KSB_Global_case_study_competition_final_round_Team_Credible_Creditors.pptx

CASE STUDY Description:
Using Data and AI to Improve Customer Experience in E-Commerce

Problem Statement: You work for a fast-growing Indian e-commerce startup that enables local
businesses to sell products online. While the company's rapid expansion has been a significant
achievement, it has also led to operational challenges, particularly around data
mismanagement. This has caused delays in customer orders, inaccurate product listings, and
an increase in customer dissatisfaction.

Your task is to propose solutions that address these key challenges:
• Data Organization and Governance
• AI for Business Operations
• Responsible AI

The Ask

• Data Organization and Governance: Establish a robust data
governance framework to streamline operations and improve
coordination across departments. Implement structured data
management practices to reduce inefficiencies and errors.
• AI in Business Operations: Deploy AI to optimize various business
functions within the e-commerce sector, ensuring smoother, more
efficient workflows and improving overall operational performance.
• Responsible AI: Ensure AI solutions are designed with transparency,
fairness, and accountability, building trust with both customers and
local business partners. Prioritize ethical data usage in customer
interactions, safeguarding privacy and ensuring responsible data
protection practices.
• Proof of Concept: Develop a Proof of Concept (PoC) or create a
detailed persona and walk through to demonstrate how these AI-driven
recommendations will optimize operations in the startup, highlighting
specific improvements in efficiency and customer satisfaction.

Deliverables
• The presentation should be tailored for the CIO, who is focused on the data
management strategy, the Head of Data and Governance, who is concerned
with the governance structure and ensuring data privacy, and the Head of
Business Operations, who is interested in understanding how these
initiatives will enhance business processes.

1. Transforming E-Commerce with AI Optimizing Operations, Improving Customer Satisfaction, and Ensuring Responsible AI Deployment Credible Creditors MSIS Global Case Competition 2024

2. The E-commerce Landscape Overview & Key numbers T he AI Opportunity Centralize data for unified access and improved coordination $ 300 Bn Expected By 2030 550 Mn Expected by 2030 Implement AI driven solutions to optimize various business functions Enhance customer experience through personalization Ensure robust data protection and privacy measures Improve operational efficiency and decision-making Rapid urbanization Rising internet penetration Increasing use of digital devices Favourable government policies Mainly due to: Four Pillars of Transformation Better data consistency and accessibility across departments $ 100 Bn Current Market Size 250 Mn Online shoppers (current)

3. Data Siloing Data Quality Privacy & Security Consistency Scalability Compliance Fragmented data across departments. Inaccurate / outdated data. Risk of data breaches. Inconsistent data formats. Overload as data volumes grow. Compliance with GDPR, etc. Centralized data platforms, data lakes. Automated data validation tools. Differential Privacy, Federated Learning. Standardize formats, Master Data Management. Cloud storage, scalable architecture. Data governance council. Challenges in Data Organization/Management and Data Governance Challenges Solutions

4. Jupiter It’s the biggest planet in the Solar System Mars Despite being red, Mars is a cold place Saturn Saturn is a gas giant and has several rings Mercury It’s the closest planet to the Sun Neptune It’s the farthest planet from the Sun Pluto Pluto is considered a dwarf planet Ceres Ceres is located in the main asteroid belt DATA GOVERNANCE FRAMEWORK Data Ownership and Stewardship Data stewards for quality Cross-department policies Data Privacy and Protection Differential Privacy (DP) Federated Learning (FL) Data Transparency and Interpretability SHAP explainability Model transparency Data Access Control Role-based access (RBAC) Access audit logs Data Quality Management Continuous data audits Real-time quality alerts Metadata Management Metadata tagging and tracking Dataset discoverability Data Lifecycle Management Retention and deletion policies. Compliance with data regulations Data Provenance and Traceability Track data origins and usage. Real-time source tracking

5. Data Management Practices Centralized platform Centralized Data Repository ETL pipelines Enhance privacy Data Anonymization Protect customer data Enhance model robustness Data Augmentation Expand dataset size Rapid retrieval Distributed Data Storage Distributed solutions (Kafka, BigQuery ) Ensure accuracy with retraining Model Monitoring, Maintenance Detect model drift Real-time access via API Data Sharing and Collaboration Facilitate secure team collaboration Data Accessibility & Consistency 30-40% Data Retrieval Time 25-30% Privacy Protection 40-50% Data Breach 30-40% Model Robustness 20-25% Model Training Time 20-25% Data Management Efficiency   30-40% System Downtime 30-40% Model Accuracy   10-15 % Maintenance Costs  10-15% Collaboration Efficiency   15-20 % Project Completion Time 25-30%

6. Pre-Purchase Phase SOURCE LLM-BASED SEARCH PERSONALIZED AD DYNAMIC PRICING VIRTUAL TRY-ON AGENT AI for Business Operations Problem Solution Benefits Inefficient inventory management leading to stockout or overstocking AI-powered demand forecasting and inventory optimization Stockouts- 25% Vendor lead time-17% Supplier Reliability-15% Poor product discovery leading to low conversion rates Natural language processing for intent-driven, conversational product searches Search Relevance- 40% Sales- 3% Generic marketing leading to high customer acquisition costs AI-driven personalized ad targeting based on user behaviour and preferences Customer acquisition cost- 30% Impressions-25% Static pricing failing to maximize revenue potential AI for real-time price optimization based on demand, competition, and customer segments Average Order Value- 15% Lack of product visualization leads to indecision and high return rates for fashion items. AI-driven virtual try-on agents enable customers to visualize products Sales conversion- 1% Return rate-20% Average order value-15%

7. Utilize various nation- wide data AI-powered inventory management for forecasting needs & o ptimize item availability Inventory cost- 25% Sales Conversion- 15% Inventory Management and Forecasting Taking care of factors like capacity, route conditions, timings, special requirements Optimized transport plans, balancing duration and cost Transportation cost- 20% Faster Delivery– 30% Transportation Optimization Customer data generates heat maps Optimize store locations to boost deliver efficiency Strategic Store Location Optimize delivery routes and schedules Reduce transportation costs & improve delivery times Transportation cost- 20% Faster Delivery– 30% Smart Logistics AI verifies orders, updates inventory, reduces errors, and speeds up fulfillment Order processing time - 50% Automated Order Processing Robots handle repetitive tasks (picking, packing) Reduces time & labor in order fulfillment Operational cost- 40% Robotic Process Automation (RPA) AI analyzes sales data, market trends to predict demand Optimizes inventory and supply chain efficiency, identifies market trends, and enables proactive strategy adjustments. Demand Forecasting Purchase Phase AI for Business Operations

8. AI streamlines feedback collection and analysis. Sentiment analysis tools automatically categorize feedback, generating actionable insights. Customer satisfaction- 25% Churn rate- 15 % Returning customer rate- 15% Loyalty and feedback RAG-powered Customer Support We've implemented context-aware chatbots that solve customer problems using knowledge from support documents and product manuals, making service faster and more interactive. Churn rate- 4% Response time- 40% Fake review identification Our AI-driven text analysis system scrutinizes review patterns, writing styles, and formatting to swiftly identify suspicious reviews, maintaining feedback integrity. Customer trust- 20% Sales conversion rate- 10% 01 02 03 Post Purchase Phase AI for Business Operations 04 S treamline returns with customer support, reverse logistics, and a Computer Vision model to authenticate returns via video or images. Refund and return rate- 6% Intelligent returns management

9. Responsible AI : Principles 01 04 Socially Beneficial AI should improve efficiency and contribute to social good (e.g., support local businesses, sustainability). Bias and Fairness Perform bias audits and ensure human oversight to address fairness. Data Privacy and Security Use rigorous safety protocols and stress testing for evolving scenarios. Implement encryption, anonymization, and access controls; conduct regular privacy audits. Accountability Assign data stewards and define departmental responsibilities to ensure accountability for AI outcomes across teams and management. Ethical Considerations Follow scientific and ethical guidelines; review AI systems regularly to serve the common good. Stakeholder Engagement Involve all stakeholder: customers, vendors, and teams—ensuring feedback and secure collaboration. 07 05 02 06 03 Continuous Monitoring Continuously monitor AI, retrain as needed, and adapt to evolving demands using customer and team feedback.

10. Customer Personas Rachel Sharma Profile: A regular customer who often buys fashion and electronics products. The Loyal Customer 01 Ross Patel Profile: New to online shopping, looking to purchase home appliances. First-time buyer 02 Monica Jain Profile: A frequent shopper who only buys items during sales or with coupons. Discount Hunter 03 Joey Singh Profile: Returns to the platform after a year’s gap. Return User 04 Aman Gupta Profile: Vendor who wants to sell his products on our platform The Vendor 05 Characteristics:   Frequent buyer in seasonal sales, uses her loyalty points, tends to compare products but prefers sticking with familiar brands Characteristics:   Hesitant buyer, researches products thoroughly before making a purchase, looking for the best deal. Characteristics:   Focuses on discounted items across various categories, from fashion to groceries. Characteristics:   Used the platform a few times but is not fully engaged. Characteristics:   Optimize vendor sourcing, onboarding, demand forecasting, cataloging, and reliability analysis.

11. Dynamic Pricing receives a 10% discount on selected items based on her purchase history RAG-powered Customer Support AI chatbot helps Rachel set up her smartwatch quickly and hassle-free. Loyalty Program Rachel receives 500 loyalty points and a recommendation for discounted wireless earbuds. LLM based Search AI provides personalized suggestions based on Rachel's past purchases. Fraud Prevention AI detects unusual login attempts and blocks suspicious transactions, alerting Rachel. Warehouse Manag e ment Her smartwatch is shipped within 2 hours for same-day delivery forecasting need of trending smartwatches and optimizing Delivery. Personalized Ads AI sends Rachel a targeted email with discounts for a smartwatch she's interested in. The Loyal Customer “ Smart people wear this – and now, Rachel, it's your turn to grab the smartwatch from BOAT at an exclusive price! " Rachel Sharma

12. Dynamic Pricing AI system recognizes Meera's high interest and offers her a 25% discount for a limited time, encouraging an immediate purchase. RAG-powered Customer Support AI chatbot helps Rachel with any her queries. LLM based Search AI provides personalized dress suggestions based on Meera’s past purchases. Fraud Prevention AI detects unusual login attempts and blocks suspicious transactions, alerting Meera. Warehouse manag e ment AI forecasts a surge in demand based on Meera’s activity, optimizing stock levels and ensuring rapid order processing and delivery within 48 hours. Personalized Ad with Virtual try-on personalized ads featuring an advanced virtual try-on tool, allowing her to visualize outfits on her own avatar. The Discount Hunter Monica Jain

13. Estimated Cost Structure Category Subcategory Costs (in INR) Centralized Data Repository Infrastructure 15 Crores Platform Development 7 Crores People 5 Crores Data Anonymization Infrastructure 3 Crores Platform Development 2 Crores People 1.5 Crores Data Augmentation Infrastructure 1.5 Crores Platform Development 1 Crores People 1 Crores Distributed Data Storage Infrastructure 12 Crores Platform Development 4 Crores People 3.5 Crores Model Monitoring and Maintenance Infrastructure 5 Crores Platform Development 2 Crores People 2.5 Crores Data Sharing and Collaboration Infrastructure 4 Crores Platform Development 2 Crores People 2 Crores Total 74 crores Data Management Business Operations Total Cost : 194 crores Category Subcategory Costs (in INR) Pre-Purchase Phase Sourcing and Inventory 23 Crores LLM-based Product Search 12  Crores Personalized Ad Campaigns 9 Crores Dynamic Pricing 14 Crores Virtual Try-on Agent 17  Crores Purchase Phase AI-Powered Inventory Management 20 Crores Smart Logistics 12 Crores Automated Order Processing 9  Crores Robotic Process Automation (RPA) 23 Crores Post-Purchase Phase AI-Powered Customer Support 7 Crores Intelligent Returns Management 9 Crores Total 120 crores

14. Financial Implications Sustainibility Initiatives AI-driven sustainability initiatives, through optimized logistics, reduce carbon footprint and curb pollution Innovative Offerings Implementing cutting edge technologies like AR shopping and leveraging to other platforms 15 crores 25 crores Long term financial goals Amount(in crores INR) O perational Efficiency 20 crores Increased Sales 30 crores Customer Retention 20 crores Scalability 25 crores Reduced Returns 10 crores Labour Savings 20 crores Cost Savings Virtual try-on agents Automation using RPA Reducing the need for increase in workforce Inventory & Transportation Optimization Enhanced customer support & loyalty programs Personalized recommendations & Dynamic Pricing Revenue Generation Developing unique products, ensuring high quality, and marketing them effectively to differentiate from competitors 35 crores Private Label Brand Combining omnichannel retailing with subscription services creates a seamless and personalized shopping experience across all channels. Omni channel and subscription 30 crores Projected revenues, cost and net cashflow

15. Strategic Investment Proposal Goal: To be the market leader in Indian ecommerce landscape with a significant market share. Investment Overview: Total Stake willing to dilute is 30% at 500 crores pre-money valuation. Financial Projections: Revenue Growth: ₹100 Crore to ₹800 Crore (Year 0 to Year 4) EBITDA Neutral by Year 3 Cash Flow Positive by Year 5 Strategic Goals: Market penetration 30% of 550 million projected online shoppers. Public Ambitions: Preparation of filling IPO after 3 years. Investor Benefits: Board Representation (3 seats) Voting Rights, Option for Secondary Sale or IPO Exit Commitments: Full Legal Compliance, Performance Milestones, Technology Implementation

16. APPENDIX

17. Future Roadmap Official launch Initiated with a comprehensive plan to transform e-commerce operations through the strategic implementation of AI and robust data governance practices while adhering to principles of responsible AI Short-Term Goals (Next 6 Months) Mid-Term Goals (6-12 Months) Scalability Improvements Develop insights for inventory management and demand forecasting Implement real-time feedback systems. Long-Term Goals (1-2 Years) Adapt AI solutions for new markets. Innovative AI Applications like exploring augmented reality shopping and predictive analytics Optimize logistics for reduced carbon footprint and promote eco-friendly products. Enhance AI models for new dynamic recommendations & pricing Implement the data governance framework Integrate new features such as virtual try-on agent and LLM based search

18. Stakeholder and Process Interconnectivity Map Local Businesses Platform Dev Team a nd Management Data Governance Team Sourcing Process Data Collection and Storage AI System Training and Testing Customer Acquisition Product Purchase Customer Support and Relationship Vendor Verification Accurate Cataloging Warehousing Products Smart Logistics Federated Learning SHAP Model Auditing Personalized Ad Campaign LLM search Dynamic Pricing Virtual Try-on Faster Delivery Fraud Prevention RAG Support Chatbot loyalty & feedback Review analysis ETL Cloud Services Data anonymity & RBAC Data Augmentation Metadata Mngt

19. Replenishment Ensures in-demand products are always available by optimizing stock from preferred vendors. Personalized Promotions AI recommends popular products for targeted campaigns, boosting sales. Sentiment Analysis AI analyzes testimonials to assess product quality and vendor performance.   Product Enrichment AI enhances product listings with metadata, increasing visibility and reach.  Demand Forecasting Predicts product demand and adjusts inventory orders to minimize stock issues.   Vendor Scoring AI evaluates vendors based on longevity, pricing, compliance, and reviews, assigning a score to help select the best options. The Vendor Aman Gupta

20. Dynamic Pricing Is offered joining discount, making the purchase more attractive RAG-powered Customer Support AI chatbot helps Neeraj facilates hassle free set-up with instructions and tutorials. Order Fulfillment AI inventory management system pinpoints the nearest distribution center with his refrigerator model, enabling same-day dispatch and two-day delivery. LLM based Search AI provides relevant suggestions for his search of a “budget-friendly refrigerator”, explaining features in easy-to-understand language. Accurate Product Description Compare refrigerator models on energy efficiency, cooling technology, and capacity, aiding his informed purchase decision The First-time Buyer Ross Patel

21. Differential Privacy Methods Clipping Bounds the sensitivity and mitigates the impact of outliers Noising Adds a Calibrated noise to make the output statistically indistinguishable Sensitivity Maximum amount that the output can change when a single data point is added or removed from the dataset. Differential Privacy (DP) in Federated Learning aims to protect the privacy of clients' data Depending on the intended level of privacy and the role of the adversary, it can be categorized into variants such as central and local Local differential privacy: It Adds noise to individual data points before collection and protects user privacy but reduces data utility Central differential privacy: Adds noise to aggregated data after collection  and balances privacy and utility but requires trusting the collector.

22. Harmonizing Centralized and Decentralized Data Management in E-commerce Centralized Data Platforms Purpose: Eliminate data silos between departments Focus: Unify operational data for internal purposes Scope: Non-sensitive, aggregate, or anonymized data Federated Learning (FL) Purpose: Protect customer privacy by decentralizing sensitive data processing Focus: Handle sensitive customer data without centralizing it Scope: Training AI models on customer data without exposing raw data Complementary Implementation Centralized Data Lakes : Aggregate non-personal operational data Federated Learning: Process sensitive customer data on local devices Differential Privacy (DP): Add noise to protect individual privacy in centralized data E-commerce Application Centralized: Inventory and order management data Decentralized (FL): Dynamic pricing and personalized recommendations

23. TEAM CREDIBLE CREDITORS KRISH PATEL DHRUV SHAH DEVARSH PATEL ARYA BHATT