Calculating ROI with Innovative eCommerce Platforms

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  • The state of retail today…

    2014 is a critical year, where old ways are no longer sufficient.

    After 20 years in retail, I have realized the important of change. I too have made a change, realizing that many things are coming together this year.

    it is time to adapt….
  • Who we are
  • 2 parts of the agenda today:

    The Retail Hype
    The need
    Why retailers and companies are working with MongoDB… to meet the needs of commerce today

    2nd half will be the technical explanation of why MongoDB is suited for today’s selling environment
    With a deep dive on one application area, the need for product information

    You may ask questions at any time and we will save 10 minutes at the end of the session for QA
  • We do have a long list of clients already

    We have many named customers and additional customers who are willing to share their stories in an anonymous fashion.

    But what we have learned over the years of being open source, is that many people adopt and use our software and we find out much later on!
  • Inventory projection for ecommerce versus stores

    Availability of analytic insight for customer impact

    Difficulty in innovating without impacting overall information delivery

    Superior, personalized user experience

  • Product is and will remain a cornerstone of retailing. It is goods and services that are packaged and sold.

    In the digital era, consumers have ‘perfect information’… both about your product as well as your competitor… and even a competitors you had not identified. The truth is products can be sold across the globe today… and the market is no longer restricted to a reasonable selling region / nor mailing area.

    In this, the need to provide the latest information on your product is critical.

    The guiding vision on this is the creation of the ‘perfect (meaing complete) product’ information.

    It starts with basic information:
    Information
    Selling price
    Availability
    To location in the supply chain
    Across an enterprise

    While that sounds easy, it is not.

    And it becomes further complicated if we want to see it it
  • Product is and will remain a cornerstone of retailing. It is goods and services that are packaged and sold.

    In the digital era, consumers have ‘perfect information’… both about your product as well as your competitor… and even a competitors you had not identified. The truth is products can be sold across the globe today… and the market is no longer restricted to a reasonable selling region / nor mailing area.

    In this, the need to provide the latest information on your product is critical.

    The guiding vision on this is the creation of the ‘perfect (meaing complete) product’ information.

    It starts with basic information:
    Information
    Selling price
    Availability
    To location in the supply chain
    Across an enterprise

    While that sounds easy, it is not.

    And it becomes further complicated if we want to see it it
  • Product is and will remain a cornerstone of retailing. It is goods and services that are packaged and sold.

    In the digital era, consumers have ‘perfect information’… both about your product as well as your competitor… and even a competitors you had not identified. The truth is products can be sold across the globe today… and the market is no longer restricted to a reasonable selling region / nor mailing area.

    In this, the need to provide the latest information on your product is critical.

    The guiding vision on this is the creation of the ‘perfect (meaing complete) product’ information.

    It starts with basic information:
    Information
    Selling price
    Availability
    To location in the supply chain
    Across an enterprise

    While that sounds easy, it is not.

    And it becomes further complicated if we want to see it it
  • Dated e-commerce site with limited capabilities
    Usability issues
    SQL database did not scale


    WHY MONGODB:
    Multi-data center replication and sharding for DR and scalability
    Dynamic schema
    Fast performance (reads and writes)

    RESULTS
    Developers, users are empowered
    Fast time to market
    Database can meet evolving business needs
    Superior user experience



  • RDBMS poorly-equipped to handle varying data types (e.g., SKUs, images)
    Inefficient use of storage in RDBMS (i.e., 90% empty columns)
    Complex joins degraded performance

    WHY MONGODB:
    Document-oriented model less complex, easier to code
    Single data store for structured, semi-structured and unstructured data
    Scalability and availability
    Analytics with MapReduce

    RESULTS
    Decreased supplier onboard time by 12x
    Grew from 400K records to 40M in 12 months
    Significant cost reductions on schema design time, ongoing developer effort, and storage usage



  • one of the world’s leading relationship service providers,
    relies on compatibility matching system to introduce potential partners,
    relies on analyzing a user’s traits and preferences. 


    To run matching across their entire use base taking 15 days on RDBMS – too long.

    Looked for alternatives – found using flex data model and rich queries, along with ability to shard to scale out, they could reduce matching time to 12 hours – 95% improvement
    Use combination of consulting and subscriptions to put dev on right path and simplify their operations
  • Mail order catalog
    Privately held
    €11.8bn turnover – total, with Otto.de @2.5billion
    53,000 employees

    400 physical stores (e.g., Crate & Barrel)
    60 different online shops

    85% of the Otto Germany revenue comes from Otto.de

    Approximately 1.5m products, which should grow to 5 million during the next couple of years.

  • Shared nothing – what does this mean?

    7 indpendent planning teams
    Working individual with 3 week sprints
    A business desginer – who is considering the BUSINESS REQUIREMENT, no obligation to understand nor ‘code’ the capabilities, simply to design
    Focus on engaging and telling a store
    Constantly updating
    Dozens and dozens of simultaneous efforts to continue innovation with ZERO dependencies for arriving at the conclusion
  • Commerce practices are changing and consumers have already changed.
    The focus is how best to adapt to modern requirements.

    And this is why we now have ….so the next question is:

    Why MongoDB to help address this new seamless digital consumer?

  • Calculating ROI with Innovative eCommerce Platforms

    1. 1. Calculating ROI with Innovative E-Commerce Platforms Enabling Omni-Channel Retailing #mongodbretail Global Business Architect, MongoDB Director, Solution Architecture, MongoDB Edouard Servan-Schreiber Rebecca Bucnis
    2. 2. “Amazon.com strives to be the e-commerce destination where consumers can find and discover anything they want to be buy online. - Jeff Bezos, founder
    3. 3. Presenters Rebecca Bucnis Global Business Architect - Business Strategy - Former Retailer Amsterdam, The Netherlands rebecca.bucnis@mongodb.com @rebeccabucnis Edouard Servan-Schreiber Director, Solution Architecture - Delivery of Solutions, Pre-Sales - North America New York, NY edouard@mongodb.com @edouardss @rebeccabucnis @edouardss
    4. 4. • Introduction • Demands of Modern E-Commerce • Why Use MongoDB for E-Commerce • Technical Capabilities and Enablers • Innovative Case Studies with ROI • Wrap Up & Next Steps Agenda
    5. 5. Introduction
    6. 6. Retail in a World with Amazon.com
    7. 7. 7 Customer-Centric E-Commerce 1. Product Available? Product Anywhere • Order Management & Fulfillment 2. Continually Fresh Content & Information • Detailed product, pricing & UGC 3. Multi-Channel Integration • Back-end systems inclusive Based upon Forrester Wave - BtoC Commerce, 2013
    8. 8. 8 Disconnected Ecommerce > ROI Speed to Innovation is Slow…. Inventory & Fulfillment more complex Single Channel Systems (or Siloed) Unable to Execute in Real-Time Static Informatio n
    9. 9. MongoDB Strategic Advantages Horizontally Scalable -Sharding Agile Flexible High Performance & Strong Consistency Application Highly Available -Replica Sets { customer: “roger”, date: new Date(), comment: “Spirited Away”, tags: [“Tezuka”, “Manga”]}
    10. 10. 10 Information Management Merchandising Content Inventory Customer Channel Sales & Fulfillment Insight Social Retail Architecture Overview Customer Channels Amazon Ebay … Stores POS Kiosk … Mobile Smartphone Tablet Website Contact Center API Data and Service Integration Social Facebook Twitter … Data Warehouse Analytics Supply Chain Management System Suppliers 3rd Party In Network Web Servers Application Servers
    11. 11. 1. Order Management & Fulfillment Theme: Product location and availability up-to-minute Business Benefits: Ability to make a sale! Modern Ecommerce
    12. 12. 12 Inventory Inventory MongoDB External Inventory Internal Inventory Regional Inventory Purchase Orders Fulfillment Promotions
    13. 13. 13 Demonstration Document Model Definitions • id: p0 Variations • id: sku0 • pId: p0 Summary • id: p0 • vars: [sku0, sku1, …] Stores • id: s1 • Loc: [22, 33] Inventory • store: s1 • pId: p0 • vars: [{sku: sku0, q: 3}, {sku: sku2, q: 2}] Product
    14. 14. 14 > db.inventory.findOne() { "_id": "5354869f300487d20b2b011d", "storeId": "store0", "location": [ -86.95444, 33.40178 ], "productId": "p0", "vars": [ { "sku": "sku1", "q": 14 }, { "sku": "sku3", "q": 7 }, { "sku": "sku7", "q": 32 }, { "sku": "sku14", "q": 65 }, ... ] } Inventory - Quantities
    15. 15. Order Management & Fulfillment Technical Challenges MongoDB Solution • Cannot see the up to date inventory by store as inventory is updated in batch processes • Inventory details are stored in systems which cannot handle the load of massive distributed reads • Need efficient geospatial lookups to find cheap fulfillment options • Fast in-place updates able to handle heavy load of real-time changes • Leveraging RAM for hot data systematically and able to fulfill massive concurrent reads • Geospatial indexing enabling easy search of inventory through nearby stores
    16. 16. 2. Latest Information in Content & Product Theme: Fresh and Engaging Content Low(est) Latency Business Benefits: Converting sale, ‘discover’ product, drive revenue Modern Ecommerce
    17. 17. Merchandising Merchandising MongoDB Product Variation Product Hierarchy Pricing Promotions Ratings & Reviews Calendar Semantic Search Product Definition Localization
    18. 18. 18 Price: { _id: <unique value>, productId: "301671", // references product id sku: "730223104376", // can reference specific sku currency: "us-dollar", price: 89.95, storeGroup: "0001", // main store group storeId: [ "1234", "2345", … ] // per store pricing lastUpdated: Date("2014/04/01"), // last update time … } Indices: productId + storeId, sku + storeId, storeId + lastUpdated Merchandising – Pricing
    19. 19. 19 • Get Variation from SKU db.variation.find( { sku: "730223104376" } ) • Get all variations for a product, sorted by SKU db.variation.find( { productId: "301671" } ).sort( { sku: 1 } ) • Find all variations of color "Blue" size 6 db.variation.find( { attributes: { $all: [ { color: "Blue" }, { size: 6 } ] } ) • Indices sku, productId + sku, attributes, lastUpdated Merchandising - Pricing
    20. 20. Continually Fresh Content & Information Technical Challenges MongoDB Solution • Enabling numerous price changes intra day and high granularity (per store/channel pricing) • Collecting and rendering users’ product reviews • Welcoming new content and be able to serve it right away • Changing the site structure and content within hours of decision • Fast updates to a pricing structure within a rich JSON document for maximum flexibiity • Able to take massive writes of loosely structured data • Storing of content using GridFS for high availability and fast retrieval • Flexible schema for easy custom changes.
    21. 21. 3. Simplistic Back-End Integration Theme: Connecting analytics to real-time execution Business Benefits: Customer satisfaction, increased revenue Modern Ecommerce
    22. 22. 22 Insight Insight MongoDB Advertising metrics Clickstream Recommendations Session Capture Activity Logging Geo Tracking Product Analytics Customer Insight Application Logs
    23. 23. 23 Streams of User Activity
    24. 24. 24 Activity logging - Architecture MongoDB HVDF API Activity Logging User History External Analytics: Hadoop, Spark, Storm, … User Preferences Recommendations Trends Product Map Apps Internal Analytics: Aggregation, MR All user activity is recorded MongoDB – Hadoop Connector Personalization
    25. 25. 25 { _id: ObjectId(), geoCode: 1, sessionId: "2373BB…", device: { id: "1234", type: "mobile/iphone", userAgent: "Chrome/34.0.1847.131" } type: "VIEW|CART_ADD|CART_REMOVE|ORDER|…", itemId: "301671", sku: "730223104376", order: { id: "12520185", … }, location: [ -86.95444, 33.40178 ], timeStamp: Date("2014/04/01 …") } User Activity - Model
    26. 26. 26 Dynamic schema for sample data Sample 1 { deviceId: XXXX, time: Date(…) type: "VIEW", … } Channel Sample 2 { deviceId: XXXX, time: Date(…) type: "CART_ADD", cartId: 123, … } Sample 3 { deviceId: XXXX, time: Date(…) type: “FB_LIKE” } Each sample can have variable fields
    27. 27. 27 Dynamic queries on Channels Channel Sample Sample Sample Sample App App App Indexes Queries Pipelines Map-Reduce Create custom indexes on Channels Use full mongodb query language to access samples Use mongodb aggregation pipelines to access samples Use mongodb inline map-reduce to access samples Full access to field, text, and geo indexing
    28. 28. Multi-Channel Integration Technical Challenges MongoDB Solution • Original legacy source systems are rigid, inflexible and do not easily exchange information • Need to add a new data source on very short notice to get larger view of customers • Keep history of customer information in loosely structured form for deep analytics • Ability to maintain original source systems, yet create a blended view without ‘rip and replace’ • Flexible schema for easy custom changes and enhancements to customer profile • Massive scaling on demand to keep historical data for as long as needed.
    29. 29. Innovative Case Studies with ROI
    30. 30. • Built custom ecommerce platform on MongoDB in 8 Months •Fast time to market •Database can meet evolving business needs •Superior user experience ROI = Original innovation, performance & flexibility Customer Examples
    31. 31. • Delivered agile automated supply chain service to online retailers powered by MongoDB •Decreased supplier onboard time by 12x •Grew from 400K records to 40M in 12 months •Significant cost reductions Customer Examples
    32. 32. Compatibility Matching System used to match potential partners “With our...SQL-based system, the entire user profile set was stored on each server, which impacted performance and impeded our ability to scale horizontally. MongoDB supports the scale that our business demands and allows us to generate matches in real-time.” Thod Nguyen, CTO, eHarmony 95% Faster Matches
    33. 33. 33 • www.otto.de • €2.5bn eCommerce site • Largest web property for female and child clothing in Europe • 1998 – 2013: based on Intershop Otto Germany
    34. 34. 34 Search & Navigate Dynamic Product Shop, Pages & Content User Experience & Personalization Customer Journey Order Management Focused Capabilities for E-Commerce
    35. 35. 35 Press Release – Otto Germany
    36. 36. 36 Executing Modern E-Commerce RevenuePotential Product Availability Unclear/ Can’t deliver Product Available – Deliver without insight Some products available Unavailable; went to store Product Available - Deliver Anywhere with insight Time to Execution
    37. 37. Then E-Commerce Island Integrated Fulfillment Static Information Continual Refresh Unknown Visitor Tailored Journey Now Enabling agile delivery of seamless interactions & selling
    38. 38. 1. Assess your retail data and omni-channel capabilities 2. Join us and Engage: • Big Data Analytics - London – 19 June • MongoDB World - New York – June 23-25 • Customer Experience Exchange – London 2-3 July 3. Start one step at a time - with “prototype” capabilities What’s Next?
    39. 39. Questions?
    40. 40. Thank You! @rebeccabucnis @edouardss Rebecca.bucnis@mongodb.com Edss@mongodb.com
    41. 41. Resources White Paper: Big Data: Examples and Guidelines for the Enterprise Decision Maker http://www.mongodb.com/lp/w hitepaper/big-data-nosql Recorded Webinar Series: Thrive with Big Data http://www.mongodb.com/lp/bi g-data-series Recorded Webinar: What’s New with MongoDB Hadoop Integration http://www.mongodb.com/pres entations/webinar-whats-new- mongodb-hadoop-integration Documentation: MongoDB Connector for Hadoop http://docs.mongodb.org/ecosy stem/tools/hadoop/ White Paper: Bringing Online Big Data to BI & Analytics http://info.mongodb.com/rs/mo ngodb/images/MongoDB_BI_An alytics.pdf Subscriptions, support, consulting, training https://www.mongodb.com/pro ducts/how-to-buy Resource Location

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