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Industries / Video game studios and publishers

Social listening for video game companies

For studios, publishers and gaming platforms. Review public player feedback and discovery questions by title, platform and release, so community, product and marketing teams can choose what to address. Use AI search visibility to understand how answers describe your product and where buyers need better information.

Read the docs

A question to track

Which co-op games suit short evening sessions and support cross-play?

Evidence to inspect

  • Saved answer for a selected prompt and engine
  • How your product and alternatives are described
  • Cited URLs and current product documentation

Use the source gaps to improve game feature pages, cross-play and accessibility FAQs, and dated patch information, then recheck the same prompts. Measure customer outcomes separately.

Explore Prompt Universe

The decision

What your team needs to decide

A publisher may need to compare reactions across titles. An independent studio may need to prioritize a few unanswered purchase questions. A platform team needs to distinguish player needs from developer integration problems.

Answer engine optimization (AEO)

Help players find the right game in AI answers

A player can ask an AI assistant which co-op game fits a short evening, whether friends can play across platforms or which title offers specific accessibility features. Review whether your game appears, how it is described and which sources support the recommendation before the player reaches a store or review page. Gaming assistants already offer game recommendations and help1. Broader publisher research reports declining search referrals2, although it does not isolate gaming audiences. Track visibility in the answer alongside visits to your own pages.

  • Turn questions from listening into prompts in Prompt Universe, such as: Which co-op games suit short evening sessions and support cross-play?

  • Read the answer and its cited sources. Check platform combinations, current features and the release the answer describes with Ground Truth, then improve game feature pages, cross-play and accessibility FAQs, and dated patch information where the evidence leaves a question unanswered.

  • Recheck the same prompts after the update. Compare how your product is described and which sources are cited. Bring unresolved questions back to the team, and measure visits and customer outcomes separately.

Working in EarShots

Start with these reviews

Mentions

Review the player need behind a question or patch reaction. Explore Mentions. Questions, friction, buying signals and praise from connected public sources.

Launch reactions

Compare reactions to the release within its watch window. Explore Launch reactions. Public reactions in a chosen release watch window; availability follows your plan.

Prompt Universe

Which co-op games suit short evening sessions and support cross-play? Explore Prompt Universe. Saved AI answers, brand mentions and cited sources for the prompts you choose.

Product-fit signals

What would make the signal useful?

Players describing repeat sessions, recommending the game for a specific experience or returning after an update can support an audience-fit hypothesis. Check retention, wishlists, purchases and repeat play in your own game and store analytics. Public enthusiasm and AI mentions do not establish sales or product-market fit.

Terms in context

A little context before you go deeper.

The terms used on this page, with definitions you can return to.

Cross-play
Players on different supported platforms being able to play together.
Buying signal
A public statement that may indicate evaluation or purchase intent, such as a specific request for an alternative. The context still needs review.
Sentiment score
A summary of the tone of the captured source sample. It describes that sample and may differ from the views of your wider customer base.
Product-market fit
Evidence that a defined group of customers repeatedly gets enough value from a product to keep using or paying for it.
Answer engine optimization
Answer engine optimization (AEO) is work to make information useful, accessible and accurate for search answers. Mentions and citations can be measured, but are not guaranteed.

FAQ

Common questions

Which sources should we start with?

Start with public Reddit, X and web discussions about your titles. Use LinkedIn and public GitHub where developer platform questions matter. This workflow does not establish direct Steam, Discord, console-store or private community coverage.

What does AI search visibility tell us?

Start with a question relevant to your audience, such as: Which co-op games suit short evening sessions and support cross-play? Answer engine optimization (AEO) means improving how useful and accurate your information is in answers. Generative engine optimization (GEO) is a related term used for generated answers. EarShots records selected answers, mentions and cited sources; these do not establish sales, adoption or coverage of every search.

Who reviews and sends a reply?

Ears can prepare documentation-grounded drafts for eligible posts on X, LinkedIn and Reddit. A person reviews the source and draft, then posts in the original thread. Other sources remain evidence to review. Connecting a source does not authorize automatic replies.

Practical guides

Read more about the next step

Choose a guide on reviewing sources, answering questions or measuring AI search visibility.

Related industries and workflows

Connect listening with AI discovery

Bring your product documentation, audience and a decision you need to make. We can review which public sources and AI search questions fit that brief.