Traditional Local Search Engine Marketing scoring factors such as location prominence also contain these primary local seo factors:
Local SEO Factor #1 : Numeric scores of the reviews (e.g., how many stars or thumbs up/down),
Local SEO Factor #2 Type of document containing the review (e.g., Yelp.com, Citysearch.com, or Insiderpages.com),
Local SEO Factor #3 The geographic distance you are from the visitors location or from their ISP IP Address
Social Networking, Local Social Networking and Local Social Networking Sites have brought additional factors that will help raise your rankings on Google, Yahoo and Bing local search results. Local Search Endorsements from users on these Social Networking sites share personalized lists of local search results and/or advertisements through their endorsements. These local search endorsement entries made in a social network including information relevant to the Local Listing an thus increase the ranking of the listings.
Further more these local social search endorsements have identified the local listings business information above and beyond the address and phone numbers and provide support local document proof of title, content, and/or category string. When the query includes multiple terms, documents that contain the terms as a phrase, include all of the terms, but not necessarily together, contain less than all of the terms, or synonyms of the terms may be included in the relevant set. The combination of location score (SEO) and topical score (Social) to order documents related to a query to improve search rankings for that query on Local Business Results for Google, Yahoo and Bing.
In Summary the combination of Local Social Marketing endorsing local search resultsA location component may analyze the query to determine a keyword, or a query topic. Of course there are other influencing factions that effect location sensitivity of the identified topic or query. Some topics are location sensitive, and some aren’t. Different topics, query types, users, geographic locales, etc. may influence a different determination of location sensitivity. An example : “pizza,” may be strongly associated with local documents or web pages (high location sensitivity), whereas a topic like “travel plans” may be less location sensitive.