Velocity Motors: Data Analysis of Success Factors and Market Insights Assignment

Assessment

Business Background

Velocity Motors is a leading automotive company specialising in selling high-quality second-hand sports cars. Established in 2005 by Sarah and Chris Larcson, the company has built a strong reputation for offering pre-owned sports cars that combine performance, style and affordability. Guided by a commitment to customer satisfaction and transparency, Velocity Motors aims to provide an exceptional buying experience for sports car enthusiasts.

Velocity Motors’ core values are:

• Quality:Ensuring that every second-hand sports car meets the highest standards of performance and reliability
• Transparency:Providing clear and honest information about each vehicle’s history and condition
• Customerfocus: Prioritising customer needs and delivering personalised service and support
• Affordability:Offering competitive pricing and financing options to make sports car ownership accessible
• Integrity:Conducting business with honesty, transparency and ethical practices

Velocity Motors sells second-hand sports cars from a range of well-known brands and offers after-sales services, including maintenance, repairs, and customisation. These services help customers enjoy their vehicles for many years. They also take pride in tailoring each car to add value and give it a distinctive character. Velocity Motors aims to expand its inventory and strengthen its online presence to reach more customers globally. The company is also focused on increasing its global market share while continuing to lead the industry in customer satisfaction and transparency.

Sarah and Chris have their own views about what contributes to the company’s growth and customer satisfaction. They believe several operational aspects may be important, although they have not formally analysed which factors truly drive success.

The first is reliable delivery. Sarah and Chris believe dependable delivery not only improves customer satisfaction but also builds loyalty, as customers are more likely to return to a company they can trust.

They also perceive customer service to be a strength. In their view, being responsive to enquiries and resolving issues promptly contributes to a positive customer experience.

A third success factor is believed to be the speed and quality of repair and maintenance services. For car parts that require ongoing maintenance or occasional repairs, the speed and quality of these services are crucial. Sarah and Chris believe that Velocity Motors’ ability to offer fast and reliable repair services has not only minimised downtime for customers but also reinforced their confidence in the brand’s commitment to quality and support.

Task 1: Success Factors

Required

(a) Justify four (4) key factors that explain Velocity Motors’ business success based on your analysis of Appendix A and Dataset A (DAI T425 A1.4.2 Dataset A v3-0). Translate each factor into one (1) measurable analytics question. Include a screenshot of any analysis performed on the dataset in your report.

(b) Review the database administrator’s comments about the following data in Dataset A (DAI T425 A1.4.2 Dataset A v3-0) and justify whether each statement is correct.

• Waitlist Months, whichmeasures how long respondents spent on a waitlist before delivery (in months), shows no outliers.
• Car Quality Rating, which measures the respondent’s perception of the car’s build and quality, hasequal intervals between scale points.
• Income Category,which measures the respondent’s approximate household income level, can be used to calculate the average income and median income.
• When no other factors are considered, After Sales Follow Up is a statistically significant predictor of Customer Service Rating.

(c) Load the provided Dataset B (DAI T425 A1.4.3 Dataset B v3-0) into Power BI and create an effective data model using the star schema. The model should not include any inactive relationships between tables. Include a screenshot of your data model from the ‘Model View’ in Power BI in your report. Identify all primary key(s) in the data model.

(d) Sarah and Chris have asked you to analyse Dataset B (DAI T425 A1.4.3 Dataset B v3-0) in Power BI to generate insights into the company’s performance. Create the following visualisations in Power BI and include screenshots of each visualisation in your report:

A table visualisation presenting the total sales price, total purchase cost, and total profit (sales price minus purchase cost) by car type, with totals displayed at the bottom.

Task 2: Predictive Analysis 

Sarah and Chris are exploring further expansion opportunities for Velocity Motors. Before opening a new store, they want to understand which factors most strongly influence overall customer satisfaction. This insight will help them design the new store and prioritise areas that affect the customer experience.

To gather insights, the customer service team conducted focus group interviews with recent car purchasers. Participants were asked to describe the factors that shaped their satisfaction with both the car and the purchase process. The key summary feedback from the focus group interviews is provided below.

‘Customers came from a range of personal and financial backgrounds. Some emphasised price as a key factor, while others were less concerned due to higher incomes. Interestingly, participants with higher incomes tended to be less satisfied, likely due to greater expectations of the overall sales process. Some customers praised the cars’ build quality, while others highlighted the professionalism, friendliness and responsiveness of the customer service team. Many participants were pleased with the finance process, describing it as fast and straightforward. Opinions on value and post-purchase support were mixed – some customers felt they received fair pricing and effective after-sales follow-up, while others noted that comparable cars were available at lower prices from other brands.’

Required

Using the data provided in Dataset A (DAI T425 A1.4.2 Dataset A v3-0) and the key summary feedback from the focus group interviews, create a predictive causal model with ‘Overall Satisfaction’ as the dependent variable and then perform a multiple linear regression testing the model by using the Analysis ToolPak add-in in Microsoft Excel.

Complete the following steps to perform the analysis:

• Based on the key summary feedback from the focus group interviews provided above, identify six (6) independent variables in Dataset A (DAI T425 A1.4.2 Dataset A v3-0) that may influence ‘Overall Satisfaction’. Name each variable exactly as it appears in the dataset. For each variable you identify, write a hypothesis summarising the expected relationship between the independent variable and ‘Overall Satisfaction’.
• In Microsoft Excel, use the Analysis ToolPak to run a multiple linear regression, using the independent variables identified in Task 2(a) and ‘Overall Satisfaction’ as the dependent variable. If dummy variablesare needed, code categories sequentially (0, 1, 2, …). Include a screenshot of your regression output in your
• Build a predictive causal model showing the direct relationships between each independent variable identified in Task 2(a) and ‘Overall Satisfaction’ as the dependent variable, ensuring that each relationship is labelled with its corresponding hypothesis. Include a screenshot of your model in your report.

Summary of Assessment Requirements

The assessment focuses on analysing the operational success factors of Velocity Motors, evaluating the quality of its datasets, creating data models and visualisations in Power BI, and performing a predictive analysis using multiple linear regression. The tasks are divided into two major components:

Task 1: Success Factors and Data Modelling

1. Identify and Justify Key Success Factors

Students must:

• Analyse Appendix A and Dataset A to identify four key success factors influencing Velocity Motors’ performance.
• Provide a justification for each factor.
• Convert each factor into a measurable analytics question.
• Include screenshots of any dataset analysis performed.

2. Evaluate DBA Data Comments

Students must critically assess whether the database administrator’s claims are correct regarding:

• Outliers in Waitlist Months
• Equal intervals in Car Quality Rating
• Whether Income Category can be used to calculate mean/median
• Whether After Sales Follow Up is a statistically significant predictor of Customer Service Rating

Students need to justify each decision using data insights and measurement scale theory.

3. Build a Star Schema Data Model in Power BI

Students must:

• Load Dataset B
• Develop a star schema without inactive relationships
• Identify primary keys
• Provide a screenshot of the model view

4. Create Power BI Visualisation

A table visualisation must be created showing:

• Total sales price
• Total purchase cost
• Total profit
  Grouped by car type, with visuals included.

Task 2: Predictive Analysis

1. Identify Predictors of Overall Satisfaction

Students must:

• Analyse customer focus group insights
• Select six independent variables from Dataset A
• Form hypotheses for each variable based on expected relationships with Overall Satisfaction

2. Perform Multiple Linear Regression

Using Excel’s Analysis ToolPak, students must:

• Run regression with Overall Satisfaction as the dependent variable
• Code dummy variables where needed
• Include screenshot(s) of regression output

3. Build a Predictive Causal Model

Students must:

• Create a model diagram showing causal paths from each independent variable to Overall Satisfaction
• Label each relationship with its hypothesis
• Include a screenshot of this model

How the Academic Mentor Guided the Student: Step-by-Step Approach

The Academic Mentor supported the student throughout the assessment in a structured, methodical manner, ensuring the student understood both analytical techniques and the business relevance of each task.

1. Understanding the Business Background

The mentor began by explaining the significance of Velocity Motors’ context, business model, and perceived success drivers.
This helped the student:

• Recognise the company’s operational strengths (quality, service, repairs, transparency)
• Connect business realities with required analytical tasks
• Clearly identify potential success factors from real-world scenarios

2. Identifying Success Factors (Task 1a)

The mentor guided the student to:

• Review Appendix A and Dataset A
• Shortlist variables relevant to business performance
• Justify each factor using supporting data
• Frame each as a measurable analytical question
  Example: “How does delivery reliability affect customer satisfaction scores?”
  The student then learned how to extract insights and present them correctly.

3. Validating DBA Comments (Task 1b)

The mentor explained:

• How to identify outliers using statistical techniques
• The difference between interval, ordinal, and categorical scales
• Why categorical income data cannot be averaged
• How to test predictor significance using simple regression
  This allowed the student to critically evaluate each DBA claim with proper justification.

4. Building the Power BI Data Model (Task 1c)

The mentor demonstrated:

• How to import Dataset B into Power BI
• How to identify fact and dimension tables
• How to construct an effective star schema
• How to avoid inactive relationships
• How to identify primary keys
  The student then recreated the model and captured the screenshot required.

5. Creating Power BI Visualisations (Task 1d)

The mentor explained:

• How to build a table visual
• How to calculate profit fields
• How to format totals
• How to ensure clarity and accuracy in the output
  This ensured the student produced a clean, correct visual for the report.

6. Identifying Predictive Variables (Task 2a)

The mentor helped the student:

• Analyse key themes from focus group feedback
• Match them with dataset variables
• Select six appropriate predictors
• Develop testable hypotheses for each
  This formed the foundation of the regression model.

7. Running Multiple Linear Regression (Task 2b)

The mentor gave guidance on:

• Preparing data for regression
• Coding dummy variables sequentially
• Using the Analysis ToolPak
• Interpreting statistical outputs (R⊃2;, p-values, coefficients)
  The student then ran the regression independently and included the outputs as required.

8. Building a Predictive Causal Model (Task 2c)

The mentor showed how to:

• Draw a causal diagram
• Connect each independent variable to the dependent variable
• Label each connection with its hypothesis
  The student built the model and captured the screenshot for inclusion.

Final Outcome and Learning Objectives Achieved

By the end of the assessment, the student successfully:

• Understood Velocity Motors’ business background and operational drivers
• Identified and justified success factors using real data
• Evaluated measurement scales, outliers, and predictor validity
• Built a professional Power BI star schema and visualisation
• Conducted a multiple linear regression using Excel
• Developed a predictive causal model aligned with customer satisfaction insights

Learning Objectives Achieved:

• Ability to interpret organisational data and link it to business performance
• Competence in validating data quality and measurement assumptions
• Skills in building effective Power BI data models
• Ability to create clear and accurate data visualisations
• Understanding of predictive modelling concepts
• Practical experience with hypothesis development and regression analysis
• Improved analytical reasoning and report-writing capability

Overall, the mentor’s structured guidance enabled the student to complete the assessment confidently, ensuring that each task met academic standards and demonstrated strong analytical competency.

Download Your Reference Sample Or Get a Fresh, Original Assignment

Need help understanding how to structure and present your academic work? Our sample solution is available for you to download and use as a reference guide. It’s designed to support your learning, help you understand formatting expectations, and give you a clear idea of how high-quality academic work is developed.
However, submitting this file as your own may lead to plagiarism penalties. Please use the sample strictly for study and reference purposes only.

If you want a fully customised, plagiarism-free solution written specifically for your topic, our academic experts are here to help. Every fresh assignment is developed from scratch, aligned with your requirements, and delivered with complete originality.

Why Choose a Fresh, Custom-Written Assignment?

• 100% original content tailored to your instructions
• Plagiarism-free work with proper referencing
• Expert academic writers across all subjects
• High-quality analysis, structure, and formatting
• On-time delivery to meet your deadlines

Get clarity from the sample or get the confidence of a fully original, customised solution crafted just for you.

Download Sample Solution            Order Fresh Assignment