Semantic Retail Presentation NRF 2011

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Semantic Retail – Competitive Advantage through Data

Since Retailers implemented Point of Sale systems data has been a driver of competitive advantage. Modern retailing operations are reliant on up-to-date and accurate information from Vendors, Logistics Partners, Websites and Customer Relationship Management systems. Over the last five years we have witnessed a data explosion: Customer Feedback, Social Media, RFID and Sensor Data. Analysis of these new data streams by a new breed of Data Scientists is the new competitive playing field. Semantic Retail is the process of exploring new sources of data, making them available and understanding their relevance to find new ways to approach old problems.
Semantic Retail Solution

Semantic enablement of a Retailer’s complex technology and data infrastructure is a significant undertaking and First Retail has joined forces with MphasiS to deliver a solution. The Semantic Retail solution combines MphasiS’ core skills of business analysis, contextual domain modeling, and process improvement with First Retail’s expertise in Machine Learning, Data Science, and Computational Linguistics. A proven methodology ensures phased projects with tightly scoped and well-defined deliverables will take the risk out of managing semantic technology and business innovation projects.

MphasiS core methodology proceeds from a holistic analysis of business touch points, contextual domain modeling, and business process modeling to arrive at an ROI-based data and process improvement plan. First Retail has an exceptional team of qualified and experienced research architects and engineers who will apply their cross-functional skills and intellect to your problems. The combined team will employ proven analysis techniques and off-the-shelf algorithms, but are eminently able to define unique new solutions. A holistic approach blends traditional analytic approaches with machine learning, high scalability, and effective use of human-supervised processes.

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Semantic Retail Presentation NRF 2011

  1. 1. Profi%ng  from  Seman%c  Web  in   RetailThe  new  tool  of  successful  prac%%oners  in  Customer  Experience  and  Opera%ons   Suresh  Nair  (MphasiS)  &  Gam  Dias  (First  Retail)  
  2. 2. Why  Seman%c?  •  Speed  &  quality  of  informa%on  =  business  agility  •  Exponen%al  increase  in  available  raw  data  •  Seman&c  technologies  map  raw  data  to  your   systems  •  Smart  applica%ons  react  to  new  informa%on  •  The  technologies  and  data  standards  are  already   available  
  3. 3. Application Screen from WATCHITOO
  4. 4. Application Screen from TOBI
  5. 5. Just browsing: vs. shopping: I want the dress that Daisy is wearing, and the song in the backgroundLet  me  learn  more  about   the  movie   OK. The cheapest I can Search  across   get both is $130 merchant   sites   Great,  go   for  it!   Find  the  soundtrack   Done. The song’s on your MP3 player. The dress will get here on Tuesday
  6. 6. Internet Studio DetailsCustomer’s IMDB MovieApplication Database Character Dress size Movie Site Data about the Merchant tie in Location customer Card # Ratings and Purchases Movie Reviews Context Dress Reviews Character Item code Merchant Product code Deeper Data Price Soundtrack # Network Social Graph Likes Posts
  7. 7. What  Cooking   Range?   Research Things I know… Key Decisions New Facts Induction range WILL IT MEET OUR NEEDS? CAN WE AFFORD IT? WILL IT MATCH THE DÉCOR? HOW WILL WE PAY FOR IT?Shortlist   New Decisions
  8. 8. What  Cooking  Range   What  COOKING   should  I  buy?  RANGE  should  I  BUY?   Public Sources What the Application knows about ME What’s  your   cooking  style?   What  décor   do  you  have?   Privileged Sources You  should   buy…  
  9. 9. Customer Aggregator Public information provider Application Application SEMANTIC  EXPOSITION   PRIVACY   PRIVACY   DECISIONS   PROMISE   Privileged data provider CUSTOMER   TRADITIONAL  DATA   &  CONTEXT   AD’  RATES   Merchant PREFERENCE   RELATIVE   PRICING & ADVERTISING DECISIONS   RANKING   PLUS MERCHANDISE PAYMENT   RECOMMEN-­‐ ACTIONS   ACCEPTED  AD  PRICING   DATIONS   PRICING  CHARTS   DECISION PUBLISHEDBEING MADE FACT
  10. 10. Vendor Supply Chain ProductsManagement What What Vendor When Why Details Where How Vendor Selection Process Quality Price Product Evolution Process Timing Segments Features Delivery In Flight Impact on Capability Shipments Product Lines Floods in Thailand Event Propagation
  11. 11. Seman%c  Web  Infrastructure   Inference The MagicConcepts Facts Query tools Federation Engine Box “Get the income (from Search “If a person the payroll agents, AI “Person” / “John was “Get all was born in system) of tools, “Date of born on Jan persons born 1971, their everyone backward Birth” 15, 1971” in 1971” age is 41” over 40 (from chaining the HR rules engines system) Semantic Web Use as input Exectute Queries QueriesConcept Query Inference AI Engine Engine Engines Model Queries Get results Conforms Fact To Store Store these As new facts Ontology Meta Data Markup
  12. 12. What’s  a  ‘Customer’   Sales: “A customer is ACME Co anyone who pays for Works Was Jan 15, a service or product for Born On 1971 PartyCompany Person Vendor Service: ACME John My Gadget “A customer is Co Smith Shop anyone who John Smith transacts (uses) a service or product Uses Provided by Pays for Product My Cool Tablet Audit / Risk / Compliance “A ACME Co customer is anyone John Smith who uses or pays for a service or product” Semantically Modeled Data Tailored Semantic Views
  13. 13. Conclusions  •  Speed  &  quality  of  informa%on  =  business  agility  •  Exponen%al  increase  in  available  raw  data  •  Seman%c  technologies  map  raw  data  to  your  systems  •  Smart  applica%ons  react  to  new  informa%on  •  The  technologies  and  data  standards  are  already   available  
  14. 14. THANK YOU Profi%ng  from  Seman%c  Web  in  Retail  Suresh  Nair  (MphasiS)  &  Gam  Dias  (First  Retail)  

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