The Road To Retail Success Passes Through Big Data And Social Physics
The retail sector has a strong desire to predict the outcome of human behaviors through big data analysis, and machine learning. However, until recent years, this has been a somewhat complex challenge.
Known as ‘Social Physics’, this new scientific field demands advanced technical capabilities to facilitate the analysis of data sets in a very short period of time, with the view of predicting the outcome of human behaviors. If successful, and by combining the many benefits of big data and machine learning with that of social physics, retailers have the potential to make smarter business decisions.
So what is social physics, how does it build on the capabilities of current machine learning protocols and can the phenomenon progress in a world of ever-growing regulatory red tape and privacy threats? Let’s find out.
Social Physics And Retail
Consumer-based retailers are faced with a constant challenge. How can retailers better understand the potential behavior of their customers, to ensure that business targets are not only met, but exceeded? This is where social physics and big data meet. To put these potentialities into a hypothetical example, think about a shopping mall plaza that wants to better understand consumer trends with the view of improving sales.
In terms of the big data itself, social physics would be interested in a range of variables linked to consumer activities, including but not limited to location data from cell phones, transaction tracking of all debit/credit card purchases, social media activities that users utilized within the plaza (such as checking-in) and the customer split between cars and pedestrians.
The aforementioned human behavior characteristics could then be analyzed with the aim of providing the shopping mall complex with highly advanced predictions, based on the previously collected empirical data sets.
A further example might see an online business utilize data sets from social media users. These data sets can be further broken down into a range of variables, such as demographics, and once analyzed, allow the online business to acquire accurate human behavior predictions in an automatic manner – all facilitated by the advanced capabilities of machine learning and artificial intelligence.
Ultimately, with retailers now operating in an ultra-competitive arena, businesses must be able to better understand not only what their customers want, but what they are going to do.
Where Did Social Physics Gain Its Roots?
In a nutshell, social physics is a branch of research that seeks to utilize data by understanding and subsequently predicting the outcome of human behavior. This could be across a range of sectors, from financial services, organizational management, governmental planning and consumer-based retail – amongst practically any other real-world setting that requires a prediction-based analysis on how humans behave.
Recognizing its revolutionary potentials, researchers at MIT began developing a further understanding on social physics through their Human Dynamics Lab, with the aim of exploring how big data analysis and computational theory can lead to the engineering of better social systems.
In one of MIT’s earlier studies, researchers sought to explore whether social physics could better predict the human behavior employed by users on hugely popular trading platform eToro. Upon supplying MIT with a data set that surpassed 10 million transactions facilitated by more than 1.6 million users, the study concluded some very interesting results. In a nutshell, the experiment was able to pinpoint the most successful traders by the way they behaved on the trading platform.
Certain Challenges Lie Ahead
Regarding the current state of play, traditional systems rely on conventional machine learning and/or deep learning technologies. Although these innovative protocols have the ability to make predictions based on historical events, they are not capable of separating the wheat from the chaff to the same extent as a model that utilizes social physics.
On the contrary, by incorporating social physics into the machine learning mechanism, tools are now capable of coping with human behavior data — something it wasn’t able to do prior to the development of social physics analytics.
Although social physics is increasing its status as a predictive phenomenon that has the potential to revolutionize the decision-making process, certain challenges still present themselves. For example, the ability to predict the outcome of future events is currently a privilege held by the few, not the many.
In most cases, these stakeholders are either large-scale financial institutions or tech companies that have the capacity to inject vast sums of capital into research and development budgets.
Furthermore, with cyber-security and data theft offenses now at record heights, organizations are somewhat fearful of utilizing a new technology that has the potential to threaten privacy. Moreover, with customers now having greater control over their own personal data, retailers will need to find a way to utilize the benefits of big data analytics, whilst at the same time ensuring that they remain complaint with GDPR regulations.
Ultimately, new and exciting startups linked to the world of social physics, machine learning and predictive analytics will need to approach industry leaders with revolutionary solutions.
New Startups Aim To Bring Predictive Analytics To The Masses
One such organization that has recognized this somewhat sizable gap in the market is U.S based startup Endor, who have created a protocol that has the ability to perform social physics analysis to retailers of all sizes.
The credentials of Endor’s founding members – Alex Pentland and Yaniv Altshuler, could not be stronger in the context of social physics, machine learning and big data. Regarding the former, Professor Alex Pentland is a Director at the MIT Media Lab, and is well known for his dedication to the phenomenon of social physics. Moreover, Pentland was also included in a recent Forbes list of the “7 most powerful data scientists in the world”.
The underlying technology has the capacity to answer an unlimited amount of predictive questions without requiring the retailer to design and build a model that is unique to their business, subsequently facilitating an accurate data science protocol that remains affordable to businesses of all sizes. Even more pertinently, with the view of solving the aforementioned issued of privacy and GDPR regulations, the Endor platform has the potential to analyze human behavior without needing to decrypt the data itself, providing end-users with the capacity to remain secure.
Although not directly targeting the retail sector, another such organization that are looking to combine machine learning and big data in a world of ever-growing regulatory burdens is that of Immuta. The company is looking to assist policymakers to reduce business risks through predictive AI-driven models.
And then we have Dr. Ryan Chin and his CityScope project, which aims to utilize the benefits of social physics and big data to revolutionize urban planning. This has a direct link to the world of retail, insofar that the predictive outcome of human behavior may assist in the planning of new locations.
Whilst machine learning and deep learning capabilities still have a major role to play in the future of predictive analytics, incorporating the phenomenon of social physics has the potential to revolutionize retail prediction models, by not only yields more accurate results but by offering a protocol that prioritizes security, cost-effectiveness and user-friendliness.
No longer do organizations need to concentrate exclusively on what the customer has previously done, insofar that the growth of social physics will result in the ability to predict what they are going to do.
Therefore, retailers can better focus their resources on what they do best – servicing the end-user.