Local Algorithm Changes – Bedlam November 2019

Google Bedlam Local Update (Nov 2019)

November 2019 had a big impact on local rankings in Google. So much so that the ranking change was called “Bedlam” because, well, for many people it produced uproar and confusion as it relates to their local rankings.

We monitor several hundred local listings across the US in different industries, and the results we are seeing are mixed. Below is a subset of around 100 locations.

Google Bedlam Local Update


Beginning in Early November, we saw a small decrease, followed by a quick recovery and small increase. Then, throughout November, we saw large swings in local results – a few days taking significant losses, followed by recovery and significant gains. This continues even into this week (December 4th, 2019).

Bedlam doesn’t look like a proximity update like Possum was in 2016. Some had originally tagged this update as Possum 2.0 based on its impact across local results, but the shifts we are seeing don’t appear to be tied to proximity. These shifts seem to be relevance related, and so far, somewhat dynamic.

We are seeing a bit of a shuffling or changing of the results in the 3 pack, giving some credence to the shuffling theory from Jason Parks (@jason_parks1). I don’t think its specifically a shuffle, but our data does show fluctuations with businesses bouncing in and out of the 3 pack, causing big swings in traffic and rankings.

(Updated Dec 5th)  – as expected there was another recovery on December 5th that has us back up above the 30 day average for rankings. The volatility continues, but at least for today, we are net positive from the recent updates.

Bedlam Rankings Update Dec 5

Over the same time period, organic results (not the map listings) have seen improvement, with the biggest gains on November 18 (a separate Google update). This update appeared to affect organic listings, and not map (local pack) listings, even though both saw gains on the 18th.

Bedlam Organic Results


Local pack results showed more volatility after November 18th than organic results did, and as of now (December 4th), we are still seeing gains in organic, and have some losses on the local map rankings.

Ranking Factors Affected

Officially, “Bedlam” was Google adding neural matching to impact the results of local searches. It is an AI based system to better match words and concepts (or entities). Much like how BERT was a better understanding of organic search queries and content, Bedlam could be seen as a better matching system to match local results with local user queries.

With Bedlam, the one thing that seems “sticky” is that the results have been more volatile. We looked at several factors, and we noticed a few things across our data set.

  • Proximity is still a very strong signal. Businesses within closer proximity to the searcher tend to rank higher.
  • Reviews seems to positively impact rankings for businesses with less proximity to the user.
  • Complete and comprehensive Google My Business listings have more overall relevance, and therefore, are often ranking higher.
  • Citations appear to have some impact, but we also see a correlation of less impact as proximity increases.
  • In order to rank over a larger area (your local ranking footprint), you need stronger signals in relevance and prominence. This includes local citations, a comprehensive Google My Business listing, better total review count, and higher review scores.
  • The strength of the entity (website) it links to also seems to play a role. Links and on-page relevance of the linked website may affect rankings, and the overall ranking footprint.

Optimizing for Bedlam and Recovery

With Bedlam, recovering from lost rankings or improving position post Bedlam isn’t much different than before. The 3 major factors for local ranking are still proximity, relevance, and prominence. Ranking changes may adjust weights in these 3 areas, so focusing on the things you can influence (relevance and prominence) should help you improve. With neural matching, the results are going through machine learning refinement, so we expect more volatility as the system continues to learn how to best match local queries with local entities.

Understanding what relevance and prominence are will help demystify this. Relevance is the collection of signals you are sending to Google that you are a match for the user’s search query. That means categorically and geographically. Neural matching is Google’s attempt to continue to get better at this. You have to be relevant for what the user needs, and your entities (website + local listing) have to work in tandem to deliver that message. This means having a relevant website that focuses on the user’s needs, having local schema and on-site signals, and having a business listing that is 100% complete and thoroughly built out. Prominence is the strength of the entity (website + local listing). This includes measurable things like links to the website, local citations, review count, review ratings, and entity recognition by Google. Prominence may also be measure by activity in Google Posts, review growth, review rating trends, and other activity related items. Some of this is speculative, but correlates with strong rankings in the local pack.

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