Day 20: Workflow optimization – audit, improve, and scale your automations

🔗 Knowledge graph – Day 20 optimizes every system

Day 1

Optimize prompts for cost/speed

Day 2

Zapier task usage optimization

Day 3

Make.com operation reduction

Day 4

OpenAI token optimization

Day 5

Lead qualifier efficiency

Day 6

Business case ROI

Day 7

3 builds performance

Day 8

Advanced qualifier optimization

Day 9

Sales assistant sequencing

Day 10

Content engine efficiency

Day 11

Support router speed

Day 12

Niche workflow refinement

Day 13

Workflow analysis foundation

Day 14

CRM sync optimization

Day 15

API call reduction

Day 16

Scraper efficiency

Day 17

Dashboard performance tracking

Day 18

Proposal generator speed

Day 19

Chatbot response time

Day 20

Workflow optimization

Shared link: Every system you've built can be faster, cheaper, and more reliable. Day 20 teaches you to audit, measure, and optimize all of them – turning good automations into great, scalable systems.

🎯 Why workflow optimization matters

📌 From "it works" to "it works perfectly"

Building automations is one thing. Optimizing them is what separates professionals. Optimization means:

Faster execution – respond to leads in seconds, not minutes
Lower costs – reduce API calls, OpenAI tokens, Zapier tasks
Higher reliability – fewer failures, better error handling
Scalability – handle 10x volume without breaking
Better ROI – more value from every automation dollar

Analogy: Building an automation is like buying a car. Optimization is like regular maintenance, tuning, and upgrades – it keeps the car running smoothly, efficiently, and reliably for years.

📋 The 5-step optimization framework

Audit & measure

Collect baseline metrics:

Execution time per workflow
Cost per run (API calls, tokens, tasks)
Error rate and types
Volume (daily/weekly runs)

Identify bottlenecks

Where are the delays? Common bottlenecks:

Slow API responses (OpenAI, external APIs)
Unnecessary steps (redundant operations)
Inefficient data processing
Rate limiting (hitting API limits)

Optimize & test

Apply optimization techniques (covered below), then A/B test.

Monitor & alert

Set up dashboards (Day 17) to track performance. Alert on anomalies.

Iterate continuously

Optimization never ends. Review monthly.

📊 Key metrics dashboard (Day 17 applied)

⏱️ Speed metrics

Average workflow execution time
API response time (p95)
Time from trigger to action
Queue wait time

2.3s avg

💰 Cost metrics

Cost per workflow run
OpenAI tokens per call
Monthly API costs
Cost per lead qualified

$0.12/lead

📈 Reliability metrics

Error rate (%)
Failed runs per day
Retry frequency
Uptime / availability

98.5% success

📦 Volume metrics

Runs per day/week
Peak load capacity
Data processed (MB)
Users/leads processed

1,250/day

⚙️ Optimization techniques for every previous day

Day / System	Common Issues	Optimization Techniques	Potential Savings
Day 1/4 – Prompts + OpenAI	High token usage, slow responses	Reduce max_tokens, use gpt-3.5-turbo, cache common responses, shorten prompts	30-50% cost reduction
Day 2 – Zapier	Task overuse, slow zaps	Combine steps, use filters early, upgrade to premium for faster execution	20-40% task reduction
Day 3 – Make.com	Too many operations, complex scenarios	Use aggregators, reduce unnecessary modules, optimize data flow	30-50% operation reduction
Day 8 – Lead qualifier	Slow qualification, multiple API calls	Combine BANT into one prompt, use webhooks for instant response	40% faster qualification
Day 9 – Sales assistant	Email delays, follow-up gaps	Use templates for common responses, batch email sends	2x faster follow-up
Day 10 – Content engine	High token usage per article	Generate outlines first, then expand section by section, cache outlines	50% cost reduction
Day 11 – Support router	Slow classification, KB search	Cache KB embeddings, use vector search, pre-classify common tickets	60% faster response
Day 14 – CRM sync	Duplicate contacts, slow updates	Batch updates, use upsert, deduplicate before creating	70% fewer API calls
Day 15 – API integrations	Rate limiting, retry storms	Implement exponential backoff, queue requests, cache responses	90% fewer 429 errors
Day 16 – Web scraping	Slow page loads, blocks	Use rotating proxies, cache HTML, respect crawl-delay	3x faster scraping
Day 18 – Proposal generator	Slow PDF generation	Use templates, pre-generate common sections, async PDF creation	50% faster proposals
Day 19 – Chatbots	Slow responses, memory loss	Use streaming, limit conversation history, cache common answers	2x faster responses

📈 Case study: Optimizing the Day 8 lead qualifier

Before optimization

4 separate API calls (budget, authority, need, timeline)
Average 8 seconds per lead
Cost: $0.08 per lead
Error rate: 5%

After optimization

1 combined API call (BANT in one prompt)
Average 2.5 seconds per lead
Cost: $0.02 per lead
Error rate: 1%

// Before: 4 separate calls
Budget prompt → Authority prompt → Need prompt → Timeline prompt
// After: One optimized prompt
"Extract BANT from this message: budget range, decision maker status, need description, timeline. Return JSON."
            

Annual savings: 1,000 leads/month × $0.06 savings × 12 = $720/year + 180 hours faster response time.

💾 Caching – the ultimate optimization

Cache OpenAI responses

Store responses for identical prompts. Use Make Data Store or Airtable. Key: prompt hash + input.

Cache API data

If data changes rarely (e.g., company info), cache for 24 hours.

Cache HTML scraped

Store scraped pages for 1-7 days to avoid repeated requests.

Cache knowledge base

Pre-compute embeddings for KB articles. Store in vector DB.

// Make.com Data Store cache pattern
Check if cache exists for today's request
If yes, return cached response
If no, call API, store response with TTL
            

📦 Batching and async processing

Batch API calls

Combine multiple operations into one API call when possible.

Async processing

For slow tasks (PDF generation), process in background, notify when done.

Queue management

Use queues to handle spikes without crashing.

✅ Optimization checklist for every system

Day 1-4: Prompts

☐ Shortened prompts
☐ Reduced max_tokens
☐ Used gpt-3.5-turbo
☐ Cached common responses

Day 2-3: No-code

☐ Removed redundant steps
☐ Used filters early
☐ Batched operations
☐ Scheduled during off-peak

Day 8-9: Sales

☐ Combined BANT prompt
☐ Used email templates
☐ Pre-generated content
☐ Automated follow-up timing

Day 10-11: Content/Support

☐ Cached KB embeddings
☐ Pre-generated outlines
☐ Used vector search
☐ Auto-escalation rules

Day 14: CRM

☐ Batched updates
☐ Deduplication before create
☐ Used upsert
☐ Synced only changed fields

Day 15: APIs

☐ Exponential backoff
☐ Request queuing
☐ Response caching
☐ Rate limit monitoring

Day 16: Scraping

☐ Rotating proxies
☐ Crawl-delay respect
☐ HTML caching
☐ Parsing optimization

Day 18-19: Docs/Chat

☐ Template reuse
☐ Async PDF generation
☐ Conversation history limits
☐ Common answer caching

8 hands-on practice exercises

📊 Exercise 1: Audit your Day 8 system

Measure current cost, speed, error rate of your lead qualifier. Document baseline.

⚡ Exercise 2: Optimize prompts

Take a Day 4 OpenAI prompt. Reduce tokens by 30% without losing quality. Test before/after.

💾 Exercise 3: Implement caching

Add a cache to your Day 10 content generator for common topics. Measure savings.

📦 Exercise 4: Batch CRM updates

Modify Day 14 sync to batch 10 updates at once instead of individual calls.

🔄 Exercise 5: Add retry logic

Improve Day 15 API calls with exponential backoff. Test with simulated failures.

⚙️ Exercise 6: Reduce Make.com operations

Take a complex Day 3 scenario. Remove 20% of modules without breaking functionality.

📈 Exercise 7: Optimization dashboard

Build a Day 17 dashboard tracking key metrics for one system. Show trends.

📝 Exercise 8: Optimization report

Write a 1-page optimization report for a client, showing before/after metrics and savings.

📄 Client proposal – Automation optimization audit

⚡ Automation Optimization Audit – Service Overview

What I'll do:

✅ Comprehensive audit of all your automation workflows
✅ Measure current performance: speed, cost, reliability, volume
✅ Identify bottlenecks and inefficiencies
✅ Implement optimizations: prompt engineering, caching, batching, error handling
✅ Deliver before/after report with metrics and savings
✅ Set up monitoring dashboard (Day 17) for ongoing tracking

Typical results:

30-50% reduction in automation costs
2-3x faster execution times
90% fewer errors
ROI in 1-3 months

Investment: $1,500 per system or $5,000 for full stack audit (up to 10 systems)

📚 Resources

Day 20: You're now an optimization expert

✔ Mastered the 5-step optimization framework
✔ Learned to measure speed, cost, reliability, volume
✔ Applied optimization techniques to every previous day
✔ Case study: 75% cost reduction on lead qualifier
✔ Implemented caching and batching strategies
✔ 8 practice exercises
✔ Client-ready optimization audit service