What I did was take three important keyword phrases for our local clients, get the top 20 local results, and compare those on these factors:

For the Google local portion:

  • Reviews
  • Number of incidences of parts of keyword in local business listing title
  • Photos/videos
  • User content
  • Webpages

localgraphs

For the destination URL:

  • Inbound links
  • Pagerank
  • Number of pages in site
  • Number of incidences of parts of keyword in homepage title tag

With all that data and my very naive statistical abilities (am I the only guy who wishes he had a statistics professor chained to a dingy, cobweb-ridden cubicle in the corner?) I created scatter graphs and found R2 values.

Unfortunately, the strongest correlation is still considered "weak" in statistical parlance. I suspect someone from Google will read this and laugh at my non-PhD-ish bafflement. Nonetheless, I know where to go next- so there will be a part two.

Strongest correlation: More reviews equals better ranking

reviewlocal

The correlation of two important SEO factors… also weak:

iblpageslocal

Other factors and correlative values (all weak, but strongest first):

  • Number of photos and videos (listed in local details): R2=0.2053
  • Number of webpages (listed in local details): R2=0.1862
  • Number of user content (listed in local details): R2=0.1788
  • PageRank: R2=0.154
  • Keyword element instances in local listing name: R2=0.079
  • Keyword element instances in homepage title tag: R2=0.0011

Next up, I'm going to examine:

  • More keywords for # of reviews, larger dataset to get more certain R2 value for this
  • Keyword prominence for homepage title and LBL name
  • Keyword integrity (whole keyword in order) in homepage title and LBL name