Amir Zakaria Marketing Branding Agency | Marketing automation
Marketing automation involves a software platform that can be used to deliver content based on specific rules set by users. The objective is to attract, build and maintain trust with current and prospective customers by automatically personalizing relevant and useful content to meet their specific needs (Hubspot, 2015; Kantrowitz, 2014). The term personalization generally refers to the customization of marketing mix elements (e.g., content personalization) at an individual scale (Montgomery & Smith, 2009). The goal is to treat a person as a maverick with individualistic needs and to design content to meet his or her expectations. According to the elaboration likelihood model (ELM), the more personal and relevant a message is, the more likely that the message will be noticed, thus increasing its effectiveness (Petty & Cacioppo, 1986). Amir zakaria اميرذكريا امير ذكريا نازلي منجم زاده
Marketing automation, customers, personalizing, marketing mix, information, software, lead generation, amir zakaria, nazli monajemzadeh, اميرذكريا, امير ذكريا, نازلي منجم زاده
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Marketing Automation

Marketing Automation

Marketing automation involves a software platform that can be used to deliver content based on specific rules set by users. The objective is to attract, build and maintain trust with current and prospective customers by automatically personalizing relevant and useful content to meet their specific needs (Hubspot, 2015; Kantrowitz, 2014). The term personalization generally refers to the customization of marketing mix elements (e.g., content personalization) at an individual scale (Montgomery & Smith, 2009). The goal is to treat a person as a maverick with individualistic needs and to design content to meet his or her expectations. According to the elaboration likelihood model (ELM), the more personal and relevant a message is, the more likely that the message will be noticed, thus increasing its effectiveness (Petty & Cacioppo, 1986).

Marketing automation capitalizes on techniques similar to Web analytics (see, e.g., Järvinen & Karjaluoto, in press; Phippen, Sheppard, & Furnell, 2004; Wilson, 2010) by tracking website visitors’ online behaviors (i.e., navigation paths and page views) through the use of cookies and IP addresses. The two tools differ in that marketing automation employs advanced capabilities for identifying individual customers and following their behaviors over extended periods of time, and these functions are typically limited in Web analytics software tools such as Google Analytics. Notably, tracking individual behaviors over time requires that a visitor first identifies him or herself by completing a website contact form.

Marketing automation exploits both active and passive means of learning about potential buyers. Active approaches involve directly asking questions, and passive approaches involve utilizing information on past transactions or clickstream data (Montgomery & Srinivasan, 2003). In the marketing automation context, active approaches refer to content delivered to customers that includes links to websites associated with questions (e.g., ‘would you like to learn more about this topic?’ or ‘would you like our sales representatives to contact you?’). Based on these active and passive tools, a software program can personalize messages and detect the buying stage a potential customer is engaged in (Kantrowitz, 2014).

Arguably, to best utilize content marketing tactics for lead generation purposes, a company would need to employ marketing automation or other IT tools to allow a quick response to online queries. More specifically, the tool should allow the company to categorize and rank leads so that the sales representatives can respond to the most profitable leads instantly. The literature shows that the effective use of IT can dramatically increase lead management efficiency (Kuruzovich, 2013; Wilson, 2006). One of the most promising avenues for IT use involves integrating web data on customer behavior with the lead qualification process, as web data are known to serve as a strong predictor of profitable customers (D’Haen, Van den Poel, & Thorleuchter, 2013; Thorleuchter, Van den Poel, & Prinzie, 2012; Wilson, 2003). Nonetheless, academic research lacks insight into how lead management processes can be improved through the use of the extensive web data available on customer behaviors and the types of IT tools required for this purpose.

Reference

  • Hubspot (2015). What is marketing automation? Available at: http://www.hubspot.com/ marketing-automation-information (Accessed 15 February 2015)
  • Kantrowitz, A. (2014). The CMO’s guide to marketing automation. Advertising Age, 85(17), 24.
    Montgomery, A., & Smith, M. (2009). Prospects for personalization on the Internet. Journal of Interactive Marketing, 23(2), 130–137.
  • Petty, R., & Cacioppo, J. (1986). The elaboration likelihood model of persuasion. In L. Berkowitz (Ed.), Advances in Experimental Social Psychology, vol. 19. (pp.  123–205). San Diego, CA: Academic Press.
  • Järvinen, J., & Karjaluoto, H. (2015). The use of Web analytics for digital marketing perfor- mance measurement. Industrial Marketing Management. http://dx.doi.org/10.1016/j. indmarman. 2015.04.009 (in press available online 21 April 2015), http://www. sciencedirect.com/science/article/pii/S001985011500139X.
  • Montgomery, A., & Srinivasan, K. (2003). Learning about customers without asking. In N. Pal, & A. Rangaswamy (Eds.), The power of oneLeverage value from personalization technologies (pp. 122–143). Penn State University: eBRC Press.
  • Kantrowitz, A. (2014). The CMO’s guide to marketing automation. Advertising Age, 85(17), 24.
  • Kuruzovich, J. (2013). Sales technologies, sales force management, and online infomediaries. Journal of Personal Selling and Sales Management, 33(2), 211–224.
  • Wilson, R.D. (2006). Developing new business strategies in B2B markets by combining CRM concepts and online databases. Competitiveness Review: An International Business Journal, 16(1), 38–43.
  • Wilson, R.D. (2010). Using clickstream data to enhance business-to-business web site performance. Journal of Business & Industrial Marketing, 25(3), 177–187.
  • Wilson, R.D. (2003). Using online databases for developing prioritized sales leads. Journal of Business and Industrial Marketing, 18(4/5), 388–402.
  • D’Haen, J., & Van den Poel, D. (2013). Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework. Industrial Marketing Management, 42(4), 544–551.
  • Thorleuchter, D., Van den Poel, D., & Prinzie, A. (2012). Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing. Expert Systems with Applications, 39(3), 2597–2605.

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