Brownstone’s FEDS database is an audited database containing current and historical information. This information ranges from demographic data to geographic data to derived variables. FEDS allows clients to enhance their existing database with information they cannot otherwise access. This allows the client to:

• Gain insight into their existing prospect pool to determine who their core customer base is, who their product appeals to, who their agents have been selling to and it is also the platform to identify additional opportunities.
• Better match their product offerings to potential clients by analysing historical product purchases and determining the likelihood of purchasing new products.
• Align the prospects they communicate with to their remuneration model. Clients that are likely to be more responsive and hence cost less to bring on book can be easily identified as can clients that might cost more to put on book but are likely to remain active and paying for longer.

The newfound ability to segment the base is certain to decrease costs as poorer leads are not contacted and therefore results in an increase in profitability as the data contacted performs better. This information is easily accessible as FEDS can be accessed online and files can be processed in a timely manner, dependent on the size of the file. In addition, Brownstone has structured its pricing to allow clients to only pay for those variables which work for them. If you don’t need a variable, you simply don’t select it and hence don’t pay for it.

The FEDS Variables

• Demographic data
Demographic data allows clients to inform their data with variables such as age, race and gender. These variables form the building blocks of any client base analysis. In addition Brownstone identifies whether the South African Identity number supplied is valid and whether the individual is a South African citizen.

• Exclusionary Variables
Brownstone flags the data provided to indicate whether anyone in the dataset is deceased, on the Direct Marketing Association (DMA) of South Africa’s Do Not Contact list or on Brownstone’s own list of sensitive individuals i.e. individuals that have expressed a desire not to be contacted by a specific brand or via a specific channel.

• Geographic Variables
Brownstone identifies the province and city an individual lives in not only from their current address but taking other factors such as the home telephone number, work telephone number and contact history into account.

• Enhanced Data Variables
Every change in contact information Brownstone receives for an individual across six contact points (physical address, postal address, home telephone, work telephone, cell telephone and email address) is retained. This information is utilised to derive powerful, contact based variables. For example, since Brownstone does not delete information on an individual, address changes can be identified. These changes can influence an individual’s responsiveness to certain product offerings.

• Lifestyle Variables
Brownstone has developed models that indicate the likelihood of an individual owning property, owning a motor vehicle and being married. In addition, Brownstone retains the outcome of every credit vetting test an individual has been subjected to. This history of these outcomes is retained although the specific scorecard they passed or failed is not revealed to protect client information.

• Income Estimator
Brownstone has developed their own income estimator allowing clients to identify whether their base is comprised of lower income, middle income or higher income individuals. The estimator can also be utilised to improve product fit, identify clients that are more likely to be able to afford the product offered and identify clients that can afford more than their current product holding.

Modeled Variables

Brownstone has been involved in a number of initiatives targeting a variety of outcomes. This involvement and the results achieved have allowed the development of the following models targeting a range of outcomes. The following are some of the models available:

• Likelihood of responding to a higher income product offering:
Individuals likely to respond positively towards being offered a product that requires a higher income, for example a comprehensive insurance offering, are identified.

• Likelihood of responding to a lower income product offering:
Individuals likely to respond positively towards being offered a product that is targeted at a lower income type individual, for example a low premium funeral insurance offering, are identified

• Likelihood of passing a credit scorecard:
Individuals likely to pass a credit scorecard are identified. Individuals less likely pass a credit score test are removed and as such bureau costs are reduced. The model allows the client to prioritise the population more likely to pass.

• Likelihood of responding to a SMS Message:
Individuals more likely to be responsive to an SMS message, regardless of the product offered, are identified.

• Likelihood of responding to an AVM Message:
Individuals more likely to be responsive to an AVM message, regardless of the product offered, are identified.

• Likelihood of responding to an email:
Individuals more likely to be responsive to an email, regardless of the product offered, are identified.


Brownstone is currently developing an online tool that will allow clients to swiftly:

• Know My Client:
Derive greater knowledge of a database using FEDS variables and Brownstone’s models by generating a summary report and/or extracting relevant data.

• Update My Client:
Update contact details and add other variables to a database.

• Build A Prospect Pool:
This enables the client to purchase the best possible leads based on criteria available within FEDS and Brownstone’s models.

• Model My Client:
Campaign specific models are built to assist with future data selection and campaign management.

News Feed
  • The pending Protection of Personal Information Act (POPI) – which is expected to cut down on unwanted telemarketing calls – could inadvertently lead to as many as 35 000 jobs being lost in three years.
  • Brownstones analytical tools are now empowered by Business Optics, which is a cloud-based computational knowledge management system.