6 Uses of Big Data for Online Retailers
Big Data refers to a large set of data too complex to be handled by conventional database management tools. It’s data that exists beyond your web analytics, ERP, or CRM databases. It often exists outside of your organization, think of customer sentiment and social sharing data owned by Facebook, Twitter and Pinterest, or competitive pricing data from comparison shopping engines.
Without Big Data, it’s impossible to get a comprehensive cross-touchpoint view of your customer, and fully understand customer behavior in order to make business decisions in real-time. (Even with Big Data, it’s still very difficult to achieve!)
Here are six uses of Big Data for online retailers.
Personalization. Consumers shop with the same retailer in different ways. Data from these multiple touch points should be processed in real-time to offer the shopper a personalized experience, including content and promotions.
For example, do not treat loyal customers the same as new ones. The experience needs to be personalized to reward loyal customers. It should look attractive and “sticky” for new customers.
Dynamic pricing. You need dynamic pricing if your products compete on price with other sites. This requires taking data from multiple sources, such as competitor pricing, product sales, regional preferences, and customer actions to determine the right price to close the sale. Large merchants like Amazon already support this functionality. Overcoming this challenge will give your business a huge competitive advantage.
Customer service. Excellent customer service is critical to the success of an ecommerce site. Zappos and Netflix are examples of terrific customer service. But Big Data has made customer service a challenge by requiring seemingly every interaction with a shopper to be used for serving that shopper. To continue to excel at customer service, online retailers need to overcome this challenge.
For example, if a customer has complained via the contact form on your online store and also tweeted about it, it will be good to have this background when he calls customer service. This will result in the customer feeling valued, creating a quicker resolution.
Managing fraud. Larger data sets help increase fraud detection. But it requires the right infrastructure, to detect fraud in real-time. This will lead to a safer environment to run your business and improved profitability.
Most online retailers need to process their sales transactions against defined fraud patterns, for detection. If it’s not done in near real-time, it could be too late to catch the fraudsters.
Supply chain visibility. Customers expect to know the exact availability, status, and location of their orders. This can get complicated for retailers if multiple third parties are involved in the supply chain. But, it is a challenge that needs to be overcome to keep customers happy.
A customer who has purchased a backordered product would want to know the status. This will require your commerce, warehousing, and transportation functions to communicate with each other and with any third-party systems in your supply chain. This functionality is best implemented by making small changes gradually.
Predictive analytics. Analytics is crucial for all online retails, regardless of size. Without analytics it is difficult to sustain your business. Big Data has helped businesses identify events before they occur. This is called “predictive analytics.” Predictive analytics is becoming an important tool for many businesses.
A good example of this is predicting the revenue from a certain product in the next quarter. Knowing this, a merchant can better manage its inventory costs and avoid key out-of-stock products.
The infographic below, courtesy of Monetate, explains structured vs unstructured data, outlines the challenges and goals of Big Data for retail, and how to make a Big Data game plan.
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