Here’s a summary of the posts and news I found most interesting in January.

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I work in spreadsheets a lot. It’s still the best way to easily slice and dice small data sets. I’ve been using Google Sheets for most of my spreadsheets for several years, but it has limits. The two biggest are memory and power, and they’ve forced me to keep a few grotesquely large spreadsheets in Excel. Another limitation to Google Sheets that I’ve experienced a lot recently is that you can’t edit row 1 of pivot tables.

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Google Analytics has a “ghost spam” issue lately and one really annoying Russian is responsible for most of it. I’ve read through buckets of posts from Analytics professionals about how to deal with this tomfoolery. I’d like to share a few of the resources and provide some commentary.

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Let’s say you’re launching a significant update to a website. A lot is changing: the theme, lots of URLs, maybe the CMS or backend, and so forth. And the team creating the website is working from various IP addresses on the development site. What’s the best way to handle the transition with Analytics?

Lately (after having a website launch without Analytics earlier this year) I’ve been handling this in a very particular way, and I’ve grown to really like it. Basically, I’m using hostnames (dev.domain.com, www.domain.com, etc.) in Google Tag Manager to automatically split traffic to different Analytics properties. I’m doing this by using a “lookup table” for my Analytics IDs (instead of using a “constant” variable).

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Recently I was with a client, browsing around their website to review some new content, and we ran across a “500 – Internal Server Error” warning on one of the most important pages. (That means their server encountered an error that prevented it from successfully serving the page.) None of us were aware of this, nor did we know how long it had been an issue.

Most websites (even big ones) contain no automatic reporting of such errors. Problems like this are usually exposed when someone else (a coworker, an angry website visitor, etc.) finds and reports them. That’s terrifying if you’re responsible for an organization’s web presence. What if visitors are experiencing errors and nobody is reporting it?

Because of this I’ve started using GTM to help me create alerts for such errors, as experienced by actual users. Errors are logged in Analytics, and when certain (custom) thresholds are met an alert is automatically sent via email. This blog post outlines how it’s done.

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After collecting data for The Worst Super Bowl Ever, I asked a bunch of other people in Boulder, CO what sort of analysis they’d like to see. The response was overwhelming: most people wanted to know who swore more — Broncos fans or Seahawks fans? This is a reasonable way to think about things, especially for those of us currently in a state of mourning.

Discussing this further, I found an implicit assumption: most people assumed that Broncos fans were much more profane during the Super Bowl than Seattle fans, mostly because of Denver’s unconditional surrender in the first few seconds of the game. Is that assumption correct? Let’s see.

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Throughout the NCAA Tournament, my brother and I captured and analyzed all tweets that contained hashtags for the 64 participating teams (e.g., Arizona: #BearDown, Florida Gulf Coast: #FGCU, Indiana: #iubb, etc.). (A full listing of the hashtags along with our methodology is provided at the bottom of this post.) In total we captured over 5 million tweets with team hashtags over 19 days. The resulting data is provided below in an elaborate collection of charts and graphs.

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