Thursday, September 26, 2013

Buck Analytics for Dummies (Part 3)

Identifying Deer Preferences through Micro Segmentation

Micro segmentation identifies each individual deer’s preferences, needs, and behaviors. In order to get down to the individual deer level, it’s necessary to score the individual deer and set location where we might intercept that deer. I score my deer and stand locations on a 1-5 scale based on

  • Recency: How recent are my deer sightings? ("1" can be a year ago; "5" can be two days ago)
  • Frequency: How frequently does he visit? ("1" can be once a week; "5" can be every other day)
  • Environmental; What environmental considerations need to met? ("1" can be any travel funnel: "5" an be a funnel with an active primary food source near by)

It’s important to establish scores which weights all factor equally: You don’t distinguish the value of one of the three measurements from the others.

For example, if a deer scores low in every measure, you would give him a score like 1-1-1. If they’re high on the frequency score, you would get something like this 1-4-1. As you an see; the first buck has a total score of 3 and the second buck has a score of 6, making the second buck twice as likely to be encountered.

Scoring allows us to weigh each deer on each day and enhance our hunting odds by focusing on relationships and not just on deer sightings. This can only be effective if we remain neutral in applying scores.

We call this making a decision tree. Decision trees show the open and interpretable patterns which were discovered. This enables us to target individual deer and locations that are relevant in real time. We can determine the right deer to pursue and how to pursue him.

We can use deer analytics to gain deeper insights into deer behavior, but only in areas with little or no hunting pressure.

More on deer analytics





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