Big Data Recommendation Engine for ecommerce

Becoming an integral part of many e-commerce websites and online shopping carts, recommendation engines use complex algorithms to determine big chunks of data and analyze the products that potential users wish to purchase as based on their online shopping preferences, stated choices or the purchases of individuals with similar kind of tastes and demographics. In addition, recommendation technology must be capable enough to reach small, medium and large sized businesses and most important be robust, scalable and cost effective. 

Most of the consumers who have used Amazon, Flipkart, Jabong and other leading e-commerce websites must be familiarized to receive recommendations for apparel, gadgets, books or any other thing in which they might be interested in purchasing in the long run. Priory, recommendation technology was somewhat filthy and savage. It used to recommend those items only which the purchaser has bought. Though, the technology has become more sophisticated and advanced and is now a vital part of many online merchandisers and vendors economic models. The strategy focuses more on determining big volumes of data and analyzes what products that targeted customers might want to purchase based on their online shopping choices, preferences, people purchases depending on their tastes and demographics. It is into building new revenue opportunities as well as booming both, the number of prospects who become purchasers and the prospect retention. Few of the top notch recommendation technology merchandisers are Google, Mavice, ChoiceStream, AgentArts and ExpertMaker. In addition, the big branded users are Apple Computer, Amazon.com, Netflix DVD rental website etc.            

The CEO of ChoiceStream Michael Strickman stated, the technology is assisting to drive and generate online sales, especially in the music industry. With such a large client centric and favorable market, it is easy to see that customizing and personalizing stuff makes sense. The customers used to download almost four times songs and music than before implementation. With the improvement in recommendation technology with time, the more concerns will be there. For example – Analytical and integrated approaches entrust on big chunks of data that are not available to other smaller merchandisers and vendors. The technology’s use of information from online trade activity and the constitution of consumer profiles have ignited privacy related enquiries and concerns.

In the early 1990s, recommendation technology began to shape up and one of the earliest pioneers was the University of Minnesota’s GroupLens Research Project. Amazon.com and Net Perceptions were amongst the first recommendation users well known as vendors for website personalization and customization software. The key forces for enterprises and organizations to drive and integrate big data recommendation engine into their e-Commerce websites is the wish not only to get the users purchase more products as well as return on their websites in the long run. They will return because these engines will make the process faster and quicker to find items they want and provide personalization that could yield favorable and useful suggestions, as said by Mavice’s chief founder and technology officer. Most of the vendors want to showcase and recommend customers with items in which they could be interested in but didn’t purchase. 

The business models usually employed by the recommendation technology vendors includes either hosting services for a company or licensing their engines basically for e-commerce businesses to run themselves. In general, there are four types of licensing engines including the ones mentioned in below.

·         Implicit Engines – These engines provide recommendations based on the activities of multiple customers while browsing a company’s website. They tell users information about the customers who have purchased ‘A’ have subsequently purchased ‘B’ too.

·         Explicit Engines – They make recommendations based on the users entering phrases or words to connote the type of products they are looking for.

·         Content based systems – They used to recommend those items to customers based on their preferences in the past. Such systems collect information about customer’s preferences through questionnaires or the past history stored in their databases.

·         Collaborative based engines – These engines provide recommendations based on the buying customers preferences with similar kind of interests, demographics, questionnaire responses or the profiles picked from customers online actions.      

In shell, as estimated and said by analyst Patti Freeman Evans with JupiterResearch, a market based research firm, about 25% of online clientele make unplanned and random purchases, a smaller percentage than users at traditional stores. Adding more, online third parties also make use of recommendation tools as a medium to optimize and promote their sites along with to generate revenue from less demanded and older stuff, for example CDs and DVDs. Businesses and enterprises use the big data recommendation engine technology to grab the information related to e-Commerce websites, along with the customer data to make better sales and marketing along with the available stock and promotional products.