In Analytics, conversions and Ecommerce transactions are credited to the last campaign, search, or ad that referred the user when he or she converted. But what role did prior website referrals, searches, and ads play in that conversion? How much time passed between the user’s initial interest and his or her purchase?
The Multi-Channel Funnels reports answer these questions and others by showing how your marketing channels (i.e., sources of traffic to your website) work together to create sales and conversions.
The Multi-Channel Funnels reports are generated from conversion paths, the sequences of interactions (i.e., clicks/referrals from channels) that led up to each conversion and transaction. By default, only interactions within the last 30 days are included in conversion paths, but you can adjust this time period from 1-90 days using the Lookback Window selector at the top of each report. Conversion path data include interactions with virtually all digital channels.
These channels include, but are not limited to:
paid and organic search (on all search engines along with the specific keywords searched)
custom campaigns that you’ve created, including offline campaigns that send traffic to vanity URLs.
What the Multi-Channel Funnels reports show
In the reports, channels are credited according to the roles they play in conversions—how often they assisted and/or completed sales and conversions. The Assisted Conversions report shows how many sales and conversions each channel initiated, assisted, and completed, along with the value of those conversions and sales.
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.
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.
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.
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 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.
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.
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.
VLOOKUP. A must have for matching keyword research data to analytics reports to show how many times a keyword is searched for a month in Google compared to how many visits or how much revenue that same keyword earned a site. I usually put the keyword data in one worksheet and the analytics data in another, and then use VLOOKUP to pull the matching keyword research data into my analytics report.
SUMIFS. When I’m looking for opportunities in keyword research, I may want to know the combined number of searches are done for keywords containing certain elements. For example, let’s say I want to know how many searches Google reports a month for keywords that contain both “black” and “dress.” SUMIFS can take doth of these conditions and sum up the number of searches for me. Its little brother, SUMIF, does the same thing with only one condition.
IF ISERROR. This nifty calculation takes those annoying #N/A results that are essentially error messages and turns them into zeroes, blanks or anything else you please. It can be a tricky formula to write outright, so I usually write the formula I wanted in the first place and then go back and paste the IF and ISERROR formulas around my original formula.
CONCATENATE. A brilliantly simple formula, CONCATENATE takes any strings of characters and cells you please and strings them together in a single cell. For example, if you’ve mapped keywords to URLs — here’s my article on keyword mapping — and you want to make a quick start of optimizing the title tags, it’s easy to concatenate the primary keyword, the page name and the brand together with some punctuation to form the start of a title tag. You’ll need to review them to make sure they’re optimal, but it’s a lot easier to tweak something that’s already written than to start writing it from scratch.
Favorite Excel 2010 Menu Items
I’ve moved these menu items into my Quick Access Toolbar for easy one-click access. I save the toolbar for commands that I use frequently that can’t be accessed with keyword shortcuts.
Freeze Panes. A must have for large spreadsheets, this command freezes the cells above and to the left of where your cursor is so that the column and row headings can be seen no matter how far down or to the right you scroll.
Remove Duplicates. I can’t count the amount of time this feature has saved me while researching keywords. With the click of a button, duplicate rows are deleted while the first instance is preserved. This can be a dangerous function, so you might want to play with it a bit before using it on critical data to understand how it works.
Manual Calculation. Tired of watching your document process formulas in “Not Responding” mode? Click the Manual Calculation Option found in the Formulas menu to prevent Excel from trying to recalculate every time a change is made. This one command has saved me weeks of compute time on formula-intensive spreadsheets.
Favorite Excel 2010 Keyboard Shortcuts
While awkward at first, forcing myself to learn these keyword shortcuts has saved me untold hours in Excel. Yes, all of these shortcuts are available in the menus, but it takes more time to execute multiple accurate clicks in the menu than to quickly type three or four keys to accomplish the same thing — once you get used to doing it. Over the course of a day or a week of executing these commands repeatedly to analyze data, those seconds add up to more time to focus on what’s important.
Highlight Range. Ctrl+Shift+Arrow Key. This is the fastest way to highlight all cells in a range when you don’t want to just click on the row or column header. For example, Ctrl+Shift+Down highlights every cell starting where your cursor is and ending with the last contiguous cell in the row. Ctrl+Shift+Down, Right would highlight every cell starting where your cursor is and ending with the last contiguous cell in the row, and then extend the highlighted section as far to the right as the right-most contiguous cell. This command is extremely handy for highlighting large ranges of cells instantly without waiting for the page to scroll until you get to the end of the cells.
Move to End. Ctrl+Arrow Key. This similar command can be used to move the cursor to the beginning or end of the range of cells without highlighting the cells in between. It’s handy in conjunction with the Page Up and Page Down buttons to scan large blocks of cells quickly
Filter: Ctrl+Shift+L. Enables the row highlighted to act as the header row from which to filter and sort the rows below it.
Delete. Alt+E, D. Quickly delete any highlighted cells, rows or columns. Unlike using the delete key or Ctrl+X, Alt+E, D removes the highlighted cells not just their contents.
Insert Cells. Alt+I, E. Inserts a number of blank cells, rows or columns equal to the number highlighted. For example, to quickly insert a blank row, I highlight the row below where I want the new blank row to be inserted and type Alt+I, E.
Paste Special > Values. Alt+E, S, V. I like to think of this as “the flattener.” It pastes the content copied onto your clipboard and pastes the numerical or textual contents without any formulas or formatting. So basically you can copy a range of cells filled with formulas that require lots of processing power to calculate, just copy those cells and Alt+E, S, V right over top the same cells to paste just the results of the calculations. You may want to save the formulas in one row or column in case you need to add more data. I’ve flattened too early many times and had to redo the formulas as I added more rows.
Transpose Rows and Columns. Alt+E, S, E. When you want to swap the axis of the cells you’re working with, simply copy the range and do a quick Alt+E, S, E. The contents of the cells will be pasted exactly as they were, except that the X-axis is now the Y-axis and vice versa.
Escape: Esc. Yes, seriously. When Excel is flashing all sorts of annoying warnings and help menus at you and you just want them to go away without bothering to click on the right button, the escape key usually does the job. Escape is also handy when you’ve started to work in a cell and want to erase the contents of that cell and cease to work in it quickly.
These are some of the commands, formulas and shortcuts I use day in and day out in Excel 2010. Sometimes it’s ugly. Sometimes it’s kludgy. But it always gets the job done.
well noted by: Jill Kocher in :
Set up advanced filters on views
Adding advanced filters to views lets you further refine the data that you collect and can even transform how that data shows up in your reports.
Set up advanced filters on views,
Create your own Custom Dimensions
Create your own Custom Metrics
Understand user behavior with Event Tracking
More useful configurations
Overview of two Analytics accounts
The following diagram shows two possible Analytics account configurations. Here both a personal Analytics account and a company account shared with co-workers. Company account tracks the company website, googleanalytics.com.
About cross domain tracking
How standard tracking compares to cross domain tracking
Cross domain tracking makes it possible for Analytics to see sessions on two related sites (such as an ecommerce site and a separate shopping cart site) as a single session. This is sometimes called site linking.
This feature is only available in Google Analytics 360, part of the Google Analytics 360 Suite. Learn more about the Google Analytics 360 Suite.
Roll-Up Reporting aggregates data from multiple Analytics properties and lets you see that data together in the same reports.
For example, if you have multiple properties for brand sites in different countries (example.fr, example.co.uk, etc.), you can aggregate that data to see global-performance metrics, and then drill down to compare brand performance among countries.
Roll-Up Reporting comprises two types of properties:
Source Properties: Individual Analytics properties that include data from a single site, app, or internet-connected device.
Roll-Up Properties: Properties that serve as aggregators of the data from multiple Source Properties.
Analytics accounts – Overview and cross domain tracking.
Every report in Analytics is made up of dimensions and metrics.
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).
A hit is the most granular piece of data in an analytics tool. It’s how most analytics tools send data to a collection server. In reality, a hit is a request for a small image file. This image request is how the data is transmitted from a website or app to the data collection server.
User > Sessions > Hits
Digital analytics data is organized into a hierarchy of hits, sessions and users. Pageviews/Screenviews: A pageview (for web, or screenview for mobile) is usually automatically generated and measures a user viewing a piece of content. A pageview is one of the fundamental metrics in digital analytics. It is used to calculate many other metrics, like Pageviews per Visit and Avg. Time on Page.
Events: An event is like a counter. It’s used to measure how often a user takes action on a piece of content. Unlike a pageview which is automatically generated, an event must be manually implemented. You usually trigger an event when the user takes some kind of action. The action may be clicking on a button, clicking on a link, swiping a screen, etc. The key is that the user is interacting with content that is on a page or a screen.
Transactions: A transaction is sent when a user completes an ecommerce transaction. You must manually implement ecommerce tracking to collect transactions. You can send all sorts of data related to the transaction including product information (ID, color, sku, etc.) and transactional information (shipping, tax, payment type, etc.)
Social interaction hit: A social interaction is whenever a user clicks on a ReTweet button, +1 button, or Like button. If you want to know if people are clicking on social buttons then use this feature! Social interaction tracking must be manually implemented.
Customized user timings:User timings provide a simple way to measure the actual time between two activities. For example, you can measure the time between when a page loads and when the user clicks a button. Custom timings must be implemented with additional code.
That’s a lot of hit types!
A session is simply a collection of hits, from the same user, grouped together. By default, most analytics tools, including Google Analytics, will group hits together based on activity. When the analytics tool detects that the user is no longer active it will terminate the session and start a new one when the user becomes active.
Most analytics tools use 30 minutes of inactivity to separate sessions. This 30-minute period is called the timeout.
Here’s where things start to get interesting. A user is the tools best-guess of an anonymous person. Users are identified using an anonymous number or a string of characters. The analytics tool normally creates the identifier the first time a user is detected. Then that identifier persists until it expires or is deleted.
The identifier is sent to the analytics tool with every hit of data. Then the analytics tools can group hits (and thus sessions) together using the identifier in the hits.
Sessions from the same user can be grouped together as long as each hit has the same user ID.
Here’s how users are detected on some of today’s most common digital platforms.
To measure a user on a website almost all analytics tools use a cookie. A cookie is a small text file. The cookie contains the anonymous identifier. Every time a hit is sent from the browser back to the analytics server identifier stored in the cookie is sent along with the data.
When measuring a website, the analytics tool usually uses a first party cookie to store an anonymous ID.
Now let’s have the cookie talk.
Google Analytics uses a first party cookie. A first party cookie is connected to the domain that creates it. A first-party can only be used by the domain that sets it. So on this site, the cookie has a domain of cutroni.com and can only be used by this website.
In Universal Analytics the cookie is named _ga and lasts for two years. In the previous version of Google Analytics the cookie was named __utma.
The good thing about a first party cookie is that almost all browsers will allow a first party cookie. It’s a very reliable piece of technology.
First party cookies are challenging when your site spans multiple domains. When a user leaves your site, and traverses to another site that you own, they do not take their first party cookies. In most situations, unless you configure analytics correctly, analytics will set another cookie when the user lands on the second domain.
Analytics uses a first party cookie to maintain a user identifier.
Now you have one user with two cookies. That could lead to double counting of users. Plus, if we want to create really cool metrics, like Revenue per user, it becomes very, very hard because we don’t know the true number of users.
The other type of cookie, a third-party cookie, can be set and accessed by domains other than the domain that creates it. Some analytics tools will let you use a third party cookie.
The value of a third party cookie is that the analytics tool can use a third party cookie to identify a user as they move from one domain to another.
A third party cookie can be used by multiple domains.
However, third-party cookies are not permitted by most browsers – that means no data.
Google Analytics does not use a third party cookie. You can read all about the Google Analytics cookies in the developer documentation.
So what’s the solution here? How do you correctly identify a user if your website spans multiple domains? In the Google Analytics world we use a feature called Cross Domain Tracking. I’m not going to talk about it in this post, but you can read about it in our support documentation.
Now let’s move on to mobile platforms – something that is very popular 🙂
Mobile tracking is similar to web tracking. There is an anonymous identifier stored on the device. The identifier is generated every time the app is installed. So if a user deletes the app the identifier will also be deleted. But if a user updates the app the identifier will not change.
The big difference between mobile and web is that the identifier is not stored in a cookie. It’s stored in a database on the mobile device – but it basically functions the same way as a cookie. The identifier is sent on every hit back to the analytics server. The analytics server then uses the identifier to create metrics like unique users.
Here’s one challenge with user measurement on an app. Many apps are not just an app. They’re a hybrid app/website. They use a browser within the app to “frame” content from a website. This can mess up the data collection.
In this situation we have two technologies with two different user identifiers. The app will measure a user based on the ID stored on the device and the website will use a cookie when a page loads in the app.
Goals are configured at the view level. Goals can be applied to specific pages or screens your users visit, how many pages/screens they view in a session, how long they stay on your site or app, and the events they trigger while they are there. Every goal can have a monetary value, so you can see how much that conversion is worth to your business. Using values for goals lets you focus on the highest value conversions, such as transactions with a minimum purchase amount.
When a visitor to your site or user of your app performs an action defined as a goal, Analytics records that as a conversion. That conversion data is then made available in a number of special-purpose reports, which are described below.
Goals fall into one of 5 types, listed in the table below:
A specific location loads
Thank you for registering! web page or app screen
Sessions that lasts a specific amount of time or longer
10 minutes or longer spent on a support site
Pages/Screens per session
A user views a specific number of pages or screens
5 pages or screens have been loaded
An action defined as an Event is triggered
Social recommendation, video play, ad click
About view filters
Filter and modify the data in a view.
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.
COUNTIF function to count the number of occurrences of each word.
The MATCH function returns the position of a value in a given range.
To find the position of the most frequently occurring word (don’t be overwhelmed), we add the MODE function
Use this result and the INDEX function to return the 2nd word in the range A1:A7, the most frequently occurring word.
This example teaches you how to perform a two-column lookup in Excel. See the example below. We want to look up the salary of James Clark, not James Smith, not James Anderson.
1. To join strings, use the & operator.
Excel formula tutorial: How to use COUNTIF, SUMIF, or AVERAGEIF functions
1. Formula and Function Tips and Shortcuts
2. Formula and Function Tools
3. IF and Related Functions
4. Lookup and Reference Functions
5. Power Functions
6. Statistical Functions
7. Math Functions
8. Date and Time Functions
9. Array Formulas and Functions
10. Reference Functions
11. Text Functions
12. Information Functions
1. Update Your Business’ Information
2. Fill Out the Local Listings
3. Create Some Buzz About Your Establishment
4. Get the Right Local Keywords and Make Good Use of Them
5. Ensure Your Website is Compatible with All Devices
6. Interact with Your Customers; Ask for Reviews
7. Make Good Use of Social Media
8. Visual Content is Important
9. Strictly Abide by Your Hours
10. Invest in a Mobile App