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Four Big Data Examples in the Retail Market

June 09, 2017

Four Big Data Examples in the Retail Market

When it comes to big data, the experts talk about big insights, and this is especially true for retailers. Perhaps more than any other market that solution providers support, retail is a numbers game with tight margins and critical delivery dates. Even the smallest savings or improvement in efficiency can translate into big bucks, which is why more resellers are convincing retailers to invest in big data projects.

A recent study by IDC shows that businesses are employing more data-driven decision-making. IDC forecasts that sales of big data products and services will increase at a compound annual growth rate of 23.1 percent through 2019, with annual spending reaching $48.6 billion in 2019. Retail in particular is looking for help to adopt big data in order to learn more about its customers, sales patterns and inventory management. Retailers and e-commerce brands are using more analytics in order to drive strategic action, especially for localization in order to create the right product mix for regional customers, and in order to forecast demand for new products.

So how can resellers help retailers harness big data in order to improve their operations? Here are just four practical examples:

1. Predicting consumer trends

Being able to accurately predict consumer trends is the holy grail for retailers. If they can identify the latest fad, retailers can make better informed decisions about product ordering and stocking strategies. However, consumers are fickle, and trends change, but big data algorithms give retailers the means by which to assess market trends using multiple data sources. Trend-forecasting algorithms can be created in Hadoop that integrate historical data, such as previous sales performance and inventory records, with external indicators such as advertising metrics, Web-browsing data, social media posts and other components in order to track consumer “buzz.” Using big data for sentiment analysis can help retailers predict top-selling products and styles across multiple categories and regions.

2. Demand forecasting

Predicting trends is one thing; you also have to map product demand in order to meet those trends. Developing accurate stocking strategies is an art form that requires matching trends with demand. Analytics can help by mapping previous buying patterns with data sources such as regional demographics, spending habits and economic indicators within targeted and regional markets. The closer retailers can get to demand-driven forecasting, the greater their profits.

3. Pricing optimization

Along with predicting demand comes pricing optimization. Analytics can help retailers get the most for goods by tracking transactions, competitors, cost of goods and other variables. Big-box retailers like Walmart spend a fortune on real-time merchandising to track millions of purchases each day to identify patterns that can point the way to higher profits. For example, a specific product may not sell well by itself, but when paired with a complementary product, overall sales increase. Target has become a master of such merchandising strategies with products like Kitchen in a Box, which has been a big seller with college students and singles.

Discounting and sales pricing also benefit from big data analytics. Statistics have shown that gradually reducing prices on goods as demand wanes is a more profitable approach than the traditional “end of season” sale. With analytics, retailers can map the rise and fall of demand and match pricing accordingly.

4. Attracting new customers

 One of the most beneficial big data applications for retailers is customer analysis. Big e-commerce players like Amazon rely heavily on recommendation engines to guide customers to new products using previous purchase histories. The more you can refine those metrics using demographics, age, income and other variables, the more goods you are likely to sell. Many stores use big data in order to build comprehensive customer profiles using data from loyalty programs, customer shadowing, consumer surveys and other sources.

In many cases, retailers are looking to attract new customers into their brick-and-mortar stores. Analytics can show them what goods to promote in order to attract new business. For example, more retailers are adopting geolocation in order to send electronic coupons to smartphone apps as users pass by the store or even when they are shopping in the store. A pop-up coupon can promote more impulse purchases as customers walk the store aisles.

Big data also can be used in order to identify gaps or weaknesses in sales programs. For example, when it analyzed its customer demographics, Macy’s determined that it was attracting fewer Millennials as customers. In order to attract younger shoppers, Macy’s opened One Below at its New York location with a selfie wall, custom phone cases created while you wait and other offers to appeal to younger consumers.

These are just some of the more obvious applications for big data in retail. There are many others, such as supply chain optimization, where you can use analytics in order to assess the ROI on specific suppliers and delivery services, or to identify fraudulent credit-card transactions in real time. Of all industries, retail tends to gather more data about customer transactions, pricing, inventory management and staffing. All of that data can be harnessed in order to make retail operations more effective and more profitable, and it’s up to resellers to show retailers the way.