Anand Srinivasan & Dr. Jon A Higbie

Transcription

1 Big Data Means Nothing Without Big Data Analytics Anand Srinivasan & Dr. Jon A Higbie October 6, 2013

2 Big Data is Defined By 3 Vs Volume, Velocity and Variety Big Data Definition Volume Amount of data being generated and collected. Velocity Rate at which the data is being generated and analyzed. Variety Different forms of data being collected. From 2005 to 2020, Global Data Will Grow From 130 Exabytes to 40,000 Exabytes (40 Trillion Gigabytes)* Traditionally, businesses have been using transactional data to make decisions With Big Data capabilities, businesses are introduced to more data sources to better understand consumer behavior and responses Currently, there are 1 2 billion people accessing the internet * About 819 million monthly active users on Facebook** Only 3% of potentially useful data is currently tagged and 0.5% used for analysis*** Big Data Is Usually Linked With Companies That Already Deal in Data Like Amazon/Google/Facebook/Twitter But a range of companies like Caesars Entertainment/Ford/UPS/IHG use Big Data and analytical capabilities in various areas of their operations**** Big Data Analytics enable using more than just transaction data in decision making * ** Facebook.com *** IDC Report: December 2012 **** Wall Street Journal 2

3 What is Revenue Management? Definition: Application of disciplined analytics that predict consumer behavior at micro market level and optimize product availability and price to maximize revenue growth* Primary Aim of Revenue Management: Selling the right product to the right customer at the right time for the right price Typical Drivers for the Adoption of Revenue Management Margin pressure Increasing competition Product differentiation Technology enables greater consumer visibility * Robert G. Cross (1997) 3

4 Differentiating Revenue Management Revenue Management requires a different mindset than other approaches Characteristics Bottom up rather than top down Quantifying what customers do rather than what they say Predictive rather than historical or reactive Approach Collect Analyze Understand Optimization rather than heuristics Collaborative rather than siloed Predict Optimize 4

5 Revenue Management Lessons Learned Streamlined processes and optimal decision making requires: Shared knowledge base. Common view of data within the enterprise Common analytical framework. Predictive analytics focused on how customers will respond to offers Targeted products and discounts. Optimized based on market response Aligned goals around growth and profitability. Not a choice between volume or profit, but increasing both! Agreed success criteria. Aligning goals with new metrics 5

6 Integrating Big Data with Revenue Management We think about Revenue Management from a holistic perspective, using an analytically driven approach to the levers that drive revenue and margin uplift BIG DATA ADVANCED ANALYTICS PLATFORM DECISION SUPPORT Disparate data sources collected and consolidated Social Media Browsing History Socioeconomic Clickstream Receipts Online Sales Weather Inventory In store Sales Promo & Discounts Fulfillment Purchase History Predictive analytic models learn from history and generate powerful insights that are actionable Scalability Discount / Incentive Optimization Price Optimization Reporting & Alerting Customer Segmentation & CLV Market Basket Analysis Performance Measurement Demand Forecasting Inventory Optimization Scenario Planning Analytically driven decisionmaking maximizes revenue at the transaction level Right Product Right Customer Right Time Right Price $ $ $ I N T E G R A T E D P R O C E S S E S 6