The more volatile the business, the more important it is to maintain a close watch on day-to-day operations—and perhaps no business is more volatile than retail. Retailers operate with very small profit margins and are subject to the caprices of the economy, consumer attitudes, inventory supply, and even the weather. The more intelligence retailers can gather to determine which factors sales most, the better their chances of yielding higher profits.
Retailers also benefit from having access to a tremendous amount of data. Every transaction tells them more about their customers, pricing, inventory controls, e-commerce effectiveness and more. With the right business analytics, all that information can be converted to intelligence in order to guide decision-making.
For example, according to the U.S. Census Bureau, online sales are climbing steadily, while retail sales overall are more volatile. Reports show that e-commerce sales increased 2.9 percent in Q3 2015, totaling $81.1 billion. Q3 sales in 2015 also were 15.2 percent higher than Q3 2014 sales, while total retail sales increased 1.6 percent in the same period. At the same time, overall retail sales in the U.S. have increased an average of 0.36 percent month over month from 1992 to 2015. Using this kind of data as a baseline, retailers can get a better understanding of their own sales performance using analytics.
Considering the quantity of data available to retailers, they can apply business analytics to almost any retail process in order to identify trends and improve profits. Here are just a few ways retailers can get more from business analytics:
E-commerce – Tracking and analyzing online shopping activity lends itself to business analytics, because each step of the process can be tracked from beginning to end. As soon as the customer enters the online store, retailers start tracking activity, assessing where they go, how they get there, how long they visit, and what seems to have the most appeal. Statistics such as number of clicks, time spent on specific product pages, and abandoned online shopping carts reveal details about shopping behavior. With the right analytics, retailers can identify the impediments to online sales, such as poor Web design, complex navigation, or confusing checkout procedures. Analytics are also essential for attracting new customers. Understanding what attracts customers to a site makes it easier to create more effective marketing campaigns.
Customer profiling – Analyzing transactions in order to create customer profiles is one of the most common analytics strategies for improving customer loyalty and increasing sales. By tracking past transactions and shopping behavior, retailers can predict future behavior. These types of analytics are useful for promoting customer loyalty and creating custom offers and marketing campaigns to increase sales volume. They also affect inventory, pricing and other aspects of retail operations.
Merchandising – Analyzing product sales can improve merchandising. By assessing the ebb and flow of sales over time, you can determine which in-store displays seem to be more effective, understand the seasonality for specific types of products, determine if specific product combinations sell well together, and reveal other trends that can improve sales volume.
Price optimization – Setting prices is one of the greatest challenges for retailers. They want prices to be competitive with similar goods and low enough to attract buyers. Even increasing pricing by a single percentage point can mean a substantial ROI when margins are tight. Using analytics, retailers can fine-tune their pricing strategies and determine optimal pricing using sales history and other factors. They even can perform “what if” simulations in order to experiment with pricing changes without risking actual sales.
Stocking strategies – In addition to increasing profits, retailers need to reduce their overhead. Maintaining the right inventory levels means they can satisfy customer needs without tying up cash and space with excess merchandise. Using analytics, they can more accurately predict inventory demand and develop timelier, more cost-effective stocking strategies.
Big data is playing an increasingly important role in retail analytics. Using Hadoop analytics, retailers can bring together larger pools of disparate data to create insights that translate into profits. All of the analytics examples described here can be combined using big data in order to uncover larger trends. For example, customer profiles affect price optimization and stocking strategies. E-commerce merchandising can benefit from analyzing other customer buying trends, assessing other Web statistics and incorporating unstructured data, such as customer support calls and social media. Big data can even help with packaging, merchandising and supply-chain strategies.
Knowledge truly is power, and retailers are awash in data that requires analytics to be turned into insight. Big data can deliver predictive analytics that can be customized for specific retail applications. By helping retailers harness business analytics, including big data, you can improve retailers’ pricing, inventory management, customer service and merchandising in order to increase sales volumes while cutting overhead. The proof of the returns will be in the analytics.