DIGITAL, DIGITAL GET DOWN
Updated: Jan 6, 2018
A digital marketplace, or what BarCard refers to as a “two-sided network,” is a new and increasingly important market organizational form in today’s economy. There is no universally accepted definition of a digital marketplace; and differentiating between a digital network and a traditional firm is becoming increasingly harder as many non-technology oriented companies have embraced new technology to develop their own internal digital marketplaces - think of Starbucks, a coffee company with their own mobile app. By doing so, these traditional companies not only create a new form of distribution for their products and services, but also harvest the data collected through in-house digital networks; creating proprietary data sets, which are commonly referred to as “Big Data.” Depending on the industry a company operates within, Big Data is used by companies for purposes such as:
Marketing: Large aggregate demographic data sets combined with location-based technology and digital transaction ledgers allow companies to better understand “real” consumer preferences to create better, more-effective marketing strategies. In the past, many companies had relied (and some still do) on traditional market research methodologies, such as consumer surveys and focus groups, to gain a better understanding of consumer preferences. However, one can argue that not even consumers truly know what they “really want” until they are able to experience the product or service. Hence why trends identified through Big Data analysis may be a more accurate method of understanding a customer as the data can illustrate “real” consumer preferences.
Advertising: The marketing example above refers to a Company that is utilizing Big Data to better market the Company’s own products and services. However, as digital media ad placement has been stealing share from traditional advertising subsets, Big Data in itself becomes a valuable resource that a company can collect from its own customers and sell to advertisers in aggregate. Privacy policies usually do not permit companies that collect data on customers sell to advertisers on an individual basis, but aggregate data sets can illustrate broader trends and be bracketed into categories such as demographic, geographic, engagement, retention, etc. For example, a company that manufactures consumer goods and distributes its products through its own digital marketplace has the ability to collect purchase order data and then can sell that in aggregate to advertising agencies and other media companies involved in the emerging digital ecosystem.
Machine Learning: For companies that utilize automated manufacturing processes and robotics as part of their operating business, Big Data can be used to power “Machine Learning,” a relatively new term that describes how computer algorithms utilize Big Data to identify historical trends and predict future outcomes and occurrences. When there are multiple variables at play, past trends cannot accurately predict what may happen in the future. However, in the context of a manufacturing company utilizing robotics to build physical products, there are fewer variables at play as an assembly line is typically continuous process involving the same steps to build the product. Should a bottleneck occur in the assembly line, an automated manufacturing system will capture that data and the “machine” will begin to “learn” what had caused that bottleneck in the first place so it will not be repeated in the future. As this process is repeated time after time, the whole system becomes more efficient as issues are immediately identified and quickly fixed.