Data analytics plays an important role in marketing management.

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Data analytics plays an important role in marketing management. There are many types of data to be gathered and studied. Structured data are quantitative data that can be stored in a fixed format, such as a spreadsheet or list. These data can be easily processed by computers. The following are examples of structured data:

E-mail address
Home address
Age
Gender
Credit card number
Unstructured data are not easily put into categories. The following are examples of unstructured data:

Internet search results
Body of an e-mail
Data from social media sites, such as Facebook or LinkedIn
Photos
Text messages
Voicemails
Semi-structured data are a combination of both structured and unstructured data. An example would be an e-mail. The To and From fields would be considered structured data that are easily categorized, and the body of the e-mail would be unstructured, which is not as easily categorized. All of these data combined, along with other types, contribute to big data.

Watch the following video for more information about big data and analytics in marketing: https://www.youtube.com/watch?v=kblH-9QrRzU

Using what you have learned, use the following questions to guide your response:

How are these data used by companies? For example, a company that makes video games for Xbox or PlayStation can track the common actions that their players take before making an in-game purchase.
Describe 2 ethical dilemmas that business organizations face when using big data. For example, sharing private customer information with your best friend without the customer’s consent would be a potential ethical dilemma because that is private information held by the business.

Sample Solution

Data analytics plays a crucial role in modern marketing, allowing companies to understand customer behavior and tailor their strategies for maximum impact. Let’s explore how structured, unstructured, and semi-structured data contribute to marketing efforts, while also considering the ethical dilemmas that arise from such practices.

How Companies Use Data

Companies leverage various data types to gain customer insights and inform marketing decisions:

  • Structured data: Demographics (age, gender, location), purchase history (frequency, amount spent), website clickstream data (pages visited, time spent) all help companies understand customer preferences and buying habits. They can then use this information for targeted advertising, personalized recommendations, and customer segmentation for more effective marketing campaigns.
  • Unstructured data: Social media posts, reviews, emails, and even open-ended survey responses provide valuable insights into customer sentiment, brand perception, and product feedback. Sentiment analysis tools can analyze the text to understand customer satisfaction and identify areas for improvement.
  • Semi-structured data: Data like website forms with both structured fields (name, email) and open text fields (feedback) offer a combination of easily categorized and qualitative information. Analyzing these aspects together provides a more complete picture of customer behavior.

Example: Video Game In-App Purchases: As you mentioned, a video game company can track player actions using structured data. By analyzing data points like time spent playing, levels reached, and items used before an in-game purchase, the company can identify patterns and predict when a player is most likely to spend money. This allows them to target in-game advertisements or promotions at these specific moments, maximizing the chance of conversion.

Ethical Dilemmas in Big Data

The power of big data comes with significant ethical considerations:

  • Privacy Concerns: Companies collect vast amounts of data, raising concerns about how it’s stored, used, and potentially shared. Customers may not be fully aware of how their data is being utilized, leading to a sense of privacy violation. Businesses must be transparent about data collection practices and obtain explicit customer consent whenever possible.
  • Algorithmic Bias: Algorithms used to analyze data can be biased based on the data they are trained on. This can lead to discriminatory marketing practices, for example, if a company’s advertising algorithms show certain products or services less frequently to users based on factors like race or income. Businesses need to be aware of potential biases within their data and algorithms and take steps to mitigate them.

Conclusion

Data analytics is a powerful tool for marketing success, but its use must be balanced with ethical considerations. Businesses have a responsibility to ensure data privacy, transparency, and fairness in their practices to build trust with their customers and maintain a positive brand image.

 

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