Hype Measurement Template: KPIs to Track for Prediction-Led Campaigns (Tarot, Easter Eggs, Theories)

Hook: Stop Guessing — Measure the Hype That Predicts Launch Wins

You’ve spent weeks seeding tarot clues, planting Easter eggs, and watching theory threads spiral — but launch day still feels like a coin flip. The missing piece isn’t creativity; it’s measurement. Prediction-led campaigns demand a different KPI set: fast-moving, predictive, and organized around rumor dynamics. This guide gives you a ready-to-build KPI dashboard, a scoring system, and actionable rules for campaigns that trade on theories, serialized reveals and prediction mechanics.

The evolution of prediction marketing in 2026 — why this matters now

In late 2025 and early 2026 we saw a wave of major brands lean into predictive mechanics: serialized reveals, tarot and oracle motifs, and seeded “theory” communities. Netflix’s tarot-themed “What Next” rollout is a headline example — Adweek reported 104 million owned social impressions and more than 1,000 press pieces after its early-January push, with Tudum hitting a 2.5M traffic day. That scale demonstrates a core truth: prediction-driven creative scales fast — but only when measurement keeps pace.

Adweek, Jan 2026: Netflix’s tarot “What Next” campaign generated 104M owned social impressions and 2.5M Tudum visits on launch day.

Two 2026 measurement realities that change everything:

• Privacy-first data flows force brands to rely more on first-party signals, social APIs, and content fingerprinting than cookies.
• AI accelerates rumor lifecycles — theory iterations and deepfake variants can explode in hours, making share velocity and theory-tracking primary predictors, not lagging conversions.

What a prediction-led KPI dashboard must do (in one sentence)

Capture rapid, multi-source signals (share velocity, theory momentum, media mentions, owned traffic) and convert them into a single, actionable Hype Score that predicts launch conversion and recommends tactics in real time.

Core KPI list — What to track and why

Below are the essential KPIs for serialized, theory-driven campaigns. For each metric I include: definition, recommended data source, update frequency, and practical threshold/trigger examples.

1. Share Velocity

Definition: Rate of shares per hour across tracked platforms (normalized against baseline). The best early predictor of viral reach.

• Data source: Social APIs, content aggregator, webhooks
• Frequency: 5–15 minute refresh for critical windows
• Trigger example: >3x baseline shares/hour → trigger paid amplification + influencer push

2. Theory Momentum (Theory Tracking)

Definition: Number of unique theory threads, cumulative audience reach, and iteration depth (how many times a theory is reshared with added detail).

• Data source: Social listening, Reddit/Discord scrapes, forum crawlers, hashtag/phrase tracking
• Frequency: 15–60 minutes
• Trigger example: A new theory with >50k reach within 12 hours → prioritize official clarifying asset or embrace the theory as a creative hook

3. Media Mentions & Quality Score

Definition: Count of earned media pieces plus a sentiment/authority-weighted quality score (give more weight to top outlets and contextual analysis).

• Data source: Media monitoring, press APIs
• Frequency: Hourly to daily
• Trigger example: >100+ high-authority mentions pre-launch → shift budget to support conversion funnels

4. Owned Traffic & Discovery Pathing

Definition: Visits to hubs (landing pages, discover pages like Tudum), scroll depth, time on page, and next-action clicks (email signups, pre-orders).

• Data source: First-party analytics, server logs, GA4 first-party events
• Frequency: Real-time to hourly
• Trigger example: High traffic with low conversion → quick UX or CTA iteration required

5. Conversion & Pre-Sale Signal

Definition: Registrations, pre-orders, waitlist signups or commerce events attributed to campaign source.

• Data source: E‑commerce, CRM, first-party events
• Frequency: Real-time
• Trigger example: Conversion rate < benchmark but share velocity high → fix funnel or add scarcity messaging

6. Sentiment & Rumor Toxicity

Definition: Sentiment score for theory conversations and toxicity index (risk of misinformation or backlash).

• Data source: NLP sentiment models, conversation classifiers
• Frequency: 30–60 minutes
• Trigger example: Rising negative sentiment >30% → deploy PR clarification and community moderation

7. Originator & Influencer Impact

Definition: Identify who seeded high-performing theories and measure their amplification multiplier.

• Data source: Social graph mapping, influencer tracking
• Frequency: Hourly
• Trigger example: High-impact originator surfaces → rapid outreach for partnership or paid support

8. Decay Rate & Persistence

Definition: How quickly attention drops after a peak — helps plan reveal cadence and drip content.

• Data source: Time-series of share velocity and mentions
• Frequency: Hourly to daily
• Trigger example: Decay half-life < 24 hours → plan immediate follow-up reveal to sustain momentum

Build the dashboard — fields, formulas and a ready-to-copy Google Sheets setup

Below is a compact, copy-paste-ready structure. Create a Google Sheet with these sheets/tabs: RawFeeds, Normalizers, KPI_Calc, HypeScore, and Alerts.

Key columns for RawFeeds tab

• timestamp (UTC)
• platform (Twitter/X, IG, TikTok, Reddit, Discord, Web)
• event_type (share, mention, comment, publish)
• content_id
• theory_id (assign via NLP clustering)
• author_id
• reach_estimate (followers * engagement rate)
• sentiment_score (-1 to 1)

Normalizers tab — why normalize

Different platforms have different cadence. Normalize on a rolling 7-day baseline per platform to compute multipliers like share_velocity_multiplier.

KPI_Calc tab — sample formulas

Assume RawFeeds data is aggregated hourly into a row per hour. Use these formulas in Google Sheets (pseudo-formulas):

• Share Velocity = SUMIF(RawFeeds!event_type, "share", RawFeeds!reach_estimate) / Normalizers!baseline_shares_hour
• Theory Momentum = COUNTUNIQUE(RawFeeds!theory_id WHERE timestamp in window) * SUM(RawFeeds!reach_estimate for those theory_ids)
• Media Quality Score = SUM(media_mentions * outlet_authority_weight)

HypeScore tab — weighted predictive index

Create a single number that predicts conversion. Example weighting (customize per brand):

• Share Velocity: 25%
• Theory Momentum: 20%
• Media Mentions (quality-weighted): 15%
• Owned Traffic (conversion intent): 15%
• Conversion Signals (pre-orders/reg): 15%
• Sentiment Net (positive minus negative): 10%

Example normalized-score formula (Google Sheets):

= (0.25 * (share_velocity / max_share_velocity)) + (0.20 * (theory_momentum / max_theory_momentum)) + (0.15 * (media_quality / max_media_quality)) + (0.15 * (owned_traffic / max_owned_traffic)) + (0.15 * (conversion_signal / max_conversion_signal)) + (0.10 * ((sentiment_score + 1) / 2))

Scale to 0–100 and color-code: 0–30 (Cold), 31–60 (Warm), 61–80 (Hot), 81–100 (On-Fire).

Action rules & playbooks tied to Hype Score ranges

Make the dashboard prescriptive. These are tested tactical responses you can automate or follow as playbooks.

1. Cold (0–30): Seed micro-theories via niche creators, refresh Easter eggs, pause paid until share velocity improves. A/B test CTA variations on owned hub.
2. Warm (31–60): Boost top-performing micro-threads with paid media, convert originators to partners, push one clarifying asset to reduce friction.
3. Hot (61–80): Ramp paid support, open-time-limited offers, brief influencers to double down on top theories, prepare a reveal micro-event.
4. On-Fire (81–100): Execute reveal conversions, add scarcity messaging, route traffic to conversion endpoint, monitor for misinformation risk and scale customer support.

Advanced prediction tactics for 2026

1. Share velocity as a leading variable in a conversion model

Build a simple regression model where the dependent variable is launch-day conversions and independent variables are rolling-window share velocity, theory momentum, and owned traffic. Use the model to estimate expected conversions and required amplification to hit targets.

2. Theory clustering + originator scoring

Use embedding models (BERT-style or newer 2025/26 transformer variants) to cluster conversation into theory cohorts. Track originators and plot theory evolution. Key metric: theory persistence score = iterations * average reach per iteration. Pair originator mapping with community programs like micro-run creator partnerships to convert top originators into activation partners.

3. Cookieless attribution & fingerprinting

2026 measurement requires first-party event tracking and creative fingerprinting for cross-platform attribution. Use hashed content IDs and UTM-like content tokens embedded in reveal assets so shares carry attribution down the funnel.

4. Real-time moderation & PR play

With AI-driven rumor velocities, have a real-time moderation and PR lane. When toxicity or misinformation crosses thresholds, run a templated statement; if a theory goes mainstream, decide quickly whether to amplify or correct. See analysis of controversy dynamics and platform reactions in coverage on how controversy drives social app behavior.

Practical setup checklist (how to implement in 48 hours)

1. Connect social streams (X, TikTok, IG, Reddit, Discord) to your aggregator and start populating RawFeeds.
2. Build Normalizers: calculate 7-day baseline by platform and hour.
3. Deploy NLP model to cluster theory phrases and assign theory_id to incoming rows.
4. Create KPI_Calc aggregations hourly and compute HypeScore.
5. Set Alerts: share_velocity multiplier > 3x, sentiment negative > 30%, HypeScore crossing 60+.
6. Run dry-runs with test seeding and confirm the playbooks fire correctly.

Example alert rules (copy/paste ready)

• IF share_velocity_multiplier > 3 AND HypeScore > 60 THEN trigger: allocate $X to paid social, notify influencer ops.
• IF sentiment_negative_ratio > 0.3 AND theory_momentum > threshold THEN trigger: PR review & official clarification asset.
• IF conversion_rate < baseline AND share_velocity_multiplier > 2 THEN trigger: UX audit + quick CTA variant deployment.

Ethics and brand safety

Prediction-led campaigns walk a fine line. Don’t manufacture false certainty or use deceptive AI-generated content that misleads audiences. Track toxicity and legal risk as part of your KPI set and put a human sign-off in the loop for actions that address misinformation.

Case study snapshot: applying the template to a tarot-themed rollout

Using public reporting from early 2026 as a guide, here’s how the dashboard would translate Netflix’s tarot push into actionable measurement:

• Share Velocity: 104M owned social impressions converted to a share_velocity_multiplier of 6x vs baseline — trigger: immediate global amplification.
• Theory Momentum: Dozens of fan theories aggregated into 12 major clusters — originator mapping prioritized community creators for partnership.
• Owned Traffic: Tudum saw a 2.5M peak — conversion play = dedicated “discover your future” hub with signups and recommender modules.
• Outcome: cross-market adaptation and incremental reveal content sustained share velocity across 34 markets — a lesson in localizing both creative and KPIs.

Make it predictive: how to turn KPIs into forecasts

Use rolling-window features (share velocity last 3, 6, 12 hours; theory momentum growth rate) in a light-weight model (XGBoost or linear regression) to output an estimated conversion range for launch day. Continuously backtest: compare predicted vs actual and update feature weights — this is how you move from reactive hype chasing to prediction-led campaign control. For guidance on turning edge signals into personalized, predictive analytics, see advanced analytics playbooks.

Downloadable dashboard — quick start CSV schema

Copy the header row below into a CSV and import into Google Sheets to create your RawFeeds starter. This gives you a working input to run the formulas above.

timestamp,platform,event_type,content_id,theory_id,author_id,reach_estimate,sentiment_score
2026-01-07T12:05:00Z,Twitter,share,post123,theoryA,authorX,12000,0.2

Then create the other tabs (Normalizers, KPI_Calc, HypeScore, Alerts) and paste the sample formulas above.

Actionable takeaways — quick checklist

• Prioritize share velocity and theory momentum as your primary early-warning signals.
• Normalize per platform — raw counts lie when channels behave differently.
• Combine first-party traffic and originator mapping to convert buzz into measurable commerce actions.
• Automate alerts for amplification and brand safety so you can move at rumor speed.
• Backtest and adapt weights in your Hype Score until it reliably predicts conversion outcomes.

Final note — prepare to act faster than the rumor

Prediction-led creative wins when measured with equal velocity. Your dashboard is not a reporting tool — it’s a control center. Use it to decide whether to clarify, amplify, partner, or pivot. In 2026, attention changes in minutes; with the right KPIs and a simple Hype Score, you can turn ephemeral theories into repeatable, monetizable launches.

Call to action

Ready to stop guessing and start predicting? Copy the CSV header above into a new Google Sheet, paste the formulas, and run a 48-hour dry-run with a small seeded theory. Want the done-for-you Google Sheets + Looker Studio dashboard and an onboarding call? Reach out to the hypes.pro team to download the complete template and get a free 30-minute setup review.

Related Reading

• Edge Signals, Live Events, and the 2026 SERP: Advanced SEO Tactics for Real‑Time Discovery
• Edge Signals & Personalization: An Advanced Analytics Playbook for Product Growth in 2026
• Developer Guide: Offering Your Content as Compliant Training Data
• Cost Impact Analysis: Quantifying Business Loss from Social Platform and CDN Outages
• The Best Budget Power Banks Under $25 That Punch Above Their Weight
• Integrating Autonomous Trucking into Global Mobility Policies: What Employers Must Update
• How Creators Can Use the ‘Very Chinese Time’ Meme Without Crossing the Line
• Monetize Smarter: Using Cashtags and Micro‑Promos to Sell Virtual Seats and Tips
• Home Gym, London Style: Display Ideas Using Miniature Big Ben Weights and Fitness Gifts