1. If you are using Google Analytics you will see that GA includes the Ipod as a mobile device. This may be misleading since behavior on the iPod is much different from a mobile phone.

You don’t have the problem of a tiny screen. In order to get more meaningful results, I recommend analyzing mobile devices with the iPad taken out.

2. After you do that the first thing to do is check the conversion rate as compared to the rest of the website. If it is much less, then your site may have major usability problems on mobile phones. Before you jump to conclusions, check that the same kind of traffic is coming to both mobile and non-mobile devices. If it is not similar that could be the reason for different conversion rates.

3. Compare mobile bounce rates vs the rest of the site. If there is a big discrepancy you may see that your navigation system doesn’t work on some or all mobile devices. Flash menu systems don’t work on the iPhone.

Make sure you continually monitor the mobile segment as its importance is growing all the time. In light of this, the Mobile Marketing Association has formed a committee to establish mobile analytics standards.

All of our clients show faster growth in mobile than almost every other segment.

One last tip: For most of our clients the iPhone and Android are the most popular mobile devices. Check your stats and make sure your Internet marketing people are testing your website using these devices.

I was recently asked to review an existing campaign. The manager I was working with was looking at Cost/Conversion information based on country.

The 1st thing I told her was not to base any decisions based on this information. Why not? Because there was no information on the quality of the leads. You can have a country with a great cost/lead and no sales. What good is that?

Cost/Conversion Metrics Can Cause You to Make the Wrong Business Decisions

Connecting Adwords or Google Analytics to your sales information or CRM can sometimes be a challenge and take time. But that is no excuse not to do a basic analysis. You can probably easily find information on sales/country from your sales department. Even better is to get the revenue information. Take that information and cross reference it to your cost/lead.

With this information you have an important metric: Cost vs. Revenue or ROI. Now you can make intelligent business decisions. For example:

• Countries which have a high cost/lead but few sales must be optimized. If there is not much left to optimize then you may want to discontinue the campaign or lower bids significantly
• A country which has many sales and a high ROI should be tested by increasing bids or adding more keywords

Next time you look at your cost/lead metrics remember to look at cost vs. revenue before making what could be the wrong decision.

Running PPC campaigns in 3 search engines has its challenges. There are many 3rd party pay per click management solutions to help, however Google Analytics can do many of the same reports.

One of the biggest challenges is to make sure all 3 campaigns are targeting all the important keywords. The best way to analyze this is to have a chart which compares the traffic for all 3 search campaigns by keyword.

How the Pivot Chart Optimizes PPC campaigns

• In Google Analytics go to Traffic Sources > Search Engines and click on paid
• On the left drop down go to Keyword
• Click on the pivot icon – the 2nd from the right

The pivot chart should be part of your pay per click analytics routine when using Adwords, Bing and Yahoo

• Compare the numbers across the search engines
• If you see a zero in one search engine you are probably missing that keyword or underbidding—because the other search engines shows clicks



The first keyword has clicks on Yahoo and MSN. However Google shows zero clicks. Find out the problem on Adwords

• Next step is to go and optimize those ad campaigns
• Find out if you:
  • Forgot to add this keyword to Adwords
  • If the bid is too low
  • If there is another reason the keyword is not showing

You should check out conversions before you do the optimization. I will show you how to do this in another post.

In the supermarket the other day I was happy to see a sale for low fat cottage cheese in a 3 pack. There were many 3 packs available and I soon found out why.

The expiry date was fast approaching and my son nixed the purchase. Unfortunately there were no single low fat cottage cheese containers for sale–only these fast expiring 3 packs.

Apparently this 3 pack idea didn’t go over well in this store.

All I could think of was the VP Marketing person looking at his graphs which show that the 3 pack idea was increasing revenue and profits. What he forgot to do is segment his analysis. If he would have checked each store type he would have found that some segments didn’t buy the 3 pack.

Segment your analysis to optimize your campaigns

Segment of people who won’t buy the 3 pack:

• Smaller families that don’t need 3 cottage cheese containers
• Or a lower income group that only buys what they need for the near future
• Or people who are trying to gain weight and skip the low fat stuff

What’s good for brick and mortar stores is good for bit and pixel stores

Even when your campaigns are successful make sure your web analysis includes segmentation in order to find out where the campaign did not succeed. Segmenting ideas include:

• Time of day
• Day of week
• Location
• Language
• Products bought

This way you will optimize your campaign even more. Don’t make the same mistake as this supermarket VP. If he had been more proactive I wouldn’t have had to leave the store without my cottage cheese.

Photo credit: stu_spivack

Understanding web analytics and statistics is becoming more important for web marketers. People who read this blog or work with us know that we are data driven. Our decisions are made after testing and analyzing. And we are not the only ones.

The New York Times as well as Wired ran interesting stories on Statistics and Data.  Google figured prominently in both articles.

The world is changing from analog to digital. Data is going to become more important to businesses as time goes on. I agree with Arthur Benjamin that statistics should be a required subject.

Replacing Calculus Education with Statistics and Probability

Maybe this will help the next generation of website analysts to be more successful.

Dashboards are important because they:

• Provide warning signals
• Provide people with an important information overview

However, they can also be very dangerous. If your job requires you to interpret data and create actionable items you must have access to all the data. Don’t rely on someone to prepare a dashboard for you. Here’s why:

• You need to look for trends and patterns in the data. Finding things that should have happened but didn’t. If you rely on someone else’s interpretations – which is what a dashboard is – you will miss things.
• Your expertise will let you know where to find more data. Drilling down or accessing additional information is critical. The right level of detail cannot be decided in advance – which is what a dashboard does.
• You need to build your own mental model – not rely on others
• You need to understand how the data was collected
• You need to be able to dissect the original data as you learn more about the problem

A web analytics professional must convert data into comprehensive explanations in the same way that expert weather forecasters analyze their information

Talented web analytics professionals know that the data is only half the story. Intuition and experience are needed to interpret the data and dashboards can get in the way. To learn more, read Gary Klein’s book, The Power of Intuition. This blog post is based on his analysis of comparing expert vs. average weather forecasters.

Photo credit: Fabiano (LicoSp)

One of the most challenging parts of my job is to convince people of the importance of AB, multivariate, and usability testing on their website. Initially, most companies want to focus on getting traffic through SEO and Google Adwords.

For new sites this is understandable–you can’t do AB testing on sites with no traffic.

However, websites with traffic should start multivariate testing immediately. It doesn’t interfere with increasing traffic through SEO or PPC. And it can improve conversions significantly.

Dan Ariely wishes his nurses would have been open to testing procedures.

It is somewhat comforting to know that Internet marketing is not the only area where people are reluctant to test. At the end of Dan Ariely’s fascinating talk about cheating, he tries to convince everyone of the importance of testing by concluding, “Unless we test those intuitions we won’t make things better.”

I recently read an interesting book based on the following ideas:

1. “Incentives are the cornerstone of modern life”
   Many websites use this fact of life by offering free products, free trials or free information.
2. “The conventional wisdom is often wrong”
   That is why we always test our new website ideas. You do test don’t you?
3. “Dramatic effects often have distant, even subtle causes”
   This is why we test small things like: the design of the call to action button, headlines, promotion code field, etc.
4. ” ‘Experts’ from criminologists to real-estate agents – use their informational advantage to serve their own agenda”
   This is why we don’t rely on web site experts to decide how to improve the website. Instead, we always test so we know what our customers want.
5. “Knowing what to measure and how to measure it makes a complicated world much less so”
   By now we all know that looking at website statistics doesn’t help us. Instead, we have to think and decide what to segment and where to drill down, resulting in actionable items that improve our website conversion rate.

What is surprising is that this is not an Internet or web analytics book. It is an Economics–or what they call Freakonomics–book. Many times we can learn a lot about web analytics from other fields.

Listen to Steven Levitt speak about crack

Web analytics and economic analysis can be dangerous

The really difficult thing about web analytics is point no. 5. Most people know that this is a challenge but unfortunately they don’t know that the most dangerous problem is how to measure.

Measuring the wrong way is much too common.

Steve Levitt and Stephen Dubner fall into this same trap.

After analyzing what matters in parenting they find out that having been adopted matters. Studies show that a child’s IQ is much more influenced by the biological parents than the adoptive parents (page 171). Now if we left it at that we would think that the adopting parents don’t have much influence on the adoptive child.

Luckily, Levitt decided to dig deeper maybe because he didn’t like the results. This happens a lot in web analytics—we don’t like the results so we dig deeper until we find what is really going on. This is ok. However you should also do this when we like the results even though it serves our agenda (see point 4 above). You do want to find out the truth, don’t you?

Getting back to the adopted baby, although he did poorly in school, another study showed that by the time they became adults they “…veered sharply from the destiny that IQ alone might have predicted. Compared to similar children who were not put up for adoption, the adoptees were far more likely to attend college, to have a well-paid job, and to wait until they were out of their teens before getting married. (page 176) ”

So the adoptive parents did matter after all. It is good that Levitt decides to dig deeper. Or maybe his decision of what to measure was wrong (see point 5 above). Maybe instead of measuring success in school he should have been measuring college attendance, jobs and marriage.

It seems like economists make the same mistakes we web analytics people do.

Read the book to get inspired about web analytics.

How Doctors Think by Jerome E. Groopman, M.D., photo credit: nele’s photostream

It seems like I am not the only one to be interested in the way doctors model their thinking. In his book “How Doctor’s Think” Jerome Groopman tells of a hand problem he had. He visited a few doctors but none inspired confidence with their diagnosis or lack thereof. Finally, a young doctor decided to compare both of Doctor Groopman’s hands – an innovative idea — and found the problem. I like that idea and use it frequently in web analytics.

By comparing two things your brain sees things it wouldn’t otherwise think of

Many times when we are looking for new insights on a web site we compare. Comparison examples include

• Conversion paths for different languages
• Conversion paths of PPC vs natural search
• Two keywords that land on the same page

What have you found useful to compare?

I ran across Bounce Rate articles on the web recently and see that many people are wasting their time with this metric. A high bounce rate can be because:

• The referring website is low quality. This happens a lot with social media sites
• The search engine is sending traffic through its images feature
• The keyword is not that relevant to your product offering but your web site ranks high on the search engine
• The traffic is coming directly. This could be from bookmarks but also from hackers, employees or robots that you web analysis software is not filtering out
• Loyal visitors may only come to see what is new (especially from a newsletter) and then leave

A high bounce rate in these cases is not something to worry about.

Bounce rate is almost meaningless unless you drill down

Avinash has put forward good ideas but in most cases we need to go further. Drill down to the:

• Keyword. This is obvious
• Page. This is not enough. When looking at the page drill down to the keywords and traffic sources
• See how the bounce rate changes over time. Map this to previous years to eliminate seasonal influences. Then map changes in bounce rate to changes made on the page, traffic source and keyword changes. This will provide further insights. You may find a change you made to a web page that you forgot to track that either caused much damage or increased success rate dramatically
  
  
  Tracking bounce rate over time can reveal website changes that were overlooked and cause damage. Track by page and by segment for best results.
• Cost of wasted money from Google Adwords and other PPC campaigns. Multiply the bounce rate by the spend for each page but don’t stop there. Analyze by keyword and ad too

After your analysis you will find website pages and/or traffic sources to optimize. Don’t forget to track the improvements you made to make sure your changes bring the desired results.