With the growth in the retail industry comes new technology to help retailers gain a competitive edge. Big data analytics can provide retail businesses with crucial insights from transaction information to social media metrics to measure customer behavior, business success, and overall market efficiency. Here are five types of big data analytics that top retailers are already using to benefit their business.
Customer Behavior Analytics
Big data can provide consumer insights to help increase revenue, avoid customer churn, and lower customer acquisition costs. Today, the myriad of opportunities for consumer-business interaction, including social media, e-commerce sites, digital communication, and physical stores, have made retailer data more complex and expansive than ever. Luckily, aggregating and analyzing the data from these various mediums can provide new-age insights into the behaviors, motivations, and communication patterns of the consumer platform, and further, illustrate who/where your highest-value customers are. Turn these insights into actions and you'll optimize market strategies for your target audience, enhance customer loyalty, and be able to streamline your business's growth longitudinally.
Personalization of the In-Store Experience
New people-tracking technology now makes it possible to analyze store behavior and measure the impacts of merchandising efforts. Data engineering platforms can help organize and clarify data to optimize merchandising tactics, personalize the in-store experience, and come up with timely offers to motivate customers to complete purchases and drive sales. These valuable insights can be gathered from all platforms: websites, point-of-sale systems, mobile apps, supply chain systems, in-store sensors and cameras, and more. Data engineering platforms will further test and quantify the impacts of your different marketing and merchandising tactics, and even use individual customers’ purchasing and browsing history to help personalize in-store service for them.
Predictive Analytics and Targeted Promotions
Predictive analytics and targeted promotions can increase conversion rates while lowering costs. It’s imperative that retailers target customer promotions effectively; to do this, they need a complete, accurate view of customers and prospects. With the large variety of ways that customers now interact with retailers, we can take the data the customers generate and turn it into insights on customer preferences, interests, and behavior. For example, if the data uncovers that high-value customers often watch cooking channels and shop often at Whole Foods, retailers can use that insight to ads and promotions for cooking shows and organic grocery stores; incredibly, just this simple change can drive up conversion rates while driving down consumer acquisition costs.
Customer Journey Analytics
With an ever-increasing retailer platform and the wide range of products available to consumers, customers now expect more from their stores; they want high-quality experiences, consistency in information and interaction, and convenience in their purchases. The customer’s desires and experience are driving sales and customer retention more than ever before. Data-driven insights can show each customer’s journey across all channels to answer the most complex retail questions: who your high-value customers are, how they behave, how and when you should reach them, and what’s going on during each step of their journey. The result of big data analytics reveals unique patterns and insights that traditional analytics can’t even produce, making them even more valuable.
Retailers can also use analytics to evaluate supply chain and product distribution, which, in turn, can reduce their overall costs. The most important factor that data engineering platforms bring is the ability to unlock insights on log, sensor, and machine data; these insights can help you improve decisions, increase operational efficiency, and save up to millions of dollars in expenses.
Plant machinery, servers, customer appliances, and product logs are all assets you can use to generate valuable data. Of course, the large amounts of data coming from all these platforms can be overwhelming and difficult to manage; and the volume of data can double every few months. With data engineering, you can combine structured data like CRM, ERP, mainframe, public data, and more with unstructured data, and then use analytical tools to detect outliers, runtime series, and root cause analyses, and other methods to get the most out of your data in less time.
In the modern world of technology, social media, and data, there are a wealth of opportunities and insights available to the retail sector. It’s important for retail companies to gather data from every platform and asset they can to help track, understand, and properly interact with customers. This is the key to boosting sales and reducing costs to get your business profiting more than ever. If you’re looking for some more sources of data and insights, check out Scanalytics Inc. for their technology and analytics that are designed to help revolutionize your business.