Valuation Basics

Key Lessons

• Focus needs to change by age
• Values need to change by age
• Consultants - Plastic Surgeons
• Growth assets - Contribute to growth
• Startup - Peak of Glory - Mature Growth - Google / FB
• Middle Ages - Decline - Nature of process
• How little capital for New Businesses
• Compressed Lifecycle
• Nokia, Yahoo, Blackberry - Short life cycles
• Valuation - Outside inn
• Finance - Inside out view
• More degrees of free give more experimentation
• Identify good projects - Fund them well
• Companies refuse to act their age

The Laws of Valuation

From "The Dean of Valuation" Aswath Damodaran

He reveals the fundamentals, myths & misconceptions: pic.twitter.com/nV1iR5iCtv

— Value Theory (@ValueInvestorAc) December 10, 2023

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Thinking Questions

Reduction in Cart Abandonment

Key Notes

• RCA
• External Factors
• Competitor Launches / Products
• Affected Customer Segment
• Macro Economic changes
• Seasonality
• User Journey in App
• Data Captured - Gender / Age
• Campaign related impacts
• No correlation to compaign 
• Any product design changes
• Catalog and inventory analysis of the product
• Which product category etc. has the highest dip.
• Any partnership change or any merchant backed. 
• Geographical distribution of the influx of users on the site like a flood, internet blackout 
• Compare the pricing of the product with the competitors 
• Ratings of the products getting moved out of the cart

Ref  - Link

Solutions Architect

Key Notes

• Challenges, Business Goals, Tech Goals
• Feature / Product Demo
• Product Integration aspects
• Customer Success Stories
• Next Steps

Ref - Link  

MLOps

Key Projects

• Loan scoring, Forecasting
• MLOps Pipeline - Data Collection, Ingestion
• Data cleaning + Feature Engineering
• Different models for different products
• Automate model selection / training steps
• Model Validation / Testing phase

Ref - Link

System Design for Recommendations and Search

Key Notes

• Batched - Store in DB, Precomputed, Refreshed, Key-value pairs
• Real-time - Time-sensitive content

Key Concepts

• Embedding creation of interests
• Features mapping
• Ranking / Retrieval
• Behavior logs - candidate sets - recommendations
• Top N neighbors, KNN, Indexes

Ref - link 

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My people / data / domain / AI ML learning's at Sensormatic

 My journey/lessons

• Setting up Team / Hiring all function QA / Development / Automation
• Transition and Team Setup
• Tools Development / Automation 
• Database Development 
• Upskill in AI / ML
• Vision patents/hackathons / Sales Analytics / Sweet Hearting
• Consulting in AI / ML
• Retail tracking, fulfillment, out of stock, complete store operations
• Holiday / Non-Holiday forecasting
• CC tier - Cycle Counting / Scaling out system learning
• It was a blend of DB, QA, AI+ML all of them
• Consulting helped me step up into AI / ML, and PI edge devices, solving a wide array of problems

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My people / data / domain / database learning's at Microsoft

My Learning's

• Dealt with hard data issues in Warranty
• Improve performance of systems from performance/finetuning (XRR App, RL feeds, Reports)
• Able to tradeoff reads vs writes for optimal performance (Order tacking / Centralized Status)
• Handle domain issues / out of sequence and customize DB for it
• Balance domain + data + customer service
• I handle conflicts in a more empathetic way, not my way or no way person
• Still, after 15 years remember the key tables/workflows/services
• What you learn/are passionate about you will never forget :)

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Good Read - How to know what customers need

Ensemble of - Data Science =  1/3 engineers, 1/3 MBAs and 1/3 social scientists

• While initial reports of customer satisfaction were positive, the strategy proved to be a failure.
• Avoid copying your competitors' solutions. Instead, create solutions tailored to the specific needs of your customers.
• When conducting customer interviews, avoid asking "yes/no" or leading questions that limit their responses.
• Don't survey customers with preconceived notions of their answers, or you’re likely to run into confirmation bias.
• Instead of asking customers what they want, focus on what they actually do.

 Ref - Link

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The Logistics at Amazon

• Delivery between fulfillment centers (FCs), where inventory is stored, and customers’ doorsteps
• Key factors demand forecasts, cost of acquiring transportation resources, staffing, buffer capacity planning
• Strategy - origin-destination targets, Demand for specific route/load - what percentage of the demand in a given locality should be met by a given fulfillment center?
• Customer demand, region-level demand
• Online controller - scan, label, apply, manifest]

Ref - Link

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Experience - Exposure - Domain - Data - Diversity Thinking

• My First year - Windows98 Testing
• Second year - C/C++ Mq Adapter Design
• Third year - Application support / migration / ITSM / Deployment / Production Support
• Fourth Year - SQL Migration / Performance Testing
• Fifth Year - Biztalk / SQL Migration / Replication / Developer
• Sixth Year - OLTP SQL Developer 
• Seventh Year - BI + OLTP Developer / Warranty migration 220 million records (Supply chain 4th year :))
• Eighth Year - QA Manager / Prod support / On-call / People Management
• 9th Year - DB / QA Developer / Product Transition
• 10th Year - Forecasting Feature in Product / DB / QA / Automation
• 11th Year - Hardware Integration / Professional Services / SQL Developer
• 12th Year - AWS Scaling / Perf testing on horizontal scaling for Tag counting / Function + Perf / DB
• 13th Year - SQL 2016 Migration Analysis / Big Data Architecture Analysis
• 14th Year - AI / ML - PG / DB Dev / BI
• 15th Year - AI / ML - PG second year / DB Dev / BI / Freelancing
• 16th - AI Freelancing / Training / DB / BI Dev / Patents
• 17th  -  AI Freelancing / Training / DB / BI Dev / Patents
• 18th - Vision / Forecasting / Recommendations  / People Management / Training
• 19th - Vision / Forecasting / Recommendations  / People Management / Training

All the different parts of experiences sum up and help me in knowing the Data journey / AI journey/business challenges 

It was varied roles / multiple domains and products but everything was worth it :)

Thanks to all my previous companies/training providers / past employers/freelance offered by startups...

Multiple lenses - Development - QA - Automation - Performance - Support - BI - Database performance tuning - Computer vision - Forecasting - Recommendations - Many times I have influenced / implemented product key features based on domain expertise.

• Warranty Migration of 220 million SKUs
• 3PL touch point integration
• Spot capabilities in Product with Data & AI (What can we do with what we have :) 

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