Wingify Conversion Optimization Blog
Tips, Tricks, How-tos, Guides, Hacks and Secrets
on Website Conversion Rate Optimization
Scientific research papers on how to increase sales or conversions are rare. Most of the articles you read on the Internet (admittedly, including a lot of posts on this blog as well) are based on what the writer thinks and what makes logical sense. But scientific research works in a different way. Authors of research papers must produce accurate, reproducible results. And their articles are reviewed by peers before getting published. Hence in most cases you can always trust results of a scientific paper.
So I fished out research papers on the internet which tell you how to increase your revenues online. Here is the list for your reading pleasure:
Usability
Miscellaneous
After compiling this list and reading most of the articles, I realized there is a large disconnect between what happens in research and what is actually applied in the market. Do you agree? Did you even know such kind of research happens at all?
All right, let’s admit it: increasing conversion rate on a website is still a voodoo science for many. With new technologies and terminology being thrown around (on Twitter, blogs, etc.) every other day, it doesn’t get any easier for people just starting to understand conversion optimization. In this post, I will try to briefly talk about all technologies and methodologies being used today for extracting more juice out of existing traffic:
Apart from these categories, numerous other tools in search analytics, PPC analytics, affiliate management, etc. are available but the above ones are the most useful ones. Even amongst the above, I will argue web analytics and split testing tools should be an absolute must for anybody serious about improving his/her website conversions.
Leave a comment here if you think you have additional toolssuggestions for conversion rate optimization which I missed here.
There are number of metrics that startups and websites obsess on. Some of the most overanalyzed yet non-useful metrics are number of visitors or pageviews on a website. The reason startups get obsessive about them is that these metrics are easy to use and are no brainer. Just slap some code on the website and you are ready to get insights on your startup’s progress, supposedly.
In fact, there are three metrics that a startup (or for that matter, any website or online business) should single mindedly obsess on accurately measuring and hence optimizing for:
The three strategies of: a) getting new customers, b) retaining existing ones and c) up-selling and cross selling new offerings are not new. Management gurus have been discussing them for ages. Even then startups and websites get drowned in a flood of metrics and forget that they are there to make money. They should better be optimizing how to make more money. And only way to optimize that is to focus on the right metrics. Do you agree?
Let’s face the truth; Alexa is not the best source of traffic data out there on the Internet. Plus, it does not have statistics on conversion rates. But, hey, Alexa is free and we are going to use it to benchmark (approximately) the conversion rates for your competition. Here is how to do it two simple steps:
Step 1. Establish industry norms using your actual conversion rate data
Suppose you are SEOMoz (I am using this website as an example and have no real data for them) and you sell paid tools for SEO. Let’s suppose your current conversion rate is a conservative 4% (again, hypothetical data) and you want to estimate how your competitor SEO Book is doing.
You and your competitors (since it is the same industry after all) follow a similar trend when it comes to relationship between conversion rates and other site metrics such as bounce rate, time on site, and page views per user. In this step, we try to calculate values for parameters which relate conversion rate to all these metrics. Using your actual conversion rate data and the stats that Alexa shows about your website, calculate X, Y and Z as the following:
The reason we don’t use your actual bounce rate, time spent and page views data is because you don’t have that data for your competitors. You only know what Alexa says about your website and what Alexa says about your competitors. So it is better to work on the Alexa data that is freely available and uses the same methodology all across.
As an example of SEOMoz, Alexa tells the bounce rate, time spent on site and page views / user is 50.7%, 219.7 seconds and 3.2 respectively. Using this data, we get the values of X, Y and Z as:
Step 2. Use the parameters to estimate competitor’s conversion rate
Now we have obtained the parameters which relate your actual conversion rate to the data that Alexa shows about your website. Next step is to simply use those parameters on your competitor’s data (as shown by Alexa) to get estimates of their conversion rate.
Competitor Conversion Rate:
Finally, to get an idea of what their real conversion rate, we simply average the estimates.
Competitor Conversion Rate = (Estimate 1 + Estimate 2 + Estimate 3) / 3
Continuing the SEOMoz example, if we were to estimate the conversion rates for SEOBook, we calculate 3 estimates of conversion rate (based on the data shown by Alexa for SEOBook):
As you can observe, the estimates are quite close. Hence, we can be pretty confident that the actual conversion rate is close to the average of these three estimates, which is:
Estimated conversion rate = 2.77%
Of course, the above estimated value is only valid if the real conversion rate for SEOMoz that I assumed (4%) is true, which may or may not be the case as I don’t have access to their real web analytic data.
Summary
Simply plug in your conversion rates in the above methodology and you should have pretty good estimate on how you are doing as compared to your competitors. You can also try to triangulate your estimates by using other data sources (apart from Alexa) such as Compete.com or Quantcast.
Do let me know if you find this approach helpful. As always, feedback and comments appreciated. Want a tool to automate all this analysis for you?
PS: The way I define Bounce Rate and Conversion Rate, they are not related. But the way Alexa defines, the two metrics are definitely related.
Lately, I have got quite a few requests for how effective call to action buttons are created in Photoshop. Though having persuasive text as call to action is important, button shape, size, color and style can also make a tremendous difference in conversion rates. So, here goes the list of free resources on creating buttons that convert and examples to get you started:
Photoshop Resources
Ideas for Buttons
This list is an ever expanding list, so feel free to suggest more (free) resources for call to action buttons. Leave a comment and I will add it in the list.
Lot of people inquire about what an ideal bounce rate or conversion rate is and if their website metrics are in the right range. One size doesn’t fit all. In this post, I fish out industry standard conversion rates and bounce rates. Though your only competition should be you, having an idea of industry metrics might help some.
| Conversion Rate | Bounce Rate | |
| Grand Average | 5.50% | 40.58% |
| Software/Product | 7.00% | 33% |
| Lead Generation | 2-3% | 47.38% |
| News/Media | - | 55.50% |
| eCommerce | 3-3.5% | 34% |
| Branding Pages | - | 43% |
Sources for these figures:
What is your bounce rate or conversion rate? Does it match with your industry average?
To set the definitions right, it is generally agreed that bounce rate is the percentage of visitors who exit the website immediately after arrival. Conversion rate is the percentage of visitors who complete website goal, which may be a signup, subscription, purchase, download, etc.
Most people believe that bounce rate and conversion rate is inversely proportional. That is, if bounce rate goes up, conversion rate would go down and if bounce rate goes down, your conversion rate will go up (because apparently you will have more interested visitors). On the face of it, this seems to be true and hence the proposition that fixing the bounce rate OR the conversion rate alone will achieve business goals seems to be true.
Sadly, this relationship between bounce rate and conversion rate is an illusion. To understand that there is NO relationship between these two metrics, you need to know what bounce rate really is. Does the bounce rate talk about visitors who viewed just one page on your website? Or should it capture more nuanced idea of visitors who stumbled across your website by chance? Most web analytics tools define bounce rate as the former: that is, a single visit is considered a bounce. Bounce rate, defined in such a manner, conveys completely wrong information.
Increasingly, visitors are becoming goal oriented. For example, if they need to see your shipping policy, they will Google it, read about it and leave your website. That visit is not a bounce: visitor got what he was looking for. Similarly, most of you will exit after reading this post for say 3-5 minutes. Do I consider you a bounced visitor? No, not at all. You were engaged for a long time, how could you be classified as a bounced visitor. However, the web analytic tool I use will classify you as a bounce because you just read one page on the website. Realize that bounce rate which you are reading out from your tool is not what it says. Scrutinize definitions and understand what the metric is saying to put it in the right context.
So, what is the best way to represent bounce rate? I think bounce rate is best captured by measuring what percentage of visitors spent less than 30 seconds on your website. Any time spent which is less will signal that visitors arrived on your website by chance and is NOT at all interested in what you are offering, hence quickly went back to what he was doing. All other visitors spending >30 seconds, even if they just see one page, should be classified as non-bounced visitors. To summarize:
Bounce rate = Number of visitors who spent < 30 seconds on the website /
Total number of visitors
Unfortunately, measuring exact time spent by a visitor by web analytics tools is difficult and most of them will approximate this number. That said, I think bounce rate should be defined by time spent on website and not by pageviews.
Coming back to conversion rate, how is it related to bounce rate? As traditional thinking goes, the visitors who bounced bring the conversion rate down as they have no chance of completing the website goal. I fully agree that bounced visitors (by definition) have no chance of completing the conversion goal. Then, I ask, why to include bounced visitors in conversion calculations at all? To truly reflect the progress you have been making on your website, conversion rate calculations should NOT include bounced visitors. Bounced visitors never really cared about your website, non-bounced are the ones who engaged and spent time going through what you are offering. Conversion rate should capture how good a job your website is doing for getting those visitors (who care about your website) to complete the goals. Conversion rate, ideally, should be calculated as following:
Conversion rate = Number of non-bounced visitors who completed the goal /
Total number of non-bounced visitors
So, now we have two metrics which are not at all related to each other: bounce rate and conversion rate. Both of these metrics convey different information regarding how you are performing. Hence, both of these metrics should be separately optimized. Optimizing bounce rate is for convincing more number of people to engage with your website. Optimizing conversion rate is for convincing the visitors who are already engaged to complete your website goals. Reducing bounce rate AND increasing conversion rate are two different activities. Remember that.
What are your views on relation between conversion rate and bounce rate? How do you and your web analytics tool measures bounce rate?
There are NOT a lot of free resources available on the Internet for A/B Testing. This post tries to lists the best tools, guides and resources for A/B Testing. As it will be an ever growing list, feel free to make suggestions for additions into the list.
Tools
Guides
Show case of existing A/B Tests
If you have any other suggestions for additions in the list, I will be happy to add them. Just leave a comment.
When people install a shiny, new (and possibly free) web analytics tool, few of the initial metrics that they obsess on are: number of pageviews and number of visitors. There is nothing wrong with measuring how many visitors come to your website; it is good metric that gives an illusion that you have everything in control on your website. If website traffic increases, it is good. If traffic decreases, it is bad. What metric can beat the effectiveness of such a simple heuristic!
The real problem arises when website owners never look beyond these simple metrics. Relying and optimizing website around these metrics is a serious error. What you indeed need to optimize should be something I call visitor lifetime value.
Visitor lifetime value, to put simply, is a concept borrowed from traditional marketing where they use something called customer lifetime value. The idea goes like this: every customer has a potential to deliver certain lifetime monetary value (read sales) to your business and a business can only survive if its customer acquisition cost (money spent on acquiring the customer) is less than customer life time value.
A similar concept can also be applied to websites. Every visitor who comes to your website holds potential to deliver certain value to you and you should know what that value is. Visitor lifetime value has following components:
Based on these parameters, a simplistic formula can be derived for visitor life time value:
Visitor life time value = [ (Goal 1 monetary value * Goal 1 conversion rate) +
(Goal 2 monetary value * Goal 2 conversion rate) + …. ] * Visitor Retention Rate
Tracking and optimizing a single metric like visitor life time value gives you THE best perspective on whether your activities on the website are really worth it. If you aren’t calculating and increasing visitor life time value, you are loosing a lot of opportunity to drive your business ahead. The metric also attaches a bound to what you should be paying for acquiring visitors through paid advertisements, banner ads or affiliates. You cannot spend more to acquire a visitor more than what you expect to derive from him.
Coming back to measuring number of visitors to your website, I lied when I said it is a worthless activity! If you see visitor life time value, it is money/sales/business that a single visitor is expected to deliver to your business. Multiply that value by the number of visitors and you get total website value (in monetary terms).
Total website value = Number of (unique) visitors * Visitor life time value
So, all in all, there are really only two ways to increase total website value:
You must be measuring number of visitors already. But ask your web analyst or web analytics tool to calculate visitor lifetime value and total website value for you. Track it, optimize your activities around it and base your decisions on these values. Because, after all, these are the only values that REALLY matter to your business. Agree?
What are your thoughts on visitor lifetime value? Any additions to the formula? Do you calculate this value for your website?
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