What is Big Data?
Big Data is a term that is becoming increasingly common within information technology, yet few people understand it well enough to describe it succinctly. Big Data is a new approach to data processing that has two primary components,
- How data is stored, and
- How data is analyzed
The Primary Components of Big Data:
- Collect everything instead of only a few things. In the past we were extremely limited in what data we could capture and store about any particular event, whether it was a census, an earthquake, or a weather pattern.Advances in storage technology enable Big Data to pull in vast quantities of data and store it for analysis.
- Let the data guide the questions, instead of the questions guiding the data. In the past we’ve always started with questions in our minds, and we’ve gathered data to try to answer them. With Big Data we often reverse that process by letting the data speak for itself, which allows us to see patterns that guide the formulation of more intelligent questions.
Versatility of Big Data
The compelling force behind big data is that it can be applied to virtually any issue we face as humans. Whether it’s energy, logistics, medicine, or social science, Big Data brings the hope of illuminating both new perspectives and new solutions to our problems.
Big Data in Retail
Retail is one of the hottest markets for big data analytics. Winning in retail sales is a game of small successes. Most retail margins are small, so keeping close track of overhead and delivery costs is essential to maintaining profitability. Finding new ways to attract and build customer loyalty is also a factor, and that means understanding customer sentiment and desires. Big data can answer these kinds of questions. By pulling together data streams from sales, operations, inventory, revenue, and other sources, big data analytics are helping retailers fine-tune their operations to reduce costs, increase customer satisfaction, and generate more profits.
Retail – Advantage Big Data
The software growth rate for big data will exponentially increase in the coming years. Companies that are investing in big data as part of their sales and marketing programs are yielding an ROI of 15-20 percent. Retail is one of the biggest sectors taking advantage of big data. Retailers are using big data to improve operations across the board, including merchandising (62 percent), marketing (60 percent), e-commerce and multichannel (44 percent), supply chain (29 percent), store management (25 percent), and operations (14 percent). Some major retailers have already reported real big data results. Macy’s says big data helped boost store sales by 10 percent, and Sterling Jewelers said it increased holiday sales by 49 percent with help from big data.
Common Retail Use Cases for Big Data
- Building a 360-Degree View of the Customer: Customer behavior and sentiment can be determined using Hadoop analytics, which can help retailers refine how they interact with customers in the store, through direct mail, and using other marketing channels. Big data can correlate transaction data, online browsing behavior, in-store shopping trends, product preferences, and more. You also can incorporate external, unstructured data streams such as social media traffic to assess customer sentiment and behavior. The resulting insight can be invaluable in guiding inventory and pricing strategies.
- Measuring Brand Sentiment: Brand studies using focus groups and customer polling techniques can be expensive and often aren’t that accurate. Using big data analytics, you can perform a customer brand sentiment analysis based on behavioral trends using sources such as Pinterest, Twitter, and Facebook, for example. The results are less biased and can be used to guide product development, advertising, and marketing programs.
- Creating Customized Promotions: Big data analytics can be used to create custom offers based on browsing history and other data sources. These customized promotions can be used for localized marketing, pushing coupons and offers to smartphone users based on their location, or to drive e-commerce sales using real-time offers delivered via online advertising or social media.
- Improving Store Layout: Big data can be used to analyze customer traffic flow within the store. Sensor data such as RFIDs or QR codes can be used to track in-store traffic and shopping habits. There also are new technologies emerging that enable in-store mapping for applications such as instant coupons that can tell retailers a lot about store flow.
- Optimizing e-commerce: Clickstream data and monitoring online behavior can help optimize e-commerce sites. Without the assistance of big data, the sheer volume of clickstream data would be difficult to analyze. Retailers can also incorporate other metrics such as social media shares, purchase history, and more to improve performance for e-commerce websites.
- Order Management: Big data can be invaluable for inventory management and tracking. For example, big data can inventory needs in order to facilitate real-time delivery. It can even be used to automate order processing to eliminate “out-of-stock” goods.
