In today’s e-tailer environment, e-commerce space is becoming increasingly competitive. With e-tailers having to constantly ﬁnd new ways to improve revenue and attract consumers, many of whom shop through a handheld device.
As the cost of acquiring new customers continues to increase, e-tailers know that encouraging those customers to remain loyal shoppers to their business is crucial to earnings. A proven strategy in maximizing the loyalty from existing customers is through upsell/cross-sell strategies. These strategies include targeted advertising and the display of relevant products and event-triggered emails through information mining of social networks, which helps deliver a personalized shopping experience and improve sales.
The Recommendation Engine is the solution for implementing upsell/cross-sell strategies. With the ever-growing product catalog on e-commerce websites, it becomes increasingly important for companies to display relevant products according to customer preferences and tastes, and provide innovative methods of intelligently searching for desired products.
Many different approaches have been applied to the basic problem of making accurate and efﬁcient recommender systems- from fairly simple database queries, nearest neighbor algorithms to Bayesian analysis. As e-commerce providers are not analytics/recommendation experts, they continue to use basic ﬁltering algorithms.
To effectively manage these e-tailer requirements, our Analytics Solution for e-commerce employs the Hybrid Recommendation Engine (HRE). Equipped with the highest AUC, the HRE empowers e-tailers to achieve an efﬁcient and cost-effective means of delivering a personalized shopping experience to its customers. Insights are derived from customer preferences, social information and shopping history.
Personalized shopping experience enabling customers to ﬁnd relevant products easily
Upsell and cross-sell based on personal attributes, past shopping history and available social information
Customer loyalty and improved sales
While the customer is viewing a list of products, our Analytics Solution provides personalized recommendations generated through Collaborative Filtering with user-based similarity through matrix factorization method with ALS and regularization
As the customer is viewing a list of items added to the shopping cart, our Analytics Solution eliminates the need for organizations to make compromises in determining what data to collect, how fast to process the data, and how and where to store it
While the customer is on the checkout page and viewing the ﬁnalized list of products, our Analytics Solution generates personalized recommendations based on product-bundling and product ontologies
Our Analytics Solution provides e-tailers with the most scalable means to centrally mine customer personal attributes, shopping history, and social information; building analytics on top of e-commerce to provide recommendations that make for a truly personalized shopping experience. It is built upon a modular architecture that takes full advantage of parallel processing, and a clustered repository – assuring consistent event collection, analysis and availability. This modular approach allows for appliances like deployment, distributed conﬁguration and high performance.
Real-time recommendations on customer’s device
Next Best Offer that optimizes value offered for right uplift
Ensemble recommender systems that ensure high relevance
Embed recommendations right into workﬂows and customer touch points through APIs
iFusion Analytics’ patented scalable and distributed platform comes with “out-of-the-box” rich analytics algorithms. It collects poly-structured data from heterogeneous sources & federated data stores. It cleans data, curates data and makes the data ready for Data Analysts and Data Scientists to accelerate insights and build solutions at a reduced cost.