I'd like to generate a report with breakdown of performance metrics for each RSA combination
Not that I'm aware of. Especially considering it's not even available beyond a very limited set of metrics in the web UI. Right now, I'm still pulling each of the assets and manually building out the ad in an ads report. Considering the push to make RSAs default, I can see this being released soon though.
Related
I am wondering that how to find the eVar names that are already implemented in the Adobe analytics.
Please let me know how to find in the bulk of all implemented eVars that will help me better understand user flows and build the charts efficiently.
There are a few ways to get bulk settings info. The most comprehensive is the Adobe Health Dashboard- https://express.adobe.com/page/tnNQGNlfzta3b/. Thisnot only tells you what each variable is named and what its settings are, but also shows you what type of data it is receiving. The downside is it requires adobe.io authorization, which can be a pain to get set up. It also only runs on PCs (because of its excel macros). I believe it also really only shows one report suite at a time.
You can also use tools like https://reportsuites.com/ to just get an export of variables and their settings.... it's particularly useful if you want ot compare across report suites. The main downside there is that adobe has made it hard to find the web services API credentials you need in order to run it.
I was reading about app indexing for iOS, First thing I found was Firebase App Indexing, which is pretty straight forward. Then I came across Mark Up Web Content, how is this different from Google's. If I have to do app indexing for my app should I implement both or which one to choose and how to choose?
Great question. Firebase App Indexing is the renamed successor to Google App Indexing. This is the method Google uses to help show app content in Google search results. In theory, Apple's Mark Up Web Content feature is used to show web content in Spotlight searches on iOS devices. I say 'in theory' because I've never actually seen it work in practice.
Both are nice concepts that don't really seem to be fully implemented yet. At Branch.io we support both for our partners out of the box, but we haven't seen a huge amount of traffic coming through from either one.
I'am creating a new feature for my iOS app. After I publish the app I wants to show the new feature only for 50% of the users, so I can do some testing which version makes more orders. I have no idea how to do it without using some third parties like Optimizely.
Also is it possible to do this using Google Tag Manager(GTM).
So can someone please help me to figure this out.
Thank you very much for your time.:)
It’s hard to do it on your own, though not impossible of course: Optimizelys of the world are just programs. You’ll need to solve these problems:
Targeting: Some algorithm that will assign user session to either control or (one of) treatment(s). This has to be random, of course, or you may as well stop there.
Routing: Send sessios to the targeted experience.
Logging: You’ll need to intelligently log events from sessions as they traverse their targeted experience. These may be many, so be careful not to add latency to your app path. Your statistical analysis will be based on these.
Experience stability: how do you ensure (if you do) that a returning user sees the same experience he’s already seen.
Note as well, that Optimizely will only help you if all your changes are on the device and not on the server. If you need to instrument server changes as well, you’ll have to look into Sitespect or Variant.
I finally figured out how to do the A/B testing with 'Google Tag Manager'(GTM).
In GTM you can create a variable called 'Google Analytics Content Experiment'. With this variable you can select how many percentage of users going to see each Variation(your experiments). You can create up to 10 variations for single experiment.
GTM is so cool and powerful. GTM contains so many features that could save lot of time and I totally recommend it for anyone who is going to do A/B testing.
I'm currently looking for a way to track basic user data for mobile iOS application:
how many times the app was launched
what was the average/by session time spent in total while using app
what was the average/by session time spent on particular screen
Additionally, I'd like the solution to:
display a heatmaps or click/tap/maps (clickstreams), to show how users tried to interact with the interface
generate visit graphs (user started from this screen, then went to this screen, etc.)
The most important requirement is that this is for internal application testing (nothing malicious), and we want to categorize data by user logged in (so that we can gather data per user, not some general average).
Can anyone recommend a suitable tool? Price or paid, doesn't matter. Is Google Analytics up for the job, or do we need something else?
Youve got several options to track the user behavior in the app. You can use frameworks like :
Flurry (http://www.flurry.com/)
Mixpanel (http://mixpanel.com/)
Localytics (http://www.localytics.com/)
Google analytics
Im pretty sure there are more. Flurry is free (for now but you have some special paid features) and it´s broadly used. It´s the framework I use the most for my apps in these moments but it will depend of the client and the information you want to track. You can track events, events with information, see the stats of use, how the user has used the application, find dead holes in your app and broadly speaking, have a general idea about how your application has been used. The other frameworks are not free and you have to pay for the services but you can always use a trial version to see if this is what you want or not. Ive used localytics and its nice.
Ive tried all of them, and there are pros and cons, but to get a general idea about your application, everyone serves. Regarding heatmaps, Im not sure about that, I mean if some of the frameworks offer a solution like that, but you can always build your reports with the provided information (I know it´s not a straightforward thing or a 5 minutes thing).
Take a look, compare and decide which one can fit the best for you.
Well these days app analysis is very important and are of great help. There are large number of analytics tools available. Some of them are free some of them are paid.
below are some of them
Flurry
Google Analytics
Heatmaps
These are few which are used most. For most list visit this link
Hope this will help you. happy coding :)
I have an iOS app which can be used offline. I need to do anonymous page view tracking, so our customers can tell which pages people are most interested in (to drive future investments). So when the user is offline, we save a timestamped page view list, and if the user happens to be online when they use the app, we send these historic records up, and also do real-time tracking.
I'm keeping some summary statistics in my GAE app, so I can report the page views with historic accuracy. However, I'm also feeding these views into google analytics, using some python code I ported from google's server-side samples.
That all works great (except for language tracking, which I may have solved thanks to a separate question here on SO). However, I'd love for google analytics to be able to understand the historical hits in context. Right now, if I connect up after looking at several pages offline, GA thinks I just popped through a bunch of pages over the course of a couple seconds.
There is no documented utm variable for timestamping. The google analytics SDK for iOS (which I'm not using) has this ominous note:
Known Issues
Possible inaccurate timestamps: timestamps are recorded at the time the application dispatches to Google Analytics, so if a user experiences long periods of offline use, the timestamps may not be 100% accurate.
That seems like a bit of an understatement. Wouldn't offline timestamps be 100% inaccurate?
Anyway, the fact that the SDK doesn't handle this right makes me think I'm not going to be able to solve this. But I figured some SO wizard might have an idea...
In fact, timestamp is a "relative" (client side) information used by Analytics to compute things like "time on page".
When the page is view in "absolute" (date and time) is always the time you send the request.