Fun with Web Metrics

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Popular Metrics: I – Benefits and Downsides

(Too) Many of the metrics requests I receive ends up being one of the “standard” requests peppered (maybe) with some custom filtering.

Almost all of them can be re-phrased to a question that start “How many…?”

  • How many (hits, views, users, etc.) did we get last (week, month, year)?
  • How many times did (insert subset of pages) get visited?

The others tend to be in terms of creating “Top 10” lists:

  • What’s our most popular (page type)?

All of these can be calculated of course, and there are lots of products out there that will handle those tabulations for you so that the effort is reduced to filling out a short form on a page and copying/pasting the results.   So the challenge isn’t getting the pertinent information to the interested party.   Instead, I find myself compelled to ask  “Why?”   More often than not, the answer is that there’s a report being crafted and the authors wanted “some numbers”…

OK – but – WHY?   What message are you trying to convey?   Are these stats really the best information that supports your message?

“Well, these are the numbers that EVERYONE reports (and therefore expects).”

Yes.   That’s very true.   But how are you putting these numbers in perspective so that comparisons site A with sites B, C, D… Z tell you something?  So, the challenge seems to be:

  1. trying to “second guess” what the REAL request is;
  2. getting the data and presenting it in a useful context;
  3. educating people to understand that yes – while having the “popular” metrics are nice, there’s almost always a better way “paint the picture” or “tell the story”.

So – all these “expected” stats have uses, but:

  • whereas in real estate it’s “location, location, location!”, for web metrics it’s “context context context!”
  • in many circumstances it’s not the value that’s important, it’s the difference between that value and some expectation of it:
    • predictions that were made have (not) been met;
    • outlying behavior indicates activity that might have been more-closely monitored (or should be in the future).
  • while these numbers are certainly obtainable for all sites (commercial and non-commercial) with the same methods, their interpretation isn’t always the same.

What do I mean by the last point?   Well, if you have a commercial site (i.e., you’re selling things online so that the point of sale happens on the site) then a one-page visit is almost always a “failure” in the sense that the sale didn’t get made:  aside from a donation system where you truly can complete the transaction with a single click (from an external site), the expectation is that the user will have to visit at least one other page to select an item to purchase.   Non-commercial sites and especially education sites frequently have visits where the user has used a search engine on a specific phrase, and “struck gold” going to the site where the information they sought was immediately available.   Objective achieved, and they move on.

So there are benefits:

  1. quick and easy retrieval and establishment of a time series of data (so you can watch the number of visitors increase weekly/monthly/yearly);
  2. comparison with stats from other sites, especially those that are most similar to yours;
  3. you’re using terms that other metrics aficionados understand;

but there are also downsides:

  1. you’re not making the most of your data because your crafting a “story” defined by someone else rather than concentrating on what makes your site special;
  2. it takes more resources to do deeper mining and spending all of your metrics budget on popular stats might not be the best investment;
  3. you could be discouraged even though your site is actually a smashing success because the numbers just don’t seem “impressive enough”.

Let’s spend a few posts with each of the “favorite” stats, point out their strengths, uses, etc. but for the most part rip them to shreds.  ☺

… Call it a statistical catharsis.


September 21, 2010 - Posted by | general, philosophy

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