Statistical normalization opposes innovation

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If you’re the sort that enjoys analytics (or your job makes you  “enjoy” it), you’ve probably encountered statistical normalization many times, even if you don’t know it. What is it? The short description is removing outliers so that you can see statistically valid trends. Remove outliers, remove bad data, and smooth over data points so that you get cleaner data to compare.

Here’s an overly simple example, averaging stuff together. Let’s say you have a month’s worth of website traffic data. If you want to see whether January’s web traffic this year was better than last year, your simplest bet (not necessarily the best) for a reasonable apples to apples comparison is to average all the visits together and see what the daily average was. If the daily average in January of this year is better than last year, things are good.

Here’s another example: look at these three charts in Google Analytics. All three are the same information. Which is the easiest to discern whether this month was better than last month? Which will your average corporate executive want to see?

Daily View
Dashboard - Google Analytics

Weekly View
Dashboard - Google Analytics

Monthly View
Dashboard - Google Analytics

Your average executive dashboard, PowerPoint, or rollup report will use the last example as the preferred data source. It’s clean, it’s obvious, and it’s easy.

Here’s the danger with smoothing things over: when you do so, you lose view of anomalies. You lose view of outliers. Look again at the daily view above. There are a few points where you have significant spikes of traffic, along with the normal, average traffic days. What happened on those days to bring in that much new audience? What did you do differently, and was it serendipity (Chris Brogan retweeted me!) or was it something under your control (paid traffic campaign kickoff)?

If you smooth out all your data in a hurry to get your reporting done and make things neat and clean for a bullet point on a slide, you lose the opportunity to dig into the anomalies. Inside those anomalies will be things that can signal opportunities for innovation. If a social media luminary retweeted your content without your asking them to, maybe it’s a signal that you need to develop a social media marketing plan. If you got some earned media coverage of a feature of your product or service, maybe that’s an area where you want to invest some more development dollars. Whatever the case may be, if the anomalies in your data are caused by something under your control, you have the potential to transform that anomaly into an innovation that will power your business, possibly in new, different, and more profitable directions.

It makes total sense to take a “high level view” if you have no responsibility for finding new ways to make your business grow. If you find the expression “getting lost in the weeds” or “getting down in the dirt” being bantered about in your marketing organization and you do have a responsibility for growth, you might have a perspective problem. Getting lost in the weeds isn’t a bad thing if you know there’s a diamond ring in there somewhere.

Be very careful about rolling up your reporting too quickly to satisfy myopic, attention-deficit reporting requirements, whether for yourself or for a company/client. You might be missing some massive innovation opportunities!

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One response to “Statistical normalization opposes innovation”

  1. Christopher, aloha. For me, your last sentence says it all. Far too often we get caught up in the reporting and miss the Why behind the numbers. You are so right: “Inside those anomalies will be things that can signal opportunities for innovation.” Thx for the clear and compelling perspective. Aloha. Janet

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