In pay-per-click advertising, keywords are what an advertiser bids on to have their ads shown in front of relevant web surfers. Advertisers have many different settings at their disposal to leverage keywords to maximize their marketing efforts. One of those settings is called the match type, and it’s used to control the relevancy, quality, and to some extent the volume of impressions and clicks that your ads will be eligible to receive.
For example, let’s say you’re bidding on the keyword “Miami Dolphins”, and you’re advertising with Google AdWords. Your match type will determine the quality and volume of traffic you receive:
– If you’re using Broad Match, your ad will appear anytime someone uses “Miami”, “Dolphins”, or any combination of those two words with any other words. So, searches like “Miami Heat”, “Miami Vice”, “Dolphins in the ocean”, and “restaurants in Miami” would all make your ads eligible when your keyword match type is set to Broad Match.
– If you’re using Phrase Match, your ad will only appear when the search term “Miami Dolphins” (in that order) appear before, after, or in between other words. Searches like “Miami Dolphins website”, “tickets to the Miami Dolphins”, or “directions to Miami Dolphins stadium” will all make your ads eligible to appear using Phrase Match.
– If you’re using Exact Match, your ad will only appear when a searcher types in “Miami Dolphins” (in that order), and with no other words before after, or in between “Miami Dolphins”. No exceptions.
(Google also has a new “Modified Broad Match” option. There’s a good article to read from the Google AdWords blog if you’re interested to learn more about it).
You must be wondering why the heck am I spending time writing about pay-per-click keyword match type options. Well, most everyone at MoreVisibility is well versed in pay-per-click, and anyone who works with Web Analytics knows at least a little bit about how pay-per-click works (and, if you didn’t know about keyword match types…now, you do!). If you’re creating and editing goals in Google Analytics, you’re going to need to be well versed in goal match types. Otherwise, what appears as a goal may not be what you wanted or expected.
In Google Analytics, there are three goal match types (for URL destination goals): Head Match, Exact Match, and Regular Expression Match.
1. Head Match.
Head Match for Google Analytics URL destination goals works somewhat like Phrase Match works in the pay-per-click advertising world.
Let’s say that I am using /thankyou as my goal URL. Using Head Match, Google Analytics will record a goal whenever any of the following pages are viewed:
Essentially, with Head Match, any time /thankyou appears in a URL (or, as we like to call it, a Request URI), and a unique page view is recorded for that URL, Google Analytics will record a goal. Notice that there are some possibilities above that you may not have considered that will count as goals using Head Match.
2. Exact Match.
This goal match type is exactly what it sounds like. It will match your goal URL exactly, without exception. This match type in Google Analytics is just like Exact Match in the pay-per-click world.
Using /thankyou as your goal URL and using Exact Match, Google Analytics will only record a goal when a unique page view occurs on:
Google Analytics WILL NOT record a goal when you’re using Exact Match for unique page views on pages like:
Notice how, using Exact Match, Google Analytics will not even count “/thankyou.html” as a goal. Exact Match is a very strict goal match type, but it may be exactly what you’re looking for, especially if you want to avoid the type of scenarios on bullet point 1 above.
3. Regular Expression Match.
The last goal match type is Regular Expression Match. Basically, this match type allows you to do all sorts of things with your goal URLs, using the power of Regular Expressions.
(Don’t know anything about Regular Expressions, or need a tune up? Don’t worry – I wrote an entire article about Regular Expressions a while back).
Regular Expression Match is great for lots of things, including combining multiple URLs into the same goal slot, or, forcing Google Analytics to match your goal URL depending on what it either starts with or ends with.
Example: you want Google Analytics to count a goal for any page that starts with /thankyou. Using the Regular Expression Match type, enter in:
^/thankyou (<– Yes, that’s the ^ symbol above your “6” key)
Using ^/thankyou will tell Google Analytics to match anything that starts with /thankyou, like:
However, it will not match anything like:
(You can use the dollar sign symbol, $, to match a URL by what the URL ends with…do read my post on Regular Expressions to know what I’m talking about).
Is your head spinning yet? Not to worry – comment below with your questions and I’ll try to answer them for you.
There is a great report in the “Goals” section of Google Analytics that is surprisingly seldom used by many people (seems like I’ve been saying that a lot recently!). It’s called the “Funnel Visualization” report. For each Goal in Google Analytics, you are allowed to create a custom path that you want the visitors to your website to take before they reach your Goal. This path can be anywhere from 1 page up to 10 different pages.
Funnels are used most commonly in Ecommerce type situations, where there is a shopping cart and a checkout process involved. Marketers and analysts usually set up a Goal Funnel that starts at a landing page of a pay-per-click or email marketing campaign, and that ends at the Goal, which is usually the “Thank You” page or “Receipt” page that a user sees after they complete a purchase. After some data has been collected, marketers and analysts will take a look at each page, or “step” in the Funnel, and see where users are abandoning the shopping process, or if they are experiencing difficulties in ultimately handing over their hard-earned money to the merchant.
However, you should also take advantage of setting up a Goal Funnel and using the Funnel Visualization reports in non-Ecommerce situations. If you have any lead generation or quote forms on your site, you can also use the Funnel Visualization report to get a good idea of how people are interacting with those particular pages, and if there are any bumps in the road that are causing detours from your main objective.
Let’s take a look at an example. The screen-shot below is showing the first three steps in an Ecommerce Goal Funnel, starting from the Shopping Cart page, and going through a “Sign-In” page, followed by a “Billing Information” page:
Before continuing, let me explain what we are looking at. First, look at the very top and middle of the image where it says “Shopping Cart – 10,214”. That is the first step in our funnel, which in this case is the Shopping Cart page, and 10,214 are the number of visitors that the Shopping Cart page had (within the period of time that I had selected – in this case it’s the last 30 days). That entire column from top to bottom represents each one of the steps in the Funnel. The figure below “Shopping Cart” – where it says 5,749 (82%) – are the number of people who went on to the next step of the funnel. You can then continue all the way down the page, until the very end of the funnel.
To the left of each step in the middle column are the top 5 entry points to each one of the step pages of your funnel. So, for our “Shopping Cart” page, 10,214 total visitors entered the shopping cart, 743 of those visitors came from a page called “Categories.bok”. Then, to the right of each step in the middle column are the top exit points from each one of the step pages of your funnel, including the total number of exits from the funnel above the top 5 exit points. For our “Shopping Cart” page, 1,244 visitors exited the funnel at this first step, with 679 of those visitors exiting the website, as represented by (exit). 33 Visitors went to a page called “Lost.bok”, 26 Visitors went to a page called “StoreFront.bok”, and so on.
So how is this information useful for me? Should I do anything to my website’s pages if a significant number of people are leaving my website from one of these shopping cart pages?
This is where you are going to have to understand what is actually on your website, and fill in the gaps of information between your knowledge of your website and the data that Google Analytics is displaying. I showed this particular funnel on purpose for exactly this reason. Here’s what I’m talking about: On the very first step of the example, the “Shopping Cart” page, 82% of people continued on to the next step. This means that 18% of people, or, 1,244 visitors, went somewhere else. We know that 679 visitors exited the website entirely, which means that this website’s marketing or IT team should probably take a look at their shopping cart page and see what technical issues or hang-ups are present in the system. But, what about the other 565 Visitors? We can only speculate, but if users can go back and continue shopping, or do other things on their shopping cart page, they may do just that, and possibly, re-enter their shopping cart at some point later.
Now, take a look at the second step – the “Sign-In Page”. This time, only 67% of visitors continued on to step 3, with 1,317 of those visitors exiting the site entirely! That is a lot of lost people! Why did they leave? Well as it turned out, this particular page had a very frustrating and annoying “Create an Account” feature that did not provide customers with an option to shop anonymously, or as a guest, without having to create a username and password for the website. You simply couldn’t get around this issue, which was very frustrating to many customers, so, a lot of them went on to other pages or left the site altoghether, which is not good news.
Since then, they have repaired this issue – and guess what started happening? Their conversion rate and Ecommerce revenue started climbing, just by making one change to their shopping cart pages!
This is a perfect example of how the Funnel Visualization report can serve as an alert system to the health and prosperity of a particular path of pages on your website that leads to a Goal – in this case, a website’s shopping cart. How else would this website’s marketing and IT department have known about the frustrations of their customers?
Is there a certain % of people that should continue to a next-step in a funnel? What’s a good “step-continuation” rate?
Ah, the 64 million dollar question! I’ll say this – you will never have a 100% “Funnel Step Continuation” rate (or, a 0% Funnel Abandonment rate). I would say that any step in the 90% range and higher is doing pretty well. Anything in the 70’s or 80’s should be cause for moderate concern, and you should open up a high priority trouble ticket, because any step that is losing 20% or 30% of it’s customers is a very substantial amount. Anything in the 60%-50% range or below means that you need to stop whatever it is that you are doing, sound the general alarm and wake the neighbors up, because there is a pretty serious issue going on – especially if your Funnel represents the pages of an Ecommerce Shopping Cart.
(Hey, there’s no shame in taking your Goal Funnel seriously. You should take it seriously – your livelihood probably depends on your website’s success, and how your website’s visitors interact with the pages in your funnel will affect the overall number of sales or leads your website generates).
Is there any other advice that you can give us?
I’ll answer your question with a question of my own: What is the shortest distance between two points? It’s a straight line. Keeping that in your mind will help you with your analysis – and help you understand why your website’s visitors may be leaving your site before filling out your Lead Generation form, or before they buy something from your online store.