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Leverage your customer data to predict your customers actions - Colin Linsky
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Presentation from an IBM Business Analytics seminar, held the 22th of november 2012 at IBM Client Center Nordic. ...

Presentation from an IBM Business Analytics seminar, held the 22th of november 2012 at IBM Client Center Nordic.

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IBM has studied the success factors needed to create optimal customer experiences. Analysis is a key factor to recognize the most profitable customers, optimize sales activity and pricing as well as improve the quality of the company's encounter with the customer. We discuss how to use your customer data actively to predict and influence future customer behavior and create loyal customers.

Colin Linsky, Predictive Analytics Worldwide Retail Sector Leader, IBM

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  • SPSS Inc. Copyright 2006 SPSS Inc.
  • SPSS Inc. Copyright 2006 SPSS Inc.
  • Sentiment evolution over time comparing Ariel and Persil. The positive peak in January relates to the Persil Gel launch by Henkel Arabia. The positive peak in July relates to the Persil Cleaner Planet Plan announcement in New Zealand.
  • From each chart one can drill down to the snippet view where concepts, hotwords and sentiment are highlighted and additional metadata is shown, including the original URL of the post.
  • The evolving topics are automatically detected without preconfiguration and are visualized in so-called „topic rivers“ that show the temporal evolution of topics along with the keywords that are most frequently mentioned within the snippets that make up the topics.
  • SPSS Inc. Copyright 2006 SPSS Inc.

Leverage your customer data to predict your customers actions - Colin Linsky Presentation Transcript

  • 1. Leverage your customer data topredict your customers actionsDr Colin LinskyWW Predictive Analytics Retail LeaderIBM SPSS Industry Solutions Team © 2012 IBM Corporation
  • 2. Agenda  Business Analytics – The Competitive Advantage  Business Analytics in Action – Customer Analytics – Market Basket Analysis – Next Best Action  The Analytics Centre of Excellence  Harvesting and Actioning Consumer Insight2
  • 3. 1. Business Analytics – The Competitive Advantage © 2012 IBM Corporation
  • 4. Business Analytics BI PA What What to do Why? happened? next? From Sense and Respond to Predict and Act4
  • 5. Predictive Analytics – What is it?• A true analytics process is the one that transforms raw data into actionable insights, the true transformation from "So What?" to "Now What?".• Business Analytics is the process that transforms raw data into actionable strategic knowledge to guide decisions aiming to increase market share, revenue and profit.• Drive your business by making informed decisions based insights derived from analyzing one of you most valuable company assets, data.• Analytics takes data and translates it into meaningful, value-added options for leadership decisions.• Actionable, statistically supported insights from data that help drive competitive advantage.• “By 2014, 30% of analytic applications will use proactive, predictive and forecasting capabilities” Gartner Forecast, 2011 http://www.readwriteweb.com/enterprise/2011/01/business-analytics-predictions.php
  • 6. Key Moments of Truth  Research and Browse  Browsing and cart use Attract  Pre-purchase  Checkout and payment  Delivery  Multi-Channel use Grow  Sign-up to a Loyalty Program  Response to a campaign or promotion  Credit application Retain  Complaint  Claim  Customer Service Request Fraud  Warranty registration  Blog/Twitter  Social Media  Product out-of-stock Risk  Destruction of perishables  Low velocity product sales  Demand forecast
  • 7. Consolidated Data Sources7
  • 8. Driving Smarter Business Outcomes Capture Predict ActEnabling a complete view of Understand customers micro-behavior Deploy predictive analytics the customer combining across channels, predict their next within business processes,enterprise and social media move and make the next best offer across access platforms, based data maximizing operational impact Text Data Statistics … Data Collection Mining Mining Deployment Platform Technologies Pre-built Content Attract Up-sell Retain …
  • 9. 2. Business Analytics in Action © 2012 IBM Corporation
  • 10. Customer Life Cycle – Customer Experience Framework Research Product Advocate Up/Cross Purchase Product Sold Product Get Customer Use Service Product
  • 11. Customer Life Cycle – Customer Experience Framework Marketing Social Intelligence Research Product Sales Advocate Up/Cross Purchase Product Sold Product Get Customer Use Service Product Feedback Management Support/Services
  • 12. Customer Life Cycle – Case Studies Marketing Social Intelligence Research Product Sales Advocate Up/Cross Purchase Product Sold Product Get Customer Use Service Product Feedback Management Support/Services12
  • 13. Customer Life Cycle – Customer Experience Framework Marketing Social Intelligence Research Cost of e-mail marketing as a Product cost percentage of revenue 71,000 responses analysed and (CPR) was cut almost by half online buzz increased by over Sales 400% Advocate Up/Cross Purchase Product Sold Product Analyzes 30 to 40 data points per customer to deliver actionable insights, giving in a Delivers preventive health Decreased churn 3.1% boost in response rate information to individuals from 19% to just Get Customer Use in a format that motivates under 2% Product Service them to take action Feedback Management More easily identify potentially Support/Services fraudulent claims, increasing customer profitability by 20%
  • 14. Example: Predictive Analytics and merchandising In-store promotion decisions Association POS Transaction Data detection Capture Predict Act
  • 15. Example: Predictive Analytics and marketing In-store promotion decisions Association POS Transaction Data detection “Blanket” marketing Demographics Customer Analysis Interactions Segments Profiles Targeted marketing Scoring models ... Attitudes Capture Predict Act
  • 16. Example: Loyalty, targeting, promotions and incentives Promotional Display Buy X get Z for only Domain Expertise $1.49! Market basket insights • If A then B Transactions • If C then D from all • If E and F then G customers • If H, then H then I Special Offer – This Week Only 10% off on any of these combinations: A + B…G + H…. Predictive Models Offers Transactions from this  % $ 1 Gillette razors customer Statement • Cardholder since YYYYMM insert  % $ 2 L’Oreal shampoo • Average transaction value • Monthly transaction value 3 13  % $ 3 House brand shampoo • Categories purchased  % $ 4 House brand hair color • Brands purchased 6 12 456  % $ 5 Colgate toothpaste Descriptive 6636 • Age  % $ 6 Nivea skin care • Gender  % $ Men’s fragrance • Family situation 7 • Zip code  % $ 8 Woman’s fragrance  % $ 9 House brand sun care Interactions Statement • Web registration insert  % $ 10 Optician • Web visits • Customer service contacts  % $ 11 Feminine hygiene • Channel preference 12 15  % $ 12 Online photo service 773 11 3 Attitudes 9245  % $ Family planning 13 • Satisfaction scores • Shopper type  % $ 14 Pampers diapers • Eco score  % $ 15 House brand diapers16
  • 17. It’s not just about marketing - what should we do for these customers?
  • 18. Example: Next Best Action Customer Reporting, KPIs and Alerts Association Browsing Business Rules LTV Transactions Domain Expertise Propensity Predictive Modeling Products Customer Predictive Engagement Model Scoring Inventory 3rd Party, CSR,Classification Social Media, Survey … Analytical Decision ManagementSegmentation Supply Chain Capture Predict Act
  • 19. The Largest Online Shopping Mall in Japan Merchants: over 37,000 Customers: over 80 million Top page PV: 8 million / day # of orders: 500,000 / day Gross Mercandise Sales (GMS): 3 billion yen GMS growth: +18% YoY
  • 20. Japanese Online Retailer Mobile Full Browser Page
  • 21. The vital ingredients… Predictive Expertise – Models predict customer segment and category affinity – Customer Segmentation (Funnel) – Market Basket Analysis (Prior sales) – Category Affinity (Products and activity – Browse/Purchase) – Current Interaction history (What’s happening during the interaction) – Price Sensitivity Calculations and Offers – Inventory Based Suggestions Decision Management – Combine predictive intelligence with business know-how – Prioritize offers based on profitability and propensity to respond. – Deliver recommendations and personalizations to a website or point of sale Business Intelligence – Understand your current state and your potential state – Monitor results and fine-tune your business – Inform strategy with a view into the future Synthesis of data sources and data types – Overlay browsing history onto purchase history to profile customers – Use profile to drive better recommendations, offers and actions
  • 22. 3. Harvesting Social Media © 2012 IBM Corporation
  • 23. Sentiment Analysis
  • 24. Snippet View
  • 25. Evolving Topics
  • 26. 4. The Analytics Centre of Excellence © 2012 IBM Corporation
  • 27. Customer analytics scenario Data and Model Customer Management Services Campaigns Data Driven Segmentation Multi-Channel ECommerce and Profiling Deployment Single View Targeting Models of the Sales Tools Customer Customer Performance Reporting Customer LTV POS Measurement Data Quality Ad hoc Queries Feedback 3rd Party Data Sources Infrastructure Modelling Data Sources Measurement Deployment Governance
  • 28. Analytics Centre of Excellence:Best practices, governance and production Collaboration – Analysts – Best Practice – Recycling – Consumers Model Management – Strategic Asset – Test & Production – Governance Automation and Scheduling – Analytics as part of business process: event or time based – Back-office actions Scoring – Batch – Real (Right?) Time Integration – Seamless integration into existing systems and business processes – Open, flexible and customizable
  • 29. Leverage your customer data topredict your customers actionsDr Colin LinskyWW Predictive Analytics Retail LeaderIBM SPSS Industry Solutions Team © 2012 IBM Corporation