Archive for May, 2010

Time scales for SEO and web analytics

Patience is a virtue, except on the internet. The sheer speed of online communication is astounding when you consider the distances involved, and we’ve all come to expect more or less instant access to data housed anywhere in the world.

However, not everything comes quickly. The benefits of SEO in particular come to those who wait. The time scale for a comprehensive search engine optimisation campaign is rarely less than a month or two for sites in a moderately competitive field. Sites that start out with very little content and few links may see gains fast if they choose keywords conservatively and keep their focus localised, but most companies looking to make serious money using the internet will have to wait a little longer to see strong progress.

That’s often said on various blogs and forums, but what is less commonly mentioned is that the initial ranking gains made during an SEO campaign may not last long. Don’t get too excited if someone gets you into the top spot for your chosen keyword, because that shift in rankings may or may not be robust. Algorithm changes- and Google makes at least one algorithm change per day on average- and action by competing sites can see your place in the result pages drop back down.

Some SEO actions produce more robust gains than others, and the stability of your step up in the rankings will also depend on how competitive your field and your keywords are. SEO is a long term process and it needs to be ongoing- a least to some degree- if you intend to keep any gains you make, but there will probably be short term fluctuations in your rankings from day to day as well.

Whether your rankings change for the better for for the worse, wait at least four or five days before celebrating or panicking.

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The difference between data and information

Data is valuable. Nobody holds data in higher esteem than data miners and web analysts, but there is a world of difference between data and information.

Take the famous (to devotees of data mining) and much admired (by devotees of data mining) Wallmart customer basket database. The implementation of barcode scanners meant that each collection of products bought by a Wallmart customers could be recorded, and they did just that. Back in the early years of the new century this database was probably the biggest in the world. It ran into TeraBytes back when that was an impressive achievement (even to devotees of data mining). Depending on how you reckon 1 TB, it’s either 1000000000000 or 1099511627776 bytes. The good folks at the University of California, Berkeley, reckon that amount of data at 50000 trees or a good sized forest worth of paper.   To put it another way, Dr Jess’ PhD thesis weighed in at 2464KB, or one 405844th of a TeraByte.

Cute arithmetic aside, a TB is a lot of data, and there are plenty of databases of that size around today. Many of the server logs we deal with are well into GB or larger. Wading through all those numbers is the job of data mining algorithms and not people, and that’s as it should be.

It’s the job of web analysts to sort through the data spat out by programs like Google Analytics and Webalizer, sometimes using algorithms and software tools and sometimes their own judgment.

The task isn’t just to build a giant database, it’s to convert that data into usable information. Wallmart doesn’t find out whether coffee and sugar are bought together by reading through their data, and nobody trying to make money from a website needs to wade through masses of facts and figures and sieve out what’s important. That’s our job.

We know what to look for and how to extract information from data, and we also know how to present that information in an easily accessible form, which is half the battle when dealing with large databases.

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