Presentation Outline:

1. Introduction
   • Company Overview
   • Objectives
2. Identified Use Cases
   • Delivery Date Prediction
   • Sentiment Analysis
   • Customer Churn
   • Customer Acquisition Cost Optimization
   • Fraud Detection
   • Price Optimization
3. Roadmap for Data Science Adoption
4. Case Study: Delivery Date Prediction
5. Case Study: Sentiment Analysis
6. Case Study: Customer Churn
7. Case Study: Customer Acquisition Cost Optimization
8. Case Study: Fraud Detection
9. Case Study: Price Optimization
10. Conclusion
11. Q&A

1. Introduction

• Company Overview: OLIST is a Brazilian e-commerce marketplace similar to Amazon.
• Objectives: OLIST aims to increase the number of active customers, revenue, efficiency of services, and improve customer experience.

2. Identified Use Cases OLIST's leadership has identified six key use cases to achieve its objectives:

a. Delivery Date Prediction

• Impact: Improve customer satisfaction and on-time delivery.
• Feasibility: High, with historical order data.
• Data & Skill Requirements: Order data, historical delivery data, machine learning expertise.
• DS Solution Approach: Predict delivery dates using ML models.
• Process Changes: More accurate delivery predictions.
• Proof of Concept: Evaluate prediction accuracy.
• Success Metric: Decrease in average delivery time.
• Estimated Benefits: Increased customer satisfaction, customer retention, and potentially reduced inventory costs.

b. Sentiment Analysis

• Impact: Understand customer experience and identify areas for improvement.
• Feasibility: High, with access to customer reviews.
• Data & Skill Requirements: Customer review data, NLP expertise.
• DS Solution Approach: Sentiment analysis of reviews.
• Process Changes: Improved customer feedback analysis.
• Proof of Concept: Evaluate sentiment analysis accuracy.
• Success Metric: Better customer reviews and ratings.
• Estimated Benefits: Improved customer satisfaction, customer retention, and product/service enhancements.

c. Customer Churn

• Impact: Identify at-risk customers and build retention strategies.
• Feasibility: High, with historical customer data.
• Data & Skill Requirements: Customer data, machine learning expertise.
• DS Solution Approach: Churn prediction models.
• Process Changes: Focus on retaining at-risk customers.
• Proof of Concept: Evaluate model's predictive accuracy.
• Success Metric: Reduced customer churn rate.
• Estimated Benefits: Increased customer retention, revenue, and improved marketing ROI.

d. Customer Acquisition Cost Optimization

• Impact: Measure the effectiveness of acquisition campaigns.
• Feasibility: High, with campaign and customer lifetime value data.
• Data & Skill Requirements: Campaign and customer data, analytical expertise.
• DS Solution Approach: Calculate customer acquisition costs.
• Process Changes: More efficient campaign spending.
• Proof of Concept: Compare campaign costs against lifetime value.
• Success Metric: Reduced acquisition cost per customer.
• Estimated Benefits: Improved campaign efficiency, marketing ROI, and increased revenue.

e. Fraud Detection

• Impact: Identify and prevent fraudulent transactions.
• Feasibility: High, with transaction data.
• Data & Skill Requirements: Transaction data, fraud detection expertise.
• DS Solution Approach: Develop fraud detection models.
• Process Changes: Enhanced security measures.
• Proof of Concept: Evaluate model's fraud detection accuracy.
• Success Metric: Reduced fraud-related financial losses.
• Estimated Benefits: Minimized financial losses, improved brand reputation.

f. Price Optimization

• Impact: Optimize product pricing for increased revenue.
• Feasibility: High, with pricing and sales data.
• Data & Skill Requirements: Pricing and sales data, pricing strategy expertise.
• DS Solution Approach: Price optimization algorithms.
• Process Changes: Dynamic pricing strategies.
• Proof of Concept: Evaluate pricing model's effectiveness.
• Success Metric: Increased sales and revenue.
• Estimated Benefits: Improved revenue and profit margins.

3. Roadmap for Data Science Adoption

• Prioritize use cases based on feasibility, potential impact, and data availability.
• Build a data science team with required skills.
• Develop proof of concepts for selected use cases.
• Monitor and fine-tune models for accuracy and effectiveness.
• Continuously measure and adapt to business needs.

10. Conclusion

• Data science adoption is key to achieving OLIST's business objectives.
• Each use case offers unique benefits and requires different data and skills.
• DS adoption will lead to improved customer satisfaction, revenue growth, and operational efficiency.

11. Q&A

• Address any questions or concerns regarding the proposed data science adoption strategy.