Reviewed by LocalCMO Editorial Team Last reviewed
Chat with LocalCMO like the generative AI tools you already know. Tell the AI marketing agent what you want to complete—check listings, respond to reviews, compare local competitors, plan a post, or build a report—and review each business step before it runs.
You enter a natural-language instruction, just as you would in a generative AI chat. The AI agent turns it into a clear plan, checks the relevant local marketing information, prepares the work, and shows which step needs your decision. This is plain-language marketing automation built around an actual business task.
A drop in Google Maps visibility may connect to incomplete listings, stronger nearby competitors, recurring review themes, or missing website evidence. LocalCMO Chat is designed to keep the instruction, public evidence, customer-visible gap, next action, approval, and follow-up check in the same thread.
Start with a real instruction in everyday language. The conversational workflow turns it into evidence, business steps, and a reviewable next action.
“Which profiles still show our old holiday hours? Prepare the corrections for review.”
Turn a plain-language request into a location-by-location list of business information that needs attention.
Explore Listings →“Summarize this month’s review themes and draft replies to the reviews that need attention first.”
Bring the original review, recurring customer theme, response draft, and approval step into one conversation.
Explore Reviews →“We still have appointments tonight. Create a useful post for the right channels.”
Connect a real business moment to an original social media draft, creative direction, channel choice, and human review.
Explore Social →“Which nearby businesses appear ahead of us, and what customer-visible gaps should we address first?”
Compare public local signals, keep the competitor set visible, and turn the clearest gap into a prioritized next action.
Check local competitors →“When customers ask ChatGPT for a nearby provider, who gets mentioned and which sources shape the answer?”
Review the exact question, answer engine, response, named competitors, and cited sources before choosing an AEO action.
Explore AEO →“Give me the three most important local marketing tasks for this week and explain why each one matters.”
Combine visible findings across listings, reviews, social, local competition, and AI search into a reviewable priority list.
Explore All Products →Every step has a clear job. The workflow keeps findings traceable and separates analysis from actions that affect customers.
Ask for a weekly local marketing plan without hiding the work inside a single answer. Each finding, draft, approval, and follow-up remains a separate item your team can understand.
Review the full reply, post, business detail, or content update before it reaches a public channel.
Keep the location, account, source, and requested action visible before granting approval.
Separate what the AI proposed, what a person changed, what was approved, and what happened next.
An AI marketing assistant helps a local business turn everyday questions into organized marketing work. In LocalCMO Chat, that means connecting a request to relevant local evidence, a prioritized action, human review, and a like-for-like recheck.
You enter a natural-language instruction, and the AI agent turns it into visible business steps: understand the request, check the relevant information, prepare the work, ask for approval where needed, and report what happened. LocalCMO applies that pattern to local marketing tasks.
You can ask about business listings, customer reviews, local social media content, nearby competitors, Google Maps visibility, AI search visibility, and a prioritized marketing action list. Available actions depend on the data sources, accounts, and permissions connected to your workspace.
The page is designed around a conversational workflow, not question answering alone. A useful response should show the evidence behind a finding, explain the gap, prepare the next action, and keep approval and follow-up visible.
A request can include several related tasks, such as checking listing accuracy, summarizing recent reviews, and preparing a social post. The work should stay separated into reviewable steps so the team can approve each affected action.
Actions that affect public business information or customer-facing content should remain visible for human review. What can be executed also depends on the connected platform and the permission level granted to the workspace.
Traditional automation usually starts with a prebuilt trigger and fixed steps. A conversational workflow starts with a plain-language business request, identifies the relevant evidence and workflow, and then presents the proposed steps for review.
The same question can be scoped to one location, a selected group, or a broader portfolio when the necessary data and permissions are available. Results should keep location differences visible instead of blending them into one unsupported average.
Use generative AI chat to turn local business instructions into evidence-backed, reviewable next steps.
