ConvoYield — Conversational Yield Optimization Engine

> "Every conversation is a financial instrument. ConvoYield tells you what it's worth."

What It Does

ConvoYield gives any bot a real-time revenue intelligence layer. On every user

message, five engines run in parallel and produce:

• Sentiment Arbitrage — Detects emotional gaps that create revenue opportunities

(frustration capture, competitor displacement, excitement amplification, etc.)

• Micro-Conversion Tracking — Finds 12 types of hidden value in every message

(email captures, budget reveals, pain points, referral signals, etc.)

• Momentum Scoring — Measures whether the conversation is gaining or losing steam
• Yield Forecasting — Predicts the total dollar value of the conversation in real-time
• Play Calling — Recommends from a 20-play behavioral economics playbook

(anchoring, loss framing, social proof, empathy bridges, urgency closes, etc.)

Zero Cost Guarantee

• Zero external dependencies — Pure Python standard library
• Zero API calls — All analysis runs locally via pattern matching and heuristics
• Zero tokens consumed — Does not call any LLM API
• Zero infrastructure — pip install and go
• <1ms per message — Adds no latency to your bot

Quick Start

from convoyield import ConvoYield

engine = ConvoYield(base_conversation_value=50.0)

# Process each user message
result = engine.process_user_message("I'm frustrated with Salesforce, it's way too expensive")

print(result.recommended_play)       # "competitor_displacement"
print(result.estimated_yield)         # 89.50
print(result.recommended_tone)        # "empathetic"
print(result.top_arbitrage.type)      # "frustration_capture"
print(result.risk_level)              # 0.21

# Record bot response for full state tracking
engine.record_bot_response("I hear you. What specifically isn't working?")

# Next message — yield COMPOUNDS
result = engine.process_user_message("The reporting is terrible and costs $500/month")
print(result.estimated_yield)         # 142.30 — value is growing!

The Five Engines

1. Sentiment Arbitrage Engine

Detects 7 arbitrage patterns via lexicon-based sentiment scoring tuned for

commercial conversations:

| Pattern | What It Detects | Value Signal |

|---------|----------------|--------------|

| competitor_displacement | Frustration with a named competitor | $45+ |

| frustration_capture | General frustration with current solution | $35+ |

| excitement_amplification | User showing enthusiasm | $25+ |

| uncertainty_anchoring | User unsure, needs guidance | $20+ |

| urgency_premium | Time pressure detected | $30+ |

| social_proof_hunger | User seeking validation | $15+ |

| budget_value_stack | User discussing budget/cost | $40+ |

2. Micro-Conversion Tracker

Detects 12 micro-conversion opportunities between "hello" and "purchase":

• Email/phone capture opportunities
• Budget and timeline reveals
• Team size and need statements
• Competitor mentions and feature requests
• Referral and testimonial signals
• Pain point articulations

Each micro-conversion has an estimated dollar value ($0.50-$15).

3. Momentum Scorer

Scores engagement momentum (-1.0 to +1.0) using four signals:

• Message length trend (expanding = engaged)
• Question frequency trend (asking = curious)
• Emotional intensity trend (feeling = invested)
• Vocabulary richness trend (elaborating = committed)

Labels: surging | accelerating | stable | declining | hemorrhaging

4. Yield Forecaster

Combines all signals to predict a dollar value for the conversation using:

• Phase multipliers (OPENING → DISCOVERY → ENGAGEMENT → NEGOTIATION → CLOSING)
• Micro-conversion portfolio value
• Arbitrage opportunity value
• Engagement and momentum premiums
• Risk assessment (0.0-1.0)

5. Play Caller

Recommends from 20 plays inspired by behavioral economics:

warm_handshake · pattern_interrupt · deep_probe · empathy_bridge ·

value_stack · competitor_displacement · social_proof_deploy ·

dopamine_ride · anchoring · loss_framing · budget_reframe ·

choice_architecture · assumptive_close · urgency_close · soft_close ·

momentum_recovery · save_attempt · upsell_bridge · referral_harvest ·

objection_reframe

Integration

Works with any bot framework — hook into your message handler:

from convoyield import ConvoYield

engine = ConvoYield()

def on_user_message(text, conversation_id):
    result = engine.process_user_message(text)

    # Shape your bot's response using:
    # result.recommended_play     → WHAT strategy to use
    # result.recommended_tone     → HOW to say it
    # result.arbitrage_opportunities → WHERE the money is
    # result.micro_conversions    → WHAT value to capture
    # result.risk_level           → HOW careful to be
    # result.estimated_yield      → HOW much is at stake

    return generate_response(text, result)

Premium Playbooks

Four industry-specific playbook packs available:

| Playbook | Plays | Price |

|----------|-------|-------|

| SaaS Sales Mastery | 25 | $49/mo |

| E-Commerce Revenue Max | 22 | $39/mo |

| Real Estate Closer | 20 | $79/mo |

| Healthcare Engagement | 18 | $99/mo |

Revenue Model

ConvoYield is free and open source. Revenue comes from:

1. Premium playbooks — Industry-specific play packs ($39-99/mo)
2. Cloud analytics — Dashboard and yield tracking ($0/49/299/mo tiers)
3. Enterprise — Custom playbooks, webhooks, white-label ($299/mo)

Architecture

convoyield/
├── orchestrator.py              # Main ConvoYield engine
├── engines/
│   ├── sentiment_arbitrage.py   # 7 arbitrage pattern detectors
│   ├── micro_conversion.py      # 12 micro-conversion trackers
│   ├── momentum.py              # 4-signal engagement scorer
│   ├── yield_forecaster.py      # Dollar-value yield prediction
│   └── play_caller.py           # 20-play behavioral economics playbook
├── models/
│   ├── conversation.py          # ConversationState, Turn, Phase
│   └── yield_result.py          # YieldResult, ArbitrageOpportunity
├── playbooks/                   # 4 premium industry packs (85 plays)
├── coin/                        # ConvoCoin — Proof-of-Yield blockchain
└── cloud/                       # Telemetry client for analytics

Tests

40 tests across 7 suites — all passing:

python -m pytest tests/ -v