2017 Local Search Ranking Factors

Local Pack/Finder Ranking Factors

  1. My Business Signals (Proximity, categories, keyword in business title, etc.) 19%
  2. Link Signals (Inbound anchor text, linking domain authority, linking domain quantity, etc.) 17%
  3. On-Page Signals (Presence of NAP, keywords in titles, domain authority, etc.) 14%
  4. Citation Signals (IYP/aggregator NAP consistency, citation volume, etc.) 13%
  5. Review Signals (Review quantity, review velocity, review diversity, etc.) 13%
  6. Behavioral Signals (Click-through rate, mobile clicks to call, check-ins, etc.) 10%
  7. Personalization 10%
  8. Social Signals (Google engagement, Facebook engagement, Twitter engagement, etc.) 4%

Localized Organic Ranking Factors

  1. Link Signals (Inbound anchor text, linking domain authority, linking domain quantity, etc.) 29%
  2. On-Page Signals (Presence of NAP, keywords in titles, domain authority, etc.) 24%
  3. Behavioral Signals (Click-through rate, mobile clicks to call, check-ins, etc.) 11%
  4. Personalization 9%
  5. Citation Signals (IYP/aggregator NAP consistency, citation volume, etc.) 8%
  6. My Business Signals (Proximity, categories, keyword in business title, etc.) 7%
  7. Review Signals (Review quantity, review velocity, review diversity, etc.) 7%
  8. Social Signals (Google engagement, Facebook engagement, Twitter engagement, etc.) 4%
Pittsburgh Local Business Ranking

Introduction

This year’s Local Search Ranking Factors marks at least one significant change: David Mihm has handed over the data collection, analysis, and publication of the survey results to me, Darren Shaw (official announcement).

Thank you, David, for trusting me with this important industry resource. It is an honor to follow in your footsteps with this, and I hope to live up to the high standards you have set for it year after year.

My apologies to the community for the delay between the last Local Search Ranking Factors (September 24th 2015) and this one. While David passed the reins to me in the summer of 2016, it has taken me this long to get everything organized and put together. I now have a much deeper appreciation for the amount of work David has invested in this for the past eight years. 🙂

Changes Made to the Survey

I have kept David’s survey style mostly intact, aside from the following 5 changes:

1) Foundational factors versus the competitive difference-maker factors

A large number of the nearby hunt positioning variables are “foundational,” in that they are needed any possibility at appearing in the neighborhood comes about, yet proceeding to concentrate on them wouldn’t move the needle (appropriate GMB classifications, for instance). Then again, a number of the elements can be viewed as “aggressive contrast creators” in that proceeding to put resources into them will push your neighborhood rankings further.

By looking over the members on which elements are foundational and which components are aggressive distinction producers, I’m wanting to give some direction on what to concentrate on in your progressing nearby hunt work, after you have set out the correct establishment.

2) Changes in approach to local search since the Possum update

Has the Possum update had much of an impact on anyone’s approach to local search? Here I ask participants to rate the top 5 factors they’re focusing on more since Possum, and which factors they’re focusing on less.

3) Breaking down citation consistency into multiple factors

How far do you need to go with citation consistency? Do you need to spend hours and hours hunting down and fixing ALL incorrect citations that exist on the web? For some businesses that have been around for a long time and have gone through many name, address, and phone number changes, this could mean hundreds or thousands of incorrect listings to clean up. Do you just do the top 10 sites? The top 30?

To answer this, I removed “Consistency of Structured Citations” as a general factor and replaced it with these 4 new factors:

  • Consistency of Citations on the Primary Data Sources (aggregators in the US and primary data sources in other countries)
  • Consistency of Citations on Tier 1 Citation Sources (the top 5 to 10 most prominent structured citation sources in the country)
  • Consistency of Citations on Tier 2 Citation Sources (the next 10 to 50 most prominent structured citation sources in the country)
  • Consistency of Citations on Tier 3 Citation Sources (the hundreds of other business listing sites out there)

4) Expanded commentary by asking direct questions

Every time of the review, I find that the genuine gold can be found by perusing the numerous clever remarks that members give. Phil Rozek recommended the magnificent thought that I could support more analysis by provoking with inquiries. I attempted to leave the inquiries open sufficiently finished to find an expansive scope of solutions, and it seems to have functioned admirably, since I’ve wound up with 33 pages of inconceivable experiences from the best in the business.

5) Factors dropped and factors added

There were a sum of 115 positioning components and 27 negative positioning variables in the 2015 study. Some of these variables simply aren’t important any longer (for instance, you can at no time in the future alter the portrayal on your Google posting), and some of them were quite recently so dark that they never made anybody’s main 20 list in any case (“Number of +1s on Website”). Likewise, numerous new variables that we’re seeing nowadays weren’t on the rundown; I experienced every one of the components, evacuating 32 of them and including 38.

For anybody intrigued, you can see the full rundown of included, evacuated, and refreshed components here.

Definitions

Is it called a snack pack, a local pack, a pak, or something else? I’m hoping to help standardize the terminology used across the industry, particularly with the pack types. I can’t think of a better place to define these than on the Local Search Ranking Factors Survey results.

GMB Listing

Google My Business Listing. Your primary listing at Google that is editable in the GMB dashboard and publicly accessible at 3 locations:

  1. Google Search (knowledge panel) (example)
  2. Google Maps (example)
  3. Google+ (example)

GMB Landing Page

The page that a GMB listing links to. Usually the homepage or a location page. (example)

Local Pack

The regular local 3-pack that appears for most local search terms. (example)

Local ABC Pack

A local 3-pack with A, B, and C to the left of each result. No review stars, ratings, or counts appear for this type. This pack type is returned for branded terms such as “Starbucks” and, inexplicably, for storage and gas station terms. (example)

Local Snack Pack

This style of local 3-pack appears for dining, hospitality, and entertainment terms. Results have a photo, no phone number, and no links to the website. (example)

Local Sponsored Pack

The special pack type that is currently appearing in San Diego for plumbers and locksmiths. It appears in addition to the regular local pack. (example 1) There are also these sponsored pack types appearing for home services businesses in the San Francisco area: (example 2)

Local Finder

The complete list of local results that appears when the “More places” link at the bottom of a local pack is clicked. (example)

The Survey

The 2017 survey is structured into five primary sections:

  • Thematic Ranking Signals
  • Specific Ranking Factors in Local Pack/Finder and Local Organic Results
  • Foundational vs. Competitive Ranking Factors
  • Impact of the Possum Update
  • Negative Ranking Factors

I. General Ranking Factors

In this section, participants are asked, “In your opinion, to what extent do each of the following thematic clusters contribute to rankings across result types at Google?” They then enter a percentage of influence for each of these 8 thematic areas, for both local pack/finder results and local organic results:

  • My Business signals (proximity, categories, keyword in business title, etc.)
  • Citation signals (IYP/aggregator NAP consistency, citation volume, etc.)
  • On-page signals (presence of NAP, keywords in titles, domain authority, etc.)
  • Link signals (inbound anchor text, linking domain authority, linking domain quantity, etc.)
  • Review signals (review quantity, review velocity, review diversity, etc.)
  • Social signals (Google engagement, Facebook engagement, Twitter engagement, etc.)
  • Behavioral/mobile signals (click-through rate, mobile clicks-to-call, check-ins, etc.)
  • Personalization

The results here give us a sense of which general ranking factor areas are more important than others.

II. Specific Ranking Factors

To some degree An of this segment, I requested that the specialists rank the main 20 singular positioning components (out of an aggregate rundown of 113) that have the greatest effect on pack/discoverer rankings.

To some extent B of this area, I requesting that they rank the main 20 components from a similar rundown, just this opportunity to rank them in view of effect on confined natural rankings.

Results were then classified by means of reverse scoring, where the main positioned calculate got the most “focuses” for that question, and the least positioned figure got the least focuses. (The components positioning outside the main 20 for all respondents wound up with zero focuses.)

III. Foundational vs Competitive Factors

In this section, I asked the experts to rank the 10 factors they think are the most important foundational ranking factors, and to rank the 10 factors they think are competitive difference makers.

Results were then tabulated via inverse scoring, where the #1 ranked factor received the most “points” for that question, and the lowest-ranked factor received the fewest points. (The factors ranking outside the top 10 for all respondents ended up with zero points.)

IV. Impact of the Possum Update

Here, I asked the experts to rank the five factors they were paying more attention to since the Possum update, and the five factors they were paying less attention to since the update.

Results were then tabulated via inverse scoring, where the #1 ranked factor received the most “points” for that question, and the lowest-ranked factor received the fewest points. (The factors ranking outside the top 5 for all respondents ended up with zero points.)

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