into and then reengage. So we'll also show you a demo of how powerful cohorts how we can define powerful cohorts very quickly in Mixpanel itself. So growth marketer, there is also like a very strong fit in terms of like using Mixpanel to speed up your day to day processes. Right. We'll go into the the last persona, which is that of the performance marketer. So for performance marketing teams, we always wanna do a lot of, like, creating key campaign metrics and also optimizing on spend. So that's where, like, you know, acquisition efficiency on, like, CAC, ROAS, CPA, all of these will be very important. Mixpanel can help you with this by bringing in data from your ad engagement partners through our native integrations. We also offer abilities for you to create shared understandings of a metric like your CAC or your ROAS or any types of custom metrics that you may have within your app and share that with everybody as well. Alright? And in terms of channel optimization, there are also options that allow you to understand how campaigns are best performing, and this also makes sense in terms of our user attribution analysis. Okay. So with that in mind, what we will do now is move into the demo. Basically, we'll be covering, like, three main questions. How can we get everybody aligned on product metrics? How can we also, you know, truly understand our funnel drop off, and how to figure out what type of users can be monetized. So I'm going to switch from the slide deck over to my live demo. So you should be able to see a dashboard now. Okay. Perfect. Now it should be up, and I just wanted to give a team a very quick introduction to the UI. We have our dashboard over here where I've put together different types of metrics that may matter to the team, such as our user life cycle, understanding who our leads are, first time buyers, active users, Chinese users, Different types of revenue related metrics such as number of orders, average card value, total revenue are also things that we can create very easily. And for our user journeys, we do also have, like, custom reports that allow us to understand conversions for the different types of campaigns that we run, and also deep dive into what these users are doing when they actually drop off. So the flow report is something that we'll also deep dive into to kind of like decipher all of these little events as well as like annotations that we're seeing on the report. Okay. So we'll start off with like the simple building block of like understanding our metrics and understanding our campaign performance. Okay. I'm going to move into the mixed panel insights report. If you are coming from, like, the GA world, you can sort of think of this as, like, your explorations. So we have our events over here. I'm just going to put something like add to cart. I can also add another event, like, catalog. And then I can also add another one like my order complete. Okay. So I have all of these metrics out of the box over here. We'll run some analysis on this in a self serve way. So breakdowns allow us to do our analysis more quickly. I'm going to go into my attribution analysis. Like what we have mentioned just now, you can actually ingest campaign data and run attribution analysis in Mixpanel. So we'll go into attribution, and then from, like, you know, all of the different campaigns that I'm running where all of my UTM parameters are going to show up automatically, I will select something like UTM source over here, and maybe I'll switch this visualization from a bar chart to a table. So I can see, right, if I'm running an attribution analysis breakdown, I'm breaking down by UTM source. I'm using the last touch attribution model of which there are also a lot more options for us, and I'm also customizing a thirty day look back window. So this is telling me, right, in terms of add to carts, Google Ads has contributed the most, 10,000. In terms of, you know, catalog views, in terms of other completes, it is also winning as well. Okay? So attribution analysis like this is very easy to run. If you want to continue breaking this down a little bit further. Alright? Let me show you a way that we can do channel classification inside Mixpanel itself. So channel classification will we are not just looking at UTM sources, but we are looking at a variety of different, you know, a variety of different types of properties to try to regroup and define the channel itself. So I'm going to close this over here. Alright. And I'll create a new custom property. Alright. So we have an example for you, and I'll walk through how Mixpanel can actually run computations on the types of data that you send in. So if I select this example for channel classifier, right, what we will be looking at, we basically have this formula over here, right, that defines, you know, the different variables for the type of domain, and I'm using this to compare. Alright? I will use these values and look through my UTM source. I will look through my referring domain, my UTM medium as well. And for the different channels that I'm defining, right, paid search, organic search, social, shopping, email, I have my own definitions. And this is something that we've prebuilt for you. But if you want to change this, maybe from, like, referring domain. Alright? I'll use, like, initial referring domain instead. I can also change this formula very quickly and then apply it on the fly. So channel classification is something that is available. If you want to modify and tweak it, you can do that as well. So I'll use this, and I'll call it channel classifier. Okay? I'm gonna apply this. It will now look through all of the data and regroup them according to the definitions that I've mentioned over here. Okay? So this is what you can do with Mechanel to quickly create and save these types of campaign attribution analysis onto your dashboards. Okay? We can also reduce any types of cohorts that we have, any types of, you know, metrics that we have and put them in any kind of follow-up behaviors. So oftentimes, what we do is, okay, we understand like number of other complaints. Alright? If I also want to save this as a metric, as a reusable metric, right, total order complaints. I'm gonna save this. Or if I also have something like another metric, like my total revenue, I can also use my units over here and run an aggregation. I'm basically summing on amount right here, and then I'll rename this as, like, revenue. Okay? These metrics over here, once I save them, I can reuse them in my user journeys. I can use them in my user segments, in my user retention as well. Alright? But what I wanted to focus on was more of, like, a life cycle of, like, what product growth and performance marketers do, which is to continue to reiterate on this and run experiments to try and improve the performance from all of these different channels. So I have an example experiment report over here. This is something that is fairly new to Mixpanel. Alright? We have released experimentation as a module in the last year. So you are able to run your AB test on your web as well as your mobile platforms, and you'll be able to measure the different variants. Alright? What the performance is on your key metrics. Alright? Oftentimes, for, like, AB test that you run, you can't bring that data into GA four itself. And, you know, with Mixpanel, it's really unified and metrics that we've talked about, like our total revenue or, like, our total number of orders. These are all references these are all reference based on prior metrics that have been created by other teams as well. Okay? So I can always change and add things like a a new metric. Let's say I want to use, like, post purchase visit as another metric for my experiment. I'll be able to add that and quickly see that like, variant b as well as variant d are the top performing variants. Alright. We'll kind of contrast these two GA four itself. Like what I've mentioned, right, the core reporting and the core types of questions that we've been asking are focused around, you know, the metrics. So in GA, what we'll be doing is we'll be looking at our free form exploration. Alright? What I wanted to show is that for the explorations over here, in order for you to specify, if I want to look at my catalogs, if I want to look at my add to carts, right, I will have to, like, build all of them over here under my value. And if I want to run a breakdown by, like, my campaign or if I want to run a breakdown by, like, my category or whatnot, I will have to take all of these different dimensions over here and then specify them, first of all, right, over here, and then drag and drop them into the columns. Alright. So once I have something like this, right, I'm just gonna close this. Close this so that we have everything inside our roles. Okay. So all of these reports over here, all of these metrics over here, right, they will be something that you have to prepare ahead of time in order to drag them inside the segments, and you can also create and reuse any definitions itself. The types of metrics and analysis that we run over here, they are also available to you in the form of, like, a template user acquisition analysis. Alright? But there is also no such ability for you to drill down and modify, maybe change, like, the look back window or change or redefine, like, the different types of metrics that we'll be looking at over here. Okay. We'll now move into the journey and drop off analysis, which is our second use case. So in Mixpanel, we have our funnels for our sign up to conversion funnel. And I'm able to see, right, at every step over here how many users we have, how many users have dropped off. I can click on these users to define who they are. So I can call these drop off users. Right? Save them. And through our native integrations, I'm able to export them to any kind of campaign and engagement tool in order to reengage these users and bring them back. Alright? For this drop off itself, if I also want to go in and understand what are they doing, right, if they did an add to cart, but they did not do a checkout. So we have two options over here. The first one is for you to actually use Mixpanel session replay capabilities to understand on a user by user basis what these users are actually doing. We'll go into that. If we launch into our replace, alright, we can see, like, you know, based off the drop off flows, right, for all of the different behaviors that we are seeing. Alright? We'll quickly show you where on the screen they were at, where were they last at before they gave up as well, and we'll also give you some hints and annotations on, like, any types of date clicks or which clicks as well. If you click if you have one all of these sections on the analysis portion, you'll be also able to understand where do users actually get frustrated and where do they actually drop off. Okay? If you contrast this to the GA four funnels itself, we have, like, session start, view product, add to cart, checkout, and then purchase. We are able to see, like, how many users there are at each particular step, but you can't interact with it to understand for these users who actually, like, you know, add to cart and did not do checkout, where did they go, what events they did, what type of sessions were they actually launching. Alright. There's no ability for us to zoom in into this and there is no like seamless transition between the static funnels report as well as any other analytics report within GA four itself. K? We'll go into our last use case over here, which is to build out our user segments. Right? So for every business, there's always going to be your new users, your newly activated users, active users, as well as your churn risk users. Right? So these different types of, like, cohort and segments, they will differ from one industry to another, and campaign engagement strategies will also differ on whether a user is an active user or whether the user is churn risk user or not. Alright? So we'll do a deep dive into Mixpanel to help you identify who these users are. Alright? I have an example of, like, my active high value users. So I'm looking at someone who has done other complete multiple times, and they have made, like, more than $5,000 in terms of transactions. What I want to do is try to add more filters so I can make my targeting to them super customized. So I will do something like my order complete. Alright. I can go into my aggregate properties over here and look for things that most frequent. So I could also specify, okay, how what are their favorite brands, for example. Right? I can select a couple of these, add them to the cohort, and Mixhella will refresh these cohorts automatically for me, you know, for my analysis as well as refresh. Once I save these cohorts over here, Alright. We'll also be able to export these cohorts very natively to any other campaign engagement tool of choice. So that makes it very seamless. We will continue to refresh and tell you if there are any new users who have, like, joined this particular definition or not. Alright? Another option that we can also look at are those churn risk users. So I could create something like this. Alright. I want to look at users who have not done any orders in the last ninety days. Alright. But they keep coming back to add to cart. That's all. Or they just keep coming back to look at something. Alright? More than 10 times. All of these definitions will be calculated for me on the fly, and I can also reuse them, funnel them towards any campaign engagement tool, and then reach out to these users. Alright? So in contrast, right, if you go back to GA four, alright, we have our segments over here. You will be able to see, right, we can firstly only look for users who did something. We cannot look for users who have not done auto complete in, the last x days or the last x months. That's one piece. And if I'm looking at users who did add to cart, can I also specify other any preferences? Do they have favorite brands when it comes to checking out from, like, specific categories? Alright? I'm only able to specify based on the custom parameters or, like, the default parameters that are track that are being that are available to me. So I can say, like, okay, maybe it matches, like, women's, for example. Alright? Okay. But I won't be able to specify that this is the most popular, you know, category for the users itself. Once we also save and create these types of user segments, there are also no native ways within GA four to export these users out to any kind of campaign engagement tool. And this primarily is because all of the users that we are seeing over here, these are 3.3 k users, they are all anonymous as well. Okay? So bringing this back to the final part of our demo. Alright? We have all of these metrics. We have all of these journeys as well as active users that we are trying to focus on. Right? What we can actually do in internal is to bring them and tie them together in this kind of executive north star view. Alright? So we have this metric tree, things that we've talked about. Right? Our total number of, like, sessions. Our total number of product views from, like, different campaigns. Our conversion rate that we've looked at. Our active users that we've looked at. All of these can be metrics that I'm putting together over here, and they all eventually roll out through my North Star KPI, which is all, like, total number of orders that I'm tracking on, like, a daily, weekly, or monthly basis as well. I will be able to assign these types of metrics to any key users inside my org regardless of whether they are from, like, you know, performance marketing or growth marketing or product team as well. So that everybody has, like, you know, some accountability in terms of the types of metrics they would that they would be focusing on as well. And for, like, a health check, I can see, like, how these, you know, metrics are moving on a month on month basis, but I can also turn on additional analysis by highlighting correlation between all of my key inputs and seeing how they actually roll out to something like the key output over here. So this is telling me, right, I have a lot of new customers, right, over the last month. Okay? But there doesn't there isn't a very strong correlation to what the number of total orders they have been receiving. If this, like, continues to be a pain point, what I would want to do is to deep dive and use this channel to understand who they are, what are they coming from, for new users who actually converted, which traffic or which campaign gave me, like, the highest quality of these new users that eventually converted. Okay? Alright. So that's it for the demo. If you have any questions, I think I've seen, like, a couple being sent in inside the q and a. Do feel free to let me know as well. Then we'll be covering them later on. Okay? So I'm going to stop share and go back to the the presentation deck. Okay. Go back to our implementation and discussion over here. Okay. Alright. So I hope that demo was helpful. If you have any questions, please like post them in the Q and A. We'll be talking a little bit more around, like, the migration approach. If, like, you know, you see anything that has been helpful and you would like to explore the possibility of introducing Mixpanel to your tech stack. So in terms of, like, user and data migration, there are, like, broad chunks that we can break this into. Alright? First step over here is just looking at the data landscape. We have our historical data that we will ideally want to bring into either our BI tools or Mixpanel itself. Alright? So from GA four, there are, like, auto exports that help you move this data into your data warehouse of choice. The good news is that GA four data, alright, now is in the now it's in, like, the concept of, like, events. Right? So the schema is actually fairly similar to what Mixpanel has as well, and we do provide support to help you transform the shape of these events into a Mixpanel shaped event. Alright? On the bottom piece, you will also see like live implementation with a different with a couple of different options. So we have like a few different keep few different integration options for you that allows you more depth in terms of the types of properties as well as event track. We have SDKs, you know, warehouse connectors as well as CDPs that we integrate with to bring in all of your events, user profiles, as well as any other type of entities such as company company related data as well. Right? On, like, you know, a more, like, practical kind of, like, tracking timeline, what we also have seen with customers working to explore, you know, this and, like, displacing GA within their tech stack is to actually run some implementation in parallel. So this is an idea of, like, what you can look forward to in terms of, like, our project planning. Alright? So first of all, we do want to, like, establish that a lot of GA4 data is sample and anonymized. So for the sake of our month on month, quarter on quarter and annual base analysis, we want to focus on key metrics. And we'll narrow down on these metrics as well as reports in order for us to bring them over into mixed panel. Alright? Following that, definitely, there is a lot of focus on making sure you do your live integration as well as historical data import at the same time in order for teams to build trust and also, like, you know, monitor any types of data discrepancies between mixed panel and GA four. So unlike the discrepancy piece, this is a very, very common stickler for a lot of customers. It is definitely something that we'll have to monitor. Definitely, when you look at two different tools with two different, like, tracking mechanisms, it's very hard to, like, reconcile and understand why did one track more than the other. Right? So we will explore this. And, you know, what we have found works for other customers is to actually use, like, more server based tracking or proxy based tracking as this prevents your events and your true user behaviors getting double countered or getting blocked by any types of ad blockers that exist as well. Okay? Now so the key takeaway here is that moving from GA four to Mixpanel can be like a very structured process. It doesn't have to be like super complex or super disruptive. We want to run our migration in, like, parallel and in different phases so that teams can get value before your full transition, the full migration is complete as well. Okay? Now so I think we have, like, come to the end of, like, you know, the demo as well as the implementation discussion. Before we open this up for q and a, we also want to keep things interactive for a minute. So we've put together a short figure round based on what we've covered today. If you were following the personas and if you were also following the path on the demo as well, it's a very quick way for you to, like, validate your ideas. And we also have prepared some small prizes for the top responses. Okay? Alright. So I'm going to move into the trivia piece. Okay. Alright. So you we will, like, start to bring live the trivia questions, and we'll give the team some time for this. Okay. So we'll start off with the first question. We'll have around, like, thirty seconds to answer this. You can ingest your campaign data and run attribution analysis in Mixpanel. We'll be shutting this off within the next ten seconds. So do put in your votes based on what you have seen in the demo as well as in the persona discussion. Right? Okay. So we've been so we've gotten quite a few votes. We'll also move on to the next question as well. Alright. So this will also be important. If you have seen it in the demo, whether it's true or false or not, if you can create channel classifiers for your trim for your campaign traffic as well in Mixpanel. Okay? So we'll give another, like, ten seconds for these, then we'll move on to the next and final question. Okay. Alright. So on to the third question now. Mixpanel has an experimentation platform. True or false? The answer was revealed in the demo. Okay. I still see some votes coming in. We'll give, like, maybe five, ten seconds more for this. Okay. Alright. So those are all of the trivia questions. Thanks for, you know, answering them. I hope, like, the session has been helpful for you. There will also be, like, a final poll, you know, for you to indicate if you would like to learn more about MiX panel after seeing this discussion as well as this demo. Alright? So we'll now move into the q and a part. Okay? And then we'll select some questions that were placed, and then we'll go through them until the end of the hour. K. So I'll move to q and a, and we'll take a look at the questions. Okay. K. They have been quite a few. So I'll need to go in and, like, understand, you know, just maybe go by chronological order. Okay. Alright. So I should be presenting one of the questions now. Can you share the question? Okay. Alright. So first question, what are some use cases where I would prefer using GA four against Mixpanel and vice versa? Okay. So based off the demo, right, you can see that Mixpanel has a lot of emphasis on self serve if you want to further explore and understand what's happening with your campaign ROI. Or if you want to switch gears and understand, you know, AOV metric and moving to something like, you know, funnel conversion metric. So if you have this kind of user behavior, right, where you want to focus a lot on switching gears between your different metrics and you care a lot about real time analysis, that's where you want to use Mixpanel for. Alright? Where GA four really excels are in the template types of reports for the traffic management and campaign performance. So those would be the main kind of, like, use cases or, like, purpose for GA four itself. Although, you know, one caveat is that, you know, all of these reports will start to have sampling upon, like, you know, around 10,000,000 events and there also wouldn't be like a very high level of trust when you have sampling as well as delay in terms of data. Okay? Alright. So let's go on to the next one. Okay. So from Navin, do you have any idea about the differences between GA three sixty and GA? I think GA also gives real time insights and can work with cross platform data. Okay. So in terms of, like, differences between GA three sixty and GA, there are no new analytics capabilities between GA three sixty and GA four. Alright? The main differences that we see are actually in terms of the sampling as well as in terms of the data latency in in terms of, like, your reporting. Alright. So GA does give real time insights, but this is only for, like, limited reports. Alright. You wouldn't be able to get real time insights on every single report itself. And cross platform analysis will work only in the context of specific dimensions and specific metrics. So it won't give you as comprehensive a platform for you to freely ask questions around what users do, how users retain, how do they engage across different platforms. Right? K. So we'll go into the next few ones. K. Take a look. Alright. So I'll share some that I am seeing right now. Okay. So this one is more on, like, event tracking. I am going to use wait. I'm just yeah. So j four has, like, snake case for event naming. What are the recommended naming conventions for events in Mixpanel? Mixpanel does has a few different, like, naming conventions that we do work with. So snake case is one option. We also do have, like, couple of case as well. So what will happen is when you start to create your different naming conventions, right, we do have a data governance suite that allows us to basically create a convention and create a standard for your event naming and help you understand a little bit and help you, like, monitor if there are any new events that come in and do not comply with your naming convention. Right? Okay. So going to the next few ones. Okay. So for this, we have a question around, like, any updates or features to prevent and filter the AI traffic for better performances check as well. Okay. So a couple of things to note over here. If you do use mixed panels SDKs for tracking, right, Our mixed our client side SDKs do have some inbuilt abilities to filter for bot related traffic. Alright? And what we'll do is drop these events. Alright? So those events do not get ingested for your analysis. And for better performance check and also because we see, like, more AI traffic coming from everywhere, that also brings me back to, like, our recommended implementation approach, which is to either use servers server side tracking via a proxy server. So you can run your own filters in terms of the types of events that look suspicious. There are also data warehouse connectors that we can use as well to let you, you know, filter through the data. Alright? We do also have a mixed panel data governance suite where what have what we'll be able to do is that you establish any types of data rules. So if you see any types of event names or event properties that do not look familiar to what you have already established or if you also have seen a sudden spike in specific types of event properties or event volumes. That's where we'll also flag all of these and suspend the event for your analysis itself. You will have to manually go inside the data governance suite and understand and let us know whether those events actually make sense or not. K? Alright. Okay. So we have, like, another question, I think, on UTM, which okay. So this is a fairly long question over here. Okay. So for UTM, with which method are we tracking our attribution sources? So the experience is more tied to web tracking, but you also want to understand how UTMs can be tracked with Mixpanel for mobile. Can this be tracked via Mixpanel s SDK or custom integrations? K. So this is a very good question. For the UTM values that we see, yes, the in the demo, what we have shown, right, those are mostly web related traffic over here. Similarly, for, like, mobile, you will also have your source, your campaigns, your creative, your socials. Alright? But those data will not be, like, freely or publicly available. What Mixpanel does is help you collect this data. We do have native integrations with attribution players that exclusively own this data so that we'll bring that in for you to understand how users from specific campaigns, you know, eventually convert or how do they eventually retain. And a couple of the players that we work with and integrate with well are AppsFlyer, Adjust, as well as Branch. Okay. Alright. Okay. So on the next one, in the funnel analysis that you showed earlier, there was a feature that lets you watch screen recordings of the user. Does this feature actually require obtaining consent from users for screen recording and data usage permission? So 100%, yes. Mixpanel will only, like, collect data that users consent to. This is due to, like, respecting, like, any kind of, like, privacy of the users itself. What I have shown you is purely in, a demo scenario as well. But in a live production environment, if you do want to use Mixpanel session replay capabilities, there are multiple options for you to mask and unmask important and sensitive properties. So as an example, text fields, images. Right? All of these can be masked automatically by default, especially if there are any things related to input from the users itself, those are automatically masked as well in order to protect, you know, and prevent, you know, any kind of, like, violation in, like, data usage as well. Okay. Alright. Okay. So we'll have a couple more questions. Alright. So how does Mixpanel integrate with Google and Meta Ads as well as other marketing platforms? Now if you go and check out, like, the Mixpanel integrations page, alright, We do have native integrations with Google Ads as well as Meta Ads as well. So the cohorts that we have shown you previously, right, like the churn risk users or the high highly active users that you want to retarget. Once you create these cohorts, we can actually use the native integration to export these users out to Google Ads, out to Meta Ads for you to do your retargeting. Alright? Now this can also be very powerful because what Google what Mixpanel does for you, right, is we actually allow we give you the option to track user information. Right? So we actually know who they are. Once you have this information, you can pass this to Google and Meta Ads in order to find your look alike audiences with a lot more accuracy. Alright? So do check out our native integrations page as well. Alright? Any other types of marketing platform data. Right? We can also explore integrations to the data warehouse connectors. So I just wanted to also share about ingesting ad spend data or ad impression data. Alright? Once you receive this aggregated data from, like, you know, your different ad engine platforms, you can also push that into Mixpanel to run comparisons and see, okay, for certain days where I had a huge bump in impressions, right, or I had a huge bump in ad clicks. Did this also correlate to the eventual sign ups that I'm seeing on my platform as well? K. Right. We'll go through some of the other questions as well. Okay. I see one online pricing over here. K. So is there a way to monitor the number of events being charged in real time? You know, this is more for, like, cost management. Okay. So, like, what we've mentioned, all of these event ingestion comes in in real time. You can definitely, like, monitor how many events are being ingested within the mixer, no project and organization itself. There are also billing mechanisms that allow you to see from, like, how many events you have budgeted for your usage. Right? You'll be able to see what is, like, the percentage of, like, event fulfillment or event utilization that you are seeing. And in terms of, like, stopping or stacking tracking instantly, there are a couple of ways that we can do that. Through the data governance, we you can ask MiXiao to stop ingesting certain types of events as well. Although what we would recommend is to, like, you know, go back use that as, like, a short term solution and then to also go back to your event implementation and, you know, remove the events that are unnecessary as well. Okay. You might okay. There is one question on, like, filtering source without the use of UTM. So I'll probably need a bit more information on these. For the UTM sources, these are all available on, like, the web traffic. If you are talking about mobile traffic as well, can we get, like, the campaign source information? Right? All of those are also available to Mixpanel through, you know, our native integrations with attribution tools. K. Alright. Share on this one. Okay. I'll go on to the next one on, like, Mixpanel. How can we set up the events? Do we do it directly to using Mixpanel or using GA for event tracking? Okay. So this is, like, fairly, like, loaded question, I will say. Now when it comes to event tracking, right, we'll always recommend using either the Mixpanel SDK or using a trusted event source for your ingestion. Alright? When you say we need to use GA for event tracking, that's using the Google Tag Manager to track and trigger events to Mixpanel. Mixpanel does have, like, you know, Google Tag Manager wrappers that allow you to do this tracking for web related traffic. Alright? However, there are no, like, GTM wrappers for, like, mobile traffic. So it becomes a little bit fragmented where you can handle and reuse your GA four tracking for your web with that traffic, but you don't actually have, like, an easy solution for your mobile traffic itself. Alright? So we'll still recommend using our SDKs additionally as well for teams who are looking at user event data migration, right, as well as comparison between different tools. Data discrepancy is always going to be one of, like, the biggest problems. So if you use, like, you know, our own tracking mechanisms, right, we'll definitely be able to, like, fully support you in troubleshooting or understanding any types of discrepancies or missing data or additional tracks as well. Okay. Alright. So we are at, like, the last sixty seconds now. I will just, like, go to, like, one final, like, question on, like, mixed panel training over here. We do have, like, our help center and also a mixed panel university that you can sign up for. I hope this session has been helpful. Do fill up the poll and let us know whether you would also like to learn more as well. Thank you so much for your time. I hope this has been helpful and do, like, let us know how this has been for everybody. Thank you.