Unify Your Selling Channels in One Product Catalog Service
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The document discusses how MongoDB can help unify product data across retail selling channels. It describes MongoDB's capabilities like flexible schemas, real-time querying, and scaling that make it well-suited for powering modern retail systems. Several use cases are provided, including maintaining a global product catalog, consolidated customer views, innovations across channels, and personalized recommendations.
![6. Product: Representative Queries – Item
• Get item by id
26
db.definition.findOne( { _id: "301671" } )
• Get item from Product Ids
db.definition.findOne( { _id: { $in: ["301671", "301672" ] } } )
• Get items by department
db.definition.find({ department: "Shoes" })
• Get items by category prefix
db.definition.find( { category: /^Shoes/Women/ } )
• Indices
productId, department, category, lastUpdated](https://image.slidesharecdn.com/141202retailrefcatalog-141210213745-conversion-gate02/85/Unify-Your-Selling-Channels-in-One-Product-Catalog-Service-25-320.jpg)
![Search – Summary Model
{ "_id": "3ZZVA46759401P", // the item id
28
"name": "Women's Chic - Black Velvet Suede",
"dep": "84700", // useful as standalone for indexing
"cat": "/84700/80009/1282094266/1200003270",
"desc": { "lang": "en", "val": "This pointy toe slingback ..." },
"img": { "width": 450, "height": 330, "src": "http://..." },
"attrs": [ // global attributes, easily indexable by SE
"heel height=mid (1-3/4 to 2-1/4 in.)",
"brand=metaphor",
"shoe size=6",
"shoe size=6.5", ...
”weather=cold"
],
"sattrs": [ // global attributes, not to be indexed
"upper material=synthetic",
"toe=open toe", ...
],
"vars": [
{ "id": "05497884001",
"img": [ // images],
"attrs": [ // list of variant attributes to index ]
"sattrs": [ // list of variant attributes not to index ] }, …
] }](https://image.slidesharecdn.com/141202retailrefcatalog-141210213745-conversion-gate02/85/Unify-Your-Selling-Channels-in-One-Product-Catalog-Service-27-320.jpg)
Unify Your Selling Channels in One Product Catalog Service
- 1. Unify Your Selling Channels in One Product Catalog Edouard Servan-Schreiber, Ph.D. Director for Solution Architecture [email protected]
- 2. Who is MongoDB? 2 Who is MongoDB: MongoDB (from humongous) is an open-source, game-changing operational database technology, designed to enable information capture, sharing and distribution to meet the needs and behaviors of today’s digitally-oriented society.
- 3. Retail at War: How to Differentiate? • Retail Information • Personalized Selling • Existing applications & technology?
- 4. MongoDB powers Systems of Engagement • A document model (holds mixed, variant data) • Ability to add new & different data (agility) • Ability to ask real-time questions based on right now 4 update (complex querying & in-place updating) • Geo-Location built-in • Power of traditional data bases (full consistency, durability, atomic operations) • Near linear expansion (scaling via sharding) • MongoDB is a unique fit for frictionless retail
- 5. 4 Use Cases: Modern, Seamless Retail 1. “Global Product 360” Theme: Product details & location up-to-minute; Vendors, payment
- 6. 4 Use Cases: Modern, Seamless Retail 2. Consolidated Customer View Theme: Single View of Customer, Consumer 360
- 7. 4 Use Cases: Modern, Seamless Retail 3. Channel Innovation (Ecommerce, Mobile, Social etc.) Theme: Back-end system integration, product details & geo-location up-to-minute
- 8. 4 Use Cases: Modern, Seamless Retail 4. Relevant Personalization: Via technology to consumer Theme: Relevance & convenience Embedding into daily life
- 9. 9 Traditional Mental Model
- 10. 10 MongoDB Mental Model
- 11. 11 Retail Data Platform
- 12. 12 Systems of Engagement
- 13. The Product Catalog Problem
- 14. Merchandising Objective: “Global Product Service” Single view of a product, one central service • Flexible schema containing all useful data • Read volume high and sustained, 100k reads / s • Can seamlessly take write spikes during catalog 14 update • Advanced indexing and querying • Geographical distribution for HA and low latency
- 15. The Reality: Many catalogs 1. One department in charge of master product works hard at fitting 15 data into SQL tables 2. Resulting data sits in a SQL server with a couple replicas. It's forbidden to hit it more than 100 times / sec 3. Other departments need to access the data way more often for their own services 4. Other departments need more information that is not available since it did not fit in that long devised rigid SQL schema 5. ETLs and Message Buses are put in place for other teams to try figure it out themselves… 6. Data becomes inconsistent, fragmented, not up-to-date… Problem visible both internally and by customers!
- 16. 16 Product Information Complexity Ecommerce Shop Catalog Stores Catalog Dozens of catalogs! Content Management Catalog Product Merchandising Operations Master Catalog Department 4 Catalog Search Catalog Store Associates Catalog Message Bus ETLs
- 17. What is hard about this? • Read intensity 18 – 100K+ reads per second (think peak time) – Omnichannel means it is not acceptable to lose a sale because your IT crawls • Flexibility – Each facet of the business needs slightly different data – And they each want to change it often • Who has the system of record when you have 12 copies around??
- 18. 19 Product Information MongoDB Architecture Product Service API MongoDB Data Layer Items Pricing Promotions Variants Ratings & Reviews … Digital Shop Store + Associates Inventory SCMS Public API Search, CMS, …
- 19. Product Page – What does it mean? 20 Product images General Informatio n List of Variants External Informatio n Localized Description
- 20. Retail Product Model - Overview • Item: the overall product info (e.g. Levi’s 501) • Variant: a specific variant of an item (e.g. in black size 6) 21 which typically has a specific SKU / UPC – Note: each model can have thousands of variants, each variant can have hundreds of attributes • Price: price information may vary based on the store, the variant, etc • Hierarchy: the item taxonomy • Facet: facets to search products by • Vendors: a given sku may be available through several vendors if the site is a marketplace
- 21. Product - Item Data Model > db.item.findOne() { _id: "301671", // main item id 22 department: "Shoes", category: "Shoes/Women/Pumps", brand: "Guess", thumbnail: "http://cdn…/pump.jpg", image: "http://cdn…/pump1.jpg", // larger version of thumbnail title: "Evening Platform Pumps", description: "Those evening platform pumps put the perfect finishing touches on your most glamorous night-on-the-town outfit", shortDescription: "Evening Platform Pumps", style: "Designer", type: "Platform", rating: 4.5, // user rating lastUpdated: Date("2014/04/01"), // last update time … }
- 22. Product – Variant Model > db.variant.findOne() { 23 _id: "730223104376", // the sku itemId: "301671", // references item id thumbnail: "http://cdn…/pump-red.jpg", // variant specific image: "http://cdn…/pump-red.jpg", size: 6.0, color: "Red", width: "B", heelHeight: 5.0, lastUpdated: Date("2014/04/01"), // last update time … }
- 23. Product Catalog - Data Layer Design Don’t do it all in a single collection! 24 MongoDB Data Store Items Summaries Pricing Ratings & Reviews Variants Promotions #1 Obtain results
- 24. Product – Summary Model The summary relies on the following parameters: • department e.g. "Shoes" • An indexed attribute 25 – Category path, e.g. "Shoes/Women/Pumps" – Price range – List of Item Attributes, e.g. Brand = Guess – List of Variant Attributes, e.g. Color = red • A non-indexed attribute – List of Item Secondary Attributes, e.g. Style = Designer – List of Variant Secondary Attributes, e.g. heel height = 4.0 • Sorting, e.g. Price Low to High
- 25. 6. Product: Representative Queries – Item • Get item by id 26 db.definition.findOne( { _id: "301671" } ) • Get item from Product Ids db.definition.findOne( { _id: { $in: ["301671", "301672" ] } } ) • Get items by department db.definition.find({ department: "Shoes" }) • Get items by category prefix db.definition.find( { category: /^Shoes/Women/ } ) • Indices productId, department, category, lastUpdated
- 26. Why is this important? • Because now I can do NEW things much more 27 easily • Suppose you want to enable search based on weather….. – “winter coats” – “shoes for the rain” – …. • Suppose you want to build a universal shopping cart to work across multiple brands and devices
- 27. Search – Summary Model { "_id": "3ZZVA46759401P", // the item id 28 "name": "Women's Chic - Black Velvet Suede", "dep": "84700", // useful as standalone for indexing "cat": "/84700/80009/1282094266/1200003270", "desc": { "lang": "en", "val": "This pointy toe slingback ..." }, "img": { "width": 450, "height": 330, "src": "http://..." }, "attrs": [ // global attributes, easily indexable by SE "heel height=mid (1-3/4 to 2-1/4 in.)", "brand=metaphor", "shoe size=6", "shoe size=6.5", ... ”weather=cold" ], "sattrs": [ // global attributes, not to be indexed "upper material=synthetic", "toe=open toe", ... ], "vars": [ { "id": "05497884001", "img": [ // images], "attrs": [ // list of variant attributes to index ] "sattrs": [ // list of variant attributes not to index ] }, … ] }
- 28. What’s marvelous about this? • No need to change a data model • No need to make this change in 29 multiple catalogs • When YOU come to my site, I can get the weather of YOUR location and show a homepage with items aligned to YOUR environment – This is demonstrated to be very effective – Think of the poor stores trying to sell sandals last spring in Manhattan…. • This took almost no effort !
- 29. Universal Shopping Cart • Persist your cart across devices and sessions • Extend the service across all your brands • Leverage MongoDB’s tag aware sharding to persist and replicate over multiple continents • Deliver low latency everywhere 30
- 30. Identify Product Trends • Retailers need to identify HOT and NEW events • Need to know what is going to happen before it happens 31 – Purchases, add to cart, browse, click
- 31. Simple Real Time Analytics • In slices of 10 minutes, just increment the events 32 as they come • Can maintain multiple trending metrics and change flexibly • Leverage results of past hour to make content dynamic • This requires no Hadoop, Kafka, Storm nor Spark Tracking samples: { "_id" : ”*********" , ”cart_count" : 14, “purchases”: 2} { "_id" : ”**********" , "cart_count" : 2} { "_id" : ”*********" , "cart_count" : 10138} { "_id" : ”**********" , "cart_count" : 1648} { "_id" : ”*********" , "cart_count" : 10852} Single operations to insert / update: Db.track.update( { _id: ”123498745" }, { $inc: {cart_count: 1 } })
- 32. Take Aways • Retail is changing and requires dynamic engagement 33 across all channels • The known IT models cannot support this appropriately • MongoDB can support these new systems of engagement by simplifying the data infrastructure and making changes flexible • MongoDB built assets for a Retail architecture • Enables powerful new business value through – Single view of product – Universal shopping cart – Streaming analytics – Single view of customer – ….
- 33. Thank You !