Yahoo Finance may not be producing ground-breaking innovation anymore, but the development mill is clearly still functioning. As anyone with a finance or investing blog can attest, getting financial data into a website is no exercise for mere mortals. Live market data, even when delayed, generally requires a contract with a data provider or the stock exchanges themselves. Yahoo appears to have resolved the legalities of sharing market data with other websites, at least for the NASDAQ, AMEX, and NYSE. International exchanges and certain indexes are still off limits, but Yahoo suggests that additional exchanges will eventually be brought into the fold. Financial blog writers should be drooling over this feature and Yahoo should benefit as influential blogs start integrating a piece of Yahoo property into their content.

When it comes to managing websites, I like to apply a fairly cut-throat approach to site optimization. Content and links which fail to perform are ruthlessly swept away in favor of those parts of the website which have performed or show potential. I like to think of this process of culling and promoting links as online natural selection or a Darwinian survival of the fittest. With today’s web analytics tools it is possible track and analyze practically every aspect of site usage, so revising a website based on behavorial data is often only a matter of taking the time to look at the data. A website subjected to this continual fine-tuning should evolve into a website which is more in line with customer needs, especially since the customers have essentially voted for the content and links which appear most prominently. I also have to admit that there is an inner sense of glee when sending non-performing links into extinction.

Unfortunately, this theory of website evolution has some strident opponents. It may sound like common sense or straight-forward logic to you and me (at least, it does to me), but there is a large and vocal community of people who do not subscribe to this line of thought at all. I am speaking about the advocates of intelligent website design, or course. These people can be found at levels of an organization, including highly experienced professionals in positions of significant decision-making capacity, as well as (gasp) e-marketing specialists in major marketing and advertising agencies. In fact, I’ve never been on a job or project which hasn’t featured a prominent respresentation of this faction. You will know when these people are in your midst, when your web analytics data is summarily dismissed and replaced by piles of indisputable market research, white papers, focus group results, and branding guidelines explaining exactly what should go where on the website. In my experience, arguing with these people usually proves an exercise in futility. Unless you are an experienced evangelist, your best chance lies with finding like-minded managers and building a base of support for your analytical approach. Or, you could try the good old “act now and ask for forgiveness later” ploy.

The point is this battle is too important to lose. Website real estate, despite widely-held beliefs to the contrary, is as precious as offline real estate. Visitors to websites are not constrained by physical distance to the competition and can leave your virtual real estate with a single mouse click. A website must engage the visitor within the first seconds and then work to help the visitor find what they seek. I don’t mean to dismiss strategic thinking out of hand. A compelling vision and well-conceived strategy are essential pre-requisites for success and should be based on solid market research, however, site optimization should not be driven by white papers and marketing egos. At that point, its best to put aside any delusions of intelligent website design and start competing on analytics. A lot of companies are already running their business on web analytics, and some are probably even using some form of natural selection when developing websites. Google has built an advertising empire based on a similar process for its AdWords. Are we soon to see websites which are programmed to evolve by themselves?

My indifference towards photo sharing networks and Flickr in particular probably could have continued forever, if it weren’t for my serendipitous discovery of Moo and subsequent order of personalized Moo Minicards using the convenient upload tool from Flickr. As fortunate would have it, I arrived on Flickr in time to learn about a relatively new feature which should really appeal to anyone with a passion for product marketing. Flickr has added a new “Camera Finder” service, ostensibly aimed at helping Flickr users find the most popular camera brands of fellow photography enthusiasts. Flickr collects the data from the meta data (EXIF data) on uploaded pictures, which usually reveals the make and model of the camera used to take the pictures. Why publish this information? The answer is self-evident from the convenient links to Yahoo! shopping (Yahoo! purchased Flickr in 2005).

For a product marketer, this information is a rare and fascinating glimpse into the lifecycle of consumer electronics. Products at all phases of the lifecycles are clearly visible on Flickr’s charts. Here we have Nikon cameras in the last 12 months. The venerable D50 shows clear uptake through most the year, but has probably reached maturity, while the D70 is showing its age and a steady decline. Meanwhile, a new entrant, the D80, is achieving market share, but is still in an early launch phase.

Most products have a steady and lengthly uptake and downtake, but certain cameras such as the Kodak EasyShare C330 Zoom, appear to have run through their entire lifecycle within the course of a year. Its possible that this model has not met consumer expectations or is running into strong competition from other models positioned in the same segment.

A marketer or photographer can easily spend a couple hours examining the Flickr camera data and, one would suspect, industry analysts and camera manufacturers are probably dissecting this data and cross-checking with their own market research at this very moment.

Even if you are not enthralled by electronic product lifecycles, this information is a wonderful demonstration of the hidden value of data in a business. Data, when linked to a business vision, can become information and knowledge. Its unlikely that the founders of Flickr expected to one day use photo meta data for marketing purposes, but somebody at Yahoo realized there was potential for this data and managed to create an amazing service.

The web analytics community has been shaken by the release of a Comscore report indicating that web audience cookie deletion leds to an average overcount by a factor of 2.5x in unique visitors. Its wonderful to have solid research on the extent of cookie deletion, but this information is not exactly news to the internet marketing community, but rather the quantification of a rather poorly-kept secret about web analytics. For years, vendors and users alike have known that cookies are an imperfect mechanism for measuring unique users. Unless you are tracking unique visitors with a site login and unique customer ID, you are left with a variety of unsatisfactory methods, from IP addresses, web beacons, 1st party cookies, 3rd party cookies, and probably a few other techniques. All will lead to some degree of under or over-counting and are guaranteed to frustrate anyone daring to seek the holy grail of perfect reporting.

It is all so dire for us marketers? Of course, it is important to have an idea of how much the audience sizes for your particular websites are overstated, in order to adjust figures where an absolute number is desirable such as calculating reach for advertising and content licensing arrangements. However, for most purposes, a good, hard look at the trend will suffice. In business, an accurate trend reading generally counts for more than a precise number.

If you’re still doubting the faithfulness of your analytics package and want a second opinion, you can always consider external traffic measurements from providers like Alexa, Hitwise, and comScore (yep, the same guys who gave you second thoughts about your analytics solution in the first place). Be warned: you won’t find exact numbers from these companies either, but rather just additional ways of measuring your audience.

Will this obsession for the exact, this passion for precise, ever go away? Its doubtful. As the fellows over at Juice Analytics mentioned in an enlightening posting called “Too Literal with Numbers” sometime ago:

People who have experience and comfort with numbers have the ability to abstract meaning from analysis for themselves–even when numbers don’t line up, the data is unclear, or the analysis has minor flaws. They can ask themselves higher-level questions: What does this mean? What are the implications for the business? How else could these results be interpreted?

In contrast, people who are uncomfortable with analytics treat numbers literally. They are disturbed by surface level inconsistencies. They expect–even need–the numbers to line up in straightforward ways. The medium simply isn’t familiar enough to abstract their own meaning.

In my day job as a marketer, I have recently undertaken a particularly daring project. I am working on a model to predict customer actions, specifically which customers will defect in the future. However, after reading up on the science of prediction, I’m starting to wonder if I’ve gone on a fool’s errand.

Prediction, as it turns out, is an extremely elusive art and the path to accurate forecasting is strewn with broken promises, false starts, disheartened scientists, and dead hedge funds. It has been said that a person could predict with absolute certainty (i.e. knowing) what number would be rolled in a craps game, if he or she knew all every detail of the die roll, such as the size and shape of the die, the material and lay of the table, the direction and velocity of the throw, and so on. The same could be said for virtually any physical action, from predicting home run swings to forecasting traffic accidents. Unfortunately, we neither have access to the entire universe of relevant data concerning the die roll/baseball swing/vehicle motion nor a computer powerful enough to process all the information and spew out an answer. And, in case we are not disheartened already, all this just applies to physical actions, not the infinitely more complex analysis of emotionally driven human actions. Customer loyalty most definitely falls in the human action category.

All is not lost. While we cannot predict things with certainty, we can ascertain the probabilities of things happening. In fact, there is rich history of calculating probabilities and, with a little searching, one quickly finds success stories from fields as diverse as quantum mechanics and behavioral finance. In fact, good statisticians have been doing this kind of probability modeling for a long time. Its interesting that predictive analytics is absent from the traditional toolset of marketers. I can certainly imagine the reason for this oversight — marketing people are notoriously shy around numbers. Now, I believe that marketers who can’t handle spreadsheets and math equations are a dying breed, but that discussion is best left for future blog posts. The point is marketing can benefit from statistics in many ways. What marketer would not want to know which customers are likely to abandon the company’s services next month?

My simple mission of predicating customer defections doesn’t seem so impossible anymore, and, furthermore, I won’t even need access to a supercomputer to do the job. I never expected to predict outcomes with absolute certainty, just to know more about future customer behavior than I know now, which is very little. I’m looking forward to plugging in a few variables culled from my web analytics reports and seeing if there is link to specific customer behavior. I’ll be bouncing off the walls with any positive results and won’t even worry about the whole correlation versus causation issue until later.

Tracking performance on this blog should prove enlightening. Aside from writing passable prose, submitting a sitemap to the search engines, and doing a fair amount of linking to internal posts and other blogs with related content, I don’t intend to do much in the way of so-called search engine optimization. In other words, the blog will have to stand on its own, without the advantage (or perhaps disadvantage) of submissions to blog aggregators, link lists to related sites, comment postings linked to this blog, pings notifications across the blogosphere, analyses of keywords, and whatever else is in vogue with my fellow bloggers. I have an instinctive feeling — completely unsubstantiated by any form of real research — that these measures are of limited usefulness with search engine positioning anyway. Having said that, I reserve the right to change my opinion should someone manage to convince me that doing such things can actually have a reasonable return on investment.

In the meantime, it appears that I have a budding fan club in the Far East. I’m not sure what compelled residents of Karatsu and neighboring cities in Japan to visit my posting on ski pistes and sunshine, however, they do appear to be the only inhabitants on Earth who are currently interested in my writings.