How Data Analysts Support Automotive Business Decisions | Project Deloitte Automotive Consumer Study

Описание: How does a data analyst help an automotive company decide where to invest?

In this video, I explain a realistic Automotive Consumer Intelligence project based on a 3,500-row synthetic dataset covering eight markets and 26 variables, inspired by the Deloitte Automotive Consumer Study.

The central idea is simple:

A data analyst does not merely calculate numbers. A data analyst reduces uncertainty so the business can make a better decision.

This project shows how consumer data can support decisions related to electric vehicles, charging infrastructure, brand loyalty, digital sales, connected-car features, software-defined vehicles, and after-sales service.

Business decisions covered
Electrification strategy

Should the company promote:

Battery Electric Vehicles, or BEVs
Plug-in Hybrid Electric Vehicles, or PHEVs
Hybrid Electric Vehicles, or HEVs
Internal Combustion Engine vehicles, or ICE vehicles

Relevant dataset fields include:

Preferred_Next_Engine and EV_Intender

Charging infrastructure

Where is the charging-access gap?

The analysis compares customer expectations with actual home-charging availability using:

Expects_Home_Charging and Home_Charger_Access

This can help identify markets where charging partnerships, home-charger installation, or public infrastructure investment may be required.

Brand retention

Which markets need stronger loyalty and retention action?

The project analyses:

Brand_Switch_Intent

This helps the business identify customers who may leave the brand and determine where retention campaigns, ownership benefits, or service improvements are most urgent.

Digital and direct sales

Should the company offer direct vehicle purchase?

The dataset includes:

Interested_Direct_Purchase

This can reveal which markets and customer segments are most open to online configuration, direct ordering, and digitally assisted vehicle purchases.

Software-defined vehicles

Which customers value:

Artificial intelligence features
Over-the-air software updates
Connected services
Software-defined vehicle capabilities

The analysis uses the relevant SDV, AI, and OTA variables to identify the strongest customer segments for premium digital features.

Service trust and retention

What drives repeat service and customer loyalty?

The project examines:

Service quality
Trust
Pricing transparency
Customer satisfaction
Repeat-service intention

These insights can support dealer-network improvements, service-process redesign, and stronger customer retention.

Dataset overview
3,500 synthetic consumers
Eight automotive markets
26 analytical variables
Vehicle and powertrain preferences
EV purchase intention
Charging expectations
Brand-switching behaviour
Digital purchase interest
Connected-car preferences
AI and OTA feature interest
Service quality and trust scores
What candidates will learn
How to understand a real business problem
How to translate business decisions into analytical questions
How to choose the right variables for each decision
How to segment customers and markets
How to create meaningful automotive KPIs
How to convert observations into insights
How to make recommendations based on data
How to explain the project in a data analyst interview
Relevant job roles

This video is useful for candidates preparing for:

Data Analyst | Business Analyst | Automotive Data Analyst | Consumer Insights Analyst | Market Research Analyst | Product Analyst | BI Analyst | Strategy Analyst | Deloitte Data Analyst Interview

Skills demonstrated

Python | Pandas | Data Cleaning | Exploratory Data Analysis | GroupBy | Pivot Tables | KPI Analysis | Customer Segmentation | Data Visualisation | Statistical Analysis | Business Storytelling | Automotive Analytics

Why this project matters for interviews

This project helps you answer questions such as:

What business problem were you solving?
How did you select the right variables?
Which customer segments did you identify?
How did you measure EV intent?
How did you identify charging gaps?
How did you analyse brand-switching risk?
Which recommendations did you give to the business?
How would you measure whether your recommendations worked?

The Talent Grid helps candidates prepare for data, AI, cloud, and technology roles through practical industry projects, resume positioning, interview preparation, mock interviews, and company-specific guidance.

Visit: https://thetalentgrid.in

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