Develop Reference

Override UserID in Google Analytics

To import my crm data into Google analytics (GA), I linked the UserID of my users with ClientID in GA.
For this, I used the following code from GA documentation:
ga('set', 'userId', '432432');
Over time, the format of the User IDs on my website has changed - instead of the numbers, hashes are now used.
Can I now use the same code above, but only with new identifiers of my users, to send UserIDs то GA without damage current analytics?
In short, can I override the current User IDs in GA so that one user is not identified by the GA system as two different people?

You can't overwrite the historical data already processed by Google Analytics, only those of the current day.
You could apply the new algorithm only to new users of the crm, from a given id on, leaving the same encoding (numbers) for the previous ones (users already processed by Analytics).

If you have a mapping table between the new and old ids there is a solution.
You need to take out the data from GA - historically. (You can use the paid service from Scitylana for this - you get data in BigQuery or in S3)
Then you need a copy of the new_id old_id mappings table in the database where you put the exported data from Scitylana.
Since you can no longer rely on the ga:userType variable (new/returning). You need to create a query that calculates it again using the consolidated new ids.
This can all be set up in a flow that updates nightly.
But you need to analyze via SQL or use a dashboard tool like Power BI, Data Studio, Tableau etc.
Since the data from Scitylana is hit-level you can calculate everything correct, no need to worry about double aggregations etc.
(I work at Scitylana)

any reason not to send data with events as opposed to custom dimensions?

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.

Is Possible to send GeoZones based pushes with Pushwoosh Remote API + Phonegap

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.

Cross Domain Conversion Tracking

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.

Twitter Streaming API Multiple Stream vs Custom Filter

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