Case Study of Zupper

We have officially posted a case study for Zupper.com.br.

The project is still a work in progress so the case study may get a few updates in the coming months but is still worth a look.  So far, in just 4 months, we have been able to more than double their conversion rate.
(http://contourthis.com/ct/resources/case-study-zupper.php)

Predictions for the next 10 years

These are my predictions of things that will happen within the next 10 years.  

• Facebook (as we know it) will be the next Myspace (and not in a good way)
• Bing will do to Google what Google did to Alta Vista in late 90's  (fitting that Bing, Yahoo! and Alta Vista are all sort of one big family)
• Google will pivot and innovate in a way that we nor Bing saw coming
• The Apple we know will fall apart in a Yahoo! style crisis.  Not knowing quite how they got where they are or where to go next.
• Amazon will take over the "ownership" level of the web by supporting the most major sites and APIs in the cloud.

Note:  This post is for entertainment only and not considered to be advice in any way.  All views are the sole opinions of Jared Smith.  I have only posted this as a sort of digital time capsule that I can dig up in 10 years and see how the world panned out.

Too small to split test

Most Conversion Rate Optimization Consultants view split testing as the Holy Grail.  Split testing is when you direct a portion of your traffic to your control, and another portion to one or more variations of the same page.  Split testing is important because the variation that works the best isn't always the most logical or appealing.  If your tests show statistically significant results, you can't go wrong with split testing.

But what if you don't have enough traffic to run a split test in an efficient manner?  If you only have a few hundred visitors each month, a split test where one variation shows twice as much success could take up to 3 months to prove statistically significant.  

The answer is:  There are other ways to measure the success of a variation.  

1. Usability Tests:  If you run a usability test before the variation and another one after, you can compare the results and see if you have fixed the problem.  
2. Surveys:  You can also do a short survey before the change and ask if participants would be willing to let you follow up with them.  After you've made your changes, follow up with previous participants and run the same survey again for new visitors.
3. User Panels:  I suggest forming 2 user panels.  The first one to consist of employees of your company and/or persons close to the company and the second of people who make up your "average" visitor but don't have any relation to the company.  This lets you see your website from both sides of the table.  Asking for a review from each panel before and after the change can help you determine if you've achieved your goal.

Split testing can be the easiest way to measure the results of your changes, but if you don't have a lot of traffic and still want to improve your site, give these other methods a try.

Abandonment starts before the form

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No matter what the form is, it is important to be well designed, user friendly, and encourage the user to fill it out.  But more often then not, abandonment starts before the form.  

How did I get here?  Was I expecting this form?  Was I expecting it to look like this?  Did I come prepared to fill it out?  Was I already convinced to fill it out?

Our expectations are like a bowling ball rolling down a hill.  Once we have expectations, it is hard to change them.  Abandonment starts before the form, and so do conversions.

Let's get personal

With all the data that exists on the web, it is possible to deduce (Sherlock Holmes style) almost everything about one of your visitors, especially if he/she chooses to log in with a social network, such as Facebook.  If you already know everything about someone, why not take them straight to what they need?  If Facebook, Google, and other large social networks succeed in advertising to their customers on a personal level, showing them what they want when they need it, their click through rates would skyrocket.  

The problem isn't that it can't be done.  The data mining is already a thing of the past.  The problem is that when someone you have never met knows your entire life story, its creepy and off putting.  While click through rates and conversions skyrocket, your visitors feel like its an invasion of privacy.  

Even though Facebook, Google+, and LinkedIn know everything about me, the problem is that I didn't tell them... I told my friends.  So how does a company make a paradigm shift from "listening in on personal conversations" to "helping you get the most out of the internet"?

Start a conversation with your customers.  Take them out for a digital date and take it slow.  When they are ready to open up about what makes them tick, you won't have to infer anything and they will lust for your recommendations.

Is your website noisy enough?

Too much noise can be a bad thing, but so is not enough noise.  When you make too much noise, people can't keep up, and can't think.  It is easier to stop listening than it is to try to make sense of it all.  But when you don't make enough noise, people feel uncomfortable.  If you're a parent you know that when your toddler stops making noise, they are probably up to no good and you will likely find a new mural in your hallway or suddenly be all out of mascara.  When you're home alone at night and there is no noise, the silence becomes frightening and every sound makes the hair on the back of your neck stand up.  

A little bit of noise helps people feel comfortable and stimulates creativity.  Its important that your website have some noise (visual, content, and social) so your visitors don't feel anxious and alone, but be careful not to overdue it to the point that they stop listening.

Statistically Significant

If you're running a split test manually, it can be hard to know when the results are statistically significant.  Some online calculators give you p-values, z-numbers and confidence intervals, none of which mean much without a stats book and a graphing calculator.  Others give you a simple yes/no answer.  To me, the simple yes/no is easy, but it doesn't really tell the story of the data.  

This morning I set out to "fix" this situation.  I wanted a visual representation of the data that was easy on the eyes and brain.  So, after a conjuring up old memories of sleeping in stats class, I came up with the idea of using venn diagrams to represent a 99% confidence level in two samples.  See my visual Confidence Calculator.

If the circles overlap the opposite label, the jury is still out.
If the circles overlap but don't overlap each other's labels, its time to keep the winner and do a new test.
If the circles don't even touch, you've already waited too long to call the winner.