数据与GIS > How Is Big Data Changing Retail Analytics?
How Is Big Data Changing Retail Analytics?
Big Data becoming a core competitive advantage in nearly every successful company.
Big Data has become a core competitive advantage in nearly every industry as successful companies are mining their data for information that might turn a series of seemingly random numbers and information into solid-gold profits. So, what does the term “big data” really mean? Big Data is just all the data that is generated and comes into the realm of companies today. And it’s growing. If the term “Big Data” makes your head swim, check out our article “5 Easy Ways To Learn About Big Data” to get some of the basics down first.
IBM, one of the leading players in this emerging space, estimates that 2.5 quintillion bytes of data are created every day from a variety of sources, including sensors, social media and billions of mobile devices around the world, making it difficult for businesses to navigate and analyse it to improve competitiveness, efficiency and profitability. IDC estimates the market for big data technology and services will grow at an annual rate of nearly 40 percent to reach $16.9 billion by 2015. Moreover, every month people send 1 billion Tweets and post 30 billion messages on Facebook. Meanwhile, more than 1 trillion mobile devices are in use today and mobile commerce is expected to reach $31 billion by 2016. Data is even being used by companies who want to be more environmental in their strategies.
Big Data for the Retail Markets
To apply big data to the retail market, in which we seek to draw actionable inferences from customers’ shopping behaviour, we must first account for relevant factors like store location quality, employees in the store, layout and store merchandising, staffing schedules, complete detail on actual sales, and surprisingly… even the weather. Input sources can include video cameras, Wi-Fi tracking tags, RFID, and other in-store systems like those for Point-of-Sale (POS), staffing, mobile APP checking in to places and events.
If you are providing Wi-Fi to retail shoppers, you can show them the most relevant websites, search terms, product review sites, and even product price points at any given time. You can collect a mountain of data across a highly distributed environment (think thousands of retail stores). This data can then be sifted and processed to presents trends in a meaningful way so that management can take intelligent action. Just a few years ago this would have been an impossible system to build, or massively expensive at the very least.
Via new data sources, retailers can gain a precise, factual understanding of how shoppers move around their stores – where they go, in what order, how long they stay, when they come to the store, and how all of these questions map to actual sales. Retailers have optimised store layouts, fixtures, staffing, and even product offerings based on what they learned.
Types of Big Data Analytics in Retail
We analysed the typical retailers and found some interesting patterns of how raw data is being converted into signals and actions. Most of the retail analytics and big data retailers tend to cluster around the following areas:
Store Operations Analytics
Vendor and SKU Management Scorecards
Returns, Fraud and Loss Prevention Analytics
Benefits of Big Data Analytics in the Retail Market
A McKinsey study released in May 2011 stated that, by using big data to the fullest, retailers stood to increase their operating margins by up to 60%, this is a pretty powerful weapon in a highly competitive space like retail. The key benefits of using big data analytics in the retail market include:
Big Data can unlock significant value by making information transparent and usable at much higher frequency.
As organisations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. Leading companies are using data collection and analysis to conduct controlled experiments to make management decisions; others are using data for basic low-frequency forecasting to high-frequency nowcasting to adjust business levers in time.
Big Data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services.
Big Data analytics can substantially improve decision-making.
Big Data can be used to improve the development of the next generation of products and services. For instance, manufacturers are using data obtained from sensors embedded in products to create innovative after-sales service offerings such as proactive maintenance.