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V imp analytics appli ed V imp analytics appli ed Presentation Transcript

  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Eric Siegel, Ph.D.Eric Siegel, Ph.D. Prediction Impact, Inc.Prediction Impact, Inc. eric@predictionimpact.comeric@predictionimpact.com (415) 683 - 1146(415) 683 - 1146 Predictive Analytics Applied: Marketing and Web Predictive Analytics Applied:Predictive Analytics Applied: Marketing and WebMarketing and Web Brought to you by Prediction Impact and World Organization of Webmasters (WOW)
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 1 Training Program OutlineTraining Program OutlineTraining Program Outline 1. Introduction 2. How Predictive Analytics Works 3. App: Customer Retention 4. App: Targeting Ads 5. Summary and take-aways
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Eric Siegel, Ph.D.Eric Siegel, Ph.D. Prediction Impact, Inc.Prediction Impact, Inc. eric@predictionimpact.comeric@predictionimpact.com (415) 683 - 1146(415) 683 - 1146 About the speaker: Eric Siegel, Ph.D. – President of Prediction Impact, Inc. – Training and services in predictive analytics – Former computer science professor at Columbia University – Before Prediction Impact, cofounded two software companies
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Business intelligence technology that produces a predictive score for each customer or prospect Predictive Analytics:Predictive Analytics:Predictive Analytics: 34 12 27 79 42 5934 1234 271234 79271234 42 5979271234 1234
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Optimizing Business ProcessesOptimizing Business ProcessesOptimizing Business Processes 4 “At a time when companies in many industries offer similar products and use comparable technology, high-performance business processes are among the last remaining points of differentiation.” - Tom Davenport
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Learn from Organizational ExperienceLearn fromLearn from Organizational ExperienceOrganizational Experience 5 Data is a core strategic asset – it encodes your business’ collective experience It is imperative to: Learn from your data. Learn as much as possible about your customers. Learn how to treat each customer individually.
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Predictive Analytics:Predictive Analytics:Predictive Analytics: 6 Your organization learns from its collective experience Collective experience: Sales records, customer profiles, … Learn: Discover strategic insights and business intelligence …and puts this knowledge to action. Discover something that makes business decisions automatically Discover business rules
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Eric Siegel, Ph.D.Eric Siegel, Ph.D. Prediction Impact, Inc.Prediction Impact, Inc. eric@predictionimpact.comeric@predictionimpact.com (415) 683 - 1146(415) 683 - 1146 How Predictive Analytics WorksHow Predictive Analytics WorksHow Predictive Analytics Works Building and Deploying Predictive ModelsBuilding and Deploying Predictive Models Predictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and Web
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Wisdom Gained: A Predictive Model is BuiltWisdom Gained: A Predictive Model is BuiltWisdom Gained: A Predictive Model is Built 8 Predictive model Customer behavior Customer contact logs Customer profiles Modeling tool •Performed by modeling software •Usually offline
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Applying a Model to Score a CustomerApplying a Model to Score a CustomerApplying a Model to Score a Customer 9 Predictive model Customer profile Customer behavior Predicted response •Often performed by modeling software •Possibly online
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Deploy to Take Business ActionDeploy to Take Business ActionDeploy to Take Business Action 10 •Usually not performed by modeling software Predicted response Business logic • Mail a solicitation • Suggest a cross-sell option • Retain with a promotion Business actions, such as: •Possibly online
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Predictive models are created automatically from your data So, they’re generated according to your: – Product – Prediction goal – Business model – Customer base This means customer intelligence specialized for your business – Customized business rules – Unique, proprietary mailing lists – Insights only your organization could possibly gain Fine-Tuned Models for Your BusinessFine-Tuned Models for Your BusinessFine-Tuned Models for Your Business 11
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Predictors: Building Blocks for ModelsPredictors:Predictors: Building Blocks for ModelsBuilding Blocks for Models 12 • recency – How recent was the last purchase? • personal income – How much to spend? Combine predictors for better rankings: recency + personal income 2_recency + personal income
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Training Data Is a Flat TableTraining Data Is a Flat TableTraining Data Is a Flat Table 13 Number purchases: Last purchase: Gender: Income: Likely to buy: 7 shoes M high gloves 3 gloves F medium hat 1 hat M high shoes 46 gloves F low piano This is the required form for training data; one record per customer.
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Why Not Memorize The Training Examples?Why Not Memorize The Training Examples?Why Not Memorize The Training Examples? 14 To learn from history is to remember history. So, we could just keep a lookup table and compare new cases to old ones. Answer: There are too many possible rows of data. That is, each customer is unique!
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Decision Tree for Cross SellDecision Tree for Cross Sell 15 Decision Trees •Non-linear •Specialized •Precise Bought shoes? Female? Hat Gloves Shoes Piano High Income? If they bought shoes and they’re not female then sell gloves. If they didn’t buy shoes and they’re high income then sell shoes.
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Response Modeling for Direct MarketingResponse Modeling forResponse Modeling for Direct MarketingDirect Marketing 16 Lower campaign costs and increase response rates • Make campaigns more targeted and more selective • Identify segments five or more times as responsive • Achieve 80% of responses with just 40% of mailing • Increase campaign ROI & profitability
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Predictive Models Score Each CustomerPredictive Models Score Each CustomerPredictive Models Score Each Customer 17 Name: • Each customer is scored with a response probability • The customers are listed in order of prediction score • The highest-scoring customers are targeted first No !14%T. Mitchel674 Yes "22%G. Clooney256 No !31%D. Leong528 Yes "35%E. Siegel429 Response:Score:ID number:
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 18 -700,000 -600,000 -500,000 -400,000 -300,000 -200,000 -100,000 0 100,000 200,000 300,000 400,000 Percent of Customers Contacted Profit 0% 25% 50% 100%75% Customers ranked with predictive analytics Customers not ranked CampaignProfitCurveCampaignProfitCurveCampaignProfitCurve
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Lift ChartLift ChartLift Chart 19 9.3 30.6 16.4 43.5 20.3 51.1 32.4 70.1 44.0 80.2 0 20 40 60 80 100 0 20 40 60 80 100 %Class % Population 0 20 40 60 80 100 • 50,000 customers 3 times as likely to buy • 200,000 customers 2 times as likely to buy
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Example Business RuleExample Business RuleExample Business Rule 20 New customers who come to the website off organic search results, buy more than $150 on their first transaction, are male, and have an email address that ends with “.net” are three times as likely to be return customers.
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Eric Siegel, Ph.D.Eric Siegel, Ph.D. Prediction Impact, Inc.Prediction Impact, Inc. eric@predictionimpact.comeric@predictionimpact.com (415) 683 - 1146(415) 683 - 1146 App: Customer RetentionApp:App: Customer RetentionCustomer Retention Retain Customers by Predicting Who Will DefectRetain Customers by Predicting Who Will Defect Predictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and Web
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Growth = Acquisition - DefectionGrowth = AcquisitionGrowth = Acquisition -- DefectionDefection 22 Customer base Acquisition: New customers Defection: Lost customers Which is the best way to increase growth? 1. Increase acquisition 2. Decrease defection
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Increase retention by 3% and boost growth by 12% Increase retention by 3%Increase retention by 3% and boost growth by 12%and boost growth by 12% 23 3%
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Predictor VariablesPredictor VariablesPredictor Variables 24 • Age of membership • External acquisition source • U.S. state of residence • Willing to receive postal mailing (opt-in) • Num days since last failed login attempt • Num days between logins
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. At-Risk SegmentAt-Risk SegmentAt-Risk Segment 25 • SOURCE_COBRAND$ == chat • SUBSCR_AGE <= 237.819 • LOGIN_DAYSSINCELAST_F > 1.85344 • LOGIN_DAYSSINCELAST_F <= 22.6578 Churn rate: 33.5% (vs. 20% average)
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Loyal SegmentLoyal SegmentLoyal Segment 26 • STATE is one of many, including ME, NE, NH, NS, QC, SK, WY • SOURCE_COBRAND$ == chat • SUBSCR_AGE > 237.819 • TIME_SINCE_JOINED <= 535.456 • LOGIN_DAYSSINCELAST_F > 99.6539 • LOGIN_DAYSSINCEFIRST_F > 112.003 • LOGIN_DAYSSINCELAST_S > 131.45 Churn rate: 6.5% (vs. 20% average) Very loyal Small segment
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 27Cost per mailing: $25, ave profit: $100, num subscribers: 100,000 Forecasted Profit of Retention Campaign
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Loyal Segment: Online RetailLoyal Segment: Online RetailLoyal Segment: Online Retail 28 • ITEM_QTY > 2.5 • TAX_SHIP <= 8.78 • 19% will return (versus 10% average)
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Eric Siegel, Ph.D.Eric Siegel, Ph.D. Prediction Impact, Inc.Prediction Impact, Inc. eric@predictionimpact.comeric@predictionimpact.com (415) 683 - 1146(415) 683 - 1146 App: Targeting AdsApp:App: Targeting AdsTargeting Ads PredictivelyPredictively Modeling Which PromotionModeling Which Promotion Each Customer Will AcceptEach Customer Will Accept
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 3030 Target content – Landing page – Featured product – Color of product displayed – Promotion or advertisement Based on: – Customer behavior profile • Browsers vs. hunters – Time of day – Geographical location – Pages – Where they came from • Ad that clicked them through • Site they came from • Search query they were on • Profile, when available Learn More From AB Testing: “Dynamic AB Selection” Learn More From AB Testing: “Dynamic AB Selection” A B C ?
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Dynamic AB SelectionDynamic AB SelectionDynamic AB Selection 31 Predictive model for B Mary’s profile Trials of B Trials of A Modeling tool Modeling tool Predictive model for ATrials of A Trials of BTrials of BTrials of B offline online Predictive score: What’s the chance Mary responds to A? Predictive score: What’s the chance Mary responds to B?
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Selecting Between 291 Sponsored PromotionsSelecting Between 291 Sponsored PromotionsSelecting Between 291 Sponsored Promotions 32 Client: A leading student grant and scholarship search service Sponsors include: – Universities – Student loans – Military recruitment – Other misc.
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. The Business: Great Potential GainThe Business: Great Potential GainThe Business: Great Potential Gain 33 • High bounties, up to $25 per acceptance • High acceptance rates, up to 5%, due to general relevancy of the promotions • Big Data: Wide and Long – Rich user profiles volunteered for funds eligibility – Over 50 million training cases: 01/05 - 09/05
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 34 Prior solicitation of this program 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% No Yes Opt_in_status 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% Y N Region of current address 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% south northeast west midwest Sority or Fraternity 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 8.00% 9.00% frat none soro Already seen? N/Y Email opt-in Y/N Region: S, NE, W, M-W Fraternity/None/Sorority
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Highly Responsive Segment for "Art Institute" AdHighly Responsive Segment for "Art Institute" AdHighly Responsive Segment for "Art Institute" Ad 35 Segment definition included: • Still in high school • Expected college graduation date in 2008 or earlier • Certain military interest • Never saw this ad before Segment probability of accepting ad: 13.5% Average overall probability of accepting ad: 2.7%
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Highly Responsive Segment for a "Navy" AdHighly Responsive Segment for a "Navy" AdHighly Responsive Segment for a "Navy" Ad 36 Segment definition included: • Has opted in for emails • Has not seen this ad before • Is in college • SAT verb-to-math ratio is not too low, nor too high • SAT written is over 480 • ACT score is over 15 • No high school name specified Segment probability of accepting ad: 2.6% Average overall probability of accepting ad: 1.6%
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Money-Making Model: Improved Resulting Revenue Money-Making Model:Money-Making Model: Improved Resulting RevenueImproved Resulting Revenue 37 A-B test deployment to compare: A. Legacy system based on acceptance rates across users B. Model-based ad selection Result: 25% increased acceptance rate 3.6% increased revenue observed; 5% later reported by the client This comes to almost $1 million per year in additional revenue, given the existing $1.5 million monthly revenue.
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Eric Siegel, Ph.D.Eric Siegel, Ph.D. Prediction Impact, Inc.Prediction Impact, Inc. eric@predictionimpact.comeric@predictionimpact.com (415) 683 - 1146(415) 683 - 1146 ConclusionsConclusionsConclusions Summary and take-Summary and take-awaysaways Predictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and WebPredictive Analytics for Business, Marketing and Web
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Predictive Analytics InitiativesPredictive Analytics InitiativesPredictive Analytics Initiatives 39 • Per-customer predictions: Unlimited range of business objectives • Management: An organizational process ensures predictions are actionable, driven by business needs; multiple roles and skills • Deployment: Mitigate risk and track performance
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. Predictive Analytics TechnologyPredictive Analytics TechnologyPredictive Analytics Technology 40 • Data preparation: An intensive bottleneck, critical to success • Modeling methods and tools: No one method that is always best; compare multiple methods
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 41 Prediction Impact Predictive Analytics and Data Mining Services: • Defining analytical goals & sourcing data • Developing predictive models • Designing and architecting solutions for model deployment • "Quick hit" proof-of-concept pilot projects Training programs: • Public seminars: Two days, in San Francisco, Washington DC, and other locations • On-site training options: Flexible, specialized • Instructor: Eric Siegel, Ph.D., President, 15 years of data mining, experienced consultant, award-winning Columbia professor • Prior attendees: Boeing, Corporate Express, Compass Bank, Hewlett-Packard, Liberty Mutual, Merck, MITRE, Monster.com, NASA, Qwest, SAS, U.S. Census Bureau, Yahoo! © Copyright 2006. All Rights Reserved Prediction Impact, Inc.© Copyright 2008. All Rights Reserved If you predict it, you own it.www.PredictionImpact.com
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 42 Prediction Impact Predictive Analytics and Data Mining © Copyright 2006. All Rights Reserved© Copyright 2008. All Rights Reserved If you predict it, you own it.www.PredictionImpact.com Applications: • Response modeling for direct marketing • Product recommendations • Dynamic content, email and ad selection • Customer retention • Strategic segmentation • Security – Fraud discovery – Intrusion detection – Risk mitigation – Malicious user behavior identification • Cutting-edge research for groundbreaking data mining initiatives Prediction Impact, Inc. Verticals: • Online business: Social networks, entertainment, retail, dating, job hunting • Telecommunications • Financial organizations • A fortune 100 technology company • Non-profits • High-tech startups • Direct marketing, catalogue retail
  • Prediction Impact, Inc.© Copyright 2008. All Rights Reserved. 43 Prediction Impact Predictive Analytics and Data Mining © Copyright 2006. All Rights Reserved© Copyright 2008. All Rights Reserved If you predict it, you own it. Team of several senior consultants: • Experts in predictive modeling for business and marketing • Relevant graduate-level degrees • Communication in business terms • Complementary analytical specialties and client verticals • Published in research journals and industrials Extended network of many more: • Closely collaborating partner firms • East coast coverage Prediction Impact, Inc. Eric Siegel, Ph.D., President Prediction Impact, Inc. San Francisco, California eric@predictionimpact.com (415) 683 - 1146 For our bi-annual newsletter, click “subscribe”: www.PredictionImpact.com To receive notifications of training seminars: training@predictionimpact.com