Library Dissertation Showcase

A Critical Investigation: To what extent are digital marketing analytics (DMA) being used to measure marketing performance (MP)?

  • Year of Publication:
  • 2018

The aim of this dissertation was to understand how, or if, marketers are exploiting the full potential of digital marketing analytics (DMA) to uncover qualitative, intangible and emotional measures of marketing performance (MP). Existing literature explores the benefits of considering these metrics in relation to the difficulties which could occur, consequently meaning MP was measured using only quantitative DMA. These findings motivated the following study to understand how marketers overcome challenges associated with measuring these metrics, or if DMA were being overlooked as a valuable and holistic measure of MP.

A sequential multi-method research design was adopted to gain qualitative data from five semi-structured interviews followed by one informal in-depth interview. This method allowed the researcher to gain a thorough understanding of different ways DMA are used to measure MP, including the importance placed on measuring qualitative metrics and understanding digital user emotions. Various participants were able to explain how DMA could be used to measure intangible outcomes such as customer satisfaction or brand equity, whilst others argued MP was determined by numbers only, showing unfamiliarity towards further exploring DMA to uncover qualitative metrics.

Overall, all participants acknowledged the importance of measuring MP using quantitative and qualitative DMA; however, all illustrated a lack of knowledge or prioritisation regarding exploiting qualitative metrics. Various responses deliberated the value of DMA, arguing inconsistencies and complexities within the data, often leading to inaccurate measures of MP. Using findings from the literature review and primary research, it was recommended for marketers to encourage the importance of measuring qualitative outcomes of DMA and to create unique DMA dashboards to alleviate complications within data, including exploring the value of insights to ensure MP was accurately measured.

PLEASE NOTE: You must be a member of the University of Lincoln to be able to view this dissertation. Please log in here.