Retail is the sale of goods, and big data refers to data sets that define a business. Big Data allows retail and storeowners to understand the market. (McAfee, Andrew & Brynjolfsson, 14). Big data is used to process information and make predictions about the retail market and landscape.
The current phase, “phase one of the second machine age describes a time when digital technologies demonstrably had an impact on the business world by taking over large amounts of routine work- tasks like processing payroll, welding car body parts together, and sending invoices to customers (18).
Digital technologies can do much of the guess-work for us, today. We just have to input the numbers.
Machine learning takes big data it gathers from computer users around the world and is used to gather and predict valuable insight regarding consumer’s spending habits and how these habits are different every day week, season and time of the year.
This is big news for retail professionals, so big in fact, that many have left their retail jobs and have started working as big data scientists.
In the summer-time, retail stores sell bathing suits, pool supplies and summer dresses. In the fall, back-to-school sales increase revenue for retail stores that sell office and school supplies. Near Christmas time, crafts and hobbies stores increase their revenue by selling holiday decorations, and displays.
Big data is indirectly able to directly influence sales by gathering data about consumer’s specific spending habits, through various human interactions: retail point-of-sales systems in malls, and stores, restaurants eat-ins, Facebook and other social media sites, Pinterest, and the internet.
Big data is great. It doesn’t care how it’s business is done; point-of sales systems in retail stores, off-line or online.
In retail, the demand goes up, as the price goes down, and vice-versa. In retail, “smart companies realize that free goods can be a complement, not a substitute, for more expensive versions: free goods increase the demand for paid versions, rather than cannibalizing them” (McAfee, Andrew & Brynjolfsson , 162).
Such free goods come in many forms, and help retail, positively, on and off the web. The Facebook app is free, but has generated 84% of total revenue for Facebook in the third quarter of 2016. These complementary services: ad revenue, pairing with products, public service, customer service, “freemium” businesses, etc., have the ability to influence sales. Businesses today, must understand the importance of being digitally open, albeit concerns of being hacked. (163).
Leading online businesses like Amazon are using ad revenue. See the below example which depicts the current homepage of Amazon.com. The main image is an advertisement for a new product, the Fire TV.
The copy reads, With access to Netflix, Hulu, HBO NOW, YouTube, Prime Video, and more—there are over 500,000 TV episodes and movies ready to stream.
This is the first copy we see on the site and it presents something new and of interest to the consumer. Directly below, for our avid consumer, there is yet another sleek copy advertisement.
This entices the consumer more. He or she is starting to feel at home with the product, but underneath the beautiful image of the “House of Cards,” show, there is an annoying paragraph, which reads “Netflix Streaming Membership Required.”
If not for the quality of the ad, the consumer would be immediately dismayed by the fact that they already have the main product on their laptop or t.v. and there is no sense of hearing about what they already know. Ad revenue is a complementary service, which dangles the main product, in this case, FireTV, in front of the consumer, allowing the marketer, and web analytics tools to gauge it’s appeal within his next designated time-frame. Amazon is watching the consumerism of the FireTV, from its inception, in this case October, and is also watching its overall sales move rapidly higher as the FireTV advertisement reminds consumers of their entertainment needs, and they stay on Amazon.com for more and more hours, more hours and more hours, and decades. American consumers will become consumed in Amazon.com, and soon the main image will be of whatever’s on each individual consumer’s mind when they wake up in the morning!
Retail being influenced by big data this October….
Hmmm…Let’s check again in December.
This is the “Golden Age of Retail”
Bernard Marr, author of Data Strategy, describes big data for retail so accurately in a Forbes article in 2015. Read his book Data Strategy
“Big Data analytics is now being applied at every stage of the retail process – working out what the popular products will be by predicting trends, forecasting where the demand will be for those products, optimizing pricing for a competitive edge, identifying the customers likely to be interested in them and working out the best way to approach them, taking their money and finally working out what to sell them next (Marr, Big Data: A Game Changer In The Retail Sector”).
For the last several years the subject of big data influencing retail has become a very digital concern, and big data analytics is being applied even further. Machine learning systems can use big data to influence what content, and advertising is shared on various social media sites, and can quickly process and analyze top consumers, or products on the web, and in the stores, each day, as well as what complementary services that should be used. Within the past few years, Point-of-Sales software has evolved to use AngularJS, NodeJS and noSQL databases, technologies that can make updates to their databases regularly and ingest big data sets rapidly each day. As a result of these modern point-of-sales systems, and internet advertising, mobile advertising, the economy is evolving rapidly into 2018. Sales for the U.S.A’s most popular stores is being influenced by consumers habits, regardless of rage, ethnicity or gender.
Retail thrives off big data, often when we least expect it, in the very private or public corners of our life, when we are watching our favorite show on t.v., back-to-school shopping, liking our friends Facebook posts, attending a free museum event, or online-shopping for a new favorite cookie recipe. Big data has extreme, often disturbingly influential power over retail.
Co-work at Aponia Data Solutions, and learn more about how big data can become a part of your business.