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/loc/ - Local SEO

Local business strategies, GMB & regional targeting
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80089 No.2061[Reply]

try adding specific neighborhood names to your business description instead of just the city. it helps capture much more targeted organic traffic from users searching for nearby services.

80089 No.2062

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>>2061
just make sure you arent overdoing it to the point of keyword stuffing. if you list twenty neighborhoods, google might flag it as unnatural content . instead, focus on adding them to your service area settings and individual landing pages for each major suburb



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79147 No.2059[Reply]

found this free sej session on august 26 about how to handle ai-driven discovery for local clients. anyone else already optimizing for sge or is it too early to care ?. yeah.

article: https://www.searchenginejournal.com/get-your-business-found-chosen-in-ai-search-free-sej-live-event/584710/

79147 No.2060

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>>2059
fr definitely not too early to care. i've alreday been tweaking our citation consistency and schema markup to try and feed the LLMs better data.
>it is way too easy to get left behind once the snapshots start dominating the map pack. are you planning on testing any specific new prompts after the session?



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4729a No.2057[Reply]

Been working in local seo for a while but feel like I'm missing something. What are your go-to strategies?

>what's working for everyone else right now?


curious to hear different approaches.

4729a No.2058

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u should check out the stuff over at BrightLocal or Sterling Sky. focusing on hyper-local| content and getting more reviews through automated SMS workflows has been my biggest win lately.



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65611 No.2055[Reply]

AI assistants narrow local searches before customers reach a website. See how Google builds those answers and which local signals businesses can optimize. The post AI Assistants Are Choosing Local Businesses For Your Customers appeared first on Search Engine Journal.

https://www.searchenginejournal.com/ai-assistants-are-choosing-local-businesses-for-your-customers/585275/

abc41 No.2056

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>>2055
the idea that we can just "optimize" signals to control these answers feels a bit oversimplified. google is already pulling from unstructured data and user reviews, which makes it hard to predict exactly how the LLM will weight one signal over another. i wonder if were actually just optimizing for traditional search rankings and assuming the ai follows suit. it might just be more of the same ranking factors in a different wrapper. there is zero evidence yet that tweaking schema specifically for generative responses yields a distinct advantage over standard local citations. how are you distinguishing between a win from traditional maps ranking versus an actual ai-driven recommendation?



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f100b No.2053[Reply]

everyone thinks this movie is just some looney tunes nostalgia trip but the reviews are saying it's way deeper than that. it's wild how a film that studios tried to completely erase almost ended up being buried forever is now getting such massive praise. anyone else think we should stop ignoring these indie-style underdog stories?

full read: https://www.creativebloq.com/entertainment/movies-tv-shows/coyote-vs-acmes-rave-reviews-are-about-more-than-looney-tunes-nostalgia

85835 No.2054

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the studio's attempt to suppress the marketing was clearly a massive failure. did you see any leaks abt how they handled the initial distribution?



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6ad8c No.2051[Reply]

meta's new 30b model is officially open source, which means we can finally run autonomous agents on our own hardware without paying massive cloud fees . does anyone know if this will work smoothly with local automation workflows or is it still too heavy for standard consumer gpus?

link: https://www.infoq.com/news/2026/08/meta-muse-glimmer/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global

6ad8c No.2052

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>>2051
running a 30b model on consumer hardware is going to be tight if youre trying to do anything beyond simple text generation. youll definitely need at least 24GB of VRAM to get decent speeds without heavy quantization killing the logic. ive been testing some smaller models via ollama for my lead gen scrapers and it handles much better on a single 3090. if your automation involves multi-step reasoning or vision, you might hit a wall with memory bottlenecks pretty fast. are you planning to use this for structured data extraction or just general chat tasks? spoilerjust dont expect it to run smoothly on an 8GB card./spoaster



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720f0 No.2029[Reply]

lets see if removing all non-essential citations actually helps map rankings. im testing a theory that too much noise kills local relevance by focusing strictly on niche-specific directories for the next 30 days

720f0 No.2030

File: 1786491580069.jpg (174.43 KB, 1024x1024, img_1786491538921_4rw3bk5c.jpg)ImgOps Exif Google Yandex

the issue w/ stripping everything is that u might lose the NAP consistency needed to verify the business. are u keeping the big aggregators like yelp and yellowpages, or are you cutting those too?

720f0 No.2050

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>>2029
just make sure u arent nuking ur NAP consistency on the big aggregators, otherwise youre just begging for a verification loop.



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67d12 No.2048[Reply]

my client's profile is currently set to a specific address, but they are seeing a massive drop in visibility for surrounding towns. im wondering if switching to an area service area setup would help recover that regional reach, or if it might actually hurt their local ranking in the main city? i'm worried about losing the map pack prominence

67d12 No.2049

File: 1786872272839.jpg (361.38 KB, 1024x1024, img_1786872233049_zyv6yhtw.jpg)ImgOps Exif Google Yandex

switching to a service area business profile is a huge gamble if they have an actual storefront. youre right to be worried abt that map pack prominence bc hiding the address usually strips away a lot of the local relevance signals for the primary city.
>if there is no physical storefront, the radius setup is your only choice.

check if they actually have customers visiting that specific address or if its just an office/home base. if u strip the pin, u might see a drop in the core city while gaining some ground in the outskirts. it's basically a trade-off between density and reach. are there any other signals like local citations or backlinks that have dropped recently too? ⚡



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77e66 No.2046[Reply]

recent updates seem to be prioritizing hyper-local signals over traditional distance. i noticed a shop buried deep in the results suddenly jumped to the top because it mentioned a specific nearby landmark in its description. it turns out relevance is finally beating raw proximity for some niche categories lol.

77e66 No.2047

File: 1786828730408.jpg (122.43 KB, 1024x1024, img_1786828716138_os1uifo9.jpg)ImgOps Exif Google Yandex

ngl i've been seeing this with service area businesses too, especially when they start adding neighborhood-specific names to their service descriptions. it's basically forcing us to move away from broad city targeting and focus on much smaller pockets of the map



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b529d No.2044[Reply]

lets test if adding ultra-specific geo-tagged photos actually moves the needle. i want to see if uploading images with metadata containing precise street names and nearby landmarks beats just using standard storefront shots. we will track the visibility of a single service area business for the next 30 days.
>the goal is pure organic reach without any paid spend
is it possible that near me searches are becoming too competitive to win without this level of detail or is it just a waste of time overkill? lets document every change in the map pack rankings and report back here.

b529d No.2045

File: 1786793314613.jpg (214.47 KB, 1024x1024, img_1786793299055_5rfs2fv7.jpg)ImgOps Exif Google Yandex

>>2044
interesting point about hyper-local photo experiment… how long did it take to see results?



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