The U.S. Local Search Ecosystem

Get a better understanding of how data providers, search engines, and directories exchange and access business data. The Local Search Ecosystem (LSE) is your resource to identify the most important sources for correct and accurate business information online.

This resource is dynamic. Hover over each segment to see the specific relationships and how information flows. Click segments to lock them.

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The Local Search Ecosystem is the brainchild of David Mihm and was first developed in 2009. The LSE shows how business information is distributed online, who the primary data providers are, how search engines use the data, and how it flows.

Whitespark and David Mihm teamed up to map the Local Search Ecosystem for the USA and provide a clearer picture of how information flows in today’s search environment.

Data Sources

In the United States there are four Primary Data Aggregators: Infogroup, Neustar/Localeze, Factual, and Foursquare (*previously Acxiom, however, with the business directory closing at the end of 2019, we believe Foursquare is in a position to take it’s place). These companies collect local business information and have massive data-sets in which they validate and vet the information provided. Search Engines like Google, Bing, and Apple license the primary aggregators data.

Other Key Sites like Facebook, Yelp, Yellowpages, CityGrid, and Dun & Bradstreet, also play a role in sending data feeds to Search Engines.

Search Engines manage their own databases, however, they utilize the information provided to them through the above mentioned sources. If the business data on any of these primary sources is incorrect it can override the information that is already available in the Search Engine’s database, this can lead to either new listings being created or changing existing listing data. Bad NAP data can negatively impact your ability to rank in Google and Bing.

There are many Geo and Vertical Directories that are valuable citation sources that can drive traffic to your website and ultimately bring in more business. These directories also access the information from the various primary services and display the data on their sites. Which further increases the importance of having correct business information (Name, Address, and Phone Number) on all of these sites.

The LSE By Data Source to Display Platform

Interactive images are rad, but sometimes it’s also nice to see data in text format. Below is the flow of data from the source to display platform. Filter the data by Source, Type, Display Platform, or Relationship to see how the data flows.

New Design

We updated the design to reflect an eco-wheel that summarizes the data sources, relationship connections, and how information is pushed and pulled throughout the local search environment. The placement of Google is a very noticeable change, as it is not central to the image. We made this change because Google is a receiver of business information, rather than a provider, so in this sense its role is similar to that of any other business directory. A huge thank you to our amazing design team, Avenir Creative, for being so awesome to work with on this.

Definitions

Understanding the Local Search Ecosystem

Before diving deeper, a couple of terms need to be introduced in order for the ecosystem to be understood properly:

One challenge with the local search ecosystem is that it goes through frequent changes. New players come in, business directories stop operating, and the significance of the different data sources shift. This prompted David to produce two updated versions of the infographic – in 2012 and in 2014.

The most significant change in the 2012 version of the infographic was the introduction of Yext, although David himself concurred that it was questionable if Yext fit the purposes of the infographic. Other important important trends noted by David at the time were:

• The emergence of Google Map Maker as an important piece of the puzzle,
• The decreasing influence of Acxiom, and
• The increasing influence of Localeze.

2012 LSE:

In the 2014 version, which was prepared when David served as the Director of Local Search Strategy at Moz, Moz Local was added as a new player, although similar concerns as the ones expressed in the 2012 version about Yext could be applied. David noted that Factual could by that time safely be considered one of the four major data aggregators.

2014 LSE:

Understanding how the local search ecosystem works is of primary importance for the successful execution of a local SEO project; especially when tackling citation related work. A business listing that appears to be correct on the surface might become incorrect in a few weeks due to incoming third-party incorrect data, or data that already exists in the database of the site, but that hasn’t been surfaced yet. Sometimes the issue can run so deep and be so obscure that only a detailed citation audit (and subsequent citation clean-up) performed with thorough understanding of the local search ecosystem’s peculiarities might be able to remedy the situation. Thus, because of the frequently changing nature of the ecosystem and its importance, we asked David if he would be interested in collaborating to create an updated version.

2017 LSE:

Data Sourcing and Analysis Methodology

Up-to-date accurate data is the most important element of the Local Search Ecosystem, and as it turns out, the toughest to obtain. As a starting point we used the same groups of sources David used when working on the previous versions of the infographic:

A) Distribution lists we received directly from the 3 oldest and most important data aggregators (Acxiom, Infogroup, and Localeze);

B) Publicly available information, including data source attributions visible on business directory listings pages, “Terms of Service” pages, or other legal pages (for instance, check out the Attributions page of Apple Maps).

SuperPages Attributions:

MapQuest Attribution:

However, we realized that this information is insufficient and might not show the whole picture. Thus, we collected data relationships information in a few additional ways:

C) We performed a “Data Aggregators Study” in 2016, the results of which were first presented by Darren at Mozcon Local. At the beginning of the study we “seeded” data about imaginary businesses on each of the data aggregators. Then, using the Local Citation Finder and manual searches, we observed how many listings would be auto-generated and which sites these listings would be auto-generated on.

D) We analyzed 20 citation audit reports of our clients (“Citation Audits Study”). We checked listings that had been auto-generated (i.e. not created as a result of manual effort) and cross-referenced overlapping NAP data for listings on different data aggregators and business directories to determine which data aggregators and business directories frequently feature same business details.

E) We directly contacted each of the top 50 most important citation sites. We only received 3 replies, however, that contained useful information from the following 3 sites: EZLocal, Cylex, and MapQuest.

After compiling all this data we had the challenging task of reconciling it. In the process, we discovered that some of the data points were contradicting each other. Thus, we decided to introduce a new classification of relationships for this version of the LSE infographic:

It is important to note that our research suffers from certain limitations. For instance, our Citation Audits Study included analysis of citation audits that had been performed over a long period of time (approximately 1 year). As mentioned above, local search ecosystem relationships change frequently, so it is possible that such changes occurred from the time the first audit had been performed to the time the last audit was completed. As a result, the audit analysis data might be contradictory or incomplete.

Additionally, we tried to not over rely on self-reported data, data aggregators distribution lists, business directories attribution pages, and written answers by business directories, which all were important sources of information used when creating the final version of the infographic.

A critical problem that we faced when analyzing the data compiled during the Data Aggregators Study, was the issue of the origin of the data. For instance, if certain business data came to Site C directly from Site A (the arch-source), or if it first made its way from Site A to Site B, and only then from Site B to Site C (refer to the section below for a specific example of this).

Infographic Design and Data Relationships Explanation

When we first started discussing the idea of updating the LSE infographic, one of the main concerns we had was with the design of the previous versions. In earlier designs Google was prioritized and occupied very prominent central place in the infographic. However, while Google is the most important display platform for most businesses, its place in the local search ecosystem is not that central. Google is a receiver of business information, rather than a provider, so in this sense its role is similar to that of any other business directory. We discussed this issue with David and he suggested a rework of the design. After a number of failed redesign attempts, David came up with the brilliant “Eco-wheel” concept and we figured we could make it dynamic.

We have divided the players in the LSE into categories:

Removal of Feed Sequence

Another feature that appeared in previous version of the LSE, but was dropped in the current version, was the different arrows that distinguished primary, secondary, and tertiary feeds. Some sites (such as the core search engines) receive data from a myriad of places. A good explicit example is the Apple Maps attributions page that features more than 50 sources of business listings data. Google and Bing obtain business data through both structured and unstructured citations (simple mentions of businesses in news articles, blog posts, forum discussions), which means that the number of potential data sources for these two search engines is practically infinite. Furthermore, it is very likely that all these search engines use “data freshness” as a major factor when deciding which data source will be displayed publicly. In some cases data from multiple sources might get merged together and certain bits of information from one source could be displayed together with bits of information from other sources.

All this makes it extremely difficult to determine which the primary, secondary, or tertiary data sources for a site at any given time are. It is much easier, however, to determine how likely it is that a site uses business data from certain source. That is why we introduced arrows that represent the likelihood of existence of a relationship instead.

Pay-to-Play Businesses Excluded

Finally, we decided to exclude agencies from this version of the LSE infographic. These include UBL, Yext, and Moz Local that were present in previous versions, as well as any other agencies that offer listing management solutions (including Whitespark). There are two reasons for this:

1. First, we wanted to create an infographic that featured natural relationships between business data providers and data receivers, and the agency way of adding business data to the ecosystem is always “pay-to-play”.
2. Second, including only some agencies that are able to add business data to the ecosystem is unfair, and there are tens, or even hundreds, of agencies that have special relationships with different data aggregators or business directories (for instance, see Factual’s list of Trusted Data Contributors).

Determining the Site Relationships

The different levels of Likelihood Of Relationships Between Sites In The Ecosystem (could be abbreviated as LORBSITE) have been mentioned a couple of times above, but some additional explanations are needed. We decided that when determining the LORBSITE we would assign different levels of trust to different information sources based on how likely it was that certain information was accurate and up-to-date. The sources were ordered in the following manner in terms of trustworthiness (starting from the most trustworthy):

1. Relationship data discovered during Data Aggregators Study
2. Relationship data discovered during Citation Audit Study
3. Relationships reported by business directories answering our email questions
4. Relationships mentioned in attribution pages (or other pages) on business directory sites
5. Distribution data provided by data aggregators

If relationship data between two (or more) information sources is inconsistent, we consider data discovered via the more trustworthy source as correct. For example, the distribution list provided by Infogroup didn’t include LocalStack.com as part of Infogroup’s distribution network. However, the results of both the Data Aggregators Study and the Citation Audit Study suggested that LocalStack does receive business data from Infogroup. Thus, an Infogroup —> LocalStack relationship was added as a “confirmed relationship” in the LSE infographic.

An opposite example would be a relationship between MapQuest and Localeze that was reported in Localeze’s distribution list. However, this relationship was not confirmed by any of our empirical studies, nor by MapQuest’s Director of Local Data and Partner Management Andy McMahon. As such a Localeze —> MapQuest relationship was added as “unconfirmed relationship” in the LSE infographic.

This way of ranking business data information relationships made it easier to analyze the results and prepare the final infographic. In some cases, however, even this way of simplifying things was insufficient to understand more complicated cases. For example, during our Data Aggregators Study we stumbled upon a very interesting discovery – business data we “seeded” on Acxiom in our data aggregator study eventually appeared on Localeze. When analyzing the data two different hypotheses emerged: