According to Cambridge Analytica CEO Alexander Nix, the success of Cambridge Analytica’s marketing is based on a combination of three elements: behavioral science, Big Data analysis, and ad targeting. Ad targeting, defined as personalized advertising, aligned as accurately as possible to the personality of an individual consumer.
Cambridge Analytica Methodology
Cambridge Analytica, the data analytics firm that worked with Donald Trumpâs election team and the winning Brexit campaign harvested millions of social media profiles of US voters and used them to build a powerful software program to predict and influence choices at the ballot box. / CA on Wikipedia
The Cambridge Analytica methodology is a massive data breach but it as well demonstrates the power of competitive monitoring in an area of ubiquitous social media sources and external databases. It outlines the shift in communication, especially when realizing the global political impact, this gangster-move finally had.
We didnÂ´t see it coming – What???
Nix introduced the Cambridge Analytica Methodology while speaking in Hamburg
„My children will certainly never, ever understand this concept of mass communication.“Cambridge Analytica CEO Alexander Nixï»¿
The Cambridge Analytica Methodology is a 2 step process:
- Cambridge Analytica buys personal data from a range of different sources, like land registries, automotive data, shopping data, bonus cards, club memberships, what magazines you read, what churches you attend.
- Cambridge Analytica aggregates this data with the electoral rolls of the Republican party and online data and calculates a Big Five personality profile. Digital footprints suddenly become real people with fears, needs, interests, and residential addresses.
For anyone seriously involved in ad tech, there is no surprise about the potential use and misuse of data for targeting purposes. Good news is that the fight against invalid traffic started through a combination of technology, policy, and operations teams. It takes time and effort to identify and fight ad fraud – but needs to be done to protect advertisers and publishers and increase transparency throughout the advertising industry.
How to identify and kill an ad fraud operation
Read: Data That Turned the World Upside Down in Das Magazin, December 2017
Why Scraping is a Billion Dollar Deal
Scraping stands for automatically collecting data from publicly available social profiles
HereÂ´s the background:
LinkedIn allows users to create profiles and then establish connections with other users. LinkedIn users create a profile on the site, they can choose from a variety of different levels of privacy protection. They can choose to keep their profiles entirely private or to make them viewable by:
- their direct connections to
- a broader network of connections
- all other LinkedIn members or
- the entire public
When users choose the last option, their profiles are viewable by anyone online regardless of whether that person is a member. LinkedIn also allows public profiles to be accessed via search engines such as Google. This comes with consequences.
hiQ is scraping LinkedIn
hiQ gathers the workforce data that forms the foundation of its analytics by automatically collecting it, or harvesting or âscrapingâ it, from publicly available LinkedIn profiles. The company sells to its client businesses information about their workforces that hiQ generates through analysis of data on LinkedIn usersâ publicly available profiles. It offers two products: âKeeper,â which tells employers which of their employees are at the greatest risk of being recruited away; and âSkill Mapper,â which provides a summary of the skills possessed by individual workers.
LinkedIn argues that it faces significant harm because hiQâs data collection threatens the privacy of LinkedIn users because even members who opt to make their profiles publicly viewable retain a significant interest in controlling the use and visibility of their data. In particular, LinkedIn points to the interest that some users may have in preventing employers or other parties from tracking changes they have made to their profiles. LinkedIn posts that when a user updates his profile, that action may signal to his employer that he is looking for a new position.
Do Not Broadcast
LinkedIn states that over 50 million LinkedIn members have used a âDo Not Broadcastâ feature that prevents the site from notifying other users when a member makes profile changes. This feature is available even when a profile is set to public.
LinkedIn also points to specific user complaints it has received objecting to the use of data by third parties. In particular, two users complained that information that they had previously featured on their profile but subsequently removed, remained viewable via third parties. LinkedIn maintains that all of these concerns are potentially undermined by hiQâs data collection practices: while the information that hiQ seeks to collect is publicly viewable, the posting of changes to a profile may raise the risk that a current employee may be rated as having a higher risk of flight under Keeper even though the employee chose the Do Not Broadcast setting.
HiQ could also make data from users available even after those users have removed it from their profiles or deleted their profiles altogether. LinkedIn argues that both it and its users, therefore, face substantial harm absent an injunction; if hiQ is able to continue its data collection unabated, LinkedIn membersâ privacy may be compromised, and the company will suffer a corresponding loss of consumer trust and confidence.
Source: www.hiqlabs.com/legal (12/2017)