Conversation with Edahn Golan and Chris Casey on diamond pricing and data business in the age of AI
"Inside the Industry, they are telling us to be very AI oreinted, and at the same time not be very AI oriented!"
In this interview I discuss the diamond price data business with Edahn and Chris from Tenoris.
The conversatin is about the status quo and the future, more towards planning and preparing for the inevitable future of AI.
Background, and why I believe this is an important topic to cover.
The advent of AI is set to revolutionize how data, including product price information, influences commerce, particularly in high-value markets like diamonds. Here’s a breakdown of how data’s importance in commerce will grow and how product pricing might change with AI integration:
1. The Growing Importance of Data in Commerce
Enhanced Decision-Making: AI thrives on high-quality data. In commerce, data about consumer behavior, demand trends, inventory levels, and competitor prices allows businesses to make data-driven decisions. This results in more dynamic and responsive pricing strategies.
Transparency and Trust: Consumers increasingly demand transparency, especially for high-value products like diamonds. Data on the origin, quality, and ethical sourcing of products can now be stored and verified on blockchains, enabling trust in commerce.
Personalization at Scale: AI uses data to tailor shopping experiences, recommending products based on preferences, search history, and even social media activity. For diamonds, this might include offering specific cuts, carats, and colors based on a customer's unique profile.
2. Impact of AI on Product Price Information for Diamonds
Dynamic Pricing Models: AI algorithms analyze vast datasets, including market demand, competitor pricing, and historical trends, to dynamically adjust prices. For diamonds, this means prices might fluctuate more frequently, reflecting real-time market conditions.
Access to Real-Time Market Insights: AI can aggregate and analyze global data on diamond prices, creating a more interconnected and competitive market. Buyers and sellers will have access to real-time insights, reducing information asymmetry and narrowing price discrepancies.
Valuation Beyond the 4Cs: Traditional diamond pricing relies on the 4Cs (carat, cut, color, clarity). AI could incorporate additional factors, such as rarity, origin, and sustainability metrics, into valuation models, potentially redefining price benchmarks.
Consumer Empowerment: AI-powered tools, like virtual diamond advisors or apps, allow consumers to compare prices, quality, and value from multiple vendors instantly. This transparency could drive prices down in competitive segments while highlighting premium options for rare or unique stones.
3. Broader Shifts Driven by AI in Commerce
Predictive Analytics for Inventory: Retailers can predict trends and adjust inventories accordingly, preventing overstocking and enabling better price management for diamonds and other products.
Supply Chain Optimization: AI can optimize the diamond supply chain, reducing costs and potentially influencing retail pricing. For instance, automated sourcing and distribution might lower operational costs, which could be passed on to consumers.
Decentralization of Pricing Power: With AI, smaller jewelers or individual sellers could access advanced pricing tools, reducing reliance on traditional pricing authorities and creating a more democratized market.
Challenges and Considerations
Ethical Pricing Models: Over-reliance on AI for dynamic pricing might lead to practices perceived as exploitative if consumers feel prices are excessively manipulated based on willingness to pay.
Data Privacy Concerns: As AI relies on consumer data, ensuring that privacy is respected while providing personalized pricing and recommendations will be critical.
Regulatory Oversight: With AI-driven price adjustments becoming commonplace, there could be a need for regulations to ensure fair competition and prevent monopolistic practices.
Although applicable in every business and personal case, specifically for diamonds, the introduction of AI will likely lead to more precise and data-driven pricing, benefiting both consumers and businesses. The shift will require careful consideration of ethics, transparency, and fairness to ensure sustainable growth in this high-value industry.
Companies that provide diamond pricing services play a critical role in the industry by aggregating, analyzing, and disseminating data to create benchmarks for valuation. However, the rise of AI and real-time data from e-commerce and POS systems could fundamentally disrupt their traditional business models. Here’s a closer look at how such companies might adapt, the impact of AI on price data, and how they can build moats in the new age of AI:
1. Current Role of Diamond Pricing Companies
Aggregating Market Data: These companies collect data from various sources, such as wholesale markets, auctions, and retailers, to establish price indices for diamonds.
Providing Transparency: They help standardize diamond pricing by analyzing the 4Cs (carat, cut, clarity, and color), making it easier for buyers and sellers to transact with confidence.
Valuation Services: These companies offer independent valuations that serve as benchmarks for retailers, insurers, and investors.
2. Impact of AI and Real-Time Transaction Data
Shift Toward Real-Time Pricing: AI can analyze vast amounts of transaction data from e-commerce platforms and POS systems to provide real-time price updates, potentially making traditional aggregated pricing models less relevant. This shift could reduce reliance on static price guides.
Data Democratization and Commoditization: AI systems that scrape e-commerce data, analyze transactions, and provide pricing insights might make price information widely accessible, eroding the exclusivity and premium pricing power of traditional pricing companies.
Granular and Contextual Pricing: AI can assess contextual factors like regional demand, consumer preferences, and even macroeconomic trends, offering a more dynamic and localized perspective on pricing. This could challenge static pricing models that rely on averages or benchmarks.
Integration with Predictive Analytics: AI can forecast future trends, such as expected price changes due to market fluctuations, consumer demand, or geopolitical events. Pricing companies that lack predictive capabilities might struggle to remain competitive.
3. Building Moats for Price Data Companies
To remain relevant and competitive, diamond pricing companies need to adapt and establish strong moats around their business models:
A. Proprietary Data Sources
Exclusive Partnerships: Establish exclusive agreements with diamond retailers, wholesalers, and miners to access proprietary transaction data that cannot be easily replicated by AI models scraping publicly available information.
Specialized Data Metrics: Go beyond the 4Cs to incorporate data on provenance, sustainability, rarity, and even historical significance, creating more comprehensive pricing indices.
B. Advanced AI and Analytics
Develop Predictive Models: Build AI tools that forecast price trends, demand shifts, and supply chain disruptions, adding predictive insights to their pricing services.
Dynamic Benchmarking: Offer real-time, dynamic pricing benchmarks by integrating AI-driven data aggregation from multiple sources, including blockchain-based tracking of diamond origins.
C. Data Privacy and Security
Ethical Data Handling: Provide assurances of secure, ethical, and compliant data management, particularly as AI-driven tools raise concerns about data misuse.
Anonymized Aggregates: Develop tools that aggregate e-commerce POS data in a way that protects the anonymity of sellers and buyers while providing actionable insights.
D. Value-Added Services
Custom Pricing Dashboards: Offer subscription-based dashboards for retailers and buyers that integrate real-time data, predictive analytics, and custom reporting.
AI-Powered Valuation Tools: Create consumer-facing tools that leverage AI to evaluate diamond prices in real time, using their proprietary algorithms.
E. Thought Leadership and Trust
Brand Authority: Maintain a reputation as the most trusted source of pricing data by publishing insights, white papers, and research about the diamond industry.
Certification Services: Provide AI-augmented certification services to validate the quality and value of diamonds, reinforcing their role as industry arbiters.
4. Challenges and Considerations
Competition from Open Data Sources: As AI enables easy access to public e-commerce data, pricing companies must differentiate by offering higher-value insights.
Maintaining Relevance: They must balance their traditional expertise with the adoption of cutting-edge AI technologies to avoid being disrupted.
Regulation and Compliance: With more data being processed, compliance with data privacy laws (e.g., GDPR) and industry regulations will be critical.
Conclusion
The advent of AI and access to transactional data from e-commerce and POS systems will significantly impact diamond pricing companies. To survive and thrive, these companies must evolve into data-driven AI-powered entities, offering proprietary insights, predictive analytics, and personalized services. By leveraging exclusive data partnerships, investing in advanced analytics, and building trust in their expertise, they can create strong moats that ensure their relevance in the AI-driven future of commerce.
Price scraping
Price scraping at the wholesale level involves gathering pricing data from various sources where wholesale transactions occur, such as online platforms, B2B marketplaces, and direct vendor systems. This process, when combined with AI, becomes a powerful tool to analyze, compare, and predict pricing trends. Here’s how it works and what factors influence it:
1. Price Scraping at the Wholesale Level
A. Data Sources for Scraping
B2B Platforms: Wholesale diamonds are often listed on platforms like RapNet, IDEX, and others. Scraping tools can gather listing prices, inventory levels, and seller details.
Manufacturer or Supplier Websites: Some wholesalers publish price ranges or product details online. AI tools can extract this data systematically.
E-Commerce Integration: Wholesale pricing data may also be accessible through APIs or web scraping from e-commerce platforms designed for bulk buyers.
Marketplaces and Auctions: Pricing data from auction platforms or online marketplaces where wholesale transactions occur can provide valuable benchmarks.
B. Techniques for Scraping
Automated Web Crawlers: Tools that visit websites and extract structured or unstructured data about prices, inventory, and product attributes.
APIs (Application Programming Interfaces): If wholesalers offer APIs, these can provide more direct and reliable access to pricing and product information.
Natural Language Processing (NLP): For unstructured data, such as descriptions or pricing embedded in documents or communications, AI-powered NLP tools extract meaningful information.
Data Normalization: Once scraped, AI systems normalize data by accounting for variables like currency differences, units of measurement, and product categories.
C. Challenges in Scraping Wholesale Data
Access Restrictions: Many B2B platforms and supplier websites restrict data scraping through CAPTCHA, IP blocking, or login requirements.
Dynamic Pricing Models: Wholesale prices often depend on volume, client relationships, and negotiation, which might not be reflected in static listings.
Data Fragmentation: Wholesale markets are less standardized than retail, with pricing spread across private networks, contracts, and non-digital systems.
2. The Role of AI in Wholesale Price Scraping
AI enhances the process of price scraping at the wholesale level in several ways:
A. Pattern Recognition
AI can detect patterns in scraped data, such as seasonal trends, bulk discount structures, and correlations between product attributes (e.g., carat size vs. price).
B. Real-Time Insights
AI systems can continuously monitor price fluctuations and alert businesses to significant changes in the market.
C. Data Cleansing and Enrichment
AI algorithms can clean, deduplicate, and enrich scraped data by cross-referencing it with additional sources, ensuring accuracy and completeness.
D. Predictive Analytics
AI uses historical pricing data to predict future trends, helping wholesalers and buyers anticipate market shifts.
E. Anomaly Detection
AI can identify outliers in pricing, such as abnormally high or low prices, which might indicate errors, scams, or unusual market conditions.
3. Implications of Price Scraping for the Wholesale Market
Price scraping combined with AI could transform the wholesale diamond market in several ways:
A. Increased Transparency
Traditionally opaque wholesale pricing will become more transparent as scraped data provides benchmarks, reducing information asymmetry.
B. Pressure on Margins
With buyers gaining access to more competitive pricing insights, wholesalers may face pressure to adjust margins.
C. Dynamic Pricing in Wholesale
Wholesalers might adopt dynamic pricing systems based on real-time data trends to remain competitive and maximize profitability.
D. Faster Decision-Making
Wholesalers and buyers can make faster, more informed decisions using dashboards powered by scraped data and AI analytics.
4. Moats for Wholesale Players Against Scraping
To counteract the risks of price scraping eroding competitive advantages, wholesale players can establish barriers:
A. Access Control
Require verified logins or memberships for price visibility.
Implement CAPTCHA or anti-bot measures to restrict automated scraping.
B. API Monetization
Provide authorized data access through paid APIs, offering structured pricing information while retaining control.
C. Proprietary Pricing Structures
Adopt pricing models that are difficult to scrape or interpret, such as negotiation-based pricing or custom quotes.
D. Value-Added Services
Offer additional services, such as quality certification, origin verification, or financing options, to differentiate from competitors beyond price alone.
E. Exclusive Networks
Cultivate exclusive relationships and agreements with buyers and sellers to maintain an ecosystem where pricing transparency benefits members without undercutting the market.
5. Ethical and Regulatory Considerations
Data Ownership: Questions about whether scraped pricing data constitutes proprietary information may arise, especially for B2B platforms.
Compliance: Wholesalers and scrapers must adhere to privacy laws and anti-competition regulations, such as GDPR or similar frameworks.
How are you planning and preparing for the future where AI will be an integral part of your business and how you capture and utilize the data you need to run your business?
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Guests:
- Edahn Golan
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- Chris Casey
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- Tenoris https://www.tenoris.bi/
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