Monthly Archives: January 2012

Snowy in Seattle

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Data Wallowing

It’s been snowing in the Seattle area and I now live on a very steep hill, so today I worked from home and have been wallowing in website analytics between a couple of sledding breaks.

My data wallows look at everything the analytics reports can serve me: visitors, page views, countries, browser versions, page paths, etc. The more esoteric the data, the more I like it, actually. I tend to find the signals in the extremes: the most popular and least popular stuff. They tell you what to focus on and what to chuck overboard.

I look at the last month, quarter, half-year, and year to get a feel for the trends over time and see how major site updates impacted traffic. I look at the top stats for each bucket and also look deep in the long tail to see what’s hiding. I sanity-check the data against my expectations, like looking at the percentage of non-U.S. visitors to the U.S. site, (it’s always higher than I expect,) and referring pages, (the top entry is really bookmarks instead of search???)

I look at clickmaps of the most and least trafficked pages to get a sense of how the page layout may be influencing clickthroughs.

Then if I have access to it, (at Microsoft I do,) I look at the data of the referring sites themselves to see where the outbound rank is for the site I’m analyzing. I look for customer satisfaction data, customer feedback, planning and marketing data, as well as industry trends for the segment the site’s in.

I search social media and look for positive and negative things about the site in question. I also see what they’re saying about the competition.

Then I spent time thinking about instrumentation gaps and how I can triangulate across or re-query the data sets I do have access to in order to guesstimate the gaps. Examples of gaps that I’ve run into in the past are not instrumenting by content types or site sections.

When I have all this data loaded into my head and spreadsheets, I can finally begin analysis by creating an empirical top task list based on what the data says and compare that to the expected or desired top task list. Further analysis is a topic for another day.

Data Acquisition and Analysis, and Startups

I had dinner at my in-laws over the Christmas break, and my brother-in-law’s father is a retired Boeing engineer in his 80’s that used to work in their wind tunnels. He shared some great stories of how moving from analog to computerized methods solved many problems for him and his team, especially around data acquisition and analysis, and it made me think of startups.

One area in particular that got much easier for them was recording air pressure over airfoils at various speeds and angles of attack. They used to have air pressure sensors attached to dozens of needle gages that were mounted on a nearby board. They’d crank up the fan to the desired speed, adjust angle accordingly and snap a few pictures of the gages. Any change of speed or angle would have them taking more photographs of the gage board.

The reason they’d take a few pictures per setting was that the needle gages had a lot of noise – they’d bounce around the scale and they needed multiple pictures to average/guess what the “real” reading was across all the photographed readings per wind speed and airfoil angle setting.

The time from taking a photo and then having it developed, examined, and compared to others in the series to record the results of the experiment in order to compare the tabulated data against the calculated model could be weeks. You can also imagine all sorts of ways that errors could creep into the dataset using this method.

In engineering today, most of this type of data is now either simulated entirely inside the computer or the computer collects massive amounts of data for further analysis. Data is the new coin of the realm, and the bigger your dataset, the larger the opportunity you have to exploit it for financial gain and hit your target.

Disk is cheap and missing the signal in the noise is expensive.

If your startup isn’t already generating and analyzing datasets or considering which you may create or have, you might as well be taking photos of physical gages in your quest to build a rocket to the moon.

Adventures in Self-Publishing

A little over a year ago I self-published a short story, SYSLOG I, to the Amazon Kindle Store.

In case you hadn’t noticed, the entire publishing industry is going through a major transformation right now, with digital books disrupting the traditional print business models.

I had longed to be an author ever since I watched my grandfather pound out his first book, Ship Benjamin Sewell, on his Underwood SX-100, (which I still have and is a lovely machine,) yet the hurdle always seemed so very high. He had self-published his book, and easily spent $10,000 or more 1983 dollars on research and printing.

Flash forward to late 2010, and self-publishing costs are $0.00 or close to zero if you go all-electronic and do your own formatting, like I did. In fact, the barriers are now so low to self-publishing electronically, I think you’d have to be nuts not to do it in addition to pursuing the traditional print path, if that’s your goal too.

Why?

You’ll actually make more money for less effort.

For transparency’s sake, here are my sales to date of SYSLOG I:

SYSLOG I sales in the Amazon Kindle Store since publication, December 2010.

While I’m not exactly burning up the charts here or earning that much, (OK, I’m hardly earning anything,) do keep in mind that I am earning and will continue to earn some money on each sale until I pull the title down or Amazon goes out of business.

Compare that to writing something on spec and then spending the time to shop it around for weeks, months, years maybe, before ever selling it, and then having to negotiate rights, earn-outs, and copyright. Ugh – no thank you!

In fact, I’ve found electronic self-publishing so friction-free, that I’ve been able to put up four other titles under pseudonyms without much trouble. One them is doing pretty OK, as far as I’m concerned. I’m currently writing for beer money and to learn the publishing platforms, so I’m already ahead anyway.

Here’s what’s happening with that one:

Not Bad for Hardly Trying

You’ll notice that sales have trended downward int he past couple of months. That’s because I changed the sale price from $0.99 to $2.99 in order to increase my royalty rate from 35% to 70%. After an initial test, I did the same thing to the other titles because while my total sales numbers are down, my payout almost doubled.

My total royalties will be over $100 for the month of December 2011 – the first time I’ve cracked that threshold. With Amazon’s release of the Kindle Fire, strong sales momentum of other Kindle models, and a two-day free promotion to get a couple of my titles in a few top 100 sales lists right before Christmas, I had strong sales momentum for the last week of the month and I’ve only seen 10-20% slowdown post holiday.

My only marketing to date has been Twitter, my blog, and spamming my colleagues at work. 🙂

So if you’re currently sitting on a title or two that you’re not earning on, what are you waiting for?

Information Architecture Planning

I’m just starting a new website project at work, and one of the first things I’m attacking is the information architecture. I’ve learned the hard way that having a solid site architecture can save boatloads of redesign pain later on.

My method is to plan it out and it goes roughly like this:

  • Learn everything I can about the topic(s) to be presented – past history, present situation, and future plans
  • Discover who all the stakeholders are
  • Perform an existing site content type and page audit
  • Build a site map of the current site
  • Wallow deeply in site metrics for top pages, visitor trends, referrers, search terms, and page flows
  • Wallow deeply in customer data around segmentation, intent drivers, and key tasks
  • Look for stuff that can be dumped overboard
  • Figure out what will need to be added in the future
  • Whiteboard out all the elements (content, information flows, external process connections, customer segments, etc.)
  • Stare at whiteboard for hours, then erase and draw, erase and draw until a model and page pattern(s) appear
  • Wireframe a few pages with the designers to get a feel if the model works across architectural segments
  • Go back to whiteboard and fix the broken stuff
  • Wireframe again
  • Look for more stuff that can be dumped overboard
  • Build high-fidelity comps, preferably on the deployment platform
  • Lock it for usability/review
  • Tweak after usability/review (if needed)
  • Hand off to production when it’s complete

Easy, eh? 😉

 

Great Merchandising

I took this a couple of years back in a Safeway. They still merchandise both of these products this way.

Genius.

Decisions, decisions...

Diamond Knot Brewery, Mukilteo, WA

The Anheuser-Busch Memorial Urinal

The Anheuser-Busch Memorial Urinal

Worth a visit.