I was watching Bloomberg TV yesterday (I’m an economics junkie) and at one point, one of the investment analysts showed an interesting methodology for choosing when to buy a stock. They showed the stock’s performance over a 30 day period aligned with the stock’s 200 day moving average, and their strategy was to buy into the stock when it was ascending past its 200 day moving average, and dump it when it was about to cross below the 200 day moving average. The rationale behind the strategy was that the 200 day moving average showed a reasonably big picture view of the stock, and you could determine whether or not the company was on the right track by how its current performance was doing versus its 200 day average.
I can’t tell you one way or another whether this investment strategy will make you a dime or lose you a fortune, but it did inspire me to look at how we look at web analytics. One of the biggest problems I see in web analytics is that we get caught up in the very short term picture. How many visitors did we get today? How many conversions did we get this week? It doesn’t occur to us very often to look at the very big picture and see how our site is doing overall compared to a long term average.
What if we took that methodology from investing and applied it to our website, asking how we’re doing compared to a long moving average? Would it tell us overall whether our recent efforts in whatever we’re doing in digital marketing make sense compared to our bigger picture view?
Here’s an example from my blog. I took my Google Analytics traffic data and plotted out 4, 13, and 26 week moving averages to see how things were going. What am I looking for? In an ideal world, the 26 week moving average should be the lowest, then the 13 week, then the 4 week, and then the weekly data should be the highest line on the graph. That would indicate consistent growth and an upward trajectory.
Instead, we have a graph that is almost a what-not-to-do. Look at the right hand side of the graph. The 26 week moving average at the very right is highest, then the 13 week, then the 4 week, then the weekly. That’s a trajectory that is headed downwards, and it’s got a decent amount of momentum behind it, which means that what I’m doing on my personal blog to market it isn’t enough. (understandably so, since my day job takes precedence).
Let’s look at the opposite situation and examine the basic traffic data for WhatCounts, which is where I spend the vast majority of my time and effort:
That is indeed a very healthy site with a lot of momentum behind it. On a consistent basis, the weekly traffic is the top-most line, followed by the moving averages at 4, 13, and 26 weeks. That’s what you want your website’s traffic to look like all the time; this is a very healthy website.
Obviously, there will always be up and down days on your website, but by looking at the bigger picture of these moving averages, you’ll see what’s truly working and what’s not – and you’ll know when things are flagging dangerously and you need to change strategies.
The next step for me on my personal website would be to look at those periods when there was consistent growth and upward momentum, check my calendar for that period, and see what practices I was doing that were working better than what I’m doing now. (I have a fairly good idea of what’s wrong, but that’s another story entirely)
The next step for me on the company website would be to look in our analytics a little more deeply and double down on the things that are working best.
For premium subscribers to my newsletter, you’ll get a sample template of the spreadsheet needed to put together this analysis for yourself in the upcoming week’s newsletter.
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Chris, this is a very interesting approach to intermediate-to-advanced analytics. I love out-of-the-box thinking like this that bring analysis techniques across industries. We don’t see enough of it. Keep up the good work.
Thanks for the kind words, sir.
This morning I was thinking it would be nice to be able to view moving averages of Analytics data, so I did some Googling and found this post. Did you plot this data by hand (or from downloaded reports) or have you integrated your Analytics data with another program to produce this?