| Project Objective |
To identify drivers of profit - customer and product groups or
segments, which would serve as basis for realigning business strategies
and facilitate restructuring of broker commissions. |
Client |
| UK based financial organization that lends mortgage loans to borrowers with poor credit record (sub prime customer group).
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Approach |
| Borrower and transaction level data from client (31000 accounts
totaling 1 million transactions) were extracted into a suitable format.
Profitability metrics Net Present Value and Return on Capital were
calculated for all closed customer accounts (nearly 18000). Initial
data analysis was aimed at data completeness and sensibility.
Historical evolution of profitability and all causal variables
borrower, product type, broker and account performance variables were
profiled to remove unusual effects characteristic of specific time
periods and arrive at a data subset more suitable for further analysis.
Further analyses focused on finding what drove profitability. Bi-variate analysis on all causal variables was conducted to see direct
impact of them on profitability, if any. Segmentation was then
performed to find if there were any combinations of variables that
drove profitability and which could not have been identified through
bi-variate analysis. Segmentation was done using CART (Classification
And Regression Trees).
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| Phased approach for Profitability Analysis |
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| Create What-if scenarios and analyze |
| Network Related Scenarios |
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From where to source raw material and how to supply FG to One Stop Shops and retailers? |
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Do they need distributors? |
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What should be the size of a storage (Silo) at plant? |
| Policy Related Scenarios |
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How responsive is my supply chain to sudden spikes in customer demand? |
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If I increase the service level from 95% to 99%, how much average inventory will increase and where? |
| Scenario Analysis for Supply Chain |
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Solution and Benefit |
| Profiles for arrears, redemptions, losses and interest incomes were
developed. A detailed analysis on profitability and consideration of
market context indicated multiple opportunities for improved
profitability such as differential pricing based on Products, Customers
and their combinations and differential commissions for brokers. Using
the current set of customers, profitability forecasts were made based
on the insights obtained from the profitability segmentation.
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Identify focus areas for client to ensure maximum profitability |
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Mortgage lenders specializing in non-conforming secured
lending in the UK |
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Identified several opportunities for improving profitability and guided development of a differential broker Commission Structure
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- Predictive Analytics helps in Strategic Marketing!
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Financial Services