Everyone loves bounce rate. Bounce rate analysis is a great way to start any optimization campaign as it gives you a quick and easy-to-understand metric that tells you what works and what doesn’t on your site. The higher the bounce rate the higher the proportion of visitors who leave immediately once they get to your site. In general, high bounce rate = bad and low bounce rate = good, but you should be less concerned about the absolute value, eg a 55% bounce rate, than with reducing the bounce rate over time, e.g. from 55% to 45%. It’s all about the journey, eh?

Although you will see global bounce rate reports, there are in fact many different bounce rates. Each landing page has a bounce rate, as do your referring keywords and sites. Use the segmentation capabilities in your web analytics tool to drill down and start investigating: Why is traffic from this keyword bouncing off my site? How come traffic from that external site is apparently unqualified? Once you start asking specific questions you will start finding specific solutions.

All ready to start bringing down that bounce rate? Great, but how much effort and time and money should you invest in bring down the bounce rate of one of your landing pages? Although you may find a page with a high bounce rate, it may not be worth optimizing it if the traffic to it is very low. In fact, I guarantee that you will have keywords that have a 100% bounce rate! But if only one person came to your site on that keyword, it’s not exactly high-value traffic. And of course, doing any kind of optimization will have a cost attached to it.

To help you prioritize, you should also take into account the value of your landing pages and keywords.

Here is an example you can use if you are using Google Analytics. You can of course do the same sort of thing in Yahoo! Web Analytics, which I have had the honour to write about on the official Yahoo! Store blog. A big hat tip also goes to my friend Rob Snell who has provided a huge amount of creative input.

As an added bonus I will be highlighting some uses of the relatively new custom report and advanced segmentation capabilities in Google Analytics. I do assume that my audience has e-commerce tracking enabled in GA, but you should be able to swap out e-commerce revenue with goal values if you don’t have an e-commerce site.

To get started, simply click here to add my Entry Pages with per visit values report custom report to your GA profile. You can of course also create it by hand. This is what it should look like:

This custom report shows you the entrances and bounces and per visit revenue for your landing pages.

After you save the report, you can pull it up in the Custom Reporting section:

Next, apply the Non-bounce Visits advanced segment alongside the All Visits segment:

Your report should look something like this:

Our custom report now shows overall metrics for all visits and non-bounce visits side-by-side. Check out the difference in per visit values! If I can move some of those 700K bounces into the non-bounce camp it would make a huge difference. Lets say I decrease the number of bounces by 20%. This equates to 724,663 x $1.96 x 20% = $284,000 in additional revenue. Of course, you can’t really take action as it is the aggregate of all your (potentially 1000s of) landing pages.

You should therefore look at individual landing pages and prioritize based on potential revenue impact.

Look for landing pages with large differences in per visit values to guide you. Not only does the last landing page in the above screenshot have a large upside in revenue per visit, but it has a very high revenue per visit to begin with for a comparatively low amount of traffic to it.

Using 20% as our target: 1,070 x $12.37 x 20% = $2,647. Depending on the reporting timeframe, get the yearly value. In this example, we are looking at 3 months worth of data, so the revenue upside is over $10,000 per year. Voilá. Now you also know how much you can invest in optimization. And this is just one page…

Finally, you should also do this kind of analysis for your keywords of course. You can grab the custom report Keywords with per visit values here.

Happy analysis!

The saying “If you can’t measure it, then you can’t manage it” definitely applies to web analytics as well. Take online coupons. If I can’t see any data about coupon usage in my web analytics reports then I won’t worry about it, let alone do any kind of optimization. But by all accounts, coupons play an important part in the marketing tactics for many online retailers.

By being able to see coupon usage in web analytics we can at least start asking some interesting questions and perhaps start uncovering some interesting nuggets.

First off, if you are giving out any kind of coupon, try to have a good estimate for the amount of visitors who have been exposed to your coupon. Email campaigns are ideal for this as you know how many email you will be sending out. If you are displaying coupons on your site, try to show coupons only to certain visitor segments by using dynamic web personalization techniques. You can also use AB testing. Once you have this number, go into your web analytics or back-end to find the number of times the coupon has been redeemed:

Now you can calculate the coupon redemption rate. In addition to knowing the number of visitors who are seeing your coupon, try to use control groups. What this means is that some visitors (the control group) should not be exposed to the coupon and you should compare their behavior – especially number of orders and order values – to the group who sees the coupon. If there is otherwise no difference between these groups you can attribute any kind of lift to the coupon.

To-do 1: Track coupon usage in web analytics.

To-do 2: Measure lift by using control groups in your campaigns.

Once we start tracking this type of data we can also start asking some related questions:

1. What is the coupon conversion rate once it has been applied?

2. What is the conversion rate of invalid coupons? If you have a coupon field on the checkout, do people who try out invalid coupons abandon their checkout?

3) Are certain visitor segments using coupons more often than others? For example, from a shopping comparison engine:

4) What is the right type of coupon? Percent off, dollar off, free shipping? In addition to conversion rate, pay attention to average order values and experiment with minimum order values before a coupon can be used.

These are some of the questions that you can get answers to before getting into optimization. Ideally, from a merchants perspective, you only want give a coupon to a visitor who “needs” a coupon, i.e. one who would otherwise not have placed an order. Sounds like a cliché, but you want to show the right coupon or offer to the right person at the right time. You also don’t want to teach all your customers to expect a coupon.

The Yahoo! Store platform offers some pretty comprehensive coupon management options. And you can really get into personalized offers by using one-off or single use coupons. And now you can track everything too!

Happy coupon analysis!

I am in the process of moving blog platforms, but if you are subscribed to my RSS feed, no update should be necessary. If you are having any issues, please let me know.

Many thanks,

Michael

I will do a demo today of the cart recovery service as part of the Store Optimization Series organized by the Yahoo! Store small business team. I will also briefly touch upon the upcoming release of PersonaQuest, our web personalization and targeting platform. Plus there will of course be something about web analytics – you know me!

Please consider joining me and other partners who will be showing off their cool tools:

Yahoo! Merchant Solutions Add-ons & Features Showcase — Register Now
Yahoo! Merchant Solutions Developer Partners showcase some of their most useful product features to help make your online business more successful.
1:00-3:00pm PT / 4:00-6:00pm ET

Live site reviews are popular sessions in conferences and webinars, but are they actually all that useful?

In a site review, an expert takes a quick look at a site and makes recommendations on the fly as to how he would improve the site. I am just doubtful that you can make any valid recommendations without knowing at least a little bit about the business behind the site. This is not a criticism of the site reviewer, but rather of the site review process itself. 

Even though it may be an expert opinion, it's still only one opinion from one person. This person will be shaped by his unique world view and past experiences and will have all sorts of hidden biases. For this reason I would take the combined behavior of thousands of actual website visitors over the opinion of one person any time. Put differently, I would trust web analytics data more than anecdotal evidence. I am also doubtful that you can easily apply the recommendations of the reviewed site to your own site.

I think the best outcome for a site review session, outside of the entertainment value, is that it gets you to think about your own site. And the starting point should be to delve in web analytics.

…at least in a Yahoo! Store with the optional cross-sell feature. Average order values are consistently higher for this retailer: 

Note that the cross-sell traffic segment includes only visitors who have clicked on a cross-sell link. It's not an AB test, but I think it does show that Yahoo!'s cross-sell algorithm does a good job of displaying relevant cross-sell items that lead to larger order sizes. Also, as I mentioned earlier, this is only one example, but I don't see how enabling cross-sells could hurt . It's easy to implement and doesn't cost anything extra.

I am a big fan of Google Analytics' event tracking feature and I think all web analytics programs should have that capability. There is one feature that would make event tracking even more useful in my opinion.

In addition to Category, Action and Label, you can send an event value, but this is supposed to be a positive integer. It would be more powerful if event value could be used also for cost data, i.e. dollars and cents, or a number with two decimals.

One of the strengths of event tracking is the ability to integrate other data sources, so wouldn't it be great to see either the cost or value of such external data sources? We could also assign a dollar value to outbound links, downloads or referral traffic coming to our site, and compare to e-commerce revenue.

As it stands now I have to multiply the cost by 100 before sending the event in order to turn cost into an integer, but then I am comparing quite literally dollars and cents.

From the excellent Neuromarketing blog comes a great post entitled "Order Effect Affects Orders" that shows that you should put your most important items first, because that's what people will click on and buy.

In other words, the item in position 0 should get more clicks than the one in position 6 on this sample section page. I thought I'd see if this is found to be true by looking directly at web analytics data.

What I did is not only track the pageview, but also the position via an onclick event using event tracking. (Side note for Yahoo! Stores: you can pretty easily determine the position of the ids in the Contents field by using the POSITION RTML operator).

What's nice is that I can now aggregate the positions across all section pages. After just a few hours of gathering data it looks like there is already a confirming pattern emerging:

The data comes from an online retailer that has section pages with three items per row. I would caution that not a lot of data has been collected at the time of this writing, but I find this type of analysis pretty amazing.

Here is what I would do with the data:

• Go through your top section pages and present your best items first, instead of other listing types, such as random/date/price/alphabetical. You can now get analytics data that shows you how important order is on your site.
• If you have an e-commerce site I think you might even be able to get a sense of the dollar value of the positions. Having a dollar value helps to make analytics actionable!
• Segment this data against screen resolution to see if there is an "above or below the fold" effect. With a low screen resolution you might find that items that are not visible without scrolling are not clicked on at all.

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.

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?