Big Data for the Retail Business I Swan Insights I Solvay Business School
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Big Data for the Retail Business I Swan Insights I Solvay Business School

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Slides of a course given at the Solvay Business School about Big Data potential in the Retail Business.

Slides of a course given at the Solvay Business School about Big Data potential in the Retail Business.

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    Big Data for the Retail Business I Swan Insights I Solvay Business School Big Data for the Retail Business I Swan Insights I Solvay Business School Presentation Transcript

    • Big Data in the Retail Business. Laurent Kinet CEO of Swan Insights
    • Who am I? My goal in my professional life has always been to deliver strategic value to my customers through the potential of new technologies. DIGITAL-IN FU AND ENTREPREUNEUR SED
    • Who am I?
    • Data is not new. Big is not new. The first and most beautiful data visualization on earth. Galileo Galilei, On Saturn.
    • Data is not new. Big is not new. The first and most beautiful data visualization on earth. “The best statistical graphic ever drawn”, Edward Tufte.
    • The new in Big Data is…
    • A couple of figures. $600 buys you a disk drive that can store all of the world’s music 7 billion mobile phones in use in 2012 40 billion Source: McKinsey pieces of content shared on Facebook every month
    • A couple of figures. 12 10 8 Data Growth 6 IT Spending 4 2 0 2013 2014 2015 2016 2017 2018 2019 Source: McKinsey 20
    • A couple of figures. Source: McKinsey Big Data: The next frontier for innovation, competition and productivity
    • Meet the demand. Data have swept into every industry and business function and are now an important factor of production. Big Data creates value in several ways. Transparency. Expose variability and Improve performance. There will be a shortage of talent necessary for organizations to take advantage of Big Data. Segment populations to customize actions. Supporting human decision making with automated algorithm. Innovate new business models and P&S. Source: McKinsey Big Data: The next frontier for innovation, competition and productivity
    • Background observations on Big Data. The first and most beautiful data visualization on earth. “The best statistical graphic ever drawn”, Edward Tufte.
    • Use cases. Source: SAP 2013
    • Big Data?
    • Big Data for Retail?
    • Big Data: the next big thing in Retail?
    • Fact. We entered a data-driven society. All decisions will soon be made out of data. WE SWITCH FROM “GUESS” TO “KNOW”. We entered the age of information. Human information is growing three times faster than structured, corporate data. We can’t ignore them both anymore. However, tons of data are still under-exploited. Huge opportunities are missed. Companies need help to take the most of external data, delivering strategic insights as the fuel for decision-making and targeted actions. Today, companies can’t ignore those facts to ensure their business sustainability and competitiveness. >
    • Holistic Data-driven Business. External Data Sources are the KEY to sustainable performance HOW DO WE DO THAT
    • CM Tools are here Analytics Prediction OUTSIDE Social Web Data Open Data VIEW Historical Social Data Analysis INSIDE VIEW Corporate Cockpits Standard B.I. PAST Machine-learning Algorithms Corporate Data Machine-learning Algorithms HOW DO WE DO THAT FUTURE
    • How can we do that? You need three things MULTIPLE DATA SOURCES. POWERFUL DATA ANALYSIS. HUMAN INTELLIGENCE. HOW DO WE DO THAT
    • DATA SOURCES WEB DATA SOCIAL DATA OPEN DATA ACQUIRED DATA YOUR DATA BIG DATA ANALYSIS METHODS MICRO SEGMENTATION CUSTOMER INTELLIGENCE PREDICTIVE MODELING PRESCRIPTIVE ANALYSIS BEHAVIORAL OUTLOOK WHAT-IF SCENARIOS SENTIMENT ANALYSIS / NLP DATA-DRIVEN OPERATIONS DATA-DRIVEN CAMPAIGNS ACTIVATION PROJECT MANAGEMENT INFORMATION SYSTEMS LOOPBACK SPECIFIC ACTIONS STRATEGIC CONSULTING
    • The DataGraph in 90 seconds. See it online on swaninsights.com/video
    • The DataGraph in 90 seconds. See it online on swaninsights.com/video
    • The DataGraph in action. Data Sources Extended range of data sources DataGraph Proprietary DataGraph Most advanced Data Analysis Methods Actions Needs Strategic Consultancy Background & Approach Sectorial Knowledge It delivers drastically better results than “mere” software.
    • The DataGraph in action. Data Sources WEB DATA DataGraph GOVERNEMENTS UNIVERSITIES INSTITUTIONS OTHER DATA DATA SUPPLIERS PARTNERS CORPORATE DATA CRM / ERP INDUSTRIAL DATA Needs DATA-DRIVEN CAMPAIGNS DATAGRAPH Insights SOCIAL MEDIA SEARCH ENGINES GOOGLE TRENDS BLOGS / FORUMS OPEN DATA Actions STRATEGIC CONSULTING GRAPH DATABASES RELATION DATABASES INFO SYSTEMS LOOPBACKS DATA ANALYSIS PROPRIETARY ALGORITHMS ADVANCED ANALYSIS METHODS DECISIONMAKING SPECIFIC ACTIONS From data sources to tangible results. IDENTIFIED NEED
    • Types of tangible benefits. Data Products. SAMPLES OF BENEFITS YOU CAN DRAW FROM THE DATAGRAPH. Lead Generation. Lead Ranking. YOU CAN GET A LIST OF LEADS THAT ARE MOST LIKELY TO PURCHASE YOUR PRODUCT. YOU CAN RANK YOUR LEADS BASED ON THEIR PROPENSITY TO CONVERT. > > Client Segmentation. Churn Prevention. Sociography. YOU CAN GET NEW, UNSUSPECTED INFORMATION ON YOUR CLIENT BASE. YOU CAN GET A LIST OF CLIENTS THAT ARE ABOUT TO LEAVE YOUR COMPANY. YOU CAN MAP AND DEFINE GROUPS AGAINST ANY GIVEN TOPIC. > > >
    • Example 1: Simple Lead Ranking for Automotive. Data Sources WEB DATA DataGraph GOVERNEMENTS UNIVERSITIES INSTITUTIONS OTHER DATA DATA SUPPLIERS PARTNERS CORPORATE DATA Needs DATA-DRIVEN CAMPAIGNS DATAGRAPH Insights SOCIAL MEDIA SEARCH ENGINES GOOGLE TRENDS BLOGS / FORUMS OPEN DATA Actions STRATEGIC CONSULTING GRAPH DATABASES RELATION DATABASES LEAD RANKING INFO SYSTEMS LOOPBACKS DATA ANALYSIS PROPRIETARY ALGORITHMS DECISIONMAKING ADVANCED ANALYSIS METHODS SPECIFIC ACTIONS CRM / ERP INDUSTRIAL DATA LEAD RANKING INCREASE CONVERSION RATE
    • Example 2: Advanced Lead Ranking for Automotive. Data Sources WEB DATA DataGraph GOVERNEMENTS UNIVERSITIES INSTITUTIONS OTHER DATA DATA SUPPLIERS PARTNERS CORPORATE DATA Needs DATA-DRIVEN CAMPAIGNS DATAGRAPH Insights SOCIAL MEDIA SEARCH ENGINES GOOGLE TRENDS BLOGS / FORUMS OPEN DATA Actions STRATEGIC CONSULTING GRAPH DATABASES RELATION DATABASES LEAD RANKING INFO SYSTEMS LOOPBACKS DATA ANALYSIS PROPRIETARY ALGORITHMS DECISIONMAKING ADVANCED ANALYSIS METHODS SPECIFIC ACTIONS CRM / ERP INDUSTRIAL DATA LEAD RANKING INCREASE CONVERSION RATE
    • Example 3: Churn Prediction for Telco. Data Sources WEB DATA DataGraph GOVERNEMENTS UNIVERSITIES INSTITUTIONS OTHER DATA DATA SUPPLIERS PARTNERS CORPORATE DATA CRM / ERP INDUSTRIAL DATA Needs DATA-DRIVEN CAMPAIGNS DATAGRAPH Insights SOCIAL MEDIA SEARCH ENGINES GOOGLE TRENDS BLOGS / FORUMS OPEN DATA Actions STRATEGIC CONSULTING GRAPH DATABASES RELATION DATABASES IDENTIFY POTENTIAL CHURNERS INFO SYSTEMS LOOPBACKS DATA ANALYSIS PROPRIETARY ALGORITHMS ADVANCED ANALYSIS METHODS DECISIONMAKING DECREASE CHURN RATE SPECIFIC ACTIONS CHURN PREDICTION
    • Example 4: 360 Client View for Retail. Data Sources WEB DATA DataGraph GOVERNEMENTS UNIVERSITIES INSTITUTIONS OTHER DATA DATA SUPPLIERS PARTNERS CORPORATE DATA CRM / ERP INDUSTRIAL DATA Needs DATA-DRIVEN CAMPAIGNS DATAGRAPH Insights SOCIAL MEDIA SEARCH ENGINES GOOGLE TRENDS BLOGS / FORUMS OPEN DATA Actions STRATEGIC CONSULTING GRAPH DATABASES RELATION DATABASES KNOW CUSTOMERS 360 INFO SYSTEMS LOOPBACKS DATA ANALYSIS PROPRIETARY ALGORITHMS ADVANCED ANALYSIS METHODS DECISIONMAKING RECOMMENDATIONS CROSS-SELL SPECIFIC ACTIONS SEGMENTATION & CHARACTERIZATION UP-SELL
    • Example 4: 360 Client View for Retail. Data Sources WEB DATA TWITTER STREAM GOOGLE TRENDS DataGraph 1 2 LOYALTY CARD PRODUCT GRAPH A- People/Product affinity B- Cross-buying MAPPING SOCIAL GRAPH A- Segmentation B- Characterization Lifestyle/Interests Lifestage Psychology traits Professional info OPEN DATA SOCIODEMOGRAPHICS & CARTOGRAPHY CORPORATE DATA LOYALTY CARDS CLIENTS / GOODS Actions 3 INTEGRATION 180* VIEW WHAT, WHEN, TO WHOM 4 MATCHING WITH SOCIO-DEMO/ CARTOGRAPHY 360* VIEW WHAT, WHEN, TO WHOM AND WHERE Needs DIRECT MARKETING SUPPLY CHAIN PLANNING KNOW CUSTOMERS 360 CRM ENRICHMENT DECISIONMAKING RECOMMENDATIONS CROSS-SELL SEGMENTATION & CHARACTERIZATION UP-SELL
    • Potential of Big Data: examples. Swan Insights’ internal work note (December 2013).
    • Potential of Big Data: 10 examples. 1.  Increase the Average Basket Price 2.  Increase the Customer Year Time Value 3.  Churn Detection 4.  Increase the share-of-caddy 5.  Segment most valuable customers Data Graphization. 6.  Purchase prediction BY THE GRAPHIZATION OF YOUR DATA, IT IS POSSIBLE TO DERIVE AFFINITY LEVELS AND RUN PREDICTIVE MODELS 7.  Bundle-purchase identification 8.  Smart Couponing 9.  Anticipate cash desk congestion > 10.  Real-time pricing changes
    • Ethics & Privacy. It is essential to comply strictly with Privacy regulations and follow a Code of Conduct. Privacy Commissions Master Contracts & NDA Security Policies & Delivery One must declare the activities to the appropriate Privacy Commissions. Service Contracts and NDA’s must foresee privacy clauses and confidentiality. Infrastructure must be protected against intrusion through the latest technologies, and the delivery channels must be adapted to corporate security policies. Master Service Contracts always must include a Security Appendix detailing all measures taken to ensure data integrity.
    • Open Discussion. What kind of Big Data initiatives has your organization started?
    • Let’s keep in touch. Laurent Kinet. Swan on LinkedIn. CEO Swan Insights sa/nv Get our news and insights about Big Data and Social Web Analysis laurent@swaninsights.com > company/swan-insights >