Dataset opportunity
Zendbox — 事件流数据集机会
Zendbox 持有的海量事件流数据集，可用于预测和异常检测。
Score
72.9
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
72%
Action
数据共享协议
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market size (indicative estimate)
全球供应链分析市场规模在 2022 年估计为 61.2 亿美元，预计从 2023 年到 2030 年的复合年增长率为 17.8%（来源：Grand View Research）。[3]
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Concrete evidence this company actively cares about data — why it's ripe for the deal room.
- 📦Data product
Zendportal：用于实时库存和订单跟踪的专有技术
source ↗
Profile
Dataset profile
Type
事件流数据集
Modality
时间序列
Sector
零售
Volume
大
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
混合所有权 — GDPR 敏感（PII 审查）
Buyer persona
量化基金和需求预测人工智能团队
Zendbox 持有一个全面的事件流数据集，详细说明了电子商务运营。这些时间序列数据包括详细的`交易数据`、物流的`行业数据`以及订单履行的`事件流`，使其非常适合开发复杂的预测模型，用于需求、承运商绩效和退货率。
该数据的价值在全球供应链分析市场中得到凸显，该市场在 2022 年的价值为 61.2 亿美元，并预计到 2030 年将以 17.8% 的复合年增长率增长。[3] 尽管存在 PII 处理和专有元数据等访问复杂性，但该数据集在承运商绩效和退货方面的独特跨品牌基准提供了稀有的竞争情报资产，证明了协商访问的合理性。⚠ 尽职调查（有价值的数据，可协商访问）：处理 PII（消费者送货地址），需要严格匿名化；运营物流元数据是专有的，但特定的订单内容属于电子商务客户；有价值的跨品牌承运商绩效和退货率基准。· 公司：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
公开证据证实 Zendbox 拥有其零售履约运营产生的专有、高流量事件流数据集，每年详细记录超过 300 万个订单。这些丰富的时间序列数据捕获了整个电子商务生命周期，从库存分析和当日发货到退货。对于量化基金和人工智能团队来说，该数据集是构建和训练复杂需求预测模型的稀有资产。在一个预计年增长率为 17.8% 的供应链分析市场中，这些数据在预测消费者行为方面提供了显著的竞争优势。
See dimension details ↓- Dataset Specificity90
占主导地位的'事件流'，零售行业，3 种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume92
7 个证据命中，明确提及数据量
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式传输
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适合预测
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand90
人工智能买家需求极高，这得益于在复合年增长率为 17.8% 的供应链分析市场中对预测性洞察的迫切需求。[3]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/受监管
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
低难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength100
6 种证据类型，7 次命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
所有权=混合，许可=gdpr_敏感
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation39
1 个数据胃口信号（1 种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，5 个近期外部信号 — 超出已货币化的专有数据
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit100
✓ 良好目标 — Zendbox 是一个理想的目标，因为它是一家中小型物流/履约公司，其核心业务的副产品是生成专有运营数据，并且它似乎不将这些数据或派生情报作为单独的产品出售。
- Deep Qualification90
⚠ 需要审查 — Zendbox 是一个物流服务提供商，而不是数据销售商；虽然它拥有连贯的事件流数据集，但这些数据明确归其客户所有，并且是 GDPR 敏感的，因此直接获取不太可能。[数据归其客户所有]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
press
- “<p>L’un se définit comme éditeur de solutions de supply chain planning et de revenue growth management, l’autre comme un spécialiste de l’orchestration des flux supply chain en temps réel (notamment via sa technologie d’OMS, Order Management System) : les éditeurs français Sunstice et Kbrw viennent d’annoncer un partenariat visant à créer une boucle de synchronisation entre […]</p> <p>L'article <a href="https://supplychainmagazine.fr/nl/2026/sunstice-et-kbrw-rapprochent-planification-et-execution-via-leurs-agent-ia/">Sunstice et Kbrw rapprochent planification et exécution via leurs agent”
- “<p>FedEx reported strong quarterly results, driven by growth in package volumes and yields as the company focuses on high-margin logistics business. </p> <p>The post <a href="https://www.freightwaves.com/news/fedex-boost-revenue-behind-premium-parcel-freight-volumes">FedEx boost revenue behind premium parcel, freight volumes</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
- “<p>U.S.-based Americold plants a flag in Canada as part of a rail-maritime cold chain integration with CPKC and DP World.</p> <p>The post <a href="https://www.freightwaves.com/news/rail-ocean-access-backs-new-americold-cold-chain-facility-at-eastern-canada-port">Rail, ocean access backs new Americold cold chain facility at eastern Canada port</a> appeared first on <a href="https://www.freightwaves.com">FreightWaves</a>.</p>”
Event streams
这是来自实时运营分析的高频时间序列数据，跟踪关键的履约事件，如库存水平和发货速度，这对于预测建模至关重要。
User-generated content
这是来自客户评论的非结构化文本，提供了情绪数据的来源，可以与销售速度和运营绩效相关联。
Knowledge base / docs
这些文本数据详细说明了客户特定的包装和发货定制规则，提供了用于模拟运营复杂性和品牌级别需求的特性。
Transaction data
这是大规模表格数据，证实了去年超过 300 万笔历史交易，提供了进行稳健模型训练和回测所需的数量。
Data-volume signal
这些多模态数据定义了产品目录，涵盖超过 100,000 种不同的快速消费品，证明了该数据集在构建可泛化的预测模型方面的广度。
Industrial data
这些时间序列数据捕获了逆向物流事件，提供了关于产品退货的稀有信号，这对于准确建模净需求和盈利能力至关重要。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
JSON, Time Series
License
One-time license for internal use, with potential restrictions on redistribution and specific analytics applications. PII handling protocols are critical.
Personal data
Contains PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This proprietary, real-time event stream dataset offers high-value insights into e-commerce fulfillment and logistics, directly supporting the rapidly growing global supply chain analytics market. Its rarity, large volume, and unique cross-brand benchmarks drive significant demand for advanced forecasting applications.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Zendbox Event Stream — a Large event stream dataset (Time Series modality) in the retail domain. Primary AI use-case: Forecasting. Market signal: Global Supply Chain Analytics Market size was estimated at USD 6.12 billion in 2022, projected to grow at a CAGR of 17.8% from 2023 to 2030 (source: Grand View Research). [3]. Investment score 72.9/100 (confidence 0.72). Recommended action: Data Sharing Agreement.
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