Big data, which, for the uninitiated are large data sets that are too complex and layered to be analyzed by database queries and conventional analysis, promise to soon become the darling in the eyes of ecommerce retailers. While ecommerce retailers absolutely love the power of analytics in analyzing and predicting customer behavior and attitudes, regular customer data such as product browsing data, content browsing data reveal only the tip of the iceberg that is the customer mind. Analytics is so much data and figure-oriented. Big data, on the other hand, encompasses a panorama of customer thought patterns and attitudes. Big data comprises data from videos, websites, scanned images, and surveys which, though difficult to analyze, can reveal a wealth of information.
Here are the top 5 ways big data analytics can help ecommerce retailers.
Personalize offerings
Big data helps identify both loyal and new customers, based on such data as browsing habits and patterns and spending patterns. Based on the data, you can customize your offerings and promotions for both loyal and new customers. For example, for loyal and long-time customers, you can identify their likes and preferences and accordingly prioritize the display of offerings when they log in. For new customers, you can offer discounts or customize content to make offerings more attractive.
Dynamic pricing
Dynamic and quick pricing changes can help you earn profits and stay competitive. Your prices need to be constantly competitive, first of all. To keep your prices constantly competitive, you need to constantly know competitor product prices, regional preferences, types of product sales and customer action data. Regular analytics face tall constraints in collecting and analyzing such huge and varied data.
Improved customer services
Data on customer (data on each customer and not a general pattern or statistics) attitudes and preferences will be extremely helpful in serving your customers better. For example, some customers may not only complain about your products or services through the official channels you offer, but may also go social about their grouses. You need to have data of such customers and exercise extra caution so that complaints of such customers are addressed double-quick.
Fraud prevention
Large data sets can, for example, identify for you regions, states or countries where credit card fraud is most frequent or where customers tend to not honor cash-on-delivery commitments. Big data can provide micro-localities where frauds or payment defaults occur the most. You can take measures to prevent frauds and monetary loss by taking steps accordingly.
Introduce products customers may like
Data collected from channels such as surveys, social media, forums, website visits may reveal that a certain product, for instance, an intelligent pedometer, is what customers want. People may have been discussing their requirements, albeit informally, on a social media forum when your data spy snooped. You instantly jump into action and procure some pedometers just to gauge their demand and the pedometers are immediately available on your website. That way, you are stealing a march on your competitors who might still be in dark about customer expectations.
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