Weighted Sort has got to be one of my favorite new features in Google Analytics. There is a nice demo for using weighted sort with bounce rate, but I thought I’d suggest another metric on which to use this new ranking algorithm.

Let’s start by asking a simple question:

What are my most valuable pages?

As a proxy for “valuable”, we’ll use the $ Index metric in the Google Analytics Top Content report. $ Index measures the e-commerce revenue divided by the number of pageviews. The problem with sorting by $ Index is that you’ll see very large $ Index values because the pages only have one or very few pageviews.

In other words, the top page only had one measly pageview out of 3.5 million and it happened to have been during a visit that resulted in one sale. Not exactly optimization material, so let’s try and find pages that are doing a bit more work by using weighted sort. Just click and look at the results:

Don’t you just love it when the data you look at tells a great story? The first few results are all checkout pages, and this of course makes perfect sense as every visitor has to go through the checkout to place an order.

But now we can go down the list and look at other pages and get a sense of their true importance. Note the relationship between pageviews and $ Index: low pageviews and high $ Index being equivalent to high pageviews and low $ Index.

What weighted sort does beautifully is put data into context. You see, I don’t care about $ Index or Bounce Rate or e-commerce conversion rate per se, I am more interested in the impact on business and prioritizing my optimization efforts.

So a logical follow-up question to the first one might be:

Which page should I optimize?

The one with a bounce rate of 60% or the one with 50%? The answer of course is: it depends. If both landing pages have the same amount of traffic I’d pick  the one with 60%. However, if landing page A has a bounce rate of 60% and 1,000 views and landing page B has a bounce rate of 50% and 10,000 views, the impact of reducing the bounce rate of B will be higher even though its bounce rate is already better than landing page A.

I really appreciate the fact that weighted sort bubbles up this valuable data with a simple click, unlike some other approaches. And once you know your most valuables pages, guess which pages make great candidates for AB or Multivariate experiments?

The only downside of weighted sort is that you don’t know it exists unless you sort in the first place. A visual cue might be nice to indicate whether a column is weight-sortable, but I definitely know where to look for it and I hope you do too.

There are a few reasons why clicks and visits from Adwords campaigns will likely never be same in your Google Analytics reports (or indeed any other web analytics tool), but you should of course make sure that you have a correct implementation as Adwords is a paid campaign…

But how can you tell if everything has been implemented correctly? If you have lots of Adwords campaigns driving traffic to lots of landing pages any errors might be hard to spot.

First of all, I would expect there to be fewer clicks than visits. If a visitor clicks multiple times on an Adwords ad during the same session you would see multiple clicks but only one visit in Google Analytics. Also, clicks are counted even before the visitor lands on your site, so if the tracking code does not execute in time on the landing page before the visitor navigates away you’d also lose visits.

In any case, even if there is a normal discrepancy you’d expect that discrepancy to be consistent. So if your normal percentage of visits to clicks is 90%, a percentage of 20% in one of your campaigns would not be normal.

You could of course look at the numbers directly in the reports table and look for unusually large differences between visits and clicks, but if you have lots of data to look at you might not spot any unusual patterns.

That’s why I like two visual approaches.

1) Using Compare Two Metrics

Pull up a new Adwords campaign report and click on the tab above the graph and select Compare Two Metrics. Choose Visits and Clicks. Data look OK to you and are clicks and visits in line?

You can then repeat this approach to drill down into Campaigns and Keywords. Unfortunately you’ll have to select those two metrics again every time you pull up a new report.

But fortunately, there is a sexier way…

2) Using Motion Charts

Pull up your Campaign or Keyword report and click on Visualize in the top half of the screen.

Motion charts allow you to see how several dimensions move over time in a graphical way. Visits should already be selected on the Y-axis. Select Clicks for the X-axis. Now you can see how visits and clicks behave over time. As I said earlier, you should expect clicks and visits to be correlated, but the motion chart should allow you to spot any outliers pretty easily.

As with all web analytics, you are looking back in time, but this is a pretty cool and powerful way to spot any errors.

What’s the only thing better than customers? Repeat Customers. According to the book Flip the Funnel the conventional wisdom is that it is far more expensive to acquire new customers than it is to retain existing ones. Many companies now base their strategy on increasing purchases from repeat customers, which in the case of Zappos is 75% of their business (again from the book Flip the Funnel).

You should be able to get repeat customer metrics directly in your e-commerce order system. If you have a Yahoo! Store, you can get a baseline metric by pulling up the Repeats report. This will show you the number of customers who have placed multiple orders. You can then divide this number by the number of total orders to get the percentage of repeat orders.

In this example, 159/3511 = 5% of orders came from repeat customers. This metric will probably not change that much over short time periods, but I would check it from time to time – perhaps on a monthly or quarterly basis. Are you able to do a better job of retaining your existing customers?

From a web analytics perspective, what I would like to examine is the behavior of repeat customers vs one-time customers vs non-customers. Are repeat customer behaving differently than other segments? Is there anything I can do to target one-time customers to get them to become loyal customers?

To address some of those questions in your web analytics you’ll have to be able to query your e-commerce order system and tag your visitors appropriately, eg as “new customer” or “repeat customer”, at the time of ordering. Implementation approaches will vary depending on your e-commerce platform, but web analytics tools such as Yahoo! Web Analytics or Google Analytics allow you to set custom variables so that you can track these customer segments.

Once you have set up your tracking, what does the data tell you? One of the first questions I would ask is how repeat customers come back to your site. Are they coming back because they know your brand and therefore type in your brand name in a search engine or visit your site directly?

My take so far is that unless you proactively target and try to get more repeat customers, don’t expect repeat customers to behave differently than one-time customers. You will have to work just as hard as getting new customers to get repeat customers in your search marketing campaigns. You will have to earn their business again every time by competing for non-branded terms:

In this example, the new and repeat customer segments show virtually no difference.

Could you be doing a better job of targeting your one-time customers either directly on your site or via email campaigns so that they become loyal repeat customers? Do you have a great process in place once customers place an order or when they call your customer support number?

Hopefully over time you will find that loyal customers will come back directly to your site thereby lowering your direct SEM costs. And finally I should mention that I have not even taken into account other advantages such as word of mouth and referrals from your loyal customers that should help to lower your overall marketing costs.

In terms of web analytics and statistics more data is always better. Ask 1000 people who they’ll vote for and you get a 3.1% margin of error. Ask a 100 people and the margin of error goes up to 9.8%. Since you are asking fewer people you are less confident about saying something about the underlying population.

Same thing for AB and MVT testing. You can see for yourself with the handy Google Website Optimizer calculator. Assuming a 2% baseline conversion rate, a 20% expected conversion rate improvement and the only change is the number of visitors who see your experiment page. If 1000 visitors see it a day, the expected experiment duration is 17 days, but if only 100 people see it per day it is 175 days! Ouch…

Add in the fact that web analysis is likely done on an ad-hoc basis without dedicated people (thereby violating Avinash Kaushik’s 10/90 rule), what’s a busy small retailer to do?

I think the answer lies in focusing on big segments, mini goals and the checkout funnel. Let me explain:

Big segments

Segmentation is the key to finding valuable nuggets in your web analytics. An average metric, such as your site conversion rate, hides the fact that your site traffic is made up of many different segments, some of which are above average and some below average. Once you see differences you can start looking at ways of doing more of the above average stuff and less of the below average stuff. However, if you don’t have much traffic to begin with, there is a risk that you will quickly look at segments that are too small to be significant. An example might be keywords that are converting very well, but only convert once or twice in a period of months. Hard to optimize that. Instead, try to work with big segments, such as:

• Visitors who added to cart. This is a much bigger segment than visitors who placed an order. What can we learn from people who add to cart vs those that don’t? Assuming that your add to cart rate is 6% (which is in line with a 2% site conversion rate), plug in this number into the Website Optimizer calculator. You now get a far less depressing duration of 56 days for 100 visitors if you were to try to optimize the add to cart rate.
• Visitors who use internal site search. I love internal site search anyway because it has lots of tactical stuff you can do, such as fixing zero results searches. But also look at how visitors are using site search, such as by source. Are you sending paid search traffic to a particular landing page, only to find that a large percentage immediately use your site search? Perhaps the landing page is not relevant enough.
• Branded keywords vs non-branded keywords. How are these segments behaving differently? Can you bucket groups of keywords to help you create more and better content that targets both the head and the long tail?

Mini goals

Getting more people to add to cart can also be viewed as a mini goal. Instead of only focusing on sales conversions try to get more visitors to add to cart first. Or you can try to get more visitors to look at product detail pages by making smart categorization choices.

Here is a very interesting metric to look at: Visits to Purchase. Pull it up in your favourite web analytics tool. You’ll probably find that most transactions happen in one visit. Big pat on the back? Not so fast. The way I see it is that you only have one shot to make a sale. After the initial visit, transactions go down rapidly. For this reason consider adding visitors to your mailing list first. This great mini goal helps you keep alive the conversation with your prospects. Obviously you should make sure you have something interesting to say to your prospects. Another example would be to provide your buyer’s guide via PDF in exchange for an email address.

Checkout Funnel

Last, but certainly not least is the checkout funnel. The checkout funnel is of course hugely important for an online store. Your visitors have to go through it before placing an order. If you have not done so, set up a checkout funnel in your web analytics tool, beginning with the cart page and ending with the transaction page. You will have one or more to steps. Make a note of the funnel conversion rate and go back to the Website Optimizer calculator. Remember the example of 175 days? Let’s plug in a typical 25% funnel conversion rate, i.e. a 75% cart abandonment rate, and keep everything else the same. You get an expected experiment duration of only 10 days for 100 visitors. If anything, you can see how important the cart page is and should feature prominently in any optimization plan.

You already know that the homepage is one of the most important pages in your online store. Many, if not most visitors, will land on your homepage, and a big chunk of your revenue will pass through the homepage. Unlike a product detail page who’s job it is to sell a particular product, the homepage has to cater to lots of different groups of visitors: not only to those who want to buy, but also to those who are doing product research, looking for support, checking order status, etc… Incidentally that is why I typically don’t recommend A/B testing the homepage, at least not right off the bat.

From a conceptual point of view there ought to be the perfect landing page for any given search query. Search engines get better and better at sending visitors to the most relevant page deep in your site, but in practice the homepage will receive traffic for all sorts of different keywords (not just branded terms) because the homepage has so much SEO pull.

How does this relate to web analytics? Using the power of segmentation I recommend that you look at your data through the lens of just visitors who landed on your homepage.

Here is the visitor segment in YWA:

and the corresponding one in Google Analytics:

Once you have applied the segments to your reports, it will first reinforce the importance of the homepage:

In this example you can see that a third of all transactions pass through the homepage. Next, look at entrance keywords and take in the scene. You may have hundreds if not thousands of different keywords landing on your homepage!

Many of them will be branded terms, but look in particular for non-branded terms. Do you match your visitor’s intent by showing them relevant content? Look at your homepage and ask yourself if you do. Just focus on the top 10 or 20 keywords because you can’t or shouldn’t optimize for everything.

Another good metric to look at is bounce rate for those keywords. Are any of them bouncing more than others?

Final Tip. Do you have product specials on the homepage? Don’t put them on willy-nilly. Folks will click on what you display (most likely in the order you present them)! The homepage *is* extremely valuable real estate.

Go treat your homepage with the respect it deserves and have fun!

Nice collection of Google Analytics custom segments from Google South East Asia. For an online store I would add:

Visitors who used site search. (click to add to your GA profile)

Why? Particularly for paid search campaigns I would like to send my visitors to the most relevant landing page. A high percentage of site search usage may suggest that visitors are not immediately finding the content they were looking for and hence using your internal site search.

I will be speaking in a couple of upcoming webinars for online retailers:

1) Friday 2/26/2010 at 4pm ET / 1pm PT. Internal Site Search analysis for online retailers. Many thanks the Shawna for the invitiation!

2) Tuesday 3/2/2010 at 2pm ET / 11am PT. Make Analytics Work: Spend Less, Make More With Segmentation. Many thanks to my friends in the Yahoo! Store team for including me alongside John Marshall and Rob Snell.

Hope to see some of you at either event.

First, what is a 404 error page? Google provides a great definition:

A 404 page is what a user sees when they try to reach a non-existent page on your site (because they’ve clicked on a broken link, the page has been deleted, or they’ve mistyped a URL)

Having a custom error page helps your visitors. Broken links or typos happen, so at the very least try to point them in the right direction. The apple.com error page is a great example.

What prompted me to write about this is that the Yahoo! Store platform presents a special case because the default behavior is to send visitors to the homepage, so it’s a “soft” 404 error page. It’s described here along with simple instructions on how to set up a custom 404 page.

We checked several hundred Yahoo! Stores and found that only a third of them are using custom error pages.

Here is why I think you should create a custom error page:

1) If a page is in fact no longer available it’s OK to let the visitor know that. Sending her to the homepage is not really what one would expect to happen.

2) As a site owner you don’t know that there is a broken link if you send visitors to the homepage. If you have a custom error page you can track it in web analytics. You can’t fix a problem if you don’t know about it.

3) Redirecting to the homepage could be seen as duplicate content and decreases the efficiency of spiders crawling your site. Returning anything but a 404 response for a non-existent page also means that you won’t be able to use the Crawl Errors report in Google Webmaster Tools – hat tip to Dave Burke for these points.

I know that to-do lists are getting longer and longer, but this is one thing you should do. So head on over here.

First of all, what is attribution management? If I may quote John Lovett from his paper “A Framework For Multicampaign Attribution Measurement“:

Multi-campaign attribution is the practice of attributing credit to all marketing exposures that led to a Web site and subsequently resulted in a conversion event, rather than attributing all credit to the exposure immediately preceding the conversion.

In other words, many of your customers are deliberatively making your life harder by visiting your site multiple times from multiple sources before converting and, to make matters even more complicated, it’s really hard to tell that this is actually happening!

Here is an example:

Standard Traffic Sources report, where one transaction is credited to Google Organic and one to Google Adwords.

I can also see the actual transaction:

Fair enough…Google gets the credit, end of story. Or so one might think… Bear in mind that this is just the last, converting visit. We can’t really see what or if the customer came to our site before. That’s where attribution analysis comes in, and here is the salient visit history from our own attribution management tool:

Turns out that the referral from flyertalk.com in spot 7 in my referral list played an important role in the conversion process and should thus get at least some if not all of the credit:

Useful to know, right? And from John’s same paper, “According to 275 Web site decision-makers surveyed in 2008, a full 52% agree that attribution would enable them to spend marketing dollars more effectively.”

OK, but back to my original question: Is attribution management right for smaller online retailers?

It’s quite a step from “useful to know” to actionable advice. The example I showed is not actionable per se. All I know is that there is more to the transaction than first meets the eye. It’s just one multi-visit trail leading to one transaction.

In order for attribution analysis to become interesting, we have to collect far more data to come up with aggregate purchase paths that exhibit distinct differences – kind of like segmenting in web analytics. For example, attribution analysis can tell you that many branded search terms are preceded by keywords that look like they are converting badly in your web analytics, but which provide a crucial assist.

The need for a lot of data could however rule out many smaller online retailers who may not have enough traffic for those patterns to emerge. Furthermore, doing any kind of decent analysis takes time and resources, possibly at the expense of other worthy initiatives, such as web analytics in general, AB and Multivariate testing, email marketing, landing page optimization, SEO and SEM, fulfilling orders, etc…

Has anyone looked into at what point attribution management becomes important? Is it in terms of revenue, traffic, marketing spend, or number of different channels? Could there be a simple metric that a busy retailer could use to determine whether she “needs” attribution management?

Is attribution management just for the big boys?

We know that visitors tend to click in order on section pages, but you can focus attention and hence click-throughs by adding a descriptive New label to an individual item.

The item with the New label has by far the highest click-through rate when looking at a site overlay report, even though it is further down the list. The effect is at least 10x more clicks than you would expect for that position.

One thing you might want to try is using another descriptor like Special, but I have a feeling that we are wired to seek out all things new. I mean, who doesn’t want the latest stuff?

Of course, you should only use such a descriptive label if it is appropriate to do so. It should also be used sparingly; if all items are labeled in this way the effect would be canceled out.