Becoming the best: Measure everything

I was reading some of my other trading blogs and I came across a post over at TraderDNA that describes some of the metrics that professional traders use to improve their performance and gauge their success.  In my own trading portfolio, I have a few metrics including % gain, buy price, sell price, % retracement, expectancy, average win, average loss and a few more.  This web page has a ton more things to track, including Time in Winners, Time in Losers, Time since last Win, Time since last Loss, Average Profit/Loss, and plenty more.  I hadn't really considered any of those metrics before, but obviously if I pay attention to them, I can use them to improve my trading.

It also gave me an idea for spam effectiveness.  In order to become the best in spam filtering, a solution needs more than block the most spam.  They need to measure a whole stack of indicators and work on improving them all.  I think looking across the following metrics would provide a cohesive set of indicators that, when improved, will greatly enhance the user experience:

- spam filtering % (general and per domain)
- false positive % (general and per domain)
- time to identify a new spam
- time to respond to new spam
- time to release false positives
- time to release blacklisted IPs
- spam-in-the-inbox (a measure of spam on the user experience)
- time-to-detect a new IP as spammy
- time-to-detect a formerly spammy IP as clean or dormant
- spam filtering % against different classes of spam (phishing, pharmaceutical, stock spam, image spam, etc)
- time-to-detect a general spam outbreak
- time-to-detect a localized spam outbreak

There's probably a whole bunch more, but I think that will do for a start.  I think that's the key for an anti-spam solution to become best in class.

Comments (9)

  1. Al Iverson says:

    You couldn’t be more right. Measure everything. Without data, one is blind.

  2. One very important one you’re missing is the other side to the coin of "spam in the inbox" – user-reported non-spams in the spam folder. That’s quite possibly the most critical metric to drive to zero, because when you get several thousand spams per month, there’s just no way you’re going to actually look through everything in your spam folder. Losing a real mail is the most dangerous thing you can do. Far worse than the occasional spam in the inbox.

  3. Matt Sergeant says:

    Terry, I think this is the best post you’ve ever made, and it’s something I’ve been saying for years.

    A lot of customers/prospects don’t understand that everyone has false positives – it is how you deal with them that is absolutely key. Something that I know one of our competitors just doesn’t seem to "get" according to various people I have heard speak on the matter.

  4. tzink says:

    Thanks for the kind words, Al.

  5. tzink says:

    Hello, Stuart,

    A non-spam in the spam folder would be a false positive (which I actually mentioned but thanks for pointing it out).  In terms of measuring spam %, there are two ways to do it, overall spam % and spam-in-the-inbox, which is a metric that Hotmail uses.  I’ll go into greater detail in another post.

  6. tzink says:

    Hey, Matt,

    Thanks for the feedback.  What I have found among customers is that people dislike spam coming through (false negatives) but they really *hate* false positives.  I used to be in charge of FP processing over here and I strove to turn them around as quickly as I possibly could.

  7. Suso says:

    Good idea.  As a sysadmin, one of the things I have trouble with is effectively determining some of these metrics from the mail and spam logs.  There aren’t any good tools out there that I have found that would give me a line for each message so I can pass it through to programs like sort, uniq, grep, etc.  So I’ve been writing one.  Its for the Postfix/Spammassassin combination, but mostly for Postfix.  I’ll release it on Freshmeat when its ready.

  8. Bart Schaefer says:

    Hey, Terry, have you seen this Register article?

    Stuart:  Driving to zero the user-reported non-spams in the spam folder may not be a realistic goal.  From what I’ve seen discussed on the Planet Antispam aggregator over the past year or so, users will report spam as non-spam almost as often as the other way around (and anyone who runs a mailing list knows there’s a lot of the other way, around).  I seem to recall that John Graham-Cumming’s SpamOrHam experiment demonstrated that most people are surprisingly bad at classifying messages.

  9. tzink says:

    Hi, Bart,

    As the one that used to be in charge of combing through false positives, I can confirm that.  When I first started here, about 80-90% of false positive submissions (non-spam that we filtered as spam) were not legitimate, that is, they were actually spam.  That’s dropped somewhat over time but even still, the greatest amount of time in processing them is the effort it takes to separate the wheat from the chaff.

    Reading through your article, I’ve actually done a lot of work with Smartscreen, the technology Hotmail uses to do their spam filtering.  It’s actually a rather clever algorithm.  Like all spam filters, it has its strengths and weaknesses and some users find it too sensitive (but it’s sensitivity can be adjusted to produce a desired False Positive rate).

    Over here in Exchange Hosted Services, we started using it a few weeks ago to supplement our own spam filtering but we use it differently than Hotmail does.  The biggest advantage that Smartscreen has is that it’s based on seeing spam from hundreds of thousands of Hotmail users and millions of Junk Mail reports; thus, it is able to incorporate a lot of different types of mail into its classification system.

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