Using SAS/Hadoop to Support Marketing Analytics with Big Data

Kerem TomakVP, 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

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

• 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

Customer Lifetime Value

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

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

Unutilized data that can be available to business

Utilized data

Hadoop & RDBMS Analogy

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

RDBMS & Hadoop is like car & trainRDBMS Hadoop

RDBMS & Hadoop Comparison*Traditional RDBMS (Oracle, DB2) Hadoop

Maximum Data Capacity Up to 100’s of TBs Up to 10’s of PBs (hundreds times more)

Processing Capacity Up to 10’s of TBs Up to 10’s of PBs (thousands times more)

Costs High software, license and hardware/storage costs

Cost effective: commodity hardware + open source software

Transactional Yes No (batch process)

Update Patterns Supported Not Supported Yet

Schema Complexity Structured (tables only) Structured or Unstructured

Processing Freedom SQL MapReduce, SQL (Hive), Streaming, Pig, HBase, etc..

Scalability Non-linear scaling Fully distributed and linearly scalable

Reliability Fault-tolerant at high cost, but without self-healing by design

Fault-tolerant and self-healing by desing

Real Time Response Yes No (HBase required)

* Cloudera comparison chart

Crystal Ball

10Source: Forrester

Toolshed

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”

Questions?

Kerem Tomak

kerem.tomak@macys.com

4154221408