Big Data Analytics for Online Retail: A Must-Have for Success

These days the capabilities for processing and analyzing big data are not just high tech luxury for large companies – online retailers of all sizes can take advantage of big data insights due to many cost-effective options both for on-premises and in the cloud. “Big data” doesn’t simply mean large volumes of data – it’s more about data that is difficult to process and analyze through traditional methods. All businesses potentially have “big data” in one form or another that could yield significant or even game-changing insight. The starting point for any company is developing a strategy and the right questions that can be answered by big data analysis.

Online retailers can use big data analytics to understand more about customer shopping and purchasing behaviors. Insights can tie behaviors to marketing and merchandising to determine which programs are most effective for particular customer segments. Product catalogs can be refined and special offers can be made more effective. Having a clear view of what is selling and what isn’t – in real-time – can greatly improve inventory management and supply chain agility. Online retailers want to increase interaction with customers to strengthen engagement and memorable experiences. The result should be more successful sales from overall customer visits to the website by delivering the product the customer wants when they want it and at the right price.
Big data analytics can greatly enhance personalization of products and information provided by the online retailer. Web content delivered to a particular customer can be tailored to preferences and shopping behaviors compiled from previous visits, and promotions can be served up that are more likely to match customer needs. Analytics can take online retailers much closer to realizing one-to-one marketing in ways that will engage customers, actually enabling customized experiences on a large scale.

Recommendations and association rules are other benefits of big data mining and analytics. When a customer selects certain items, recommendations can be made for other items based on the patterns of buying for similar customers, and / or based on previous buying behaviors of the customer. Association rule learning focuses on which products are usually purchased together, based in part on shopping cart analysis. Using analytics to determine the relationships between certain products that map to purchasing patterns can help create better processes for ordering and stocking such products.

Predictive analytics are becoming mission-critical for online retailers. Beyond determining customer behavioral patterns, predictive analytics are needed to identify future trends and events, to determine new customer types and new products, and even to take a look at new business models. Supply chain, inventory management, and revenue outlooks are also areas that can benefit from predictive analytics. Initiatives to leverage big data through various analytics can make use of technology tools to continuously explore, test and refine different ideas, or search for focused answers that can help with business agility and competitiveness.

Real-time analytics for online retail do provide much needed insight, but such analytics processes do not take place in a vacuum. When analytics are running in the background in response to customer interactions on the online retailer’s website, website performance comes into question. It is essential to coordinate analytics process performance with website optimization. The best personalization and recommendations results will be meaningless if speedy web page loading is compromised. Most customers will abandon any online site that is slow to load and refresh, which obviously will result in a negative shopping experience. Shopping cart analytics cannot be performed if there’s nothing in the cart.

About Julie Hunt

Julie is an accomplished consultant and analyst for B2B software solutions, providing services to vendors to improve strategies for customers, target markets, solutions, vendor landscape, and future direction. For buyers of software, she helps companies make purchase decisions for software by working from a business-technology strategy. Julie has the unique perspective of a software industry “hybrid”: extensive experience in the technology, business, and customer-oriented aspects of creating, marketing and selling software. She has worked in the B2B software industry on the vendor side for more than 25 years in roles from the very technical (developer, SE, solutions consultant) to advisory roles for developing strategies for products, markets and customers, and go-to-market initiatives. Julie is an accomplished consultant and analyst for B2B software solutions, providing services to vendors to improve strategies for customers, target markets, solutions, vendor landscape, and future direction. For buyers of software, she helps companies make purchase decisions for software by working from a business-technology strategy. Julie has the unique perspective of a software industry “hybrid”: extensive experience in the technology, business, and customer-oriented aspects of creating, marketing and selling software. She has worked in the B2B software industry on the vendor side for more than 25 years in roles from the very technical (developer, SE, solutions consultant) to advisory roles for developing strategies for products, markets and customers, and go-to-market initiatives.

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