Using LLMs for Game Analytics: A Practical Workflow (and Where It Fails)

What can LLMs actually do for a game analytics team?

• Draft queries from plain-language questions, against your schema.
• Explain a query someone else wrote, and spot likely errors.
• Summarise results — cohort tables, experiment readouts — into a first-draft narrative.
• Generate hypotheses for a metric movement, which analysts then test.
• Document metrics, tables and dashboards faster.

They do not replace knowing what the data can and cannot support.

What does a reliable workflow look like?

1. Load the context. Table schemas, your metrics dictionary (definitions, filters, grain), and known caveats — revised ad revenue, delayed iOS postbacks, test devices.
2. Ask a scoped question. “D7 retention by source for iOS installs in August, classic definition” beats “why did retention drop?”.
3. Read the SQL before the answer. Check joins, filters, date logic and the cohort definition.
4. Verify against a trusted number. One known-good query or dashboard figure per analysis.
5. Let the analyst write the conclusion. Use the model’s summary as a draft, not a verdict.

Where do LLMs fail in game analytics?

• Invented definitions. Without a dictionary, “retention” becomes whatever the model assumes.
• Blended averages. Models happily report the overall number; you must ask for the segment that receives the decision — see The Averages Trap.
• Immature data. A fluent summary of two-day-old cohorts is still two-day-old data.
• Confident causality. Correlation dressed as explanation.

What if different models give different answers?

Treat it as a diagnostic. When models disagree, it can mean the question is ambiguous, context is missing, or the trade-off genuinely needs human judgment. Clarify the brief; if the disagreement remains, you have found the judgment boundary. See The Two-Model Disagreement Signal.

Related: A Metrics Dictionary Is a Product · Prompting Is Not Briefing · A Dashboard Should Compress a Decision