Every company in the category gets scored on 5 dimensions. Each score maps to specific evidence from that week's source corpus: app store rankings, revenue data, content volume, partnership announcements, engagement metrics.
The composite score is a weighted sum. The weights reflect which dimensions matter most for structural brand power in a given vertical. The delta column shows week-over-week movement. You can see exactly who moved, how much, and why.
| # | Company | Composite | Delta | Content | Narrative | Distrib. | Community | Monetize |
|---|---|---|---|---|---|---|---|---|
| 1 | ReelShort | 84.05 | ▼ -0.55 | 85 | 78 | 88 | 76 | 95 |
| 2 | DramaBox | 78.75 | ▲ +1.25 | 82 | 72 | 87 | 62 | 92 |
| 3 | Disney | 74.25 | ▲ +3.25 | 50 | 92 | 87 | 68 | 70 |
| 4 | iQiYi | 64.50 | — | 75 | 65 | 70 | 50 | 60 |
| 5 | Netflix | 62.80 | ▼ -3.00 | 28 | 38 | 93 | 68 | 85 |
| 6 | CandyJar | 58.65 | NEW | 65 | 52 | 58 | 65 | 55 |
| 7 | JioHotstar | 58.30 | ▲ +5.50 | 55 | 40 | 82 | 55 | 52 |
| 8 | GoodShort | 57.10 | ▲ +2.80 | 68 | 58 | 57 | 47 | 55 |
Notice Netflix: #5 composite, but #1 in distribution (93). Amazon: #10 composite, also massive distribution (83). Both are falling because distribution power without content commitment is a wasting asset in this category. The SBPI captures that structural weakness in a way a headline never would.
What this tells Nuri: When a client in an adjacent vertical asks "who's winning in micro-drama?" the answer is a structural map of 17 players with different strengths, different trajectories, and different vulnerabilities. That's a BI recommendation engine.
This is the part that takes a second to click. Once it does, you won't look at competitive rankings the same way.
Five horizontal layers, one per SBPI dimension. Every company is a dot on each layer, positioned by its score (0 to 100). Dots are color-coded by tier. When you hover over a company, dashed lines connect its five dots across all dimensions, tracing a shape.
That shape is the company's structural fingerprint.
Try hovering ReelShort. Its shape is wide and high across all five layers. That's a balanced powerhouse. Now hover Netflix. Massive distribution spike, near-zero content. That's a sleeping giant with a structural hole. Hover COL/BeLive. Almost nothing across four dimensions, then a spike to 90 on monetization. That's a pure infrastructure play.
Each shape tells a different story. A diagonal line from low-left to high-right is a company that's strong on monetization but weak on content. A flat high line is a dominant player. A spiky, uneven shape is a company with a single bet.
No other intelligence system visualizes competitive position this way. It turns a 17-row spreadsheet into a pattern you can read in two seconds.
Week 10 ran the full pipeline for the first time. It tracked 16 companies, scored them, wrote the report. Solid output. But there was a blind spot.
This is the difference between a static report and a system. A consulting team that misses a player has to redo their research. SHUR IQ gets a permanent upgrade to its search methodology. Every correction compounds.
Next training target: Curated source lists. Right now the system searches broadly. A Google Sheet of 50-100 vetted sources per vertical would double the signal quality. A 2x improvement from a spreadsheet update.
A year ago, building a multi-language competitive intelligence pipeline across Chinese, Korean, and Hindi media would have taken a team of analysts, translation services, and months of development. We added it in one session.
Chinese sources (Tier 1) run every week because that's where the market originated. Search terms include 微短剧 (micro-drama), 短剧出海 (short drama overseas expansion), and company-specific queries for parent entities like 九州文化 (Jiuzhou Culture, ReelShort's parent).
Korean and Indian sources are conditional, triggered when signals from English-language monitoring suggest activity in those markets. This week, JioHotstar's 100-microdrama commitment for IPL triggered Hindi/India searches, and the Korean "Warring States" platform launches triggered Korean searches.
The corroboration rule: No foreign-language finding enters the scoring rationale without confirmation from at least two independent sources. Translated findings are tagged [translated:zh], [translated:ko], [translated:hi] so you can see exactly what came from which market.
Each weekly run builds on the previous one. The search term registry grows. The source list improves. The translation accuracy compounds. After 10 weeks, you have a multi-language industry database that no Google Trends dashboard can replicate.
The SBPI framework you see in the micro-drama report (Content Power, Narrative Control, Distribution Power, Community Depth, Monetization Maturity) is one configuration. The system runs on whatever ontology you give it.
For a SaaS vertical, you might weight Distribution Power at 10% and add a "Platform Stickiness" dimension at 25%. For a consumer brand vertical, Community Depth might jump to 30%. For a regulated industry, you'd add a "Regulatory Positioning" dimension entirely.
The dimensions, the weights, the tier thresholds, the scoring criteria, the search terms, the company registry. All configurable per client, per vertical, per subscriber. The Totem Protocol that powers SHUR IQ treats ontology as a first-class parameter.
What this means for the business: One system serves any number of verticals. A subscriber tracking the micro-drama category and another tracking edtech startups would get structurally identical reports with completely different ontologies. The analytical engine is the same. The lens it looks through is different.
This ontology layer is the least-tuned part of the current system. The micro-drama SBPI weights have not been calibrated against historical outcomes. Once we invest in ontology design (expert interviews, backtesting against known market shifts), the accuracy jumps significantly. A content investment. The technology is already built.
SHUR IQ doesn't produce raw data dumps. It produces publication-grade editorial content with interactive visualizations. Thumbnail cards with og:image pulls. Ranked tables with delta tracking. Gap analysis with severity badges. Breaking news with source links and "Why It Matters" callouts.
This scales to 100+ verticals with the same production quality. A micro-drama report, a fintech report, an edtech report. Same editorial design system, same analytical rigor, different data. Each one looks like it was hand-produced by a team of analysts and designers.
Week 10 established baselines for 16 companies. Week 11 added CandyJar, tracked deltas for all 17, and expanded foreign language coverage. Week 12 will add Watchlist companies (ShortTV, Holywater, DramaWave) and deepen the persistent knowledge graph.
The state file tracks per-company: composite score, previous composite, delta, tier, key signal, and signal URL. The InfraNodus persistent graph accumulates cross-week relationships. Gap objects track first-identified dates and open/closed status. All of this compounds.
After 10 weeks, you have a longitudinal database of category dynamics that doesn't exist anywhere else. After 6 months, you have a proprietary industry dataset that competitors would need to rebuild from scratch. After a year, you have an asset.
Google Trends shows you that "microdrama" searches went up 40% in February. It can't tell you that JioHotstar jumped +5.5 SBPI points because of a 100-title IPL commitment, that CandyJar appeared from nowhere at #6, or that Netflix is bleeding structural position despite having the best distribution score in the category.
Google Trends is a useful tool. It shows search interest over time, geographic distribution, and related queries. For quick pulse checks on consumer interest, it works. But competitive intelligence requires more than search volume trends.
The comparison isn't really fair. Google Trends measures consumer attention. SHUR IQ measures structural competitive position. They answer different questions. But if the question is "what's actually happening in this category and who's winning," one of these tools can answer it and the other can't.
The Week 11 report is published. 17 companies scored. 5 structural gaps tracked. 52 sources across 4 languages. Breaking news with thumbnails and editorial analysis.