Module 03: Written On Data Mining Process Criteria And Weigh

Module03 Written The Data Mining Processcriteriaweightprocess Map De

Module03 Written The Data Mining Processcriteriaweightprocess Map De

Identify the core assignment question: You are asked to explain the six steps of the Cross-Industry Standard Process for Data Mining (CRISP-DM) with examples, and to develop questions based on the ADAPT framework (Assess, Discover, Activate, Persuade, Transition) tailored to your product, a hoverboard, that you are presenting to a prospective client. You need to prepare a PowerPoint presentation that clearly identifies business requirements, explains their importance, and defines project scope, along with crafting appropriate ADAPT questions to engage potential customers about the hoverboard.

Paper For Above instruction

The assignment requires a comprehensive understanding of the CRISP-DM process, which is instrumental in guiding data mining projects through six iterative steps: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. For each of these steps, specific examples should be provided to illustrate how they can be practically applied in real-world scenarios, particularly in a project involving data analysis to support decision-making or product improvement contexts.

Following the data mining focus, the project shifts to a marketing and sales context involving the hoverboard product. The objective is to craft strategic questions based on the ADAPT framework to effectively engage a prospective customer. ADAPT, which stands for Assess, Discover, Activate, Persuade, and Transition, offers a structured approach to understanding customer needs and steering the conversation toward a sale.

To begin, assessing involves asking questions that identify the prospects' current situation and needs related to personal transportation devices, such as: “What do you look for in a modern, eco-friendly personal transportation option?” This question helps gauge the customer's openness to innovative products like hoverboards and their current preferences.

Discover questions delve deeper into the prospect's requirements and perceptions, such as: “Have you used or considered using a hoverboard before? What features matter most to you in such a device?” This probes their past experience and expectations, guiding tailored communication.

Activation involves encouraging the prospect to imagine adopting the product. An effective question could be: “Can you see yourself commuting comfortably around the city on a hoverboard during your daily routine?” This helps foster a mental image of product usability.

Persuasion questions aim to highlight benefits and address potential concerns, for example: “Would the safety features and ease of use of our hoverboard make you more willing to try it?” This addresses objections and reinforces product advantages.

Transition questions facilitate moving toward closing the sale or next steps. An example is: “Would you like to schedule a demonstration or try out the hoverboard to experience its benefits firsthand?” Such questions help convert interest into action.

In addition to crafting these targeted questions, the PowerPoint presentation should clearly identify the business requirements for the project, emphasizing their importance in ensuring the final deliverable aligns with stakeholder needs. This includes defining specific project parameters, scope boundaries, and the key goals the project aims to achieve, such as increasing sales or customer engagement through effective marketing inquiries.

Overall, this assignment combines technical understanding of data mining processes with practical marketing techniques, presented in a professional manner to support business goals and foster customer engagement for the hoverboard product.

References

  • Cheng, J., & Ruhollah, O. (2020). Data Mining Techniques for Customer Segmentation. Journal of Business Analytics, 4(2), 85-98.
  • Kantardzic, M. (2011). Data Mining: Concepts, Models, Methods, and Algorithms. Wiley.
  • Bagging, J., & Van den Poel, D. (2008). Customer Segmentation in E-Commerce. International Journal of Electronic Commerce, 13(2), 41-57.
  • Shmueli, G., Bruce, P. C., Gedeck, P., et al. (2020). Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. Wiley.
  • Pyle, D. (1999). Data Preparation for Data Mining. Morgan Kaufmann.
  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI magazine, 17(3), 37–54.
  • Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Review. Mobile Networks and Applications, 19(2), 171–209.
  • Market Research Future. (2022). Hoverboard Market Trends and Forecast. Retrieved from https://marketresearchfuture.com/
  • Gustafsson, A., Johnson, M. D., & Roos, E. (2005). The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention. Journal of Marketing, 69(4), 210-218.
  • Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service Quality Delivery and Customer Loyalty. Journal of Marketing, 66(2), 33-46.

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