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April 24, 2007

The stupidest web analytics app ever? Perhaps not.

When I first came across 3D Live Stats my first reaction was to groan at yet another over-wrought method for conveying (I use that term loosely) web analytics data. The conceit here is that your traffic is shown as rays of light emerging from a rotating globe, whilst your search keywords float across the screen. It's sort of like a cross between the Wrath of Khan and the Star Wars credits.

However, I'm prepared to give 3D Live Stats the benefit of the doubt, since their app is designed to hang in reception areas and on the walls of the offices of company CEOs, to  give web data (or any other data, apparently) the glamour of a "Hollywood Movie" (according to their site). Since it's always a challenge to get the high-ups at a company engaged with web analytics data, this may be a good thing. And at least it's less ridiculous than VisitorVille.

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April 19, 2007

Experian consumes Hitwise

Today's news (so much more interesting than the Google earnings call) is that credit-rating and demographic data provider Experian has bought online competitive data provider Hitwise, for $240m in cash. The rationale for Experian is that Hitwise complements its existing portfolio of marketing services, which mainly lie in the area of data provision for 1:1 marketing efforts (i.e. lists). The main other offering Experian has in this area is CheetahMail, an e-mail management solution.

Apparently Experian has been partnering with Hitwise for the past four years, so the companies clearly know each other well (others put the acquisition down to Hitwise's excellent blogs which contain a lot of free and very interesting data). Clearly Experian hopes to enhance its targeting profiles with information about the kinds of website that people in a certain demographic use, which is (I think), a smart move - assuming Experian/Hitwise can pull it off technically, and, more important, from a legal/PR point of view.

Like Allen Stern, I am a little concerned that Experian may be looking to attach browsing history to my credit score, but fortunately the technical and privacy hurdles make this a distant possibility. And I presume that Experian expect a big dividend from the synergy between the two companies, since at Hitwise's current rate of earnings (negligible profit on sales of $40m for the year just ended) it will be a long time before Experian gets its investment back.

But congratulations to the folk at Hitwise. I know a few of the guys there from my London days, and they're a good bunch. Let's hope they thrive within the Experian fold.

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April 17, 2007

Cookies are evil! Burn them!

Click for original on www.webflyer.comThere's a lot of chatter on the wires here (ooh, I make it sound so glamorous and newsroom-y - it would be more accurate to say there are a lot of e-mails going back and forth) about Comscore's press release about cookie deletion. It makes for somewhat alarming reading - according to the report, 31% of Internet users delete their first-party cookies at least once a month, with 7% deleting them more than four times. Comscore estimates that this means that a cookie-based count of unique users would be overstated by a factor of 2.5, or 150%.

Of course, Comscore is hardly likely to come out with a piece of research that provides a glowing endorsement of cookies, since their measurement methodology - panels (or, more accurately, sampling using a piece of client software that users install on their machines) competes directly with regular web analytics solutions, which rely on cookies for user counts and persistence. But is this study as alarming as it seems?

One thing that caught my eye about the study is that the first-party counts excluded log-in cookies. I'm not quite sure what they meant by this (i.e. whether those were just session cookies), but a lot of sites' first-party cookies are login cookies. So the first-party cookies measured were 'non-essential' cookies; perhaps much more likely to be deleted.

Furthermore, if your site is issuing log-in cookies (assuming they're persistent), you can use these cookies to generate UU numbers, even if not all users have them (assuming your web analytics solution is sophisticated enough to do this). The great thing about a log-in cookie is that, even if the user clears their cookie, when they come back and log in again, their log-in cookie looks identical, even if it's not the same actual cookie. So you can have users delete their cookies 10 times a month with no problems, as long as the new cookie you give them looks the same as the old one.

Which leads me onto the point I made in my previous post - sites need to work with their web analytics vendor to implement strategies to limit the impact of cookie deletion on their numbers. It's still very common to encounter a site which is issuing a high-quality cookie associated with a log-in, but is using a 'junk' first- (or even third-)party cookie for sessionizing, UU counts and persistence.

The other point I'd make is that debates about the absolute accuracy of web analytics have been raging for as long as the industry has existed, and the main answer to this kind of thing remains the same today as it always have - don't rely on your web analytics solution for absolute numbers. Instead, focus on trends and comparisons - which have a much higher chance of being accurate if your measurement technique is consistent across your audience and over time. If you want absolute numbers, use a panel.

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April 13, 2007

The mysteries of measuring marketing response, part 3: Reconciling post-click behavior

Apologies for the rather slower pace of posts of late, but life has been a little busy here, what with moving house in Seattle, taking a trip to London to attend E-metrics, and succumbing to a nasty cold this week. Hopefully this post, the latest in my 'mini-series' on online marketing measurement techniques, will make up for things.

In my first and second posts on this topic, I discussed the techniques that ad servers (and other kinds of marketing delivery systems) and web analytics apps use to decide which marketing has delivered a click to an advertiser's website. If you know what marketing has delivered a specific visit, you can associate any 'conversion behavior' (e.g. the customer buying something) with that marketing, and use this information to generate ROI information about that marketing. Here's a simple example:

  • Keyword "bananas" delivers 1,000 visits to www.bananas-r-us.com
  • CPC (cost per click) for this keyword is $1; so campaign costs $1,000 to run
  • Of 1,000 visits, 30 include a purchase
  • Average purchase value is $50 (that's a lot of bananas)
  • Total revenue generated - 30 x 50 = $1,500; so ROAS (return on ad spend) is 50% ((1,500 - 1,000)/1,000)

So far, so much egg-sucking tutorial. The wrinkle is that not all visitors who click through the ad will go on to buy something there and then (that is, within the visit that started with the ad click). A proportion will visit the site, undertake some serious banana research, and then go home in the evening to consult with their spouses about whether their family really needs a 60lb bargain bucket of over-ripe bananas., and then place the order. In the industry, this is known as a deferred conversion.

In certain retail sectors, such as technology, travel, and insurance, deferred conversions are the norm. So you might be spending lots of money on search engine marketing, but your web analytics tool is telling you that no one is buying anything on the back of your keywords, whilst what is in fact happening is people are coming back later of their own accord and buying stuff.

Individual marketing delivery systems (like Google Adwords) solve this problem by giving the user a cookie when they first click through an ad, and then tying subsequent conversions (typically within 30 days) back to this click if that same user (identified by the cookie they still have) comes back and buys something. However, as I mentioned in my first post on this topic, this doesn't work well when you are using multiple marketing channels (e.g. search + e-mail), as they will 'compete' to claim credit for the same conversions, and over-report the ROI of the marketing you're doing.

Under the influence

The only way round this is to get your web analytics system (which will not double-count conversions, because each conversion only occurs once in a web analytics system's database) to make some intelligent decisions about what marketing actually drove (or at least 'influenced') the conversion. Consider the following example sequence of visits to a site from the same user (who, for the sake of this example, we can assume has a persistent cookie for the duration of the set of visits):

 

Date Marketing Source Purchase value
April 1 2007 Paid Search $0
April 10 2007 E-mail $0
April 13 2007 none [direct] $1,000

In this scenario, how do you decide what (if any) marketing contributed to the $1,000 purchase? There are a number of methods you could use:

1. 'In visit' allocation
This method allocates the conversion to the visit that contained it, and no other. This method would allocate the $1,000 to a "no marketing" or "direct URL" bucket; i.e. the paid search and e-mail campaigns would get no credit.

2. Last marketing source
Here, the last marketing that drove this user to the website gets exclusive credit for the conversion. So in this example, the e-mail campaign would be credited with the $1,000. Paid search gets nothing, despite the fact that it may have been this marketing that alerted the user to the site in the first place.

3. First marketing source
The first marketing that drove the user to the site gets the credit - in this example, the paid search campaign gets the $1,000 in its ROAS calculations. E-mail gets nothing, despite the fact that it drove the customer's most recent interaction with the site. Another problem with this approach is that when users churn their cookies, their "true" first marketing source is lost, and a new (semi-random) one is allocated based upon the first marketing they respond to since cookie-churning.

4. Simple shared allocation
All historical marketing gets a share of the credit for the conversion. So paid search gets $500 and e-mail gets $500. This is probably closer to the truth (both had some hand in creating this eventual conversion), but is a pretty crude model, since different kinds of marketing have radically different 'engagement profiles' associated with them. For example you could argue that paid search clicks have a higher engagement profile than banner ad clicks, since when someone clicks a paid search ad they've already entered a relevant search term, so are clearly in the market to some extent.

5. Age-based shared allocation
Another over-simplification in the last method is that the age of the click (i.e. how long ago it happened) is not taken into account. The e-mail click happened only 3 days ago, whereas the paid search click was 13 days ago. Taking this into account, you could allocate, say, $250 of the $1,000 to paid search, and $750 to e-mail. The maths for doing this systematically are non-trivial - you have to model in influence 'curves' that tail off to zero at some point (say, 30 days back) and then allocate the conversion value on a pro-rata basis based upon each click's position along the curve.

6. Age and channel-based shared allocation
This method combines the idea of age-based and marketing channel-based allocation together to create what is probably the truest (but certainly the most complex) picture of conversion influence. In this method you create influence curves for each marketing channel that you're using, which reflect the different rate at which the 'influence' of each channel wanes over time (the influence of an ad might wane very quickly, for example, whilst an e-mail's influence might linger longer).

For each historical clicks that was driven by marketing, you then locate the position on the influence curve for that kind of marketing and read off the number; and you then add up the values from each curve and pro-rata allocate the conversion value on this basis.

In our example, you might find that the paid search curve yields a value of 4 at the "-13 days" position, whilst the e-mail curve yields a value of 6 at the "-3 days" position, reflecting the fact (or, more accurately, someone's opinion) that paid search click influence lasts longer than e-mail click influence. Allocating the $1,000 on this basis would mean that paid search gets $400, whilst e-mail gets $600.

Confused?

You may be asking, "but where do these influence curves come from?" The answer is, your head. Or mine. or the head of (the head of) your marketing agency. But they don't exist at the moment, and, with all the other uncertainties and lack of standards in the online marketing world, I can't see a commonly agreed-upon set of marketing channel influence curves coming out any time soon.

The tricksy thing about this field is that the more you look at the way marketing is allocated to conversions at the moment, the more you realize how broken and simplistic those methods are. I've never met a client or agency who's implemented anything like nos. 5 or 6 above, though I have seen no. 4 used.

But in a world where everyone increasingly understands that you need to use multiple touch points to reach consumers, intelligently allocating conversions to multi-channel marketing efforts will become increasingly important, even to a little guy running some paid search campaigns with a bit of e-mail thrown in. So if you can come up with some plausible influence curves, turn them into a fancily-named methodology and set yourself up as a consultant, and you can make a bunch of money.

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April 09, 2007

Online ad serving 101

Do you know how online ad serving works? Are you familiar with the difference between a publisher-side ad server and an advertiser (or third-party) ad server? No? Then read this excellent article on the topic by my colleague and ad industry veteran Eric Picard. The article's actually an updated version of one that he wrote almost six years ago; amazingly, despite the changes in the online world, and the emergence of major new kinds of online ads, the basic principles remain unchanged.

However, as Eric points out, there is a lot of room left for innovation in online ad serving, particularly in the planning/buying and trafficking tools that are available for agencies. It's still remarkably labor-intensive to plan and deploy a major campaign across a large number of publishers; it's even more complex to understand the performance of such a campaign in the context of other kinds of online marketing that you might be doing.

I'll be returning to this topic in the continuation of my 'Mysteries of online measuring online marketing response' series. Stay tuned.

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