In a previous post describing a simple approach to de-seasonalizing your data, I covered how marketers can examine, at a rough level, the impact of seasonality on metrics. Obviously, your data science team would be looking at this data in greater depth and, to be sure, a more precise calculation of seasonality is a far […]
A few posts back, I examined a simple technique for using an exponential moving average (EMA) on your time-series metrics. This has the advantage of smoothing out the metrics while at the same time keeping a “memory” of all previous values of the metric that came before. It also has the side benefit of being […]
Often, you want to look at your metrics over a longer time period than usual in order to identify long-term trends. This is especially true if your metrics are “jagged” and move around a lot. You have two choices: You could use a larger time period. If you usually watch daily metrics, switch to weekly. […]
“Negative” metrics — you might prefer the term “De-optimization Metrics” — can be just as important to your continuous optimization efforts as your positive ones. The purpose of a negative metric is to isolate for you the deleterious effects you may inadvertently be having on your positive metrics. A negative metric is not necessarily something […]
“I Before E, Except After C” — do you remember that from 4th grade spelling? Well, it works just as well when you’re concentrating on your metrics and optimization program. What do the letters mean for metrics optimization? I is for Insight — learnings you derive from your continuous optimization efforts E is for Earnings — which […]
Many organizations get fixated on metrics data, without thinking of where it comes from. But, what if your metrics indicate a problem which originates somewhere else? Huh? What I’m asking is, “why should we believe that the place at which we measure our metrics is also the place where the problem occurs?” Perhaps the metric […]
Now that the 2012 U.S. Presidential election is over, there’s a bit of buzz around why some folks thought the election would be close and others who, using some solid statistical techniques, predicted a definitive (and it turns out, accurate) outcome. I want to try to explain the latter, especially for marketers. Why? Because political […]
This is a follow up to my previous article “Oh, What A Tangled Web We Weave” which discusses eight common ways we practice self-deception, particularly in the context of metrics, analytics, and continuous improvement testing. Continuing with our earlier list, here are four more ways a marketer might self-deceive: #5: Confuse Accuracy With Precision Example: […]
“Oh, What A Tangled Web We Weave, When First We Practice To (Self) Deceive” With apologies to Sir Walter Scott for my mangling of his most popular turn of phrase, I’d like to take some time now to discuss how people −and particularly people involved in metrics and evaluation of “how did we do this week/month/quarter?” […]
When is nothing worth something? Turns out, just about all the time. Whenever you look at your metrics, consider that a metric is, in virtually every case, designed to measure a specific outcome. It may be a positive outcome, one that is beneficial to your company: more sales, more customers, less inventory shrink, etc. Or it may […]
A few years back, I wrote a column about a common worry people express when they use analytics to inform their optimization testing plans. Somehow they figure that the simpler tools on the market (such as Google’s free Content Experiments tool) won’t be able to handle their site. A more expensive tool, mistakenly, is assumed […]
In many ways, being effective online means paying attention to the things that matter. That recipe includes conversion, and analytics, and metrics — important ingredients, all — but it also means using your brain to pay attention, as a human, to what your customer, another human, needs. Waitresses are great at this. And as far […]
How do you measure the impact of conversion improvement on your site? Most marketers know the basics of how to calculate this, but might get confused when it came to details — or do they? Astute readers will recall I began a series on calculating the impact of your continuous improvement efforts a few months […]
As I’ve mentioned in earlier articles, the biggest threat to effective improvement on a regular continuous basis is looking at too much data. The more metrics you’re looking at, the less likely you are to follow through on any of the signals they’re telling you. Less is always more. If you hear about a million […]
Everyone knows the question: “What one [book|person|food|movie|etc] would you take with you to a deserted island?” That is to say, what one thing can’t you live without? Most of us have a pretty narrow list. Perhaps it’s a book you’ve read over and over again. Or your favorite cousin — the one you would like […]
I’m often amazed at how naive companies can be about their online testing efforts. The bigger the company, it seems, the more disconnect there can be between the talented optimization team and the senior management. And that, as you might guess, often ends up undermining the revenue and efficiency improvements that come from continuous testing. […]
The start of a new year is a popular time for resolutions. Many people resolve to eat better (read: ‘eat less’). Or exercise more. You know the drill. And right about now, three weeks into a new year, is when the vast majority of those resolutions start to break down. Of course, even among marketing […]