Love it when web analytics data paints a clear picture (even if it is not a pretty one).

Love it when web analytics data paints a clear picture (even if it is not a pretty one).

I was happily segmenting data in the quest for actionable insights when I came across segmentation by page type (hat tip to Gabriel), specifically for e-commerce sites. Outside of the homepage you typically have two main types in an online store:

• section/category pages and
• item/product detail pages

You should be able to tag/segment your data by page type with any web analytics package - if you have a Yahoo! Store and Yahoo! Web Analytics you get the page type tagging out of the box (very cool).

Then pull up your top landing page report, segmented by page type and look at some conversion and revenue metrics. Where does most of your traffic land? On a section or item page? What is the conversion rate per page type?

(Click for larger versions)

Answering these questions is inherently interesting, but the main value is to further segment by paid source, e.g. Adwords or Yahoo! Search Marketing.

In one particular case, I found that most Adwords traffic was being sent to section pages (not sure if this was intentional), but the data now shows me that sending folks to item pages could yield far better results:

Note that no matter what tool you use, you should be able to get this sort of data pretty easily as long as you have proper campaign tagging enabled.

Even if your data is not as clear-cut as in this example, you can now review your PPC strategy and make changes if necessary as you are in charge of specifying the landing page URLs.

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Segment by page type

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Time to look at some data after gathering checkout error data for a while. Surprise #1 43% of all transactions had at least one error message during checkout. I expected there to be fewer although I don't know why.

Time to look at some data after gathering checkout error data for a while.

Surprise #1

43% of all transactions had at least one error message during checkout. I expected there to be fewer although I don't know why.

Surprise #2

Seeing an error message does not necessarily mean that those people don't convert. In fact, 91.7% of visitors who saw an error message still completed the purchase. Forgot to put in the state or the email address? Just hit the back button and try again. This is what most people (thankfully for retailers) seemed to have done.

Surprise #3

Not all error messages are created equal. In particular, if the error message has to do with an incorrect CVV value, the conversion rate drops from 91.7% to just 61.9%. Or to put it another way: of all visitors who see an error message during checkout, those who don't see a CVV-related error message are almost 50% more likely to convert than those who do. Using the sample data above, it looks like over 210 transactions are lost due to CVV issues.

Perhaps this is not a huge surprise though. Here are some plausible reasons I can think of why CVV errors might lead to fewer conversions:

• If you supply the wrong CVV value or leave it blank the transaction may not go through at all (as opposed to a typo in your shipping address).
• Could it be a symptom of fraud? You need the card present to see the CVV value.
• Do visitors have trouble understanding or locating the CVV value?
• Or perhaps the error message itself (e.g. "You seem to have supplied an invalid Credit Card Verification value.") is not as helpful as others in actually helping visitors to correct the error.

One caveat

This sample data is from one source. I have seen checkout errors on other sites that are completely different, so use caution - as you always should - when using anecdotal data or drawing general conclusions.

Finally, I am wondering if your aim should be to reduce error messages in general, either by making (design) changes to your checkout pages or by limiting the amount of data you try to collect?

Read more:
Surprising checkout error analysis

Popularity: 10% [?]

Optimizing for better online conversion is, for the most part, something we do by examining our content and its layout, such as landing pages and immediate pages in our funnels – and hopefully applying some sort of testing methodology.

Optimizing for better online conversion is, for the most part, something we do by examining our content and its layout, such as landing pages and immediate pages in our funnels – and hopefully applying some sort of testing methodology. This is a standard practice I most certainly agree upon, but I also believe that one needs to look outside this, sometimes, closed loop of pages.

I suggest, as an alternative, that you try using a Page Revenue Participation metric for your next Conversion Optimization routine, as it might reveal opportunities away from your traditional funnel. When debating this, please note that, I am purely talking about Controlled On site Content, as defined by the Online Business Measurement Quadrant.

The reason for brining this up; I only just returned from Boston, where I attended the Internet Retailer conference, and probably even more exciting the Yahoo! Merchant Summit, which was delivered in conjunction with the show (On that note, the ystoreblog folks did a respectable pre-show interview about YWA 9.5 and my book). Anywho; the merchant owners had some of the most honest analytics questions I’ve heard I in a long time. You simply cannot wow these folks with a posh looking four-dimensional bubble chart – they wanted to know exactly HOW we are going to help them make more money!!) – Refreshing!

A small disclaimer; all of the below screen-shots are from Yahoo! Web Analytics, but you have the same metric available in e.g. Omniture Site Catalyst and even though Google Analytics have a different term ($ index) and slightly different calculation, it is the same optimization attitude.

So how do I use a Page Revenue Participation metric for my next Conversion Optimization routine ?

1.

Go find your most popular pages report, which in most tools, means a report that shows a sorted list of all your pages, with the one receiving the most page views on top. By most standards, this report does not provide much insight, unless you derive your revenue from page views (such as advertising) - fair enough, then this might not be a bad proxy for $money.

2.

Customize this particular report in a way, so that you get your tools revenue participation metric appended to it. In Yahoo! Web Analytics the Revenue Participation metric resides under the sales group under metrics, when using the Custom Report wizard.

3.

The below figure, shows a standard most popular pages report, which have had the revenue participation metric added to it. This is almost per default, and with out much fingering, a better choice than just looking at raw page view numbers.

4.

Fingering or not! I am a big fan of visualizing data and the below screen shot is essentially just an advert for data visualization (but also, potentially, a  honest report export, that will create better value than a non colored bar chart). The below bar chart was extended to 100 bars and had its bars colored by the revenue participation metric. This alone brings instant insight, as we can spot easily spot “small green bars” – or more precisely, low page view volume combined with high revenue participation (a.k.a. important pages). We could of course expand this with more rows of data or just sift through the bars a hundred at a time.

5.

What I really like for you to do, is to change the sorting from page views to revenue participation. This changes the picture completely and it is the essence of my suggestion for you trying out a Page Revenue Participation metric for your next Conversion Optimization routine. This should in a best case scenario bring forth a set of pages, that you might not see as active drivers for conversion today.

The last X steps in your funnels, which are required for people to check out, will of course always be on top, and if you are anal (like me), I suggest you go filter them away, so you only have pure content in this report. Especially if you have to communicate this beyond yourself or your close team.

The below shows a astonishing set of facts:

• The “About us” Page Touched 23% of revenue in < 1% of visits
• Site Search participates in about 50% of all my revenue.

Knowing these two facts (which will be completely different from site to site) I hope one would set out to work on the “About us” pages AND closely look at those internal site search result pages. For these folks, their business clearly depend on it.

6.

As a closing comment; not that the upper commentary need further input, it is obvious that neither “About us” or “Search” is part of your traditional funnel analysis as below – and as such we sometimes forget to look outside our funnel.

Doing a better “About us” page, in this example, even though it is not part of the funnel, is likely to widen the funnel – and if that is not exciting I don’t know what it.

This is not the first time I talked about revenue participation per say and if you are still listening, the post about how you can Use Web Analytics to determine the width of your Internal Search Query box might be worth a read.

Cheers
/ Dennis Mortensen (@dennismortensen)

Original post: 
Using a Page Revenue Participation metric for Conversion Optimization

Popularity: 20% [?]

Big hat tip to Joe Megibow for providing the inspiration for this post. At the recent eMetrics conference Joe talked about how Expedia is listening to their customers and using web analytics and monitoring techniques to fix problems with their site, particularly in the checkout process. Especially for online retailers fixing and reducing checkout problems means higher conversion rates and money in the bank.

Big hat tip to Joe Megibow for providing the inspiration for this post. At the recent eMetrics conference Joe talked about how Expedia is listening to their customers and using web analytics and monitoring techniques to fix problems with their site, particularly in the checkout process. Especially for online retailers fixing and reducing checkout problems means higher conversion rates and money in the bank.

Of course the first step to fixing something is knowing that there is a problem in the first place, and customers may not always be kind enough to tell you about it. As it happens, event tracking is ideally suited to tackle this issue and track error messages that customers see as they progress through the checkout pages. All we have to do is fire off events when these various errors messages are displayed on the page. Event tracking is very flexible in that I don't have to know the actual error message ahead of time; we just send the error message itself as an action. The additional benefit of event tracking is that it is completely separate from the traditional web analytics pageview model. Just say no to fake pageviews!

Anyway, here is an example of a Yahoo! Store checkout page, but you should be able to do this in other platforms.

(Click for larger)

Not only are we seeing the various error messages (there are probably more than a dozen) that can happen, but two of them are already standing out. I will not attempt an analysis here, but the point is that we can start asking the right questions by seeing this type of data.

Since event tracking is still in beta and implementation of this technique depends on how error messages are displayed on the page I won't try to detail the steps involved, but anyone interested to learn more, feel free to contact me or ping me on Twitter/monitus and I will send you in the right direction.

The rest is here:
Reducing checkout errors using event tracking

Popularity: 16% [?]

Few years ago, I learned an important fact about the online retail industry and that was, on average, every online customer visits a retailer site at least 2-3 times before he goes for a final conversion. However, the definition of conversion can vary from retailer to retailer depending on their business model, online/offline store presence, type of merchandise they deal in, competitive landscape, online marketing mix and on top of that, what their target customer segment is and their objectives?

Recently, I unsurfaced some really interesting facts while doing an analysis for one of my client, and I thought it would be better to share these facts with other members in this association and listen their perspective.

20% of the monthly revenue comes from the first touchpoint visitors.
The next 30% comes from second touchpoint visitors.
The next 28% comes from third touchpoint visitors.

If we aggregate the above numbers you will notice that, the first 3 touchpoints of the visitor makes all the difference, and if the retailer is not able to assist the visitor in his conversion process, then he might lose this visitor to his competitor.

The above % can vary from retailer to retailer depending on the marketing mix, and how the retailer hand hold the visitor from one marketing channel to another until the customer converts. Now the question is, what marketing channels makes the most difference and what should be the recency between the first 3 touchpoints to generate the above results? To learn more about this, check the second part of this article.

26-Apr-09 10:00 AM
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Do the first 3 touchpoints make all the difference?

Popularity: 10% [?]