PREDICTIVE ANALYTICS. The power to predict who will click, buy, lie, or die

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2 PREDICTIVE ANALYTICS The power to predict who will click, buy, lie, or die

3 Why Use Predictive Analytics in Business Why use predictive analytics in business? right business decision > customer behavior, dealing with overwhelming complexity, hundreds or even thousands of factors, a universe of thousands or millions of customers, people just cannot connect the dots to make the ideal decision, DECISION Predictive Analytics connects the dots scientifically, guiding each decision to greater success.

4 Example organizations that use predictive analytics Google: Predicts which new ads will get many bounces (when people click on an ad, but then immediately click the back button). Stanford University: Derived with predictive modelling, an innovative method that diagnoses breast cancer better than human doctors in part by considering a greater number of factors in a tissue sample.

5 Example organizations that use predictive analytics Obama was re-elected in 2012 with the help of voter prediction. The leading career-focused social network, LinkedIn, predicts your job skills. Target Co. predicts customer pregnancy in order to market relevant products accordingly Online dating leaders Match.com, OkCupid, Tinder predict which hottie on your screen would be the best bet on your side

6 Example organizations that use predictive analytics Computers can literally read your mind. Hewlett-Packard (HP) earmarks each and every one of its more than 330,000 worldwide employees according to Flight Risk the expected chance he or she will quit their job, so that managers may intervene in advance where possible and plan accordingly otherwise. Inspired by the TV crime drama Lie to Me about a micro-expression reader, researchers at the University at Buffalo trained a system to detect lies with 82 percent accuracy by observing eye movements alone.

7 Example organizations that use predictive analytics amazon.com: 35 percent of sales come from product recommendations. Netflix: Sponsored a $1 million competition to improve movie recommendations; a reported 70 percent of Netflix movie choices arise from its online recommendations Target: Increased revenue 15 to 20 percent by targeting direct mail with product choice models.

8 Half the money I spend on advertising is wasted; the trouble is I don t know which half. John Wanamaker BIG If you torture the data long enough, it will confess. RONALD COASE, Professor of Economics, University of Chicago DATA

9 Insider Predictive Analytics Technology that learns from experience (data) to predict the future behaviour of individuals in order to drive better decisions.

10 Insider Predictive Analytics PAST PRESENT FUTURE Predictive analytics simplifies data to amplify value Predictive analytics can navigate overwhelming complexity to give you a clearer view of the future. Data Predictive Analytics Uncertainty Future Outcomes

11 Analytics Reporting Optimal Action Predicted Customer Insights Campaign Execution Auto Optimization Partner Website Which action will drive the best result What is likely to happen in the future Advanced Analytics Analyze the Customers Behaviors Predictive Analytics Auto Segmentation Advanced JS Mobil SDK Surveys Offers Data Integration & Optimization Real-time Updates Information from business systems

12 Likelyhood yerine likelihood olacak Predictive Segmentation Predict a visitor s likely next move and act on real time via our machine learning platform. BEHAVIORAL SEGMENTS NEVER PUCHASED NOT PURCHASED FOR 6 MONTHS WHICH BEHAVIORAL SEGMENTS PRODUCE MOST REVENUE? DISCOUNT SHOPPERS LIKELYHOOD TO PURCHASE AT LEAST PURCHASED ONCE AT LEAST PURCHASED ONCE DISCOUNT SHOPPERS SLEEPLESS LUXURY SHOPPERS LUXURY SHOPPERS WINDOW SHOPPERS WINDOW SHOPPERS LIKELYHOOD TO PURCHASE NOT PURCHASED FOR 6 MOUTHS RECENT VISITORS RECENT VISITORS SLEEPLESS NEVER PUCHASED

13 Rule Model : Likelihood to Purchase Characteristic of and individual Predictive Model Predictive Score & & & & If the visitor comes to website from Facebook has searched on website more than 3 times has checked an item more than once average spent time on product detail pages between 120 seconds and 480 seconds has been less than 20 days since his/her last purchase THEN the probability of purchasing at the end of this session is %75.

14 Auto Segmentation Let algorithms discover your customer segments which acquired best performance High value, fewer orders, Big spend on 1 st order Segment Categories o Demographic Segments $124 average order $595 total revenues 67 days between orders 5 total orders 14 total items $164 first order revenues 3.3 products in first order 3% of orders on clearance +10 more o Behavioral Segments Example : Cluster DNA o Interest Segments Long term, high value, frequent buyers $99 average order $2.261 total revenues 24 days between orders 24 total orders 57 total items $76 first order revenues 1.7 products in first order 6% of orders on clearance +10 more

15 Demographic Segments Gender Male Female Age Over 45 Income High Marital Married Medium Low Status Not married

16 Behavioral Segments Churn / Loyal Customers Never purchased Purchased at least once Frequent buyers Non recent visitors Sleepless Discount Shoppers Window Shoppers Luxury Shoppers Likelihood to purchase (High, medium, low) Spending Pattern (Beginning of month, end of month etc.)

17 Interest Segments Example : Brand Clusters DNA Cluster X Brand Scale Cluster Y Brand Scale Least Interest Pleasure Doing Business Wow Coutre Desigual 6126 L*Space Preferred Brands Tahari Arthur S.Levine Adrienne Vittadini Calvin Klein Eliza J Nine West Preferred Brands Desigual Dzhavael Couture Custo Barcelona Smash! Salvage Least Interest Collective Concepts Wow Couture Max and Cleo Rene Rofe Steven

18 Thank you Yeşilce Mah. Dalgıç Sk. 4.Levent / İSTANBUL burakozencs

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