Same message to everyone:
"Our comprehensive solution offers robust
features including X, Y, and Z..."

Results:
- Analytical: "Give me the data"
- Driver: "Get to the point"
- Expressive: "This is boring"
- Amiable: "Too salesy"

One message, four misses.

Same information, adapted:

Analytical: "Based on our data from 500+
implementations, the ROI averages 3.2x..."

Driver: "Bottom line: 50% faster, 30% cheaper.
Ready to see it?"

Expressive: "Imagine your team actually enjoying
their workflows for once..."

Amiable: "Let me show you how other teams like
yours have approached this..."

Dominant (D):
- Direct, results-oriented
- Wants bottom line fast
- Competitive, decisive
- Values efficiency

Influential (I):
- Enthusiastic, optimistic
- Relationship-focused
- Wants to collaborate
- Values recognition

Steady (S):
- Patient, supportive
- Team-oriented
- Wants stability
- Values security

Conscientious (C):
- Analytical, systematic
- Detail-oriented
- Wants accuracy
- Values quality

For practical bot use:

Analytical:
- Data-driven
- Wants details
- Skeptical until proven
- Long consideration

Driver:
- Action-oriented
- Wants results
- Impatient with fluff
- Quick decisions

Relationship:
- People-focused
- Wants trust first
- Values consensus
- Considers impact on others

PERSONA_INDICATORS = {
    "analytical": {
        "patterns": [
            r"what('s| is) the (data|evidence|proof)",
            r"how (exactly|specifically)",
            r"(numbers|metrics|statistics)",
            r"can you (explain|clarify)",
            r"(detail|specifics|breakdown)"
        ],
        "characteristics": {
            "avg_message_length": "long",
            "question_frequency": "high",
            "requests_documentation": True,
            "mentions_competitors": True
        }
    },
    "driver": {
        "patterns": [
            r"(bottom line|get to the point)",
            r"(quickly|fast|asap|now)",
            r"what('s| is) the (cost|price|roi)",
            r"(let's|let us) (move|proceed|start)",
            r"(results|outcome|impact)"
        ],
        "characteristics": {
            "avg_message_length": "short",
            "response_time": "fast",
            "direct_statements": True,
            "impatience_signals": True
        }
    },
    "relationship": {
        "patterns": [
            r"(my team|our team|the team)",
            r"(everyone|all of us|together)",
            r"how (do others|have others)",
            r"(trust|relationship|partnership)",
            r"(feel|comfortable|confident)"
        ],
        "characteristics": {
            "avg_message_length": "medium",
            "mentions_others": True,
            "asks_about_support": True,
            "consensus_oriented": True
        }
    }
}

def analyze_persona_signals(messages):
    signals = {
        "analytical": 0,
        "driver": 0,
        "relationship": 0
    }

    for message in messages:
        text = message.text.lower()

        # Check patterns
        for persona, config in PERSONA_INDICATORS.items():
            for pattern in config["patterns"]:
                if re.search(pattern, text):
                    signals[persona] += 10

        # Check message length
        word_count = len(message.text.split())
        if word_count > 50:
            signals["analytical"] += 5
        elif word_count < 15:
            signals["driver"] += 5

        # Check response time
        if message.response_time_minutes < 5:
            signals["driver"] += 5
        elif message.response_time_minutes > 60:
            signals["analytical"] += 3

        # Check for team mentions
        if re.search(r'\b(team|we|us|our)\b', text):
            signals["relationship"] += 5

    return signals

def classify_persona(signals):
    total = sum(signals.values())
    if total == 0:
        return {"persona": "unknown", "confidence": 0}

    scores = {p: s/total for p, s in signals.items()}
    top_persona = max(scores, key=scores.get)

    # Confidence based on margin
    sorted_scores = sorted(scores.values(), reverse=True)
    margin = sorted_scores[0] - sorted_scores[1] if len(sorted_scores) > 1 else sorted_scores[0]

    return {
        "persona": top_persona,
        "confidence": margin,
        "scores": scores
    }

def adapt_message_for_persona(base_message, persona):
    adaptations = {
        "analytical": {
            "add": ["data points", "sources", "detailed explanations"],
            "remove": ["hype", "generalizations", "assumptions"],
            "tone": "precise and thorough",
            "cta": "Let me send you the detailed documentation..."
        },
        "driver": {
            "add": ["results", "bottom line", "timeline"],
            "remove": ["excessive detail", "caveats", "long explanations"],
            "tone": "direct and efficient",
            "cta": "Want to see it in action? 15 minutes."
        },
        "relationship": {
            "add": ["social proof", "team impact", "support emphasis"],
            "remove": ["aggressive urgency", "pressure tactics"],
            "tone": "warm and collaborative",
            "cta": "Would it help to chat through how this works for your team?"
        }
    }

    config = adaptations.get(persona, adaptations["relationship"])
    return apply_adaptations(base_message, config)

PERSONA_TEMPLATES = {
    "analytical": {
        "greeting": "Thanks for your question—let me give you the specifics.",
        "value_prop": "Based on analysis of {n} implementations, customers see {metric}. Here's the breakdown: {details}",
        "objection_response": "That's a fair concern. The data shows {evidence}. Here's the methodology: {explanation}",
        "cta": "I can send you our technical documentation and case study data. Would that be helpful?"
    },
    "driver": {
        "greeting": "Got it.",
        "value_prop": "{result} in {timeframe}. {roi}.",
        "objection_response": "Fair point. Here's how we solve that: {solution}. Results: {outcome}.",
        "cta": "Free for a 15-minute demo this week?"
    },
    "relationship": {
        "greeting": "Great to hear from you! Thanks for sharing that.",
        "value_prop": "Teams like yours have found {benefit}. {customer_name} mentioned their team really appreciated {aspect}.",
        "objection_response": "I totally understand that concern—it's common. Here's how we support teams through that: {support}",
        "cta": "Would it help to connect you with someone at a similar company who's been through this?"
    }
}

def adapt_pacing_for_persona(persona, default_delay):
    pacing = {
        "analytical": {
            "response_delay": default_delay * 1.2,  # Slightly slower, thorough
            "follow_up_interval": "longer",
            "detail_level": "high",
            "question_limit": "none"
        },
        "driver": {
            "response_delay": default_delay * 0.5,  # Faster
            "follow_up_interval": "shorter",
            "detail_level": "low",
            "question_limit": 2
        },
        "relationship": {
            "response_delay": default_delay,
            "follow_up_interval": "standard",
            "detail_level": "medium",
            "question_limit": 3
        }
    }
    return pacing.get(persona, pacing["relationship"])

class PersonaClassifier:
    def __init__(self, prospect_id):
        self.prospect_id = prospect_id
        self.signals = {"analytical": 0, "driver": 0, "relationship": 0}
        self.message_count = 0
        self.classification = None
        self.confidence = 0

    def update(self, message):
        self.message_count += 1

        # Analyze new message
        new_signals = analyze_persona_signals([message])

        # Update cumulative signals
        for persona, score in new_signals.items():
            self.signals[persona] += score

        # Reclassify
        result = classify_persona(self.signals)
        self.classification = result["persona"]
        self.confidence = result["confidence"]

        # Check if confident enough to adapt
        if self.confidence >= 0.3 and self.message_count >= 3:
            return {
                "persona": self.classification,
                "ready": True,
                "confidence": self.confidence
            }
        else:
            return {
                "persona": "unknown",
                "ready": False,
                "confidence": self.confidence
            }

def generate_response(conversation, intent):
    prospect = conversation.prospect

    # Get current persona classification
    classifier = get_persona_classifier(prospect.id)
    persona_result = classifier.classification

    # Generate base response
    base_response = generate_base_response(intent)

    # Adapt if confident
    if persona_result["ready"]:
        adapted = adapt_message_for_persona(
            base_response,
            persona_result["persona"]
        )
        return adapted
    else:
        # Use default/neutral style
        return base_response

Track:
- Classification stability (does it change?)
- Confidence levels achieved
- Conversion rate by persona
- Response rate by persona

Validate:
- Survey customers post-sale
- Compare to rep assessments
- Check against known personalities

A/B test:
- Adapted vs non-adapted messages
- By persona type
- By stage

Measure:
- Engagement rate
- Response sentiment
- Conversion rate