Financial Services Audience Segments

We’ve identified the likely financial needs and product holdings of Australian households.

How we do it

We combined consumer research, credit risk, product demand, and high net worth data to develop a set of 42 models that identify consumer’s likely financial needs and product holdings.

  • Property: ownership status (renting, saving, mortgaged, or freehold) and incidence of property investment

  • Mortgage behaviour: likelihood to refinance their mortgage and/or make interest-only repayments 

  • Credit cards: ownership, payment behaviour (e.g revolver or transactor) and loyalty/rewards 

  • Wealth: incidence of term deposits, SMSF, and/or private health plan. Estimated net worth, income, disposable income, total debt, mortgage value, equity & value of superannuation. Price sensitivity and likelihood of financial stress

  • Advisors and mortgage brokers: likelihood of relationship

  • Product profitability: of various financial products including mortgages, investment products, credit cards, transaction account, deposits, and personal loans

Use cases​

The possibilities are endless, but the most common applications for this data asset include:

  • Risk Assessment: Enables banks and credit providers to make decisions based on the overlay of credit risk and credit demand to enable differentiated offer, product, and interest rate combinations.

  • Acquisition & Cross-Sell: Banks can identify households with a likelihood to be a first-time home buyer, refinancing, or looking to release equity. Professional Services can target prospects based on their predicted level of interest in financial advice. Wealth brands can segment households based on their interest in SMSF and investment products, and also target high net worth individuals for wealth management offerings.

  • Analytics: Overlay customer data with smrtr variables for custom analytics such as segmentations. For example, differentiate households based on the products they hold, the profitability and value of those households, and the type of products they might be interested in.

How we do it

We combined consumer research, credit risk, product demand, and high net worth data to develop a set of 42 models that identify consumer’s likely financial needs and product holdings.

  • Property: ownership status (renting, saving, mortgaged, or freehold) and incidence of property investment

  • Mortgage behaviour: likelihood to refinance their mortgage and/or make interest-only repayments 

  • Credit cards: ownership, payment behaviour (e.g revolver or transactor) and loyalty/rewards 

  • Wealth: incidence of term deposits, SMSF, and/or private health plan. Estimated net worth, income, disposable income, total debt, mortgage value, equity & value of superannuation. Price sensitivity and likelihood of financial stress

  • Advisors and mortgage brokers: likelihood of relationship

  • Product profitability: of various financial products including mortgages, investment products, credit cards, transaction account, deposits, and personal loans 

Use cases

The possibilities are endless, but the most common applications for this data asset include:

  • Risk Assessment: Enables banks and credit providers to make decisions based on the overlay of credit risk and credit demand to enable differentiated offer, product, and interest rate combinations.

  • Acquisition & Cross-Sell: Banks can identify households with a likelihood to be a first-time home buyer, refinancing, or looking to release equity. Professional Services can target prospects based on their predicted level of interest in financial advice. Wealth brands can segment households based on their interest in SMSF and investment products, and also target high net worth individuals for wealth management offerings.

  • Analytics: Overlay customer data with smrtr variables for custom analytics such as segmentations. For example, differentiate households based on the products they hold, the profitability and value of those households, and the type of products they might be interested in.

Got Questions?

We have all the answers! Well, maybe not all but we’ll do our best to help.