A local SEO checklist is the ordered set of audit and remediation steps that gets a business found in geographically-qualified search \u2014 Google’s local pack, Maps, AI Overviews on “near me” intent, and the organic results that sit underneath them. The checklist below is what we run when we onboard a multi-location brand or rebuild an under-performing single-location listing. It is structured so a non-specialist can work through it in sequence and produce defensible improvements before bringing in a specialist for the harder items.<\/p>\n
Local search has changed in two practical ways since 2023. First, the local pack now competes for vertical space with AI-generated answers that sometimes pull from review content, business profile attributes, and on-page entity signals rather than just classic ranking factors. Second, review velocity, response cadence, and category-specific attributes have become weightier signals than they were when the discipline was mostly about citations and proximity. The checklist reflects both shifts.<\/p>\n
Use this as an audit document. Each section is a yes\/no check with a remediation pointer when the answer is no. The order matters \u2014 fix profile fundamentals before chasing citations, fix on-page entity signals before chasing links, and fix reviews velocity before paying for outreach.<\/p>\n<\/p>\n
Before anything else, the public business profile on the dominant map and search platform must be claimed, accurate, and complete. Most ranking and visibility issues at the local level trace back to a profile that was set up once, never revisited, and is now missing fields the algorithm has started weighting.<\/p>\n
Claim and verify.<\/strong> The profile must be claimed by an owner-controlled account, not a third party. If a previous agency or an unknown actor controls the profile, request ownership through the platform’s recovery flow. Verify the listing through the platform’s preferred method (postcard, video, phone) \u2014 unverified profiles are visibly de-ranked.<\/p>\n Primary category.<\/strong> The single primary category is the most influential field on the profile. Pick the most specific category that accurately describes the core business (e.g., “Italian restaurant” not “Restaurant”; “Personal injury attorney” not “Lawyer”). A wrong primary category caps how high the listing can rank for relevant local queries regardless of other signals.<\/p>\n Secondary categories.<\/strong> Add secondary categories for genuine service overlaps, but do not stuff. Two to four well-chosen secondaries is normal; eight is a signal of category gaming and tends to weaken the primary.<\/p>\n Attributes.<\/strong> Complete every applicable attribute \u2014 accessibility, payment methods, dietary options, service options (delivery, takeaway, dine-in), amenities. Attributes increasingly drive AI answers on filter queries (“wheelchair accessible cafes near me”, “dim sum with kids menu”). Profiles missing attributes are invisible on attribute-filtered queries.<\/p>\n Service area or address.<\/strong> A storefront business should show its address. A service-area business (mobile, no public-facing storefront) should hide the address and define the service area accurately. Profiles with both a hidden address and a public address are misconfigured and trigger trust flags.<\/p>\n Hours including special hours.<\/strong> Standard hours, holiday hours, more-hours fields (delivery, takeaway, brunch). Inaccurate hours are one of the highest causes of negative reviews and trust loss.<\/p>\n Photos and the visual surface.<\/strong> A baseline of 20-40 high-quality photos covering exterior, interior, products or service evidence, and team. Refresh quarterly. Profiles without recent photos are visibly de-ranked in pack and Maps results.<\/p>\n<\/p>\n NAP (Name, Address, Phone) consistency across the web tells search engines this is a single, real, verifiable business. Inconsistent NAP \u2014 two different phone numbers across directories, an old address still showing on aggregators, name variants like “ABC Co.” vs “ABC Company Pte Ltd” \u2014 fragments the entity signal and depresses rankings.<\/p>\n Audit the existing citation footprint.<\/strong> Pull the brand name and phone through a citation aggregator audit (the local SEO tool category). Export the list. Mark each citation: correct, inconsistent, or duplicate. Inconsistent citations need updating; duplicate citations need merging or removal through the platform’s process.<\/p>\n Prioritise the structured data layer first.<\/strong> The major data aggregators (the location-data category that feeds many smaller directories) are where to fix NAP first; corrections at the aggregator level propagate downstream. Then fix the major individual directories: maps, social platforms, industry-specific directories, local chamber and tourism listings. Then the long tail.<\/p>\n Don’t bulk-submit to low-quality directories.<\/strong> A common mistake is paying for citation-building packages that submit the business to hundreds of low-quality directories. The marginal value is near zero, and a number of these directories are scraped and re-scraped, which can re-introduce old NAP variants and make cleanup harder later. Volume is not the goal; consistency in directories that real people use is.<\/p>\n Industry-specific citations.<\/strong> A higher-value place to invest citation effort is the directory layer for the specific industry \u2014 legal directory listings for law firms, medical directories for clinics, hospitality platforms for accommodations and restaurants. These pass relevance signals on top of NAP signals.<\/p>\n NAP on the website itself.<\/strong> The footer of the website should contain the primary NAP. The contact page should repeat it with a clickable phone number, an embedded map, and structured data (LocalBusiness schema with the same NAP). The website is the canonical source of truth for the entity; if NAP on the website is wrong, every downstream cleanup is undermined.<\/p>\n<\/p>\n On-page work for local SEO is often under-invested compared to citations and links, but it is where many of the larger ranking gains come from in 2026. Search engines and AI answer surfaces are pulling entity-rich on-page content into local results more aggressively than they used to.<\/p>\n Location landing pages.<\/strong> A multi-location brand needs a dedicated landing page per location with substantive, location-specific content \u2014 not duplicated boilerplate with the city name swapped. Each page should cover services offered at that location, team or premises specific to that location, parking and access information, neighbourhood context, and location-specific testimonials. Thin location pages are a common ranking ceiling for chain businesses.<\/p>\n LocalBusiness schema.<\/strong> The location page (and the homepage for a single-location business) should carry LocalBusiness JSON-LD or a more specific subtype (Restaurant, Dentist, AutoRepair, etc.) with name, address, telephone, geo coordinates, opening hours, price range, and review aggregate. The schema should match the public profile exactly. The most specific applicable subtype outperforms the generic LocalBusiness type for entity recognition.<\/p>\n Embedded map.<\/strong> An embedded map iframe pointing to the verified profile passes a soft entity signal and improves user experience on the contact page. It is not a major ranking factor on its own, but it is a small consistent signal and the absence of one is a missed cue.<\/p>\n Title tags and H1.<\/strong> Location-aware title tags use the format “[Service] in [Location] | [Brand]” or a close variant. The H1 reinforces the primary service and location entity. Avoid keyword stuffing \u2014 one clean primary phrase is better than three crammed variants.<\/p>\n Internal links from service pages to location pages.<\/strong> Service pages should link to relevant location pages (“Available at our [Location] branch”) and location pages should link to the services offered there. This builds the internal cluster between service and location entities, which both classic ranking and AI extraction benefit from.<\/p>\n City and neighbourhood mentions in body content.<\/strong> Location pages should mention the city, neighbourhood, and adjacent landmarks naturally in the body content. “Five minutes from [Landmark], serving [Neighbourhood] since [Year]” is the kind of geographic context that strengthens entity recognition without reading as keyword-stuffed.<\/p>\n<\/p>\n Reviews are now a three-axis ranking and conversion factor: count and average rating (the historical metric), velocity and recency (the trending metric), and content depth and owner-response cadence (the engagement metric). Each axis is checked independently in modern local-pack ranking.<\/p>\n Volume and average rating.<\/strong> Aim for a review count that is at or above the median of competitors in the local pack \u2014 this varies sharply by category. A four-star average with 200 reviews typically outperforms a 4.9-star average with 12 reviews on the volume axis.<\/p>\n Velocity and recency.<\/strong> A profile with 200 reviews where the most recent is from 18 months ago looks dormant to the algorithm. Aim for a steady cadence of new reviews \u2014 for most service businesses, at least 2-4 per month. Reviews older than 12 months are weighted less than recent reviews on most surfaces.<\/p>\n Don’t run incentive schemes that violate platform policy.<\/strong> Cash, discounts, or other compensation in exchange for reviews violates major platform policies and can result in review removal or profile suspension. The right pattern is a polite post-service ask, ideally automated through the booking or invoicing flow, with no incentive attached.<\/p>\n Response to every review.<\/strong> Owner responses to reviews \u2014 both positive and negative \u2014 are a visible engagement signal and a conversion factor for users reading the profile. Respond to negative reviews calmly and constructively (acknowledge, take it offline if needed, do not argue in public). Respond to positive reviews briefly and warmly. Profiles with high response rates outperform comparable profiles with low response rates on the same review base.<\/p>\n Content depth in reviews.<\/strong> Long, specific reviews that mention services, products, staff names, and use-case context are weighted more than short reviews on AI answer surfaces. The post-service ask can include a soft prompt for specifics (“Mention which service you booked and how it went”) without crossing into solicitation of false content.<\/p>\n Negative review handling.<\/strong> Do not ignore one-star reviews, do not request removal of legitimate complaints, do not engage in arguments. Acknowledge, offer to take it offline, follow through. Pattern of unresponded negative reviews is a visible weakness in the profile.<\/p>\n<\/p>\n AI-generated answers on “near me” and category queries pull from a broader signal set than classic local-pack ranking. Optimising for them is a small additional layer on top of the checklist above, not a replacement for it.<\/p>\n Business Profile attributes drive filter answers.<\/strong> When users ask AI “what’s a good wheelchair-accessible cafe near me” or “vegan restaurant near me with outdoor seating”, the answer engine filters on the corresponding profile attributes first, then on review content. Profiles missing attributes are invisible on these queries even if they meet the criteria in reality.<\/p>\n Review content as answer source.<\/strong> AI answers increasingly quote from reviews (“customers praise the bread pudding and quick service”). The pattern that performs is reviews with specific service or product mentions \u2014 which means the post-service review prompt should be designed to elicit specifics rather than generic praise.<\/p>\n Entity-rich on-page content.<\/strong> AI answer engines extract from the location landing page when it has structured, entity-rich content: services offered with descriptions, team with names and roles, hours and access details, neighbourhood context. Thin or boilerplate location pages are passed over.<\/p>\n FAQ schema on location pages.<\/strong> A short FAQ section on each location page \u2014 covering parking, access, opening hours edge cases, service availability \u2014 with FAQPage schema gives AI answer engines extractable Q&A pairs that map cleanly onto user questions.<\/p>\n Cross-platform consistency.<\/strong> AI answer engines triangulate across platforms. If the verified business profile says “open Sunday” but the website footer says “closed Sunday” and the social profile says “open by appointment”, the answer engine flags the entity as inconsistent and may suppress it or qualify the answer with uncertainty. Cross-platform consistency on opening hours, service area, and primary attributes is a citation hygiene factor.<\/p>\n<\/p>\n Run the audit in this order. Tick each item; if any item is no, that is a remediation task. Do not skip ahead \u2014 fixing on-page before profile fundamentals is wasted effort.<\/p>\n Profile fundamentals.<\/strong> Profile claimed by owner account; verified; primary category set to most specific accurate option; secondary categories appropriate (2-4); attributes complete; service area or address configured correctly; standard hours set; special hours set for the next 12 months; 20+ recent photos; description complete and entity-rich.<\/p>\n NAP and citations.<\/strong> NAP on website footer; NAP on contact page with clickable phone and embedded map; NAP exactly matches profile; major data aggregators showing correct NAP; major individual directories audited for inconsistencies; duplicates removed or merged; industry-specific directories present.<\/p>\n On-page.<\/strong> Dedicated location page per location; substantive non-duplicated content on each; LocalBusiness or specific subtype schema with full fields; embedded map; location-aware titles and H1; internal links between service and location pages; geographic context (neighbourhood, landmark, city) in body content.<\/p>\n Reviews.<\/strong> Review count at or above local-pack median; recent reviews (within last 90 days); steady velocity (2-4+ per month for most service categories); owner responses to all reviews in last 12 months; review prompt flow active in booking or invoicing process; no incentivised review schemes.<\/p>\n Local AI readiness.<\/strong> Profile attributes complete for all relevant filters; review prompts elicit specifics; location page has entity-rich content; FAQ section on location pages with FAQPage schema; cross-platform consistency on hours, service area, attributes.<\/p>\n Cadence.<\/strong> Re-run this checklist quarterly. Profile fields, photos, and review velocity drift; competitors keep updating theirs. The discipline is maintenance, not a one-off.<\/p>\n<\/p>\n Local SEO in 2026 is a layered discipline: profile fundamentals first, then NAP and citations, then on-page entity signals, then reviews velocity and response, then the AI-answer optimisation layer on top. Each layer has a defined set of yes\/no checks, and each layer is a prerequisite for the layers above it \u2014 fixing on-page before fixing the primary category does not produce results, and chasing AI answers before fixing review velocity is premature. The checklist above is a maintenance instrument, not a one-off. The profiles that win local search are the ones whose owners run the audit quarterly, action the gaps, and keep the entity signals consistent across platforms over time. The work is not exotic; the discipline is staying current as the platform fields and the AI answer surfaces keep evolving.<\/p>\n<\/p>\n If you want a structured local SEO audit run on your locations \u2014 profile, citations, on-page, reviews, AI readiness \u2014 we can run it and produce a remediation plan.<\/p>\n<\/p>\nNAP consistency and citation cleanup<\/h2>\n
On-page local signals \u2014 landing pages, schema, entity prominence<\/h2>\n
Reviews \u2014 velocity, recency, response, and content depth<\/h2>\n
Local AI answers and citation hooks for 2026<\/h2>\n
The full checklist \u2014 running it as an audit<\/h2>\n
Conclusion<\/h2>\n
Frequently Asked Questions<\/h2>\n
How often should a local SEO audit be run?<\/summary>\n
What is the most common local SEO mistake?<\/summary>\n
Do citations still matter for local SEO in 2026?<\/summary>\n
How many reviews do I need to rank in the local pack?<\/summary>\n
Are location landing pages necessary if I only have one business location?<\/summary>\n
How do I optimise for AI Overviews on near-me queries?<\/summary>\n
Should I pay for a citation-building service?<\/summary>\n