With 526 million users posting 3.2 billion “Likes,” comments, picture uploads, and shares on a daily basis, the task of managing data on Facebook’s nearly 1 billion users once seemed insurmountable. Facebook has an estimated 60,000 or more servers that store daily content, resulting in the construction of a supplementary 300,000-square-foot facility in Prineville, Oregon, and another center located in Rutherford County, North Carolina. Regarding the organization of data, in 2012 Facebook launched its Timeline feature, which organizes, tracks, and displays a user’s activity in the form of milestones ranging from sign-up through the present day. Facebook’s ability to successfully organize a user’s information and activity opens a new revenue stream known as data mining.
Data mining is the process of discovering patterns in a consumer’s online behavior, habits, and preferences. This information is then categorized into fields where relationships have been identified. Facebook has been able to successfully collect and organize user data, transforming user information into targeted advertisements. User data is highly coveted by private businesses, public companies, and non-profits, such that companies pay Facebook for advertising space to reach a targeted consumer base. A business seeking to advertise on Facebook completes fields specifying its desired demographics, such as country, gender, age, interests, etc. Subsequently, Facebook returns the estimated impressions an advertisement will receive based on this target information.
During Facebook’s infancy, data management strategies were nebulous, focusing primarily on creating bandwidth to support the website’s growing customer base. Now, with Facebook’s highly active user base and data management timeline tool, Facebook has ultimately laid the foundation to crowd source, or derive insights from its large user base and technology platform. The acquisition of user data remains an important revenue generator for Facebook. However, to compete in the 21st century, Facebook must continue to diversify its monetization strategy.
Predictability in the Financial Industry
Current forecasting tools in the financial industry include the Index of Leading Economic Indicators, the Consumer Sentiment Index (CSI), the Conference Board’s Consumer Confidence Index (CCI), and the American Association of Individual Investors (AAII). These indices are used to measure the health of the U.S. economy based on consumer/investor opinion. However, indices produce only marginal indicators of the market.
What do other forms of media imply?
Regarding media, Tetlock states that the “high values of media pessimism provoke downward pressure on market prices, which leads to high market trading volume.” Internet chat rooms concentrating on stocks, however, did not reveal any material correlation between positive messages and market returns. More accurate market forecasts require a platform with “real-time data sets, from thousands of data points, from historical archives of data collection agencies” according to Ludvigson.
We suggest that the real-time platform of Facebook’s millions of users be used to create an algorithm based on user sentiment to predict more accurate, short-term forecasts of market movement. Previous and current market resources use data from much smaller populations that are solicited, not voluntarily given. Often these resources are stale, as they are aggregated and released from the previous quarter. Past studies also relied on the interpretation of chat room feedback, thus implying a degree of subjective error.
Subscribers voluntarily submit positive responses via Facebook’s “Like” function for businesses and products. Though “Likes” provide only positive feedback, the information is captured immediately. By gathering subscriber feedback and data mining for trends, Facebook can use real-time data with other traditional resources to produce more accurate forecasts of the financial market. These forecasts would contain more frequent data points, which produce market correlations and lead to more precise market movement predictions.
However, the positive feedback communicated by the “Like” function does not convey the extent to which the subscriber feels positive toward another person, company, and/or product. The integration of a user’s comments on a particular company, brand, or the economy as a whole may provide better insights. Whether the information Facebook has acquired, and continues to acquire, is both genuine and robust enough to provide psychographic insight is debatable. Nevertheless, this information should be used in conjunction with the other indices and market information, such as industry experts and analysts, to ensure accuracy.
User-generated content from social media or other mediums may present a suitable method to gauge sentiment. However, the same study determined that inaccuracies arose from using sentiment from an entire social media population; therefore, experts within the masses should be identified. The aggregation of data from Facebook utilized in conjunction with more traditional resources could produce more accurate market predictions, otherwise known as crowd sourcing.
Practitioner Take-Aways & Implications
In the previous sections, the analysis presupposes the possibility of Facebook predicting activity in sentiment-based markets. If Facebook is able to predict an outcome, a working model or theory could be built around that outcome. With a working model, Facebook could conceivably influence an outcome to reflect an individual’s preference, especially if the predicted outcome is contrary to the individual’s desire. However, Facebook is not acting contrary to their customer’s desires, the company is leveraging proprietary customer information to exploit a potential revenue stream. The constant updating of customer information is the feedstock to predictive algorithms and stock market forecasting, which in turn is the potential revenue stream. This assumption recognizes that Facebook could not only predict financial markets, but also influence and ultimately control them.
A critical mass of users is needed to support this assumption, which Facebook currently offers, through a user base characterized by diverse demographics, achieved by offering free accounts. It seems free accounts provided by Internet services follow the adage, if you cannot quite figure out how a company is making money, it is you (specifically your information) that is being sold. In Facebook’s business model, users are not only customers, but the inventory as well. User information is sold every time users engage free services. Facebook users are thus engaged in a tacit agreement with Facebook through which their information will be used and possibly sold.
How is Facebook relevant to the reader and the reader’s business?
One area of opportunity Facebook offers businesses and entire industries is to better understand their customers through the rich set of data and analysis provided by its users. However, this opportunity relies on the presumption that Facebook would be willing to share or sell its data and analysis. These companies would then use the information to their own advantage, and possibly sell the insights derived to the highest bidder(s).
Validation & Proof of Concept
While much of this discussion could be considered conjecture, the business of data mining the past to predict the future is neither a pipedream nor a future technology. In fact, several third-party platforms have utilized social media to predict a movie’s success once released, while other platforms use sentiment analytics to measure the popularity of a company’s brand amongst the general population. When Facebook is able to properly mine and analyze the data it collects from its users, it will be able to predict its users’ wants and needs. Facebook will also be able to predict trends in different markets (i.e., consumer products and financial).
In January 2010 a small start-up company based in Massachusetts, Recorded Future, predicted Yemen’s impending famine and conflict. This was a full year ahead of the actual conflict, something Recorded Future was able to predict based on public information gathered from various resources across the Internet by using algorithms built “in-house.” With Facebook’s enormous repository of information and the already established practice of mining data to predict the future, Facebook could soon adjust its business model to not only sell predictions of the future, but also influence the future based on those very same predictions.
Facebook is in a prime position to transition from a social media platform into a revenue-generating predictor of sentiment-based markets. Facebook started out as a platform for users to connect with friends, and quickly grew into one of the most visited websites in the world. The implications are potentially endless with the growing number of users Facebook possesses and the massive warehouse of data, which can be analyzed. A continual loop can be created through user-generated content continually feeding Facebook’s user data repository and prediction algorithms, and thus further influencing outcomes. Facebook stands at the precipice of a major decision. If Facebook utilizes the data it is collecting, the firm could create a market disruption not only in the information technology sector, but also in sentiment-based markets.
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 Tetlock, P. C, “Giving Content to Investor Sentiment: The Role of Media in the Stock Market,” Journal of Finance, 62:3 (2007): 1139-1168.
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 Ludvigson, S. C., “Consumer Confidence and Consumer Spending,” Journal of Economic Perspectives, 18:2 (2004): 29-50.
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 “Yemen Heading for Disaster in 2010?” RecordedFuture.com, January 12, 2010. Accessed April 8, 2012, https://www.recordedfuture.com/2010/01/12/yemen-heading-for-disaster-in-2010/.