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When using traditional marketing methods to understand consumer behavior, managers must consider “whether the participating groups are truly representative of the larger population, whether the answers given in these formats represent actual market behavior, (and) how to isolate the effects of different variables that influence demand” (Farnham, 2014, p. 93). The use of technology in marketing to expand the use of these traditional methods can create biases that may unduly influence manager decisions about consumer behavior.
For example, a type of bias that can result from the use of technology is sampling bias. “A common cause of sampling bias lies in the design of the study or in the data collection procedure, both of which may favor or disfavor collecting data from certain classes or individuals or in certain conditions. Sampling bias is also particularly prominent whenever researchers adopt sampling strategies based on judgment or convenience, in which the criterion used to select samples is somehow related to the variables of interest” (Panzeri, Magri, and Carraro, 2008).
A second type of bias that may result from the use of technology is response bias. “In survey sampling, response bias refers to the bias that results from problems in the measurement process. This bias may result from leading questions, in which the wording of the question may be loaded in some way to unduly favor one response over another, or social desirability, because most people like to present themselves in a favorable light, so they will be reluctant to admit to unsavory attitudes or illegal activities in a survey” (Statistics and Probability Dictionary, 2014).

The use of technology in data collection is evident in TV advertising and credit card purchases and could be subject to bias. “Given the hundreds of companies tracking consumers’ online behavior, information, such as gender, age, location, income, and interests, is now a commodity” (Farnham, 2014, p. 87). In addition to tracking online behavior, “Data-gathering firms and technology companies are aggressively matching people's TV-viewing behavior with other personal data -- in some cases, prescription-drug records obtained from insurers -- and using it to help advertisers buy ads targeted to shows watched by certain kinds of people. (Vascellaro, 2011).
Take for instance that “New data from cable TV boxes combined with other household data now allow advertisers to target audience segments more narrowly. Cable companies such as Cablevision Systems Corp. are also developing systems that can show different commercials at the same time to different households viewing the same program” (Farnham, 2014, p. 87). Another example of a company using new technology with regards to consumer behavior is Rentrak Corporation. This is “a company that started out distributing videocassettes, (and) has diversified into TV ratings, using data from cable set-top boxes. Rentrak has signed up more than 70 TV networks and nearly 300 TV stations, about 50 in the last quarter, to track their audiences' viewing behavior. Rentrak is also challenging Nielsen in the growing business of linking TV viewership with consumer information gathered about specific households” (Sharma, 2014). If the geographic region of the target audience is too narrow, this would create a bias that may not be accurate. Likewise, a target group may be largely one ethnicity or age group and this would create bias in the results of the data.
In Bible times, the Phoenician economy was affected by the use of technology. “The early Phoenician economy was built on timber sales, wood working, and cloth dyeing. Dyes ranging in color from a pink to a deep purple were made from the rotting gland of a sea snail. Gradually the Phoenician city-states became centers of maritime trade and manufacturing. Having limited natural resources, they imported raw materials and fashioned them into more valuable objects that could be shipped profitably, such as jewelry, metalwork, furniture, and housewares. They borrowed techniques and styles from all corners of the world that they touched as traders’ (Phoenician culture, n.d.). With regard to the cloth dyeing, “The association of royalty with the color purple stems from the ancient reddish-purple dye made from the glands of murex mollusks. The expense of producing the dye was so prodigious—many thousands of mollusks were needed to produce one ounce of dye—that only the very wealthiest could afford it. It was said to be worth the price, because the dye, once set, would not run or fade. Tyre and other Phoenician cities traded fine garments dyed purple (or reddish purple—the actual hue is debated) and spread the dye-making technology to their settlements around the Mediterranean” (Gore, 2004).
Managers can overcome biases, such as sampling and response bias, and still influence demand for their product or service. By building on the traditional methods of marketing research, they can uses statistical or econometric techniques to include a multitude of independent variables in a multiple regression analysis. Another strength of regression analysis is that a manager can include cross-sectional data, time series data, or a combination of both, which is panel data, in their analysis. This wide range of data points helps to eliminate bias and still enable the manager to advertise their product to the segment of the population where their products are in demand.

References
Farnham, P. (2014). Economics for managers (3rd ed.). Upper Saddle River, N.J.: Pearson Education, Inc.
Gore, R. (2004). Who were the Phoenician’s? Retrieved November 24, 2014, from http://ngm.nationalgeographic.com/ngm/0410/feature2/
Panzeri, S., Magri, C., & Carraro, L. (2008). Sampling Bias. Retrieved November 24, 2014, from http://www.scholarpedia.org/article/Sampling_bias
Phoenician culture. (n.d.). Retrieved November 24, 2014, from http://www.angelfire.com/empire2/unkemptgoose/Phonecian.html
Sharma, A., & Stewart, C. S. (2014, Feb 13). Nielsen feels heat as rivals promote digital measures. Wall Street Journal Retrieved from http://0-search.proquest.com.oak.indwes.edu/docview/1497354933?accountid=6363
Statistics and Probability Dictionary. (2014). Retrieved November 24, 2014, from http://stattrek.com/statistics/dictionary.aspx?definition=response_bias
Vascellaro, J. E. (2011, Mar 07). What they know: A wall street journal investigation -- TV's next wave: Tuning in to you. Wall Street Journal Retrieved from http://0-search.proquest.com.oak.indwes.edu/docview/855167593?accountid=6363

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