Filters may be applied retroactively to any data that has been processed.
True
False
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By admin
Filters may be applied retroactively to any data that has been processed.
True
False
By admin
If a filter excludes data from a view, that data can never be recovered for that view.
True
False
By admin
Which reports require you to activate Advertising Features?
Cohort Analysis reports
Real-time reports
Demographics and Interests reports
Geo reports
Explanation:
Read more here: https://support.google.com/analytics/answer/2819948
Enable Demographics and Interests reports
When you enable Advertising Reporting Features, you allow Analytics to collect additional information from the DoubleClick cookie (web activity) and from Device Advertising IDs (app activity).
Enable Demographics and Interests reports
You can enable the Demographics and Interests reports from either the Admin or Reporting tab.
To enable the reports from the Admin tab:
To enable the reports from the Reporting tab:
You should see data in your reports within 24 hours of enabling.
By admin
Which of these user characteristics CANNOT be used to create a Custom Segment?
Users who have children
Users that viewed a page on your website and then watched a video
Users 25 to 34 years of age who have their browser set to Spanish
Users that engaged in your social media or email campaigns
By admin
What is the “Bounce Rate” in Google Analytics?
The percentage of total site exits
The number of times unique users returned to your website in a given time period
The percentage of visits when a user landed on your website and exited without any interactions
The percentage of sessions in which a user exits from your homepage
Explanation:
A bounce is a single-page session on your site. In Analytics, a bounce is calculated specifically as a session that triggers only a single request to the Analytics server, such as when a user opens a single page on your site and then exits without triggering any other requests to the Analytics server during that session.
Read more here: https://support.google.com/analytics/answer/1009409
Bounce rate
About bounce rate
A bounce is a single-page session on your site. In Analytics, a bounce is calculated specifically as a session that triggers only a single request to the Analytics server, such as when a user opens a single page on your site and then exits without triggering any other requests to the Analytics server during that session.
Bounce rate is single-page sessions divided by all sessions, or the percentage of all sessions on your site in which users viewed only a single page and triggered only a single request to the Analytics server.
These single-page sessions have a session duration of 0 seconds since there are no subsequent hits after the first one that would let Analytics calculate the length of the session. Learn more about how session duration is calculated.
Is a high bounce rate a bad thing?
It depends.
If the success of your site depends on users viewing more than one page, then, yes, a high bounce rate is bad. For example, if your home page is the gateway to the rest of your site (e.g., news articles, product pages, your checkout process) and a high percentage of users are viewing only your home page, then you don’t want a high bounce rate.
On the other hand, if you have a single-page site like a blog, or offer other types of content for which single-page sessions are expected, then a high bounce rate is perfectly normal.
Lower your bounce rate
Examine your bounce rate from different perspectives. For example:
The Audience Overview report provides the overall bounce rate for your site.
The Channels report provides the bounce rate for each channel grouping.
The All Traffic report provides the bounce rate for each source/medium pair.
The All Pages report provides the bounce rate for individual pages.
If your overall bounce rate is high, then you can dig deeper to see whether it’s uniformly high or whether it’s the result of something like one or two channels, source/medium pairs, or just a few pages.
For example, if just a few pages are the problem, examine whether the content correlates well with the marketing you use to drive users to those pages, and whether those pages offer users easy paths to the next steps you want them to take.
If a particular channel has a high bounce rate, take a look at your marketing efforts for that channel: for example, if users coming via display are bouncing, make sure your ads are relevant to your site content.
If the problem is more widespread, take a look at your tracking-code implementation to be sure all the necessary pages are tagged and that they’re tagged correctly. And you may want to reevaluate your overall site design and examine the language, graphics, color, calls to action, and visibility of important page elements.
You can use Optimize to test different versions of your site pages to see which designs encourage users to engage more.
If you have a single-page site, learn about non-interaction events that you can implement to better capture user engagement and identify single-page sessions that are not bounces.
By admin
Which kinds of hits does Google Analytics track?
Page-tracking hit
Event-tracking hit
Ecommerce-tracking hit
All of the above
Explanation:
An interaction that results in data being sent to Analytics. Common hit types include page tracking hits, event tracking hits, and ecommerce hits.
Each time the tracking code is triggered by a user’s behavior (for example, user loads a page on a website or a screen in a mobile app), Analytics records that activity. Each interaction is packaged into a hit and sent to Google’s servers.
Read more here: https://support.google.com/analytics/answer/6086082
Hit
An interaction that results in data being sent to Analytics. Common hit types include page tracking hits, event tracking hits, and ecommerce hits.
Each time the tracking code is triggered by a user’s behavior (for example, user loads a page on a website or a screen in a mobile app), Analytics records that activity. Each interaction is packaged into a hit and sent to Google’s servers. Examples of hit types include:
By admin
What is the set of rules that determines how sales and conversions get attributed based on touch-points in the conversion path?
Conversion tracking
Multi-Channel Funnels
Attribution modeling
Channel Groupings
Explanation:
An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths.
Read more here: https://support.google.com/analytics/answer/1662518?hl=en
Attribution modeling overview
Assign credit for sales and conversions to touchpoints in conversion paths.
An attribution model is the rule, or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths. For example, the Last Interaction model in Analytics assigns 100% credit to the final touchpoints (i.e., clicks) that immediately precede sales or conversions. In contrast, the First Interaction model assigns 100% credit to touchpoints that initiate conversion paths.
You can use the Model Comparison Tool to compare how different attribution models impact the valuation of your marketing channels. In the tool, the calculated Conversion Value (and the number of conversions) for each of your marketing channels will vary according to the attribution model used. A channel that predominantly initiates conversion paths will have a higher Conversion Value according to the First Interaction attribution model than it would according to the Last Interaction attribution model.
Attribution modeling example
A customer finds your site by clicking one of your AdWords ads. She returns one week later by clicking over from a social network. That same day, she comes back a third time via one of your email campaigns, and a few hours later, she returns again directly and makes a purchase.
Last Interaction model icon In the Last Interaction attribution model, the last touchpoint—in this case, the Direct channel—would receive 100% of the credit for the sale.
Icon for Last Non-Direct and last AdWords Click In the Last Non-Direct Click attribution model, all direct traffic is ignored, and 100% of the credit for the sale goes to the last channel that the customer clicked through from before converting—in this case, the Email channel.
Icon for Last Non-Direct and last AdWords Click In the Last AdWords Click attribution model, the last AdWords click—in this case, the first and only click to the Paid Search channel —would receive 100% of the credit for the sale.
First Interaction model icon In the First Interaction attribution model, the first touchpoint—in this case, the Paid Search channel—would receive 100% of the credit for the sale.
Linear model icon In the Linear attribution model, each touchpoint in the conversion path—in this case the Paid Search, Social Network, Email, and Direct channels—would share equal credit (25% each) for the sale.
Time Decay model icon In the Time Decay attribution model, the touchpoints closest in time to the sale or conversion get most of the credit. In this particular sale, the Direct and Email channels would receive the most credit because the customer interacted with them within a few hours of conversion. The Social Network channel would receive less credit than either the Direct or Email channels. Since the Paid Search interaction occurred one week earlier, this channel would receive significantly less credit.
Position-based model icon In the Position Based attribution model, 40% credit is assigned to each the first and last interaction, and the remaining 20% credit is distributed evenly to the middle interactions. In this example, the Paid Search and Direct channels would each receive 40% credit, while the Social Network and Email channels would each receive 10% credit.
By admin
Which report shows users who initiated sessions over 1-day, 7-day, 14-day, and 30-day periods?
Active Users report
User Explorer report
Users Flow report
Cohort Analysis report
By admin
What is a “dimension” in Google Analytics?
The total amount of revenue a business has made in a given date range.
A comparison of data between two date ranges.
A report that offers information about your audience.
An attribute of a data set that can be organized for better analysis.
Explanation:
Dimensions are attributes of your data. For example, the dimension City indicates the city, for example, “Paris” or “New York”, from which a session originates. The dimension Page indicates the URL of a page that is viewed.
Read more here: https://support.google.com/analytics/answer/1033861
Dimensions and metrics
Understand the building blocks of your reports.
Dimensions are attributes of your data. For example, the dimension City indicates the city, for example, “Paris” or “New York”, from which a session originates. The dimension Page indicates the URL of a page that is viewed.
Metrics are quantitative measurements. The metric Sessions is the total number of sessions. The metric Pages/Session is the average number of pages viewed per session.
The tables in most Analytics reports organize dimension values into rows, and metrics into columns. For example, this table shows one dimension (City) and two metrics (Sessions and Pages/Session).
In most Analytics reports, you can change the dimension and/or add a secondary dimension. For example, adding Browser as a secondary dimension to the above table would result in the following:
Valid dimension-metric combinations
Not every metric can be combined with every dimension. Each dimension and metric has a scope: user-level, session-level, or hit-level. In most cases, it only makes sense to combine dimensions and metrics that share the same scope. For example, Sessions is a session-based metric so it can only be used with session-level dimensions like Source or City. It would not be logical to combine Sessions with a hit-level dimension like Page.
For a list of the valid dimension-metric pairs, use the Dimensions and Metrics Reference.
How metrics are calculated
In Analytics, user metrics are calculated in two basic ways:
As overview totals
where the metric is displayed as a summary statistic for your entire site, such as bounce rate or total pageviews.
In association with one or more reporting dimensions
where the metric value is qualified by selected dimension(s).
The following diagram illustrates these two types of calculations with a simple example. On the left side, user data is calculated as an overview metric, while the same data is calculated via the New User dimension on the right side.
In the Overview Report example, calculations for time on site are computed using the time difference between each user’s initial session and the exit, with the sum of each session length averaged across three sessions. This number is based on a relatively simple calculation achieved by gathering time stamp data at the request level.
In the New vs Returning Report example, averages are not computed for all sessions, but rather via the User Type dimension. By pairing the Time On Site metric with a dimension, you can analyze this metric via returning vs new users, where the calculations are modified by the requested dimension. The use of the dimension offers an insight into user behavior not provided in the overview report: it’s clear that new users are spending more time on your site than returning users.
Metrics calculation is also affected by stacking more than one dimension with a given metric. In both the preformatted and custom reports, you can use multiple dimensions together. For example, suppose you use both the User Type dimension and the Language dimension to analyze time on site for your website. In this case, the calculation for new versus returning users is the same, but when you drill down to view new users using the Language dimension, the calculation is further modified by the additional dimension. So, for example, your user breakdown might look like this, where the top site times are listed in order:
Attribution models
Because Analytics attempts to answer a variety of questions about user behavior, it uses different calculation types or attribution models to arrive at the data that you see in the reports. Think about each Analytics report as a response to a particular kind of user analysis question. Often, these questions fall into distinct categories:
Content: How many times was a particular page viewed?
Goals: Which pages URLs contributed to the highest goal conversion rate?
Ecommerce: How much value did a given page contribute to a transaction?
Internal Search: Which internal search terms contributed to a transaction?
For each of these major categories and the reports that they contain, Analytics uses a distinct attribution model. Because each attribution model is designed to calculate a known set of metrics, you might notice that some metrics—such as Pageviews—appear only in certain reports and not in others. This is due to the attribution model that is used for that report.
The Analytics reports use three attribution models:
Per Request
Page value
Site Search attribution
Per Request attribution
This attribution gives aggregate values for a single metric or for a metric/dimension pairing. This is the most common and simplest type of Analytics attribution, since values are determined from individual user GIF requests. Thus, for any given request, it is possible to look up a particular dimension and/or metric.
Most dimension values are available at the request level and remain persistent either via the HTTP/GET request itself, or in the GIF request, for every page or event request made to your site. Some common dimensions available at the request level are:
page URI—available with every request to your site, this indicates the path of the page being accessed
campaign—if a user comes in via a campaign, that campaign remains persistently available with every subsequent request, until the campaign itself changes
user agent—every request from a user contains the browser information for that user, sent in via the HTTP/GET request from the browser and stored in the log files directly.
Page Value attribution
The purpose of this attribution type is to answer the question: “How useful was my page in relation to a goal or revenue value?” This attribution model is used to determine the Page Value value for a page or set of pages. The following illustration shows a series of user pageviews in relationship to goals and purchases, such as what might occur on your site.
Legend: P1 through P4 represent pages. The shopping bags indicates a receipt page, and the flag image indicates a goal.
This attribution model is referred to as a “forward looking” attribution model, because it applies value to a page by looking forward to the goals and/or purchases that take place after the page was visited. The following table shows the value attributed to each page in this sequence.
In this model, transactions or goals are attributed to the search term immediately preceding the goal or transaction.
By admin
By default, which of these is NOT considered a “source” in Google Analytics?
googlemerchandisestore.com
(direct)
Explanation:
Email comes under “Medium”.
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