Is possible to send pushes from backend (using Pushwoosh remote API) to any device thats be inside a defined geozone? Can I use for do that the getNearestZone function? I don't understand it at all, I figured out that the getNearestZone function let me know the distance to the nearest geozone configurated by the pushwoosh panel?
GeoZones by default are intended to work automatically. When users enter a GeoZones, they receive push message triggered by it.
Currently, the manual/scheduled push messages can be delivered via Tags only, i.e. to Cities and Countries.
There is an Enterprise method, that lets you get recent coordinates of all devices (!) with their IDs via API. In this case you can target these devices via their IDs directly, or put a Tag on them. You can also dynamically set a GeoZone in the area of your interest via API (also an Enterprise method), for example in the zone with high density of your subscribers.
Related
I have a table that gets filled with data received from an API call. Is it possible to somehow "lock" Chrome's network inspector on this element (= the table) and make it only show calls related to it and not every single one made by the rest of the page? Maybe this is doable with an extension?
I would say it's not possible to achieve, because:
The view and the logic are also seperated in (modern) frontend applications. So the app can request a JSON file from a server when it starts, and then passes those informations through the entire application (tables etc.)
A table itself is not calling the endpoint, and can be just generated by a function with the prefilled data.
The closest you can get is:
Record screenshots in Chrome to see when the table gets filled. There you see which calls got made during this period of time:
Afterwar you know what's the "name" of the call, you can put it in the filter/search field and reload the page. Afterwards, you will always just get shown the call you selected and is possibly responsible for filling the table.
Of course, you can also go by hand and say:
What is inside the table? Which information does it display?
Then go through the api calls and look for the information in the response bodies
Once you identified the call, filter it inside the chrome dev tools.
And if you want to go to the next level:
Copy the call als a cUrl (via right-click on the call) and insert it in Postman and also install Postman Inceptor. Afterwards you can pass website calls (the one for your table) to Postman so you can have a closer look at it and also re-call it every time you need the specific call.
To summarise:
Figure out which call has the information for your table, extract the call using Postman Inceptor and pass it to Postman with it. Then, you can analyse or do whatever you want with it.
It seems that Custom Dimensions are the new way to send custom data from a website to GA. I'm new to GA but my manager has used GA in the past and I'm guessing this was before the CD structure existed in GA. Do you know when the CD structure was introduced in GA?
He has sent custom data to GA in the past using events. This seems like a viable way of sending data and another manager at my company had referred to this approach last week so it seems like maybe this was a standard approach before GA introduced CD's. So given the following request:
var myRequest =
{
UserID:1234,
SelectedReportType:1,
};
What are the tradeoffs between sending this request data to GA as a CD like this:
ga('set', 'dimension1', JSON.stringify(myRequest));
ga('send', 'pageview');
Vs sending this request data to GA as event data like this:
ga('send', 'event', {
'eventCategory':'MyWidgetUserSelection',
'eventAction':JSON.stringify(myRequest)
});
?
Custom dimension where introduced with the switch from "classic" Analytics to Universal Analytics (IIRC that was in 2012), where they replaced (more or less) custom variables.
"Classic Analytics" (not an official name, AFAIK the previous GA version did not have a name other than GA) was a pretty messy thing that pretty much used the technology of the original Urchin tracker (Urchin was a web tracking company Google acquired in the early 2000s and rebranded their product as Google Analytics). Classic analytics pre-computed a lot of data on the client side (using up to five different cookies), including traffic source attribution, before it made a rather convoluted image request to the Google server.
In contrast Universal Analytics was designed on top of a clean protocol, the measurement protocol. It is "universal" because any device or program that can make a http request can now send data to Google Analytics. Universal Analytics does not compute any data on the client side, the data is processed only after it arrives at the Google tracking servers.
"Classic" Analytics had up to five custom variables in different scopes (hit, session,user)). They were displayed in the "custom" menu item of the GA interface (which is still there, but is now useless unless you have old data that was collected with classic analytics). Five variables posed a pretty tight limit, plus it was not always easy to understand how exactly they were supposed to work. So people developed a habit of storing additional data not in custom variables, but in events.
Universal Analytics in the free (commercial) version offers 20 (200) custom dimensions in four different scopes, to wit hit, session, user and product (and an additional 20 (200) custom metrics, although very few people seem to use custom metrics). "Hit scope" means you can add a dimension to every single interaction. "Session scope" only retains the last value for a session. "User scope" is primarily for values that are set once per recurring user (i.e. a user turns into a customer). With the product scope you can add additional properties to the products in an ecommerce-transaction (or production impression etc. if you are using enhanced e-commerce tracking).
Conceptually event tracking and custom dimensions are not remotely comparable. A dimension is a property that is connected to an interaction hit (or a collection of interaction hits like a session or a user) and allows to break down metrics into indivual rows. For example the "pageview" metric can be broken down by page path or page title, which are automatically collected. You might add a custom dimension "page category" and you can break down your total number of pageviews into separate rows that show the number of pageviews per category.
Custom dimensions do not have their own report; you can select them as secondary dimension in a standard report, or create custom reports based on them. You can also use custom dimensions to segment sessions or users by the respective values for the dimension.
Events on the other hand are interactions in their own right, with their own set of default metrics and dimensions (in fact you can amend events with their own custom dimensions). Proper usage of events is to track interactions that not load a new page (or do not change the page content enough to warrant a pageview call).
You can use events for segmentation (i.e. "show only sessions where the user had a certain event"), but you cannot break down pageview metrics by event properties. That is actually the main difference.
A more practical concern is that events, unlike custom dimensions, count toward you data collection limit (the free version of Google Analytics allows for 10 mio hits per month only, although the limit is so far not strictly enforced). Since custom dimension are not interactions by themselved they do not count towards the quota.
SETUP:
Having a few different information sites/domains about my products and one single site/domain shop-site where you the purchase, checkout and so on happens, I'm having troubles to find a proper solution for a comprehensive tracking of all the pages including the conversion tracking.
IDEAL:
What I want is to get reports seperated for each site (shop-site as well as information-sites), but add some conversion tracking. Ideal would be to be able to track a conversion when the user gets to the checkout process, so that I can see it in the statistics of the shop-site as well as on the site that referred to this sale/conversion and maybe even a funnel visualisation for the whole action.
WHAT I ALREADY GOT:
I already set up statistics for each site/domain. I do have set events for a referal to the shop-site as well as an event when the user checks out.
WHAT I TRIED:
I set up cross domain tracking for one of my information-sites and the shop-site, so now the events show up in that single property.
Unfortunately thats not the statistic I intend to get, as the whole data got consolidated from those 2 properties. Also it made the tracking goal/conversion only accessible for that single property, while I can't distinguish between a conversion made originally from this site or one of the others.
Is it possible to achieve what I actually intend to do or what's the proper way to track such a setup??
You can either use an additional tracker (so you have one UA id that goes in your "normal" domain, one that goes in your shpooing site and one that is placed on both sites) - you'd still have both pages tracked in separate properties, but you'd have one rollup property that tracks all your sites. This might however unwanted complexity if you use event tracking etc (since you'd have to see to it that events are always pushed to the correct tracker).
An easier solutions is via views/profiles - use the same UA id for both sites; create views based on domain name (filter in the admin section) to track each site separately; for the common data view that display data from both sites create a filter that includes the hostname in the reports so you can tell both sites apart in the report.
I'm building a node.js application that opens up a connection to the Twitter Streaming API (v1.1)
I would like to filter multiple keywords (hashtags & words) as separate queries. My original idea was to have multiple public streams.
However, I understand that I can only have one open connection to the Twitter streaming api per application and per IP address and that Twitter encourages us to come up with creative solutions to get what we want.
So my question is this:
If I stream with no filters, such as using statuses/sample (which I believe is 1%) and use custom javascript to filter the output, would I get the same tweets if I used the API method of filtering (i.e track='twitter').
Edit: I have created a diagram explaining this:
As you can see, I want to know if the two outputs wil be the same. I suspect that they won't be because although both outputs are effectively the same filter, one source is a 1% sample, and maybe the other source is a 100% sample but only delivering 1% tweets from that.
So can someone please clarify if both outputs are the same?
Thank you.
According to the Twitter streaming api rules, if the keywords that you track doesn't exceed 1% of the whole global traffic you will receive all data (some tweets might be lost due to network issues etc but it is not significant). This is called garden-hose (firehose is a special filter which gives you all the data but it is given as a paid service through third parties such as http://datasift.com/)
So if a tweet is filtered through public stream then it would be part of your custom filter too unless your keyword set is too broad.
By using custom filters you can track multiple search keywords, and if you miss some data because your keyword set is too broad twitter sends a track limitation notice indicating how much data you are missing.
My suggestion to you would be to use a custom filter and analyze what you get from the stream and what you get as a result for the same keywords from twitter. And when you start getting track limitation notice from twitter, it is time for you to split your keyword set into chunks and start streaming through different streamers by running them from different machines.
The details of the filter streaming is below (taken from official website https://dev.twitter.com/docs/api/1.1/post/statuses/filter)
Returns public statuses that match one or more filter predicates. Multiple parameters may be specified which allows most clients to use a single connection to the Streaming API. Both GET and POST requests are supported, but GET requests with too many parameters may cause the request to be rejected for excessive URL length. Use a POST request to avoid long URLs.
The default access level allows up to 400 track keywords, 5,000 follow userids and 25 0.1-360 degree location boxes. If you need elevated access to the Streaming API, you should explore our partner providers of Twitter data here.
I would like to answer my question with the results of my findings.
I tested both side by side in the same time frame and concluded that the custom filter method, whilst it supports multiple filters does not provide enough tweets to create an interesting enough visualisation.
I think the only way to get something more interesting with concurrent filters is to look at other methods but I am wondering if its not possible. Maybe with a third party.
I have attached a screenshot of the visualisation tracking 'barackobama' The left is the custom filter, the right is statuses/filter.
The statuses/filter api operate on all tweets, instead of those returned by statuses/sample, you can tell by looking at their tweet id's: sample tweets all come from a specific time window. So from millisecond-resolution creation time, you can definitely tell that filter returns tweets outside of sample.
For more details about getting creation time from tweet id and the time window on sample tweets, consult this post: http://blog.falcondai.com/2013/06/666-and-how-twitter-samples-tweets-in.html
I have a google analytics account for an asp.net application which determines one of many clients based on a queryString within the URL. The snippet for the GA page is located within the master page, so the GA code is consistent across all clients.
What is the most efficient process for getting a set of basic analytics through the GA portal, per client (per queryString)?
---Edit---
Off hand , but never accomplished, I want to know if I can set a variable var1 within the snippet that gets set to GA identifying a client, then get discrete but identical reports, per var1 on the users. So that var[0] .. var[n-1] = Visitors where n is the number of clients.
You can just push the unique query string (assuming it's some sort of uID) as a custom variable into analytics. If it's sequential, you can assign ranges to clients (once again, am just guessing what your setup is like).
Alternatively, you can use the uID as key and have visit frequency as the value. (here is some prototyping code to check custom var key values https://github.com/vly/js_ga_cvars)
Just remember, when you set the custom vars you have to push a pageview or event to actually pass it to GA.
If you have access to the GA Universal beta program, you have the opportunity to define custom metrics (in case of a numeric data) or dimensions which would make aggregation reporting a lot easier.
If the client is an individual person, this is not allowed by the google terms of service.
You will not (and will not allow any third party to) use the Service
to track, collect or upload any data that personally identifies an
individual
http://www.google.com/analytics/terms/us.html