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    The Problem With Reading Site Tools

    May 6th, 2010

    Businesses want to see the product that they have.  That is fairly obvious.  What may be slightly less obvious, though we probably all know it if we stop and think, is that businesses want to provide us with the tools we need to engage with them.  A basic offline example is carts at the grocery store.  This is provided to you simply to make shopping with that store easier and so that you can buy more stuff (as opposed to if you had to carry it all in your hands).

    These same sorts of tools exist on almost all e-commerce websites in one form or another.  A simple example is a search box.  The option to search provides the visitor with a tool to find what they are looking for.  Another example is product categories.  A company doesn’t just put all of the products they have and place them into a single index page.  They try to group them by how they think their (potential) customers would group them.

    All of these things make it easier to engage with the business.  The tools make it easier to shop or discover product.  This, hopefully, will get you to buy more product which is how the business can justify the investment in whatever tool they have developed.  Some, like the search box, are easy to see the value in while others may just be for fun.  Either way the end goal is the same.

    Clearly it costs money to develop and implement these tools, therefore the initiative has to generate money.  This could be directly (sale now) or indirectly (brand goodwill for later purchase and/or consideration).  The hard part is measuring the value of one of these tools retrospectively.  If it was upfront, then test, test test.  But, in some cases, that just isn’t possible or wasn’t done for some other reason and yet the leadership will still want some number to hang their hat on even if it is just a very rough estimate.

    The problem with reading tools after the fact is that it is very hard to prove direct value.  Sure you can see how much demand came through the tool.  Or how many visits interacted with it and how many times it was used in total.  You can look at participation metrics for the tool.  You can look at data point after data point but one very tough question will remain: how much of this is incremental?.  Even if you launch something and it is used by a significant portion of your visits, you will still have a hard time saying what is truly incremental.

    For instance, mother’s day is coming up.  Let’s say you launch a page that lists many products for mom.  You may see that for those visits the % of time that a product is viewed and then purchased will decline.  But don’t worry, they are just browsing for gifts.  They are using the tool exactly as expected.  But if this change in behavior happened on the whole site you would have an issue because people are viewing product and simply not buying.  You might read that as a poorly designed product page or that your visitor can’t find the information they want.

    On the flip side, you may launch some tool and find that anybody that touches the tool is like pure gold.  Conversion is up 50% for visits that use the tool.  Everything looks great, right?  Well, what if all of those people were your best customers anyway; what if they are your most engaged and most likely to buy?  Perhaps your tool is actually lowering conversion for these people, but the fact that they perform so well compared to the site as a whole causes you to miss this issue.  And, at worst, to declare the new tool a success!

    The solution, of course, is to test everything.  That way you will have a true and clean read of the exact lift of a new tool.

    What are your favorite tools that you wish all sites had?

    This has been a Thought From The Cake Scraps.



    The Best Use Of Participation Metrics

    January 28th, 2009

    In my previous post on participation metrics I got a great question about how participation works across multiple visits.  And as long as I was going to write about that question, I thought it would be a great time to also state the single best reason to use participation metrics.

    First, I will address the question about multiple visits.  It is a great question, and I would expect nothing less from a guy who is crunching Milwaukee Brewers stats dating back to pre-1900 over at Brewer Leaders.  Hell, Ted “Double Duty” Radcliffe wasn’t even playing yet (bonus points if you tell me why he was famous in the comments).

    Getting back to the subject at hand.  Participation metrics are generally only good for a single visit and a visit is arbitrarily defined by ‘the industry’ as you leaving the page or being idle for 30 min.  A full dictionary (pops a PDF) can be found here.  Therefore if you added a bunch of stuff to your cart and ended your visit the next time you came only the pages that you then touched would be counted.  This is the same for the non-participation metrics as well.  Therefore it would be possible to have a visit that only has one page and has an order and revenue associated with it.  Just think of a person sitting on the order confirm page, going idle for 31 min, then checking out.

    There are metrics that will track across multiple visits, but because this is all based on cookies, to say they are reliable would just be wrong.  They are only as right as cookie deletion rates allow them to be.  Anyway, that is a whole post in and of it self.  But now you know about participation metrics (or most other metrics) across visits.

    The other things I wanted to touch on – and the title of the post – relates to when to use participation.  The single biggest reason, in my opinion, is ease of communication.  People like to see big round numbers.  If you can say that a promotion page was viewed by 50% of your visitors by participated in 60% of your revenue that is easy.  There are less moving levers.  With revenue distributed, when page views go up for some reason (say you are doing a lot of liquidations and people have to click around to find something is a size or color they want) it is going to give an odd looking number compared to what people might be used to seeing.

    Giving a nice number or % of total revenue just makes communication much easier.  And sometimes, when you’re working on something that you don’t really want to work on, finding the answer and communicating it out and being done with it is all you want.

    These metrics discussions sure are fun, no?

    This has been a Thought From The Cake Scraps.


    Participation Metrics

    January 27th, 2009

    I have not had a post on some basic elements of Web Analytics in quite some time.  Previously I have talked about how a person is tracked on a web site both with internal campaigns and e-mails.  I think that stuff is great to know for anybody surfing the internet.  It gives you an idea of what all that stuff in the URL is.  Check it out if you haven’t.

    Once question that comes up quite a bit centers around what a report or analysis means when it talks about Revenue Participation or Order Participation or other ‘Participation’ branded metrics.  The first thing you need to know is that it is not the same thing as non-participation branded terms (i.e. revenue <> revenue participation).  The second is that participation metrics are related to single pages within a website.

    Simply put, when a metric has “participation” attached to it, the metric changes from being a distributed metric to a non-distributed metric.

    Lets just concentrate on revenue, but know that the example is not specific to revenue.

    Lets assume that I came to a site and purchased $100 worth of stuff.  Let us also assume that I saw 20 pages in that time, including checkout pages.  When an analyst is looking at reoprts on a page basis there are 2 ways to look at that $100 I spent.

    The first way is to attribute (or distribute – however you want to think about it) that $100 across all 20 pages.  This means that each page gets $4 worth of demand.  This would include any page that I viewed, including the checkout pages.  This is nice because no matter how many pages I am looking at I am not double counting revenue.  It makes it simple.  You can just add up whatever pages you are interested in and you have your revenue number.

    The second way is participation.  this would give each page that I saw $100 worth of revenue attributed to it.  You probaly don’t need me to tell you – but I will anyway – you cannot add up multiple pages with this method.  If you did that for my hypothetical purchase you would get $2,000 worth of revenue participation.

    It seems a bit odd, but there are definite uses for each way of looking at revenue.  The first way – distributed – seems logical at first, but then you are giving revenue away from an index or homepage and giving it to a checkout page.  There is not $4 worth of demand on each checkout page.  With participation, each page gets full credit, but then you cannot add up multiple pages.  Each has its place.

    I hope that that clears up the difference between participation metrics and non-participation metrics, at least as far as it relates to those metrics from a page within a website standpoint.

    Do you have a preference between these metrics?

    This has been a Thought From The Cake Scraps.


    Estimating Demand Impact And Conversion Rates

    December 30th, 2008

    I was recently working on an interesting project where I was estimating the demand impact of a change that we had implemented to our site.  Without getting into the details, a change was made so that the customer would be less distracted during their shopping experience.  This then – hopefully – keeps the visitor more engaged with the site and if everything else goes well, they will then buy.

    The tricky part is that the likelihood to convert changes at different points on the site, though it is a bit difficult to get it.

    For example, if all a visitor sees is the homepage, they are going to convert at some percent.  Assume 10% for easy math.  If a visitor doesn’t bounce – meaning come to the site, see one page, and leave – say the percent to convert increases to 20%.  If the visitor then sees a product page they are now going to convert 30% of the time.  And finally if they enter the checkout process they will convert 40% of the time.  The point is that the level that the customer is at in the site changes the likelihood of conversion.

    This seems like it would be a very obvious thing, and to a certain extent it is.  The key component here is not that these differences exist and you know about them.  The key is taking that knowledge into account when making an estimate for demand impact of a change.

    If visitors have all of these different conversion points and a change is made that causes 1,000 visitors to not leave the site you need to take these conversion points into account.  Saying that the 100 more people will buy (using 1,000 visitors * 10 % conversion from homepage) is just as misleading as saying that 400 people will buy (using 1,000 visitors * 40% conversion rate from checkout pages).  When making a demand impact, make sure that you include a few inputs for these different areas.

    For example: 100 visitors * 10% + 300 * 20% + etc.  As long as the percents add up to 100% and the visitors add up to your total you are in good shape.  You can then take this number times your average order value and you now have a demand estimate.  Note that you could even take this a step more and apply a different average order value to people who have been in different areas of the site.  For instance someone who is shopping for Outerwear or A laptop will probably have a different average order value then an individual looking at flip-flops or computer cables.

    Ultimately you can segment this to any level that you are able to get.  Just make sure that the work that you put into arriving at the final number is worth it – especially if you are using the Omniture Excel Client.  Make a judgement call.  If it is just going to be small dollars or you really just need a ballpark then take the 1,000 visitors * 25% or something like that.  It is a guess, but it should be an educated guess.  Each different analysis will require varying levles of confidence.

    Have you done anything like this before?  How did it go?

    This has been a Thought From The Cake Scraps.


    You Are Being Tracked: Internal Campaigns

    September 22nd, 2008

    So you know that you are tracked by e-mails.  You are going to beat the system.  You are not even going to use a search term to get to the website because you know Google will track you along with the website.  You are going to direct load – typing the URL into the address bar – and avoid being tracked.  Almost, but no.  Chances are that the site gave you a cookie last time you were there.  Oh well, you tried.  But that is not what this post is about.  Just because you got to the site without tracking, does not mean that you will not be tracked.

    Internal campaigns are exactly what they sound like.  They are campaigns that are internal to the site.  A campaign is anything that the site is doing to try to get you to buy more stuff (or whatever the conversion metric would be, such as filling out a survey or something).  E-mails are campaigns.  Billboards are campaigns.  A site or company runs advertising campaigns. You get the idea.

    The banner that you see across whatever site you are on is sure to include an element that says that you clicked it – a tag.  Note that I am only talking about a banner that is on the site and for the site, not an advertisement for a different site.  The advertisement for a different site would be an external campaign for the company that bought the ad.  We are talking about an ad for another item on your site – perhaps for an LCD monitor when you are looking at computers.  I call this a real estate campaign or an internal campaign.  I call it real estate because the site is tracking based on the tag on the banner and the site knows the location of the banner, the real estate. I call it an internal campaign because it is for another product that will take the visitor somewhere else internal to the site, not push them out the door to an external site.

    So you clicked the banner and were tagged.  It is in this way that the site can track how often the banner is being used (instances) as a rate of how many people saw the page it was on (page views).  This also allows the site to understand where someone is clicking on the site.  Each area of the banner could contain a different tag, thus if you clicked the t-shirt you could get tagged with a value of tshirtclick while if you clicked the jeans on the same banner you would get tagged with a value of jeansclick.

    Internal campaigns are very useful for a site because they allow for a wide variety of reporting.  The site will know how many conversions they got that clicked on the banner and how much revenue is associated with it.  This also is a much easier way to track traffic from a page.  Perhaps a single page has multiple banners and the site wants to know how many people clicked the banners.  With no tagging on the banners, all the site would be able to do is look at what pages visitors went to next and add them up.  For instance if there is no way to get to the jeans page from your home page and yet 20 of the 100 visitors took that path you can assume that they must have clicked on the jeans banner.  But then to add that up with the page that took them to the t-shrits and the page that took them to the pants, and to…etc. is a huge pain. By the time you get to the number of estimated clicks (because in theory they could have the page bookmarked or something like that) you won’t care any more.

    Look for more the post forthcoming about purchase influencer tagging on Thoughts From Thee Cake Scraps.


    Google Search History Update

    September 9th, 2008

    In my post You Are Being Tracked: E-mail Style there was some discussion/confusion about when I said:

    Hopefully you know that Google keeps track of everything you have searched for.  Ever.

    Well this it true and false depending on how you read it.  GHamilton noted that Google does not keep everything you searched for.  Rather they keep it for 18 months.  The key word here is “YOU”.  In a recent post on the Google Blog Google announced:

    Today, we’re announcing a new logs retention policy: we’ll anonymize IP addresses on our server logs after 9 months. We’re significantly shortening our previous 18-month retention policy to address regulatory concerns and to take another step to improve privacy for our users.

    You may see where I am going here.  GHamilton is correct in that from and individual IP perspective after 18-months, or rather 9 months now, Google no longer has history on you specifically.  I am correct in that Google really does have “a history of everything you have searched for.  Ever.” with the caveat that they no longer know that you were the one that searched for it after 9 months.  If you are interested CNET does a great job of getting into the nitty-gritty and explains why the ACLU is so critical of Google’s privacy policy.

    Hopefully that helps clear things up a bit for people.  Let me know your thoughts.  Do you care that Google keeps your data for 9-months?  Should it be longer/shorter?  Should they keep anonymous search history forever?


    You Are Being Tracked: E-Mail Style

    September 6th, 2008

    Most people probably already know that they are being tracked.  There are all sorts of programs and ways to do this at all sorts of levels.  For instance your ISP may track you and give (sell) your data to a company like Hitwise – privacy policy can be found here.  I actually saw this in a newscast last week.   They interviewed some guy about what popular search terms are and tried to make it sound creepy.  Amazing! People search for weird stuff on the internet like “how to make bombs” and *gasp* “porn”.  This guy must be some sort of genius!  And he looks at historical data! Brilliant!

    Hopefully you know that Google keeps track of everything you have searched for.  Ever.  Anyway, the part that people probably don’t know as much about is how individual sites track you.  One way a site can track you is by tagging you when you click through on an e-mail they send you – the focus of this post.  Think of tags as dated stamps in your passport book.  Interestingly enough, some of this tagging can be easily found in the address bar of your browser.

    When you see something in the address bar that looks like emid=584783 that is telling the website that your internal – meaning site specific- e-mail address ID is 584783.  This value is unique to a single e-mail address. Each e-mail sent to that e-mail address will have their unique emid attached to all links in the e-mail. This also allows a site to build a history of that e-mail address – not only for activity, but for response rate as well.  Now every time you click through an e-mail for that site they have more history.  Note that larger sites rarely look at individual behavior but instead classify a behavior and then analyze that group.  Still, the information is there.

    In addition to an e-mail ID, there is usually a campaign variable such as cid=Sep08FreeShipping.  This allows the site to report on everything with Sep08FreeShipping stored in the cid variable. All of this information is contained within the link that you click from the e-mail. If you get the e-mail and directly load their site, not through the e-mail, the activity will not be tracked because in a direct load no value would have been assigned to cid.

    These variables do not have to remain in the web address the entire time.  They are stored in the background after the initial click. So when you no longer see emid or cid in the address bar, but originally arrived at the site through the e-mail, you and your activity is still being tracked.

    Look for at least one more installment of how you are tracked. There I will focus more on how a site tracks internal campaigns. Hope this helped give some people a better understanding of how websites track you.