Streaming subscriptions evolve quickly. Prices shift monthly, trials vary by region, bundles tie platforms to telcos, music, or gaming. Internal trackers often miss these changes, while generic reports stop at averages.
Without structure and comparability, strategy teams can’t size markets reliably or anticipate churn. You need outlet-level tiers, normalized prices, and churn-risk reasoning — not just numbers.
Standardizes tiers, trials, and bundles into a single taxonomy.
Normalizes local prices and exposes a USD median for benchmarking.
Estimates active subscribers and revenues transparently.
Adds churn-risk signals with short explanatory notes.
Scores every enriched field (source availability, freshness, methods).
This segment is activated with a blend of trusted sources and your own inputs
AI reasoning
Open Datasets
Web intelligence
Curated APIs
Comparable tiers across countries with normalized USD medians.
Trials & bundles classified by partner type, churn-risk labeled with rationale.
Estimated active subscribers and monthly revenues per row.
BI/CRM-ready exports with field-level confidence scores.
Trust line: Every enriched field is sourced, scored, and explained.
Your questions on this segment, answered
Your questions on this segment, answered
How does this differ from using generic trackers or reports?
Generic reports provide static averages, while trackers list raw prices. This dataset adds structure, normalized comparisons, subscriber/revenue estimates, and churn-risk reasoning, with field-level confidence scores. It transforms fragmented signals into actionable market intelligence.
How are local prices normalized into USD for comparison?
Prices are collected in local currency, then converted into USD at the analysis date. Median values are calculated to avoid bias from extremes. This allows direct benchmarking across countries and tiers.
Can I adapt the taxonomy for tiers, bundles, and trials?
Yes. The segment uses a standardized taxonomy (e.g., basic, standard, premium; telco, music, gaming), but it can be adjusted to match your internal categories. This ensures seamless integration with your segmentation.
How are churn-risk signals determined and explained?
Churn risk combines pricing, trial offers, and bundling strategies. Each row has a qualitative label (low, medium, high) plus a short note explaining the signal. This gives not just a risk tag, but the reasoning behind it.
What does the confidence score mean, and how often are updates applied?
The confidence score reflects reliability based on source availability, freshness, and applied methods. Each enriched field is scored separately so you can judge data quality field by field. Updates follow web monitoring and API refreshes; cadence depends on platform changes and data volume.
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