- You have been hired as a marketing consultant in a company. The organization asks you to identify a sample population to determine the demand for a product or service they are planning to offer. You must provide an unbiased sample survey that best represents the population.
In your post,
State the organization’s industry or purpose.
Identify the product or services selected for conducting the survey.
State the problem that the product or service will solve.
Explain what the organization wants to learn about the product or services from the survey.
Explain how and where you determined your sample population and why your sample choice will get the needed data.
Determine a sample size that will adequately represent the population for your example and how you determined this is an adequate sample size in your survey.
200 words 1 scholarly resource, NO AI
- There is no standard definition for big data or data mining. In this discussion forum, follow the general definitions used in your textbook. “Big data” refers to a data set that is too complex and big to apply traditional data analysis methods. “Data mining” is discovery-oriented compared to traditional databases when users know what they are looking for in the database.
In your post,
Provide an example of a company collecting big data for competitive advantage.
Explain why you chose this example.
Describe the value data mining brings to this business and at least three pieces of evidence of how they use these insights.
200 words 1 scholarly resource NO AI
- Piggy back the 2nd one but different forum In this discussion, you will continue to review big data and data mining.
Instructions:
Find a credible article on data mining practices by an organization in the library or online.
Post a link to the article and/or upload the library document for your classmates.
Discuss the organization’s data mining practices and how that helps them understand their customers and/or market.
Full Answer Section
Sample Population and Rationale: Our sample population will be homeowners in urban and suburban areas with a disposable income that allows for such investments. We will focus on areas with higher existing adoption rates of smart home technologies or higher electricity costs, as these individuals are more likely to be early adopters. We will determine the sample through random digit dialing within targeted zip codes and online panels specializing in homeowner surveys. This choice is unbiased because it directly targets the demographic most likely to be interested in and capable of affording the product, ensuring the data reflects genuine market potential rather than general public opinion.
Sample Size and Determination: For an adequate representation, we will aim for a sample size of 500 homeowners. This size is determined based on achieving a statistically significant margin of error (typically 5%) with a 95% confidence level for a large population. This sample size allows for sufficient data segmentation and analysis across various demographic groups within our target population while remaining feasible within budget and time constraints. A larger sample would provide slightly more precision but with diminishing returns on effort.
Scholarly Resource:
- Malhotra, N. K., Birks, D. F., & Wills, P. (2018). Marketing Research: An Applied Orientation. Pearson Education.
2. Big Data and Data Mining: Amazon
Company Collecting Big Data: Amazon is an exemplary company that leverages big data for competitive advantage.
Why This Example: Amazon's business model is inherently data-driven. Every customer interaction, from Browse to purchasing to customer service inquiries, generates vast amounts of data. This extensive data collection across numerous product categories and geographical regions perfectly aligns with the definition of "big data" – too complex and voluminous for traditional methods. Their success is undeniably tied to their ability to process and act upon this data.
Value of Data Mining and Evidence: Data mining brings immense value to Amazon by providing deep insights into customer behavior, preferences, and market trends. This allows for highly personalized experiences and optimized business operations.
- Personalized Recommendations: Amazon's "Customers who bought this also bought..." and "Recommended for you" features are direct results of data mining. They analyze vast purchasing patterns and Browse histories to suggest relevant products, significantly increasing sales and customer engagement.
- Dynamic Pricing: Amazon uses data mining to constantly analyze competitor pricing, demand fluctuations, and inventory levels to dynamically adjust product prices. This optimizes revenue and competitiveness, often offering different prices to different customers based on their Browse history and perceived willingness to pay.
- Optimized Inventory and Logistics: By mining historical sales data, seasonal trends, and even external factors like weather forecasts, Amazon can predict demand for specific products. This enables them to optimize inventory levels, strategically place products in their warehouses, and streamline their logistics networks, leading to faster delivery times and reduced operational costs.
Scholarly Resource:
- Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
3. Data Mining Practices: Netflix
Credible Article:
Here's a link to a relevant article:
https://towardsdatascience.com/how-netflix-uses-data-science-and-machine-learning-a-look-into-their-strategies-3286f76c374b
Organization’s Data Mining Practices:
Netflix, the global streaming giant, heavily relies on data mining to understand its customers and the broader market. Their data mining practices are sophisticated and touch almost every aspect of their business, from content acquisition to user experience. They collect data on everything from what users watch, when they watch it, where they pause, rewind, fast-forward, and even the search queries they enter. This behavioral data is then combined with demographic information and content metadata.
How it Helps Understand Customers/Market:
Netflix's extensive data mining helps them deeply understand customer preferences, viewing habits, and content trends. For customers, this translates into a highly personalized experience. For example, their recommendation engine, a prime example of data mining in action, suggests content tailored to individual tastes, significantly improving user engagement and retention. They understand what genres, actors, directors, and even specific narrative structures appeal to different segments of their audience. This allows them to effectively categorize and present their vast library.
From a market perspective, data mining informs Netflix's content creation and acquisition strategies. By analyzing what content performs well, what gaps exist in their library, and what new trends are emerging, they can make informed decisions about which shows and movies to greenlight or license. This minimizes risk and maximizes the potential for hits. They can even identify niche markets or specific demographics that are underserved, leading to the creation of highly targeted original content. Ultimately, their data mining practices drive customer satisfaction and maintain their competitive edge in the crowded streaming market.
Sample Answer
Product Demand Survey: Smart Home Energy Management System
Organization’s Industry/Purpose: The organization operates in the renewable energy and smart home technology sector, aiming to provide sustainable and efficient energy solutions for residential customers.
Product/Services Selected: We will survey a "Smart Home Energy Management System." This system integrates solar panels, battery storage, and smart appliances, allowing homeowners to monitor and optimize their energy consumption.
Problem Solved: This product addresses the problem of rising electricity costs, grid instability, and the desire for greater energy independence and environmental sustainability among homeowners. It provides real-time energy insights and automated optimization.
What the Organization Wants to Learn: The organization seeks to understand potential customers' willingness to adopt smart home energy solutions, their perceived value of energy independence, their price sensitivity, and their preferred features (e.g., app control, integration with existing smart home devices). They also want to gauge the importance of environmental impact in their purchasing decisions.