Shopin project $1TN of purchase data transactions in the Retail Intelligence Data Engine data fabric by end of Q4

CTO Professor Georgi Gospodinov, estimates 98% of US ecommerce and 20% of brick and mortar retail will be represented in Shopin’s retail data engine in 2019, to finally bring an end to the data-power monopolies of mega-retailers. Live and in pilots with major retailers, ShopScore and R.I.D.E. provide retail brands a fighting chance in personalization, marketing and business intelligence.

| Source: Shopin Shopin

New York, Oct. 23, 2019 (GLOBE NEWSWIRE) -- Shopin’s continues on-track to decentralize and democratize the purchase data of mega retailers like Amazon and major retailer brands (including Macy’s, Coach, Michael Kors, Kohls, Finish Line and many more). 

The Retail Intelligence Engine™, delivers a never-before seen global view of retail’s purchase data by acquiring and reverse engineering purchase data from retail brand websites. The platform produces ShopScore™: a quantitative scoring model which measures the strength of influence each brand or product have over each and their category, whilst providing actual recommendations with predictions.

R.I.D.E.’s aggregated purchase data view of the world of retail delivers these recommendations programmatically to power a revolution in accuracy for personalization, advertising, marketing and apps. Retail brands, consultants, funds and traders also may leverage business intelligence reporting to make better quantitative-driven decisions and recommendations.

Georgi Gospodinov, Shopin’s CTO, projects that by the end of 2019, the data fabric will provide:


  • $1TN of purchase data transactions represented
  • 500MM connections between 350MM SKUs
  • 350K brands tracked
  • 98% of US eCommerce and 20% of brick-and-mortar covered


Along with these projections, Shopin reveals that over 30,000 lines of code have been written this year powering ShopScore and R.I.D.E., and includes an overview of their underlying technology stack.

Eran Eyal, Shopin’s CEO and founder shares, “You cannot deny the stats. Retail is in the midst of an apocalypse, and the lack of purchase data in scale as an oracle is at the core of not understanding the customer across baskets and retail brands. The customer has all access to retail, while the brands have limited access to the customer. Only mega-retailers like Amazon have enough purchase data and technical sophistication in enough scale. So much so that 35% of their revenue (17% of US eCommerce) is driven by this data. We are revealing all of retail’s purchase data, including theirs, to level the playing field. This will be the backbone of Retail 3.0.”

He further intimates “We are excited to have also launched our Series A equity funding round, and are looking forward to getting the fuel we push through to go to the next phase and beyond”.

Shopin invites technology-driven businesses, brands, and consultancy companies to partner and pilot with them, as well as investors to contact them here: https://www.shopin.com/contact-us/ 

About Shopin:

Shopin is dedicated to ensuring that timeless brands can remain timeless. We leverage groundbreaking artificial intelligence, blockchain, distributed networks and learnings from incredible ecommerce giants like Amazon and Alibaba to bring consumers closer to the brands they love, whilst enabling them to control their data more securely than ever before.

We have two core products: 

  1. ShopScore - aka Retail Intelligence Data Engine, where we have invented a patent-protected technical methodology to acquire and reverse engineer the purchase data insights of Amazon, eBay, Macys, Coach and many other major retailers to deliver unparalleled personalization and actionable recommendations for multiple divisions within the brand/ retailer.
  2. Shopin - aka ShopChain™, which is our longer term vision for consumers to own and control their purchase data to drive the most personal experience in every site, app and store, with a cryptocurrency to reward shoppers for engaging with ads, marketing and loyalty campaigns.

Shopin’s approach is to firstly create a massive data fabric decentralizing the retail industry’s purchase data and the underlying tools to interpret that data into actionable recommendations available through retorts and an API feed. 

This data fabric is the core of ShopScore, and of ShopChain’s ability to make recommendations and personalization at heretofore unforeseen accuracy and performance with even minimal data as the entire retail industry’s purchase data is driving the results.

ShopChain’s unique architecture which combines an asynchronous, asymmetrical DHT and Blockchain ensures that the retailer and shopper’s data is highly performant, yet incredibly secure.

The Lead named Shopin as retail’s “Moonshot of the Year” in the Leading 100. Shopin is the winner of BTC Miami (North American Bitcoin Conference), CoinAgenda Global and Bitcoinference 2018. 

Recent and relevant articles:

https://www.shopin.com/shopin-q2-2019-update-from-ceo-and-founder-eran-eyal/

https://www.shopin.com/the-lead-announces-the-leading-100/

https://wwd.com/business-news/technology/shopin-ride-citation-1203142258/

https://www.einnews.com/pr_news/492456032/professor-georgi-gospodinov-and-eran-eyal-s-shopin-retail-intelligence-data-engine-exceeds-4bn-purchase-transactions

http://retailtechpodcast.com/podcast/interview-with-shopin-ceo-eran-eyal-on-building-a-universal-shopper-profile-on-blockchain

https://www.einpresswire.com/article/488982338/blockchain-decentralization-and-retail-innovator-shopin-ceo-eran-eyal-delivers-2019-q2-update-to-participants

https://www.fierceceo.com/human-capital/eran-eyal-ceo-shopin-taking-retail

https://picante.today/blockchain-pr/2019/09/18/89185/shopin-welcomes-retail-and-fashion-industry-leader-randy-shifrin-as-vp-of-revenue/

https://www.globenewswire.com/news-release/2019/04/10/1802024/0/en/SHOPIN-LAUNCHES-THE-RETAIL-INTELLIGENCE-DATA-ENGINE-TO-BRING-AMAZON-LIKE-DATA-INSIGHTS-TO-THE-DECENTRALIZED-WORLD-OF-RETAIL.html

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                Shopin’s Retail Intelligence Data Engine / ShopScore technology is end-to-end proprietary, built on approximately 30,000 lines of code written by Shopin’s own subject matter experts and stored on our internal Github repository.

This team is lead by Professor Georgi Gospodinov (ex Walmart director of insights and analytics, professor of mathematics), Professor Richard Linares (M.I.T.), and Mark Plaskow.

Some of the proprietary components (many already patent protected): 
- Data acquisition* (massive scale scraping and ingestion) 
- Data lake
- Data fabric
- Taxonomy model with reinforcement learning
- Analytics suite with proprietary algorithms
- Visual AI with proprietary image decomposition and algorithms
- Visual AI auto-training framework*
- NLP with proprietary attribution and algorithms
- Universal catalogue model
- Hybrid platform architecture
- Customer preference model
- Recommendation engine API architecture

                
            
            
                R.I.D.E. is a patent-protected proprietary
artificial intelligence and data fabric, that
acquires, and reverse-engineers purchase data
from retailer websites.

Our visual artificial intelligence and NLP tools
detect products across the entire landscape of
this massive data fabric, revealing a view of how
every product is sold and which items or brands
are the influencers that trigger the sale.

Who’s in the fabric? Amazon, Macy’s, eBay,
Bloomingdales, Coach and many more
This data measurement is what we call
“ShopScore” 

                
            

        

    





        

            

                

                    
Mot-clé

                            
                                Retail Intelligence Data
                            
                            
                                Shopin
                            
                            
                                Eran Eyal
                            
                            
                                ShopScore
                            
                            
                                Retail data
                            
                            
                                Purchase Data
                            
                            
                                Artificial Intelligence
                            
                            
                                Georgi Gospodinov
                            

                



        


    

        
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