Luxury Watch Market Pricing Intelligence Engine

@kamran2012

This looks like a great fit, I will build your pricing intelligence engine with marketplace scraping from Chrono24 and eBay, data normalization by reference number, condition, and year, a pricing engine that calculates market floor, median, and liquidity per SKU, a WhatsApp listing parser for incoming dealer messages, and an internal dashboard with margin simulation and alerts. For the WhatsApp message parser, I will use an LLM-based extraction pipeline that handles inconsistent dealer shorthand (e.g., "126710BLNR mint box papers 12.5k") and maps it to structured fields with a confidence score that flags uncertain parses for manual review. Questions: 1) How frequently should the marketplace scraping run - hourly, daily, or on-demand? 2) Do you want the Slack and email alerts triggered by specific margin thresholds you define, or auto-detected? Looking forward to your response. Best regards, Kamran

@muneeb6767

I can architect your full Pricing Intelligence & Automation Engine—marketplace aggregation, data normalization, rule-based pricing logic, inbound listing parsing, and a real-time internal dashboard. Using Python pipelines, structured storage, and clean business-rule modeling, I’ll build a scalable system that reduces manual comp checks and improves margin control with data-driven buy/list recommendations.

@Azursol

Hello,! I’m excited about the opportunity to help with your project. Based on your requirements, I believe my expertise in Python, Web Scraping aligns perfectly with your needs. How I Will Build It: I will approach your project with a structured, goal-oriented method. Using my experience in Python, Web Scraping, Data Mining, Market Research, Business Intelligence, Data Visualization, Data Analysis, Automation, Database Management, I’ll deliver a solution that not only meets your expectations but is also scalable, efficient, and cleanly coded. I ensure seamless integration, full responsiveness, and a strong focus on performance and user experience. Why Choose Me: - 10 years of experience delivering high-quality web and software projects - Deep understanding of Python, Web Scraping and related technologies - Strong communication and collaboration skills - A proven track record — check out my freelancer portfolio. - I’m available for a call to discuss your project in more detail - Committed to delivering results on time, every time Availability: I can start immediately and complete this task within the expected timeframe. Looking forward to working with you! Best regards, Ali Zahid India

@AZTURK1

Hi there, I understand you need an automated Pricing Intelligence & Automation Engine for secondary luxury watches. I can design a production-ready pipeline to ingest marketplace data, run pricing intelligence, and feed a sourcing dashboard with parsed dealer listings. - Marketplace ingestion & normalization (Chrono24, eBay) with outlier removal and historical store - Pricing Engine: market floor/median, liquidity, delta vs cost, rule-based buy/list recommendations - Messaging parser for dealer inputs (WhatsApp) and auto-push to sourcing dashboard + alerts - Internal dashboard: SKU search, real-time comps, margin simulation, Slack/email alerts Skills: ✅ Market Research ✅ Python ✅ Web Scraping ✅ Business Intelligence ✅ Database Management ✅ Data Analysis and Automation Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I can start immediately and deliver a phased MVP in 6-8 weeks with clear milestones. Which marketplaces beyond Chrono24 and eBay must be included initially, and do you have sample dealer WhatsApp messages for parser training? Best regards,

@Joelite17

I have experience working on data-driven systems, including building structured pipelines, handling data normalization, and implementing logic-based workflows. I am particularly interested in the Marketplace Data Aggregation and Pricing Intelligence components of your project, as they involve extracting actionable insights from large datasets and translating them into real-world business decisions. In addition, I have worked with tools and frameworks that support automation and backend development, enabling efficient data processing and system integration. I am comfortable working with APIs, web scraping techniques, and building structured data models that support analytics and dashboarding. Hence i am a good fit for this huge project.

@Shrutibimpl

Hello, I will design and build a centralized pricing intelligence platform to automate your watch trading operations. I will develop a scraping module using a popular automation library to ingest data from major marketplaces, applying a normalization engine to clean references and conditions while filtering outliers. For intelligence, I will implement a rules-based engine to calculate medians and liquidity, recommending prices based on your margin and age thresholds. I will also create a custom NLP parser to translate shorthand messages from dealers into structured data for your dashboard. The final dashboard will feature real-time market comparisons, margin simulators, and automated alerts for your sourcing team. This approach will significantly reduce manual checks and centralize your data into a single source of truth. 1) Which specific database technology do you prefer for storing historical pricing trends? 2) Are the dealer messages from WhatsApp coming through an official API or a specific export format? 3) What is the highest projected number of unique watch references the system needs to track? Thanks, Bharat

@devanshkhandekar

Hi,I’m a seasoned Applied Data Scientist with more than 6 yeras of experience.I can build for you a pricing/market intlligence pipeline: data ingestion, normalization, outlier control, forecasting signals & rule-based decision engines with auditability My Approach: *Data aggregation: ingest Chrono24/eBay feeds(scrape or API),store raw snapshots,then normalize to a canonical schema: ref #, model, condition, year, region, currency, seller rating, box/papers & listing quality flags. Deduplicate, standardize FX & maintain historical time series per SKU *Data quality: robust filtering (IQR/MAD), rule-based sanity checks & source credibility scoring to remove low-quality comps while keeping traceability *Pricing intelligence: compute market floor/median, dispersion, days-on-market proxies & liquidity/velocity tiers per reference. Track deltas vs target cost/margins, inventory age & apply transparent business rules to recommend buy/list prices *Message parser: extract structured fields using regex + lightweight NLP, normalize currencies & set status; push into sourcing dashboard with confidence + manual review *Dashboard + alerts: SKU search, comp set inspection, margin simulator & alerting for price drops/opportunities Relevant experience * Built competitive pricing systems :scraped marketplaces -> normalized SKUs -> outlier control -> pricing recommendations * Developed entity resolution pipelines (product identifiers -> canonical ref ID) & time-series trend/velocity indicators

@MQamar123

With nearly a decade of experience in software development, I am well-placed to deliver a highly effective internal Pricing Intelligence & Automation Engine for your luxury watch market platform. My proficiency across multiple languages and frameworks including Python -- a powerful tool for web scraping -- and my knack for leveraging technology to solve complex problems makes me an excellent fit for this project. In past projects, I have successfully developed data aggregation and analysis systems, similar to what you require. My coding arsenal combines the likes of Node.js and React.js to build scalable and data-intensive applications. A testament to these abilities is my expertise in Artificial Intelligence projects where I utilized Python to create sophisticated solutions. To add, I consider myself exceptionally skilled in identifying business pain points and translating them into optimized solutions, which aligns perfectly with your objective of automating pricing decisions using data-driven insights. My thoroughness in removing outliers coupled with my ability to calculate market indicators precisely provide the ideal foundation for your Pricing Intelligence Engine. Rest assured, choosing me would enable faster pricing, data-driven buy and listing decisions as you envision.

@jonhw5

Hello, thanks for posting this project. Your goal to build an automated Pricing Intelligence & Automation Engine for luxury watch sourcing is clear and exciting. I have hands-on experience designing robust data aggregation pipelines, advanced pricing engines, and real-time dashboards using scalable modern stacks. My work often involves custom scraping/ingestion, data normalization, and rule-based price calculations, with a focus on security, accuracy, and actionable insights. I understand the importance of reliable data sources, latency, outlier removal, and flexible business rule logic in high-value, fast-moving sectors like luxury goods. I have architected market comparison tools with alerting, structured sourcing flows, and integrations with communication platforms for deal intake and team notifications. Throughout, I prioritize maintainability, transparency, and margin optimization. Could you please share which tech stack or cloud services you anticipate leveraging for this system? Looking forward to discussing further.

@growwithgdp

✅ Design scalable data ingestion layer using official APIs/approved feeds, with SKU normalization, outlier filtering, and historical price storage. ✅ Build pricing intelligence engine calculating market floor, median, liquidity, margin thresholds, and rule-based buy/list recommendations. ✅ Develop structured messaging parser (e.g., WhatsApp Business API) to extract dealer listing data into sourcing dashboard. ✅ Create internal dashboard with SKU search, market comparison, margin simulation, and Slack/email alerts. ✅ Deliver centralized pricing intelligence database with clean architecture, documentation, and scalable automation strategy.

@nin1

With expertise in data aggregation and automation, I can develop a robust pricing intelligence engine tailored for your luxury watch market. Leveraging real-time marketplace insights, I’ll ensure accurate and faster pricing decisions. Pro tip: Implementing AI-driven adjustments can further enhance your pricing precision and increase profitability. Let's discuss!

@Hemantt841

I was thrilled to come across your project for the Luxury Watch Market Pricing Intelligence Engine. The idea of automating competitive price monitoring and rule-based pricing decisions in the secondary luxury watch market really caught my attention. With over 7 years of experience in software development, I have worked on similar projects that required data aggregation, pricing intelligence, and automation. Here's how I would approach this project: - Develop a robust Marketplace Data Aggregation Layer using web scraping tools like Beautiful Soup and Python to ingest listings from platforms like Chrono24 and eBay. - Implement a Pricing Intelligence Engine using machine learning algorithms to calculate market indicators and recommend optimal buy and list prices. - Create a Messaging Listing Parser to extract structured data from dealer listings and automate data input into a sourcing dashboard. - Design an Internal Dashboard with real-time market comparison and margin simulation features. In a recent project for a similar industry, I built a pricing intelligence system that reduced manual comp checking by 85% and improved pricing accuracy by 20%. The outcome was a centralized database that streamlined pricing decisions and increased profitability. I'm curious to know more about your vision for this project. How do you envision the integration of the Pricing Intelligence Engin

@cemguv

Hi — this is exactly the kind of data-driven system I specialize in. I’ve built similar pricing intelligence and automation pipelines combining scraping, normalization, and rule-based engines for marketplaces. How I’d approach your system: Scalable scraping layer (Chrono24, eBay) with proxy handling, deduplication, and historical storage Data normalization engine (reference matching, condition scoring, outlier filtering) Pricing engine with margin rules, liquidity signals, and buy/list recommendations WhatsApp ingestion pipeline using WhatsApp Business API + NLP parsing for dealer messages Internal dashboard (real-time comps, alerts, margin simulation) Clean architecture built for scaling (Python + Node + PostgreSQL + Redis) I focus on accuracy, speed, and automation, ensuring your sourcing team can act instantly with reliable pricing insights. I can deliver a robust MVP that’s production-ready and easy to extend as your data grows. Let’s build this right

@ziadhosaaam

Hello, I’m specialized in building pricing intelligence systems and data-driven automation engines, including marketplace aggregation, normalization pipelines, and rule-based decision layers. I’ve worked on similar setups involving scraping/ingesting marketplace data, cleaning and standardizing listings, storing historical price trends, and building engines that calculate floor/median pricing with margin and velocity logic. I’ve also implemented structured parsers for inbound messages (e.g., WhatsApp) that extract and normalize pricing data into dashboards with alerts and simulations. Please confirm your preferred stack (Python + FastAPI, Node, etc.), hosting environment, and whether you already have internal cost/margin rules documented, and I’ll outline the architecture and timeline for an MVP. Best regards, Ziad

@ModN42

Hi, This is exactly the kind of system I like building: practical, data-driven, and focused on real trading decisions. I can help you design and build a pricing intelligence engine that: Pulls and normalizes watch listings from sources like Chrono24 and eBay Cleans the data (outliers, bad sellers, inconsistent refs) and stores price history Calculates real market signals (floor, median, liquidity, trends) per reference Applies your pricing rules to suggest buy and list prices automatically Parses dealer messages (WhatsApp-style text) into clean, structured sourcing data Exposes everything in a simple internal dashboard with alerts when opportunities appear I work mainly in Python for scraping, parsing, and pricing logic, with a clean database layer and a lightweight dashboard (tables, filters, margin simulations). The focus would be reliability, transparency of rules, and easy iteration as your strategy evolves. Happy to start with an MVP, validate on a subset of SKUs, and then expand. If you want, we can also phase this so value shows early instead of waiting for a “big bang” build. Let me know how you’d like to approach the first phase.

@sinchana0123

Hello, I can build your Pricing Intelligence & Automation Engine using Python and a scalable backend architecture. The system will include marketplace data aggregation, structured normalization, historical price storage, and a rule-based pricing engine to calculate market floor, margins, and optimized buy/list recommendations. I will also implement a dealer message parser for structured intake and an internal dashboard with SKU search, margin simulation, and Slack/email alerts. The solution will be modular, well-documented, and designed for long-term scalability. I suggest a phased rollout over 3–4 weeks. Ready to start immediately.

@Iitiancraft

Hi, This is a sophisticated data engineering + pricing automation problem — and I’d approach it as a structured, scalable intelligence system rather than a scraping script. I can build a Python-based engine that: ✔ Aggregates & normalizes marketplace listings (Chrono24, eBay) ✔ Cleans outliers & low-quality sellers ✔ Stores historical pricing for trend & liquidity analysis ✔ Calculates floor/median + margin simulations ✔ Applies rule-based pricing logic (min margin %, velocity, aging) ✔ Parses inbound dealer listings (WhatsApp format → structured data) ✔ Feeds a real-time internal dashboard with alerts Tech Stack Suggestion: Python (scraping + rule engine), PostgreSQL, FastAPI backend, React dashboard, Slack/email alert integration. Estimated Timeline: 6–8 weeks (phased delivery) This will reduce manual comp checks dramatically while improving pricing precision and margin discipline. I’d be happy to outline a phased architecture before we begin.

@ibrahiman58

Hello, I’ve reviewed your project and clearly understand your goal to build a Pricing Intelligence & Automation Engine for the secondary luxury watch market. The platform will automate competitive price monitoring, ingest and normalize marketplace listings, parse inbound dealer data, and provide rule-based pricing recommendations through a centralized, real-time dashboard. With several years of experience developing data-driven automation and analytics systems, I can deliver a secure, scalable solution using Python, Django/Flask, and relevant scraping and data processing tools. I’m confident in implementing your full pipeline—from data aggregation to margin simulations and alerting—ensuring reduced manual effort and faster, accurate pricing decisions. Looking forward to discussing your approach and aligning on the execution plan. ✅ Best Regards.

@domingof3

I’ve built data-driven pricing and intelligence systems with marketplace scraping, normalization pipelines, and historical trend storage. I’m experienced with automated ingestion (eBay/Chrono24-style sources), outlier filtering, SKU-level analytics, rule-based pricing engines, and NLP parsing for unstructured inputs like WhatsApp dealer feeds. I can deliver a scalable dashboard with alerts and margin simulations. I’m ready to begin immediately and can work efficiently within your deadline. I’d appreciate the opportunity to discuss how I can help move your project forward.