Big data vs. Small dataFrom a start-up to $100MM EBITDA:

the power of data driven strategies

Peter ZhangDirector of Customer Analytics, Sears Holding Co.

Founder & President, Simon Peter LLC

Background

• High risk, below-the-sub-prime industry.

• Fragmented market.

• A start up in online consumer lending.

• An efficient on-line platform, a strong database team, and, a lot of data.

• An innovative founder/leader.

Old Strategies

• Purchase the leads (loan applications) from leads providers. – costs were high

• Organic traffic (loan applications online) from company’s web page. – traffic was low

• Risk-based lead purchasing strategy. – high default

• Dilemma:Driving up volume vs. cut default

Marketing vs. Risk

What is the goal?

• Risk management: cut default rate

• Marketing goal: increase lead/loan volume

Show me the money! Maximize long-term profit: Optimize both

marketing and risk management on one platform

Strategy: Use data-driven strategies to maximize profit, no matter data is big or

smallImprove risk management: go deeper in data-mining Instead of using off-shelf credit score, build customized model based on target population to improve prediction of default. Dig into the details of data.

“Devil hides in details. ”

Improve marketing: go broader in data-miningBuild profit model based on short-term and long-term profitability. Use profit score to decide lead price and underwrite loans. Drive up good lead traffic. “We want market share, but only the good market share.”

Strategy: Continuously optimize operations using data-mining

• Improve organic web traffic: Search engine optimization, Google search rank, improve the cost-

efficiency of the banner ads.

• Improve operation efficiency: Use historical data to optimize the call center resource allocation.

• Improve collection operation: Build collection model to decide when, what channel and how to do

collection.

• Improve debt selling strategy: Build debt evaluation model, Sell the bad debts that is not worth to

collect in early stage and get better return.

Strategy: Continuously optimize operations using data-mining

• Improve organic web traffic: Search engine optimization, Google search rank, improve the cost-

efficiency of the banner ads.

• Improve operation efficiency: Use historical data to optimize the call center resource allocation.

• Improve collection operation: Build collection model to decide when, what channel and how to do

collection.

• Improve debt selling strategy: Build debt evaluation model, Sell the bad debts that is not worth to

collect in early stage and get better return.