GEO SEO for LLMs (Geographic Search Engine Optimisation) has become a very important part of my SEO strategy. Having succeeded in my GEO / LLM strategy allows me to coach others in this.
GEO SEO for LLMs Target Audience
Before I develop an area of SEO coaching, I always start by defining my target audience. Understanding your buyer persona is key, not just for the attendees, but also to ensure the sessions are taught at the right level. For this SEO lesson / module I have aimed the course content at: SEO Students, SEO professionals and marketing teams interested in AI, SEO, and location-based marketing.
GEO Learning Objectives:
- Understand the concept of Geo SEO and its importance for LLMs.
- Learn how to incorporate location-specific data into LLM prompts and applications.
- Explore practical applications of Geo SEO for LLMs in various industries.
- Identify potential challenges and ethical considerations related to Geo SEO for LLMs.
What is Geo SEO?
Here I expand on Geo SEO (Geographic Search Engine Optimisation) focusing on optimising online content to rank higher in local search results. I also emphasise the importance of location signals for both traditional search engines and LLMs. Use examples: “SEO Coach near me,” “SEO Coach in Cape Town,” “Carla dos Santos SEO Coach South Africa”
What are LLMs?
I Briefly explain what Large Language Models are, their capabilities, and how they generate text based on input prompts. I Highlight that LLMs, while powerful, need contextual information, including location, to provide relevant responses.
Why SEO Matters for LLMs:
Here I expand on LLMs, when used in applications like chatbots or virtual assistants, need to understand user location to provide accurate and helpful information. We also discuss the growing importance of personalised, location-aware experiences.
Incorporating Location Data:
Explicit Location Prompts: Demonstrate how to explicitly include location information in LLM prompt, and I show how specific location data improves the relevance of LLM responses.
Examples: “Who are the best SEO Coaches in Cape Town South Africa?” “Find the nearest SEO Coach to my current location.” “Give me directions to The SEO Coach from Camps Bay.”
Implicit Location Data:
- How LLMs can infer location from user context (e.g., IP address, device location services).
- Explain how APIs and location services can be integrated with LLMs to provide real-time location data.
- How location data can be extracted from user profiles, previous interactions, or other available data.
Location-Specific Knowledge Bases: - Explain how to create or use location-specific datasets and knowledge bases to enhance LLM responses.
- Examples: Local business listings, city guides, event calendars.
- Discuss the importance of keeping location data up-to-date and accurate.
LLM / GEO – Practical Applications – Local Business Discovery:
So here we will do a real world set up of your google business listing and also discuss local directory links – these local directory backlinks were very relevant in my local SEO success.
- Show how LLMs can be used to help users find local businesses, services, and attractions.
- Discuss how LLMs can provide personalised recommendations based on user location and preferences
- Explain how LLMs can be used to provide location-based emergency assistance.
Discuss how LLMs can be used to find information based on user location, and other parameters.
Challenges and Ethical Considerations
- Data Privacy: Discuss the importance of protecting user location data and complying with privacy regulations (e.g., GDPR, CCPA).
- Accuracy and Reliability: Address the potential for inaccuracies in location data and LLM responses.
- Bias and Fairness: Discuss the potential for bias in location data and LLM algorithms, which can lead to unfair or discriminatory outcomes.
- Accessibility: Discuss the need to make location-based LLM applications accessible to all users, regardless of their technical skills or disabilities.
Hands-on Activity – searching with LLM
I provide my students with sample prompts and ask them to incorporate location data to improve the relevance of the responses. I use a simple LLM interface or API to demonstrate the impact of location data on LLM output.
