Audience Intelligence
A Sonar analysis runs through a multi-stage pipeline. Each stage is a named, versioned step that consumes the upstream output and produces structured JSON; deterministic post-processors check the math and shape before the next stage runs. Every stored report records the engine version that produced it, so analyses are reproducible and stale ones can be re-run on the same inputs.
This page is a reader’s guide to the report — what each stage looks at, what you’ll see in your output, and how the diagnostic layer turns the read into recommendations.
Backwards-looking — what the audience already looks like
Adjacency
Sonar starts with your comp set and identifies the audiences adjacent to those games — which existing fandoms overlap with the target, where they congregate, and which platforms index strongest for each comp.
What you get: A ranked list of communities discussing each comp title with size estimates, activity signals, and a per-platform read.
Fandom type classification
Not every fan plays the same way. Sonar maps each comp’s community into a five-segment taxonomy:
- Tourist — low-depth, high-volume engagement (memes, surface shares)
- Enthusiast — reviews, recommendations, ongoing discussion (typically the largest segment)
- Lorekeeper — theory posts, wiki contributions, long-form discussion
- Creator — fan art, cosplay, mods, UGC video
- Evangelist — high share rate, converts tourists into enthusiasts, grows the community
Segment sizes come from per-platform engagement signals, not from general knowledge about the IP. If a signal is missing, Sonar uses a low-confidence baseline rather than inventing numbers.
Quantic Foundry motivation mapping
Each comp is scored 1–10 across six motivation dimensions — Action, Social, Mastery, Achievement, Immersion, Creativity — using Quantic Foundry’s player-motivation framework (a 250,000+-respondent dataset). Sonar then computes the composite for the comp set: which motivations the addressable audience cares about most.
A community of story-focused players responds to different messaging than a competitive community, even when both play the same comp.
IP recommendations
Comps you may not have considered. Sonar performs a per-comp audience-overlap deep dive — articulating the specific behaviour, motivation, or community pattern that drives overlap, not just “same genre.” Sometimes the strongest matches aren’t the obvious ones.
Weighted audience scoring
Every detected segment is scored on relevance, reachability, and aggregate size. A 50K-member subreddit that ignores external posts scores differently from a 10K-member subreddit that actively engages with developers. The output is a seeding-priority ranking — focus your effort here first.
Comp analysis, genre baseline, and audience overlap
Sales context grounded in Gamalytic data: genre saturation, regional revenue distribution, comparable-title performance, and overlap between comp audiences. The deterministic comp engine (revenue distribution, tier classification, regional cost adjustment) runs as part of these stages — math is enforced in code, not estimated in prose.
Game design — features, difficulty, narrative
Three stages translate the audience signals into design implications: which features are non-negotiable for this audience, what difficulty philosophy fits the motivation profile, what narrative positioning competes effectively. These are advisory — Sonar surfaces the implications; the studio makes the call.
Executive summary
The headline read for non-marketing readers. Audience profile, key risks, and the 2–3 most actionable recommendations.
Forward-looking — diagnostic layer
The diagnostic stages move from “here’s the current situation” to “here’s what to change and why.” All four are live today:
Audience alignment
A three-band verdict — aligned, mixed, or misaligned — describing how well the game’s stated design serves the audience identified in the upstream stages, with a concise narrative grounded in those signals. Honest about both strengths and risks. Designed for delta-tracking across iterations of the same game, not for cross-game comparison.
Market trajectory
Directional signals — what’s growing, what’s declining, what’s changing — rather than a snapshot of the current state. Tailwinds and headwinds are named and tied to specific signals in the comp set.
Gap analysis
The specific gaps between your game’s current design and the competitive position it needs to reach. Every gap must ground in upstream evidence — the stage explicitly forbids inventing gaps the data does not support.
Prioritised recommendations
Actionable changes ranked by impact, each tied back to a gap, risk, or trajectory signal.
Pitch deck digest (optional)
If you upload a pitch deck (PDF or PPTX) when starting an analysis, a separate digest_deck stage extracts genre, audience, comps, and design intent so the rest of the pipeline has a richer brief than a one-line concept description.
Reading your report
The report opens on the executive summary. Diagnostic findings (audience alignment, gaps, recommendations) sit alongside the audience description so you can read either order. Most studios start with the recommendations and the weighted segment ranking, then dive into the upstream stages if they want to see the reasoning.
Reports are stored, versioned, and re-runnable. When the engine version moves, prior reports are flagged stale; you can re-run the same inputs to compare directly.
Next steps
Use your Sonar report alongside your marketing strategy. The community rankings should inform your PR list and early-access priorities. The diagnostic recommendations should feed into design iteration and post-mortems. If you want to act on the audience read in campaigns, see Launcher — it consumes Sonar’s output to generate a media plan and stage paused ad campaigns in your own ad accounts.
See Using Sonar for help navigating the report interface.