E-Commerce industry is predicted to have exponential growths in the next 5-10 years at least in the developing countries and it's going to re-define the way the products are priced, served and sold. Customers decision to buy products is driven by many factors such as relevance, customer service experience, promotions etc apart from just the price. To succeed in ecommerce space, companies should be equipped with superior knowledge on how to price products, which products to keep on hand and better ways to market these products in real time. Present techniques are ill equipped to deal with above problems on real time, and large data from varied sources.
With SpotDy, e-commerce companies can offer in real-time the shopper a personalized experience, including unique content and promotions.
With SpotDy, e-commerce companies can analyze competitor pricing, customer preferences and customer actions to determine the right price to close the sale.
With SpotDy, e-commerce companies can identify events before they occur, such as predicting customer buying patterns or predicting inventory needs.
With SpotDy, e-commerce companies can Optimize the site structure and content for organic search.
With SpotDy, e-commerce companies can understand customer's intent to deliver what they want.
A large e-commerce company wants to improve its customer's online experience. The company's customer online activities generated vast amounts of data about its customer's interactions, but the company was able to collect and analyze only 20% of the data. The company's service depended a lot on third party providers for product delivery and inventory management. This was proving to be a challenge, and affecting the company's ability to provide its services faster. Moreover, its business was seasonal making its technology infrastructure underutilized for more than half a year.
The company turned to big data technologies. The company replaced its traditional technologies with big data computing and cloud databases thereby improving its integration with third parties, delivering the products to customer 2X times faster and significantly improving its inventory management. The newer system helped the company understand its customers better and used it to personalize its offering and put relevant promotions thereby improving its sales by 25%. More importantly, as the business was seasonal, the company was able predict its technology infrastructure usage and took advantage of the distributional nature of big data technologies to add and remove its computing power and resources based on customer traffic and session.
Today, the company improved its speed to market by 30% and significant increase in amount of data used, allowing deeper insights on how customers used its services. The company improved its technology infrastructure utilization by 25% and saved lots of money on software licenses due to migration to open sources technologies.