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Market Analytica

Case study · 52 franchises scored

LootSignal

The question the system answers

Which entertainment franchises deserve merchandise and licensing investment next — and in what order?

Scope

LootSignal is an internally-built research system that scores 52 entertainment franchises across seven weighted dimensions, so a licensing or merchandising decision starts from a comparable, current ranking instead of a one-off deck.

52
Franchises scored
Observed · as of 2026-07-01High confidence
7
Scoring dimensions
Observed · as of 2026-07-01High confidence
5
Source classes ingested
Observed · as of 2026-07-01High confidence
DemandFandomDesign potentialLicensing feasibilityAudience fitPricing powerMarket whitespace

Data and research used

  • Search and interest trend dataWeekly refresh

    Public interest signals, normalized per franchise.

  • Social audience and engagement signalsWeekly refresh

    Fandom size and engagement direction, not raw follower counts.

  • Marketplace listing and pricing dataWeekly refresh

    Merchandise breadth, price points, and sell-through proxies.

  • Release and licensing calendarsMonthly refresh

    Upcoming content that moves demand windows.

  • Category market researchQuarterly

    Licensed category-level context for whitespace estimates.

What was not available

Rights-holder internal sales data was not available and is not modeled. Where a dimension depends on it (pricing power in particular), the score uses marketplace proxies and is flagged at lower confidence.

The scoring model

The model is a weighted sum with explicit, versioned weights. Nothing about it is hidden — that is the point.

franchise_score = Σ (dimension_score_i × weight_i)  where Σ weight_i = 1

dimension_score_i ∈ [0,100], normalized within the 52-franchise set
weights are explicit, adjustable, and versioned with each scoring run

Try the weighting yourself

Ten sample rows, three of the seven dimensions. Move a weight and watch the ranking respond — this is the same mechanic the full system runs across 52 franchises.

  1. 01Sample J74.2
  2. 02Sample C70.2
  3. 03Sample G66.4
  4. 04Sample D65.7
  5. 05Sample E61.2
  6. 06Sample I57.4
  7. 07Sample F56.7
  8. 08Sample B53.4
  9. 09Sample H47.7
  10. 10Sample A43.7

Features shipped

  • Ranked franchise table with adjustable dimension weights
  • Side-by-side franchise comparisons
  • Demand-window forecasts tied to release calendars
  • Category benchmarks
  • Methodology documentation inside the product
  • A data room with source lineage per figure

Decisions supported

  • Which three franchises to pitch for licensed merchandise next quarter
  • Which existing lines to expand, hold, or wind down
  • When to time a launch against a franchise's next content window
  • Which whitespace categories justify a licensing conversation

Limitations and assumptions

  • Scores are relative within the tracked set of 52 franchises; adding franchises re-normalizes the field.
  • Pricing-power scores rely on marketplace proxies, not rights-holder sales data, and carry medium confidence at best.
  • Demand-window forecasts assume announced release dates hold; slips are ingested on the next refresh, not predicted.
  • The model ranks opportunities — it does not model licensing negotiation outcomes or contract economics.
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