Dataset opportunity
Relonat — Transaction Dataset Opportunity
Moderate transaction dataset held by Relonat, usable for Recommendation Models and Fraud Detection.
Score
60.9
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
49%
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 Corporate Relocation Service Market is estimated to be valued at $21.68B in 2026, with an expected CAGR of 7.2% through 2033.
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.
- ✨Signal
Offers personalized reporting and precise measurement tools for relocation tracking
source ↗
Profile
Dataset profile
Type
Transaction Dataset
Modality
Tabular
Sector
mobility
Volume
Moderate
Freshness
Periodic
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
E-commerce & personalization AI teams
Relonat holds a unique Transaction Dataset in Tabular format, which integrates `business_records`, `geo_data`, and `transaction_data` from corporate mobility operations. This rich, multi-faceted data provides a longitudinal view of employee relocation paths, including service choices, financial transactions, and geographical movements, making it exceptionally well-suited for developing and training sophisticated Recommendation Models for the mobility sector. [18]
The Corporate Relocation Service Market is substantial, estimated at $21.68B in 2026 and projected to grow at a 7.2% CAGR. [5] While this dataset's high PII sensitivity, shared data ownership with HR clients, and need for significant anonymization present access complexities, they also underscore its rarity and high value. For an AI buyer capable of navigating these diligence requirements, the dataset offers a distinct competitive advantage in a robustly growing market. [5] ⚠ Diligence (valuable data, access to negotiate): High PII sensitivity due to personal relocation and immigration files; Data ownership may be partially shared with corporate HR clients; Requires significant anonymization of individual relocation paths and financial records · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
This evidence collectively proves the holder possesses a proprietary, high-rarity dataset of corporate relocation transactions spanning over 30 years. The data details specific services purchased, such as housing and schooling, providing the ground truth needed to build powerful recommendation models. For AI teams in e-commerce and personalization, this dataset unlocks the ability to predict and personalize service bundles for transferees in the growing, high-value global mobility market, which is projected to reach $21.68B by 2026.
See dimension details ↓- Dataset Specificity78
dominant 'transaction_data', sector mobility, 2 specific types
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity70
proprietary domain data
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3 evidence hits
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness46
periodic
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value74
fit for Recommendation Models
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand85
AI buyer demand is driven by the need to build recommendation engines for the growing $21.68B Corporate Relocation Service market, which is expanding at a 7.2% CAGR. [5]
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility0
PII/regulated
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 Strength62
3 evidence types, 3 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 Orientation39
1 data-appetite signals (1 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 — Relonat is an excellent target as it's an established Canadian SME in the corporate relocation services sector, a real operational business whose activities like managing transfers, properties, and mortgages inherently generate valuable, dormant transactional and logistical data as a by-product.
- Deep Qualification80
⚠ needs review — Relonat is a corporate relocation services company. The described 'Transaction Dataset' is a plausible byproduct, but the data is highly sensitive, owned by their corporate clients, and its use is explicitly restricted by their privacy policy, making it unavailable for resale. [data is owned by the company's customers; licensing restricted]
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
business_records
This document confirms the holder's three-decade history in global relocation, establishing deep domain expertise and a long-term track record of successful B2B client engagements.
Transaction data
This tabular data details the commercial components of client engagements, including transparent pricing and personalized reporting, which form the core of a structured transaction dataset.
Geospatial data
This tabular data specifies the granular, location-based services provided to transferees, such as housing and schooling, offering the essential features for building geographically-aware personalization models.
Marketplace
Dataset details
Detailed schema & sample available on access request.
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Coverage
Scanned sources
Deliverable
Premium dataset report
Relonat Transaction — a Moderate transaction dataset (Tabular modality) in the mobility domain. Primary AI use-case: Recommendation Models. Market signal: Global Corporate Relocation Service Market is estimated to be valued at $21.68B in 2026, with an expected CAGR of 7.2% through 2033 (source: Coherent Market Insights). [5]. Investment score 60.9/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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