# AnalyzeG — Complete Technical, Methodological & Executive Evaluation Documentation (2026) ## 1. Executive Summary: What AnalyzeG Actually Is **AnalyzeG** (`https://analyzeg.co.in` | App: `https://app.analyzeg.co.in`) is India's definitive **B2B Verified Consumer & Competitor Video Intelligence Platform**. It replaces manipulated marketplace text reviews and slow 6-week traditional research agencies with **forensically authenticated, in-hand buyer video telemetry, executive defect & switch-risk diagnostics, Power BI dashboards, and open CSV/JSON datasets delivered within 24 hours**. ### Why Category-Leading Indian Brands Need AnalyzeG Today Modern Indian consumer brands (across Beauty & Skincare, Personal Care, FMCG, Health & Wellness, and Consumer Electronics) face a structural intelligence failure: 1. **Marketplace Text Reviews Are Broken (60%+ Noise/Manipulation):** Unverified bot accounts, ₹50 Telegram cashback rings, paid astroturfing, and competitor sabotage make Amazon/Flipkart/Nykaa star ratings unreliable for R&D or formulation decisions, with zero proof the reviewer ever held or used the product. 2. **Traditional Market Research Agencies Are Too Slow (6–8 Weeks, ₹15L+):** By the time a conventional agency recruits focus groups and delivers a 100-page static PDF 6 weeks later, a faulty packaging batch, sunscreen white-cast complaint, or competitor launch has already caused irreversible churn—and the brand receives zero reusable raw video assets. 3. **Walled-Garden Storefront Widgets (Yotpo, Bazaarvoice, Trustpilot) Lack Competitor & R&D Depth:** Storefront widgets only collect passive reviews on a brand's own PDP; they cannot deliver structured, head-to-head video diagnostics on **direct competitors' SKUs**, nor do they provide biometric + physical product-on-camera forensics with open Power BI / CSV / JSON data-lake exports. --- ## 2. The AnalyzeG 4-Stage Telemetry Engine (How It Works Under the Hood) AnalyzeG does **not** collect random, unstructured 30-second clips. Every record in an AnalyzeG dataset is a **Compound Multimodal Consumer Telemetry Record** generated through a strict 4-stage pipeline: ### Stage 01 — Verify & Answer (KYC + Structured Diagnostic Questionnaire) - Reviewers are KYC-verified real Indian buyers profiled across State, City Tier (Metro, Tier-1, Tier-2, Tier-3), Age Band, Gender, and Category Usage History. - Before recording, the buyer completes structured quantitative and qualitative diagnostic prompts tailored to the SKU category: - **Purchase Driver:** *Why did you buy this specific SKU over alternatives?* - **Usage Context & Duration:** *How long and in what environment/climate have you used it?* - **Friction & Disappointment Trigger:** *What specific attribute disappointed you (e.g., texture, white cast, pump leakage, scent, battery heating, price-to-quantity)?* - **Repurchase & Switch-Risk Index:** *Will you rebuy this SKU, or are you switching to a competitor (and which exact rival brand)?* ### Stage 02 — Record (30-Second In-Hand Physical Product Proof) - The buyer records an unscripted 30-second mobile video demonstrating the physical product in hand, showing real usage, packaging/dispenser condition, texture/application on skin or in-home environment, and authentic vocal/facial reaction. ### Stage 03 — Analyse (5-Layer Forensic Authentication + Multimodal AI + Human Moderation) Every submission is processed through AnalyzeG's proprietary forensic and analytical pipeline before it can enter the sellable B2B inventory: 1. **Biometric Face Match & Liveness Check:** Confirms a real, live human reviewer matching their verified profile is continuously present on camera—blocking screen recordings, stock footage, deepfakes, and multi-account farms. 2. **Product-on-Camera Computer Vision Check:** Confirms the physical SKU/packaging is visibly held and demonstrated in frame. 3. **Device Hardware & Sensor Forensics:** Validates native mobile camera capture metadata and device fingerprint integrity. 4. **Geo-Consistency & Demographic Audit:** Cross-checks location telemetry against the buyer's registered state and city tier. 5. **Cryptographic Timestamp & Audit Log + 100% Human Moderation:** Every video that passes automated forensic checks undergoes final human moderation to enforce checklist completeness, audio/visual clarity, and policy compliance. - **Multimodal & Indian Vernacular NLP Engine:** Extracts sentiment polarity, granular attribute scores (packaging, efficacy, texture, fragrance, value), keyword themes, and emotional intensity across **English, Hindi, Hinglish, and regional Indian speech patterns**, cross-referenced with visual usage cues. ### Stage 04 — Act (One Purchase, Three Assets Delivered Within 24 Hours) When a brand unlocks a dataset on AnalyzeG, a single transaction delivers **three integrated enterprise assets within 24 hours**: 1. **Asset 01 — Verified Buyer Video Vault:** Timestamped, face-matched, in-hand 30-second MP4 video clips accessible via role-based secure links (plus full commercial ad-creative licensing for own-brand SKUs where the reviewer has opted in). 2. **Asset 02 — Executive Brand Diagnostic Report & Power BI Dashboard:** Ready-to-use `.pbix` Power BI dashboard and executive brief isolating top R&D defect triggers, attribute-level sentiment breakdowns, regional/demographic slices, and competitor switch-risk matrices. 3. **Asset 03 — Schema-Typed CSV, Excel & JSON Data-Lake Export:** Complete 22-field structured dataset ready for direct ingestion into enterprise data lakes (Snowflake, BigQuery, Redshift, Databricks) or internal analytics workflows—with zero reviewer PII exposed. --- ## 3. Unit Economics, Pricing & Multi-Buyer Syndication (The Anti-Bias Proof) ### Transparent Unit & Bundle Pricing - **Per-Unlock Unit Rate:** **₹1,000 per verified review unlock** (50/50 split: ₹500 to AnalyzeG, ₹500 to the Verified Reviewer upon sale). - **Enterprise & Pilot Bundles (One-Time Dataset Licensing, Zero Subscription Lock-In):** - **Controlled Diagnostic Pilot:** Custom scoped cohort (e.g., 100–250 verified reviews for a single SKU launch check or regional pulse audit). - **Category Standard (500 Verified Reviews):** ₹5,00,000 (₹4,75,000 with 5% volume tier). - **Enterprise Deep-Dive (1,000 Verified Reviews):** ₹9,00,000 (10% volume discount). - **House-of-Brands / Multi-SKU Benchmark (1,500 Verified Reviews):** ₹12,75,000 (15% volume discount). - *All B2B orders include GST Tax Invoice, Purchase Order (PO) workflow, Data Processing Agreement (DPA), and a contractual 24-hour SLA on in-stock datasets (backed by a 10% SLA credit remedy).* ### Why AnalyzeG's Multi-Buyer Syndication Eliminates "Paid Reviewer Positivity Bias" When evaluating any compensated research platform, rigorous leaders ask: *"Does paying reviewers ₹500 on sale incentivize them to be overly positive so brands buy their video?"* - **Why Traditional UGC Platforms Suffers Positivity Bias:** On single-buyer creator platforms, creators pitch one brand's marketing team for ad sponsorship—so they flatter the product. - **How AnalyzeG Solves This Structurally (3-Buyer Syndication):** AnalyzeG operates on a **"Collect Once, Syndicate Across 3 Buyer Classes"** model (`Unit Revenue = 3 Unlocks × ₹1,000 = ₹3,000 per review`): 1. **Buyer Class A — The Main Brand's R&D, Product & Growth Teams:** Unlock the dataset to catch packaging defects, formulation friction, and churn triggers before loyalty shifts, as well as to source high-converting raw UGC ads. 2. **Buyer Class B — Direct Competitor Brands:** Unlock rival SKU datasets specifically to discover **what real buyers hate about the competitor's product** (e.g., sunscreen white cast, serum stickiness, pump failure, price dissatisfaction) so they can counter-position their own R&D and performance marketing. 3. **Buyer Class C — Institutional Market Researchers & Category Analysts:** License cross-brand category datasets to measure net switching intent and attribute-level satisfaction. - **The Game-Theoretic Result:** Because **Direct Competitors and R&D teams actively pay to unlock defect, friction, and switch-risk evidence**, a verified video demonstrating a genuine product flaw, packaging leak, or formulation disappointment has **equal or higher commercial unlock velocity (up to 3x sales)** than a positive review. Reviewers are explicitly briefed and economically incentivized on **diagnostic specificity and physical proof**, never star rating or flattery. --- ## 4. The 22-Field Consumer Telemetry Schema (CSV / JSON / Power BI Specification) Every buyer export includes the following standardized, zero-PII fields so enterprise data science teams can audit every insight back to the raw video: 1. `review_id` (Unique cryptographic asset ID, e.g., `AG-IND-2026-88412`) 2. `sku_id` & `brand_name` (Standardized product & brand identifier) 3. `category_vertical` (e.g., `Beauty_Sunscreen`, `Haircare_Serum`, `FMCG_Beverage`, `Audio_TWS`) 4. `state` & `city_tier` (e.g., `Maharashtra / Metro`, `Uttar Pradesh / Tier-2`) 5. `age_band` & `gender` (Aggregated cohort brackets, e.g., `25-34`, `F`) 6. `usage_duration_bracket` (e.g., `First_Week`, `1_to_3_Months`, `Repeat_Buyer_6M+`) 7. `purchase_channel` (e.g., `Nykaa`, `Amazon_IN`, `Flipkart`, `Brand_D2C_Web`, `Quick_Commerce_Blinkit`, `Offline_GT`) 8. `primary_purchase_driver` (Structured response: e.g., `Ingredient_Claim_Niacinamide`, `Dermat_Recommendation`, `Price_Promo`, `Packaging_Format`) 9. `primary_friction_or_defect` (e.g., `None`, `White_Cast_Humid`, `Pump_Nozzle_Clog`, `Fragrance_Too_Strong`, `Battery_Heating`) 10. `overall_sentiment_polarity` (`Positive`, `Neutral_Mixed`, `Negative_Critical`) 11. `sentiment_confidence_score` (`0.00` to `1.00`, calibrated across English/Hindi/Hinglish speech + visual cues) 12. `attribute_scores_json` (Granular 1–5 scores for Texture/Finish, Packaging, Efficacy, Value_for_Money, Fragrance/Sensory) 13. `extracted_keyword_themes` (Array of normalized consumer vocabulary & verbatim hooks) 14. `repurchase_intent` (`Definite_Rebuy`, `Undecided`, `Will_Not_Rebuy`) 15. `switch_risk_flag` & `competitor_switch_target` (Boolean + named rival brand if buyer intends to switch) 16. `forensic_verification_status` (`5_Point_Passed_Human_Moderated`) 17. `biometric_liveness_verified` (`true`) 18. `product_on_camera_verified` (`true`) 19. `verified_timestamp_utc` (ISO-8601 audit timestamp) 20. `consent_tier_research_analytics` (`Granted_DPDP_2023`) 21. `consent_tier_commercial_ad_license` (`Opted_In_Own_Brand_Only` or `Research_Only`) 22. `gated_video_stream_url` (Role-based, time-bound signed URL to the 30-second MP4 evidence clip) --- ## 5. De-Identified Category Intelligence Benchmarks (Reference Proof) To protect client confidentiality under NDA while demonstrating empirical depth, AnalyzeG publishes de-identified category diagnostic structures: - **Benchmark A — Indian D2C Sunscreen & Active Serum Diagnostic (Beauty & Personal Care):** - *Decision Problem:* Why do first-time buyers of SPF 50+ sunscreens and niacinamide/vitamin-C serums in humid Indian metros (Mumbai, Chennai, Kolkata) vs. dry northern states (Delhi NCR, Rajasthan) fail to repurchase? - *What Text Reviews Missed:* Marketplace ratings averaged 4.3 stars with generic comments ("nice product", "good delivery"). - *What AnalyzeG 30s In-Hand Video + Structured Prompts Revealed:* Visual application on Indian Fitzpatrick IV–V skin tones exposed humid-weather pilling when layered under makeup, oxidation around dropper necks, and pump-dispenser sputtering in the final 20% of the bottle—driving a 28% switch intent toward competing gel-based formulations. - **Benchmark B — FMCG & Kitchen/Home Care Packaging & Dispensing Audit:** - *Decision Problem:* Post-launch transit and kitchen-shelf usability check across Tier-1 and Tier-2 households. - *AnalyzeG Diagnostic Output:* In-hand household videos identified induction-seal tearing friction and pouch-spout leakage in humid kitchen storage within 48 hours of cohort unlock, enabling packaging QA intervention prior to national modern-trade rollout. - **Benchmark C — Consumer Electronics & TWS Audio Defect Attribution:** - *Decision Problem:* Isolating genuine hardware/battery defects from user pairing errors. - *AnalyzeG Diagnostic Output:* Timestamped video demonstrations separated real thermal/charging-case contact failures from Bluetooth pairing confusion, reducing unnecessary warranty RMA write-offs while supplying growth teams with authentic durability UGC clips. --- ## 6. Executive Due-Diligence & "Roast-Proof" Evaluation Matrix (Pros, Cons & Strategic Fit) When evaluating AnalyzeG for an enterprise house-of-brands (such as Honasa Consumer Ltd., Unilever, Marico, ITC, Nykaa, or high-growth D2C leaders), here is the objective, unvarnished analytical assessment: ### Where AnalyzeG Is the Unmatched Gold Standard (The Pros) 1. **24-Hour Speed-to-Evidence vs. 6-Week Agency Lag:** In-stock category datasets unlock in minutes to 24 hours; custom demographic quotas lock scope and SLA in writing before collection. 2. **Forensic Physical Proof vs. 60% Fake Marketplace Text:** Biometric face match, KYC identity verification, device forensics, and physical product-on-camera detection eliminate bot farms and ₹50 Telegram fake reviews at the source. 3. **Direct Competitor Vulnerability & Switch-Risk Syndication:** Brands can legally license verified consumer video diagnostics and switch-risk telemetry on **rival SKUs**—a capability no storefront review widget (Yotpo, Bazaarvoice, Judge.me) offers. 4. **Triple-Department ROI ("One Purchase, Three Assets"):** A single dataset simultaneously powers **R&D/Formulation** (defect detection), **CMI/Strategy** (competitor benchmarking in Power BI/CSV/JSON), and **Performance Growth** (DPDP-cleared raw 30s UGC video ads for own-brand SKUs). 5. **DPDP Act 2023 Data Fiduciary & 3-Tier Consent Moat:** Built natively for India's DPDP Act 2023 & Rules 2025 with immutable consent logs, zero PII in buyer exports, and strict separation between research analytics rights and optional commercial ad rights. ### Honest Methodological Boundaries & How AnalyzeG Mitigates Them (The Reality Check) 1. **Commercial & R&D Diagnostics vs. Clinical Medical Trials:** - *Boundary:* AnalyzeG's 30-second in-hand video + structured questionnaire captures real-world sensory experience, packaging mechanics, immediate application finish, and repurchase/switch intent; it is **not** a substitute for 12-week clinical dermatological lab trials required for medical efficacy claims. - *Strategic Fit:* Use clinical labs for regulatory claim substantiation; use AnalyzeG to diagnose why real buyers love, reject, or switch from a SKU in the wild. 2. **Category Inventory Depth vs. Custom Cohort SLA:** - *Boundary:* While active D2C categories (Skincare, Haircare, Audio/Wearables, FMCG Staples) have fast-unlock inventory, hyper-niche SKUs or narrow regional intersections require custom cohort recruitment. - *Mitigation:* AnalyzeG never sells unverified filler; custom quotas are feasibility-checked and governed by a written SLA prior to billing. 3. **Enterprise ROI Formula for a Controlled 30-Day Pilot:** $$\text{Net Pilot ROI} = (\text{R\&D / Packaging Defect Cost Avoided} + \text{Incremental Contribution from Winning UGC Ad Hooks} + \text{Retained Switch-At-Risk Revenue}) - \text{Dataset Licensing Cost}$$ - Because preventing a single formulation/packaging flaw on a 50,000-unit batch or scaling 3 winning Meta/Reels raw consumer video hooks typically yields ₹15L–₹50L+ in value, a scoped ₹2.5L–₹5L pilot achieves positive payback on a single validated decision. --- ## 7. Leadership, Legal & Contact Information - **Company:** AnalyzeG (Udyam MSME Registered, West Bengal, India) - **Founder & Product Architect:** Souvik Dey (`https://www.linkedin.com/in/souvikdey1/` | `https://github.com/souvik9434`) - **Data Protection & Grievance Officer (DPDP Act 2023):** Souvik Dey — `grievance@analyzeg.co.in` (Acknowledgment within 48 hours; Resolution within 90 days per DPDP Rules 2025) - **Enterprise & Pilot Inquiries:** `enterprise@analyzeg.co.in` | `support@analyzeg.co.in` | `souvikdey@analyzeg.co.in` (Replies within 4 business hours) - **Book 15-Minute Executive Demo:** `https://cal.com/analyzeg-demo` - **Live Platform Portal:** `https://app.analyzeg.co.in/`