FINANCIAL SERVICES CASE STUDY

P.E. Funded Alternative Lender Profitability Study

Mettacite Capabilities Overview:

Whether your goal is to increase top line revenue or position your team or organization for rapid growth, Mettacite helps you better understand and uncover new and existing opportunities to create long-term profitable client relationships.

We create simple, sustainable customer acquisition and retention processes to help you grow your business. Using data from as many customers touch points and internal systems as possible, we identify key insights that inform and guide the implementation and success of critical growth strategies.

We use these key insights to help uncover the most profitable/ valuable prospective and current customers so that you can maximize your time, effort, and energy to grow your business.

Private Lender Case Study:

The client is a private lender to small businesses engaged in buying and renovating residential properties.  Since its inception, the client has captured every detail from its extensive loan application form, as well as data relevant to the terms of the loan and the outcomes – in this case the payback and/or modification of the loan.  They also have limited data pertaining to the location of the subject property and its surrounding area.

The data is culled from the client’s CRM system.  Many individuals have been involved in the capture and recording of application data, so data quality is an issue.  Customers range from those with single loans to those with more than a hundred.  Any effort to use the data as a basis for decisions and insight would require a preliminary effort to clean and normalize the CRM data.

Desired outcome of the Mettacite engagement:

Create a clean, normalized data file in which each row represents a completed loan.  Determine whether any of these variables, alone or in combination, can predict early loan repayment, which has been identified as a highly desirable outcome.  Although there was significant company folklore tying early repayment to the Net Worth of the borrower, Mettacite began with the null hypothesis rather than with the goal to prove the client’s belief.

Process/Outcome:

Mettacite created the necessary clean file, calculated the value of some 990 unique correlations using two different statistical methods.  Mettacite then performed a Principal Components Analysis to expose dimensions and patterns in the dataset that were not made visible through simple bivariate analysis.  Finally, Mettacite made several attempts to create multilinear regression models, in an effort to find out whether early repayment could be predicted by means of such an algorithm.

Value provided to the client:

Knowing whether early repayment is predictable and to what extent (given the available data) was crucial to the client for two reasons:  1) It would help them eliminate a significant uncertainty in assessing the health of the current portfolio.  2) It would prevent their making decisions and taking actions predicated on unsupported received wisdom.  

Outcome:

We determined beyond the shadow of a doubt that the available data was insufficient to predict early repayment with any acceptable degree of certainty.  Along the way, we disproved the prevailing wisdom that high Net Income was a key driver of repayment behavior.  Finally, we suggested several other data points and data sources that may contribute to a successful model in the future.  Additionally, though the data is insufficient to predict repayment timing, we were able to identify some variables that were weak to moderately correlated with early repayment.  On the basis of these, we were able to suggest some very inexpensive operational changes that may result in a higher incidence of early repayment.    

Mettacite creates a process to help companies measure and link revenue, acceleration strategies, and results to objective metrics that more accurately reflect the overall value of a given customer relationship and portfolio.

Mettacite makes it possible for senior leadership to pursue company strategy on the basis of fact-based findings.