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Predictive Algorithm in Marketing Research Paper


Introduction

People are drowning in data. People who are supposed to make sense of data are drowning in them. Business owners, manufacturers, analysts, business retailers are just some of the people that need to know how to make sense of significant amounts of data that are being collated each day. The proponent of this study attempts to provide a solution for business retailers. The solution utilizes a predictive algorithm to make sense of consumer behavior, and use insights gleaned from the said process for enhancing the retailer’s marketing scheme. A practical way to leverage the power of predictive algorithm is to make it a backbone in the development of a marketing scheme that specifically targets customers by offering them products through the store’s digital signage system.

Methodology

Understanding the Issue through an Anthropology-based Analytical Framework

It is important to use an anthropology-based analytical framework in order to highlight the fact that the software design is dependent on the prior understanding of how human beings behave in certain circumstances. In other words, it is crucial to have insights into human psychology before acquiring the ability to create an effective predictive capability. In this case, the anthropological framework focuses on the effort on patterns of human behavior in the context of acquiring and consuming products sold in supermarkets and malls.

Key Definition of Terms: Surveillance and Privacy

It is also important to point out that instead of simply restating the technical definition of privacy and surveillance, it is best to look at a more nuanced analysis of these concepts. Consider for example Phil Agre’s capture model. In this theoretical model, Agre attempts to uncover the systematic and institutionalized use of surveillance (Wardrip-Fruin and Montfort 737). Agre’s “capture model” attempts to predict and explain how conglomerates and national governments are going to make surveillance appear natural and commonplace in order to downplay its ubiquity and pervasiveness in society (Wardrip-Fruin and Montfort 737).

Agre was able to prove his point when he presented two approaches to understanding the issues surrounding privacy. Agre pointed out that people normally assess privacy using the “surveillance model” (101). However, there exists another method of data collection, and Agre made the revelation that the use of the “capture model” creates a less obvious strategy of collecting personal information. In the “capture model,” the current use and design of data-capture technologies condition the minds of people regarding the inevitable collection and use of personal information. Examples of these strategies are commonplace, and the target market or the people that are being surveilled are not going to feel any type of coercion and they are not going to notice the tell-tale signs of privacy violations that they were accustomed to seeing using the “surveillance model” of data collection (Agre 102). In the first model, the target market resists the obvious privacy violations in the use of cameras and deliberate profiling. However, in the new method, the “capture model” exposes how business groups and other entities are capturing information in a subtle way. For example, the collection of personal information is embedded in a system designed to improve the service delivery process.

With regard to the concept of privacy, it is critical to look beyond the use of conventional spying techniques. National governments and business establishments use sophisticated computer systems in order to acquire personal information related to human behavior (Botibol 1). Thus, these entities are spying on people without seeking prior consent. Finn Brunton and Helen Nissenbaum labeled the process as “information asymmetry” the deliberate collection of personal information. However, it is imperative to point out that the presence of cutting-edge technologies related to facial recognition, data mining, and data retrieval creates more sinister ways of violating people’s privacy. Brunton and Nissenbaum pointed out that a surveillance camera may look innocent in the corner of a building, however, the simple data capture on this device gets complicated if paired with facial recognition software and the ability to tie it up with a recent credit card purchase (Ebeling 52). These are some of the ideas that critics are going to use against the development of the proposed project to use a predictive algorithm for the enhancement of marketing strategies.

Evidence

Popular examples of subtle surveillance mechanisms in the context of the “capture model” form part of everyday existence. Consider for instance UPS’ business model. At the end of a typical business transaction, the firm delivers a package to a recipient, and the said recipient has to affix his or her signature in order to complete the deal. The receiver affixes his or her signature on a digital tablet that captures the signature and sends the package owner’s signature to the company’s database for verification (Agre 102). Here is a good example of data capture without explicit content but packaged in such a way that it is acceptable to allow corporations to collect sensitive information from the customers.

In North America, the Canadian Ministry of Transportation implemented a tracking system monitoring the movement of all commercial vehicles (Agre 102). In the United States, a trucking firm called American Inc. uses the same platform that enables the company to use wireless communication and Global Positioning System for the purpose of monitoring trucks (Agre 102). In these instances, tracking replaces the less acceptable term of surveillance even if the said term effectively describes the activities mentioned earlier.

Agre’s “capture model” leaps forward into prominence if viewed from a certain perspective. Using the insights gleaned from studying the model and combining it with revelations concerning the recent activities of America’s National State Agency or NSA reveal a serious problem with regards to surveillance and privacy. According to a non-government organization called Electronic Frontier Foundation, news reports had unveiled the illegal surveillance practices of the NSA (1).

Although the average person complains about the threat of unlawful surveillance. Most people are unaware of the subtle use of information as evidenced by the popularity of predictive analytics. A predictive algorithm or predictive analytics requires sophisticated computer systems that allow business retailers to comb through customer-related information (Popky 10). The use of the anthropological framework helps in the analysis of past behavior. Thus, predictive software programmed to see patterns in consumer behavior provides insights on future preferences. Thus, business retailers are going to know in advance that customers are going to visit the store on certain dates. In addition, they are going to look for certain products to fulfill a certain need at certain times during the week, month, or year. However, the tailor-made advertising content or sales promotions are made possible using the said system. It is possible to transmit the personalized advertising content to the person’s email or mobile phones.

Amazon, Apple, and Facebook are some of the world-renowned companies leveraging the power of predictive algorithm (Hildebrandt and Vries 98). Amazon makes recommendations and Apple makes suggestions regarding products on sale based on the customer’s purchasing history. Facebook on the other hand collaborates with other websites and analyzes the data that customers provide as feedback on those websites (Hildebrandt and Vries 98).

Discussion

A predictive algorithm is a branch of computer science that enables software creators to develop a way of understanding human behavior. In the context of a business retailer’s needs, a predictive algorithm analyzes data related to consumer behavior, specifically shopping patterns or purchasing behavior. The acquisition of appropriate software that was programmed with a predictive algorithm capability enables business retailers to gain insight about customers’ preferences or wants.

It is not going to be an easy task from the point of view of business retailers. There are numerous challenges that are blocking the way towards a successful implementation of the said business model. There are surveillance and privacy issue that are going to be raised in order to block the establishment of the said marketing ploy. However, business retailers are going to prevail in the end. There are several reasons why it is difficult to prevent the development and deployment of the said strategy. First, the predictive algorithm is no longer veiled in secrecy or limited to theoretical discussions. In fact, there is now a better name to describe the real-world application of a predictive algorithm and it is called predictive analytics (Siegel 1). Companies like Apple and Facebook utilize predictive analytics to predict behavior based on the anthropological study of past events. In other words, computer systems make sense of customer’s past purchases and expressed preferences.

The phenomenon called the “Googlization of us” refers to a recent social development that is a direct outcome of the extensive and prevalent use of Google products (Vaidhyanathan 83). If one combines Vaidhyanathan’s insight with that of Agre, it is easy to make the conclusion that the conditioning of people’s minds had begun in earnest. Thus, it becomes harder to detect privacy violations in the near future.

Although the application of the “capture model” and the pervasive impact of “Googlization” makes it easier to force people in accepting the inevitable use of predictive algorithm or predictive analytics, people are going to resist the outright collection of personal data. Thus, retailers are going to develop strategies for the subtle collection of data. They are going to use the “loyalty card” system and enhance it using new technologies. In the “loyalty card” scheme, customers find it hard to resist the use of the said system because it offers discounts. However, aside from ensuring the growth of returning clients, the use of the loyalty scheme enables the organization to track the purchases and preferences of the said consumers.

In the first roll-out of the predictive algorithm-based marketing strategy, the loyalty cards are enhanced with a microchip that computers can read in order to identify the customer that enters the business establishment. Once the customer passes a certain point, digital signages in the form of flat TV screens or mobile devices installed in strategic areas are going to display information based on a predictive pattern of behavior.

In the near future, business retailers are going to push the envelope even further and the use of the loyalty cards with an embedded microchip is merely the starting point. They are also going to pair these technologies with facial recognition software and other data-capture tools.

Conclusion

Business retailers are desperate in the need to design a better way to connect and interact with customers. Traditional ways of reaching out to consumers via conventional advertising strategies are ineffective. It is better to develop a personalized way to present advertising content or sales promotions directly to the consumer. The initial phase of the strategy rollout requires the use of a “loyalty scheme” as this is in keeping up with the “capture model” described by Agre. In other words, it is important to condition the minds and pave the way for a more intrusive and more pervasive way of collecting personal information. Business retailers are going to pay special attention to the insights provided by Agre due to the expected complaints related to privacy issues.

However, business retailers are aware of the impact of the phenomenon called the “Googlization of us” making it easier for their respective customers to embrace the subtle way of collecting personal information. Customers are going to play along because they are made to believe that the new system was installed for their benefit. Nevertheless, the use of the loyalty card acts as a way of testing the tolerable limits of data capture. Once the use of the new card with an embedded microchip becomes commonplace, they are going to enhance the system using facial recognition software. Combining facial recognition software and predictive analytics allow business retailers to anticipate future needs. They are always one step ahead of their consumers. They are going to provide better products and provide it in a timely manner.

Works Cited

Agre, Philip. Surveillance and Capture. Taylor & Francis, 1994, Web.

Botibol, Anthony. BlueVenn. 2016, Web.

Ebeling, Mary. Healthcare and Big Data. Springer, 2016.

Electronic Frontier Foundation. 2016. Web.

Popky, Linda. Marketing Above the Noise. Routledge, 2013.

Siegel, Eric. Predictive Analytics. John Wiley & Sons, 2016.

Vaidhyanathan, Siva. The Googlization of Everything. University of California Press, 2011.

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1. IvyPanda. "Predictive Algorithm in Marketing." November 23, 2020. https://ivypanda.com/essays/predictive-algorithm-in-marketing/.


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IvyPanda. "Predictive Algorithm in Marketing." November 23, 2020. https://ivypanda.com/essays/predictive-algorithm-in-marketing/.

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IvyPanda. 2020. "Predictive Algorithm in Marketing." November 23, 2020. https://ivypanda.com/essays/predictive-algorithm-in-marketing/.

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IvyPanda. (2020) 'Predictive Algorithm in Marketing'. 23 November.

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