BUS625: Data & Decision Analytics Week 3 Response 1 Guided

BUS625: Data & Decision Analytics Week 3 Response 1 Guided Response

Review and respond to two of your peers’ sample selections. Propose an alternative sample choice with justification, special circumstances, or issues that may need attention in your peer’s sample. Note that this discussion is reinforcing the fact that you can get many different samples from the same population.

Based on your peer’s response, provide recommendations regarding how to improve their networking plan and overcome the obstacles currently holding them back from networking. Per their value proposition, why would they be a good person to have in your network? Additionally, make sure to send your peer a connection request using their LinkedIn URL.

Paper For Above instruction

Introduction

In the realm of data and decision analytics, selecting an appropriate sample from a target population is crucial for deriving accurate and actionable insights. The process of sampling influences the reliability and validity of the research findings. This paper aims to critique and propose an alternative sampling approach for a case involving T-Mobile's DIGITS product, while also providing feedback on networking strategies as outlined by two peers. The focus is on enhancing sampling methodologies and fostering professional relationships to support future research and career development.

Critique and Alternative Sampling Approach for T-Mobile Case Study

The original sampling method proposed by the peer involved selecting a subset of the entire customer base based on preliminary filters such as creditworthiness and usage behavior, focusing on approximately 43 million customers out of 86 million. This approach leverages stratified sampling, considering specific customer attributes like data usage patterns, payment reliability, and interest in wearable technology. While practical, this method might overlook the dynamic nature of customer preferences and behaviors, which can fluctuate over time and across segments.

An alternative sampling strategy would be to utilize stratified random sampling combined with a cluster sampling component. Firstly, customers can be divided into strata based on demographic variables such as age, geographic location, and device usage patterns, which influence wearable adoption. For instance, younger demographics and urban residents are more likely to adopt wearable technology. Random samples can be drawn within each stratum to ensure representation across key segments. Additionally, cluster sampling within geographic regions—such as specific cities or zip codes—can enable cost-effective data collection while capturing regional differences in customer needs and preferences.

This hybrid approach would allow for more nuanced insights into customer behaviors and needs, accounting for variations across different segments. It also improves the generalizability of the findings, reducing sampling bias that might occur if only credit data and usage are considered. Furthermore, ongoing sample adjustment through weightings can enhance the accuracy of predictive models, providing a robust foundation for strategic decision-making about the DIGITS product and similar innovations.

Feedback on Peers’ Networking Strategies

Regarding the networking plans shared by Jairo and Paul, both offer practical step-by-step approaches to expanding professional connections. Jairo’s plan focuses on developing relationships with leadership at various levels within a target organization through visits and online platforms like LinkedIn. This proactive engagement, coupled with goal-setting for specific roles, is strategic and aligned with career aspirations.

Paul emphasizes leveraging existing experience to build relationships nationally, with a focus on internal and external connections that can facilitate career advancement within his engineering role. His plan emphasizes relationship cultivation and prioritizes network expansion, which is vital for professional growth.

To improve these plans, I recommend incorporating regular networking activities such as attending industry conferences, joining professional associations, and engaging in online communities related to their fields. These steps can help overcome barriers like limited interaction with unfamiliar contacts and time constraints. Building a diverse network that includes mentors, peers, and industry leaders will enrich their perspectives and opportunities.

In terms of their value propositions, Jairo’s focus on leadership development and diverse experience positions him as a potential mentor for aspiring managers, while Paul’s broad technical knowledge and problem-solving skills make him a reliable contact for technical collaborations and insights.

Sending connection requests tailored with specific comments about shared goals or mutual interests can foster stronger relationships. Maintaining consistent follow-ups and providing value to connections through sharing relevant insights or offering assistance will further strengthen their networks.

Conclusion

Effective sampling and robust networking are vital components of successful research and professional advancement. Alternative sampling methods such as stratified random sampling combined with cluster sampling can produce more representative and comprehensive data sets, especially in complex consumer markets. Simultaneously, proactive and strategic networking efforts, including targeted activities and relationship cultivation, can overcome barriers and open new opportunities for career growth. Both elements, when executed thoughtfully, significantly enhance the quality of insights and professional development.

References

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