Share these talks and lectures with your colleagues
Invite colleaguesPractice paper
Logistic regression to identify organisational opportunities in customer surveys using R
Applied Marketing Analytics: The Peer-Reviewed Journal,
2
(2),
133-143
(2016)
Abstract
Historically, identifying specific focal points to improve the customer experience using information from customer surveys has proven to be difficult. The large quantity of responses and the nature of the responses themselves often do not translate into specific areas for improvement that have an impact on satisfaction. Using logistic regression, however, one can identify the impact on overall customer satisfaction of various survey responses. This practical paper demonstrates a method to analytically understand customer responses and create a compelling visualisation using the R programming language.
Keywords: customer satisfaction; customer survey; market research; logistic regression; R programming
The full article is available to subscribers to the journal.
Author's Biography
Ted Kwartler is a data-driven expert in analytics applied to production environments and text mining for actionable insights. While at Amazon.com, Ted used analytics for coaching, product launches, forecasting and developing Amazon’s social media customer care unit. Later he worked as a director of advanced analytics, focusing on a national customer service workforce for a Fortune 100 company. His team used analytics to improve efficiency and to understand the voice of the customer. Ted has worked in successful start-ups and also Fortune 100 companies in analytics roles to improve customer interactions. Ted holds an MBA from the University of Notre Dame with a citation in analytics and marketing.
Citation
Kwartler, Ted (2016, June 1). Logistic regression to identify organisational opportunities in customer surveys using R. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 2, Issue 2. https://doi.org/10.69554/ZCCC1606.
© Henry Stewart
Publications LLP
Publications LLP