How Data Analysis Works in Search party
The core way to explore data in Search Party is through filters. Filters let you cut through large prompt sets and view analytics by the dimensions that matter most to you.
For filters to be most effective, you should tag prompts as your prompt sets grow. Tags are completely flexible and can align to your business structure. Common examples include:
Topic
Funnel stage
Prompt cluster
Product line
Brand
Geography
By tagging prompts consistently, those tags carry across the entire product, allowing you to filter and analyze data in a structured way.
Auto-Generated Filters
In addition to custom tags, Search Party provides several built-in filters:
Provider: Slice by foundation model (Claude, Grok, Google, Perplexity, ChatGPT, etc.)
Intent: Filter by automatically classified intent types (recommendation, comparison, purchase, etc.)
Competitors: Analyze visibility by specific competitors
Domain / Domain type: Drill into citations by site or category of site
Page URL / Page type: Review analysis at a granular page level
Date range: Track how visibility shifts over time
These filters can be combined with your own tags for powerful cross-cut analysis.
Key Benefits
Flexible tagging system aligns analytics to your business priorities
Ability to slice by both human-defined and system-generated filters
Consistent tagging creates a data model that scales as your prompt sets grow
Common Use Cases
Marketing teams: Filter by funnel stage tags to see how visibility shifts across the customer journey
Product teams: Analyze sentiment and share of voice by product line
Executives: Benchmark visibility by geography or brand-level tags
Why It Matters
Insights are only as strong as the dimensions you can cut them by. Tags and filters make Search Party’s analytics human-led: you define the categories that matter, then use system filters to enrich and expand the analysis. This structure ensures your visibility insights stay actionable as your prompt sets scale.