MIT Sloan Linked Data Ventures - Jay Myers


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  • Intro
  • This is one of my favorite quotes – one of the reasons we adopt and promote new technologies is to solve problems.I’m here to talk about real world business problems that we as a retail organization run into every day and how we are using these technologies to solve problems.This will be the theme of my talk today –problem solving using Linked Data and Semantic WebI will be highlighting two real world solutions we have right now using data to help address business needsI’ll then go into my vision of how using linked data address a number of other challenges we as a retail enterprise are facing
  • Best Buy has a large presence in the USOver 1110 physical brick and mortar store locations hereOf course, with the advent of ecommerce, our business models have been shifting toward a blended customer experience – in store and onlineNow 67% of US consumers used the Net at some point in their buying processIt would make sense that consumers would want to be informed about where to touch, look and feel purchase desired products, and hopefully purchase While we have been trying to maximize the visibility of our products on the web, we haven’t done the same for our brick and mortar stores.These stores are amazing little bundles of rich data – with unique attributes like store name, basic location data addresses, all the way up to more advanced but important attributes like geo coordinates. Many of the stores have events specific to their location that benefits customers (sales, learning opportunities) and stores (foot traffic).Looking around the industry, retailers tend keep their valuable store location data locked up in data silos – we typically refer to them as store locators…an application where you have to enter a zip code or postal code to get a data result back.Back in 2008 we were trying to solve this problem – trying to establish a home on the web for our retail stores.After a couple of failed attempts, we decided to establish blogs for each retail store and give the ability for every store to publish their own data.
  • So we created a network of over 1100 store blogs and built some pretty basic tools for collecting dataWe asked our employees to help us out, populating valuable location data, event data, random dataAnd we made this semantic and linked through a basic templating system using the open source blogging platform wordpressThe end result are store blogs that are visible to humans and machines Store sites are available for human consumptionAdditionally, if you look in the source code, you’ll see all the blogs produce RDFa for better machine readability, and non human-readable RDF/XML for linking stores into a rich data network, making their data available to queryThis project has some interesting outcomes. We’ve achieved a 95% adoption rate at the store level – it seems that employees at our local store love to share their dataWe also noticed a sharp increase in organic search traffic to these sites. This was not intended, but was a positive result.Every store has a unique URI – a true home on the web
  • Using this same toolset, we are addressing the problem of open box and returned itemsAre you all familiar with open box products? Open box product are fully functioning products with significant markdownsRepresents a significant challenge to our stores – some have as many as 500 open box products in store – but we haven’t provided any visibility to these products on the web.On average return rate for electronic devices is 11-20%Cost to the CE industry: $13.8 billion (2007)We ask our store employees to fill out a simple form, providing the SKU, markdown price, and reason the item was returned.Then using wordpress and pinging our open catalog API Remix, we are able to publish localized, unique, data-rich product detail pages that make these products visible.Again, if you look in the source code, you’ll see these pages are coded using RDFa for better machine readability, and non human-readable RDF/XMLSeveralreasons why I think these two examples are significant:1. Simple2. Organic3. Harnessing human-generated input and turning it into machine-readable data. We made semantic web accessible to everyday store employees4. Removed valuable store data from silos publishing it in an open format for everything to consume
  • I want to shift gears and explore a more global vision of where linked data and semantic web can take our business Best Buy is made up of many different parts – stores, employees, products, etc.Take these parts and pieces as a whole, and we have millions, maybe even billions of data touchpoints out there, where we’re either publishing and displaying data (store information, product information) or receiving and consuming data (forums, ratings and reviews, twitter/ facebook/ social)Businesses can take this wealth of data and utilize it to improve our business, our bottom line and our customer’s lives
  • I’ve started to challenge my colleagues on both the business and technical side to start looking at all these different entities from a data perspective – imagine all this data in a global graphImagine linking all of these entities into a huge global graph of dataIt adds up incredibly fastThis slide is just a small representation of the vast amount of data we can start using to solve additional issues plaguing our retail industry todayLet’s look at some examples of current problems and what we might do to address these problems using data
  • It’s no secret that margins on many retail products are extremely thinIn order to stay profitable, retailers have to sell other products in addition to the primary product (attach other products)Ecommerce sites struggle with basket sizeIn a recent article, Alexander Gruensteidl talks about “surviving the future of retail”. He finds that retailers aren’t building the types of user experiences that engage customers in a way that promotes deeper product discovery and curiosity. While much of this article focuses on customer experience design, there is a definite tie in to information and data that cannot be ignoredI believe we can promote deeper discovery of products, goods and services by utilizing linked data
  • We can start developing deep relationships between primary and secondary products with open and linked dataTake a popular product like a netbook. To drive traffic into the store or web site, retailers will offer primary products at negative margin in hopes of being able to attach secondary products that have positive margin.Many times we do not provide adequate connections from primary to secondary products to promote these attachmentsWe should be exploring relationships between products to drive sales and increase attachment rates while providing the complete solution to the consumerWe could be providing pathways of exploration -- going beyond the expected into products that the consumer may not have exploredThink of it as “degrees of product separation” going from the obvious to the less expected, connecting all products through data and their inherent relationshipsThere are several benefits:Offering paths of exploration and choice creates a perceived valueUtilizes the strength of a retailer’s large catalog3. Extends the product long tail4. Achieves positive business results (offset negative margin, increase attach rates) while helping the consumer with a complete solution and improving the customer’s life with products she/ he may not have thought of
  • On the technical side, we can achieve this with a SPARQL query on our global graph of data
  • An ongoing study by IPG Media Lab reveals that shopper satisfaction at retail stores is declining up to 15% per year ( are more empowered than ever before, and more knowledgeable – often times will have performed more researched than store employees and may be more knowledgableCustomers perceive this lack of knowledge as poor service, which drives people awayThe traditional solution to this issue has been more training for our store employeesBut take these factors into consideration:1. Training to create a more informed staff through traditional learning methods drives up costs and squeezes already tight margins2. Compound this with the fact that retail employee turnover is extremely high3. And -- the products we sell can change rapidly, old ones go away and new ones appear in a matter of weeksAll of these issues add to a decline is customer serviceNot having the same or better tools as customers puts employees at a disadvantageThere’s a solution for this involving data
  • We can empower our employees with data-drive apps and devices that fit in the palm of their handNot only can they assist and explore products with customers, but these tools can serve as on the floor trainingApps that explore a large graph of data can be extremely powerful and allow the employee to fully serve the customer’s needs, and in return, slow the decline in customer service rates These devices and apps are not only an output, but can harness real time sentiment and trend data directly from employees, adding data back to the graph
  • Our CEO Brian Dunn is driving the idea of the “connected world” a place where everything everyone is connected – companies, employees, customers, brands, devices – a ubiquitous layer of connectivity Traditional digital marketing and advertising methods will not be enough to fuel a connected worldA connected world relies on open, accessible and queryable data to provide a solid foundation for companies to be everywhere in the connected worldLinked data will connect corporate entities to other entities and fuel valuable insight to customers everywhere they are atData is device, platform and trend agnostic and it has the power to “future proof” your businessAllows to business to adapt and use gathered insights to navigate many different business scenarios
  • Thank you!
  • MIT Sloan Linked Data Ventures - Jay Myers

    2. 2. "Many of our greatest companies did not start because they thought there was a big pot of gold at the end of the rainbow. They started because they thought there was an interesting problem to be solved." - Tim O’Reilly, Web 2.0 Summit 2008
    4. 4. Simple form/ Basic transform Human & machine user input engine readable data
    5. 5. Simple form/ Basic transform Human & machine user input engine and API readable data Catalog API +  
    6. 6. MILLIONS OF DATA TOUCHPOINTS 1,100+ Stores 155,000 Employees 460,000+ 6 Countries Products 10 Brands 1,400 Domains
    7. 7. BBY US @BestBuy BBY UK BBY US BBY US BBY UK Local Twi er Customer Facebook Customer Facebook Stores annot. BBY UK Insights Insights Employee Carphone Reward Insights Warehouse BBY US Zone @twelp- force Twi er BBY UK Products Best Buy annot. Site Mobile @BestBuy Best Buy Analytics UK UK Twi er BBY UK BBY QR Products BBY US Best Buy Code .com Employee US Data Insights BBY UK Site Analytics BBY Mobile BBY US Site Apps Analytics Geek Best Buy Squad Global BBY CN BBY US Best Buy Site Mobile App Magnolia Pacific Graph China Analytics Data Sales BBY CA BBY CA Employee Insights BBY CN Local Five Star Products Stores BBY MX Products Site Analytics BBY CA Customer Best Buy Best Buy BBY TK Insights Canada BBY CA Mexico BBY MX Products Customer Products Best Buy Insights Turkey BBY CA BBY MX Products BBY CA BBY MX Customer BBY TK Site BBY MX Employee BBY TK @BestBuy Insights Site Analytics Local Insights Employee CA Analytics Stores Insights Twi er
    8. 8. PROBLEM: SHRINKING MARGINS & ATTACH RATES “…e-commerce still lacks browsing and discovery experiences that satisfy curiosity." -  Alexander Gruensteidl. “Four Keys to Surviving the Future of Retail”. 09 September 2010 . <h p:// retail-currency>
    9. 9. CREATE PRODUCT RELATIONSHIPS Margin: 49% Margin: 10% Margin: 17% Margin: 9% Margin: 31% Margin: 49% Margin: 10% Margin: 61% Margin: 19% Margin: -15% Margin: 8% Margin: 25% Margin: 12% Margin: 21% Margin: 40%
    10. 10. SPARQL Global Graph select distinct ?o as ?uri, bif:sprintf("%.2f",?p2) as ?price, ? currency, ?text, ?label, ?thumb, ?ean, ?order_link where of data { ?s1 rdfs:comment ?text . ?text bif:contains ’”Netbook”’.
    11. 11. PROBLEM: DECLINING CUSTOMER SERVICE "Poor service in the guise of ill-informed store staff creates lack of trust and drives shoppers to look for alternatives." - Nigel Fenwick. “Industry Innovation: Retail”. Forrester Research. 28 July 2010 .
    12. 12. SPARQL Global Graph select distinct ?o as ?uri, bif:sprintf("%.2f",?p2) as ?price, ? currency, ?text, ?label, ?thumb, ?ean, ?order_link where of data { ?s1 rdfs:comment ?text . ?text bif:contains ’”LCD TV”’.
    14. 14. THANK YOU! @jaymyers