If you have an e-commerce site with section and item pages, you’ll want to be able to tell those page types apart in your web analytics. Unless you have a URL structure where you can tell directly what type of page it is (e.g. www.yoursite.com/?item=xyz or /?section=abc), you must tell your web analytics tool what type of page it is by doing some manual page tagging.

For Google Analytics I use custom variables to tag each page as ItemPage and SectionPage (along with some other types of pages).

In the case of Yahoo! Store, I use a bit of RTML at the top of each page to set a simple JS variable. The rule is literally as simple as “If the page has a price on it, it’s an item page. If not, it’s a section page.” The output on the published site would look something like this for an item page:

<script type=”text/javascript”>var pagetype=”ItemPage”;</script>

This can then be used in the tracking script

_setCustomVar(2,”PageType”,pagetype,3);

which is then sent along with the pageview. Note that pagetype does not have quotes because it is a variable and it’s value will be sent.

Big segments and mini goals

So why am I doing this? Because if I don’t get visitors to an item page, which has the add to cart button, there won’t be any transactions. I want to know how effective I am at getting visitors from the landing page to an item page. It’s a great mini optimization goal.

While I can get a nice graphical view of links being clicked with the new In-Page Analytics, I’d like to get a simple number for all my main landing pages and I can do so easily with an advanced segment:

which I can then compare to all my traffic in the Top Landing Pages report:

All these landing pages have similar bounce rates, but some pages are obviously better at getting visitors down the conversion funnel than others…

You already know that there are lots of conversion rates in your web analytics: Every PPC, keyword or email campaign has a specific conversion rate. But what about the stuff that really matters: the actual products you sell? Shouldn’t products have a conversion rate as well?

When a transaction occurs you can (and should) send the order details to Google Analytics, such as product name, SKU, price, order number. You’ll get very rich data and be able to attribute revenue to your various campaigns.

But this is not enough

What I also want to know is how many times that product was viewed and whether it was added to cart. You see, Google Analytics doesn’t know that SKU abc123 corresponds to item page abc123.html. With transactions I only know about the (few) successes and not about the (many) misses.

I don’t think there is a decent built-in report that would give you this information out of the box. In order to see the number of add-to-carts I tried using the Navigation Summary report on the cart page.

It shows me the top 10 previous and next pages. I figured that in order to get from one page to the cart page you’d have to hit the add to cart button. Based on the percentage of clicks you can figure out the number of add-to-carts for each of those top 10 previous pages and then compare to the number of SKUs sold. However, the SKUs are only accessible in the e-commerce section so it’s hard to do this efficiently. I am also not sure about the true number of add-to-carts as visitors hit the back button and view other pages, all triggering additional pageviews.

The solution

The reason for the long intro is that I’d always prefer to get the data directly or without any additional tagging (Occam’s Razor, anyone?), but in this case I don’t think it’s possible. I personally recommend that you use event tracking to capture add-to-carts. I suspect that most add-to-carts are a form post and this is certainly the case for Yahoo! Stores. In the case of Yahoo! Store the product SKU is also the product page so it makes it pretty straightforward to analyze. Here is what I end up with:

The letters correspond to SKUs added to cart. Now I get an e-commerce conversion rate for each event, which translates to: What’s the percentage of visitors who placed an order after adding to cart?  I can now start asking some really good questions, such as: Why does H have a far better conversion rate than A? Can I ramp up my targeting for H?

Note that events only live as long as the session. So if someone adds to cart, leaves for a while and lets the session expire, and then finalizes the order in a new session the transaction would not be attributed to the add-to-cart event, but I can live with that.

Although I tend to be skeptical of best practices – they might be a starting point, but I prefer doing testing to find out what works or not – there are some things that are always better than others. A fast-loading site always beats a slow-loading one,  working links are always better than broken links, and reducing errors in general makes obvious sense.

In the same vein I would say that a short checkout form is better than a longer one. Of course you need a certain amount of information to be able to process and fulfill an order, but you should only ask for the information you absolutely need. Reduce friction and remove any obstacles that make it more difficult for your customers to buy from you.

But can we back up this assertion with web analytics?

I am a big fan of tactical web analytics, such as tracking 404 pages, zero results searches and checkout errors.

This report shows the number and type of checkout errors seen by visitors:

The number one error message customers see is the missing phone number.

In a default Yahoo! Store checkout, the phone number is required:

But do you really need the phone number? If there is a problem with the order, can’t you just email the customer? I think the data shows that your customers don’t think you need their phone number either! And if you do require the phone number, you should at least tell your customers why you need it.

If you do not absolutely need to have the phone number, then how about making the phone number field optional?

Fortunately, this is easy to do in the checkout manager in your Yahoo! Store. Just edit the phone field on the shipping page and uncheck the required field:

Because this is just the data from one store I don’t know if making the phone field optional in the checkout qualifies as a best practice , but at least the data gives you a clear picture of the problem. And in this case it would be easy to fix it.

Now on to the other error messages!

(This post applies only to Yahoo! Store merchants).

If you are using Trackable Links you can make a small change in the trackable link URL that allows you to track it directly in web analytics too.

When you set up a trackable link it looks something like:

http://store.yahoo.com/cgi-bin/clink?yhst-12345678910+VQASpg+index.html+testit

When you click on a link like this one (which is a non-working sample link), you are redirected to the landing page – in this case the homepage. What if you want to track this link also in web analytics? In this case you might be tempted to add a tracking parameter at the end of the page you are linking to, e.g.:

http://store.yahoo.com/cgi-bin/clink?yhst-12345678910+VQASpg+pageid.html?coupon=testit+testit

Unfortunately, this tracking parameter will be stripped during the redirect to the landing page and your web analytics tool will just count another pageview for the page.

There is however a simple solution, which involves using the full URL to the page:

http://store.yahoo.com/cgi-bin/clink?yhst-12345678910+VQASpg+http://www.yourdomain.com/pageid.html?coupon=testit+testit

In this case the tracking parameter is preserved and can be read by your web analytics tool:

allowing you to do advanced web analysis of the performance of these trackable links.

You might therefore wonder: Why use trackable links at all? Would it not be more elegant to send visitors from your paid campaigns directly to your desired landing pages using URL tracking parameters?

There is one good reason in my opinion: When you use a trackable link in conjunction with coupons, clicking on the trackable link automatically applies the coupon!

How about that for being nice to your customers?

I should also add that you can send visitors straight to your cart page if you want.

http://store.yahoo.com/cgi-bin/clink?yhst-12345678910+VQASpg+https://order.store.yahoo.net/cgi-bin/wg-order?catalog=yhst-12345678910&coupon=testit+testit

Just note that if you are using tracking parameters you must use the parameter catalog=storeid otherwise it won’t work. If you want to use multiple tracking parameters, just separate them by ampersands “&”, e.g.:

coupon=testit&campaign=email

Finally, I just realized that the fine folks at YWA have already written a help document about tracking trackable links! They also rightly point out that you should encode the full URL of the page you are linking to.

Happy tracking of trackable links!

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.

You can now use single-use coupons in our cart recovery service and our web personalization platform PersonaQuest. As the name suggests, single-use coupons can only be used once. One issue with generic coupons is that they could be disseminated far more widely than intended. This can not only result in lower margins, but it’s also hard to tell how effective coupons are if you don’t know how many people have been exposed to them. Plus of course you may be giving coupons to visitors who do not “need” them – those folks who would have placed an order without the coupon.

What I am most excited about single-use coupons is in website testing.  Particularly with dynamic onsite targeting you can display the coupon to only a specific segment of visitors. Use your web analytics to find out when visitors abandon – look at landing pages and keywords with high bounce rates, visits to transaction or page depth – and target those.

It’s of course still about the offer, right? You can test the type of coupon – $ off, % off, free shipping – and see what works best. You might find that the type of coupon varies across segments! Well, you can now test this and have all the necessary data directly in web analytics to draw conclusions from, including actual coupon usage.

So how does this all work?

You start by creating a series of single-use coupons in the coupon manager of your Yahoo! Store.

Then you download the coupon series

and upload in your Monitus Tools account:

You will get a nice table of imported coupons:

As coupons are distributed to visitors you can see the numbers decreasing. You can set up an alert if the number of coupons is getting below a certain threshold and you can add more coupons.

Now for the fun stuff!

All you then have to do is specify the coupon code pool when you set up a new PersonaQuest rule and add the tag on the desired page and location:

and PersonaQuest will start distributing those single-use coupons to each visitor in the desired segment (in this case to visitors to the site on a particular date).

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 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.