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Impact Data

Collection Agency Service Provider Non Profit Debt Buyer Credit Grantor Direct Marketer Submit Data File

Debt Buyer

debtOur client had purchased a 1.8 million account portfolio and segmented it with a fresh credit bureau recovery score. When the client contacted Impact Data, its method had been to work the accounts on a descending recovery score, with a manual or automatic contact strategy and no emphasis placed on balance. It was performing below expectations.

The number of accounts by recovery score range (Figure 1) shows a classic bell curve, but the ability to manage the inventory is limited because most of the accounts are clustered in the 533-576 range.

Figure 1

number-of-accounts-by-recovery-score-range

Opportunities Identified

Clearly, the Client’s “Call Everything” strategy wasn’t helping to achieve the maximum revenue. Impact Data captured all cumulative payment activity since the portfolio was purchased, in addition to Zip and balance. A review of the results-based data clearly identified inconsistencies with work effort and portfolio performance expectations. The unit yields for the highest scored accounts was true to the design of the score, however, the volume of accounts was not sufficient to justify the strategy (Figure 2).

The volume of accounts in the 533—576 band was over 770,000 and was not providing the yields necessary to support the return objectives on the portfolio. Impact Data offered a plan that would identify accounts with a higher propensity to perform based on geo-economic segmentation with the existing credit bureau recovery score as an overlay. The effect would be a simple 30-segment matrix that could be used to quickly identify operational strengths to reduce overhead expense and increase revenue.

Figure 2

dollers-per-account-recovered-by-recovery-score-range

Impact Segmentation Matrix

The matrix was created to incorporate 3 Impact ranges and the existing 10 Credit Bureau recovery score ranges from the client. The table below (Figure 3) shows the distribution across the 30 segments. Two items that clearly stand out are the Rank Ordering of accounts from Impact High to Low and the total accounts in the Impact High column compared to the total accounts in the 533—576 Recovery score range.

Figure 3

total-accounts-recovery-score

Impact Segmentation Results

The results of applying an Impact segmentation model created a huge opportunity for our client in regard to improvement to overall results and workforce allocation. By focusing solely on credit bureau recovery score, our client was clearly wasting effort on accounts that did not have a strong propensity to perform (Figure 4).

The Impact High range had a comparable number of accounts to the 533-576 recovery score range but produced a $2.6 million revenue improvement. Our client was also able to identify segments that could be outsourced to agencies thus reducing a significant amount of overhead expense that was applied needlessly to this portfolio.

Figure 4

payments-by-segments

What can Impact Data do to help you make smart decisions about buying and selling debt? We’ll prove our performance before we start with a sample of your data. Contact us to get started improving your revenues and lessening your overhead. Or, register to receive our free eBook and learn more about the ways Impact Data can transform profitability through data intelligence.

 

Submit a sample data file to see how Impact Data can point you to clients with the highest propensity to perform.