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

Manya Mayes
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Manya Mayes: VP Data Science, 1440 Consulting, University of Liverpool, UK

Applied Marketing Analytics: The Peer-Reviewed Journal, 2023, vol. 9, issue 2, 153-161

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 (search for similar items in EconPapers)
JEL-codes: M3 (search for similar items in EconPapers)
Date: 2023
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