Search arXivSearch

arXiv · 2405.04692

Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development

Abstract

This paper explores the application of AI and NLP techniques for user feedback analysis in the context of heavy machine crane products. By leveraging AI and NLP, organizations can gain insights into customer perceptions, improve product development, enhance satisfaction and loyalty, inform decision-making, and gain a competitive advantage. The paper highlights the impact of user feedback analysis on organizational performance and emphasizes the reasons for using AI and NLP, including scalability, objectivity, improved accuracy, increased insights, and time savings. The methodology involves data collection, cleaning, text and rating analysis, interpretation, and feedback implementation. Results include sentiment analysis, word cloud visualizations, and radar charts comparing product attributes. These findings provide valuable information for understanding customer sentiment, identifying improvement areas, and making data-driven decisions to enhance the customer experience. In conclusion, promising AI and NLP techniques in user feedback analysis offer organizations a powerful tool to understand customers, improve product development, increase satisfaction, and drive business success

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Tian Tian, Liu Ze hui, Huang Zichen, Yubing Tang. 2024-05-07. Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development. https://arxiv.org/abs/2405.04692

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Local Media and the Shaping of Social Norms: Evidence from the Ebola outbreak

Media's influence on norms and behavior is widely recognized. Less is known about the role played by media being local. I examine this in a high-stakes context, the Ebola outbreak in Guinea. I exploit quasi-random variation in access to radio and the timing of a public-health campaign aired on community radio. I find that 12-17% of Ebola cases could have been prevented if places with access to a neighboring community radio station had instead had their own. Impacts are driven by radio being local rather than by ethno-linguistic belonging. Local media facilitates coordination in behaviors observed and sanctioned locally.

econ.GN

Productivity Shocks and Input Misallocation: A Decomposition

This paper asks how much input misallocation productivity uncertainty generates and at which stage of input decisions it arises. I separate revenue productivity by when each component is revealed and trace each into the gap between an input's marginal revenue product and its price. In six European countries, shocks revealed after an input is committed account for 20 percent of capital gap dispersion and 5 percent of labor gap dispersion. An unanticipated one percent rise in productivity raises the capital gap by 0.92 percent and the labor gap by 0.19 percent, because most of the shock passes into the wage.

econ.GN

When Do Type-Specific Wages Buffer Distributional Incidence in TANK?

When do relative wages buffer the unequal incidence of aggregate shocks? I derive a consumption-gap decomposition and a present-value condition for partial offset in a TANK model. An extension separates wage-setting demand elasticity from substitution between labor segments and allows each segment to contain both financial types. With a zero inherited wage gap and a same-sign discounted wedge, substitution above one gives offsetting earnings reallocation; substitution below one gives amplification. The channel disappears when financial types have identical segment exposure. Numerical experiments assess these mechanisms, shock persistence, policy feedback, and aggregate-IRF matching. In the nested perfect-alignment monetary benchmark, the peak consumption gap is about two-fifths smaller under type-specific wages than under the common-wage closure. These are conditional model comparisons, not empirical effect estimates or welfare rankings.

econ.GN