In this ever evolving technological world, it is no secret that everyday devices and internet services are collecting data on the consumers using them. In simple terms, almost every action taken in a digital space is being recorded, categorized, and used in various ways in the name of business. For those who weren’t aware of these privacy settings (or lack there of), this probably opens up a number of questions. When you look much closer into the uses of that information, it is clear that data is the future of marketing. At the same time, it can feel like a violation, so why is do businesses do it?
In the past most companies would market a similar message across an entire major area, and hope that the individuals who are most receptive to their product would be among those in the chosen demographic group. It lead to a small return on investment mostly driven by luck or assumptions. More recently the method of predictive analytics transforms the consumer data being stored in order to better target market groups that will want to purchase a product. This micro-targeting allows marketers and businesses to be more far more efficient with their advertising dollars since there is a much higher chance that their ads are being displayed in front of the right individuals.
Recently Bill Morris came to talk to my capstone class more about this industry. He works for Faraday which specializes in this of predictive analytics. They originally were tasked by the solar energy businesses who were being pushed by the government to lower their costs. Over the years they have fine tuned their process of “data plumbing” to expand is several other industries. Where does this data, however, come from? The immediate answer would be via your social media, but also extends past what you put online and moves towards public records about your home and the patterns of your everyday lifestyle. Faraday’s data plumbing uses nine major sources through a combination of public data such as the census and data purchased from private sources.
The B2B company uses large amounts of consumer data and complex methods of analysis in order to lower the costs of customer acquisition. Thanks to this extensive research and adaptable algorithms, Faraday provides a platform based around a map for the most statistically similar individuals to their current customer base. This type of method calls for us to “check out personal biases at the door” as Morris says. Lead generation of this kind is scientific and not feelings based. Consumers can for instance ask for a list of customer leads for males in a certain zip code with a specific level of education. That short list of characteristics is just the tip of the iceberg. From there the businesses can move forward and create marketing campaigns that are specific to that list. This has even lead Faraday to reduce acquisition expenditures by as much as 50% for some clients.
Personal data however can be tricky in this type of industry. One concern that I was happy to hear Morris address was privacy. Data is not shared between clients, and they only receive the generated leads rather than the specific data. They have even situated a system to avoid reverse engineering of results, so if there are less than fifty results for a certain set of data points the map will not provide the leads.
Predictive analytics is undoubtedly driving the world of consumerism and beyond. I look forward to see how the precision of marketers transforms, and I think this new age of business will be fascinating as the robotic nature of algorithmic innovation is paired with the human experience.