using machine learning to measure advertising campaign effctivness

Use of Machine Learning in Advertising Campaign
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Abstract
Advertising of products remains the critical approach adopted by marketers in sensitizing the targeted market on the respective product or service that they sell to the customers. It is ideal to measure and determine the efficiency of advertising mechanisms utilized by the individual business. In the current world of marketing, for instance, the use of social network has remained the primary advertising approach being used. The successfulness of an advertising campaign depends on the eloquent use of the generated data which describes the market trends of the respective business typically. In this document, the primary purpose is to measure the advertising campaign effectiveness by the use of machine learning. In particular, the focus will be on the appropriateness of the AIDA model and the social media analytics. Therefore, the paper will outline how the two objects can provide a mechanism for understanding a given advertising campaign effectiveness in regards to the online data.
 
Use of Machine Learning in Advertising Campaign
In the current advanced computing segment, knowledge development and management have led to the realization of various concepts. Machine learning is a model of artificial intelligence (AI). It is primarily defined with the role of allowing software applications to accurately predict specific outcomes within a given context without having explicit programming.

The process involved in AIDA model and social media data are more of machine learning because the operations endured in the machine learning models do involve data mining as well as predictive modeling. A machine learning process will engage the searching through provided data in the determination of patterns and thus outline necessary understanding of the data prior adjusting of program operations accordingly.
As defined by Rawal (2013), an AIDA model is illustrated with four steps which provide a straightforward description of a process thus giving an explicit understanding of the practice appropriateness. The four individual stages of the AIDA steps are attention, interest, desire, and action. While conducting any given advertising campaign, the marketer will first attract the intended market attention by the use relevant advertising materials such as a social network (Ghirvu, 2013). Once the attention of the targeted consumer is piqued, the arousing of the customer interest in the respective service or product is spontaneously induced. This defines the stage of maintaining interest. The practice may engage the method of describing the product via the website or twitter handle.
The marketers are then followed with the role of persuading the customers to go for the product. The desire of the consumers highly depends on the arousal. The customer desire well defines the effectiveness of the advertising material that creates the attention. This directly affects the arousal of the consumer. The advertisement done will produce the intended desire to own the product. However, failure to realize the same, then the advertisement campaign is ineffective. The action fulfills the end product of advertisement which defines the purchasing instead owning of the respective product or service. The realization of action well reflects on the efficiency of the campaign.
Social Media Data involves the collection of data which is analyzed to determine the aspects that are and aren’t working on the social networks as advertising material. This machine learning practice relies on the social media KPIs which are the primary business metrics applied in the analysis of the intended specific facets of the business (Gilbert & Karahalios, 2009). It’s required to identify the respective social media key performance indicators (KPIs) that will be tracked and analyzed. By doing so, one is in the right position to have an idea of the performance of the respective social strategy tool. These social media KPIs will measure the target audience reach, consumer engagement and the overall response times.
In regards to Denecke & Nejdl (2009), social networks have an explicit understanding of the benefits of analytics thus offering tools on their platforms for the purpose. Taking the case of Facebook insights, some KPIs can be analyzed in regards to essential Facebook metrics. The engagement metric will indicate the actual recorded number of post clicks, views, Likes, comments and possible shares by taking a comparison of given time span. Impressions will be indicated by the number of Facebook Page displays. The organic Likes shows the persons who indeed Likes the Page without the connection form the advertisement campaign.
The models engage the processes that tend to extract some facts that drive towards the understanding of the advertising process without conducting any specific programming. Therefore, this illustrates the execution of machine learning practice in measuring the effectiveness of advertising campaign (Lin & Huang, 2006).
References
Denecke, K., & Nejdl, W. (2009). How valuable is medical social media data? Content analysis of the medical web. Information Sciences, 179(12), 1870-1880.
Ghirvu, A. I. (2013). The AIDA model for advergames. The USV Annals of Economics and Public Administration, 13(1 (17)), 90-98.
Gilbert, E., & Karahalios, K. (2009, April). Predicting tie strength with social media. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 211-220). ACM.
Lin, Y. S., & Huang, J. Y. (2006). Internet blogs as a tourism marketing medium: A case study. Journal of Business Research, 59(10), 1201-1205.
Perlich, C., Dalessandro, B., Raeder, T., Stitelman, O., & Provost, F. (2014). Machine learning for targeted display advertising: Transfer learning in action. Machine learning, 95(1), 103-127.
Rawal, P. (2013). AIDA Marketing Communication Model: Stimulating a purchase decision in the minds of the consumers through a linear progression of steps. International Journal of Multidisciplinary Research in Social & Management Sciences, 1(1), 37-44.