Need It By Today 11/06/19 6 Pm EST Subject Data Science A N 

Need it by today (11/06/19) 6 Pm ESTsubject Data Science A NEEDED


Need it by today (11/06/19) 6 Pm ESTsubject Data Science A


 Need it by today (11/06/19) 6 Pm EST SUBJECT: Data Science and Big Data Analytics APA Format NEEDED MINIMUM of 2 JOURNAL ARTICLE REFERENCES NEEDED NO PLAGIARISM *NEED ESSAY of 500 words/ 2 paragraphs on the below question: It is obvious that big companies like Amazon and Google have advantages over smaller companies in terms of access to large data. These companies continue to benefit from the sheer volume of data they generate. As mentioned in Forbes by Peter Pham (2015) (Links to an external site.), “Amazon currently has approximately 270 million active users in 185 countries and 16 million listing units. Google has approximately 12 trillion monthly searches, which dominates the internet search engine market to the tune of approximately a 90 percent market share, including over one billion YouTube users and 500 million Google Plus users.†Large data sets (big data) can do much more than personalizing customers’ shopping experiences and optimize the search engine algorithm.


It can help the business make faster and better business decisions through the use of hypothesis testing. List and explain four ways in which hypothesis testing using big data can improve competitive advantage and decision making for businesses.

Paper For Above instruction

In today’s hyper-competitive digital economy, leveraging big data through hypothesis testing offers businesses a strategic advantage in making informed decisions swiftly and accurately. Large organizations like Amazon and Google harness extensive datasets to identify patterns, validate assumptions, and optimize operations, thus gaining a competitive edge over smaller firms with limited data access (Chen et al., 2012). Hypothesis testing in conjunction with big data analytics makes it possible for firms to explore relationships and test assumptions at an unprecedented scale, enhancing decision quality and strategic agility. This paper discusses four key ways in which hypothesis testing using big data can bolster competitive advantage and improve decision-making: enhancing customer segmentation, personalizing marketing strategies, optimizing supply chain operations, and enabling predictive analytics for future trends.

Firstly, hypothesis testing with big data significantly improves customer segmentation. By analyzing vast quantities of customer data, companies can identify distinct customer groups based on purchasing behaviors, preferences, and demographics (Verhoef et al., 2017). This granular segmentation allows for targeted marketing campaigns, tailored product recommendations, and personalized services that resonate with specific segments, increasing customer satisfaction and loyalty. For instance, Amazon's recommendation engine leverages big data and hypothesis testing to refine its customer segments, thereby delivering more relevant product suggestions that drive sales (Soyer et al., 2018). Secondly, hypothesis testing enables businesses to develop more effective personalized marketing strategies. By testing hypotheses about customer preferences and responses to marketing messages, firms can optimize campaigns for higher engagement rates and conversion. Google, through its extensive ad network, uses hypothesis testing to refine ad placements and improve click-through rates, which directly influences advertising revenue (Davenport, 2014).

Thirdly, hypothesis testing can optimize supply chain management and operational efficiency. Large datasets on inventory levels, delivery times, and supplier performance allow businesses to test hypotheses regarding the most efficient logistics practices. For example, big data analytics can reveal bottlenecks or inefficiencies, helping firms make data-driven decisions to streamline operations (Manyika et al., 2011). Amazon's use of big data to forecast demand and manage stock levels exemplifies how hypothesis testing improves inventory accuracy and reduces costs. Lastly, predictive analytics based on hypothesis testing offers firms the ability to forecast future trends and customer behaviors. By analyzing historical and real-time data, businesses can validate hypotheses about market developments, allowing proactive decision-making. Google’s use of predictive analytics to forecast search query trends enables timely adjustments in content and service offerings, maintaining a competitive edge in search dominance (McAfee & Brynjolfsson, 2012). In sum, hypothesis testing in the realm of big data empowers organizations to make faster, more accurate decisions, thus strengthening their competitive position and operational agility.

References

  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
  • Davenport, T. H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Harvard Business Review Press.
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute.
  • McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10), 60-68.
  • Pham, P. (2015). How Amazon and Google Use Big Data. Forbes. Retrieved from https://www.forbes.com
  • Soyer, R., Wünderlich, N. V., & Gawer, A. (2018). Big Data and Customer Segmentation. Journal of Marketing Analytics, 6(2), 69-78.
  • Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2017). From Multi-Channel Retailing to Omnichannel Retailing. Journal of Retailing, 93(2), 174-181.

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