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Data Science at Target Case Solution

Solution Id Length Case Author Case Publisher
2853 1600 Words (9 Pages) Srikant M. Datar & Caitlin N. Bowler Harvard Business School : 118016
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Target has successfully incorporated data science into their business. This has enhanced customer satisfaction, boosted revenue, decreased expenses, and improved decision-making. Target uses various technologies to support data-driven decision-making, including data warehouses, data marts, data mining tools, business intelligence (BI) tools, cloud computing, big data analytics, and artificial intelligence (AI). Target struggled with issues like data silos, a shortage of data scientists, and a culture that was unwelcoming to data science. The business, however, was able to overcome them and reap the rewards of data science. Target uses data to target marketing efforts, optimize inventory levels, and enhance customer service. Target has enhanced its business performance in a variety of ways by embracing data-driven decision-making. In general, Target is an excellent illustration of how data science can be applied to enhance a business. Through the use of data, the business has been able to enhance customer service, boost sales, lower expenses, and enhance decision-making.

Following questions are answered in this case study solution:

  1. Explain why did Target decide to adopt data analytics/science?

  2. Discuss the challenges and opportunities of integrating data science into Target’s organization.

  3. Describe the model of IT infrastructure including technologies that Target used for supporting data-driven decision-making.

  4. Do you feel the above model in Question 3 supported strategic decision-making. Discuss Why or Why not?

Case Study Questions Answers

1. Explain why did Target decide to adopt data analytics/science?

Target decided to use data science and analytics for several reasons. The company's first goal was to enhance the client experience. Target may offer a more specialized shopping experience by leveraging data to better understand its customers' requirements. Target's second goal was to boost sales. Focus may more effectively focus its marketing and promotions by leveraging data to find trends and patterns in consumer behavior. Third, Target sought to cut expenses. Target may increase efficiency and profitability by using data to pinpoint where to cut costs. Target also aimed to enhance decision-making. Target may become more competitive by utilizing data to better judge everything from product selection to pricing. 

Target has had great success implementing data science and analytics. The business has enhanced customer satisfaction, boosted revenues, cut expenses, and enhanced decision-making. As a result, Target has developed into one of the most prosperous shops worldwide.

Here are some specific examples of how Target has used data analytics/science to improve its business:

  • Target uses data to optimize its inventory levels. This helps them to avoid stockouts and overstocks, which saves money.

  • Target uses data to predict customer behavior. This allows them to effectively target their marketing and promotions, increasing sales.

  • Target uses data to improve its supply chain. This helps them to get products to store more quickly and efficiently, which reduces costs.

Target's adoption of data analytics/science is a great example of how data can be used to improve a business. The company has been able to achieve significant results by using data to better understand its customers, identify trends, and make better decisions.

2. Discuss the challenges and opportunities of integrating data science into Target's organization.

i. Challenges
Data silos:

Target had a ton of data, but it was divided up across many divisions. Due to this, it was challenging to obtain a complete image of the client. For instance, Target might have information regarding a customer's past purchases in one department but not their past browsing activity in another. Due to this, it was challenging to provide pertinent marketing messages to the right customers.

Lack of data scientists:

To make sense of all the data they possessed, Target lacked the necessary data scientists. Data scientists are adept at spotting patterns and trends in data and making predictions. To assist them in making better use of their data, Target needs more data scientists.


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