custom-field-population

カスタム項目の自動入力

あなたは、会話データから CRM の項目を自動更新するセールスボットの構築に精通しています。自然言語での会話から項目を抽出して入力し、手動でのデータ入力をなくすシステムを開発者が作れるよう支援することが目標です。

自動化が重要な理由

手動入力の問題

After every conversation:
- Rep opens CRM
- Types notes
- Updates 10+ fields
- Often forgets or skips
- Data quality suffers

Time wasted: 30-60 minutes/day
Data accuracy: 50-70%

自動入力を導入した場合

During/after conversation:
- Bot extracts key data
- Fields update automatically
- Rep reviews/confirms
- Data always current

Time saved: 25-50 minutes/day
Data accuracy: 90%+

項目の抽出

連絡先情報

Extract from conversation:

"My email is [email protected]"
→ email: [email protected]

"Best number is 415-555-1234"
→ phone: +14155551234

"I'm the VP of Marketing"
→ title: VP of Marketing

"We're based in San Francisco"
→ city: San Francisco, state: CA

"We have about 500 employees"
→ company_size: 500

Pattern matching + NER for extraction.

適格性評価データ（BANT）

Budget extraction:
"We've allocated around $50k for this"
→ budget: 50000, budget_currency: USD

"Budget isn't approved yet"
→ budget_status: not_approved

Authority extraction:
"I make the final call on this"
→ decision_maker: true

"I'll need to run this by my CEO"
→ decision_maker: false, economic_buyer: CEO

Need extraction:
"Our main problem is lead conversion"
→ primary_need: lead_conversion

Timeline extraction:
"We want to implement by Q2"
→ target_timeline: Q2, urgency: medium

"This is urgent, need it next month"
→ target_timeline: next_month, urgency: high

商談データ

Product interest:
"We're looking at your enterprise plan"
→ product_interest: enterprise

"Specifically interested in analytics"
→ features_of_interest: [analytics]

Deal size signals:
"We'd need licenses for 100 users"
→ estimated_users: 100
→ estimated_deal_size: 100 * price_per_user

Stage indicators:
"We're just researching right now"
→ stage: research

"Ready to see a proposal"
→ stage: proposal_requested

エンゲージメントデータ

Preferences:
"Email works best for me"
→ preferred_channel: email

"I'm usually free mornings"
→ preferred_time: morning

"Don't call my cell"
→ contact_preference: no_cell_calls

Relationships:
"Talk to Sarah, she handles vendor selection"
→ additional_contact: Sarah, role: vendor_selection

"Our IT team will need to review"
→ stakeholders: [IT team]

抽出の実装

ルールベースの抽出

EXTRACTION_RULES = {
    "email": {
        "patterns": [
            r"[\w\.-]+@[\w\.-]+\.\w+",
            r"email is ([\w\.-]+@[\w\.-]+\.\w+)",
            r"reach me at ([\w\.-]+@[\w\.-]+\.\w+)"
        ],
        "field": "email",
        "validation": validate_email
    },
    "phone": {
        "patterns": [
            r"\+?1?[-.\s]?\(?[0-9]{3}\)?[-.\s]?[0-9]{3}[-.\s]?[0-9]{4}",
            r"call me at ([\d\s\-\(\)]+)",
            r"number is ([\d\s\-\(\)]+)"
        ],
        "field": "phone",
        "validation": validate_phone,
        "transform": normalize_phone
    },
    "company_size": {
        "patterns": [
            r"(\d+)\s*employees",
            r"team of (\d+)",
            r"about (\d+) people",
            r"(\d+)-person company"
        ],
        "field": "company_size",
        "transform": int
    },
    "budget": {
        "patterns": [
            r"\$(\d+[,\d]*)\s*(k|K|thousand|million|M)?",
            r"budget.*?(\d+[,\d]*)\s*(k|K|thousand|million|M)?",
            r"allocated.*?(\d+[,\d]*)\s*(k|K|thousand|million|M)?"
        ],
        "field": "budget",
        "transform": parse_currency
    }
}

def extract_field(text, field_config):
    for pattern in field_config["patterns"]:
        match = re.search(pattern, text, re.IGNORECASE)
        if match:
            value = match.group(1) if match.groups() else match.group()
            if "transform" in field_config:
                value = field_config["transform"](value)
            if "validation" in field_config:
                if not field_config["validation"](value):
                    continue
            return value
    return None

NLP ベースの抽出

def extract_with_nlp(text, field_type):
    # Use NER for entity extraction
    entities = ner_model.extract(text)

    # Use intent classification for context
    intent = classify_intent(text)

    # Combine for extraction
    if field_type == "budget":
        money_entities = [e for e in entities if e.type == "MONEY"]
        if money_entities and intent in ["budget_discussion", "pricing"]:
            return money_entities[0].value

    if field_type == "timeline":
        date_entities = [e for e in entities if e.type == "DATE"]
        if date_entities and intent in ["timeline_discussion", "urgency"]:
            return parse_timeline(date_entities[0].value)

    return None

LLM ベースの抽出

def extract_with_llm(conversation, fields_needed):
    prompt = f"""
    Extract the following information from this conversation:
    {fields_needed}

    Conversation:
    {conversation}

    Return as JSON with extracted values. Use null for any
    fields not mentioned. Include confidence score (0-1).
    """

    response = llm.generate(prompt)
    extracted = json.loads(response)

    return extracted

# Example output:
{
    "budget": {"value": 50000, "confidence": 0.9},
    "timeline": {"value": "Q2 2024", "confidence": 0.85},
    "decision_maker": {"value": false, "confidence": 0.7},
    "company_size": {"value": 500, "confidence": 0.95}
}

CRM 連携

項目のマッピング

CRM_FIELD_MAPPING = {
    # Contact fields
    "email": "Contact.Email",
    "phone": "Contact.Phone",
    "title": "Contact.Title",
    "preferred_channel": "Contact.Preferred_Channel__c",

    # Account fields
    "company_size": "Account.NumberOfEmployees",
    "industry": "Account.Industry",
    "city": "Account.BillingCity",
    "state": "Account.BillingState",

    # Opportunity fields
    "budget": "Opportunity.Amount",
    "target_timeline": "Opportunity.CloseDate",
    "stage": "Opportunity.StageName",
    "product_interest": "Opportunity.Product_Interest__c",

    # Custom fields
    "primary_need": "Opportunity.Primary_Pain_Point__c",
    "decision_maker": "Contact.Is_Decision_Maker__c",
    "urgency": "Opportunity.Urgency__c"
}

更新ロジック

def update_crm(extracted_data, record_id, crm_client):
    updates = {}

    for field, value_data in extracted_data.items():
        # Check confidence threshold
        if value_data["confidence"] < 0.7:
            continue  # Skip low-confidence extractions

        # Get CRM field name
        crm_field = CRM_FIELD_MAPPING.get(field)
        if not crm_field:
            continue

        # Check if update is appropriate
        current_value = crm_client.get_field(record_id, crm_field)
        if should_update(current_value, value_data["value"]):
            updates[crm_field] = value_data["value"]

    # Apply updates
    if updates:
        crm_client.update(record_id, updates)
        log_updates(record_id, updates, extracted_data)

    return updates

def should_update(current, new):
    # Don't overwrite with null
    if new is None:
        return False

    # Don't overwrite non-null with different value (require confirmation)
    if current is not None and current != new:
        return False  # Or queue for review

    return True

バッチ処理

def process_conversation_for_crm(conversation):
    # Extract all fields
    extracted = {}

    for message in conversation.messages:
        if message.sender == "prospect":
            message_extractions = extract_all_fields(message.text)
            for field, data in message_extractions.items():
                # Keep highest confidence extraction
                if field not in extracted or data["confidence"] > extracted[field]["confidence"]:
                    extracted[field] = data

    # Validate and clean
    validated = validate_extractions(extracted)

    # Update CRM
    updates = update_crm(
        validated,
        conversation.prospect_id,
        crm_client
    )

    return updates

人による確認

確認キュー

When to queue for review:
- Low confidence (<70%)
- Conflicting extractions
- Significant value change
- Critical fields (budget, close date)

Review interface:
"Extracted: Budget = $50,000 (85% confidence)
From: 'We've set aside around $50k for this project'
Current value: [blank]
[Approve] [Edit] [Reject]"

信頼度のしきい値

CONFIDENCE_THRESHOLDS = {
    # Auto-update
    "high": {
        "threshold": 0.9,
        "action": "auto_update"
    },
    # Update but notify
    "medium": {
        "threshold": 0.7,
        "action": "update_and_notify"
    },
    # Queue for review
    "low": {
        "threshold": 0.5,
        "action": "queue_for_review"
    },
    # Discard
    "very_low": {
        "threshold": 0.0,
        "action": "discard"
    }
}

フィードバックループ

def record_feedback(extraction_id, approved, corrected_value=None):
    # Log the feedback
    feedback = {
        "extraction_id": extraction_id,
        "approved": approved,
        "corrected_value": corrected_value,
        "timestamp": datetime.now()
    }

    # Use for model improvement
    if not approved:
        add_to_training_data(
            original=get_extraction(extraction_id),
            corrected=corrected_value
        )

    # Update extraction rules if pattern failing
    if not approved:
        increment_failure_count(extraction_id)
        if should_disable_rule(extraction_id):
            disable_extraction_rule(extraction_id)

項目ごとの処理

日付／タイムライン項目

def parse_timeline(text):
    # Relative dates
    relative_patterns = {
        r"next month": lambda: today() + timedelta(days=30),
        r"next quarter|Q[1-4]": parse_quarter,
        r"end of year": lambda: date(today().year, 12, 31),
        r"(\d+) (days|weeks|months)": parse_relative_duration
    }

    for pattern, handler in relative_patterns.items():
        if re.search(pattern, text, re.IGNORECASE):
            return handler()

    # Absolute dates
    parsed = dateparser.parse(text)
    if parsed:
        return parsed.date()

    return None

通貨項目

def parse_currency(text):
    # Remove formatting
    cleaned = re.sub(r'[,$]', '', text)

    # Handle multipliers
    multipliers = {
        'k': 1000, 'K': 1000, 'thousand': 1000,
        'm': 1000000, 'M': 1000000, 'million': 1000000
    }

    for suffix, multiplier in multipliers.items():
        if suffix in text:
            cleaned = re.sub(r'[a-zA-Z]', '', cleaned)
            return float(cleaned) * multiplier

    return float(cleaned)

選択リスト項目

def map_to_picklist(extracted_value, picklist_options):
    # Exact match
    if extracted_value in picklist_options:
        return extracted_value

    # Fuzzy match
    best_match = None
    best_score = 0

    for option in picklist_options:
        score = fuzzy_match(extracted_value.lower(), option.lower())
        if score > best_score and score > 0.8:
            best_match = option
            best_score = score

    return best_match  # May be None

指標

抽出品質

Track:
- Extraction accuracy (vs human verification)
- Confidence calibration
- Field coverage (% of fields populated)
- False positive rate
- Human override rate

Improve:
- Rules with low accuracy
- Fields with low coverage
- High override rate patterns

時間の削減

Measure:
- Data entry time before automation
- Data entry time after automation
- Data quality improvement
- Rep satisfaction

"Automated field population saves reps
35 minutes/day on average."