DATA6000: Industry Research Report Assignment Help
Question
DATA6000: In this Kaplan Business School Assignment, the MBA students are supposed to develop an industry research report demonstrating their learnings from this Master of Business Analytics degree. For this, the students will be required to identify one business problem related to a specific industry and apply the techniques of descriptive and predictive analytics to analyze this problem to propose certain recommendations for addressing the problem. These recommendations would need to be communicated to a wider audience through this research report.
Solution
Our experts have written a comprehensive industry research report as a part of this solution for an organization named Instacart. It is important that the problem identified can be addressed through the use of data analytics, which is why extensive research is required even for identifying the topic of this report. This is why many students get stuck in writing this report and need help. We, at OAS, cater to the needs of our students and offer high-quality Business analytics assignment help in Sydney. The business problem that is explored in this industry report is the issue of excessive reorder placements by the customers of Instacart.
Executive Summary
Firstly, a brief summary of all the important findings of the report has been highlighted to orient the reader to what this report is about. You can read a snippet of this complete summary below:
Instacart faces a challenge with reorder placements by customers. To address this, we propose leveraging market basket analysis to uncover associations between products. By analysing transactional data, we can identify frequently co-occurring products and provide personalized reorder recommendations to customers.
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Industry Problem
In providing DATA6000 Industry Research Report Assignment Help, the first step involves providing a foregrounding to the chosen industry. Here, our experts have described the industry problem in detail, followed by highlighting why it becomes necessary to solve this problem, along with a justification of how data analytics can be employed to solve this issue. Additionally, as per the assessment instruction, our experts have also reflected on the availability of data in conducting this research.
2.1 Provide industry background
The e-commerce and grocery delivery industry has witnessed significant growth in recent years, driven by changing consumer preferences and increased online shopping. Instacart, a prominent player in this industry, operates as a platform connecting customers with local grocery stores for convenient and timely delivery (www.instacart.com).
2.2 Contemporary business problem in this industry
One contemporary business problem in the e-commerce and grocery delivery industry is the intensifying competition among major players. As more companies enter the market and existing players expand their services, the industry becomes highly saturated, leading to challenges in acquiring and retaining customers.
2.3 Argue why solving this problem is important to the industry
According to (Alsghaier 2017) solving the problem of increasing competition and operational complexities in the e-commerce and grocery delivery industry is crucial for its sustained growth and success.
2.4 Justify how data can be used to provide actionable insights and solutions
Data can be leveraged to provide actionable insights and solutions by uncovering patterns, trends, and correlations that may not be immediately apparent.
2.5 Reflect on how the availability of data affected the business problem you eventually chose to address
The availability of data plays a critical role in addressing the business problem of reorder placements by customers on Instacart.
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Data processing and management
Here, the source of the data used has been outlined by our expert, in addition to underscoring how descriptive and predictive analytical techniques would be applied to this problem. Furthermore, this section also elaborates upon how the data was cleansed, prepared for, and mined in this context.
3.1 Data Sources:
For the Kaggle competition that Instacart hosted and that was open in early 2016, there was a market basket dataset available. We were given transactional data from client orders placed over time by Instacart, which enabled us to predict which previously bought items would be included in a user’s subsequent order (Kaggle.com).
3.2 Outline the applicability of descriptive and predictive analytics techniques to this data in the context of the business problem
According to (Hamed Taherdoost, 2020) descriptive analytics techniques can be applied to the transactional data to gain a deeper understanding of customer reorder behaviour.
3.3 Briefly describe how the data was cleansed, prepared and mined (provide one supporting file to demonstrate this process)
Dataset Collection The files used to create the data set for the report are trustworthy records of customer orders throughout time. They are unidentified user personal details and include information on over 200,000 Instacart users and 3 million grocery purchase records (Bertoni & Larsson 2017).
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Data Analytics Methodology
The following section remarks on the methodology chosen for approaching this business problem. Our experts have outlined additional information about this methodology used in the appendix. This is how our experts ensure the development of well-articulated and thorough details in the Kaplan Business School Assignment Help provided to MBA students.
Data analytics methodology refers to the systematic approach and processes used to analyze data and derive insights from it. It involves a series of steps that guide the overall data analysis process. While specific methodologies can vary, here is a general outline of a data analytics methodology:
Problem Definition: Clearly define the business problem or objective that needs to be addressed through data analysis. Identify the key questions to be answered and the desired outcomes.
Data Collection: Gather relevant data from various sources, such as databases, APIs, surveys, or external datasets. Ensure data quality and integrity by validating and cleaning the data to remove errors, inconsistencies, or missing values.
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Visualisation and Evaluation of Results
Here, the insights pertaining to descriptive and predictive analytics have been visualised by our experts. While providing Master of Business Analytics assignment help, our expert also outlines the importance of visuals here, in addition to reflecting upon the usefulness of these techniques used.
Evaluate the significance of the visuals for addressing the business problem
Visualizations play a significant role in addressing the business problem of reorder placements on Instacart. Visuals highlighting reorder patterns, popular products, and product associations aid in identifying opportunities for targeted promotions and personalized recommendations.
Reflect on the efficacy of the techniques/software used
The efficacy of the techniques and software used in addressing the business problem of reorder placements on Instacart is notable. According to the (Kaur & Kang, 2016) market basket analysis proves effective in uncovering product associations and frequent item set.
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Recommendations
The next section presents the recommendation for addressing the reorder placement problem. This section also elaborates upon how these insights would be efficiently communicated to a wider audience, the shortcomings of this technique, and data analytics’ role in approaching this issue, while also suggesting data analytics techniques and technologies that can be used in the future.
6.1 To address the business problem of reorder placements on Instacart and leverage data visualizations and outputs, here are some recommendations:
Visualize Reorder Patterns: Create a line graph or heatmap to visualize reorder patterns over time. Identify peak reorder periods and low-demand periods. Utilize this information to optimize inventory management, resource allocation, and targeted promotions during peak periods to encourage reorders.
6.2 To effectively communicate the data insights to a diverse audience, it’s important to present the information in a clear, concise, and visually appealing manner. Here’s a recommended approach:
Use Engaging Visuals: Choose simple and intuitive visualizations that can be easily understood by diverse audience members, regardless of their level of data literacy (Colla & Lapoule 2012).
Provide Context: Accompany the visuals with concise and meaningful captions or descriptions that provide context and highlight the main findings.
6.3 Reflect on the limitations of the data and analytics technique
For addressing the business problem of reorder placements on Instacart, there are some limitations to consider: Data Limitations: The accuracy and completeness of the data are crucial. Incomplete or inaccurate data can lead to biased insights and flawed recommendations. Missing data points, inconsistent formatting, or limited historical data can hinder the effectiveness of the analysis (Yusuf Perwej, 2017).
6.4 Evaluate the role of data analytics in addressing this business problem
Data analytics plays a crucial role in addressing the business problem of reorder management in Instacart. It enables Instacart to analyze vast amounts of customer data, identify patterns, and derive meaningful insights.
6.5 Suggest further data analytics techniques, technologies and plans which may address the business problem in the future
In the future, Instacart can consider incorporating advanced data analytics techniques such as deep learning and reinforcement learning to improve the reorder system. These techniques can help in understanding complex customer behaviours and preferences.
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Data Ethics and Security
The next section presents the recommendation for addressing the reorder placement problem. This section also elaborates upon how these insights would be efficiently communicated to a wider audience, the shortcomings of this technique, and data analytics’ role in approaching this issue, while also suggesting data analytics techniques and technologies that can be used in the future.
Privacy, legal, security, and ethical considerations are crucial when conducting data analysis to ensure responsible and compliant practices. Here are key considerations relevant to data analysis:
Privacy: Safeguarding customer data is paramount. Anonymizing and protecting personally identifiable information (PII) is necessary to prevent unauthorized access or breaches. Adhering to privacy regulations, such as GDPR or CCPA, is essential when handling customer data.
Legal Compliance: Ensure compliance with relevant data protection laws, industry regulations, and contractual agreements. Understand the legal obligations for data collection, storage, processing, and sharing.
Data Security: Implement robust security measures to protect data from unauthorized access, breaches, or cyber threats. Use encryption, secure data storage, access controls, and regularly update security protocols to mitigate risks.
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