[...] More on GA Visitor Loyalty (and unique visitors) | Increasing Your … [...]

Thank you both for your comments.

As I understand, your original request is to get a measure of the number of VISITORS who returned X (1, 2, 3 etc) times to the site over the 2007 period?
vs the number of VISITS which is what the Loyalty report gives.

Your solution is the exactly the sort of thing I was talking about. Pick a method that gets close to the answer, knowing it’s not perfect, and use that.
And try and validate via some other method.

As for the 20%? In our case we have “83.21% New Visits”, it goes up and down marginally on a month by month basis, but is around 82-84% consistently.
Which when rephrased is 16-18% repeat visitors, or rounding for simplicity, 20%.

Currently we only have GA data from mid 2007. But we still need to report to seniormost management, and higher, visitor numbers back to 2003. So we rely on the tool I wrote for getting visitor numbers out of … funkier… log files. (Use Apache’s mod_usertrack module, and log the unique id cookie. Some additional funky algorithms around that fwiw.)

I automatically produce weekly numbers. Say we had 10 visitors last week. And 10 this week. Well, we know 20%, or 2 visitors THIS week, were included in last weeks numbers – giving 18 visitors for the combined two week period, vs a simple addition *ONLY* giving 20.
ie: 10 + (10 – 2).

So in the interests of *SPEED*, I simply add up all 52+ weeks of VISITORS in my master spreadsheet and subtract 20% to give a NUMBER for each year. Note this can also include parts of Jan “next year” (eg 2008), or miss parts of Dec “this year” (eg 2007) – weeks rarely start/ending on a year boundary. Shrug – it’s a fudge.
Which having *just* compared with the same period GA numbers is so close as to make no significant difference.

ie. The fudge is, once again, validated.
To put in perspective on the Speed issue, reprocessing the logs from scratch would take around 1-2 hours on the hardware we currently have, for each year. Vs my fudge which is pre-calculated, ie 5-20 seconds, to open the spreadsheet.

On the Uniques vs 1st Time Visits difference? Same here.
In our case (~ 1.4M for 6 months), the diff was ~ 920. Or ~0.07%, if I’ve done the math right.

Make sense?
Cheers!
- Steve

Your explanations related to the cookies are correct and could explain this problem if there were more visits than unique visitors. But that is not what’s happening. There are more unique visitors than visits which is not coherent (30.661>30.234) . I believe there is a bug on the collection or presentation of this data.

Anyway, I thought of another idea to get the results on the estimate number of visitors for each row in the table. If the number of visits is “inversely cumulative”, as to say that the “1 time” row includes all visitors throughout the period, the “2 times” row includes all the visitors that visited at least 2 times the site, and so on…
So I have collected the data related to the entire life of my Google analytics which was shown in the table above. The diference is in the calculations of the visitors figures: now I have displayed a column which calculates the “net visits” on each row. These “net visits” are calculated by subtracting the number of visits on the row above by the number of visits on the row below.
This method is not perfect, firstly because there is the “interval problem”. As we have intervals above the 9th times, it is difficult to calculate the number of “net visits” above the “8 times” row. I still have to figger a way to contour this problem on the intervals and an immediate solution would be to create an interval on the 8+ times. Anyway, these are the calculations on the non-intervalled rows:
Number of visits Visits % of visits Net visits
1 times 30.234 29,79% 20.272
2 times 9.962 9,82% 4.190
3 times 5.772 5,69% 1.787
4 times 3.985 3,93% 1.042
5 times 2.943 2,90% 606
6 times 2.337 2,30% 350
7 times 1.987 1,96% 250

As you can notice the calculations are made only on the rows till the 8th one, because of that “interval problem”. With this data on “net visits” I can estimate the number of visitors the same way I was calculating above, by dividing the number of visits by the number of times visited. This way I’ll get an estimate of the visitors for all GA statistics period and I can calculate a % of visitors for each row, getting a profile of use for the whole period.
Now I have a second problem: how can I extrapolate this results for the period I am studying (the whole 2007)?
I came up with a solution that, despite not being perfect, can give an aproximation. Using the absolute unique visitors number for the whole year displayed at the visitor overview tab you can extrapolate the results for each row, by multiplying that number by the percentage obtained previously for each row.
And that’s basically it. I still have to find a way to go over the intervals, but these will be the figures I will use.

Steve:
I didn’t get what you meant by fudge factor, nor how you calculate the 20% number. Could you give some explanations, please?

Thanks.

Now, I definitely caught a version of that in my screen shot on the original post, where I showed how my 16 visits were all in the 201+ category. But your questions made me start to thing about this issue. In fact, GA only caught that snapshot because my cookies had me at lots of visits already, and my profile was new. (So the best it could do was say, she visited 16 times during the life of this week-old profile, and they were her 255th, 256th etc visit. Or wherever they actually were in the 201+ neighborhood.) But that really isn’t the problem for you, because you captured the entire life of your Google Analytics.

So I checked out a whole bunch of sites, using their best, all-inclusive profile, for the life of their experience with GA. And over and over again, I find the same thing: their one-time visits and their absolute unique visitors are almost identical. Just like you would expect them to be if everyone has to be a one-time visitor the first time they visit. In fact, yours are not that different – your absolute unique visitor count is only 400 visitors higher than your one-time visits, less than a 2% deviation.

What are some of the reasons that they are not identical? If they pull the data from the same cookie, they should be identical (and I *think* they both come from the cookie called utma, but I could be wrong.) Filters could certainly screw it up – for example, you might filter out your own visits by IP address, your dynamic IP address changes, your have a couple of days where you visit (but it is your millionth visit, not your first, and your cookies already know that). Or, I can imagine a scenario where your GA breaks but the visitor’s cookies continue to increment. (Stranger things have happened.) This is a worthy exercise, because the delta between absolute unique visitors and one time visitors is small enough to be explained by little problems such as these.

Anyway, thanks for your thoughts. They certainly helped my thinking. STEVE, I will have to go back and reread your stuff when my eyes are more wide open.

Robbin, I know you’re aware I’ve written my own tools for some of these numbers – mainly the “visitors” stat, as that is the one that senior management and above want.
My reporting of this “number” has a fudge factor built in when I’d aggregate over longer periods of time.

Because the tools would be run over a weekly period, you couldn’t simply add # of visitors over 52 entries to get # of visitors for a year. It’d be too high, as the individuals would get counted multiple times. Once this week, once last week and so on.

But this problem is more around the Repeating visitors.
And I also track how many of those we get.
So I do a simply fudge downwards to account for them.

eg. 20% Repeat Visitors? Fudge the figures for a yearly report down by 20%.
Sure it isn’t perfect.But when I have run a full years worth of logs through the same tool. The numbers are close enough (by luck or accuracy I don’t know… ) that I’ve lived with the simply fudging for several years now.

The bonus with the fudge is that it’s *fast*. And my time can be at a premium. Shrug. YMMV.

HTH?
Cheers!
- Steve