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Using SAS/Hadoop to Support Marketing Analytics with Big Data

Using SAS/Hadoop to Support Marketing Analytics with Big Data. Kerem Tomak VP, Marketing Analytics, Macys.com. Agenda. Who is the customer? Life and death of a customer Data galore Crystal Ball What matters the most…. Who is the customer?. .com stores. Life of a customer.

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Using SAS/Hadoop to Support Marketing Analytics with Big Data

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  1. Using SAS/Hadoop to Support Marketing Analytics with Big Data Kerem Tomak VP, Marketing Analytics, Macys.com

  2. Agenda • Who is the customer? • Life and death of a customer • Data galore • Crystal Ball • What matters the most…

  3. Who is the customer? • .com • stores

  4. Life of a customer • Present value of all future profits obtained from a customer over his or her life of relationship with a firm.

  5. Customer Lifetime Value • The CLV of a customer i is the discounted value of the future profits yielded by this customer • Where • CFi,t = net cash flow generated by the customer i activity at time t • h = time horizon for estimating the CLV • d = discount rate • The CLV is the value added, by an individual customer, to the company

  6. Why is CLV important ? • By knowing the CLV of the customers, one can • Focus on groups of customers of equal wealth • Evaluate the budget of a marketing campaign • Measure the efficiency of a past marketing campaign by evaluating the CLV change it incurred • Focus on the most valuable customers, which deserve to be closely followed • Neglect the less valuable ones, to which the company should pay less attention • Use CLV to introduce new segmentation opportunities

  7. Tapping into the data • Data Storage • Reporting • Analytics • Advanced Analytics • Computing with big datasets is a fundamentally different challenge than doing “big compute” over a small dataset Utilized data Unutilized data that can be available to business

  8. Hadoop & RDBMS Analogy RDBMS & Hadoop is like car & train RDBMS Hadoop Cargo train: • rough • missing a lot of “luxury” • slow to accelerate • carries almost anything • moves a lot of stuff very efficiently Sports car: • refined • has a lot of features • accelerates very fast • pricey • expensive to maintain

  9. RDBMS & Hadoop Comparison* * Cloudera comparison chart

  10. Crystal Ball Source: Forrester 10

  11. Toolshed

  12. What matters the most • Building data infrastructure • Fast processing of large amounts of data and deployment of model scoring on the same environment • Business task execution • Real-time optimization for customized offer management • Planning tools • Give analytical guidelines to campaign management • Strategic support • Develop robust analytics that look at customer’s environment “Making sense out of models” “Deploying in production”

  13. Questions? Kerem Tomak kerem.tomak@macys.com 4154221408

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