Machine Learning - eBook (EN)

Economic Validation: Analyzing the Economic Benefits of Intelligent Search using Amazon Kendra 4 © 2022 TechTarget, Inc. All Rights Reserved. ESG Economic Validation ESG completed a quantitative analysis of Amazon Kendra, an intelligent search service, focusing on the economic benefits organizations across a wide range of domains can expect when leveraging Amazon Kendra versus traditional search solutions. ESG's Economic Validation process is a proven method for understanding, validating, quantifying, and modeling the economic value propositions of a product or solution. The process leverages ESG's core competencies in market and industry analysis, forward-looking research, and technical/economic validation. ESG conducted in-depth interviews with end-users and reviewed case studies to better understand and quantify how Amazon Kendra as a managed service helps organizations improve their customer experience, increase employee productivity, and lower TCO in comparison with traditional search solutions. ESG used the qualitative and quantitative findings as the basis for a simple economic model comparing the expected cost savings of implementing and using Amazon Kendra versus traditional search solutions. Amazon Kendra Economic Overview ESG's economic analysis revealed that the Amazon Kendra intelligent search solution provided its customers with significant savings and benefits in the following categories: Improved Operational Efficiency, Improved Knowledge Management, Improved Customer Experience, Lower Total Cost of Ownership, and Reduced Risk and Liability. Improved Operational Efficiency Amazon Kendra delivers improved operational efficiency to its customers, as they search through unstructured data and discover the right answers to their questions when they need them. According to one customer, "Amazon Kendra provides a direct connection (to answers in our data) with unbelievable levels of efficiency and accuracy." • Increased employee productivity–A research and development organization using Amazon Kendra reported that it improved employee productivity by 25%. Multiple factors drove this, including the time savings from not having to manually open documents in search results, finding the right answer with their first query, and having answers extracted from within documents and presented as text snippets. In contrast, traditional search provides links to documents that users still need to open and search manually, which is time-consuming and often inaccurate. • Easy to find what you are looking for--ESG verified that Amazon Kendra addressed the accuracy challenge of traditional search solutions by supporting natural language queries, enabling intuitive search within unstructured content. Its engine is built on natural language understanding (NLU) and ML, which facilitate instant answers, FAQ matching, and document ranking using broad domain expertise, as the solution is pre-trained for 14 domains and industries. The last component provides continuous improvement by incrementally learning from user feedback. One customer commented, "Unlike conventional search technology, Kendra's natural language search capabilities help us answer questions quickly and accurately—no matter how deep the information lives within the Index." Another customer mentioned, "With Amazon Kendra, instead of going down the road of classifying data, tagging documents, and managing taxonomies, we were able to apply Amazon Kendra's intelligent search capabilities so employees could ask natural language questions and get answers to HR or benefit-related topics quickly." "We have been able to reduce the amount of time that it takes to search for relevant information across systems by roughly 50%, increasing staff productivity by 25%." "Amazon Kendra allows product managers to ask questions in everyday language, quickly surfacing an answer…previously not possible with keyword search and connecting them to relevant content across the enterprise."

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