Wingify Conversion Optimization Blog
Tips, Tricks, How-tos, Guides, Hacks and Secrets
on Website Conversion Rate Optimization
Wingify Conversion Optimization Blog
Tips, Tricks, How-tos, Guides, Hacks and Secrets
on Website Conversion Rate Optimization
Many consumers read product reviews when deciding which is best to buy. Consumers are taking advantage of widely available wireless internet to share their experiences with products in online communities. Potential buyers also want to gain information from others who have purchased the products they are considering. According to a survey in October 2008 by the consulting and research firm Penn, Shoen & Berlin, 70% of Americans say they consult consumer ratings or product reviews before purchasing an item.
The point of writing a product review is to share information and experiences with the product. There is a lot of information that can be shared using a well written product review. Six main elements should be written in a great product review that will generate sales. The first is product description. Describing the product should be followed by proof that you know the product and what it’s capable of. A description of the product’s buyer should also be included. Videos and images are great tools to enhance a product review. They may be followed by any criticism of the product that is necessary to give the reader a balanced product review. A review should end with a call to action. You want the reader to feel compelled to purchase the product.
A product review should give a good description of the product. The size, weight, look, smell, and feel of the product should be described. The ordering process and customer service practices of the company should also be described. Consumers want to know if you had a great experience talking with customer service. They also want to know how long it took from the time you ordered your item until it was received. Describing the ideal buyer of a product is very helpful to potential buyers. One of the things that a potential buyer wants to know is whether the product is for them. When describing an item it is important to avoid too much detail. Your review should not be longer than a few paragraphs.
Potential buyers want to know how the product compares to similar products. Comparing and contrasting similar products helps readers decide which product is the best match for them. Comparison reviews are great for attracting readers. When you are comparing, make sure that you define which product is better and why.
Giving background information about why you ordered the product and how it has helped you is great for generating sales. Storytelling also helps customers empathize with your situation and think about how they are like you. Storytelling is a great tool for connecting with the reader emotionally.
Consumers want to know that they’re getting the most for their money. Most consumers don’t mind spending more money for a superior product. All opinions shared in the review should be supported by facts where possible. Using expert sources such as statistics is a great way to lend credibility to a product review. Using personal information is valuable when talking about personal products. Your section about price and value is a great place to share an opinion about why someone should buy a certain product.
Words are very important, but nothing tells a product’s story like pictures. According to gibLink, including photos allows the reader to visualize exactly what you are reviewing. Images are also great for breaking up large amounts of text. Making a video of the product in action or even showing the content of the box and describing the items is very helpful to prospective buyers. Submitting the video to YouTube can generate even more traffic to your review.
It may seem counterproductive to criticize a product you would like to sell, but many readers won’t believe a review that is completely positive. Some aspects of a product can be seen as either positive or negative, and its important to be balanced in your review. Writing an objective review is important because your writing needs to read like a review and not like an advertisement. A review should sound like the writer is trying to help the reader, not sell something to them.
Covering specific details of the product is important because they show you actually used the product and have experience with it. You may want to talk about what the product is best used for and what may not be a good use for this product. Giving very specific details about the product and situations that may be encountered while using the product will prove to the reader that the product is genuine and the results you discuss will actually be produced by this product.
It’s great to describe the product, but consumers need to understand how the product can lessen or solve a problem they have. Consumers want to know why they should buy this particular product. What are the benefits? Discussing the benefits of certain ingredients is helpful to many consumers. Be positive and remember the type of reader you are trying to influence.
A product review tells the potential buyer what to do next. It’s a good idea to link a website where the product can be purchased. Listing a phone number or email to contact for more information is also helpful. A quick summary of your review should be included right before the call to action. The potential buyer should understand what product they are buying, what problem the product will help them solve, and how the product will benefit them. It’s also helpful if they understand why a particular product is a better buy than the competition.
Note: this post is from a guest author David Murton. Email us if you’d like to contribute a guest post yourself.
Here is a complete blueprint of how to get more customers for your service or product:
Have I missed any aspect? Please leave a comment below and I will add it.
The future of web analytics is certainly a shift of focus from reporting metrics to mining interesting information by applying statistical and machine learning techniques to web logs. In other words, web analytics will increasingly become “web-log mining“.
In this post, I compile some of the most pioneering research papers that will shape up web analytics in years to come. As you will see, some of the papers are more than 10 years old. This shows that academia has long been doing interesting research in web analytics and will also make you realize just how basic our existing web analytics capabilities are. So, get ready dive into the future of web analytics:
Review / Introduction
Applications / Techniques
Examples / Implementations
Hope you liked the list. If you know any specific paper that I may have missed, please leave a comment. I will add it in the list.
For any business, two pieces of information are most important to its survival. One, in order to make decisions, a business needs to know the ground reality of where it stands in the market now. Second, in order to plan forward and determine progress, it needs to know where it stood in the market in the past. These two sources of information individually don’t convey much information. But combined together, they provide actionable insights. Where am I now and where was I is what you need to know if you need to plan for where I want to go.
Making parallels to web analytics, current set of tools (unfortunately) only provide information on what is happening now. Your favorite tool will churn out data on number of visitors, page views, countries, referrers and what not. This exactly tells you how your website is doing today. However, this completely misses out macro trends. Sure, you can see a historical graph of number of visitors and all sorts of other metrics but that is only the first step towards knowing what has changed.
Ideally a web analytics tool should go deep on the segment level and mine signals in the (historical) data and correlate different metrics automatically for you. Here are some of the examples that I expect an analytics tool to mine automatically for me:
Most of what I have written above isn’t super hard. Some of it can be done by having simple heuristics built into the tool. Moreover, data mining and machine learning has progressed a lot and I am surprised web analytics industry has been so slow at adopting the methodologies. Though Google is taking the right steps with their intelligence feature but it still it leaves a lot to be desired: where are correlations, recommendations, trend mining and other interesting stuff? Nuconomy was doing the right stuff but they took far too long, didn’t innovate a lot and end up getting bought by a company for in house analytics.
Web analytics shouldn’t be simply a data collection and reporting tool. It should actually be collection, reporting and mining tool. My tool gives me 100s of metrics to look at which I can’t keep looking at day after day (unless it is my full time job). Instead it should mine all 100s of reports for me, and show me interesting nuggets on what has changed (and possibly what could change). So I ask: where is the innovation in web analytics? All I see around is dumb reports ready to get mind by a human.
What is your perspective on this? Do you think web analytics is ripe for a major change?
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?
