Maximize Your Wine Budget by Going META?


I’m a geek. I’m not the pencil-neck variety of yesteryear, but rather a modern geek. I dig numbers and analytics and I have an obsessive tendency to dive into the nitty gritty of topics I’m interested in until I have what appears to some to be encyclopedic knowledge of a topic. It’s fun for my friends for a while… until it’s not. Only the other geeks really get me, but that doesn’t mean I don’t have something useful to tell you as a result of my geekery.

Odds are good you have no idea what I’m talking about with that goofy title since you’re probably NOT a geek. Let me break it down for you:

META data is a way for us computer geeks (among others) to store additional information about an item. Let’s say that item is a song… We’ll stay on topic and use the song “Red Red Wine” as an example. The song itself has “common attributes” like Artist: UB40, Album: Labour of Love, and Year released: 1983. There are also many “hidden attributes” like Genre, Instruments used, Beats per minute, Track number, Composer, Lyricist, etc. We call this META data.

Wine has META data, too! Its common attributes are Year vinted, AVA, Varietal, and of course Winery. But its META data has a wealth of additional information about the wine that contributes to how the wine tastes.

A wine's META data

You may recognize some of these and not others but, here goes:

  • Percentage of each varietal
  • Age of vines
  • Type of oak used (if any)
  • Winemaking techniques
  • Alcohol percentage
  • Particular aromas/flavors
  • Brix level
  • pH
  • Clone
  • Winemaker
  • Importer
  • And don’t forget all the little mini-bits of information that make up “Terroir,” each of which could be its own piece of META data (was it planted on a slope? which way does the slope face? how many minutes of sunshine did each row of vines get that year? how much granite is in the soil? is the soil calcareous? and on and on.)

Lately I’ve been wondering how useful this META data might be in helping to select wines you’ll like. If it’s effective at choosing wines you’ll like, you might even be able to use the information to maximize your wine budget. Here’s a geeky little example:

  1. Assume that 100% of your wine budget is spent each month
  2. Assume a bell curve for your enjoyment of the wines you’ve purchased
    • 2% of the wines are totally amazing
    • 8% of the wines are really really good
    • 15% of the wines are good and you’d drink them again
    • 50% of the wines are average. You could take ’em or leave ’em.
    • 15% of the wines are not good and you’d prefer never to drink them again
    • 8% of the wines are so bad you don’t finish the bottle
    • 2% of the wines are corked and undrinkable
  3. Pretend that all adds up to an “enjoyment score” of 50.
  4. Now imagine based on hidden characteristics of the wines you buy, you can limit your buying to wines that are only in the first three categories. Suddenly your “enjoyment score” could jump to 80! And now you’re getting more value for your wine because you’re enjoying more of the wine you buy AND you’re getting more wines you are excited to be drinking!

There are a PILE of companies, wine writers, retailers, etc. out there who believe that they can tell you that if you like Wine A, you’re gonna love Wine B. When they’re making those statements, it’s often based on just a few characteristics of the wines or even worse, subjective statements/opinions about the wines.

A lot of geeks like me think that if you collect enough data about what people like and don’t like, they’ll be able to recommend wines even better than their competition in an objective way. Fundamentally though, I think computers are a LONG way from being able to make the subjective into something objective for purchasing recommendations.

Instead of relying on mountains of data and algorithms, or a “consistent palate” from a wine reviewer, what if the WINE could tell you if you’re gonna like it based on its META data? Could these methods be used to create groups of remarkably similar wines and then find the ones at the best prices within those groups?

It seems that I’m not alone in this line of thinking, this is a snippet of today’s WineAccess special deal:

The concept illustrated by WineAccess using the Old Wente Clone

The concept illustrated by WineAccess using the Old Wente Clone

To see more about this wine & deal, check it out at WineAccess.

Posted on by Arianna Armstrong in How to Buy Wine at Good Prices

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