Big data analytics can provide value in the field of digital commerce in a number of ways, including supply chain management. In this instance, vendors can collect large amounts of data on competitors who are ‘out of stock’ on certain items and then be sure to drastically increase shipments of those items in order to appeal to eager shoppers with little patience. Other eCom applications include employing a dynamic pricing model, as well as optimizing shipping, warehousing, and supply routes. The former can be accomplished by collecting information on industry-wide pricing, enabling increases or drops aimed at maximizing conversions/profits. The latter can be accomplished by mapping out where clusters of products are being ‘ordered from’/’shipped to’, so that ballpark quantities can be kept in warehouses in targeted geolocations.

Exercise input and output data from wearables and fitness apps are shedding light on what types of workout routine people like doing, where they like doing them, as well as the biggest fitness challenges amongst different target audiences. Good sources of relevant data points include posts on social media of people sharing their fitness accomplishments, such as ‘Huray, I just finished running 10 miles in Central Park’. Enough of these posts, and companies can deduce that running in parks is a major trend in New York and use that to promote healthcare products such as knee braces for runners’ damaged cartilage.

Marketing teams/agencies can benefit from big data analytics by understanding current search trends on engines, by analyzing competitor marketing campaigns, as well as analyzing target audience engagement on social media. A shoe retailer may cross-reference a top-trending search query (‘where to find vintage sneakers?’), with competitor ads pushing ‘worn shoes’ together with high engagement on Instagram posts featuring shoes of this variety. Each insight on its own is ‘interesting’ but as a large-scale, cross-reference is no longer a correlation but a concrete trend to be capitalized on.

The insurance industry can see major benefits from big data patterns as far as risk mitigation is concerned. For example, analyzing crime rate data in an area where a company is applying for insurance coverage can shed light on the potential risk involved as well as the pricing of a policy premium. Weather data from previous years can also help calculate the probability of climate-based damage on a structure’s future insurance plan.