The Future of Split Testing and Conversion Rate Optimization

I’ve been fortunate enough to see and experience first hand the evolution of the Internet, from even before the web till today.

I’ll spare you a lengthy history lesson explaining how we’ve gone from brochureware sites to where we are today, but I do want to share some thoughts and perspective on where I think things are going.

When marketers started to understand the potential of dynamic web sites, there were two terms everyone was throwing around:

Personalization & Customization.

Fast forward to today (2011). The user experience is still exactly the same for all visitors (other than on a handfull of sites such as Amazon.com).

For the most part, web site Personalization has failed. Sure it sounds good in theory, but trying to tailor the web site experience at the individual level is extremely difficult. It is difficult both from a technological perspective but mostly by trying to create an optimal user experience based on data from a single individual.

There is no doubt in my mind that in the future (and to some extent today) the user experience when visiting a web site will be created dynamically based on what gets the best results, but based on “anonymous” information which is common to large groups of visitors, and not based on a single person.

This reminds me of the concept of Psychohistory from the science fiction series “Foundation” by Isaac Asimov.
Wikipedia explains it better than I can:

The premise of the series is that mathematician Hari Seldon spent his life developing a branch of mathematics known as psychohistory, a concept of mathematical sociology (analogous to mathematical physics). Using the law of mass action, it can predict the future, but only on a large scale; it is error-prone on a small scale. It works on the principle that the behaviour of a mass of people is predictable if the quantity of this mass is very large. The larger the number, the more predictable is the future.

I also like to think of this in terms of what usually happens at (successful) brick and mortar stores.

When you walk into a store, the salesperson probably doesn’t know you personally, but will probably try to help you based on certain public traits such as gender, age, if you’re by yourself or with someone else, etc.

Which brings me back to what actually prompted me to write this article in the first place :)

While I’ve been split testing since 2005 in order to improve conversion rates, the majority of the time, it’s still about what works best for the site as a whole, opposed to split testing together with segmentation (which is what we really want).

Until recently, there haven’t been many options out there to achieve this level of targeting and testing (at least not priced for small to mid sized businesses) but over the past few months, I’ve been starting to see more and more startups trying to bring this level of sophistication to the masses.

While I haven’t had a chance to use any of these services first hand, there is no doubt in my mind that business that truly embrace this level of targeting and split testing will eventually lead the pack and leave most one-size-fits all web sites in the dust.

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Web Analytics Career Advice

The past couple of months I’ve been seeing more and more discussion about breaking into web analytics as a career.

There are plenty of articles out there with great career advice.
Jim Sterne recently put together a list of resources and links for starting a career in web analytics.

I don’t want to repeat what others have already said, but I do want to add some fodder for thought that I don’t seem to be seeing other people mentioning.

As I mentioned in my previous post about selling web analytics, web analytics is just a tool.
A means to an ends.

Think of a carpenter’s tool box – hammer, saw, screwdriver, drill, etc.
Yes, you need to be proficient using these tools to be a good carpenter, but the point is that you’re using these tools to make something!

Which brings me to my point.

In order to be successful in web analytics, you need to “make something with your tools”.

You should be using web analytics in order to provide value, specifically providing actionable information.

In order to provide information that is actionable, you need to have a good understanding and some real world experience in the realm you’re trying to improve.

While many online activities can be optimized using web analytics, here are some categories that are commonly optimized using web analytics.

  • Website Conversion Rate Optimization
  • Paid Search
  • Search Engine Optimization
  • Social Media
  • Email Marketing
  • Online to Offline Marketing

Each of the above topics can and should be using data in order to improve the results.

A common “life-cycle” using web analytics to improve something is:

  1. What happened (make sure you’re measuring everything that needs to be measured)
  2. Why it happened (analysis of the data together with domain expertise)
  3. What’s next (domain expertise on how to make improvements)
  4. Start over again

For example:

What happened
The bounce rate on our homepage is 68%. Domain experience tells you that this is abnormally high and can/should be reduced.

Why it happened (analysis of the data together with domain expertise)
Looking at the homepage I see it’s way to busy. Two column layout with equal size columns makes it hard know what to focus on. No clear headline and multiple competing calls to action.

What’s next (domain expertise on how to make improvements)
Split test different layouts, calls to action, headline, amount of content.
Ideally you should be able to provide recommendations for the actual variations to be tested, and not just say you should test this.

So, if you want a career in web analytics, you need to become proficient in one or more of the above topics so you’ll know how to answer the questions “why it happened” and “what’s next”.

It’s not enough to just tell people what’s broken –
You need to tell them how to fix it.

As always, comments are greatly appreciated.

– Ophir

Selling Web Analytics

How to Sell Web Analytics

While I was at the Google Analytics Certified Partner summit this year, someone came up to me and asked:

How do you sell web analytics?

This is a question I deal with quite often, both internally within the agency I work for and when trying to convey the value of web analytics to clients.

The short answer is that you shouldn’t be trying to sell web analytics.

Web analytics is a tool, a means to an ends.

It has no inherent value by itself. It’s only through the analysis of data and providing actionable insights that it creates value.

You should be selling the value that can be gained by a proper web analytics implementation and actionable analysis.

Marketing people talk about selling the benefits, not the features.

Web analytics is a feature.
Improving the bottom line is a benefit.

A simple analogy is HTML (the code used to create web pages).
Imagine if you tried to pitch HTML services to a business. They would probably be scratching their heads as to why they need your services.

Now imagine pitching web site creation services, providing real world examples on how businesses have improved their bottom live with their web site.
Now they’re listening.

When selling services that include web analytics, try starting with the end result and then work your way backwards. If you start with the prize, people will usually pay more attention.

Here’s an example:

  1. I can help you make an additional $80,000 a year
  2. It will cost you a one time investment of $25,000 investment and $1,000 a month
  3. You’re currently doing 3,000 sales a year
  4. I will get you 400 additional sales a year (average sale value is $200)
  5. The additional sales will come from improving your overall conversion rate by 13.3%
  6. Improving the overall conversion rate will coming from decreasing bounce rates, increasing “add to cart” rates and decreasing cart abandonment.
  7. The above improvements will come from making changes on the web site
  8. We’ll test a few options until we find something that works (split testing)
  9. We’ll know what changes to test based on data that tells us what people are doing on your web site
  10. In order to get the data that we need, we have to install web analytics on the site and then analyze the data

The problem is that many web analytics businesses start their pitch with step 10 and then work their way to step 1.

When people ask me what I do for a living, the answers have changed over the years, but now I usually answer along the lines of:

I help web sites do better at whatever they’re try to do.
Specifically, I learn the business objectives and then do some detective work, finding issues that can be improved and then I improve them.

As always, comments and suggestions are appreciated.

Ophir