Market research of commercial recommendation engines for online and offline retail

Abstract

In the era of big data and predictive analytics, recommendation systems or recommendation engines that recommend merchandise or service offerings based on individual preferences have had a revolutionary impact on retail businesses by making "personalization" a reality. As recommendable engines enable retailers to develop an unprecedented 360 degree understanding of their consumers at an individual level, retailers that are early adopters of recommendation engine technologies have gained competitive advantages with sales increase, targeted marketing and customer loyalty. This thesis aims to conduct a comprehensive research of the market for commercial recommendation engines in both online retail and offline retail. The market research covers industry situation overview, market size, industry trend, competitive landscape, major vendors of recommendation engines and their differentiated technologies. This thesis also investigates into the unaddressed customer needs based on the voice of recommendation engine customers and proposes corresponding solutions. As recommendation engines have been widely accepted and proven effective in online retail, this thesis explores how recommendation engines, in combination with other big data technologies, can be used to transform the brick and mortar offline retail. KEYWORDS: Recommendation algorithms, recommendation engines, recommendation systems, onmi-channel personalization technology, data management platforms, online retail, offline retail, digital advertising, emarketing, social log-in, point of sales, mobile payments, geo-location targeting, digital wallet, natural language processing, data aggregation, data warehouse, and data normalization.