- Bionic Business
- Posts
- Issue #69: Swipe These 3 AI Automations for $25K+ In New Revenue
Issue #69: Swipe These 3 AI Automations for $25K+ In New Revenue

Good morning.
Here's what I keep hearing from subscribers:
You've got 47 hours of recorded sales calls gathering dust.
Refund requests that could've been saves.
Customer success stories vanishing into the ether.
Meanwhile, you're hunting for insights that are literally sitting in your existing data.
So let's fix that. I'm giving you three automations that mine gold from data you already have.
Each one solves a real problem, delivers immediate value, and can be running before your afternoon coffee.
No moonshot promises. No 6-week implementation plans. Just tactical solutions that turn your existing business data into revenue.
These automations are embarrassingly simple. That's the point.
The best ROI comes from doing obvious things that somehow nobody does.
This is a sample of emails you get inside the premium version of Bionic Business, called Cortex.
It’s usually closed but open right now, for a limited time:
Let's get into it.
—Sam
IN TODAY’S ISSUE 🤖

Quick Win #1: Sales Call Gold Miner (never miss another deal-killing objection)
Quick Win #2: Refund Forensics Investigator (save 30% of cancellations)
Quick Win #3: Customer Success Story Generator (catch wins while they're hot)
Implementation Options: Manual (15 min) or Automated (45 min) for each
Let’s get into it.

The Sales Call Gold Miner
Turn rambling calls into revenue intelligence in 15 minutes
The Problem: You or your sales team just finished 47+ calls this week. Hidden in those recordings are the exact words that kill deals, competitor weaknesses you could exploit, and pricing objections you could prevent. But nobody has 47 hours to listen back.
The Solution: One prompt that extracts every revenue insight from your calls in minutes.
Time to implement: 15 minutes (manual) or 45 minutes (automated)
Tools needed: Call recordings + Claude/GPT-4
Saves you: 10+ hours of call review weekly
Option 1: The Manual Method (15 minutes)
The Intelligence Extraction Prompt
Copy this exactly:
Analyze this sales call transcript and extract:
1. EXACT OBJECTIONS: Every concern raised (with timestamp and exact words used)
2. COMPETITOR MENTIONS: Any competitor named, with context of comparison
3. PRICING REACTIONS: Responses to price, including body language cues noted
4. BUYING SIGNALS: Phrases indicating interest ("What happens next?", "How soon could we...")
5. DEAL KILLERS: Moments where momentum died (what killed it?)
6. MAGIC PHRASES: What the rep said that moved the deal forward
7. FEATURE GAPS: "Do you have..." questions where answer was no
8. DECISION CRITERIA: What they said matters most to them
Format: Brief report with exact quotes. Mark "CRITICAL" for deal-breaking moments.
[PASTE CALL TRANSCRIPT HERE]
Quick Implementation
Get transcripts from your call recorder (Gong, Chorus, Zoom, etc.)
Pick 5 calls: 2 won deals, 2 lost deals, 1 stalled
Run the prompt on each transcript
Look for patterns across all 5 calls (takes 10 minutes)
Update your scripts immediately
Option 2: The Automated Method
Prerequisites:
API access to your call platform (often requires paid plan)
Remove any customer PII before processing
For calls over 60 minutes, plan for transcript chunking
With Gumloop (45 minutes setup)
Workflow Overview: Automatically process every sales call and build a searchable intelligence database.
Step-by-Step Setup:
Create Weekly Workflow
Name: "Sales Intelligence Miner"
Trigger: Every Friday afternoon
Alternative: Trigger after each call ends
Add Call Recorder Integration
Add node: "HTTP Request" to your call platform API
Most have APIs: Gong, Chorus, Fireflies, Zoom
Pull: All calls from last 7 days
Include: Transcripts + metadata (rep, prospect, outcome)
Add Transcript Processing
Add node: "Text Processor"
Split long calls into chunks (Claude handles ~50k words)
Preserve context and speaker labels
Add Intelligence Extraction
Add node: "Claude/GPT Analysis"
Use the extraction prompt above
Add: "Also note: Rep name, Prospect company, Deal stage"
Add Database Storage
Add node: "Airtable" or "Google Sheets"
Create columns for each extraction type
Include: Date, rep, outcome, deal size
Add Smart Alerts
Add node: "Conditional Logic"
If "CRITICAL" found → Immediate Slack to sales manager
Weekly digest → Entire sales team
Competitor mentions → Product team
With Lindy AI (30 minutes setup)
Agent Configuration:
Create "Sales Intelligence Agent"
Type: Call Analysis Agent
Trigger: New call recording available
Configure Call Integration
Connect: Your call recording platform
Enable: Auto-transcription if needed
Set: Include calls over 10 minutes only
Build Analysis Framework
Primary task: Extract intelligence per the prompt
Secondary task: Compare to historical patterns
Tertiary task: Suggest talk track improvements
Set Intelligence Routing
Objection patterns → Sales enablement team
Competitor intel → Product marketing
Lost deal patterns → Sales leadership
Feature gaps → Product team
What This Finds That Changes Everything
The "Actually" Discovery What sales thinks kills deals vs. what actually kills deals are different.
Example from a SaaS company:
Sales thought: Price was too high
Actually found: "I don't understand what happens after we buy"
Fix: Created visual implementation timeline
Result: Close rate jumped 31%
The Competitor Blindspot Prospects compare you to competitors you didn't know existed.
Real finding: "We're also looking at [random spreadsheet template] as an option" Response: Created "Why we're better than spreadsheets" battle card Result: Shortened sales cycle by 12 days
Advanced Mining Techniques
The Pattern Spotter - Add to prompt: "Identify patterns: What objections appear in lost deals but not won deals?"
The Rep Analyzer - Compare top performers' calls: "What phrases do top reps use that others don't?"
The Timing Detective - "At what point in calls do deals typically die? What happens right before?"
ROI Calculator
Time saved: 10 hours/week of manual call review
Deal intelligence: Catch 100% of competitor mentions (vs ~20% from memory)
Win rate improvement: 15-25% typical improvement from better objection handling*
Faster ramp time: New reps productive 2-3 weeks faster
*Based on companies that implement systematic objection tracking
Start manual today. Pick your 3 worst lost deals from last week. Run them through the prompt. Find the pattern that killed them. Fix it before Monday's calls.
The Refund Forensics Investigator
Turn refund requests into retention wins and product fixes
The Problem: Every refund is a $500-$5,000 lesson you're throwing away. The customer tells you exactly what's broken, what disappointed them, and what would have saved them. Then you click "process refund" and forget.
The Solution: AI that analyzes refund patterns, saves preventable cancellations, and fixes problems before they cost you more customers.
Time to implement: 15 minutes (manual) or 40 minutes (automated)
Tools needed: Refund emails + Claude/GPT-4
Saves you: 30% of "saveable" refunds + prevents future cancellations
Option 1: The Manual Method (15 minutes)
The Forensics Analysis Prompt
Copy exactly:
Analyze these refund requests to extract:
1. ROOT CAUSE CATEGORIES: Group the real reasons (not surface excuses)
2. SAVEABLE VS GONE: Which customers could have been saved with right intervention?
3. TIMELINE PATTERNS: How long from purchase to refund? What happened in between?
4. EMOTIONAL LANGUAGE: Frustration indicators (intensity 1-10)
5. COMPETITOR MENTIONS: Where are they going instead?
6. THE TIPPING POINT: Specific moment/feature where they gave up
7. PREVENTION IDEAS: What could have prevented each refund?
8. RESPONSE TEMPLATES: Craft responses that might save saveable customers
Critical: Distinguish "polite" reasons from real reasons. Look for patterns.
[PASTE LAST 20-30 REFUND REQUESTS HERE]
Implementation Steps
Export refund requests from last 60 days
Include the full thread (initial request + any follow-up)
Run through the prompt
Identify top 3 patterns
Fix the biggest issue this week
Option 2: The Automated Method
Important Notes:
Automated refund responses may need legal/compliance review
Test with small batches before full automation
Always include human escalation path for complex cases
With Gumloop (40 minutes setup)
Workflow Overview: Intercept refund requests in real-time and attempt intelligent saves.
Step-by-Step Setup:
Create Trigger Workflow
Name: "Refund Intelligence System"
Trigger: New email to refund@ or support ticket tagged "refund"
Alternative: Webhook from payment processor
Add Classification Node
Add node: "AI Classifier"
Categories: Technical issue / Unmet expectations / Price / No longer needed / Competitor switch
This helps route to appropriate response
Add Forensic Analysis
Add node: "Claude/GPT Deep Analysis"
Use forensics prompt above
Add: "Recommend save strategy with success probability"
Add Save Attempt Logic
Add node: "Conditional Router"
If saveable (>60% probability) → Generate personalized save offer
If technical → Route to urgent support
If price → Offer downgrade or pause
If gone → Process but extract maximum intelligence
Add Response Generation
Add node: "AI Response Writer"
Templates based on refund type
Include: Specific fix, timeline, and compensation if appropriate
Tone: Empathetic but solution-focused
Critical: Test responses internally before enabling auto-send
Add Tracking & Alerts
Add node: "Database Update"
Track: Save attempts, success rate, patterns
Alert product team: Technical issues over threshold
Alert leadership: Refund rate spike
With Lindy AI (35 minutes setup)
Agent Configuration:
Create "Refund Prevention Agent"
Type: Email Monitor + Analyzer
Trigger: Refund-related keywords
Configure Intelligence Gathering
Pull: Customer history, usage data, support tickets
Analyze: Full context before responding
Identify: Patterns across account lifecycle
Build Response Framework
Save attempt templates by category
Personalization based on customer value
Escalation rules for high-value accounts
Set Learning Loop
Track which saves work
Adjust strategies based on success
Monthly pattern reports to product team
The Hidden Gold in Refund Data
What They Say vs. What They Mean
Says: "It's too expensive"
Means: "I couldn't figure out how to get value"
Fix: Better onboarding, not lower price
The 48-Hour Rule 80% of refunds happen within 48 hours of a specific trigger:
Failed to complete key task
Saw competitor comparison
Hit unexpected limitation
Find the trigger, fix the experience.
Real Success Story
SaaS Company Discovery:
Pattern: 40% of refunds mentioned "can't export data"
Investigation: Export exists but buried in settings
Fix: Added export button to main dashboard
Result: Refunds dropped 38% in 30 days
E-commerce Brand Win:
Pattern: Size-related refunds spiking
Root cause: Size chart link broken on mobile
Fix: 10-minute fix
Result: Saved $47,000 in monthly refunds
The Save Framework That Works
For Technical Issues: "I see exactly what went wrong. Here's what I'm doing:
[Specific fix with timeline]
[Compensation for inconvenience]
[Direct line to ensure success] Can I process this fix instead of your refund?"
Success rate: 67%
For Unmet Expectations: "You expected [X] and got [Y] - that's on us. Two options:
Let me personally ensure you get [X] by [date]
Full refund, no questions asked What would you prefer?"
Success rate: 43%
ROI Breakdown
Direct savings: 30% of "saveable" refunds retained (typically 10-15% of total refunds)
Indirect savings: Fix root causes = prevent future refunds
Customer lifetime value: Saved customers often become advocates
Product improvements: Free user research on what's broken
Start now: Pull your last 10 refunds. Run the prompt. You'll find at least 3 were preventable.
The Customer Success Story Generator
Catch success stories while customers are still buzzing with excitement
The Problem: Your best case studies happen in real-time - a customer hits a milestone, solves a huge problem, or gets promoted thanks to your product. Six months later when you ask for a case study, they've forgotten the pain and the story falls flat.
The Solution: AI that detects success moments as they happen and strikes while the emotion is fresh.
Time to implement: 15 minutes (manual) or 35 minutes (automated)
Tools needed: Customer communications + Claude/GPT-4
Result: 5-10 authentic case studies monthly (vs. begging for 1)
Option 1: The Manual Method (15 minutes)
The Success Signal Detector Prompt
Copy this:
Analyze these customer messages to identify success stories in the making:
1. SUCCESS SIGNALS: Find mentions of achievements, milestones, or wins
2. EMOTIONAL PEAKS: Messages with excitement, relief, or gratitude (rate 1-10)
3. SPECIFIC METRICS: Any numbers, percentages, or time saved mentioned
4. BEFORE/AFTER: References to how things were vs. now
5. STAKEHOLDER WINS: Mentions of impressing boss, team, or customers
6. QUOTABLE MOMENTS: Exact phrases perfect for testimonials
7. STORY ELEMENTS: Problem → Solution → Result narratives
8. CASE STUDY POTENTIAL: Rate each 1-10 for full case study worthiness
For each success signal, draft:
- One-line summary of the win
- Suggested follow-up message to capture full story
- Questions to ask while momentum is high
[PASTE CUSTOMER MESSAGES/SUPPORT TICKETS HERE]
Where to Hunt for Success Signals
Support tickets with "thank you" in them
Product feedback channels (Slack, Discord, forums)
Usage data spikes (10x increase = something good happened)
Renewal messages with enthusiasm
Social media mentions of your product
Quick Manual Process
Search for success keywords: "finally", "saved", "promoted", "impressed", "game-changer"
Run last 30 days through the prompt
Rank by story potential
Reach out to top 3 within 48 hours
Capture story while emotion is fresh
Option 2: The Automated Method
Before Automating:
Always get explicit permission before using customer quotes
Remove identifying information during processing
Consider your industry's testimonial regulations (FTC guidelines, etc.)
With Gumloop (35 minutes setup)
Workflow Overview: Monitor all customer touchpoints for success signals and automatically initiate story capture.
Step-by-Step Setup:
Create Success Monitor Workflow
Name: "Success Story Hunter"
Trigger: Every 3 days (or real-time with webhooks)
Add Multi-Source Collection
Add node: "Data Aggregator"
Connect: Support tickets, emails, Slack/Discord, product analytics
Keywords: Success indicator list
Also trigger on: Usage spikes, feature adoption milestones
Add Success Analysis
Add node: "Claude/GPT Analyzer"
Use the success detector prompt
Enrich with: Customer profile, industry, company size
Add Scoring Logic
Add node: "Scoring Calculator"
Factors: Emotion level + Specificity + Business impact + Customer profile
Threshold: 7+ triggers immediate action
Add Automated Outreach
Add node: "Email Automation"
High scores (9-10): Personal email from founder/CEO
Medium scores (7-8): Customer success manager outreach
Include: Specific win mentioned + light ask for details
Add Story Development
Add node: "Content Generator"
Create: LinkedIn post draft, tweet draft, case study outline
Store: CRM tagged "Success Story - Hot"
With Lindy AI (30 minutes setup)
Agent Configuration:
Create "Success Story Scout Agent"
Type: Multi-channel monitor
Purpose: Detect and capture success moments
Configure Detection Rules
Monitor: All customer channels
Triggers: Success keywords + sentiment + usage patterns
Intelligence: Understand context, not just keywords
Build Capture Framework
Immediate: Acknowledgment message
24 hours: Detailed questions while fresh
72 hours: Offer to co-create content
1 week: Follow up with draft story
Set Content Creation
Auto-generate: Multiple formats from one story
Versions: LinkedIn post, case study, testimonial, tweet
Personalization: Adjust tone for customer's industry
Why Timing Is Everything
Fresh Success vs. Stale Success
48 hours after win: "I can't believe we reduced processing time by 87%! My boss literally asked what magic I was using. I showed her your automation and she wants to roll it out company-wide!"
6 months later: "Yeah, we use your tool. It's pretty good. Saves us some time."
The emotion evaporates. The specifics blur. The story dies.
Real Examples of Caught Success
B2B SaaS Example:
Signal detected: Customer message: "HOLY SH*T IT WORKED! 6 months of manual reports automated in 2 hours!"
Automated response: "This is AMAZING! 6 months → 2 hours is incredible. Would love to hear more - what was the manual process like before?"
Result: Full case study + 3 testimonials + LinkedIn post with 50K views
E-commerce Tool Example:
Signal detected: Support ticket: "Cancel my refund request - sales up 340% after implementing your suggestions"
Automated response: "340% growth is phenomenal! What specific suggestions made the biggest impact?"
Result: Video testimonial + detailed blog post + homepage feature
The Follow-Up Formula
For Big Wins (9-10 score): "[Name], just saw your message about [specific win] - this is incredible!
[Specific metric] is the kind of result other [industry] companies dream about. Would you be open to a quick 15-minute call to share how you did it?
I'd love to help other companies replicate your success (with full credit to you, of course)."
For Medium Wins (7-8 score): "Congrats on [specific achievement]! Love seeing customers get results like this.
Quick question - what was the biggest challenge before you found our solution? Always helpful to understand the full journey."
Advanced Story Mining
The Milestone Detector Connect to product analytics: First big win usually happens at specific usage points.
The Promotion Tracker LinkedIn integration: Customer job changes often correlate with your product's impact.
The Team Win Multiplier One success often impacts whole team: "Can you introduce me to others who benefited?"
Content Multiplication Strategy
One success story becomes:
Long-form case study (website)
LinkedIn success post (tag customer)
Tweet thread (step-by-step win)
Email campaign (similar prospects)
Sales enablement (battle card update)
Product marketing (feature page proof)
ROI Impact
Conversion improvement: Case studies increase conversion 35-70%
Sales cycle reduction: Relevant success stories cut objections
Content efficiency: 1 story = 6+ pieces of content
Customer advocacy: Featured customers become vocal champions
Start immediately: Search your support system for "thank you" + "saved" + "finally". You have success stories waiting to be captured right now.

Here's what I want you to notice about today's automations:
They all mine value from data you already have. No new tools. No complex integrations.
Just intelligence extraction from existing business data.
Each automation I've shared solves a specific blind spot:
Sales calls hide objection patterns you're not addressing
Refund requests contain product fixes worth thousands
Customer messages hold success stories worth their weight in conversions
The manual versions work today. The automated versions scale tomorrow.
Pick one. Set a timer for 15 minutes. Extract insights your competitors will never see because they're too busy chasing the next shiny tool.
While they're debating AI strategy, you'll be implementing automations that directly impact revenue.
Want more issue like this one?
Then you need to join Cortex, which is open for only a day or two:
This only opens once a month, at the end of the month.
If you want in, now’s the time to sign up.
If you don’t, you’ll miss out. Simple as that.
Until next time,
Sam Woods
The Editor