📦 Chapter 9: Batch – PART 1
Welcome to Part 3: ACCELERATE!
We’ve completed Part 1 (VISION) and Part 2 (STEER). Now we enter Part 3: ACCELERATE! This section focuses on how to move through the Build-Measure-Learn loop FASTER than your competitors. Speed is your ultimate competitive advantage!
The Core Question of Part 3
How can we maintain our learning speed as we grow?
Most startups face a paradox:
- Early stages: Small team, moves FAST, learns quickly
- Growth stage: Bigger team, moves SLOW, learning slows down
The challenge: How do you scale WITHOUT losing the agility and speed that made you successful?
Eric’s promise: The techniques in Part 3 will help you stay fast even as you grow from 5 people to 50 to 500!
Chapter 9: The Power of Small Batches
This chapter introduces one of the most counterintuitive concepts in Lean Startup: working in small batches is FASTER than working in large batches! This goes against everything your intuition tells you, but the data proves it!
The Famous Envelope-Stuffing Experiment
Eric Ries begins with a story from the book Lean Thinking by James Womack and Daniel Jones:
The Setup: A father and his two daughters (ages 6 and 9) need to stuff 100 newsletters into envelopes. Each envelope needs to be:
- Folded
- Inserted into envelope
- Sealed
- Stamped
Two Approaches:
Method 1: Large Batch (What the kids suggested)
- Fold ALL 100 newsletters first
- Then insert ALL 100 into envelopes
- Then seal ALL 100 envelopes
- Then stamp ALL 100 envelopes
Method 2: Small Batch / Single-Piece Flow (What the father suggested)
- Complete ONE envelope at a time (fold → insert → seal → stamp)
- Then move to the next envelope
- Repeat until all 100 are done
The kids’ prediction: “Daddy, your way won’t be efficient! We should do it our way!”
The experiment: They raced! Each took 50 envelopes and competed to see who would finish first.
The result: The father’s “one envelope at a time” method was FASTER!
☕ Hamed’s Analysis: Why Does This Seem So Wrong?
When I first teach this concept to Iranian entrepreneurs, 99% of them say “No way! Large batches must be faster!”
Why our intuition fails:
Reason 1: We forget about “hidden time”
- In large batches, you spend time sorting, stacking, organizing piles
- In small batches, you never have piles to manage!
Reason 2: We assume “repetition = efficiency”
- We think: “If I fold 100 letters in a row, I’ll get really good at folding!”
- Reality: The individual task improvement doesn’t offset the system slowdown!
Reason 3: We don’t account for “Work-in-Progress (WIP) inventory”
- Large batches create piles of half-finished work sitting around
- This takes up space, creates confusion, and slows everything down
The key insight: In process-oriented work, individual performance is less important than overall system performance!
Why Small Batches Win: The Hidden Advantages
Eric Ries explains that even if both methods took the EXACT same amount of time, small batches would STILL be superior for several critical reasons:
Advantage #1: You Catch Problems IMMEDIATELY
Scenario: What if the letters don’t fit in the envelopes?
Large Batch Method:
- You fold all 100 letters
- You try to insert them into envelopes
- PROBLEM DISCOVERED: They don’t fit!
- Now you have 100 folded letters that are useless!
- You have to refold all 100 in a different way!
Small Batch Method:
- You fold the first letter
- You try to insert it
- PROBLEM DISCOVERED: It doesn’t fit!
- You only wasted time on 1 letter!
- You adjust your folding method immediately!
Time saved: MASSIVE! You discovered the problem after wasting 1 letter instead of 100!
Advantage #2: You Get Finished Products SOONER
Large Batch Method:
- First finished envelope: After ALL 100 are processed (maybe 30 minutes)
- If someone needs to mail something urgently → They have to wait!
Small Batch Method:
- First finished envelope: After ~20 seconds!
- Someone needs to mail urgently? Here’s one ready to go!
Key insight: Small batches reduce your “time to market” dramatically! Your first product ships WAY sooner!
Advantage #3: You Reduce Rework and Waste
Scenario: What if the envelopes are defective and won’t seal?
Large Batch Method:
- You’ve folded 100 letters
- You’ve inserted 100 into envelopes
- NOW you discover: The envelopes won’t seal!
- You have to remove all 100 letters from the bad envelopes
- Get new envelopes
- Re-insert all 100 letters
- MASSIVE waste of time!
Small Batch Method:
- You complete the first envelope
- You try to seal it → Doesn’t work!
- You immediately get new envelopes
- You’ve only wasted time on 1 envelope!
Eric’s key point: “What if customers decide they don’t want your product? Which method helps you discover this sooner?”
☕ Hamed’s Real Example: Mobile App Development
Let me give you a PERFECT example from my consulting work:
Client: Food Delivery App
Two Development Approaches:
Team A: Large Batch Method
- Spent 4 months building ALL features:
- User registration
- Restaurant listings
- Menu browsing
- Shopping cart
- Payment integration
- Order tracking
- Rating system
- Launched everything at once
- PROBLEM DISCOVERED: Users hated the payment process!
- Result: 4 months of work, but users dropped off at payment!
Team B: Small Batch Method
- Week 1: Built basic registration + restaurant list
- Week 2: Tested with 10 users → Learned they wanted restaurant photos!
- Week 3: Added photos + basic menu browsing
- Week 4: Tested payment with 20 users → Discovered people preferred credit cards over cash on delivery!
- Week 5: Optimized payment based on feedback
Result:
- Team A: 4 months → Failed launch → Had to redo payment → Another 2 months wasted!
- Team B: 5 weeks → Successful launch with features users actually wanted!
Lesson: Small batches let you LEARN what users want BEFORE you waste months building the wrong thing!
Small Batches in Manufacturing: The Toyota Story
Eric Ries explains that Toyota discovered the power of small batches decades ago! This is where the concept of “Lean Manufacturing” comes from!
How Toyota Beat American Carmakers
The Context: Post-World War II Japan
- American carmakers dominated with MASSIVE factories and large-batch production
- Toyota couldn’t afford huge factories or massive production runs
- Toyota was forced to innovate!
What Toyota Did Differently:
Traditional American Method (Large Batches):
- Produce 10,000 identical cars
- Store them in warehouses
- Distribute to dealers
- Hope customers want to buy them!
Toyota’s Method (Small Batches / Just-In-Time Production):
- Produce small batches of diverse models
- Respond quickly to what customers actually want
- Minimal inventory (reduces storage costs!)
- Faster adaptation to market changes!
The Advantages Toyota Gained:
1. Flexibility: Could produce different models for different customer segments
2. Lower Capital Requirements: Didn’t need massive factories or warehouses
3. Faster Learning: Could test new designs quickly and iterate based on customer feedback
4. Less Waste: If a model didn’t sell well, they hadn’t overproduced it!
Result: Toyota went from a small Japanese manufacturer to one of the largest carmakers in the world!
☕ Hamed’s Analysis: Just-In-Time for Startups
Toyota’s “Just-In-Time” production is EXACTLY what startups should do!
What “Just-In-Time” means for startups:
DON’T do this (Large Batch):
- Build 20 features
- Wait 6 months to launch
- Hope customers like all 20 features!
DO this instead (Small Batch / Just-In-Time):
- Build 1 feature
- Launch it in 1 week
- Measure if customers like it
- Based on feedback, decide what to build next!
Real Iranian Startup Example – E-Learning Platform:
Original Plan (Large Batch):
- Create courses for Math, Physics, Chemistry, Biology, English
- 12 months of content creation
- Launch with ALL subjects at once
My Advice (Small Batch):
- Start with JUST Math
- Create 10 lessons
- Launch in 1 month
- See which lessons students actually watch!
What We Discovered:
- Students loved video lessons but HATED multiple-choice quizzes!
- They wanted interactive problem-solving instead!
- If we’d spent 12 months creating multiple-choice quizzes for ALL subjects, we would have wasted a YEAR!
Lesson: Build a little, learn a lot! Don’t build a lot and learn you wasted your time!
Small Batches in Software: Continuous Deployment
Eric Ries introduces the concept of Continuous Deployment – the software equivalent of small-batch manufacturing! This is how modern tech companies work!
What is Continuous Deployment?
Definition: The practice of releasing code changes to production CONSTANTLY – sometimes dozens of times per day!
Traditional Software Development (Large Batch):
- Developers write code for 3 months
- QA tests for 2 weeks
- Product managers review
- Finally release to customers
- Time from “idea” to “customer sees it”: 3-4 months!
Continuous Deployment (Small Batch):
- Developer writes a small feature
- Automated tests run immediately
- If tests pass, deploy to production INSTANTLY!
- Time from “idea” to “customer sees it”: Hours or minutes!
Eric’s Example: IMVU
At IMVU (Eric Ries’ company), they deployed code to production 50 TIMES PER DAY!
- Every engineer could ship code multiple times daily
- Customers saw new features and improvements constantly
- If something broke, they knew IMMEDIATELY which change caused it!
- They could roll back instantly!
Why this works:
- Small changes are easier to test and debug
- Faster feedback from customers
- Less “inventory” of unshipped code sitting around
How Continuous Deployment Prevents Disasters
Scenario: A Bug Gets Into Production
Traditional Method (Large Batch):
- You deployed 50 features at once
- Suddenly the site crashes!
- Which of the 50 features caused it?
- Engineers spend DAYS debugging!
- Meanwhile, your site is broken!
Continuous Deployment (Small Batch):
- You deployed 1 small feature
- Site crashes immediately
- You KNOW exactly which feature caused it!
- Roll back in 30 seconds!
- Site is back up!
Eric’s key insight: “The smaller the batch, the easier it is to pinpoint problems!”
☕ Hamed’s Practical Guide: How to Implement Small Batches
Most Iranian startups I work with say “We want to do small batches, but HOW?”
Here’s my step-by-step framework:
Step 1: Break Features Into Tiny Pieces
DON’T think like this:
- “We’re building a social media feature” (Way too big!)
DO think like this:
- Week 1: Add a “Like” button
- Week 2: Add a comment box
- Week 3: Add notifications for likes/comments
- Week 4: Add ability to share posts
Each piece is shippable on its own!
Step 2: Ship Every Friday
- Set a rule: Whatever is ready by Friday gets deployed!
- Even if it’s just one small feature!
- This creates a rhythm of constant shipping!
Step 3: Measure Immediately
- As soon as you ship, track the metrics!
- Did usage go up?
- Did the feature get used?
- Are there bugs?
Step 4: Iterate Based on Data
- If the feature works → Build the next piece!
- If it doesn’t work → Pivot or remove it!
- Don’t wait months to find out!
Real Example – Online Marketplace:
What they wanted to build: A full marketplace with buyer profiles, seller profiles, ratings, reviews, messaging, payment, dispute resolution
What I told them to do instead:
- Week 1: Just let sellers list products (no payments yet!)
- Week 2: Add a “Contact Seller” button (transactions happen offline)
- Week 3: Track how many people contact sellers
- Week 4: If lots of contacts → Add payment!
- Week 5: If few contacts → Figure out why BEFORE building payment!
What we discovered: People contacted sellers but then complained sellers were unresponsive! The problem wasn’t payment – it was seller quality! We pivoted to focus on vetting sellers instead of building fancy payment systems!
Lesson: Small batches let you discover the REAL problem early, before you waste months building the wrong solution!
End of Part 1
In Part 2, we’ll cover: The Waterfall Model vs. Small Batches, How to Implement Continuous Deployment, Real Examples from Tech Companies (Dropbox, Amazon, Facebook), and How Small Batches Accelerate the Build-Measure-Learn Loop!
📦 Chapter 9: Batch – PART 2 (COMPLETE)
The Waterfall Model vs. Small Batches
Eric Ries contrasts the traditional “Waterfall” software development model with the Lean Startup approach of small batches. Understanding this difference is CRITICAL!
The Traditional Waterfall Model (Large Batches)
How it works: Work flows sequentially from one department to the next, like a waterfall flowing downhill.
The Steps:
- Requirements Gathering: Product managers talk to users, write a spec (2 months)
- Design: Designers create mockups and user interfaces (1 month)
- Development: Engineers build the product (4 months)
- QA Testing: QA team tests for bugs (1 month)
- Launch: Finally ship to customers! (8 months total!)
The Problems with Waterfall:
Problem #1: Communication Breakdowns
- By the time engineers start coding, the designers have moved on to other projects
- Engineers have questions → Designers are unavailable → Engineers guess!
- Result: Product doesn’t match original vision!
Problem #2: Late Problem Discovery
- QA discovers major bugs AFTER 4 months of development!
- Engineers have to go back and fix them
- But now engineers have forgotten how the code works!
- Debugging takes FOREVER!
Problem #3: No Customer Feedback Until the End
- After 8 months, you finally show customers the product
- Customers: “This isn’t what we want!”
- Too late! You’ve already invested 8 months!
Eric’s verdict: “Waterfall maximizes individual department efficiency while destroying overall system efficiency!”
The Small Batch Alternative: Cross-Functional Teams
How it works: Instead of sequential handoffs, have a CROSS-FUNCTIONAL team work together on small features from start to finish!
The Small Batch Process:
- Week 1: Product manager, designer, and engineer work TOGETHER on Feature A
- Product manager defines the goal
- Designer sketches a quick mockup
- Engineer builds a basic version
- Ship to a small group of users!
- Week 2: Review feedback from users, adjust Feature A, and start Feature B
The Advantages:
Advantage #1: Constant Communication
- Team members are working together daily
- Questions get answered immediately!
- No miscommunication between departments!
Advantage #2: Immediate Problem Discovery
- If there’s a bug, you find it within days (not months!)
- Engineer’s memory of the code is fresh → Easy to fix!
Advantage #3: Continuous Customer Feedback
- Every week, customers see new features
- If they don’t like something, you pivot immediately!
- You’re never more than 1 week away from validated learning!
Eric’s key point: “Cross-functional teams working in small batches accelerate the Build-Measure-Learn loop!”
☕ Hamed’s Real Example: Comparing Waterfall vs. Small Batches
I worked with two e-commerce startups at the same time. Perfect A/B test!
Startup A: Used Waterfall (Traditional)
Their Process:
- Months 1-2: Product team researched and wrote detailed specs for the entire website
- Month 3: Design team created complete mockups for all pages
- Months 4-7: Engineering team built everything
- Month 8: QA tested the site
- Month 9: LAUNCH!
What Happened:
- After launch, they discovered users hated the checkout process!
- Conversion rate: Only 2%!
- Engineers had to spend another 2 months redesigning checkout
- Total time to get it right: 11 months!
Startup B: Used Small Batches (Lean)
Their Process:
- Week 1: Built basic homepage + product listing page → Launched!
- Week 2: Added shopping cart → Launched!
- Week 3: Added checkout → Launched!
- Week 4: Discovered checkout had problems! (Conversion rate: 2%)
- Week 5: Tested 3 different checkout designs with users
- Week 6: Implemented winning design → Conversion rate jumped to 8%!
Total time to get it right: 6 weeks!
Comparison:
- Startup A (Waterfall): 11 months to success
- Startup B (Small Batches): 6 weeks to success
- Startup B was 7X FASTER!
Lesson: Small batches don’t just save time – they save you from building the wrong thing!
Implementing Continuous Deployment: The Technical Side
Now Eric Ries gets technical! How do you actually implement continuous deployment? What infrastructure do you need?
The 4 Pillars of Continuous Deployment
Pillar #1: Automated Testing
You CANNOT deploy 50 times per day without automated tests!
What you need:
- Unit tests: Test individual functions and components
- Integration tests: Test how components work together
- End-to-end tests: Test the entire user flow
At IMVU:
- Every code change triggered thousands of automated tests
- If ANY test failed → Code couldn’t be deployed!
- If all tests passed → Code automatically deployed to production!
Pillar #2: Incremental Rollout
Don’t deploy to ALL users at once! Deploy gradually!
The Strategy:
- Deploy to 1% of users first
- Monitor metrics closely
- If everything looks good → Deploy to 10%
- Then 50%
- Then 100%
- If anything breaks → Roll back instantly!
Why this matters: If a bad deploy affects only 1% of users, you’ve limited the damage!
Pillar #3: Instant Rollback
You need the ability to undo a deployment in SECONDS!
At IMVU:
- If metrics dropped after a deployment, they rolled back immediately
- Rollback time: Under 60 seconds!
- This safety net made engineers confident to deploy frequently!
Pillar #4: Real-Time Monitoring
You need dashboards showing key metrics in REAL-TIME!
What to monitor:
- Error rates
- Page load times
- User engagement metrics
- Conversion rates
Why this matters: You need to know within MINUTES if a deployment caused problems!
The Immune System Metaphor
Eric Ries uses a brilliant metaphor: Continuous deployment is like your body’s immune system!
How Your Immune System Works:
- Constantly monitoring for threats
- When it detects a problem → Immediate response!
- Isolates the threat before it spreads
- Learns from each encounter (builds antibodies)
How Continuous Deployment Works:
- Constantly monitoring for bugs and performance issues
- When it detects a problem → Immediate rollback!
- Prevents the bug from affecting all users
- Engineers learn from each failure (write better tests)
The key insight: Instead of trying to prevent ALL problems upfront (impossible!), build a system that can DETECT and RECOVER from problems quickly!
Eric’s comparison:
- Traditional approach: “Let’s make the product perfect before shipping!” (Takes forever, still has bugs)
- Lean approach: “Let’s ship quickly and fix problems as they arise!” (Faster, actually works!)
☕ Hamed’s Practical Implementation Guide
Most Iranian startups tell me: “This sounds great, but we don’t have IMVU’s resources!”
Here’s how to start continuous deployment with MINIMAL resources:
Phase 1: Start Weekly Deployments (Month 1)
- Pick ONE day per week (e.g., Friday afternoon)
- Deploy whatever features are ready
- Have the whole team monitoring for 2 hours after deployment
- If something breaks → Everyone helps fix it immediately!
Phase 2: Add Basic Automated Tests (Months 2-3)
You don’t need thousands of tests! Start small!
Test Priority Order:
- Test the user registration/login flow
- Test the payment process (if applicable)
- Test the core user action (e.g., posting content, making a purchase)
Tools for Iranian startups:
- For web apps: Selenium (free, open-source)
- For mobile apps: Appium (free, open-source)
- For backend: Jest, PyTest (free)
Phase 3: Move to Daily Deployments (Months 4-6)
- Once you have basic tests, deploy every day!
- Set a rule: “If tests pass, we deploy!”
- Monitor metrics for 30 minutes after each deploy
Phase 4: Implement Gradual Rollout (Months 7-12)
Simple approach:
- Use a feature flag system (can be as simple as an if/else in your code!)
- Deploy new features to 10% of users first
- If metrics look good → Increase to 50%, then 100%
Real Iranian Startup Example – Ride-Sharing App:
Before Continuous Deployment:
- Deployed once per month
- Each deployment took 8 hours (including testing)
- Frequently had bugs that took days to fix
- Customer complaints were high
After Implementing My Framework:
- Month 1: Weekly deployments → Bugs got caught faster!
- Month 3: Added automated tests for critical flows → Confidence increased!
- Month 5: Daily deployments → Features shipped 4X faster!
- Month 8: Gradual rollout → Major bugs affected fewer users!
Results after 1 year:
- Development speed increased 5X
- Customer complaints decreased 70%
- Team morale improved (less stress from big deployments!)
Key lesson: You don’t need perfection! Start with weekly deployments and improve gradually!
Real-World Examples: Tech Giants Using Small Batches
Eric Ries shares examples of major tech companies that adopted small-batch thinking and continuous deployment:
Example #1: Amazon
Amazon’s Deployment Frequency:
In the early 2010s, Amazon was deploying code to production every 11.6 seconds on average!
How they achieved this:
- Massive investment in automated testing infrastructure
- Every team could deploy independently
- Instant rollback capability
- Culture of “two-pizza teams” (small, autonomous teams)
The result: Amazon could test ideas faster than any competitor!
Example: Amazon tests hundreds of variations of their homepage daily!
- Different button colors
- Different product placements
- Different copy
They measure which variations increase sales and keep the winners!
Eric’s point: Amazon’s competitive advantage isn’t just low prices – it’s their SPEED OF LEARNING!
Example #2: Facebook
Facebook’s Famous Motto: “Move Fast and Break Things” (later changed to “Move Fast with Stable Infrastructure”)
How Facebook works:
- Engineers can deploy code to production on their first day of work!
- New features get tested with small groups first (using their “Gatekeeper” system)
- If metrics improve → Roll out to everyone!
- If metrics decline → Kill the feature immediately!
Famous example: The Facebook “Like” button!
- Started as an internal experiment
- Tested with 1% of users
- Engagement went up significantly
- Rolled out to everyone
- Now it’s one of the most iconic features on the internet!
Eric’s insight: Facebook’s willingness to experiment frequently is WHY they stay ahead of competitors!
Example #3: Dropbox
Dropbox’s Continuous Deployment Journey:
When Dropbox was small, they deployed manually and infrequently. As they grew, this became a bottleneck!
Their transformation:
- Invested heavily in automated testing
- Built custom tools for continuous deployment
- Deployed multiple times per day
The impact:
- Feature development accelerated
- Engineers spent less time on manual deployments
- More time for innovation!
Eric’s lesson: Even companies with complex infrastructure (like Dropbox, which has to work across multiple platforms) can adopt continuous deployment!
☕ Hamed’s Analysis: What Iranian Startups Can Learn
Most Iranian entrepreneurs tell me: “But we’re not Amazon or Facebook!”
True! But the PRINCIPLES still apply!
What you should copy from the big tech companies:
Lesson #1: Speed Matters More Than Perfection
- Amazon doesn’t wait for perfect code
- They ship quickly and fix problems as they arise
- You should do the same!
Lesson #2: Test with Small Groups First
- Facebook doesn’t launch features to all 3 billion users at once!
- They test with small groups first
- You can do this too! Even if you only have 1,000 users, test with 100 first!
Lesson #3: Kill Features That Don’t Work
- Tech giants ruthlessly kill features that don’t improve metrics
- Don’t fall in love with your features!
- If the data says it’s not working → Remove it!
Real Iranian Example – Social Media App:
They wanted to add a “Stories” feature (like Instagram Stories).
What they did right:
- Built a basic version in 2 weeks (not months!)
- Tested with 5% of users
- Measured engagement metrics daily
What they discovered:
- Only 2% of test users posted Stories!
- But 60% of users VIEWED Stories!
- Insight: Users wanted to consume Stories, not create them!
What they did next:
- Pivoted to focus on showing Stories from brands/influencers
- Engagement skyrocketed!
- If they’d spent 6 months building a perfect Stories feature, they would have built the WRONG thing!
Key takeaway: Small batches aren’t just for startups – they’re how the biggest tech companies in the world operate!
How Small Batches Accelerate the Build-Measure-Learn Loop
Eric Ries brings it all together: Small batches are THE key to accelerating the Build-Measure-Learn loop!
The Connection: Small Batches → Faster Learning
Remember the core Lean Startup question: “How do we learn as quickly as possible whether our ideas will work?”
The answer: Small batches!
Why Small Batches Accelerate Learning:
Reason #1: Shorter Feedback Loops
- Large batch: Build for 6 months → Get feedback → Too late to change course!
- Small batch: Build for 1 week → Get feedback → Pivot immediately if needed!
Reason #2: More Iterations in the Same Time
- In 6 months with large batches: 1 iteration
- In 6 months with small batches: 24 iterations!
- More iterations = More learning = Better product!
Reason #3: Reduced Risk
- Large batches: If you’re wrong, you waste months!
- Small batches: If you’re wrong, you waste days!
Eric’s mathematical insight:
“If you can go through the Build-Measure-Learn loop 10 times while your competitor goes through it once, you have a 10X learning advantage!”
This is your unfair competitive advantage as a startup!
The Virtuous Cycle of Small Batches
Eric Ries describes a “virtuous cycle” that small batches create:
The Cycle:
- Small batches → Faster deployment → You ship features weekly instead of monthly
- Faster deployment → Quicker feedback → You learn what customers want faster
- Quicker feedback → Better decisions → You build the right features
- Better decisions → Higher engagement → Customers use your product more
- Higher engagement → More data → You have better metrics to make decisions
- More data → Faster learning → You iterate even faster!
- Faster learning → Competitive advantage! → You outpace competitors!
The opposite (vicious) cycle of large batches:
- Large batches → Slow deployment → You ship features every few months
- Slow deployment → Delayed feedback → You don’t know if customers like it for months
- Delayed feedback → Poor decisions → You build the wrong features
- Poor decisions → Low engagement → Customers don’t use your product
- Low engagement → Less data → You have worse metrics to guide you
- Less data → Slower learning → You iterate even slower!
- Slower learning → Competitive disadvantage! → Competitors outpace you!
Eric’s warning: “Large batches create a death spiral! Small batches create a growth spiral!”
☕ Hamed’s Final Advice: How to Start TODAY
You don’t need to transform your entire company overnight!
Start with ONE small batch experiment:
Week 1: Pick Your Smallest Shippable Feature
- Look at your roadmap
- Find the smallest thing you can build in 1 week
- Cut scope ruthlessly! (Example: Instead of “complete user profile system,” just add “profile photo upload”)
Week 2: Build and Ship It
- Build the minimal version
- Don’t make it perfect!
- Ship it to a small group of users (10-100 people)
Week 3: Measure and Learn
- Did users use the feature?
- Did engagement go up or down?
- What feedback did you get?
Week 4: Iterate or Pivot
- If it worked → Build the next small feature!
- If it didn’t work → Try something different!
The Key Mindset Shift:
Old thinking: “Let’s spend 6 months building the perfect product, then launch!”
New thinking: “Let’s spend 1 week building something tiny, learn from it, then build the next thing!”
Challenge to Iranian entrepreneurs:
What’s ONE feature you can ship in the NEXT 7 DAYS?
Not a complete product. Not a perfect feature. Just ONE small thing!
Ship it. Measure it. Learn from it.
That’s how you start the small batch journey!
Remember: Amazon didn’t start deploying every 11 seconds on day one! They started with one small step. So can you!
🎯 Chapter 9 Summary: Key Takeaways
The Big Ideas from Chapter 9
1. Small batches are FASTER than large batches!
- Counterintuitive but proven by data!
- The envelope-stuffing experiment proves it!
2. Small batches catch problems immediately!
- Discover errors after 1 unit, not 100 units!
- Reduce rework and waste dramatically!
3. Continuous deployment is the software equivalent of small-batch manufacturing!
- Deploy multiple times per day
- Get instant customer feedback
- Iterate faster than competitors!
4. The 4 pillars of continuous deployment:
- Automated testing
- Incremental rollout
- Instant rollback
- Real-time monitoring
5. Cross-functional teams beat sequential handoffs!
- Product, design, and engineering work together
- No communication breakdowns!
- Faster decision-making!
6. Tech giants like Amazon, Facebook, and Dropbox use small batches!
- It’s not just for startups!
- It scales as you grow!
7. Small batches accelerate the Build-Measure-Learn loop!
- More iterations = More learning!
- More learning = Better products!
- Better products = Competitive advantage!
The ultimate lesson: Your speed of learning is your competitive advantage! Small batches make you learn faster!
📦 End of Chapter 9: Batch 📦
Next up: Chapter 10 – GROW!
We’ll explore the three engines of growth and how to choose the right one for your startup!
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