Data-Driven Decisions: Accelerate Starbucks Growth
Unlock the power of data and transform your approach to driving growth at Starbucks! This comprehensive course, developed by The Art of Service, equips you with the skills and knowledge to make strategic, data-backed decisions that can significantly impact key business areas. From optimizing store operations to crafting personalized marketing campaigns, you'll learn to leverage data to enhance the Starbucks experience and fuel sustainable growth. Participants receive a prestigious Certificate of Completion issued by The Art of Service upon successful completion of the course, validating their expertise in data-driven decision making within the Starbucks ecosystem.Course Overview This course is designed to be Interactive, Engaging, Comprehensive, Personalized, Up-to-date, Practical, and packed with Real-world applications. You'll benefit from High-quality content, Expert instructors, Flexible learning, and a User-friendly platform. Learn at your own pace with Mobile-accessible materials and join a vibrant Community-driven environment. Gain Actionable insights through Hands-on projects and enjoy Bite-sized lessons with Lifetime access. Gamification and Progress tracking will keep you motivated throughout your learning journey.
Course Curriculum Module 1: Foundations of Data-Driven Decision Making at Starbucks
Gain a solid understanding of the fundamental principles that underpin effective data-driven decision making within the context of Starbucks' business model. - Introduction to Data-Driven Decision Making: Overview of the principles and benefits of using data to inform strategic decisions.
- Understanding the Starbucks Business Model: Deep dive into Starbucks' key revenue streams, operational processes, and customer segments.
- Data Sources within Starbucks: Exploration of the various data sources available, including point-of-sale (POS) systems, customer loyalty programs, mobile app data, and social media analytics.
- Data Quality and Integrity: Best practices for ensuring data accuracy, completeness, and consistency to avoid biased or misleading analyses.
- Ethical Considerations in Data Analysis: Understanding and adhering to ethical guidelines for data privacy, security, and responsible use.
- Introduction to Key Performance Indicators (KPIs) for Starbucks: Defining and understanding essential metrics like same-store sales growth, customer satisfaction, and market share.
- Setting Data-Driven Goals: How to align data analysis with strategic business objectives for Starbucks.
- Data Visualization Principles: Introduction to effectively communicating data insights using charts, graphs, and dashboards.
Module 2: Data Collection and Preprocessing
Learn the techniques for collecting, cleaning, and preparing data from various sources to ensure its suitability for analysis. - Data Extraction from Starbucks Systems: Hands-on exercises on extracting data from POS systems, loyalty programs, and other relevant databases.
- Data Cleaning Techniques: Mastering techniques for handling missing values, outliers, and inconsistencies in data.
- Data Transformation and Integration: Learning how to transform data into a consistent format and integrate data from multiple sources.
- Data Warehousing and Data Lakes for Starbucks: Understanding the role and architecture of data warehouses and data lakes in managing large volumes of Starbucks data.
- Data Security and Compliance: Implementing security measures to protect sensitive data and comply with relevant regulations.
- Introduction to Data Pipelines: Understanding and building automated data pipelines for efficient data processing.
- Version Control for Data: Managing data versions to ensure reproducibility and track changes.
- Data Anonymization Techniques: Methods for protecting customer privacy while still enabling data analysis.
Module 3: Data Analysis Techniques for Retail Operations
Explore a range of analytical techniques specifically applicable to optimizing retail operations within the Starbucks context. - Descriptive Statistics for Retail Analysis: Using measures of central tendency and dispersion to understand key trends in sales, customer demographics, and inventory levels.
- Inferential Statistics for A/B Testing: Conducting A/B tests to evaluate the impact of different promotions, menu items, or store layouts.
- Time Series Analysis for Sales Forecasting: Using time series models to predict future sales trends and optimize inventory management.
- Regression Analysis for Identifying Key Drivers of Sales: Identifying factors that influence sales performance, such as marketing spend, weather patterns, and competitor activity.
- Geospatial Analysis for Store Location Optimization: Using geospatial data to identify optimal locations for new Starbucks stores.
- Menu Analysis and Optimization: Analyzing menu item performance to identify popular and underperforming items and optimize menu offerings.
- Staffing Optimization: Using data to optimize staffing levels based on customer traffic patterns and demand fluctuations.
- Supply Chain Optimization: Using data to improve supply chain efficiency and reduce waste.
Module 4: Customer Analytics and Personalization
Master the art of understanding customer behavior and preferences through data analysis, enabling personalized marketing campaigns and improved customer experiences. - Customer Segmentation Techniques: Using clustering algorithms to segment customers based on demographics, purchase history, and behavior.
- Customer Lifetime Value (CLTV) Analysis: Calculating CLTV to identify high-value customers and prioritize retention efforts.
- Churn Analysis and Prediction: Identifying customers at risk of churning and implementing strategies to prevent customer attrition.
- Sentiment Analysis of Customer Reviews and Social Media Data: Analyzing customer feedback to identify areas for improvement and track customer sentiment towards Starbucks.
- Personalized Marketing Campaigns: Designing and implementing targeted marketing campaigns based on customer segments and individual preferences.
- Recommendation Systems for Menu Items: Building recommendation systems to suggest relevant menu items to customers based on their past purchases.
- Loyalty Program Optimization: Using data to optimize the Starbucks Rewards program and increase customer engagement.
- Understanding Customer Journey Mapping through Data: Visualizing the customer journey and identifying pain points.
Module 5: Marketing and Promotion Optimization
Learn how to leverage data to optimize marketing campaigns, promotional offers, and advertising strategies to maximize return on investment. - Marketing Attribution Modeling: Determining the contribution of different marketing channels to sales and customer acquisition.
- A/B Testing of Marketing Campaigns: Conducting A/B tests to optimize email subject lines, ad copy, and landing pages.
- Social Media Analytics: Measuring the effectiveness of social media campaigns and identifying key influencers.
- Search Engine Optimization (SEO) for Starbucks: Optimizing Starbucks' website and online content to improve search engine rankings.
- Paid Advertising Campaign Management: Managing paid advertising campaigns on platforms like Google Ads and social media.
- Promotional Offer Optimization: Using data to design and implement effective promotional offers that drive sales and customer acquisition.
- Understanding Brand Awareness Metrics: Measuring the impact of marketing campaigns on brand awareness.
- Analyzing the Effectiveness of Influencer Marketing Campaigns: Tracking the ROI of collaborations with influencers.
Module 6: Store Performance Analysis and Optimization
Gain insights into store performance through data analysis, enabling you to identify areas for improvement and implement strategies to enhance profitability. - Analyzing Key Performance Indicators (KPIs) by Store: Tracking KPIs such as sales per square foot, transaction volume, and average order value.
- Identifying Underperforming Stores: Using data to identify stores that are not meeting performance targets and diagnose the underlying causes.
- Optimizing Store Layout and Design: Using data to optimize store layout and design to improve customer flow and increase sales.
- Analyzing Customer Traffic Patterns: Understanding customer traffic patterns to optimize staffing levels and product placement.
- Benchmarking Store Performance: Comparing store performance against industry benchmarks and best-performing stores within the Starbucks network.
- Implementing Operational Improvements: Using data to identify and implement operational improvements that enhance efficiency and reduce costs.
- Analyzing the Impact of Local Events on Store Performance: Understanding how local events impact store traffic and sales.
- Using Data to Improve In-Store Customer Experience: Identifying factors that contribute to a positive customer experience.
Module 7: Supply Chain and Inventory Management
Learn how to use data to optimize the supply chain and manage inventory levels effectively, reducing waste and ensuring product availability. - Demand Forecasting for Inventory Planning: Using time series models and other techniques to predict future demand for key ingredients and products.
- Optimizing Inventory Levels: Determining optimal inventory levels to minimize holding costs and prevent stockouts.
- Supplier Performance Analysis: Evaluating supplier performance based on factors such as on-time delivery, product quality, and pricing.
- Waste Reduction Strategies: Using data to identify and implement strategies to reduce waste in the supply chain.
- Predictive Maintenance for Equipment: Using data to predict equipment failures and schedule maintenance proactively.
- Route Optimization for Deliveries: Optimizing delivery routes to minimize transportation costs and delivery times.
- Analyzing the Impact of Weather on Supply Chain: Understanding how weather patterns can disrupt the supply chain.
- Using Data to Improve Traceability and Transparency: Tracking products throughout the supply chain to ensure quality and safety.
Module 8: Data Visualization and Storytelling
Master the art of presenting data insights in a clear, concise, and compelling manner to influence decision-making. - Data Visualization Best Practices: Applying principles of visual design to create effective charts, graphs, and dashboards.
- Choosing the Right Chart Type: Selecting the appropriate chart type for different types of data and analytical objectives.
- Creating Interactive Dashboards: Building interactive dashboards that allow users to explore data and gain insights.
- Data Storytelling Techniques: Crafting compelling narratives around data to engage audiences and drive action.
- Presenting Data to Different Audiences: Tailoring data presentations to the specific needs and interests of different stakeholders.
- Using Data Visualization Tools: Hands-on training with popular data visualization tools such as Tableau and Power BI.
- Best Practices for Creating Executive Summaries: Distilling key insights into concise and actionable summaries.
- Data Presentation Etiquette: Presenting data with confidence and clarity.
Module 9: Advanced Analytics and Machine Learning for Starbucks
Explore advanced analytical techniques, including machine learning, to gain deeper insights and develop predictive models for various business applications. - Introduction to Machine Learning: Overview of machine learning concepts and algorithms.
- Predictive Modeling for Customer Churn: Building machine learning models to predict customer churn and identify at-risk customers.
- Fraud Detection: Using machine learning to detect fraudulent transactions and prevent financial losses.
- Image Recognition for Quality Control: Using image recognition to automate quality control processes and identify defects in products.
- Natural Language Processing (NLP) for Customer Feedback Analysis: Using NLP to analyze customer reviews and social media data and identify key themes and sentiment.
- Reinforcement Learning for Dynamic Pricing: Using reinforcement learning to optimize pricing strategies in real-time.
- Building a Recommendation Engine using Machine Learning: Suggesting items to customers.
- Time Series Forecasting with Advanced Models: ARIMA, LSTM and other models.
Module 10: Building a Data-Driven Culture at Starbucks
Learn how to foster a data-driven culture within Starbucks by promoting data literacy, encouraging data sharing, and empowering employees to make data-informed decisions. - Promoting Data Literacy: Educating employees on the importance of data and how to interpret data insights.
- Establishing Data Governance Policies: Developing policies for data quality, security, and access.
- Creating a Data-Sharing Culture: Encouraging employees to share data and insights across departments and teams.
- Empowering Employees to Make Data-Informed Decisions: Providing employees with the tools and training they need to make data-driven decisions.
- Measuring the Impact of Data-Driven Initiatives: Tracking the ROI of data-driven initiatives and communicating the results to stakeholders.
- Leading with Data: Championing a culture of data-driven decision-making.
- Data Democratization: Making data accessible to all employees.
- Case Studies of Successful Data-Driven Organizations: Learning from other companies.
Module 11: Real-World Case Studies: Data-Driven Success at Starbucks
Analyze real-world examples of how data-driven decision-making has been successfully applied at Starbucks to drive growth, improve efficiency, and enhance the customer experience. - Case Study 1: Optimizing Menu Offerings Based on Sales Data: Analyzing how Starbucks uses sales data to identify popular and underperforming menu items and optimize menu offerings.
- Case Study 2: Enhancing Customer Loyalty Through Personalized Marketing Campaigns: Exploring how Starbucks uses customer data to design and implement targeted marketing campaigns that increase customer engagement and loyalty.
- Case Study 3: Improving Store Performance Through Data-Driven Operational Improvements: Analyzing how Starbucks uses data to identify and implement operational improvements that enhance efficiency and reduce costs.
- Case Study 4: Optimizing Supply Chain and Inventory Management to Reduce Waste: Examining how Starbucks uses data to optimize the supply chain and manage inventory levels effectively, reducing waste and ensuring product availability.
- Case Study 5: Using Location Analytics for Site Selection: How Starbucks picks a location of its store through data.
- Case Study 6: Improving Customer Experience in Drive-Thru: Improving speed of service through data.
- Case Study 7: Managing Product Launches: Successfully launching new products.
- Case Study 8: Improving the Starbucks App Experience Through Data Analytics. Optimizing the digital experience.
Module 12: Capstone Project: Develop a Data-Driven Growth Strategy for Starbucks
Apply the knowledge and skills acquired throughout the course to develop a comprehensive data-driven growth strategy for a specific area of the Starbucks business. This project will be a culminating experience that demonstrates your ability to leverage data to solve real-world business challenges. - Project Selection: Choose a specific area of the Starbucks business to focus on, such as store operations, customer engagement, or marketing.
- Data Collection and Analysis: Gather and analyze relevant data from various sources.
- Strategy Development: Develop a comprehensive data-driven growth strategy that addresses the chosen area of the Starbucks business.
- Implementation Plan: Create a detailed implementation plan that outlines the steps required to execute the strategy.
- Presentation and Feedback: Present your strategy to the instructor and classmates and receive feedback on your approach.
- Final Report: Submit a final report that summarizes your project findings and recommendations.
- Peer Review: Review other students' projects to gain a broader understanding of data-driven strategies.
- Iterative Project Improvement: Incorporate feedback to refine the project.
ENROLL TODAY and embark on your journey to becoming a data-driven decision-maker at Starbucks! Upon successful completion of this course, you will receive a Certificate of Completion issued by The Art of Service.
Module 1: Foundations of Data-Driven Decision Making at Starbucks
Gain a solid understanding of the fundamental principles that underpin effective data-driven decision making within the context of Starbucks' business model.- Introduction to Data-Driven Decision Making: Overview of the principles and benefits of using data to inform strategic decisions.
- Understanding the Starbucks Business Model: Deep dive into Starbucks' key revenue streams, operational processes, and customer segments.
- Data Sources within Starbucks: Exploration of the various data sources available, including point-of-sale (POS) systems, customer loyalty programs, mobile app data, and social media analytics.
- Data Quality and Integrity: Best practices for ensuring data accuracy, completeness, and consistency to avoid biased or misleading analyses.
- Ethical Considerations in Data Analysis: Understanding and adhering to ethical guidelines for data privacy, security, and responsible use.
- Introduction to Key Performance Indicators (KPIs) for Starbucks: Defining and understanding essential metrics like same-store sales growth, customer satisfaction, and market share.
- Setting Data-Driven Goals: How to align data analysis with strategic business objectives for Starbucks.
- Data Visualization Principles: Introduction to effectively communicating data insights using charts, graphs, and dashboards.
Module 2: Data Collection and Preprocessing
Learn the techniques for collecting, cleaning, and preparing data from various sources to ensure its suitability for analysis.- Data Extraction from Starbucks Systems: Hands-on exercises on extracting data from POS systems, loyalty programs, and other relevant databases.
- Data Cleaning Techniques: Mastering techniques for handling missing values, outliers, and inconsistencies in data.
- Data Transformation and Integration: Learning how to transform data into a consistent format and integrate data from multiple sources.
- Data Warehousing and Data Lakes for Starbucks: Understanding the role and architecture of data warehouses and data lakes in managing large volumes of Starbucks data.
- Data Security and Compliance: Implementing security measures to protect sensitive data and comply with relevant regulations.
- Introduction to Data Pipelines: Understanding and building automated data pipelines for efficient data processing.
- Version Control for Data: Managing data versions to ensure reproducibility and track changes.
- Data Anonymization Techniques: Methods for protecting customer privacy while still enabling data analysis.
Module 3: Data Analysis Techniques for Retail Operations
Explore a range of analytical techniques specifically applicable to optimizing retail operations within the Starbucks context.- Descriptive Statistics for Retail Analysis: Using measures of central tendency and dispersion to understand key trends in sales, customer demographics, and inventory levels.
- Inferential Statistics for A/B Testing: Conducting A/B tests to evaluate the impact of different promotions, menu items, or store layouts.
- Time Series Analysis for Sales Forecasting: Using time series models to predict future sales trends and optimize inventory management.
- Regression Analysis for Identifying Key Drivers of Sales: Identifying factors that influence sales performance, such as marketing spend, weather patterns, and competitor activity.
- Geospatial Analysis for Store Location Optimization: Using geospatial data to identify optimal locations for new Starbucks stores.
- Menu Analysis and Optimization: Analyzing menu item performance to identify popular and underperforming items and optimize menu offerings.
- Staffing Optimization: Using data to optimize staffing levels based on customer traffic patterns and demand fluctuations.
- Supply Chain Optimization: Using data to improve supply chain efficiency and reduce waste.
Module 4: Customer Analytics and Personalization
Master the art of understanding customer behavior and preferences through data analysis, enabling personalized marketing campaigns and improved customer experiences.- Customer Segmentation Techniques: Using clustering algorithms to segment customers based on demographics, purchase history, and behavior.
- Customer Lifetime Value (CLTV) Analysis: Calculating CLTV to identify high-value customers and prioritize retention efforts.
- Churn Analysis and Prediction: Identifying customers at risk of churning and implementing strategies to prevent customer attrition.
- Sentiment Analysis of Customer Reviews and Social Media Data: Analyzing customer feedback to identify areas for improvement and track customer sentiment towards Starbucks.
- Personalized Marketing Campaigns: Designing and implementing targeted marketing campaigns based on customer segments and individual preferences.
- Recommendation Systems for Menu Items: Building recommendation systems to suggest relevant menu items to customers based on their past purchases.
- Loyalty Program Optimization: Using data to optimize the Starbucks Rewards program and increase customer engagement.
- Understanding Customer Journey Mapping through Data: Visualizing the customer journey and identifying pain points.
Module 5: Marketing and Promotion Optimization
Learn how to leverage data to optimize marketing campaigns, promotional offers, and advertising strategies to maximize return on investment.- Marketing Attribution Modeling: Determining the contribution of different marketing channels to sales and customer acquisition.
- A/B Testing of Marketing Campaigns: Conducting A/B tests to optimize email subject lines, ad copy, and landing pages.
- Social Media Analytics: Measuring the effectiveness of social media campaigns and identifying key influencers.
- Search Engine Optimization (SEO) for Starbucks: Optimizing Starbucks' website and online content to improve search engine rankings.
- Paid Advertising Campaign Management: Managing paid advertising campaigns on platforms like Google Ads and social media.
- Promotional Offer Optimization: Using data to design and implement effective promotional offers that drive sales and customer acquisition.
- Understanding Brand Awareness Metrics: Measuring the impact of marketing campaigns on brand awareness.
- Analyzing the Effectiveness of Influencer Marketing Campaigns: Tracking the ROI of collaborations with influencers.
Module 6: Store Performance Analysis and Optimization
Gain insights into store performance through data analysis, enabling you to identify areas for improvement and implement strategies to enhance profitability.- Analyzing Key Performance Indicators (KPIs) by Store: Tracking KPIs such as sales per square foot, transaction volume, and average order value.
- Identifying Underperforming Stores: Using data to identify stores that are not meeting performance targets and diagnose the underlying causes.
- Optimizing Store Layout and Design: Using data to optimize store layout and design to improve customer flow and increase sales.
- Analyzing Customer Traffic Patterns: Understanding customer traffic patterns to optimize staffing levels and product placement.
- Benchmarking Store Performance: Comparing store performance against industry benchmarks and best-performing stores within the Starbucks network.
- Implementing Operational Improvements: Using data to identify and implement operational improvements that enhance efficiency and reduce costs.
- Analyzing the Impact of Local Events on Store Performance: Understanding how local events impact store traffic and sales.
- Using Data to Improve In-Store Customer Experience: Identifying factors that contribute to a positive customer experience.
Module 7: Supply Chain and Inventory Management
Learn how to use data to optimize the supply chain and manage inventory levels effectively, reducing waste and ensuring product availability.- Demand Forecasting for Inventory Planning: Using time series models and other techniques to predict future demand for key ingredients and products.
- Optimizing Inventory Levels: Determining optimal inventory levels to minimize holding costs and prevent stockouts.
- Supplier Performance Analysis: Evaluating supplier performance based on factors such as on-time delivery, product quality, and pricing.
- Waste Reduction Strategies: Using data to identify and implement strategies to reduce waste in the supply chain.
- Predictive Maintenance for Equipment: Using data to predict equipment failures and schedule maintenance proactively.
- Route Optimization for Deliveries: Optimizing delivery routes to minimize transportation costs and delivery times.
- Analyzing the Impact of Weather on Supply Chain: Understanding how weather patterns can disrupt the supply chain.
- Using Data to Improve Traceability and Transparency: Tracking products throughout the supply chain to ensure quality and safety.
Module 8: Data Visualization and Storytelling
Master the art of presenting data insights in a clear, concise, and compelling manner to influence decision-making.- Data Visualization Best Practices: Applying principles of visual design to create effective charts, graphs, and dashboards.
- Choosing the Right Chart Type: Selecting the appropriate chart type for different types of data and analytical objectives.
- Creating Interactive Dashboards: Building interactive dashboards that allow users to explore data and gain insights.
- Data Storytelling Techniques: Crafting compelling narratives around data to engage audiences and drive action.
- Presenting Data to Different Audiences: Tailoring data presentations to the specific needs and interests of different stakeholders.
- Using Data Visualization Tools: Hands-on training with popular data visualization tools such as Tableau and Power BI.
- Best Practices for Creating Executive Summaries: Distilling key insights into concise and actionable summaries.
- Data Presentation Etiquette: Presenting data with confidence and clarity.
Module 9: Advanced Analytics and Machine Learning for Starbucks
Explore advanced analytical techniques, including machine learning, to gain deeper insights and develop predictive models for various business applications.- Introduction to Machine Learning: Overview of machine learning concepts and algorithms.
- Predictive Modeling for Customer Churn: Building machine learning models to predict customer churn and identify at-risk customers.
- Fraud Detection: Using machine learning to detect fraudulent transactions and prevent financial losses.
- Image Recognition for Quality Control: Using image recognition to automate quality control processes and identify defects in products.
- Natural Language Processing (NLP) for Customer Feedback Analysis: Using NLP to analyze customer reviews and social media data and identify key themes and sentiment.
- Reinforcement Learning for Dynamic Pricing: Using reinforcement learning to optimize pricing strategies in real-time.
- Building a Recommendation Engine using Machine Learning: Suggesting items to customers.
- Time Series Forecasting with Advanced Models: ARIMA, LSTM and other models.
Module 10: Building a Data-Driven Culture at Starbucks
Learn how to foster a data-driven culture within Starbucks by promoting data literacy, encouraging data sharing, and empowering employees to make data-informed decisions.- Promoting Data Literacy: Educating employees on the importance of data and how to interpret data insights.
- Establishing Data Governance Policies: Developing policies for data quality, security, and access.
- Creating a Data-Sharing Culture: Encouraging employees to share data and insights across departments and teams.
- Empowering Employees to Make Data-Informed Decisions: Providing employees with the tools and training they need to make data-driven decisions.
- Measuring the Impact of Data-Driven Initiatives: Tracking the ROI of data-driven initiatives and communicating the results to stakeholders.
- Leading with Data: Championing a culture of data-driven decision-making.
- Data Democratization: Making data accessible to all employees.
- Case Studies of Successful Data-Driven Organizations: Learning from other companies.
Module 11: Real-World Case Studies: Data-Driven Success at Starbucks
Analyze real-world examples of how data-driven decision-making has been successfully applied at Starbucks to drive growth, improve efficiency, and enhance the customer experience.- Case Study 1: Optimizing Menu Offerings Based on Sales Data: Analyzing how Starbucks uses sales data to identify popular and underperforming menu items and optimize menu offerings.
- Case Study 2: Enhancing Customer Loyalty Through Personalized Marketing Campaigns: Exploring how Starbucks uses customer data to design and implement targeted marketing campaigns that increase customer engagement and loyalty.
- Case Study 3: Improving Store Performance Through Data-Driven Operational Improvements: Analyzing how Starbucks uses data to identify and implement operational improvements that enhance efficiency and reduce costs.
- Case Study 4: Optimizing Supply Chain and Inventory Management to Reduce Waste: Examining how Starbucks uses data to optimize the supply chain and manage inventory levels effectively, reducing waste and ensuring product availability.
- Case Study 5: Using Location Analytics for Site Selection: How Starbucks picks a location of its store through data.
- Case Study 6: Improving Customer Experience in Drive-Thru: Improving speed of service through data.
- Case Study 7: Managing Product Launches: Successfully launching new products.
- Case Study 8: Improving the Starbucks App Experience Through Data Analytics. Optimizing the digital experience.
Module 12: Capstone Project: Develop a Data-Driven Growth Strategy for Starbucks
Apply the knowledge and skills acquired throughout the course to develop a comprehensive data-driven growth strategy for a specific area of the Starbucks business. This project will be a culminating experience that demonstrates your ability to leverage data to solve real-world business challenges.- Project Selection: Choose a specific area of the Starbucks business to focus on, such as store operations, customer engagement, or marketing.
- Data Collection and Analysis: Gather and analyze relevant data from various sources.
- Strategy Development: Develop a comprehensive data-driven growth strategy that addresses the chosen area of the Starbucks business.
- Implementation Plan: Create a detailed implementation plan that outlines the steps required to execute the strategy.
- Presentation and Feedback: Present your strategy to the instructor and classmates and receive feedback on your approach.
- Final Report: Submit a final report that summarizes your project findings and recommendations.
- Peer Review: Review other students' projects to gain a broader understanding of data-driven strategies.
- Iterative Project Improvement: Incorporate feedback to refine the project.