Sales Target Documentation¶

Overview¶

The Sales Target module (sale.target) provides sales goal-setting and performance tracking capabilities for salespeople and teams. It enables management to set realistic, measurable targets based on territory, product category, and time period, then track actual achievement through sales opportunities. This module is essential for performance management, commission calculations, and sales team motivation.

Model Information¶

Model Name: sale.target Display Name: Sales Target Key Fields: None (allows multiple targets per combination)

Features¶

❌ Audit logging enabled (_audit_log)
❌ Multi-company support (company_id)
❌ Full-text content search (_content_search)
✅ Performance tracking through sale.opportunity integration
✅ Both amount and quantity targets
✅ Expected vs actual achievement calculation

Target Setting Framework¶

Sales targets follow SMART goal principles:

Principle	Implementation
Specific	Define by salesperson, product category, and tracking category
Measurable	Track both amount (revenue) and quantity (units) targets
Achievable	Based on historical performance and market conditions
Relevant	Aligned with business objectives and sales strategy
Time-bound	Set clear date ranges (daily, monthly, quarterly, annually)

Target Types¶

Type	Description	Use Case
Amount Target	Revenue/sales amount goal	Overall sales performance, commission tiers
Quantity Target	Unit/volume goal	Product-focused targets, volume incentives
Combined	Both amount and quantity	Comprehensive performance management
User-Specific	Assigned to individual salesperson	Individual accountability
Category-Specific	By product category	Product line focus
Tracking-Specific	By account tracking category	Territory or segment focus

Key Fields Reference¶

Target Definition Fields¶

Field	Type	Required	Description
`user_id`	Many2One	❌	Salesperson/user assigned this target (base.user)
`prod_categ_id`	Many2One	❌	Product category for target segmentation (product.categ)
`track_id`	Many2One	❌	Accounting tracking category for territory/segment
`date_from`	Date	✅	Start date of target period
`date_to`	Date	✅	End date of target period

Amount Target Fields¶

Field	Type	Description
`amount_target`	Decimal	Target revenue/sales amount for the period
`amount_actual`	Decimal	Actual achieved revenue from won opportunities (computed)
`amount_expected`	Decimal	Expected revenue from open opportunities (computed)

Quantity Target Fields¶

Field	Type	Description
`qty_target`	Decimal	Target quantity/volume for the period
`qty_actual`	Decimal	Actual achieved quantity from won opportunities (computed)
`qty_expected`	Decimal	Expected quantity from open opportunities (computed)

Relationship Fields¶

Field	Type	Description
`comments`	One2Many	User comments and notes (message model)

API Methods¶

1. Create Record¶

Method: create(vals, context)

Creates a new sales target for a specific period and optional filters.

Parameters:

vals = {
    "user_id": 5,                      # Optional: Specific salesperson
    "prod_categ_id": 10,               # Optional: Product category
    "track_id": 3,                     # Optional: Tracking category
    "date_from": "2026-01-01",         # Required: Period start
    "date_to": "2026-03-31",           # Required: Period end
    "amount_target": 500000,           # Optional: Revenue target
    "qty_target": 10000                # Optional: Quantity target
}

context = {
    "company_id": 1                    # Current company context
}

Returns: int - New target ID

Example:

# Create quarterly target for salesperson
target_id = get_model("sale.target").create({
    "user_id": 5,
    "date_from": "2026-01-01",
    "date_to": "2026-03-31",
    "amount_target": 500000,
    "qty_target": 10000
})

2. Get Achievement Amounts¶

Method: get_amount(ids, context)

Computed field function that calculates actual and expected achievement from sales opportunities.

Behavior: - Queries sale.opportunity for won and open opportunities - Filters by date_close within target period - Optionally filters by user_id if specified - Optionally filters by product category if specified - For won opportunities: sums full amount and quantity - For open opportunities: sums probability-weighted amounts (amount * probability / 100) - Returns multi-field computed values

Returns: dict - Achievement metrics:

{
    target_id: {
        "amount_actual": Decimal,      # Total from won opportunities
        "amount_expected": Decimal,    # Probability-weighted from open opportunities
        "qty_actual": Decimal,         # Total quantity from won
        "qty_expected": Decimal        # Probability-weighted quantity from open
    }
}

Example:

# Automatically calculated when browsing target
target = get_model("sale.target").browse(target_id)
print(f"Target: ${target.amount_target}")
print(f"Actual: ${target.amount_actual}")
print(f"Expected: ${target.amount_expected}")
achievement_pct = (target.amount_actual / target.amount_target * 100) if target.amount_target else 0
print(f"Achievement: {achievement_pct:.1f}%")

3. Generate Daily Targets¶

Method: gen_targets(context)

Utility method to automatically generate daily targets for the current month.

Behavior: - Calculates first day and last day of current month - Iterates through each day in the month - Creates target record for each day if it doesn't already exist - Uses default date_from and date_to values

Example:

# Generate daily targets for current month
get_model("sale.target").gen_targets()
# Creates target records for each day of the month

Search Functions¶

Search by Salesperson¶

# Find all targets for a specific salesperson
condition = [["user_id", "=", 5]]

Search by Period¶

# Find targets for specific period
condition = [
    ["date_from", ">=", "2026-01-01"],
    ["date_to", "<=", "2026-12-31"]
]

Search by Product Category¶

# Find targets for specific product category
condition = [["prod_categ_id", "=", 10]]

Search by Tracking Category¶

# Find targets by territory/segment
condition = [["track_id", "=", 3]]

Search Active Targets¶

# Find targets for current period
from datetime import date
today = date.today().strftime("%Y-%m-%d")
condition = [
    ["date_from", "<=", today],
    ["date_to", ">=", today]
]

Computed Fields Functions¶

get_amount(ids, context)¶

Calculates actual achievement from won opportunities and expected achievement from open opportunities (probability-weighted). Returns both amount and quantity metrics for comprehensive performance tracking.

Best Practices¶

1. SMART Goal Setting¶

Follow SMART principles when creating targets:

# Bad: Vague, unrealistic target
target = {
    "date_from": "2026-01-01",
    "date_to": "2026-12-31",
    "amount_target": 10000000  # $10M for entire year, no breakdown
}

# Good: Specific, measurable, time-bound
q1_target = {
    "user_id": 5,                    # Specific person
    "prod_categ_id": 10,             # Specific product line
    "date_from": "2026-01-01",       # Clear timeframe
    "date_to": "2026-03-31",
    "amount_target": 500000,         # Realistic quarterly target
    "qty_target": 10000              # Volume component
}

Why: SMART goals are more achievable and provide better performance measurement.

2. Hierarchical Target Structure¶

Create targets at multiple levels for comprehensive tracking:

# Level 1: Overall company target
company_target = get_model("sale.target").create({
    "date_from": "2026-01-01",
    "date_to": "2026-03-31",
    "amount_target": 2000000  # Total company Q1 target
})

# Level 2: Product category targets
electronics_target = get_model("sale.target").create({
    "prod_categ_id": 10,             # Electronics
    "date_from": "2026-01-01",
    "date_to": "2026-03-31",
    "amount_target": 800000
})

furniture_target = get_model("sale.target").create({
    "prod_categ_id": 15,             # Furniture
    "date_from": "2026-01-01",
    "date_to": "2026-03-31",
    "amount_target": 600000
})

# Level 3: Individual salesperson targets
sales_rep_1 = get_model("sale.target").create({
    "user_id": 5,
    "prod_categ_id": 10,
    "date_from": "2026-01-01",
    "date_to": "2026-03-31",
    "amount_target": 400000
})

Why: Hierarchical targets enable drill-down analysis and ensure alignment across organization.

3. Regular Performance Reviews¶

Monitor target achievement throughout the period:

# Weekly performance review
targets = get_model("sale.target").search_browse([
    ["date_from", ">=", "2026-01-01"],
    ["date_to", "<=", "2026-03-31"]
])

for target in targets:
    # Calculate achievement
    if target.amount_target:
        actual_pct = (target.amount_actual / target.amount_target * 100)
        pipeline_pct = (target.amount_expected / target.amount_target * 100)
        total_pct = ((target.amount_actual + target.amount_expected) / target.amount_target * 100)

        user_name = target.user_id.name if target.user_id else "All Users"
        category = target.prod_categ_id.name if target.prod_categ_id else "All Products"

        print(f"\n{user_name} - {category}")
        print(f"  Target: ${target.amount_target:,.0f}")
        print(f"  Actual: ${target.amount_actual:,.0f} ({actual_pct:.1f}%)")
        print(f"  Pipeline: ${target.amount_expected:,.0f} ({pipeline_pct:.1f}%)")
        print(f"  Total Expected: {total_pct:.1f}%")

        # Alert if significantly off track
        if total_pct < 70:
            print(f"  ⚠️ WARNING: Below target!")

Why: Regular reviews enable early intervention and course correction.

4. Historical Baseline¶

Use previous period performance to set realistic targets:

# Calculate historical average for Q4
historical_targets = get_model("sale.target").search_browse([
    ["user_id", "=", 5],
    ["date_from", ">=", "2023-10-01"],
    ["date_to", "<=", "2025-12-31"]  # Last 2+ years of Q4
], order="date_from")

# Calculate average achievement
total_achievement = sum(t.amount_actual for t in historical_targets)
avg_achievement = total_achievement / len(historical_targets) if historical_targets else 0

# Set new target with 10% growth
new_target = get_model("sale.target").create({
    "user_id": 5,
    "date_from": "2026-10-01",
    "date_to": "2026-12-31",
    "amount_target": avg_achievement * 1.10  # 10% growth
})

print(f"Historical Q4 average: ${avg_achievement:,.0f}")
print(f"New Q4 target: ${new_target.amount_target:,.0f}")

Why: Data-driven target setting improves accuracy and fairness.

5. Commission Integration¶

Link targets to commission structures:

# Define tiered commission structure
def calculate_commission(target):
    if not target.amount_target or target.amount_target == 0:
        return 0

    achievement_pct = (target.amount_actual / target.amount_target) * 100
    actual_amount = target.amount_actual

    # Tiered commission rates
    if achievement_pct < 80:
        rate = 0.03  # 3% base
    elif achievement_pct < 100:
        rate = 0.05  # 5% for 80-100%
    elif achievement_pct < 120:
        rate = 0.07  # 7% for 100-120%
    else:
        rate = 0.10  # 10% for >120%

    commission = actual_amount * rate
    return commission

# Calculate commissions for all salespeople
targets = get_model("sale.target").search_browse([
    ["date_from", ">=", "2026-01-01"],
    ["date_to", "<=", "2026-03-31"],
    ["user_id", "!=", None]
])

for target in targets:
    commission = calculate_commission(target)
    print(f"{target.user_id.name}: ${commission:,.2f}")

Why: Clear target-commission linkage motivates sales team.

Database Constraints¶

Default Values¶

_defaults = {
    "date_from": lambda *a: date.today().strftime("%Y-%m-01"),  # First of month
    "date_to": lambda *a: (date.today() + relativedelta(day=31)).strftime("%Y-%m-%d")  # Last of month
}

New targets default to the current month period.

Model	Relationship	Description
`base.user`	Many2One	Salesperson assigned to target
`product.categ`	Many2One	Product category for segmented targets
`account.track.categ`	Many2One	Tracking category for territory/segment
`sale.opportunity`	Computed	Source of actual and expected achievement data
`message`	One2Many	Comments and notes

Common Use Cases¶

Use Case 1: Set Quarterly Sales Targets for Team¶

# Define Q1 2026 targets for sales team

# Sales team roster
sales_team = [
    {"user_id": 5, "name": "John Smith", "target": 500000},
    {"user_id": 8, "name": "Jane Doe", "target": 450000},
    {"user_id": 12, "name": "Bob Wilson", "target": 400000}
]

# Create targets
for rep in sales_team:
    target_id = get_model("sale.target").create({
        "user_id": rep["user_id"],
        "date_from": "2026-01-01",
        "date_to": "2026-03-31",
        "amount_target": rep["target"]
    })
    print(f"Created Q1 target for {rep['name']}: ${rep['target']:,}")

# Total team target
total_target = sum(rep["target"] for rep in sales_team)
print(f"\nTotal team target: ${total_target:,}")

Use Case 2: Performance Dashboard¶

# Build comprehensive performance dashboard

# Get all active targets
targets = get_model("sale.target").search_browse([
    ["date_from", ">=", "2026-01-01"],
    ["date_to", "<=", "2026-03-31"]
], order="user_id,prod_categ_id")

print("SALES PERFORMANCE DASHBOARD - Q1 2026")
print("=" * 80)

# Group by salesperson
from collections import defaultdict
by_user = defaultdict(list)

for target in targets:
    user_key = target.user_id.name if target.user_id else "Company-Wide"
    by_user[user_key].append(target)

# Display by user
for user_name, user_targets in sorted(by_user.items()):
    print(f"\n{user_name}")
    print("-" * 80)

    total_target = sum(t.amount_target or 0 for t in user_targets)
    total_actual = sum(t.amount_actual or 0 for t in user_targets)
    total_expected = sum(t.amount_expected or 0 for t in user_targets)

    for target in user_targets:
        category = target.prod_categ_id.name if target.prod_categ_id else "All Categories"

        if target.amount_target:
            achievement = (target.amount_actual / target.amount_target) * 100
            print(f"  {category:30} Target: ${target.amount_target:>12,.0f}  "
                  f"Actual: ${target.amount_actual:>12,.0f}  "
                  f"({achievement:>5.1f}%)")

    if total_target:
        overall_achievement = (total_actual / total_target) * 100
        pipeline_achievement = ((total_actual + total_expected) / total_target) * 100

        print(f"\n  {'TOTAL':30} Target: ${total_target:>12,.0f}  "
              f"Actual: ${total_actual:>12,.0f}  "
              f"({overall_achievement:>5.1f}%)")
        print(f"  {'PIPELINE':30}                           "
              f"${total_expected:>12,.0f}  "
              f"({pipeline_achievement:>5.1f}%)")

Use Case 3: Territory-Based Targets¶

# Set targets by sales territory using tracking categories

# Define territories
territories = [
    {"track_id": 1, "name": "North Region", "target": 1000000},
    {"track_id": 2, "name": "South Region", "target": 800000},
    {"track_id": 3, "name": "East Region", "target": 900000},
    {"track_id": 4, "name": "West Region", "target": 700000}
]

# Create territory targets
for territory in territories:
    target_id = get_model("sale.target").create({
        "track_id": territory["track_id"],
        "date_from": "2026-01-01",
        "date_to": "2026-12-31",
        "amount_target": territory["target"]
    })
    print(f"Created annual target for {territory['name']}: ${territory['target']:,}")

# Later: Review territory performance
territory_targets = get_model("sale.target").search_browse([
    ["track_id", "!=", None],
    ["date_from", "=", "2026-01-01"]
])

print("\nTERRITORY PERFORMANCE")
for target in territory_targets:
    achievement_pct = (target.amount_actual / target.amount_target * 100) if target.amount_target else 0
    print(f"{target.track_id.name}: {achievement_pct:.1f}% of target")

Use Case 4: Product Line Targets¶

# Set targets by product category

# Define product line targets for Q1
product_targets = [
    {"categ_id": 10, "name": "Electronics", "amount": 800000, "qty": 5000},
    {"categ_id": 15, "name": "Furniture", "amount": 600000, "qty": 2000},
    {"categ_id": 20, "name": "Office Supplies", "amount": 400000, "qty": 10000}
]

# Create category targets
for product in product_targets:
    target_id = get_model("sale.target").create({
        "prod_categ_id": product["categ_id"],
        "date_from": "2026-01-01",
        "date_to": "2026-03-31",
        "amount_target": product["amount"],
        "qty_target": product["qty"]
    })
    print(f"Created Q1 target for {product['name']}:")
    print(f"  Amount: ${product['amount']:,}")
    print(f"  Quantity: {product['qty']:,} units")

# Review product line performance
category_targets = get_model("sale.target").search_browse([
    ["prod_categ_id", "!=", None],
    ["date_from", "=", "2026-01-01"],
    ["date_to", "=", "2026-03-31"]
])

print("\nPRODUCT LINE PERFORMANCE")
for target in category_targets:
    if target.amount_target:
        amount_achievement = (target.amount_actual / target.amount_target * 100)
        print(f"\n{target.prod_categ_id.name}:")
        print(f"  Amount: {amount_achievement:.1f}% of ${target.amount_target:,} target")

    if target.qty_target:
        qty_achievement = (target.qty_actual / target.qty_target * 100)
        print(f"  Quantity: {qty_achievement:.1f}% of {target.qty_target:,} unit target")

Use Case 5: Commission Calculation¶

# Calculate sales commissions based on target achievement

def calculate_tiered_commission(target):
    """Calculate commission with tiered rates based on achievement"""
    if not target.amount_target or target.amount_target == 0:
        return {
            "amount": 0,
            "rate": 0,
            "tier": "None",
            "achievement_pct": 0
        }

    achievement_pct = (target.amount_actual / target.amount_target) * 100
    actual_amount = target.amount_actual

    # Define commission tiers
    if achievement_pct >= 120:
        rate = 0.10
        tier = "Platinum (≥120%)"
    elif achievement_pct >= 100:
        rate = 0.07
        tier = "Gold (100-119%)"
    elif achievement_pct >= 80:
        rate = 0.05
        tier = "Silver (80-99%)"
    else:
        rate = 0.03
        tier = "Bronze (<80%)"

    commission = actual_amount * rate

    return {
        "amount": commission,
        "rate": rate,
        "tier": tier,
        "achievement_pct": achievement_pct
    }

# Get Q1 targets for all salespeople
targets = get_model("sale.target").search_browse([
    ["date_from", "=", "2026-01-01"],
    ["date_to", "=", "2026-03-31"],
    ["user_id", "!=", None]
], order="user_id")

print("SALES COMMISSION REPORT - Q1 2026")
print("=" * 100)
print(f"{'Salesperson':<20} {'Target':>15} {'Actual':>15} {'Achievement':>12} "
      f"{'Tier':<20} {'Commission':>15}")
print("-" * 100)

total_commission = 0
for target in targets:
    comm_data = calculate_tiered_commission(target)

    print(f"{target.user_id.name:<20} "
          f"${target.amount_target:>14,.0f} "
          f"${target.amount_actual:>14,.0f} "
          f"{comm_data['achievement_pct']:>11.1f}% "
          f"{comm_data['tier']:<20} "
          f"${comm_data['amount']:>14,.2f}")

    total_commission += comm_data["amount"]

print("-" * 100)
print(f"{'TOTAL COMMISSIONS':>77} ${total_commission:>14,.2f}")

Performance Tips¶

1. Efficient Target Queries¶

# Bad: Multiple separate queries
for user_id in user_ids:
    targets = get_model("sale.target").search_browse([
        ["user_id", "=", user_id],
        ["date_from", ">=", "2026-01-01"]
    ])

# Good: Single query with all user IDs
targets = get_model("sale.target").search_browse([
    ["user_id", "in", user_ids],
    ["date_from", ">=", "2026-01-01"]
], order="user_id")

2. Minimize Computed Field Access¶

# Bad: Multiple accesses trigger multiple queries
for target in targets:
    if target.amount_actual > 0:        # Query 1
        pct = target.amount_actual / target.amount_target  # Query 2

# Good: Access once, cache result
for target in targets:
    actual = target.amount_actual       # Single query
    if actual > 0:
        pct = actual / target.amount_target

3. Use Date Indexing¶

The model is ordered by date_from,user_id,prod_categ_id for efficient date-range queries:

# Efficient: Uses indexed date field
current_targets = get_model("sale.target").search_browse([
    ["date_from", ">=", "2026-01-01"],
    ["date_to", "<=", "2026-12-31"]
])

Troubleshooting¶

"amount_actual showing zero despite won opportunities"¶

Cause: Opportunities may have date_close outside target period, or wrong user/category Solution: Verify opportunity dates and assignments:

# Check opportunities for target period
opps = get_model("sale.opportunity").search_browse([
    ["state", "=", "won"],
    ["date_close", ">=", target.date_from],
    ["date_close", "<=", target.date_to]
])

if target.user_id:
    opps = [o for o in opps if o.user_id.id == target.user_id.id]

for opp in opps:
    print(f"Opportunity {opp.number}: ${opp.amount}, Date: {opp.date_close}")

"amount_expected not reflecting pipeline"¶

Cause: Open opportunities missing probability values Solution: Ensure opportunities have probability set:

# Find open opportunities without probability
opps = get_model("sale.opportunity").search_browse([
    ["state", "=", "open"],
    ["probability", "=", None]
])
print(f"Found {len(opps)} opportunities missing probability")

"Targets not aligning with actual sales"¶

Cause: Using sale.order instead of sale.opportunity for achievement Solution: Remember that targets track opportunities (pipeline), not orders:

Target achievement comes from sale.opportunity, not sale.order
- Won opportunities = actual achievement
- Open opportunities = expected achievement (probability-weighted)

Testing Examples¶

Unit Test: Achievement Calculation¶

def test_target_achievement():
    # Create target
    target_id = get_model("sale.target").create({
        "user_id": 5,
        "date_from": "2026-01-01",
        "date_to": "2026-03-31",
        "amount_target": 100000,
        "qty_target": 1000
    })

    # Verify target
    target = get_model("sale.target").browse(target_id)
    assert target.amount_target == 100000
    assert target.qty_target == 1000

    # Check computed fields exist
    assert hasattr(target, "amount_actual")
    assert hasattr(target, "amount_expected")
    assert hasattr(target, "qty_actual")
    assert hasattr(target, "qty_expected")

    # Calculate achievement percentage
    if target.amount_target:
        achievement = (target.amount_actual / target.amount_target) * 100
        assert 0 <= achievement <= 200  # Reasonable range

Security Considerations¶

Permission Model¶

sale.target.create - Create new targets (typically managers only)
sale.target.write - Edit existing targets (managers only)
sale.target.delete - Delete targets (restricted)
sale.target.read - View targets (salespeople can view own targets)

Data Access¶

Consider implementing record rules to limit visibility
Salespeople should see only their own targets
Managers should see team targets
Executives should see all targets

Configuration Settings¶

Required Settings¶

Setting	Location	Description
Users	base.user	Active salespeople with proper permissions
Opportunities	sale.opportunity	Must track date_close and probability

Optional Settings¶

Setting	Default	Description
date_from	First of current month	Default target start date
date_to	Last of current month	Default target end date

Integration Points¶

Internal Modules¶

Sales Opportunities: Primary source of achievement data (won and open opportunities)
User Management: Links to salespeople and user accounts
Product Categories: Enables product-line focused targets
Accounting Tracking: Supports territory/segment-based targets
Commission Calculations: Can drive performance-based compensation

Version History¶

Last Updated: 2026-01-05 Model Version: sale_target.py (98 lines) Framework: Netforce

Additional Resources¶

Sales Forecast Documentation: sale.forecast
Sales Opportunity Documentation: sale.opportunity
User Management Documentation: base.user
Product Category Documentation: product.categ
Commission Documentation: Related seller.commission or similar modules

Support & Feedback¶

For issues or questions about this module: 1. Check sale.opportunity configuration and data 2. Verify date ranges align with opportunity close dates 3. Ensure probability values are set on opportunities 4. Review user and category assignments 5. Test achievement calculations in development environment

This documentation is generated for developer onboarding and reference purposes.

Sales Target Documentation¶

Overview¶

Model Information¶

Features¶

Target Setting Framework¶

Target Types¶

Key Fields Reference¶

Target Definition Fields¶

Amount Target Fields¶

Quantity Target Fields¶

Relationship Fields¶

API Methods¶

1. Create Record¶

2. Get Achievement Amounts¶

3. Generate Daily Targets¶

Search Functions¶

Search by Salesperson¶

Search by Period¶

Search by Product Category¶

Search by Tracking Category¶

Search Active Targets¶

Computed Fields Functions¶

get_amount(ids, context)¶

Best Practices¶

1. SMART Goal Setting¶

2. Hierarchical Target Structure¶

3. Regular Performance Reviews¶

4. Historical Baseline¶

5. Commission Integration¶

Database Constraints¶

Default Values¶

Related Models¶

Common Use Cases¶

Use Case 1: Set Quarterly Sales Targets for Team¶

Use Case 2: Performance Dashboard¶

Use Case 3: Territory-Based Targets¶

Use Case 4: Product Line Targets¶

Use Case 5: Commission Calculation¶

Performance Tips¶

1. Efficient Target Queries¶

2. Minimize Computed Field Access¶

3. Use Date Indexing¶

Troubleshooting¶

"amount_actual showing zero despite won opportunities"¶

"amount_expected not reflecting pipeline"¶

"Targets not aligning with actual sales"¶

Testing Examples¶

Unit Test: Achievement Calculation¶

Security Considerations¶

Permission Model¶

Data Access¶

Configuration Settings¶

Required Settings¶

Optional Settings¶

Integration Points¶

Internal Modules¶

Version History¶

Additional Resources¶

Support & Feedback¶