A property is a website, mobile application, or device (e.g. a kiosk or point-of-sale device.) An account can contain one or more properties. Within an Analytics account, you add the properties from which you want to collect data. When you add a property to an account, Analytics generates the tracking code that you use to collect data from that property. The tracking code contains a unique ID that identifies the data from that property, and makes it easily identifiable in your reports. Analytics also creates one unfiltered view for each property you add.
A session is a group of interactions that take place on your website within a given time frame. For example a single session can contain multiple screen or page views, events, social interactions, and ecommerce transactions.
Credit card number is very sensitive information which should never be collected or try to collect with any script. It may land you in trouble and you may face legal proceedings.
Events are user interactions with content that can be tracked independently from a web page or a screen load. Downloads, mobile ad clicks, gadgets, Flash elements, AJAX embedded elements, and video plays are all examples of actions you might want to track as Events. An Event has the following components. An Event hit includes a value for each component, and these values are displayed in your reports.
The First Interaction model attributes 100% of the conversion value to the first channel with which the customer interacted. When it’s useful: This model is appropriate if you run ads or campaigns to create initial awareness. For example, if your brand is not well known, you may place a premium on the keywords or channels that first exposed customers to the brand.
The next step is to have the web development team, or the mobile team, actually implement the tracking recommendations that you’ve made. Some website technologies will require additional planning, such as:
With Real-Time, you can immediately and continuously monitor the effects that new campaigns and site changes have on your traffic. Here are a few of the ways you might use Real-Time:
Intelligence monitors your website’s traffic to detect significant statistical variations, and generates alerts when those variations occur.
The Time Lag report counts the number of days from the first user interaction (e.g., impression, click, direct session) to conversion. Some Analytics users like to compare this report with the Ecommerce > Time to Purchase report, which counts the number of days from the first campaign referral to conversion. Given the different starting points for the two timelines, the reports do not matc
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Filters allow you to limit and modify the data that is included in a view. For example, you can use filters to exclude traffic from particular IP addresses, focus on a specific subdomain or directory, or convert dynamic page URLs into readable text string
The Goal Flow report shows the path your traffic traveled through a funnel towards a Goal conversion. This report can help you see if users are navigating your content as expected, or if there are problems, such as high drop-off rates or unexpected loops.
The Site Speed reports measure three aspects of latency:
Filters allow you to limit and modify the data that is included in a view. For example, you can use filters to exclude traffic from particular IP addresses, focus on a specific subdomain or directory, or convert dynamic page URLs into readable text strings.
The Last Non-Direct Click model ignores direct traffic and attributes 100% of the conversion value to the last channel that the customer clicked through from before buying or converting. Analytics uses this model by default when attributing conversion value in non-Multi-Channel Funnels reports. When it’s useful: Because the Last Non-Direct Click model is the default model used for non-Multi-Channel Funnels reports, it provides a useful benchmark to compare with results from other models. In addition, if you consider direct traffic to be from customers who have already been won through a different channel, then you may wish to filter out direct traffic and focus on the last marketing activity before conversion.
By default, the channel labels that you see in Multi-Channel Funnels reports (Paid Search, Organic Search, Social Network, etc.) are defined as part of the MCF Channel Grouping. If you have specific analysis requirements, you may wish to create your own Custom Channel Grouping(s), each with its own set of labels. When you share a Custom Channel Grouping, only the configuration information is shared. Your data remains private. The following channel labels are defined as part of the MCF Channel Grouping and are the labels used by default in your reports. The channel definitions are not case sensitive.
