Marketers and advertisers
Marketers are considered to be among the most likely potential adversaries that have the motivation to attempt a re-identification (identity disclosure, link disclosure and content disclosure), buy data from brokers directly, and are considered to have the necessary tools, or are indirect adversaries in hiring data analytics agencies that buy data from data brokers. Advertisers are no direct threat, but are important enabling players.
Traditional marketing research often involves assessing the overall market for a good or service, surveying consumers about their likes and dislikes, and conducting focus groups to gauge consumer responses to a new product.
The growth of information technology has transformed market research, with a growing number of analysts learning about consumer preferences and buying habits by mining massive sets of quantitative data and employing complex algorithms to uncover patterns and correlations that enable more effective marketing and advertising.
Most used are correlations between different factors and variables in large data sets, often measured in terabytes. Data mining often gives businesses enormous amounts of information about their customers’ behaviours and buying habits, enabling them to more effectively market and advertise their goods and services.
Amazon’s feature matching algorithm tells a potential customer that people who like one particular product also like certain other items, is an example. The “Frequently Bought Together” of Amazon and The “Genius Recommendations” feature of iTunes make recommendations which are similar to what we already like.
Credit card issuers generating lists of products and services that consumers are likely to buy based on characteristics of customer credit card accounts for customer service representatives is another.
Often mentioned three major benefits of using a recommendation engine:
Increase in revenue.
Increase in customer satisfaction leading to customer retention.
Elimination of the need for market research.
Threat modelling note