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Next-generation recommendation engines delight customers with personalized experiences in real time—and at scale.
Choice is a good thing. But too much of it can be overwhelming—even paralyzing. In today’s crowded digital marketplace, consumers want product and service choices that cut through the clutter because they are curated expressly to their needs. It’s about serving up the most relevant possibilities, not the most popular ones.
By delivering personalized recommendations based on data-driven understanding of customers’ history, behaviors, reviews and a raft of personalized attributes, companies across industries can stand apart from their competitors.
Next generation recommendation engines, which use a multi-tiered algorithmic approach to understand the personal context of each potential buyer, can help companies earn customer engagement and loyalty—while increasing sales.
What is the real potential of these recommendation engines to deliver customer relevance at scale? What capabilities must they have to deliver individually tailored and effective suggestions at scale and in real time?
Explore the possibilities of personalized recommendation tools—and maximize them.
Pioneering companies began to use basic, personalized recommendation services for customers more than a decade ago. Recommendation engines have evolved considerably since then, helping companies to understand their customers better and tee up purchase options that align with unique customer attributes.
Despite this evolution, there are challenges that businesses must address to deliver truly personalized recommendations in today’s digital landscape—characterized by a vast range of products and services, cultures and channels.
Companies must be able to:
Process massive volumes of data from all types of consumer interactions
Interpret results of consumer interactions in real time with a micro-second response turn around
Create a holistic, personalized and consistent experience across channels when making recommendations
Invest in advanced, analytical methodologies to gain insights from the sparse data generated by new and occasional consumers
Companies today must go beyond an 80-20 sales strategy—where the majority of their profits are generated by selling a few products and services to a fairly small customer segment.
They must find ways to attract and get purchases from the 80 percent too. But making personalized recommendations at scale to these consumers is not an easy proposition.
Suboptimal recommendation engines tend to propose the most popular, rather than the most relevant, options in response to consumer searches. Multi-tiered recommendation engines can alleviate this “Harry Potter Effect” because they support the ability to curate personalized experiences, even with different levels of available data to assess.
By understanding consumers’ preferences, analyzing purchasing behavior and building individual profiles, the multi-tiered recommendation engine recommends a purchase that meets their needs.
To make individually tailored and effective suggestions at scale in real time, next generation recommendation engines must be enabled by several key capabilities. They should:
Understand context. Know the location of each consumer, and what they are doing at a particular moment.
Adjust based on insight. Make automatic improvements, based on feedback from previous recommendations.
Scale and flex. Have a robust and dynamic technical architecture to manage the sheer volume of data arising from multiple sources.
Test and learn. Allow business users to quickly simulate performance before moving into production, to minimize risk at critical customer touchpoints.
Be analytics masters. Balance breadth of data from a diverse customer set—from regular shoppers to infrequent customers.
Support business users. Give business users greater control over performance.
By learning about consumer preferences and adapting to changes in their lifestyle, interests, location or circle of influence, companies can significantly increase their ability to stay relevant—and competitive.
February 28, 2014
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