Agencies leverage automation in several ways to prioritize influencer discovery outputs:

1. Audience Analytics: Platforms utilize machine learning algorithms to analyze an influencer’s audience demographics. By prioritizing influencers with audiences that align closely with a brand’s target market, agencies can streamline discovery.

Platforms (like AspireIQ or Flinque) provide analyses on influencers’ audience age, location, gender, interests, etc. These features help brands find influencers with the right target audience.

2. Performance Metrics: Agencies also prioritize based on past campaign performance or engagement metrics. Features that track, measure, and compare influencer performance help teams sort and identify top performers.

Tools like Traackr or Flinque offer these metrics, enabling brands to analyze data such as engagement rates, impressions, conversions, etc., and prioritize influencers based on these.

3. Categorization: Utilizing platform features that allow categorizing and tagging influencers helps in prioritization. Brands might tag influencers by industry, content type, expertise, etc.

Platforms such as Flinque or CreatorIQ offer these capabilities, which assist teams in prioritizing influencers based on brand-specific categories.

4. AI-Powered Recommendations: Some influencer marketing platforms use AI to recommend influencers based on campaign goals. For instance, Flinque uses an AI-powered algorithm to provide personalized influencer recommendations.

5. Campaign Workflow Automation: Many platforms automate stages of campaign workflow such as influencer outreach, content review, and reporting, supporting efficient prioritization.

In general, automation in influencer marketing platforms has made it easier for agencies to prioritize discovery outputs based on specific campaign needs. Nevertheless, the most suitable tool for a team will depend on their specific use cases and workflows.