Organizations have increased expectations for expertise in data analytics by marketing students. The chapter describes the change taking place in business in general and in marketing specifically and the disconnect between demand and supply. While tools have been available to teach marketing research using survey, experimental, and qualitative methodologies. However, a lack of materials and a huge learning curve are major reasons for methodologies for analyzing digital data, big data, or social media data not being used. Teradata, Inc., worked with Marketing Information Systems academics to create TeradataUniversityNetwork.com (TUN) as a place for sharing tools, software, articles, and data so analytics can be taught in the classroom. As of August 2014, (TUN) is a resource for sharing tools, software, articles, and videos that focus on marketing analytics. This chapter describes the range of materials available and how they can be used in the classroom.
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The future of marketing lies in companies' abilities to collect and connect large amounts of data and rapidly analyze it in order to make their marketing interactions relevant for each individual customer. Marketing students today need to enter the workforce with a solid foundational understanding of the technology, tools, and processes required to make this happen. (Darryl McDonald, President, Teradata Applications “Global Skills Shortage,” 2014)
Understanding where consumers go, how they use their time, the kind of messages they send, the searches they conduct before purchasing products, and what devices they prefer using when searching or making purchases are all important issues to understand when creating and evaluating marketing strategies and tactics used to draw consumers to retail outlets, services, or products. To gather this information, aggregate it, analyze it, and create a format that conveys relevant insights to decision makers, marketers need to interface with technology and software. The movement to use marketing automation and marketing analytical tools by companies is gaining momentum, thereby changing the requirements for the skills of students graduating from marketing programs.
Organizations such as Accenture, Deloitte, and IBM are opening new analytics centers (Chen, Chiang & Storey, 2010; IBM, 2009; Luftman & Ben-Zvi, 2010; Pettey & Goasduff, 2011; Turban, Sharda, Dursun & King, 2011). Research by McKinsey Global Institute forecasts a 50 to 60 percent shortfall of qualified people for analytics positions, which is about 140,000 to 190,000 unfilled positions by 2018 (Manyika et al., 2011). In addition, about 1.5 million current managers and analysts do not have the necessary skills to understand and make decisions based on the analysis of large amounts of data (Manyika, et al., 2011). Business schools need to prepare graduates in business intelligence (BI) (Connolly, 2012; Conway & Vasseur, 2009; “Global Skills Shortage,” 2014; Sircar, 2009; Watson, 2008; Wixom et al., 2011).
Business professionals now expect that marketing students will have experience with BI tools (Connolly, 2012; Conway and Vasseur, 2009; “Global Skills Shortage,” 2014; Sicar, 2009; Watson, 2008; Wixom et al., 2011). Interfacing with technology and software beyond Excel, SPSS or SAS has not been a significant part of marketing classes for a number of reasons. One major reason for not incorporating automation and analytics tools is the lack of materials for demonstration and class assignments (“Intelligence in Harmony, “2012; Wixom et al., 2011; “State of the Industry,” 2012). Without materials faculty can only talk about the role of marketing analytics. However, a faculty member talking about the process does not allow students to achieve higher levels of learning, i.e., analysis and synthesis. Students develop analysis and synthesis skills through practice. Access to data, case materials, and marketing-related software is necessary for creating student assignments that provide students the opportunity to practice with the tools.
The purpose of this chapter is to describe activities and materials that can be used with students to develop marketing analytics skills and Teradata University Network (TUN) which is a repository for such material. The first section of the paper provides a rationale for why analytics needs to be taught in marketing classes. The second section provides an example of the resources available on www.teradatauniversitynetwork.com (TUN). The third section presents results when using this material in the classroom.