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IT, Web 9 Pages

Questions On Data Science

Question

Try to give your own definition and description of the following problems. You may look up the textbook, Internet, or other resources. Your answer to each question shall not exceed one page.

  1. What is the custom churn problem?
  2. What is firmographic data? And how it is related to data mining?
  3. What is a market basket problem?
  4. Can you describe the online recommendation system?
  5. What is the link prediction problem for social network? Can you give examples?
  6. What is A/B testing? Describe its use in the online advertising setting.
  7. What does the term “customer profiling” mean?
  8. What is the placebo effect in data mining?
  9. What is the OCR recognition problem? What are the major techniques involved?
Solution

Title: Questions On Data Science
Length: 9 pages (2557 Words)
Style: MLA

Preview

Answers to Questions

Answer 1

 It is old marketing wisdom that it is better to retain an existing customer other than acquiring a new one. Companies, therefore, expend vast resources to handle instances where the customer may desert the firm either through setting up churn call centers and use of toolkits with specialized offers to the customers likely to desert. The act of a client leaving one firm and becoming a customer of another is referred to as customer churning. Late intervention employed by companies often enjoy 30 to 40 percent save rate of clients probable to churn. The interventions are taken towards reversing the decision of the customer to churn occur too late when the customer is already upset about the present value proposition offered by the company in question (Cios 37). The saves que model adopted by most companies is thus inadequate, especially in the present da where shoppers exchange information on how to maximize the potential benefits of a bluffed churn call while also collectively figuring out the reasons that would avoid long save conversations, thus getting out of the service. Further, companies possess declining ability to address churn. Companies are increasingly turning to the adoption of churn models aimed at identifying the root causes of customer churn, and addressing them before the company can lose substantive profitability owing to customer churn. Customer churn models rely on the available customer data held by the organization to help predict whether the customer is likely to churn shortly. Despite the increased efficiency achieved by modern statistical analysis, a churn statistical model lacks the capacity to inform the company on the root causes of customer churn.

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