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Documentation Index

Fetch the complete documentation index at: https://docs.influship.com/llms.txt

Use this file to discover all available pages before exploring further.

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Expand a Roster with Lookalikes

You have creators that work. Now you need more like them. This recipe uses the lookalike endpoint with weighted seeds to find similar creators, filtered to your requirements, and scored for campaign fit. The key insight: if you have performance data from previous campaigns, encode it in the weights. Your best-performing creator gets the highest weight, and the API finds more creators who skew toward that profile.

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The Flow

  1. Identify your and assign weights based on performance
  2. Run a lookalike search with filters
  3. Score results against your next campaign (optional but recommended)

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Step 1: Build Weighted Seeds

Start with creators you’ve worked with. If you have ROI or engagement data, normalize it to weights: 
import Influship from 'influship';

const client = new Influship();

// Your past campaign data
const campaignResults = [
  { username: 'wellness_maya', roi: 4.2 },
  { username: 'fit_with_dani', roi: 2.8 },
  { username: 'nutrition_neil', roi: 1.5 },
];

// Normalize ROI to 0-1 weights
const maxRoi = Math.max(...campaignResults.map((c) => c.roi));
const seeds = campaignResults.map((c) => ({
  platform: 'instagram' as const,
  username: c.username,
  weight: Math.round((c.roi / maxRoi) * 100) / 100,
}));

// seeds:
// [
//   { platform: 'instagram', username: 'wellness_maya', weight: 1.0 },
//   { platform: 'instagram', username: 'fit_with_dani', weight: 0.67 },
//   { platform: 'instagram', username: 'nutrition_neil', weight: 0.36 },
// ]
No performance data? Start with equal weights (1.0). You can also manually weight based on gut feel — give your favorite seed a 1.0 and the others less. 
const similar = await client.creators.lookalike({
  seeds,
  filters: {
    followers: { min: 15000, max: 300000 },
    engagement_rate: { min: 2.5 },
  },
  limit: 25,
});

console.log(`Found ${similar.data.length} similar creators`);

for (const result of similar.data.slice(0, 5)) {
  console.log(
    `${result.creator.name} (@${result.primary_profile?.username})`,
    `— similarity: ${result.similarity.score}`,
  );
  console.log(`  Shared traits: ${result.similarity.shared_traits.join(', ')}`);
}
At 1.5 per result, 25 results costs 37.5 credits ($0.375).

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Step 3: Score for Campaign Fit (Optional)

Lookalikes find similar creators — but similar to your past performers doesn’t automatically mean they’re right for your next campaign. If the campaign is different, score the results: 
const candidateIds = similar.data.map((r) => r.creator.id);

const scored = await client.creators.match({
  creator_ids: candidateIds,
  intent: {
    query: 'Launch campaign for a new organic snack brand',
    context:
      'We need creators who feel authentic, not overly polished. ' +
      'The product is a healthy snack bar. Target audience is active ' +
      'millennials who care about ingredients.',
  },
});

const goodFits = scored.data
  .filter((r) => r.match.decision === 'good')
  .sort((a, b) => b.match.score - a.match.score);

console.log(`${goodFits.length} of ${candidateIds.length} passed campaign fit`);

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Combining Similarity and Fit Scores

If you want a single ranking that blends both signals, weight them based on what matters more: 
// Build a combined score
const combined = similar.data
  .map((lookalike) => {
    const matchResult = scored.data.find(
      (s) => s.creator.id === lookalike.creator.id,
    );
    if (!matchResult || matchResult.match.decision === 'avoid') return null;

    return {
      creator: lookalike.creator,
      profile: lookalike.primary_profile,
      similarityScore: lookalike.similarity.score,
      campaignScore: matchResult.match.score,
      // 40% similarity, 60% campaign fit
      combined: lookalike.similarity.score * 0.4 + matchResult.match.score * 0.6,
      traits: lookalike.similarity.shared_traits,
      reasons: matchResult.match.reasons,
    };
  })
  .filter(Boolean)
  .sort((a, b) => b.combined - a.combined);
The blend ratio depends on your use case. If you’re expanding a proven formula, lean toward similarity (0.7 / 0.3). If the new campaign is different, lean toward campaign fit (0.3 / 0.7).

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Total Cost

StepEndpointCreditsCost
Lookalike (25 results)POST /v1/creators/lookalike37.5$0.375
Match scoring (25 creators)POST /v1/creators/match25$0.25
Total62.5$0.625

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The Feedback Loop

This workflow gets better over time:
  1. Run a campaign with your shortlist
  2. Measure performance (ROI, engagement, conversions)
  3. Feed those numbers back as weights in the next lookalike search
  4. Repeat
Each iteration refines what “similar to our best creators” means. The weights encode accumulated campaign knowledge that no amount of manual research can replicate.

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Tips

  • Don’t over-seed. 3-5 seeds is usually enough. More seeds dilute the signal unless each one genuinely represents a different trait you want.
  • Vary the weights meaningfully. If all your seeds have weight 1.0, you’re not using the feature. Even rough estimates (1.0, 0.7, 0.3) are better than equal weights.
  • Filter aggressively. Lookalike search returns creators who are stylistically similar — they may not meet your follower or engagement requirements. Filters prevent you from scoring creators you’d never work with.