Why Big Data Is a Game Changer For The Retail

Big data is changing online and offline retail in transformative ways. The shopping experience is becoming personalized and tailored to individuals according to their needs, digital footprints, and global trends. Modern retail requires a blend of physical and digital methods to increase market share. This is where Big Data comes in the picture, where retail giants use and curate data for understanding various touchpoints of the consumer including, behavior, interests, age group, and create personalized products and services.

What is Big Data?

Big Data includes large data sets, which can be processed with traditional methods and provide patterns of trends according to user behavior and more. Data is the oil for the 21st century, and information is extremely valuable for both online and offline retailers in the global economy. As a result, many retail giants have been heavily investing in Big Data, tools to collect and analyze Big Data, and are gaining a massive advantage over the competitors by using technology extensively.

Big data and Advanced Analytics can be applied at every stage of the retail process, including data curation, demand for predictive trends, forecasting the market demand, optimizing pricing, identifying customer interests and behavior, and iterating according to the changing trends.

Predicting Trends

Big Data helps in predicting trends for retailers by understanding user behavior, items of interest in particular geographical location, social media influence over fashion and lifestyle. These trend forecasting algorithms include web browsing habits and social media trends to understand what is creating a buzz around the globe and analyze the set of audiences. By using sentiment analysis and machine learning-based algorithms, retailers can determine how consumers are consuming around the world and determine the top-selling products in the category.

Optimized Pricing

Competitive pricing plays a vital role in online and offline retail as consumers are always looking to achieve an enhanced value of their money. Retailers like Walmart are spending billions on creating private clouds to track and analyze millions of transactions, footfalls, and user behavior from their stores. Big Data algorithms can track and check competitive pricing, inventory levels, competitors’ activities, and respond to market needs accordingly in real-time. These insights can help businesses to make better decisions and gain market share.

These algorithms can also help retailers to indicate when they should drop the prices and use the markdown optimization. Before the technological era, retailers would reduce the prices at the end of the season when the demand is low and increase the prices when the demand is high in seasonal times like Christmas and New Year. However, with the implementation of analytics, retailers have understood that they can optimize prices from time to time to increase revenue and use a predictive approach to determine the rise and fall and demand for a product, irrespective of seasonality.

Forecasting Demand

Retailers are always looking to forecast demand to mitigate the risk, and not overload their warehouses with products which are not in demand. By gathering demographic data and economic indicators like spending habits, purchasing power, retailers can forecast demand efficiently. For example, certain retailers have found that the demand for books increases as the weather gets colder in European nations. This could be due to that people opt to stay at home in the winter season and seek indoor activities compare to users booking for entertainment-related experiences like travelling in there summer vacations.

Identifying customers audiences

It requires a systematic approach to identify customer audiences for a particular product, and the best way to move forward is with data. Modern retailers have to rely on recommendation engine based technology in which data is collected through online and offline transactional records. The demand forecast for products is based on geographic location, demographics, seasonality, and customer preferences. Retailers can mitigate risk and fulfil orders more quickly and efficiently by the influx of technology and attract customers to their declining brick and mortar stores. By using predictive analytics and targeting the millennial demographics, companies can integrate technology in their retail shops, including virtual mirrors, selfie walls, omnichannel experience, 3D printing of products, and attracting customers to the store to increase lifetime value to the business.

Big data will only get bigger

Big Data in shaping how retailers approach consumers and how consumers connect to the retailers in the online and offline world. With the influx of data, retailers will continue to optimize their offerings, expand their market share, and have a personalized approach to target the consumers. For consumers, they will get better services from the retailer, achieve better value for their spending, and will engage more with brands. As big data tools collect and analyze data in real-time, it will provide help companies to analyze trending products and services according to the consumer’s needs and preferences.