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Graphs in Retail: Know Your Customers and Make Your Recommendations Engine Learn

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At Neo4j we believe that “Graphs Are Everywhere”. In this session, we’ll be exploring graphs within the Retail industry. We’ll discuss a range of data that are commonly available within a retail organisation, both online and “brick and mortar". We’ll illustrate some graphs which can be created by linking together different elements of that data and discuss the retail use cases those graphs can enable and transform.

We’ll specifically focus on use cases like Personalised Recommendations (with a live demo), Supply Chain Management, Logistics, and Customer 360. We'll also look at some relevant graph algorithms and talk about opportunities for integration with Artificial Intelligence/Machine Learning technologies, which can be used along with Neo4j to generate new value using retail data.

Walmart, Wobi, and others already deploy Neo4j for use cases like price comparison or real-time contextual and learning recommendation engines. Read about their use cases!

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Graphs in Retail: Know Your Customers and Make Your Recommendations Engine Learn

  1. 1. Graphs in Retail Know Your Customers and Make Your Recommendation Engine Learn Joe Depeau Sr. Presales Consultant, UK 14th August, 2019 @joedepeau http://linkedin.com/in/joedepeau
  2. 2. • Introduction to Graphs and Neo4j • Retail Data Overview • Use Cases • Customer 360 View • Recommendations (with demo) • Logistics • Supply Chain Management • Others … • Q&A 2 Agenda
  3. 3. Introduction to Graphs and Neo4j 3
  4. 4. Relational vs. Graph Databases 4
  5. 5. Graphs in the Age of Connections 5
  6. 6. 6
  7. 7. 7 Car DRIVES name: “Dan” born: May 29, 1970 twitter: “@dan” name: “Ann” born: Dec 5, 1975 since: Jan 10, 2011 brand: “Volvo” model: “V70” Anatomy of a Property Graph Database Nodes • Represent the objects in the graph • Can be labeled Relationships • Relate nodes by type and direction Properties • Name-value pairs that can go on nodes and relationships. LOVES LOVES LIVES WITH OW NS Person Person
  8. 8. Retail Data Overview 8
  9. 9. 9 Some Examples of Typical Retail Data Event DataProduct Data Customer DataOrganisational Data 3rd Party Data Documentation Facilities Processes Systems and Databases KPIs and Reports Personal Data Customer Relationships Documentation Processes Brand Data Product Hierarchy Pricing Data Clickstream Data Searches Customer Contact Social Media Market Data Organisational Hierarchy Purchase History Supply Chain Data Supplier Data Logistics Data Inventory Data Local Data
  10. 10. Use Case Examples 10
  11. 11. Customer 360 Example Graph Organisational Data Customer Data Product Data Event Data 3rd Party Data Supply Chain Data 11
  12. 12. 12 Customer 360 Graph Uses • Can I use the graph to help me improve customer experience? • Can the graph help me determine Customer Lifetime Value (CLV)? • Can I spot and prevent churn using a graph? • Can the graph help me spot influencers in my customer base? Yes! Yes! Yes! Yes!
  13. 13. Recommendations Example Graph Organisational Data Customer Data Product Data Event Data 3rd Party Data Supply Chain Data 13
  14. 14. 14 Product Recommendations Effective product recommendation algorithms has become the new standard in online retail — directly affecting revenue streams and the shopping experience. Logistics/Delivery Routing recommendations allows companies to save money on routing and delivery, and provide better and faster service. Promotion recommendations Building powerful personalized promotion engines is another area within retail that requires input from multiple data sources, and real-time, session based queries, which is an ideal task to solve with Neo4j. Today Recommendation Engines are at the Core of Digitization in Retail
  15. 15. 15 Mobile Brick & Mortar Web Multi-channel
  16. 16. 16 Recommendations Graph Uses • Can I use the graph to help me improve sales with better recommendations? • Can the graph help me make real-time recommendations across channels? • Can I integrate my recommendations graph with AI and Machine Learning techniques? • Can the graph help me with other types of recommendations besides products? Yes! Yes! Yes! Yes!
  17. 17. Demo 17
  18. 18. Supply Chain Example Graph Organisational Data Customer Data Product Data Event Data 3rd Party Data Supply Chain Data 18
  19. 19. 19 Supply Chain Graph Uses • Can I use the graph to help me improve my ordering and procurement processes? • Can the graph help me save money on orders? • Can I optimize my inventory using a graph? • Can the graph help me with comparative analysis of my suppliers and their products? Yes! Yes! Yes! Yes!
  20. 20. 20 Other Retail Graph Use Cases • Identity and Access Management • Infrastructure and Network Management • Master/Meta-data Management • Regulatory Compliance (i.e. GDPR)
  21. 21. Case Study Examples 22
  22. 22. 23 Case studySolving real-time recommendations for the World’s largest retailer. Challenge •In its drive to provide the best web experience for its customers, Walmart wanted to optimize its online recommendations. •Walmart recognized the challenge it faced in delivering recommendations with traditional relational database technology. •Walmart uses Neo4j to quickly query customers’ past purchases, as well as instantly capture any new interests shown in the customers’ current online visit – essential for making real-time recommendations. Use of Neo4j “As the current market leader in graph databases, and with enterprise features for scalability and availability, Neo4j is the right choice to meet our demands”. - Marcos Vada, Walmart • With Neo4j, Walmart could substitute a heavy batch process with a simple and real-time graph database. Result/Outcome
  23. 23. 24 Top Tier US Retailer Case studySolving Real-time promotions for a top US retailer Challenge •Suffered significant revenues loss, due to legacy infrastructure. •Particularly challenging when handling transaction volumes on peak shopping occasions such as Thanksgiving and Cyber Monday. •Neo4j is used to revolutionize and reinvent its real- time promotions engine. •On an average Neo4j processes 90% of this retailer’s 35M+ daily transactions, each 3-22 hops, in 4ms or less. Use of Neo4j • Reached an all time high in online revenues, due to the Neo4j-based friction free solution. • Neo4j also enabled the company to be one of the first retailers to provide the same promotions across both online and traditional retail channels. “On an average Neo4j processes 90% of this retailer’s 35M+ daily transactions, each 3-22 hops, in 4ms or less.” – Top Tier US Retailer Result/Outcome
  24. 24. 25 Case Study : Wobi uses Neo4j to enable ‘Whole Customer Understanding’ The World’s Leading Graph Database CASE STUDY www.neo4j.com Wobi Price Comparison Site Wobi Builds ‘Whole Customer Understanding’ with Neo4j The success of price comparison websites rests on their ability to make customers compelling ‘value offers’ – and to do that they need to capture, organise and instantly analyse masses of customer data. Israel-based Wobi has achieved that aim of ‘whole customer understanding’ using Neo4j. The Company Founded five years ago, price comparison website Wobi is already one of Israel’s best-known com- panies. Owned by the White Mountain investment group, Wobi has over 500,000 customers and millions of site visitors every month, who use Wobi to compare and choose their pensions and car, home, mortgage and travel insurance. Wobi has around 100 staff and, bolstered by a high-profile TV advertising campaign, will expand further this year by launching a banking and finance compar- ison service. The Challenge Wobi aims to give its customers best ‘value offers’, and to do that it needed to build a detailed picture of each customer and their full financial situation – savings, pensions, insurance policies, accounts and family background. As Chief Technology Officer, Shai Bentin, explained: “We look to give our customers great value offers and so, as our CEO says, we want to look at the customer’s account in such depth that we can tell them they have a leak in their house because they have been paying more for their water every month! That’s the idea...we can offer to move a customer from, say, one phone company to another that better suits their needs – and we can read that information off their account, their credit account and the way the customer behaves.” To achieve that level of understanding, Wobi needed a single customer database where it could rapidly drill-down into each individual’s history and add new information on the fly. It faced two key issues. “One is that we need to extract a lot of customer information very, very fast from the database,” Shai said, “and the second is the way we get the information. It’s a tree-like structure – under each customer will hang a lot of information, and for performance we needed to pull up all that information at once.” When Wobi began searching for the ideal database, it realised “that structure really suited working with Neo4j”, Shai said, because Neo4j organises data into ‘nodes’ and ‘relationships’. This enabled Wobi to define its customers as ‘nodes’, and to hang off them every piece of information relating to that customer as ‘relationships’. Shai explained: “Instead of having to break up our data into tables like with an SQL database and make thousands of joins, with Neo4j we could just ‘save the tree’ and do a single look-up to the person, to grab everything at once.” The Strategy Neo4j is now Wobi’s core customer database, sitting at the heart of a network of around 20 servers, with a team of five people doing Neo4j development and testing work. Wobi started using Neo4j after coming across the product by chance. Shai explained: “I felt that working with a normal SQL database would be too much work for us, and I actually started out look- ing for object databases, because our programming language is Java which is very object oriented. Then I stumbled on Neo4j – we tried it out and it worked for us.” INDUSTRY Finance USE CASE Graph-Based Search/ Recommendations GOAL Make customers ‘value offers’ based on in-depth understanding of their current financial situation and needs CHALLENGE Rapidly analyse large volumes of ‘whole customer’ information SOLUTION Store all customer data into Neo4j database RESULTS – Data on half-a-million customers is accessed exponentially faster – All data consolidated in Neo4j for ‘whole customer understanding’
  25. 25. Q & A 26
  26. 26. 27 Thank you!

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