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Practice paper

Using customer feedback to prioritise remediation return on investment and improve customer experience

Manya Mayes
Applied Marketing Analytics: The Peer-Reviewed Journal, 9 (2), 153-161 (2023)
https://doi.org/10.69554/XIEJ4146

Abstract

With the myriad of customer comments available on digital media, it is paramount for organisations to identify the breadth of compliments and complaints that customers are discussing, to analyse and understand drivers of customer sentiment and to know where to prioritise available resources such that the resolution of issues produces the biggest rewards for customers — and the business. Many organisations already successfully identify the issues that customers report (and there may be dozens of them). Often, they select the issues with the highest volume (impacting the most customers) and focus on resolving those first. Additionally, they may target the issues that have the highest negative sentiment. While both approaches are useful, they usually lack the ability to track newly developing issues/trends and, most importantly, are unable to accurately prioritise where to start (in the case of issues with similar volumes) and how to quantify the return on investment (ROI) associated with the remediation of each issue. This paper focuses on the technical capabilities needed to be able to identify the drivers of customer conversations and sentiment, the approaches needed to quantify both the importance of taking action from the customer's standpoint and the impact for the business in doing so, allowing a measured approach to improved customer experience. Analysing, prioritising and resolving cross-channel issues creates happier customers, higher rates of acquisition, increased repeat business and, ultimately, improves the bottom line.

Keywords: sentiment analysis; natural language processing; sensitivity analysis; brand and reputation management; competitive intelligence; customer experience; marketing analytics; digital analytics

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Author's Biography

Manya Mayes has a Master of Science in big data analytics from the University of Liverpool, UK, and has been immersed in natural language processing (NLP) since 1998. Her love of everything NLP has improved vehicle safety, supported a US$1.8bn class-action lawsuit settlement and improved customer lifetime value across numerous industries. Manya has also played an instrumental role in developing NLP software for companies in the tech industry.

Citation

Mayes, Manya (2023, October 1). Using customer feedback to prioritise remediation return on investment and improve customer experience. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 9, Issue 2. https://doi.org/10.69554/XIEJ4146.

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cover image, Applied Marketing Analytics: The Peer-Reviewed Journal
Applied Marketing Analytics: The Peer-Reviewed Journal
Volume 9 / Issue 2
© Henry Stewart
Publications LLP

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