Examples I saw in a website I launched last year: * High bounce rate showed that people did not find the content engaging / not what they were looking for, confirming a hypothesis I had; this was particularly true for certain sections of the website. * Time spent on pages was another metric that was helpful to find problems in some sections. * Easy slicing of traffic sources by referrer and country, showed me that most of my "good" traffic was coming from facebook, so I invested more time there.

To optimize the bounce rate metric even further a funnel can be created and measured. This allows to see bounces for each part of the funnel up to your conversion(s). It is often really valuable to see how a change on your website improves one part of the funnel but impairs another one, and than to think about the reason why this happens.

There are millions of possible answers to this question... Examples:

- Conversion rate grouped by browser and browser version to find browser specific bugs. - Total revenue per user per marketing channel / search keyword / ... to optimize budget allocation. - Revenue by mobile OS version share to decide testing procedures to not optimize for users that don't contribute to your bottom line. - Client side loading times per user location and provider to optimize infrastructure placement. - ...

One of the reason Google Analytics (and logging solutions like it) is popular is that people don't have access to server logs.

Server side logs aren’t an accurate way of doing analytics and bring up compliance challenges (storing access logs and using them for purposes other than security etc.)

> Server side logs aren’t an accurate way of doing analytics ...

Based on what? Server side logs records what the server itself sent. That sounds like it should offer far more accuracy than what a JS only solution would do.

That being said, if the code for analysing the server logs is lousy, it's not going to help. :)

The identifier in server logs is IP address, which is useless for distinguishing users. On the corporate side, NAT hides all the visits of many people behind a single IP. On the consumer side, ISPs change IP addresses all the time so a single person will show up as multiple IP addresses.

Note that I'm just talking about distinguishing people, not identifying or tracking. A basic question like "how many people came to my website today" is more accurately answered with client-side analytics than by analyzing web server logs.

> A basic question like "how many people came to my website today" is more accurately answered with client-side analytics than by analyzing web server logs.

How can client side analytics be more accurate, when ad blockers stop visits from even being registered by client-side analytics?

Before looking at accuracy, you already have compliance challenges with logging IPs / user agents and using it for analytics purposes.

Please don't try and change the goal posts. You said:

> Server side logs aren’t an accurate way of doing analytics...

What is your thinking behind that statement, as it sounds incorrect?

What compliance is challenged by logging user agents?

Can you talk a little bit more about why server side logs aren't accurate for analytics? I was planning on doing basic analytics (pageviews mostly) using Cloudfront access logs stored in an S3 bucket and queried through Athena for a site I'm working on which uses the AWS stack.

I know Cloudfront logs can sometimes drop, but is there a more important reason you're talking about?

I think with server-side only is harder to filter bots, crawlers and get accurate bounce rates or session length times.

Shameless plug, you might want to have a look at https://github.com/ownstats/ownstats

You mean unpopular?

Popular. Because adding lines of javascript is much easier than processing server log files.

I find time spent on page is a great way to measure performance of redesigns on page. There are numerous actionable points.

It might be, assuming two things:

1) That you actually care about this metric. I don't, I do not get paid by the number of minutes spent on pages, I get paid by the number of signed-up subscribers who use my software to make their workflow easier. I can (and prefer to) use bandit testing to measure the performance of redesigns.

2) that it can be reliably measured, which I don't think it can.

We are a SaaS company also, and time spent reading manual/tutorial pages is important to us.

With regards to point two, being reliably measured sounds to me like perfection is the enemy of adequate. Perhaps in low volumes you can't measure certain stats like this reliabily but in large volumes I think it's useful.

Tom Gilb once said that "everything can be measured so accurately that the result is better than having no measurement at all" or words to that effect. I'm sure he phrased it better. In this case, some users aren't measured, which drags down the accuracy of the measurement.

Even if the numbers are off by quite a few per cent, I can easily see how some site operators might benefit from knowing e.g. that visitors close one tutorial much quicker than the others.

As long as your measurement is a random sample everything is okay. Even if it is not, it is much more information than you had before. You just need to keep it in mind when evaluating conclusions. We are not talking about drug tails here and no one dies if the measurement is not 100% accurate.

I am able to measure everything 100% accurate. But this is really really expensive. It's a trade-of.

Unless your page is google.com, where more time spent on page means the experience is worse, not better.

Context is important, and it's one metric amongst others that should be used.

The point is, there are clearly actionable points GA can offer. It's disingenuous to think there are not.

I agree, saying that there are no actionable points in GA is like saying analytics in general are useless.

Counter example: I search for information on a subject. I get many interesting and relevant results and therefore spend more time on the page.

This makes it even more obvious that a longer or shorter session length is not clearly better or worse, it depends on a lot of other things.

Hook into onbeforeunload and call your own javascript to post back to your own site to let you know when they've left.

I know how I'd do this, but have no reason to do it.

Well, you don't specifically need GA for it, but tracking referers to sales, for example...is often actionable. Similar for a funnel view to see where visitors drop off. Especially combined with some A/B testing.