A regional comparison of the skills sought by employers for entry-level data scientists, data analytics, business analytics, marketing analytics and digital analytics professionals
Abstract
Fact-based decision making is changing job functions within organisations more than any other technology. Analytics, once the purview of the data scientist, is now spread throughout organisations. No longer is there a single job title, job function or set of required skills and credentials for an analytics career. Companies now recruit analytics talent based on required skill sets. Part 1 of this research1 focused on a cross-region comparison of the demand and employer requirements in job ads posted on LinkedIn for entry-level marketing analytics professionals. In Part 1, the authors reviewed the skills employers seek when hiring entry-level marketing analytics professionals in three geographic regions. The authors presented evidence of the growth in marketing analytics worldwide and how it has become a global phenomenon. Using job postings scraped from LinkedIn, Part 1 of the study extended previous research by focusing only on entry-level marketing analytics professionals' skills, knowledge and abilities in the United States, the United Kingdom and the European Union. The authors presented findings across the three regions, clearly noting where distinct differences were identified in job postings by employers seeking entry-level marketing analytics professionals. Finally, the authors provided emerging and creative methods for recruiting analytics talent in a very complex and dynamically changing environment.
This paper, Part 2 of the research project, focuses on a cross-region comparison of demand and employer requirements in job ads posted on LinkedIn for entry-level data scientist, data analytics, business analytics, marketing analytics and digital analytics professionals. The authors examined the similarities and differences in employer expectations by region across requirements for hard skills, soft skills, software skills and credentials.
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Author's Biography
Angela D'Auria Stanton is a professor of marketing at Radford University, where she teaches undergraduate courses in marketing research and marketing analytics and graduate courses in the foundations of data analytics and advanced data visualisation. Angela began her career as an IT professional with the US Navy, Advanced Technology and Information Technology Solutions (where she also served as Vice President). As a marketing research and analytics consultant, she served as Vice President of Stanton & Associates and President of The Strategy Group. Angela holds a BBA in Information Systems, an MBA and a PhD in business with a major in marketing and an emphasis in international marketing and cross-cultural research methodology from Old Dominion University.
Wilbur W. Stanton is a professor of marketing at Radford University, where he teaches undergraduate courses in marketing research, advertising and consumer behaviour and graduate courses in predictive analytics and data mining. His research and consulting focus on ad testing, consumer behaviour / insights, A / B (split) testing, competitive analysis, business intelligence, market segmentation, product / brand development and marketing research. Wil applies advanced predictive analytics and data mining techniques using SAS and SAS Enterprise Miner, SPSS and SPSS Molder and R to bring about positive and innovative solutions to business problems and the optimisation of business decisions. He holds a PhD in decision science with an emphasis in applied statistics and strategic decision making, master’s in decision science, an MBA and a BBA in marketing from Georgia State University.
Citation
Stanton, Angela D'Auria and Stanton, Wilbur W. (2023, August 1). A regional comparison of the skills sought by employers for entry-level data scientists, data analytics, business analytics, marketing analytics and digital analytics professionals. In the Applied Marketing Analytics: The Peer-Reviewed Journal, Volume 8, Issue 4. https://doi.org/10.69554/CACC7233.
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