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How Big Data is Changing the e-Commerce Landscape

How Big Data is Changing the e-Commerce Landscape

The changes that come with Big Data create many opportunities for e-commerce companies to improve their operations. The faster they start exploring these opportunities the better their position in the e-commerce space will be.

What we have seen in the past few years is an important rise in the volume, variety and velocity of data. This being said, what we can expect for the future is more, more, and more!

Indeed, a larger volume of data in different format is available at an accelerating pace. In the E-Commerce space, the different forms of data collected fall under two categories; structured and unstructured data. The structured data refers to the regular data that can be easily defined. The challenge is with unstructured data, which is data that is harder to define. This includes tweets, clicks, videos, comments for instance. However, by using the proper tools, businesses can extract a lot of value from this data which used to go “unnoticed”. E-Commerce can leverage on this information to personalize the customer experience, exploit dynamic pricing, and reduce frauds.  


Companies are able to enhance and personalize the customer experience at a larger scale. Indeed, having access to so much information means that companies are able to create personalized offers targeted toward different sub segment of customers. However, this is only feasible with the use of "marketing automation software" to effectively manage the marketing campaigns. Many studies have shown that personalization can deliver significantly higher returns. 

Dynamic Pricing:

Having access to live data also means that the e-commerce world can adjust their prices according to the demand. E-Commerce can collect data from historical product pricing, customer activities, preferences, order history, demand for the product and the price of competitors to support real-time pricing.  So far, only 22% of retailers have adopted a dynamic pricing strategy and more than 36% expect to implement it in the next year. This is not surprising since dynamic pricing has the potential to improve gross margins by 10%.

Fraud Reduction:

In the past, predictive analytic solutions were used to detect and prevent frauds. Retailers would analyze the way users would interact with the website to try to prevent fraud. Now, retailers and payment processors automate this process through the use of machine learning which automatically defines new "features" to detect and prevent fraud. Knowing that fraud causes the loss of billions of dollars every year, machine learning arrives as a relief for retailers.