Dataset opportunity
Shippr — Mobility Event Dataset Opportunity
Moderate mobility event dataset held by Shippr, usable for Forecasting and Anomaly Detection.
Score
69
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
56%
Action
Data Sharing Agreement
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)
Global Mobility Data Analytics Platforms Market = $2.16 billion in 2024, CAGR 18.1%. [2, 5].
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.
Profile
Dataset profile
Type
Mobility Event Dataset
Modality
Time Series
Sector
mobility
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
Quant funds & demand-forecasting AI teams
Shippr holds a Mobility Event Dataset structured as Time Series data. This includes granular `api` logs, real-time event_streams of deliveries, precise geo_data, and related `transaction_data`, making it exceptionally suited for AI Forecasting applications like demand prediction and route optimization.
The global Mobility Data Analytics Platforms Market was valued at $2.16 billion in 2024 and is projected to grow at a CAGR of 18.1%. [2, 5] This high-growth market underscores the rarity and value of Shippr's multi-jurisdictional dataset. While access requires navigating GDPR-sensitive PII and clarifying data ownership across France, Belgium, and the UK, the asset's richness presents a significant opportunity for AI buyers. ⚠ Diligence (valuable data, access to negotiate): Data involves third-party delivery partners and end-customer PII (GDPR sensitive).; Ownership of routing data vs. client delivery instructions needs clarification.; Operates across multiple jurisdictions (France, Belgium, UK) with varying data regulations. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Evidence confirms Shippr owns a proprietary, high-frequency mobility event dataset, generated from over 5,000 commercial clients and its real-time delivery operations. This data offers a direct signal into urban logistics and commercial activity, enabling quant funds and AI teams to build powerful forecasting models for demand and supply chain dynamics. In a global mobility analytics market growing at over 18% annually, this dataset provides a rare, alpha-generating edge on economic activity in key European hubs.
See dimension details ↓- Dataset Specificity90
dominant 'event_streams', sector mobility, 3 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume58
4 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
real-time/streaming
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
fit for Forecasting
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand85
AI buyer demand for rich mobility data is strong, driven by the Mobility Data Analytics Platforms market's projected 18.1% CAGR for applications like forecasting. [2, 5]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
open/API access
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility0
medium difficulty, independent
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength74
4 evidence types, 4 hits
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License28
ownership=mixed, licensing=gdpr_sensitive
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
independent
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation56
2 data-appetite signals (2 types)
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
surplus=high — proprietary data beyond what's already monetised
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
✓ good target — Shippr is an excellent target as it's an SME operating a logistics platform for same-day B2B deliveries, which generates valuable mobility data as a by-product of its core operational business. Issues: The company is described as both a logistics provider and a tech company developing 'web-based delivery management solutions'. [9] It's crucial to confirm their; There is a separate company in India also named Shippr, founded in 2014, which could cause confusion. [7, 11] The target company is the Belgian one founded in 2
- Deep Qualification80
✓ pass — Shippr is a data_holder with a coherent Mobility Event Dataset generated as a by-product of its B2B delivery platform, but data ownership is mixed and access is complicated by GDPR and the role of independent delivery partners as separate data controllers.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
API access
The company's public API demonstrates structured data exchange with commercial ERP and e-commerce systems like Shopify, indicating a high-quality, automated data capture pipeline for transaction events.
Event streams
This confirms the existence of high-fidelity event streams from a network of over 5,000 companies, capturing real-time tracking data that is essential for time-series forecasting models.
Geospatial data
The dataset contains granular geo-data from major European economic hubs like Paris, Brussels, and London, offering a precise view of urban logistics patterns for location-based analysis.
Transaction data
Evidence points to detailed transaction data that categorizes deliveries by high-value sectors like medical and food wholesale, providing specific insight into commercial supply chain activity.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
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Shippr Mobility Event — a Moderate mobility event dataset (Time Series modality) in the mobility domain. Primary AI use-case: Forecasting. Market signal: Global Mobility Data Analytics Platforms Market = $2.16 billion in 2024, CAGR 18.1% (source: Global Market Insights Inc.). [2, 5]. Investment score 69.0/100 (confidence 0.56). Recommended action: Data Sharing Agreement.
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