The Costco Company Business Data Analytics

Introduction

Businesses in the modern economy are increasingly using data analytics to improve their competitiveness. Business executives believe that they can obtain valuable information concerning market needs and the general competitive environment by analyzing both internal and external data. This paper will focus on the use of data analytics in Costco. It will begin with an overview of the evolution of the use of data analytics in business. This will be followed by a discussion on the advantages and disadvantages of data analytics. The challenges that impede the adoption of data analytics will also be discussed. The last part of the paper will provide a discussion on how data analytics has transformed Costco.

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Overview

Data analytics refers to “the science of collecting, organizing, and analyzing large sets of data to discover patterns and other useful information” (Runkler, 2012, pp. 1-3). The purpose of data analytics is to enable businesses to gain insights into the information contained in the large volumes of data that they possess.

In the 1950s, data analytics mainly focused on descriptive analysis of structured data. Businesses analyzed internal data to support management decisions. In the 1970s and 1980s, IT companies such as SAS began to develop special analytics applications (Runkler, 2012). However, the use of the applications was limited due to their high costs. Data analytics depended heavily on historical data, which was analyzed to generate accurate and timely information.

In the last decade, businesses shifted their attention to big data analytics. This involves the analysis of very large volumes of structured and unstructured data using advanced analytics tools. Companies are analyzing both historical and real-time data to gain a clear understanding of their operations (Marz & Warren, 2014). Moreover, analytics is inbuilt in every business process to enable companies to be proactive rather than reactive to market needs.

Advantages and Disadvantages

Costco enjoys the following benefits by using data analytics. First, data analytics enables Costco to understand how customers perceive its products and services. This involves analyzing unstructured data from the company’s sales website and social media to uncover customer sentiments about products (Runkler, 2012).

The insights obtained from the analysis allow Costco to improve its products to meet customer expectations. Second, data analytics facilitates effective risk analysis in the company. Costco uses predictive analytics to scan and analyze external data from varied sources such as government reports and newspapers to identify the most important trends in its business environment. Moreover, the company uses predictive analytics to prevent fraud by analyzing large volumes of transaction data. Third, data analytics facilitates accurate evaluation of the effectiveness of marketing activities such as sales promotions, advertising, and merchandising (Marz & Warren, 2014). The ability of Costco to set prices that customers are willing to pay is attributed to the fact that it uses data analytics to monitor market conditions.

The disadvantages of data analytics include the following. First, data analytics is a routine process that can lead to boredom and low morale among employees. Moreover, overreliance on automated data analytics can lead to the deskilling of the workforce (Marthandan & Tang, 2010). Second, data analytics can lead to loss of customer information. Analyzing data involves sharing large volumes of customer information with third parties. This increases the risk of losing vital information, which can lead to losses or expensive lawsuits. Finally, data analytics can lead to information overload. For instance, Costco generates several reports on a daily basis concerning its operations through data analytics. This can slow the process of making decisions if analytics tools fail to generate concise information.

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Challenges in Implementing Data Analytics

Costco and other businesses face several challenges when implementing data analytics. To begin with, the total cost of acquiring and using a data analytics system is often high. Adopting data analytics is expensive due to the high cost of acquiring advanced software and hardware that are needed for effective storage, analysis, and retrieval of data (Marthandan & Tang, 2010). This means that only companies with adequate financial resources can implement data analytics projects.

Lack of talented employees is another challenge that managers must overcome to implement data analytics. The arrival of big data calls for the employment of data scientists who possess skills in computer technology, mathematics, and analytics to analyze and manage data effectively (Marz & Warren, 2014). However, data scientists are in short supply in nearly all parts of the world due to the high demand for their skills.

Ensuring data security is also a major challenge that must be addressed. Storing data safely is a challenge due to the increase in cybercrimes. Lack of data security limits the use of data analytics by discouraging customers from providing their information to retailers such as Costco.

The challenges discussed in the foregoing paragraphs can be addressed through the following strategies. First, Costco should focus on training its own data scientists to ensure the effective use of data analytics (Runkler, 2012). This will reduce the high cost of hiring external data scientists. Second, Costco has to establish effective information security policies to protect data and the insights generated through data analytics. As a result, it will avoid the losses associated with losing customer information. Finally, adequate financial resources must be allocated to data analytics projects through effective planning and budgeting. This will prevent failures that are likely to arise due to underfunding.

Company Transformation

Data analytics has greatly transformed Costco. To begin with, data analytics facilitates the delivery of shopping experiences that are aligned with the needs of individual customers. The company analyzes customer characteristics such as “loyalty, past purchases, cross-channel preferences, and service incidents to gain a consolidated view” (Costco, 2015). This enables its sales associates to provide personalized services such as identifying customers by their names, thereby enhancing customer loyalty.

Guiding customers to identify related products is another transformation that Costco has achieved through data analytics. The company’s online retail store has adopted intelligent algorithms that use past shopping data to make real-time product recommendations to customers (Costco, 2015). Similarly, its physical stores are using intelligent interactive display units that analyze customer data and make appropriate product recommendations. This improves the ease of shopping and customer satisfaction.

Data analytics has also transformed pricing strategies in the company. In the past, Costco priced its products based on historical data, such as production costs. This led to product failures because most prices did not reflect customers’ willingness to pay. However, data analytics has enabled the company to engage customers in real-time two-way conversations to determine the right prices. As a result, Costco is pricing its merchandise based on customer value rather than historical data. This strategy has greatly improved customer responsiveness in the company.

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In the next decade, data analytics will become a major source of revenue rather than a mere means of generating information in the company. Costco often acquires huge volumes of data concerning citizens from different socio-economic backgrounds (Runkler, 2012).

It can analyze, organize, and package the data in a manner that is usable by companies in the banking, insurance, and manufacturing industries. As the world moves towards a knowledge economy, Costco will earn a lot of money by selling data to third parties. Advancements in computer technology will lead to the development of data analytics applications that are tailor-made for specific purposes. For instance, Costco is likely to adopt “customer relationship-oriented applications such as sales pipeline conversion analytics to improve sales” (Runkler, 2012, p. 78). Data analytics will also become an integral aspect of marketing in the future. Costco is likely to use data analytics to provide personalized marketing messages to increase sales.

The types of data that can be collected through data analytics include provoked and user-generated data. Provoked data is “generated whenever people are requested to express their opinions” (Marz & Warren, 2014, p. 96). Thus, it can be collected by analyzing data obtained from rating websites such as Yelp. User-generated data refers to the content that people upload on the internet. Thus, it can be collected by analyzing data from popular websites such as Facebook and Tweeter.

Conclusion

Data analytics has become an important aspect of every business because it generates valuable insights that inform management decisions. Costco uses data analytics to understand market needs, evaluate marketing strategies, and prevent fraud. However, the use of data analytics also exposes the company to the risk of losing customer information. The main challenges associated with using data analytics include high cost of implementation, lack of talent, and poor data security. Costco should focus on addressing these challenges in order to realize the full potential of data analytics.

References

Costco. (2015). About us. Web.

Marthandan, G., & Tang, C. (2010). Information technology evaluation: Issues and challenges. Journal of Systems and Information Technology, 12(1), 37-55. Web.

Marz, N., & Warren, J. (2014). Big data: Principles and best practices. London, England: Manning Publications. Web.

Runkler, T. (2012). Data analytics: Models and algorithms for intelligent data analysis. Berlin, Germany: Springer. Web.

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StudyCorgi. (2021, January 13). The Costco Company Business Data Analytics. Retrieved from https://studycorgi.com/the-costco-company-business-data-analytics/

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