Big data vs small data peter zhang

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Presentation at “Big data vs. Small data” Panel, Kellogg Marketing Conference 2013, Kellogg School of Management.

Big data vs. Small data
From a start-up to $100MM EBITDA:
the power of data driven strategies

Peter Zhang
Director 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
small
Improve 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-mining
Build 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.

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Big data vs small data peter zhang

1. Big data vs. Small data From a start-up to $100MM EBITDA: the power of data driven strategies Peter Zhang Director of Customer Analytics, Sears Holding Co. Founder & President, Simon Peter LLC

2. 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.

3. 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

4. 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

5. Strategy: Use data-driven strategies to maximize profit, no matter data is big or small Improve 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-mining Build 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.”

6. 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.

7. 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.

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Big data vs small data peter zhang

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