How your scores are calculated

Every number in theviral.me is grounded in your real data. Here's exactly what goes into each score, in plain language — including when a score honestly says 'not enough data yet' instead of guessing.

Profile Reach Score

Your single headline number (0–100): are you actually growing? It is computed from your day-by-day cross-platform stats, so it can be tracked over time and climb as you grow. It blends four parts:

  • Growth velocity (30%) — your 7-day follower growth rate. Flat sits at the middle; growing pushes it up, shrinking pulls it down.
  • Engagement health (30%) — your weighted average engagement rate, measured against a ~6% “excellent” benchmark.
  • Reach efficiency (20%) — how far your content travels relative to your audience size (reach ÷ followers). Reaching twice your follower count scores full marks.
  • Consistency (20%) — your posting cadence over the last 7 days, against a healthy rhythm of about five posts a week.

Until there is enough daily history to be meaningful, this shows a “needs more data” state rather than a made-up number.

Creator Score

The score brands see when evaluating you (0–100). It is a blend of Influence and Reliability: 70% Influence + 30% Reliability once you have a collaboration track record, and Influence alone before then.

Influence ranks you against every other creator on the platform:

  • Follower count (35%)
  • Engagement rate (30%)
  • Average views (20%)
  • Your Creator DNA viral-potential (15%)

Reliability reflects how you handle brand deals:

  • Acceptance rate — how often you accept or complete proposals (40%)
  • Completion rate — how often accepted deals get finished (30%)
  • Response speed — how quickly you resolve inquiries (30%)

If you have no real signal yet (no synced audience or performance data), you appear as a “New creator” rather than being given a misleading low score.

Viral Predictor (Predicted Score)

Before you post, the Predictor estimates a draft’s engagement, virality, retention and audience match (each 0–100); the headline Predicted Score is their average. Predictions are grounded against your own past posts on the topic and real top-performing posts in your niche when that evidence exists.

Confidenceis honest about how much to trust each prediction. Once enough of your past predictions have been checked against what actually happened, confidence is anchored to that real accuracy. Before then it reflects how much comparison data backed the prediction — and it is capped, so a prediction made from the draft text alone can never claim high certainty it hasn’t earned.

Creator DNA

Six 0–100 traits describing your style: writing style, emotional range, hook strength, consistency, viral potential, and audience alignment. These are calculated deterministically from your real post history — engagement-rate statistics, post lengths, hook patterns and topic mix — not guessed. They unlock once you have enough analyzed posts.

Algorithm Scores (draft quality)

When you check a draft, the analyzer rates the text itself across dimensions like hook, readability, curiosity, retention, emotion, shareability and call-to-action. This is a quality read on the writing before it goes out — it is separate from predicted results and from actual performance.

Actual post performance

On posts you’ve published, we show measured results from real platform metrics:

  • Engagement score — your real engagement rate on that post.
  • Virality score — a weighted blend of engagement relative to how many people saw it: (likes + comments×2 + shares×3 + saves×1.5) ÷ impressions.

These are kept strictly separate from the predicted and draft-quality scores, so a “before you post” estimate is never confused with what actually happened.

Try it yourself

These interactive calculators run the exact formulas the product uses. Drag the inputs to see how each score is built up from its parts.

Profile Reach Score

score = 0.30·growth + 0.30·engagement + 0.20·reach + 0.20·consistency growth = 50 + (7d follower growth% ÷ 5) × 40 engagement = (avg ER% ÷ 6) × 100 reach = (reach ÷ followers ÷ 2) × 100 consistency = (posts in 7d ÷ 5) × 100 (each clamped 0–100)

Reach Score57
Growth velocity · 30%66
Engagement health · 30%50
Reach efficiency · 20%50
Consistency · 20%60

Creator Score

Creator Score = 0.7·Influence + 0.3·Reliability (Influence only, before any deals) Influence = 0.35·followers%ile + 0.30·engagement%ile + 0.20·views%ile + 0.15·viralPotential Reliability = 0.40·acceptRate + 0.30·completionRate + 0.30·responseSpeed responseSpeed: ≤24h=100, ≤72h=80, ≤7d=60, ≤14d=40, else 20

Creator Score63
Influence · 70%57
Reliability · 30%79

Percentiles are your rank against every other creator — 60 means you beat 60% of them. That’s why the score is relative and moves as the population changes.

Viral Predictor — Confidence

if graded ≥ 5: confidence = w·(100 − MAE) + (1 − w)·raw, w = min(0.8, graded ÷ 20) otherwise: confidence = min(raw, evidenceCap) evidenceCap = min(90, 45 + evidencePosts×6 + ownHistory×5) (clamped 5–97)

Confidence80%

Basis: track record (weight 0.5). With a real track record, confidence anchors on 100 − your error. Without one, it’s capped by how much evidence backed the prediction — so an over-confident model can’t overstate it.

Post performance (measured)

virality = min( (likes×1 + comments×2 + shares×3 + saves×1.5) ÷ impressions × 100 , 100 ) engagement rate = (likes + comments + shares + saves) ÷ impressions × 100

Virality score2.7
Engagement rate2.1%

Creator DNA — Viral Potential

viralPotential = min(avgER × 8, 60) + (stdev < avgER ? 30 : 15) (clamped 0–100) — rewards a high, consistent engagement rate

Viral potential62

Consistency bonus: +30 (steady) — steadier engagement scores higher than the same average that swings wildly.

These calculators run the exact formulas the product uses. Percentile-based inputs (Creator Score) depend on the whole creator population, so plug in your estimated rank to see how the pieces combine.

Our principles

  • Grounded in your data. Scores come from your real metrics, not placeholders.
  • Honest empty states. When there isn’t enough data, we say so instead of fabricating a number.
  • Self-correcting. Predictions are graded against reality, and confidence adjusts to the model’s actual track record for you.