Analyst, LLMO

Job Description:

Job Description

Analyst, LLMO

Overview

The Analyst, LLMO is a key support role within the dentsu’s LLMO specialty practice, responsible for conducting in-depth analyses, preparing reporting deliverables, and extracting actionable insights from LLMO efforts. This individual will work closely with technical, content, and client teams to monitor performance, identify trends, and support data-driven decision-making for brand discovery and presence across AI-driven search and generative platforms.

Key Responsibilities

Data Analysis & Insights

• Perform comprehensive analyses on LLMO initiatives to assess effectiveness, measure performance, and identify opportunities for optimization.
• Develop clear, compelling dashboards and visualizations to communicate findings and progress to stakeholders.
• Generate actionable insights and recommendations to inform LLMO strategies and client reporting.
• Support the implementation of measurement frameworks and tracking solutions for LLMO initiatives

Reporting and Deliverables

• Prepare regular and ad-hoc reports on LLMO performance, including KPIs, trends, and ROI for internal teams and clients.
• Ensure accuracy, consistency, and timeliness of all reporting outputs.
• Assist in the preparation of client presentations, case studies, and strategic recommendations.
• Identify opportunities to enhance analytical methodologies, tools, and reporting processes.

Industry Research and Competitive Analysis

• Stay current with developments in LLMs, generative AI, and AI-driven search platforms, as well as evolving SEO best practices and industry trends.
• Conduct category & industry research  to identify opportunities for improving discoverability and relevance within LLM and generative engine environments.
• Monitor competitor activities and benchmark LLMO performance against industry standards and best practices.
• Provide recommendations for content and strategy adjustments based on competitive insights.

Key Skills

Data Analysis & Interpretation

• Cross-Platform Correlation: Ability to compare visibility from rankings, "AI mentions and citations" in LLMs like Gemini or ChatGPT to understand impact of downstream actions.
• GA4, Adobe & Web Analytics: Strong knowledge of Google Analytics, Adobe Analytics, GSC, Bing Web webmaster Tools to track user behavior that leads to GAI SERPs and AI summarized answers.
• Sentiment Analysis: Evaluating whether an AI describes a brand positively or negatively.
• Reporting Tools: Skills to use Looker Studio or Tableau to visualize and create new views that help measure “success” of AI Search via combined metrics and new KPI’s.

2. Search Monitoring Platform Experience

• Search Platforms & Tools: Proficiency in navigating tools like Ahrefs, SEMrush, or Search Console to monitor rankings and technical health, export and make use of nibble datasets.
• AI Monitoring Tools: Ability to navigate and use tools like Profound, BlueFish, SEMRush, BrightEdge (others) to see how often a brand is mentioned in conversational AI.
• "Prompt-as-Audit": Knowing how to write precise prompts to test if an AI knows a brand and where it is getting its information.

Data Extraction & "Wrangling"

• API Connectivity: Ability to use Python or specialized connectors (like Supermetrics etc…) to pull data directly from the Google Search Console API or Profound API and others.
• Log File Analysis: Proficiency with tools like Screaming Frog Log File Analyser to see exactly how often "AI Bots" (like GPTBot or PerplexityBot) are hitting your server compared to Googlebot.

4. Advanced Segmentation (The "Insight" Layer)

• Regex (Regular Expressions): This is a mandatory hard skill. You use Regex to filter thousands of search queries into "intent buckets" (e.g., separating "How-to" questions from "Buy" keywords) within Google Search Console or BigQuery.
• Natural Language Clustering: Ability to use programming languages like Python scripts or VLOOKUP/XLOOKUP in Excel to group prompt/queries by topic or "Entity." Apply to query fanout analysis, etc.
• Sentiment Tagging: Ability to label data based on how an AI describes you. You’ll use automated scripts to tag rows as Positive, Neutral, or Hallucination to track brand health over time.

5. Data Organization & Environment

• SQL (Structured Query Language): As your data grows, Excel will crash. You need basic SQL to run queries in BigQuery (where Google Search Console can bulk-export its data). This allows you to join "Citations" data with "Sales" data to see if being mentioned in ChatGPT actually makes money.
• Power Query / Power Pivot: Inside Excel, these tools allow you to connect multiple data sources (e.g., a list of your backlinks + a list of LLM citations) to find the overlap.
• Database Management: Understanding how to structure a simple relational database so that your "URL list" stays synced with its "Ranking," "Citation Rate," and "Word Count."

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