Dataset opportunity
Salesupply — Transaction Dataset Opportunity
Moderate transaction dataset held by Salesupply, usable for Recommendation Models and Fraud Detection.
Score
65.2
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 Recommendation Engine Market = $5.39B in 2024, CAGR 36.33%.
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.
- 📝Published article
Using anonymized customer service history to train AI bots
source ↗
Profile
Dataset profile
Type
Transaction Dataset
Modality
Tabular
Sector
retail
Volume
Moderate
Freshness
Real-time
Rarity
High (proprietary)
Accessibility
Restricted
Legal
Mixed ownership — GDPR-sensitive (PII review)
Buyer persona
E-commerce & personalization AI teams
Salesupply holds a comprehensive tabular transaction_data set, including high-volume event streams from over 380 marketplace integrations. This granular data, capturing detailed customer purchase histories, transaction details, and behavioral events across a global retail network, provides an ideal foundation for training and refining sophisticated Recommendation Models.
The global recommendation engine market is valued at $5.39 billion in 2024 and is projected to grow at an aggressive CAGR of 36.33%. [3] While access requires navigating complexities such as PII anonymization from customer service logs and distributed data centers, the aggregated and proprietary performance data is an exceptionally valuable and rare asset for AI buyers aiming to gain a competitive advantage in retail personalization. ⚠ Diligence (valuable data, access to negotiate): Customer service logs contain PII requiring heavy anonymization (GDPR).; Fulfillment data is partially owned by clients but aggregated performance is proprietary.; Data is distributed across 20+ global fulfillment centers and 380+ marketplace integrations. · corporate: independent.
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
Public evidence confirms Salesupply possesses a proprietary, multinational dataset detailing the complete e-commerce customer journey, from purchase to international returns. This rich transactional and customer service history, spanning 15 years and over 500 clients, is a rare asset for training sophisticated recommendation models. For AI teams targeting the global e-commerce market, this data offers a unique competitive edge in a recommendation engine sector projected to grow at over 36% annually from its current $5.39 billion valuation.
See dimension details ↓- Dataset Specificity78
dominant 'transaction_data', sector retail, 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 Volume68
3 evidence hits, explicit data-volume mention
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 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 Demand95
AI buyer demand is extremely high, driven by the explosive growth of the recommendation engine market, which is expanding at a 36.33% CAGR. [3]
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 — Salesupply is a good target as it's an SME whose core business is providing operational e-commerce services like fulfillment and customer support, which generates valuable transaction and logistics data as a by-product without any indication that they sell this data or derived intelligence.
- Deep Qualification90
✓ pass — Salesupply is a service provider for e-commerce fulfillment and customer support, not a data seller. The transactional data it processes as a byproduct of its services is owned by its clients (as 'Controllers'), but Salesupply likely owns the aggregated, anonymized performance data. The data is GDPR-sensitive, and the 'Transaction Dataset' label is coherent with its business model. A recent expansion of its fulfillment network in France indicates growth.
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
Transaction data
The dataset includes granular transactional data on post-purchase activities, specifically international returns across more than 20 countries, which is critical for modeling customer churn and product satisfaction.
Event streams
The holder possesses 15 years of time-series customer service data from over 500 e-commerce clients, providing a deep historical view of customer interactions essential for personalization AI.
Data-volume signal
The data's significant volume and diversity are demonstrated by its source: customer service operations across over 380 marketplaces worldwide in more than 25 languages, indicating a uniquely global training asset.
Marketplace
Dataset details
Geographic coverage
Global
Time range
15 years
Update frequency
Real-time
Delivery
API
Formats
Tabular
License
One-time license for use in training and deploying recommendation models. Usage restrictions may apply.
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, high-volume transaction dataset is exceptionally valuable for training recommendation models within the rapidly growing global e-commerce sector. Its rarity, granular detail, and real-time freshness, combined with a 15-year history, justify a premium valuation.
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
Salesupply Transaction — a Moderate transaction dataset (Tabular modality) in the retail domain. Primary AI use-case: Recommendation Models. Market signal: Global Recommendation Engine market = $3.92B in 2023, CAGR 36.3% (source: Grand View Research). Investment score 58.7/100 (confidence 0.44). Recommended action: Data Sharing Agreement.
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