Online Marketers spend a good amount of time and money designing websites and campaigns to drive visitors to the site. When a visitor clicks though from a banner, marketers need to ensure that the visitors just doesn’t remain a visitor but becomes a customer. In order for a visitor to become a customer the value proposition needs to be clear and the landing page, the page visitors enter the site from, needs to provide a compelling reason for visitor to stay on that site and become a customer. The actual process of becoming a customer might involve few more pages on the sites but the landing page is the first impression that a visitor has about the site.
Marketers need to constantly optimize their landing pages (and other pages) to ensure they can get more and more visitors engaged and drive them though the conversion funnel.
The key to any landing page optimization campaign is to ignore your own biases and listen to your customers. Your customers are your best landing page designers. Their actions on the site tell you how they feel about your it along with what is working and what is not. They will tell you which landing page design is best convincing them to become customers or do what you want them to do.
Everything that you have on a page impacts visitors actions. Every little thing contributes the page’s success or failure. Which means, you can test pretty much everything on your web page. After testing for while you will figure out things that do make impact and things that do not have any significant impact and are not worth testing going forward. Examples of things that you can test are:
If I told you that I recently increased the conversion rate by 65%, just with few minor changes, will that get you excited? Won’t you like to have that kind of increase in your conversion rate? Do the math and find out why you should be testing right now. (I will share the example a little later)
Convinced? Ready to know how we can start testing?
Get your web analytics tool in action. As I have written in past on my blog, don’t just do reporting, analyze the data and see what your customers are telling you. Start testing the landing pages which are bleeding the most visitors, say your home page for example. If it is the top entry page and you see that it has a bounce rate higher than 30% then that might be the low hanging fruit that you might want to go after first.
The Process of Testing
A/B Testing:
A/B (or A/B/C/etc.) testing is the simple and easy way to explore the world of testing. A/B testing is a process of testing multiple versions (version A, B, C etc) of a page to see which page is better than others (statistically significant) in driving user take action that you want them to take e.g. drive user to signup for email newsletter.
In A/B testing you (the tool you implement on your site) randomly sends the visitors to one of the various versions of the pages. The tool then tracks the number of visitors and those who take the specified end action as defined by you based on your business goals.
Once enough visitors have been exposed to the test to get a statistically valid result, the tool will declare the winning page version. (Note: Depending on the complexity and goals of your business there might be more data that you will need to look into to determine if the winner declared by the tool is actual winner or not and if there are additional test you will need to run before you can find the winner.)
Multivariate Testing:
Multivariate testing is powerful way of testing specific elements within each page that you are optimizing. When your optimization tool declares a winner of an A/B test, it tells you which version of the page is best. It does not, however, tell you which elements within the winning page caused the increase in conversions. This is where multivariate testing can help.
In a multivariate test, you test multiple elements on a page (headlines, buttons, text, images) against different versions and combinations of those same elements. I know it sounds confusing, so let me give you a simple example:
Say you are testing a headline and a button. Headline 1 says, “Sign-up to get access now.” Version 2 of this headline might say, “Become a Member and Get Instant Access.” You might have version 1 of your button say “sign-up.” Version 2 could say “Get Access.”
Your multivariate test would test Version 1 of your headline with Version 1 of your button against version 1 of your headline with version 2 of your button, and so on for each of the possible combinations of headlines and buttons.
One of the major drawbacks of the Multivariate testing is that you need to have a lot of traffic to get statistically valid (accurate) test results. Depending on which tool you use, you will need a different amount of traffic. A/B testing does not require as much traffic for valid results.
I hope this helps get you started with optimizing your website.
Ready for an Example?
Here is an example of the page that I ran on RoommateHub.com.
Original Registration Page
Version 1 of Registration Page (Can you spot the differences between original and this version)
Google Website Optimizer Declares the Winner. 65% Increase in registrations, how cool is that.
There are few good books, websites, and other resources on the subject. Here are two books that I recommend:
Further Reading
Marketers need to constantly optimize their landing pages (and other pages) to ensure they can get more and more visitors engaged and drive them though the conversion funnel.
The key to any landing page optimization campaign is to ignore your own biases and listen to your customers. Your customers are your best landing page designers. Their actions on the site tell you how they feel about your it along with what is working and what is not. They will tell you which landing page design is best convincing them to become customers or do what you want them to do.
Everything that you have on a page impacts visitors actions. Every little thing contributes the page’s success or failure. Which means, you can test pretty much everything on your web page. After testing for while you will figure out things that do make impact and things that do not have any significant impact and are not worth testing going forward. Examples of things that you can test are:
- Headlines
- Images
- Text Size
- Font Color
- Place of images and text
- Call to Action
- Buttons/Text/Images – Size, Color, Font etc.
- Promotions – example: Does 20% of $100 works better than $20 off on $100?
If I told you that I recently increased the conversion rate by 65%, just with few minor changes, will that get you excited? Won’t you like to have that kind of increase in your conversion rate? Do the math and find out why you should be testing right now. (I will share the example a little later)
Convinced? Ready to know how we can start testing?
Get your web analytics tool in action. As I have written in past on my blog, don’t just do reporting, analyze the data and see what your customers are telling you. Start testing the landing pages which are bleeding the most visitors, say your home page for example. If it is the top entry page and you see that it has a bounce rate higher than 30% then that might be the low hanging fruit that you might want to go after first.
The Process of Testing
A/B Testing:
A/B (or A/B/C/etc.) testing is the simple and easy way to explore the world of testing. A/B testing is a process of testing multiple versions (version A, B, C etc) of a page to see which page is better than others (statistically significant) in driving user take action that you want them to take e.g. drive user to signup for email newsletter.
In A/B testing you (the tool you implement on your site) randomly sends the visitors to one of the various versions of the pages. The tool then tracks the number of visitors and those who take the specified end action as defined by you based on your business goals.
Once enough visitors have been exposed to the test to get a statistically valid result, the tool will declare the winning page version. (Note: Depending on the complexity and goals of your business there might be more data that you will need to look into to determine if the winner declared by the tool is actual winner or not and if there are additional test you will need to run before you can find the winner.)
Multivariate Testing:
Multivariate testing is powerful way of testing specific elements within each page that you are optimizing. When your optimization tool declares a winner of an A/B test, it tells you which version of the page is best. It does not, however, tell you which elements within the winning page caused the increase in conversions. This is where multivariate testing can help.
In a multivariate test, you test multiple elements on a page (headlines, buttons, text, images) against different versions and combinations of those same elements. I know it sounds confusing, so let me give you a simple example:
Say you are testing a headline and a button. Headline 1 says, “Sign-up to get access now.” Version 2 of this headline might say, “Become a Member and Get Instant Access.” You might have version 1 of your button say “sign-up.” Version 2 could say “Get Access.”
Your multivariate test would test Version 1 of your headline with Version 1 of your button against version 1 of your headline with version 2 of your button, and so on for each of the possible combinations of headlines and buttons.
One of the major drawbacks of the Multivariate testing is that you need to have a lot of traffic to get statistically valid (accurate) test results. Depending on which tool you use, you will need a different amount of traffic. A/B testing does not require as much traffic for valid results.
I hope this helps get you started with optimizing your website.
Ready for an Example?
Here is an example of the page that I ran on RoommateHub.com.
Original Registration Page
Version 1 of Registration Page (Can you spot the differences between original and this version)
Google Website Optimizer Declares the Winner. 65% Increase in registrations, how cool is that.
There are few good books, websites, and other resources on the subject. Here are two books that I recommend:
Further Reading
0 comments:
Post a Comment
Note: Only a member of this blog may post a comment.