Google Analytics Graph Values Do Not Add up to Number in Box - google-analytics

Chart from Google Analytics
As I worked on my sites monthly report for visits/trends, I noticed that the user number provided in text (value 4539) is different the the number you get when you add each day's plot point together along the blue line (value of 5110). I have the graph set for users, and also made sure the time frame for data was the same, but I am not sure what why these numbers differ so much.
Can someone explain this to me? Apparently I am an idiot.
Edit #1: This is the default settings under Reports > Audience > Overview. I have no dimensions added or anything more than just the strictly default settings.

User numbers are time dependent. That is to say, a user may come back more than once in a month, and GA knows that because it's detecting the cookie dropped in their browser. So, if Person A visited your site on Monday he counts as one user, and a report for Monday counts him as that. If he comes back on Tuesday and you look at a user report just for Tuesday, he is again one user. So, in individual daily reports Person A is counted as one user, in 2 reports. If you look at a report for the week, he counts as one user, because GA knows that he was person coming back to your site twice in one week.

Related

What is the chart lines in google analytics overview display?

when I look in Google analytics under visitors overview there is a line chart that tell me how many users per day I have had. But these numbers does not add up to the ones below that show users, new users, sessions and so on. What does the line chart actually tell me? If I for example export the report to an excel file by day I get a lot higher number of users per day compared to exporting by month which is much lower. Can someone explain the difference. I wanted to know the number of visits to the site per day....
While the trend tells you how many individual users visited the site per day, the "Users" below represents you the de-duplicated count of users who came to the site during the time frame applied.
Example: you visit the same site on 4 separate days during a particular week, the line chart will identify you as a visitor on all 4 days (4 daily users). While the User count below counts you an "one" user for the week.

Unique Users in Google Analytics

I'm trying to get all unique visitors for a selected time period, but I want to filter them by date on the server. However, the sum of unique visitors for each day isn't the number of unique visitors for the time period.
For example:
Monday: 2 unique visitors
Tuesday: 3 unique visitors
The unique visitors for the two days period isn't necessarily 5.
Is there a way to get the results I want using the Google Analytics API (v3)?
You're right that Users aren't additive, so you can't simply add them day by day. There are several ways around this.
The fist and most obvious is that if you've implemented the User-ID you should be able to straight up pull and interrogate the data about which users saw your site on which days.
Another way I've implemented before is to dynamically pull the number of Users from the Google Analytics API whenever you need it. Obviously this only works if you're populating a live web dashboard or similar, but since it's just the one figure you're asking for, it wouldn't slow down the load time by much. Eg. if you're using a dashboarding tool such as Klipfolio, you may be able to define a dynamic data source, and query Google whenever you needthe figure (https://support.klipfolio.com/hc/en-us/articles/216183237-BETA-Working-with-dynamic-data-sources)
You could also limit the number of ways that the data can be interrogated, and calculate all of them. For example, if you only allow users to look at data month-by-month or day-by-day, then you only need those figures.
Finally, you can estimate the figure with reasonable accuracy by splitting it into two parts. New Users are equal to New Sessions (you're only new on your first Session), which is additive, so that figure can be separated out and combined as required.
Then, you could take a rough ratio of new to returning Users (% New Users) from, say, 1 year of data, and use that with the New Users figure to generate an average on any level.

Difference between Active Users and Retained Users

Can someone explain me the difference between the 2 APP metrics, Active Users and Retained Users. I am looking at these metrics through flurry API and I am grouping my data at month level.
I am not able to understand what is my Retained users number mean at a month level.
Retained users plots two lines. The blue line is the number of users who installed the app in a given month, week or year. The green line identifies how many of these users had at least one session with your app in the past week.
The two lines will generally converge for the current month so use weekly or daily views to analyze the current month.

Doing cohort analytics on Google Analytics

Suppose I have 65 people that register on January 1, 2012.
I want to find out how many of those 65 people returned to the site that same week. (More generally, if n people signup on date A, I want to be able to find out how many of those n people return in a given date range.)
Is there a way to do this using Google Analytics? If so, how? I am currently getting the user's username for each page hit.
If you only need to track people who sign in then you don't need to get very fancy. You can copy the relevant user attributes, such as sign up date, from your DB to GA using events or session level custom variables.
But if you want to track everyone, including those who don't sign up, then you'll need to use visitor level custom variables (GA cookies).
I explain how to set this up in detail in this post so I'll just highlight the key points here:
First, decide how to layout the data in Google Analytic's custom variables based on your requirements. For example, are you storing retention dates for daily, weekly or monthly tracking? Do you also want to track cohort goals? Partition this data into the available custom variable slots.
Write the cohort data to these custom variables when visitors arrive or achieve goals using Google Analytic's _setCustomVar function. Setting the fourth parameter of that function to 1 indicates you want to do visitor-level (cookie) tracking.
For each cohort you wish to analyze, create an advanced segment in Google Analytics. Using a regex expression in the condition will give you the flexibility to segment for interesting cohorts. ex: "All users whose first visit was the week before Christmas".
Analyze the results with reports by specifying a date range and the corresponding cohort-sliced advanced segments. Another option is to extract the data using the Google Analytics Data Feed Query Explorer or their API.
Once you've put in the work your new visitors will be stamped by their first visit date and nicely fall into each daily or weekly retention bucket. This is what it might look like if you were tracking weekly retention, for example:
This is not a full solution, but here are some points on how I would approach this problem with the help of Google Analytics:
You have to make sure that you somehow store the registration date of each user, either in your database or in a cookie. Then have a look at Google Analytics Event Tracking. You could for example set up a new category based on the registration date. On every page load in your page, you then have to set up this event tracking call, for example like:
_trackEvent("returns", "2012-01-01", "UserId:123123123")
This way you will receive all page views for users that registered on that particular date. To add a date range in this, you have to make sure that these events only get fired for the number of dates after the signup (e.g. 7 days).
After your date range, you will be able to see how many page views and how many users returned - you even know which users came back.

Google Analytics and Piwik Discrepancy

Hi guys, I was wondering if anyone have the same problem as I do. I have 2 trackers which are Google Analytics and Piwik but after sometime I found out there is a discrepancy. Please read below for more information.
Here is data for yesterday (with New Piwik Last Week v1.7.1 version then).
GGA : 14 803 visits (Unique Visistors)
Piwik : 10 254 visits (Unique Visistors)
31% discrepancy.
Question
What do i have to do to match the records? or which of the statistics is the correct ones?
Any advice would be much appreciated.
Respective to the different programs they are both correct. The difference comes in in HOW they calculate what a unique visitor is. No two stats aggregators work the same.
Google Analytics What's the difference between the 'Absolute Unique Visitor' report and the 'New vs. Returning' report?:
Absolute Unique Visitors
In this report, the question asked is: 'has this visitor visited the website prior to the active (selected) date range?' The answer is a simple yes or no. If the answer is 'yes,' the visitor is categorized under 'Prior Visitors' in our calculations; if it is no, the visitor is categorized under 'First Time Visitors.' Therefore, in your report, visitors who have returned are still only counted once.
Piwik FAQs:
How is a 'unique visitor' counted in Piwik?
Unique Visitors is the number of visitors coming to your website; Unique Visitors are determined using first party cookies.
If the visitor doesn't accept cookie (disabled, blocked or deleted cookies), a simple heuristic is used to try to match the visitor to a previous visitor with the same features (IP, resolution, browser, plugins, OS, ...).
Note that by default, Unique Visitors are available for days, weeks and months periods, but Unique Visitors is not processed for the "Year" period for performance reasons. See how to enable Unique Visitors for all date ranges.
They both use cookies to determine uniques, but both go about it calculating them in different ways. It's apples and oranges when comparing stats packages side by side.
Examine the rest of the stats beyond unique visitors. If there is a wide margin across the board, take a close look at the implementation of both.
If all is well with both implementations, then pick one and go with it for the stats. Overall trends is what you are looking for. Are the stats you want to go up going up? Are the stats you want to go down going down?

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