Using Data Mining and Machine Learning in Retail

Transcription

1 Using Data Mining and Machine Learning in Retail Omeid Seide Senior Manager, Big Data Solutions Sears Holdings Bharat Prasad Big Data Solution Architect Sears Holdings

2 Over a Century of Innovation A Fortune 100 company, nearly $40 billion in annualgrowing revenue. data volumeslargest fourth The nation s broad line Shortened Tight IT retailer with almost 2,500 full-line processing and budgets windows specialty retail stores in the US and Canada. A front runner inthe Big Data effortsescalating Latency in Challenge costs including driving personalized data marketing and generating savings from legacy migration. Hitting ETL Running one of the biggest scalability rewards complexity ceilings programs that captures and analyzes a Demanding business very large number of customer requirements transactions quickly. 2

3 What is Big Data? Big Data can no longer be defined by the amount of data, but by the type, speed, and storage capacity needed to compute and analyze that data. 3

4 Data, Data, and More Data We are creating so much data, so quickly, that 90% of the data in the world today has been created in the last 2 years. 4

5 The Problem with Large Scale Data Processing With traditional computer processing--it can be difficult to compute everything, due to storage space, processing time, and cost. This typically leads to incomplete computations, data latency, and overall lack of quality analysis. Hadoop brings infinite scalability, extremely large storage capability, and fast data processing. 5

6 Enter Hadoop Apache Hadoop is a framework which: Runs applications on a large cluster built of commodity hardware. Provides reliability and data motion to applications. Implements a computational paradigm named MapReduce. Applications divided into small fragments of work for execution/ re-execution on any node in the cluster. Provides a Distributed File System (HDFS) that stores data on compute nodes, resulting in high aggregate bandwidth across the cluster. Both Map/Reduce and the Distributed File System Framework automatically handle the node failures. 6

7 Why Use Hadoop? Stability: Hadoop is horizontally scalable. Easily stores and processes petabytes of data, just by adding hardware. Economical: Uses commodity based hardware. Efficient: Extremely powerful processing ability. Reliability: Data is replicated 3x times (min) in different locations; failed tasks are rerun. Storage space & Capacity: Central Repository; Keep everything forever. 7

8 Big Data Analytics in Retail How can I better manage my inventory? How can I better understand my customers buying habits? How can I detect fraudulent activity? How can I create better targeted interaction with my customer? How do I get customers to purchase more products? 8

9 The Evolution Data Analysis 9

10 What is Mahout? Top Apache Foundation software project Uses Scalable Machine Learning algorithms Collection of pre-built data-mining libraries Primary focus on collaborative filtering, clustering & classification Houses a Java based math library that uses common math operations Uses MapReduce paradigm 10

11 Examples of Data Mining & Machine Learning 11

12 3 Primary Algorithms Clustering Recommendation Systems Market Basket Analysis 12

13 Clustering A process of grouping similar things in such a way, so that like items are grouped together with other items that most closely represent themselves. 13

14 Motivation behind Clustering Why use Clustering?? To better understand a customer s buying behavior To develop targeted marketing campaigns To understand interest, motivation, and lifestyle, in order more effectively move merchandise in and out of stores 14

15 Recommendation Systems An information filtering system that is used to predict a users rating or preference, typically using a collaborative, content-based or hybrid approach to recommendations. 15

16 Collaborative Filtering Framework that filters and recommends items based on user behavior, preferences and activities. Based on their similarities to others. Recommenders User based Item based Online and Offline support Can utilize Hadoop Uses numerous similarity measurements, such as Cosine, LLR, Tanimoto, Pearson, and more. 16

17 Content- Based Filtering Looks at the item and the users preference in order, and provides a recommendation. Users Ratings A B Feature Values User Profile C Content used in the past Allows for highly precise recommendations. Difficulty when making recommendation over cross-sections of service when used for crossselling. Matching X Y Z Feature Values Content with similar feature values is recommended Content Profile profile Contents 17

18 Market-Basket Model A model used to describe the commonality of several relationships between two objects. Items: anything that is purchased Basket: a set of items The numbers of items in a basket is typically small, and the number of baskets is typically large 18

19 Market Basket Models A list of Purchasers Additional Purchaser data is can be useful (but is not needed) A list of transactions Seek to identify purchasing patterns What items are normally purchased together What is the purchasing sequence Is there a seasonality effect to purchasing Categorize buying behavior Translate buying behavior into actionable insight Targeted promotions Inventory placement Store layout Cross- Selling 19

20 Frequent Itemsets Any set of items that appears regularly within multiple baskets Originally used to analyze a physical supermarket basket Best used to link commonly bought together pairs that often have no relationship to each other Example: Diapers & Beer A major store chain discovered that diapers and beer were regularly appearing in baskets together. Theory was that if you bought diapers you are likely to have a baby at home, with a baby at home it is less likely that you go to a bar to drink, and more likely you will have a beer at home. 20

21 Applying Market Baskets Models Retail Stores Showroom floor planning Catalog layout Crossing selling Fraud Analysis 21

22 Big Data Stack Data Visualization & Reporting Consumption Consumption Semantic Semantic Computation/Acc Computation/Acc ess ess Storage Storage Data Analytics Hive/Pig Advance Query Hive/Pig Storage-hdfs On-Promises Integration Integration Integration Integration Advance Query Storage-hdfs Cloud NOSQL NOSQLDB DB Security Security Security Frequency Frequency Data Mining Metadata Data Governance & Integration --ETL/ELT On demand Real-Time Streaming Time series 22

23 Open vs Closed Stack Distribution Distribution Consump Consump tion tion Semanti Semanti c c Computat Computat ion/acces ion/acces s s Storage Storage /NO /NOSQL SQL DB DB Security Security Integratio Integratio n n Source Source Blo g 23

24 Questions? 24