An alternative small business lender wanted to target creditworthy small businesses and small business owners with a pre-qualified loan offer. Their aim was to leverage their marketing spend for a higher return on investment (ROI).
Using the Powerlytics Data Platform that includes the anonymized financial statements of over 30 million for profit business in the US, we developed a score to prioritize businesses for marketing campaigns. The score identified the industry sector and ZIP Code combinations that contain the most qualified prospects based on the lender’s underwriting criteria.
To be considered a pre-qualified business prospect, the lender provided Powerlytics with these requirements:
The following highlights the results just for the San Antonio MSA. In the San Antonio MSA there are about 164,000 NAICS6/ZIP Code combinations. Powerlytics limited the 164,000 combinations to only those that would pass the lender’s underwriting requirements noted above.
By limiting the data, Powerlytics identified approximately 9,400 partnerships and corporations and 1,100 sole proprietorships that would be good candidates for the lender.
We then created a score for each NAICS6/ZIP Code combination based on the Return on Sales metric, and rank-ordered the highest scoring NAICS6/ZIP Code combination for the lender.
Small Business Owner
The Powerlytics data platform also provides a comprehensive and anonymized financial view of over 150 million households in the US.
Using this data, we developed a score to prioritize small business owners for marketing campaigns, identifying the ZIP+4 where business owners live and have businesses that qualify for the lender’s criteria. ZIP+4 is a very granular targeting approach since, on average, there are only 3-4 homes within a ZIP+4.
In the San Antonio MSA, there are approximately 196,000 ZIP+4s. Less than 200 of the 196,000 ZIP+4s in the area have business owners with metrics that met the lenders criteria. There are approximately 1,500 households in those areas.
Powerlytics created a score that rank orders the qualifying ZIP+4s based on the % of Households with Business owners as well as Return on Sales for those businesses.
Our clients have run multiple campaigns using this and similar approaches, such as conducting a correlation analysis with their portfolios and developing look-a-like algorithms to identify prospects that resemble their best performing customers. Booked loans on average have doubled compared to previous results when not leveraging Powerlytics data and scores. As a result, clients have been able to allocate their marketing spend on prospects that score higher and reduce spend on prospects that are less likely to produce a positive ROI.