Partner Capacity Dashboard

January 2026 vs February 2026 Analysis
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📋 Framework & Methodology

Framework & Methodology
How monthly capacity targets are built, protected, and aligned.
Model: Multi-layer rules Cycle: Jan -> Feb Signals: Perf + FR + Ops

Background

Targets are built monthly to match demand, protect quality, and keep partner capacity healthy using performance and compliance signals.

Context (Feb 2026)

Stabilize good partners, reduce more from average/bad, protect new partners, and allow limited PMM exceptions.

Challenges

Keep good partner capacity stable with a 13% overall cut; driver card constraints shift some share to average/bad.

Strategic Goals

Demand Coverage Quality & Compliance Risk Mitigation Budget Adherence

Model Layers

Baseline (size, performance, FR) -> New Partner Protection -> Termination/Blocking -> Quality Brakes -> Manual Adjustment.

Calculation Flow (Feb) For more info about the rules check out this link Capacity target rules details →
Short, auditable path from Jan baseline to Feb targets
1
📍 Start Point
Jan final target per partner serves as the baseline for Feb calculations.
2
⚙️ Base Multipliers
Performance, FR%, and ops factors reduce low performers and protect high performers.
3
🛡️ Protected Partners
≤1 month: T1 min 20, T2 min 15 (18 partners). <3 months: min by tier (150 partners). Zero after factors → restore safe minimum.
4
⚠️ Term & Warnings
Terminated = 0 (3 partners). Warning letters (34): Bad/Avg → 50% of Jan, Good → 90%.
5
📸 FR% Fine-Tune
Apply FR% slabs by performance tier to fine-tune output and reward compliance.
6
✏️ Manual Override
PMM/ops exceptions for contracts, launches, staffing, or data issues.
7
🎯 Align to Feb Plan
Scale to Feb national total, then apply city-level scaling factors.
🚫

Compliance Blocked Riders

Waiting for final list from compliance team. PMMs confirm recovery plans; actions follow on flagged risks. Critical for maintaining fleet quality standards.

🪪

Driver Card Slab

Not a core factor (overall rate 40.5%). Used to keep targets stable with smaller cuts for good/average partners >80%. Ensures compliance-ready capacity.

🏗️

Model Structure

Baseline + protection + controls + manual review produce explainable targets per partner and city. Transparent, auditable, and defensible.

Baseline Protection Controls
🔄

Flexibility

Monthly tuning of coefficients and thresholds keeps alignment with CPO, service, and partner mix goals. Adapts to market conditions.

Monthly Tuning CPO Alignment Adaptive
🔐

Governance

Manual overrides are limited and audited to keep results consistent and defensible. SOP + maker/checker ensures accountability.

Audit Trail Maker/Checker SOP

Business Metrics Dashboard

📊 Executive Briefing ● AUTO
▶ 📋 Highlights, Lowlights & Notes Expand ▾
📌 Need to Know
• Dashboard reflects first version of capacity target — mid-month adjustments may apply.
• Partner data is based on last week of prior month (e.g., Feb target uses late-Jan active partner data).
• Performance MoM comparison is target vs. target, not actual performance — partner categories may shift between months.
✅ Highlights
🛡️ Good partners protected
Lowest target reduction at only 6.6% — prioritized in allocation rules.
📉 Bad partners minimized
Count dropped 73 → 20 MoM; target cut from 808 → 370.
⚖️ Average partners stable
Target maintained at same level MoM with +1.0% change.
🤖 Manual adjustments halved
Reduced by 50.4% — rules automation improving.
🎯 FR achievement rate at 92.4% — strong face recognition rate against target.
⚠️ Lowlights
😞 Partner experience impact
Target declining for 3rd consecutive month affects partner sentiment. Additionally, 70 partners phased out adds to negative experience.
🪪 Low driver card rate
Driver card rate at 40.5% — limiting full rule application and requiring exceptions.
▶ ⚡ Key Challenges & Outcomes Expand ▾
1 Maintain avg scale with -13% target reduction
Challenge
Achieve scale target despite +13% overall reduction in capacity.
Solution
Identified & phased out low-compliance partners to protect avg scale.
Outcome
Avg scale 35.1 / 36 target — 98% achievement.
2 Preserve good partners target under reduction pressure
Challenge
Keep high-quality partners at prior-month level despite overall target cuts.
Solution
Rules prioritize good partners first — absorb reduction from lower tiers.
Outcome
Good partners target -6.6% (only from city-level constraints); on track — most reduction tied to DC issue.
3 Driver card constraints limiting rule execution
Challenge
Low driver card rate (40.5%) prevents full rule application — forced manual exceptions.
Solution
Coordinating with CH/PMM to align partners on driver card issuance timeline.
Outcome
Assigned conservative targets until commitment shown; reassessment next month.
Total Riders 👥
19,365
↓ -3,088 (MoM -13.8%)
Active Partners 🎯
551
out of 643 partners
Good Partners 📈
252
↓ -17 (MoM -6.3%)
Jan: 269 → Feb: 252
At-Risk Partners (bad partners) ⚠️
20
↓ -53 (MoM -72.6%)
Jan: 73 → Feb: 20
Zero-Target Partners ⭕
92
3 Terminated, 70 Phased Out, 19 No Jan Target
Partners not given target in Feb
Avg Scale per Partner 📊
35.1
↓ -0.8 (MoM -2.2%)
Jan: 35.9 → Feb: 35.1
Highest Partner Total Capacity 🏆
200
highest net target given to a partner (60 riders)
Highest Partner with Capacity Reduction 📉
-85
lowest total target given to a partner (10 riders)

Performance Breakdown

📌

Note: The target change shown per category reflects the overall capacity reduction, not a per-performance-level adjustment. Partners may have a different assessment month-over-month (e.g. shifted from Good to Average), so the numbers also reflect partner movement across categories.

Good Partners

Partners: 252 (47.2%)

Feb Target: 10,192 (53.7%) MoM -17.5%

Scale: 45.9 → 40.4 -12.0%

Average Partners

Partners: 262 (49.1%)

Feb Target: 8,414 (44.3%) MoM +1.0%

Scale: 30.4 → 32.1 +5.6%

Bad Partners

Partners: 20 (3.7%)

Feb Target: 370 (1.9%) MoM -75.9%

Scale: 21.0 → 18.5 -11.9%

📋
Final Capacity Target — Our Rules vs Last Month Achievement (Performance breakdown)
Total Delta -3,366
💡

Note: Target is assigned only to last week active partners of the month — inactive partners are excluded. | This table shows the capacity rules impact on target assignment per performance level (Good, Average, Bad). | Targets are based on each partner's latest assessment — some partners may have shifted category since last month.

Last Month Total 22,342
New Month Total 18,976
Net Delta -3,366
Partners with Target 534
Category # of Partners
with Target		 Last Month Final Target New Month Final Target Delta MoM %
Good
252 10,914 10,192 -722 -6.6%
Average
262 10,620 8,414 -2,206 -20.8%
Bad
20 808 370 -438 -54.2%
Total
534 22,342 18,976 -3,366 -15.1%
Manual Adjustment Summary - Feb 2026
Capacity Added
Unchanged
Capacity Reduced
MoM Partners -50.4% ▼
Net Impact -8.6% ▼
Partners Adjusted 61 ▲
Total Moved 1,019 ▲
Top Issue Driver Card ⚠
Partners Adjusted
61 ▼
Jan: 123 → Feb: 61
-62 Partners (MoM)
Net Capacity Change
-173 ▼
Calculation = Added - Reduced
+423 added - 596 reduced
Capacity Movement Breakdown
423
596
Added (41.5%)
Reduced (58.5%)
⚠️ TOP DRIVER
Driver Card Issues
23 partners (37.7%)
▶ How We Reduced Manual Adjustments — Feb Approach Click to expand ▾

We reduced manual edits by tightening rules, requiring evidence, and aligning stakeholders early — so targets follow one consistent logic.

→
→
1
Same-Level Shift Rules
Good → Good only
Average → Average or Good
Bad → Bad or higher
Purpose: Keep growth on strong partners & protect avg scale.
Result: Fewer random reductions; PMMs push delivery or reassign within tier.
2
No Removal Without Proof
🚫 → 📄 → ✅
No ad-hoc removal → Provide evidence → Approved
Rule: PMMs need valid proof (screenshot/record) to remove any target.
Purpose: Eliminate decisions not backed by data.
Result: Adjustments drop — all exceptions are controlled & traceable.
3
Upfront Alignment & PMM Ownership
Pre-cycle session → Share wiki:
✓ Target rules & logic
✓ Stratification metrics & criteria
✓ Examples & common cases
After alignment → PMM authority:
PMMs can request adjustments without pre-approval — as long as they follow the shared rules and support Keeta's monthly target. Frontline teams are closest to reality and partners.
Purpose: Align first, then empower — fewer blockers, faster decisions.
Result: PMMs act confidently within clear rules — less back-and-forth, more ownership.
⚠️ Real Example — Push-back on removing a good partner's target

Context: A PMM requested to set a good-performing partner's target to zero based on city-level cancellation rate. We refused — the partner had 100% FR, 100 Driver Card and was rated Good across all 8 metrics over 3 months. Removing their target would violate our data-driven rules. The case was escalated and resolved through a meeting.

R
Ruaa Alabdulrahman 01-27 09:09 AM
performance all month is below city average @Adeel Ahmed @Mohamed Gamal
M
Mohamed Gamal 01-27 10:02 AM
@Ruaa Alabdulrahman
Performance used for three month and they are good performance with 100% FR, 100 Driver card, what's the logic to give them zero target?

@Adeel Ahmed ^
R
Ruaa Alabdulrahman 01-27 10:13 AM
@Mohamed Gamal shared above their Jan overall performance ^
M
Mohamed Gamal 01-27 10:30 AM
Yes, as mentioned above, we are considering 3 months performance assessment not MTD, we explained this
M
Mohamed Gamal 01-27 10:32 AM
The option we can do is to reduce their target a bit and give the reduction target to same level partners (good one)
R
Ruaa Alabdulrahman 01-27 10:56 AM
in 3 months they been average with a constants high cancelation and Al Ahsa overall cancelation is very high so we need to reduce those who contribute the majority of the city cancelation thu have low capacity
M
Mohamed Gamal 01-27 10:58 AM
Kindly note that the performance level has a score with 8 metrics not only cancellation rate, if you a confusion on that level, pls arrange for a meeting to discuss further, for now let's pls work on the data we added

thank you
R
Ruaa Alabdulrahman 01-27 11:02 AM
@Mohamed Gamal you asked for justification im justifying my actions if i need to raise a request ill. Im well aware of the 8 matrics as ill follow up on daily.
M
Mohamed Gamal 01-27 11:05 AM
The justification is against our data statement, we can't terminate good partners without valid reason

@Adeel Ahmed If you have time, let's discuss this today me, you, and Ruaa
R
Ruaa Alabdulrahman 01-28 09:17 AM
CC: @王奉策(Chandler Wa...
C
王奉策 (Chandler) 01-28 09:38 AM
@Adeel Ahmed check this, driver card is priority
C
王奉策 (Chandler) 01-28 09:39 AM
@Mohamed Gamal fyi, in KAS, cost is first, compliance is second, customer experience is third, all others are rest
👍 Mohamed Gamal, Adeel Ahmed
✅

Outcome: Request to zero the target was rejected. The partner kept their target with a minor reduction, redistributed to same-tier (Good) partners — fully aligned with our rules. 📄 View full case details →

Net Manual Impact
—
—
Largest Segment Shift
—
—

Feb Target vs After Manual Adjustment (Good / Average / Bad Partners)

Capacity targets for partners by performance segment (Good / Average / Bad) before and after PMM manual adjustments; shows how manual overrides impacted our rule-based targets.

Increase Partners
—
—
Decrease Partners
—
—

Partner Capacity Changes

Distribution of 551 partners with active targets showing capacity direction: increases, unchanged, or decreases vs previous month.

📌 Performance & Scale Summary (Jan vs Feb)

👥
Partners with Target
551 -11.8%
Jan 625 → Feb 551
🎯
Total Target
19,365 -13.7%
Jan 22,453 → Feb 19,365
📊
Avg Scale
35.1 -2.2%
Jan 35.9 → Feb 35.1
🚴
Delivery Riders
20,489 -12.8%
Jan 23,511 → Feb 20,489
🧭 Filter by period, performance, or scale to view avg scale & capacity breakdown. Overall avg scale is always included.
Period:
Performance:
Scale:

Avg Scale Overview (Total Partners)

Riders breakdown (Jan vs Feb)

Avg Scale (Partners with Target)

📊 FR & Driver Card Compliance Analysis

📋 Overview: Analyze partner distribution by Face Recognition (FR) and Driver Card compliance rates across all segments. (Data updated till 25th Feb 2026)

📸 AVG FR RATE KPI
All / With Target
71.9%
All
73.8%
Target
26.9%
All
28.7%
Target
🪪 AVG DC RATE KPI
All / With Target

Face Recognition (FR) Rate - All Partners vs Partners with Target

Comparison:
All Partners:
With Target:
Performance:

🏙️ City Tier Performance Analysis (Jan vs Feb)

🏆
Highest City
Riyadh
200 partners (Feb)
📉
Lowest City
Jazan
3 partners (Feb)
📊
Total (Feb)
551
partners with target
📈
Biggest MoM Change
Jazan
-40.0% MoM
Metric:

City Comparison (Jan vs Feb)

Note: Use slicers to compare Jan vs Feb by city and tier, with MoM% changes highlighted.

🎯 Target Movement Analysis (Jan vs Feb)

MoM Partners
-11.8% ▼
Net Movement
-131 ▼
Increased
153 MoM -27.8%
Decreased
284 MoM +18.3%
No Change
114 MoM -34.1%
View:

📈 Overall

Note: MoM% shown on Feb values. Bars: Jan vs Feb comparison.

🧩 Breakdown

Breakdown (Jan vs Feb)

📊 Riders Slab Distribution (Jan vs Feb)

Total Partners
551 MoM -11.8% ▼
Small Scale
141 partners MoM -22.1%
Mid Scale
316 partners MoM -3.7%
Large Scale
94 partners MoM -19.0%
Peak Slab
20-30 160 partners
View:

Riders Slab Distribution (Jan vs Feb)

📌 Top & Bottom Partners Overview (Jan vs Feb)

Partners
20
Total Target
3,833 19.8%
Good Perf.
14 partners
Avg Perf.
6 partners
Avg FR / DC %
96.1% / 40.0%
# Partner # of cities New/Old Delivery riders Total target MoM %
(Target)		 Performance Avg FR % Avg Driver Card %
Jan Feb Jan Feb Jan Feb Jan Feb Jan Feb Jan Feb Jan Feb

📋 Note: Table compares Jan vs Feb per partner. MoM% shown for Total Target only. Total target reflects riders across all covered cities. Performance is based on highest-target city. FR & Driver Card rates are averaged across cities.

✅ February 2026 Success Metrics

Performance Breakdown: Toggle between Capacity Target & Avg Scale, and Next Month Target to review achievement, gaps, % by segment, and strategic planning.

💡 Key Observations

  • Loading insights...

📋 Executive Summary

February 2026 Partner Capacity Analysis — High-Level Management View

-13.8%
MoM Change
19,365
Total Riders
551
Active Partners
🚴
19,365
Total Riders (Feb)
↓ 3,088 from Jan (22,453)
👥
551
Partners with Target
of 643 total partners
📊
35.1
Avg Target/Partner
Stable vs January
🆕
23
New Partners
17 out of assessment
⚠️
92
Zero-Target Partners
3 Term, 70 Phased, 19 No Jan
✅
252
Good Partners
↓ 17.5% capacity vs Jan

📈 February 2026 Capacity Strategy

  • Re-balancing: Strategic redistribution, not uniform cuts
  • Overall Reduction: 13.8% below Jan (-3,088 riders)
  • Headcount Alignment: Restructure base for high avg scale
  • FR% Primary Gate: Low FR = no growth; severe = zero target
  • Performance-Based: Quality + scale → allocation
  • New Partner Protection: Shield new partners from sharp cuts
-13.8%
Overall
92
Zero Target
FR%
Primary Gate

✅ Positive Highlights

  • Bad Partners ↓72.6%: Dropped from 73 → 20 partners
  • Average Partners Stable: 262 partners at 8,414 riders (+1.0%)
  • Avg Target Held: 35.1 riders/partner despite reduction
  • Quality Improvement: Portfolio mix shifting toward compliance
-53
Bad Partners
+1.0%
Avg Partners
35.1
Avg Target

⚠️ Concerns & Risks

  • Overall Decline: Total capacity down 13.8% (-3,088)
  • Good Partners Down: target reduction by 6.6%
  • Zero-Target Impact: 70 partners phased partners with no allocation (Impact on partner experience)
  • Capacity Contraction: Fleet size reduction pressure
-3,088
Riders
-17.5%
Good Cap.
92
Zero Target

🎯 Q1 2026 Success Targets

  • Avg riders/partner: Raise from ~33 to ≥45
  • Stability: Lock 3-month bands; ≥70% peak retention
  • CPO impact: ≥0.39 SAR/order reduction
  • Controls: SOP + maker/checker; -10% manual adj.
  • Mix shift: Good 58-60%; Bad 5%; add driver card
  • Compliance: FR 98-100% by March
  • Scale top: ≥20 partners to ≥150 riders
≥45
Avg Target
58-60%
Good Share
98%+
FR Target
💡 Strategy Logic: Scale reliable partners, tighten controls, and shift mix toward compliant segments to raise output and reduce CPO. Focus on headcount alignment to maintain high average scale for partner profitability while protecting priority cities and high-quality performers.

🎯 Future Strategy & Roadmap

📅 Planning Horizon

Next Implementation Phase: February 2026 or Post-Ramadan (depending on timing)

🔧

1. Refine Logic & Factors

  • Adjust performance and FR thresholds based on January results
  • Fine-tune coefficients using real performance data
  • Optimize balance between quality gates and growth targets
🏙️

2. City-Tier Minimums & Order Targets

  • Set differentiated minimum targets per city tier (T1 vs T2)
  • Ensure city-level capacity aligns with demand
  • Balance service level requirements with budget constraints
📋

3. Partner Willingness & Commitment

  • Introduce PMM-recorded partner willingness sheet
  • Track recruitment plans and fleet investment commitments
  • Integrate as model signal post-Ramadan (when demand stabilizes)
🤝

4. Long-Term Strategic Partnerships

  • Share capacity targets 2–3 months in advance for key partners
  • Enable early hiring and operational planning
  • Build longer-term, win-win partnership relationships
🛡️

5. Partner Retention Focus (Critical Priority)

⚠️ Priority Goal: Prevent loss of good, compliant partners due to unrealistic capacity reductions

✅ Protection Strategies

  • Provide reasonable, profitable targets for quality partners
  • Ensure targets support sustainable partner economics
  • Regular follow-up on partner concerns and feedback
  • Quick resolution of operational blockers

🎯 Success Metrics

  • Partner satisfaction and engagement levels
  • Retention rate of "Good" performing partners
  • Partner profitability and earning potential
  • Long-term partnership commitment indicators

📊 Implementation Timeline

📅

Phase 1: Immediate

Logic refinement
Partner retention focus

🗓️

Phase 2: Post-Ramadan

City-tier targets
Willingness tracking

📆

Phase 3: Long-term

Strategic partnerships
Advance planning (2-3 months)

🎯 Strategic Outcomes

By implementing these initiatives, we aim to achieve:

  • Enhanced Predictability: Partners can plan ahead with confidence
  • Improved Retention: Keep quality partners engaged and profitable
  • Better Alignment: Partner capacity matches actual demand and capability
  • Stronger Partnerships: Move from transactional to strategic relationships
  • Operational Excellence: Right capacity, right place, right time

🗓️ Monthly insights (6 months comparison)

Sep 2025 – Feb 2026 Structure (capacity target monthly overview)
What's inside: 📊 Overview & TrendsKPIs, targets, scale & MoM 🚗🏍️ Vehicle TypeTarget split by vehicle 📊 Partner MovementJoiners, leavers & net flow 👥 New vs OldTenure-based contribution 🏙️ City DistributionAllocation across cities 🏢 Tier City (T1 vs T2)Tier performance compare 🤝 Partner WillingnessSentiment & growth intent

📊 Monthly Overview & Trends

Active Partners (with target) ✅
—
—
Total Partners 🏢
—
—
Total Target 🎯
—
—
Average Scale (riders / active partner) 📏
—
—
# Of Partners with target increase in a month (all) 📈
—
—
MoM change (Increase all) 🔁
—
—
Increase > 20% 🔥
—
—
Increase > 30% 🚀
—
—

Monthly trend: Total Target & Total Partners

Tracks total capacity targets and number of total partners month-over-month. Shows overall market size and partner base growth trends. Dual-axis visualization with combined insight indicator.

Hover a month to see details.

MoM Change in Total Target (%)

Shows month‑over‑month % change in Total Target based on the Monthly Insights totals. Green = increase vs previous month, Red = decrease. First month is the baseline (no MoM).

🚗🏍️ Monthly Target by Vehicle Type (Cars vs Bikes)

Shows capacity target breakdown by vehicle type across months. Cars dominate but Bikes share is growing (15.8% → 25.4%). Toggle between Monthly overview and City-level breakdown.

🚗 Avg Cars Explained
Average monthly car-based capacity targets across all cities from September 2025 to February 2026.
22,493 = 134,957 total ÷ 6 months
(27,599 + 28,372 + 28,257 + 18,845 + 17,441 + 14,443) ÷ 6
Represents 80.5% of total vehicle capacity. Cars remain the dominant vehicle type.
🚗 Avg Cars (per month)
22,493
80.5% of total
total: 134,957
🏍️ Avg Bikes Explained
Average monthly bike-based capacity targets across all cities from September 2025 to February 2026.
5,447 = 32,679 total ÷ 6 months
(5,194 + 5,634 + 6,580 + 5,337 + 5,012 + 4,922) ÷ 6
Represents 19.5% of total vehicle capacity. Bikes share is growing steadily.
🏍️ Avg Bikes (per month)
5,447
19.5% of total
total: 32,679
📈 Bikes Share Growth Explained
+9.6pp Share Increase: Bikes' share of total capacity grew from 15.8% (Sep) to 25.4% (Feb).
Share: 25.4% - 15.8% = +9.6 percentage points
-5.2% Volume Change: Bike count went from 5,194 (Sep) to 4,922 (Feb).
Volume: (4,922 - 5,194) / 5,194 × 100 = -5.2%
Note: Share increased mainly due to cars declining faster (-47.7%) than bikes (-5.2%).
📈 Bikes Share Growth (Sep→Feb)
+9.6pp share increase
15.8% → 25.4% bikes share
Cars declined -47.7% vs Bikes -5.2%
📊 Avg Total Explained
Average monthly capacity targets for both Cars and Bikes across all 17 cities.
27,939 = 167,636 total ÷ 6 months
Period: September 2025 → February 2026
Monthly trend: Peaked at 34,837 (Nov) → dropped to 19,365 (Feb) due to seasonal adjustments.
📊 Avg Total (per month)
27,939
Sep - Feb 2026
total: 167,636

Average Scale by Performance Level

Average riders per partner by performance level (Good/Average/Bad) across months.

Hover a month to see details.

Average Scale by Size Level

Average riders per partner by size level (Big/Mid/Small) across months.

Hover a month to see details.

Monthly trend: Average Scale

Average riders per active partner each month. Indicates typical partner capacity and operational efficiency trends. Declining trend shows efficiency optimization efforts.

Hover a month to see details.
Partners Share (Performance & Size)

Partners performance mix (Good / Average / Bad)

Distribution of partners by achievement level. Good (≥80% FR), Average (50-80% FR), Bad (<50% FR). Shows quality composition evolution.

Quick Metrics: ✓ Good Quality Trending ⚠ Watch Average Decline
Total Target Share (Performance & Size)

Total Riders Share by Performance Level (Good / Average / Bad)

Distribution of total target riders share with % across performance levels. Shows capacity concentration by quality tier.

Capacity Allocation: ⭐ Share by Quality
▶ 📊 Partner Movement Analysis (Increase / Decrease / No Change) — click to expand/collapse

Track partner target capacity movements: partners who increased, decreased, or maintained their targets across 6 months. Segmented by quality performance (Good ≥80% FR, Average 50-80% FR, Bad <50% FR).

📊 Key Insight: Average of 195 partners increased targets monthly, while 243 decreased. December saw the sharpest decline (79 increases vs 457 decreases). February closed with 136 increases vs 284 decreases.

Partner Movement by Type (Trend)

Quality Distribution by Movement Type

ℹ️ Note: Table shows partners who completed assessment during each month. Partners without assessment data (new sign-ups, incomplete metrics) are excluded from these numbers.

▶ 📋 View Detailed Movement Table — click to expand
Month Total Increase Total Decrease No change Subtotal
Good Average Bad Good Average Bad Good Average Bad
▶ 👥 New vs Old Partners Analysis (by Partner Age) — click to expand/collapse

Analyze partner composition by tenure: newly onboarded (<1 month), recently added (<3 months), and established (>3 months). Compare capacity (Target), engagement (Active Count), scale efficiency (Avg Scale), and quality breakdown across all 6 months. Green/Red arrows show month-over-month trends.

📊 Key Insight: Loading...

Total Partners by Age (Trend)

Avg Scale by Partner Age (Trend)

▶ 🏙️ City-wise Target Distribution (with MoM trends) — click to expand/collapse

City-level analysis showing total target riders and active partners (with targets) across all 6 months (Sep–Feb). Green/Red arrows indicate month-over-month percentage changes. Filter by two months to compare city performance trends.

🔵 T1 Cities (Major Markets)
🟢 T2 Cities (Secondary Markets)

Total Target by City (Trend)

Active Partners by City (Trend)

Highest Avg Scale
—
—
—
Lowest Avg Scale
—
—
—
Best MoM Improver
—
—
—
Steepest MoM Decline
—
—
—

City-wise Avg Scale Trend (Target ÷ Partners)

City-wise Monthly Target & Partners Overview

Complete view of all cities across 6 months showing partner counts and total target capacity. MoM % change calculated from previous month.

▶ 📊 Tier City Overview (T1 vs T2) — click to expand/collapse

Takeaway: T1 drives ~90.2% of targets while T2 holds ~9.8% in Feb. T1 shows a sharper pullback in Feb while T2 stays relatively flat; maintain T2 density (avg scale ~21.0) and prioritize T1 recovery.

📌 Highlighted Arrows: Green ↑ or Red ↓ with percentage show month-over-month change for each metric within the same tier (T1 compared to previous T1, T2 to previous T2). This helps identify growth or decline trends for each tier independently.

T1 vs T2 Total Target (with combined trend)

Partners with Target (T1/T2) + Total Trend

Tier Overview by Month (counts shown with share%)

Comprehensive view of T1 vs T2 cities showing monthly targets, active partners, partners with targets, and average scale. Weighted Avg Scale represents overall market capacity efficiency. Last row shows 6-month averages.

📊 Partner Willing Analysis (Monthly Comparison)

📋 Definition: "Willing Partners" are those who increased their capacity target compared to the previous month. "Not-Willing Partners" have decreased or maintained the same target (Target Increase ≤ 0).

Feb-26 Willing %
24.6%
↓ -8.1pp
vs Jan-26: 32.7%
Feb-26 Willing
130
↓ -35.3%
vs Jan-26: 201
Feb-26 Not-Willing
398
↓ -3.6%
vs Jan-26: 413
Feb-26 New Partners
23
↑ +109.1%
vs Jan-26: 11
5-Mo Avg Willing %
30.0%
AVG
Range: 12.8% - 40.5%
Month Total Partners ✓ Willing (>0) ✗ Not-Willing (≤0) 🆕 New Partners Willing % Not-Willing %
BASE Sep-25 615 Baseline month (no comparison data)
Oct-25 697 ↑ +13.3% 213 356 128
37.4%
62.6%
Nov-25 767 ↑ +10.0% 259 ↑ +21.6% 381 ↑ +7.0% 127 ↓ -0.8%
40.5% ↑ +3.1pp
59.5% ↓ -3.1pp
Dec-25 674 ↓ -12.1% 81 ↓ -68.7% 554 ↑ +45.4% 39 ↓ -69.3%
12.8% ↓ -27.7pp
87.2% ↑ +27.7pp
Jan-26 625 ↓ -7.3% 201 ↑ +148.1% 413 ↓ -25.5% 11 ↓ -71.8%
32.7% ↑ +19.9pp
67.3% ↓ -19.9pp
LATEST Feb-26 551 ↓ -11.8% 130 ↓ -35.3% 398 ↓ -3.6% 23 ↑ +109.1%
24.6% ↓ -8.1pp
75.4% ↑ +8.1pp
Average (Oct-25 - Feb-26) 663 177 420 66 30.0% 70.0%

Monthly Willing vs Not-Willing Partners

Stacked bars show monthly willing vs not‑willing partners (counts). Use the filter to switch to % trend.

Partner Willingness Trend (%)

Line trend shows willingness share over time (percent). Use the filter to switch to counts.

Growth Category Breakdown (Increase Slabs)

Partners by % target increase (0%, 1-10%, 11-20%, 21-30%, >30%). Line shows total partners with any increase.

Growth Category Breakdown (Decrease Slabs)

Partners by % target decrease (0%, -1 to -10%, -11 to -20%, -21 to -30%, <-30%). Line shows total partners with any decrease.

Partner Size Breakdown - Willingness by Target Size

Size Oct-25 Nov-25 Dec-25 Jan-26 Feb-26
Overall Big Mid Small Overall Big Mid Small Overall Big Mid Small Overall Big Mid Small Overall Big Mid Small
Total 213 (100%) 28 ↓ (13.1%) 116 ↑ (54.5%) 69 ↑ (32.4%) 258 (100%) 24 ↓ (9.3%) 129 ↑ (50.0%) 105 ↑ (40.7%) 79 (100%) 7 ↓ (8.9%) 32 ↑ (40.5%) 40 ↑ (50.6%) 201 (100%) 22 ↓ (10.9%) 96 ↑ (47.8%) 83 ↑ (41.3%) 129 (100%) 13 ↓ (10.1%) 62 ↑ (48.1%) 54 ↑ (41.9%)
≥1%-≤10% 60 (28.2%) 11 ↑ (39.3%) 37 ↑ (31.9%) 12 (17.4%) 73 (28.3%) 16 ↑ (66.7%) 47 ↑ (36.4%) 10 ↓ (9.5%) 22 (27.8%) 4 ↑ (57.1%) 12 ↑ (37.5%) 6 (15.0%) 79 (39.3%) 16 (72.7%) 40 (41.7%) 23 (27.7%) 46 (35.7%) 8 ↑ (61.5%) 27 ↑ (43.5%) 11 ↓ (20.4%)
>10%-<20% 86 ↑ (40.4%) 12 ↑ (42.9%) 47 ↑ (40.5%) 27 ↑ (39.1%) 112 ↑ (43.4%) 4 (16.7%) 59 ↑ (45.7%) 49 ↑ (46.7%) 31 ↑ (39.2%) 3 ↑ (42.9%) 13 ↑ (40.6%) 15 ↑ (37.5%) 58 (28.9%) 5 (22.7%) 30 (31.3%) 23 (27.7%) 51 ↑ (39.5%) 4 ↑ (30.8%) 22 ↑ (35.5%) 25 ↑ (46.3%)
≥20%-≤30% 30 ↓ (14.1%) 3 ↓ (10.7%) 15 ↓ (12.9%) 12 (17.4%) 35 ↓ (13.6%) 4 (16.7%) 12 ↓ (9.3%) 19 (18.1%) 14 (17.7%) 0 (0.0%) 5 (15.6%) 9 (22.5%) 28 (13.9%) 1 (4.5%) 14 (14.6%) 13 (15.7%) 16 (12.4%) 1 ↓ (7.7%) 5 ↓ (8.1%) 10 (18.5%)
>30% 37 (17.4%) 2 ↓ (7.1%) 17 ↓ (14.7%) 18 (26.1%) 38 ↓ (14.7%) 0 (0.0%) 11 ↓ (8.5%) 27 (25.7%) 12 (15.2%) 0 (0.0%) 2 ↓ (6.3%) 10 (25.0%) 36 (17.9%) 0 (0.0%) 12 (12.5%) 24 (28.9%) 16 (12.4%) 0 (0.0%) 8 ↓ (12.9%) 8 (14.8%)
Shows willingness to increase targets by partner size reveals which size segments drive growth across all months.

Partner Performance Breakdown - Willingness by Target Size

Performance Oct-25 Nov-25 Dec-25 Jan-26 Feb-26
Overall Good Average Bad Overall Good Average Bad Overall Good Average Bad Overall Good Average Bad Overall Good Average Bad
Total 213 (100%) 28 ↓ (13.1%) 116 ↑ (54.5%) 69 ↑ (32.4%) 258 (100%) 24 ↓ (9.3%) 129 ↑ (50.0%) 105 ↑ (40.7%) 79 (100%) 7 ↓ (8.9%) 32 ↑ (40.5%) 40 ↑ (50.6%) 201 (100%) 109 (54.2%) 77 (38.3%) 15 (7.5%) 129 (100%) 82 ↑ (63.6%) 46 ↑ (35.7%) 1 ↓ (0.8%)
≥1%-≤10% 60 (28.2%) 10 ↑ (35.7%) 35 ↑ (30.2%) 15 (21.7%) 73 (28.3%) 7 (29.2%) 41 ↑ (31.8%) 25 (23.8%) 22 (27.8%) 3 ↑ (42.9%) 13 ↑ (40.6%) 6 (15.0%) 79 (39.3%) 46 (42.2%) 29 (37.7%) 4 (26.7%) 46 (35.7%) 32 ↑ (39.0%) 14 ↑ (30.4%) 0 (0.0%)
>10%-<20% 86 ↑ (40.4%) 13 ↑ (46.4%) 45 ↑ (38.8%) 28 ↑ (40.6%) 112 ↑ (43.4%) 12 ↑ (50.0%) 60 ↑ (46.5%) 40 ↑ (38.1%) 31 ↑ (39.2%) 2 (28.6%) 14 ↑ (43.8%) 15 ↑ (37.5%) 58 (28.9%) 32 (29.4%) 20 (26.0%) 6 (40.0%) 51 ↑ (39.5%) 33 ↑ (40.2%) 18 ↑ (39.1%) 0 (0.0%)
≥20%-≤30% 30 ↓ (14.1%) 3 (10.7%) 19 (16.4%) 8 ↓ (11.6%) 35 ↓ (13.6%) 3 ↓ (12.5%) 16 ↓ (12.4%) 16 (15.2%) 14 (17.7%) 2 (28.6%) 3 ↓ (9.4%) 9 (22.5%) 28 (13.9%) 15 (13.8%) 11 (14.3%) 2 (13.3%) 16 (12.4%) 10 ↓ (12.2%) 6 ↓ (13.0%) 0 (0.0%)
>30% 37 (17.4%) 2 ↓ (7.1%) 17 ↓ (14.7%) 18 (26.1%) 38 ↓ (14.7%) 2 ↓ (8.3%) 12 ↓ (9.3%) 24 (22.9%) 12 (15.2%) 0 (0.0%) 2 ↓ (6.3%) 10 (25.0%) 36 (17.9%) 16 (14.7%) 17 (22.1%) 3 (20.0%) 16 (12.4%) 7 ↓ (8.5%) 8 (17.4%) 1 ↑ (100.0%)
Shows willingness to increase targets by partner performance reveals which level segments drive growth across all months.

Partners by Riders Slab Distribution

Number of partners in each rider count range across all months. Helps identify capacity distribution patterns.

💡 Key Insights & Takeaways

📈 Overall Trend: Partner willingness started at 37.4% in October (213 willing partners), improved in November (40.5%, 259 partners), but plummeted in December to the lowest point at 12.8% (81 willing partners). January showed strong recovery to 32.7% (201 partners), but February declined again to 24.6% (130 partners), indicating fluctuating engagement patterns.

🎯 Best Performance: November demonstrated the highest willingness rate at 40.5%, with 259 out of 640 sub-total partners willing to increase targets. October followed with 37.4% willingness (213 partners).

⚠️ December Challenge: December saw the most dramatic drop with only 12.8% willingness (81 partners), and 554 partners (87.2%) not willing to increase targets. This represents the most significant resistance to growth observed across all months.

🔥 January Recovery & February Decline: January demonstrated a remarkable 20% recovery jump to 32.7% willingness (201 partners), rising from December's 12.8% low. However, February declined to 24.6% (130 partners willing out of 528 sub-total), showing continued volatility in partner engagement.

📊 Partner Base Dynamics: Total partners contracted from peak of 767 (November) to 551 (February), representing significant portfolio consolidation. Average willingness across Oct-Feb was 30.0% for willing partners vs 70.0% not-willing, showing persistent engagement challenges.

💼 Strategic Recommendation: Address the February decline by implementing targeted retention and growth incentives. The fluctuation between January's recovery and February's drop suggests partners respond inconsistently to interventions. Focus on sustaining engagement momentum while addressing root causes of the repeated declines.

🔧 Target Adjustment Analysis - February 2026

📊 Strategic Capacity Reduction via Manual Adjustments

This analysis examines manual target adjustments for February 2026. 61 partners received manual adjustments, resulting in strategic capacity reduction: net -173 riders (-8.6%) through +423 additions and -596 reductions. Primary drivers: driver card compliance issues and capacity rebalancing. MoM: Partners ↓50.4% (123→61)

Total Partners 👥
61
MoM: ↓50.4% (was 123)
Given Target 📋
2,012
MoM: ↓52.3% (was 4,221)
Adjusted Target 📉
1,839
-173 (-8.6%)
Adjustments (Total Capacity Moved) 🔄
1,019
MoM: ↓47.7% (was ±1,949)

💡 Total Capacity Moved (1,019): Total Additions: 423 • Total Reductions: 596 • Total: 423 + 596 = 1,019 • Net value: -173 riders. This reflects strategic capacity reduction focused on driver card compliance. MoM: Capacity Moved ↓49.8%

📊 Given Target vs Adjusted Target - Deep Dive

🎯 Target Allocation Comparison

GIVEN TARGET (BEFORE)
2,012
Algorithmic allocation
Avg: 33.0 / per partner
↓52.3% vs Jan (4,221)
ADJUSTED TARGET (AFTER)
1,839
Manual adjustment
Avg: 30.1 / per partner
↓56.7% vs Jan (4,246)
NET CHANGE
-173
Overall -8.6%
+423 additions / -596 reductions
↓792% vs Jan (+25)
Change Direction # Of Partners % of Total Total Capacity Moved Avg Change Status
📈 Target Increases 27 44.3% +423 riders +15.7 riders 🟢 Capacity Addition
📉 Target Decreases 34 55.7% -596 riders -17.5 riders 🔴 Capacity Reduction
➡️ No Change 0 0% 0 riders - ⚪ No Change
Total/Net 61 100% -173 riders -2.8 avg 🔴 Net Decrease

💡 KEY INSIGHT: February shows net capacity reduction (-8.6%) vs January's slight increase (+0.6%). More partners received decreases (55.7%) than increases (44.3%), with driver card compliance being the primary factor. MoM: Shift from +25 to -173 net change

🎯 ADJUSTMENT REASON CLASSIFICATION

📋 Categorized Adjustment Reasons (PMM Comments Analysis)

Detailed analysis of PMM manual adjustment comments, categorizing partners into 12 reason segments. Used to identify operational gaps, performance patterns, and strategic priorities.

Rank Reason Category Partners % of Total Typical Action Priority Level
1 Driver Card Issues (Low/No Cards) 23 37.7% ⬇️⬇️ Heavy Reduction 🔴 CRITICAL
2 FR/Performance Non-Compliance 8 13.1% ⬇️⬇️ Target Cut 🔴 COMPLIANCE
3 2% Buffer/Demand Gap Coverage 7 11.5% ⬆️ Capacity Buffer 🟡 STRATEGIC
4 New Partner Onboarding 5 8.2% 🆕 Initial Setup 🔵 SUPPORT
5 Capacity Transfer/Redistribution 4 6.6% ↔️ Transfer to capable 🟢 REBALANCE
6 High Performance Reward 3 4.9% ⬆️ Capacity Increase 🟢 REWARD
7 Partner Inability to Fulfill 2 3.3% ⬇️ Reduce Target 🟠 REVIEW
8 City Capacity Need 2 3.3% ⬆️ Fill Gap 🔵 STRATEGIC
9 Other/Mixed Reasons 7 11.5% 🔄 Varied ⚪ MIXED
TOTAL 61 100.0% - -

🚨 Top Reduction Reasons

50.8%

31 partners reduced for driver card issues (37.7%) and FR/performance non-compliance (13.1%). Driver card availability is the #1 adjustment driver in February.

📊 Strategic Allocation

26.2%

16 partners adjusted for strategic reasons: 2% buffer (7), new partner support (5), and capacity transfer (4). Focus on demand coverage and redistribution.

🚗 Driver Card Compliance Pressure

February shows driver card issues as the dominant factor affecting 50.8% of all adjustments (31 partners). This represents a significant shift from January's performance-based adjustments. MoM: Driver card issues now primary driver vs performance penalties

🏙️ City-Level Adjustment Analysis

City Partners Given Target Adjusted Target Net Change Avg Change Increases Decreases % Change Status
Riyadh 40 1,769 1,620 -149 -3.7 14 25 -8.4% 🔴 Major Reduction
Jeddah 8 119 0 -119 -14.9 0 8 -100% 🔴 Complete Removal
Tabuk 2 45 50 +5 +2.5 2 0 +11.1% 🟢 Growth
Taif 4 35 45 +10 +2.5 3 1 +28.6% 🟢 Growth
Makkah 2 44 44 0 0 1 1 0% ⚪ Balanced
Madinah 2 0 29 +29 +14.5 2 0 N/A 🟢 New Activation
Jubail 1 0 25 +25 +25.0 1 0 N/A 🟢 New Activation
Al Ahsa 1 0 16 +16 +16.0 1 0 N/A 🟢 New Activation
Jazan 1 0 10 +10 +10.0 1 0 N/A 🟢 New Activation
TOTAL 61 2,012 1,839 -173 -2.8 27 34 -8.6% -

🔍 Key City Insights: Riyadh accounts for 65.6% of all adjusted partners (40/61), net change -149 riders (-8.4%). Jeddah: 8 partners had targets zeroed out (-119, -100%). Newly activated cities: Madinah (+29), Jubail (+25), Al Ahsa (+16), Jazan (+10).MoM: Significant drop from Riyadh's +18 net in January

City-Level Target Changes (Top Cities)

📊 Performance Impact Analysis

Net Change
-173
-8.6% vs Jan
Capacity Moved
1,019
-47.7% vs Jan
Driver Card 0-50%
-280 net
44 partners impacted
High FR (80-100%)
-322 net
Primary reduction pool

📅 Quarterly Performance Impact

Performance Partners Given Target Adjusted Net Change (vs Jan) Avg
Good 43 1,615 1,267 -348
Jan -431 | MoM +19.3% ^
 -8.1
Average 47 277 247 -30
Jan -61 | MoM +50.8% ^
 -0.6
Bad 25 32 149 +117
Jan +322 | MoM -63.7% v
 +4.7

"Bad" partners net value remains positive (+117), but below Jan (+322); "Good" partners improved vs Jan but overall still net negative.

🆕 New vs Old Partners

Type Partners Given Target Adjusted Net Change (vs Jan) Avg
New 13 133 188 +55
Jan +149 | MoM -63.1% v
 +4.2
Old 40 1,801 1,490 -311
Jan -319 | MoM +2.5% ^
 -7.8

📊 New partner support declined vs Jan (+55 vs +149); old partner reductions remain large (-311 vs -319).

📸 FR Rate Impact

FR Range Partners Given Target Adjusted Net Change (vs Jan) Avg
0-30% 1 10 15 +5
Jan +106 | MoM -95.3% v
 +5.0
30-50% 0 0 0 0
Jan +39 | MoM -100% v
 0.0
50-70% 3 22 11 -11
Jan -144 | MoM +92.4% ^
 -3.7
70-80% 5 110 182 +72
Jan -33 | MoM +318.2% ^
 +14.4
80-100% 44 1,792 1,470 -322
Jan -240 | MoM -34.2% v
 -7.3
No FR Rate 8 78 161 +83
Jan - | MoM N/A
 +10.4
Total 61 2,012 1,839 -173
Jan - | MoM N/A
 -2.8

High FR (80-100%) absorbed most reductions (-322 vs Jan -240); mid-tier 70-80% shifted from -33 to +72.

🪪 Driver Card Impact

Range Partners Given Target Adjusted Net Change (vs Jan) Avg
0-10% 28 898 746 -152
Jan - | MoM N/A
 -5.4
10-30% 8 400 342 -58
Jan - | MoM N/A
 -7.3
30-50% 8 326 256 -70
Jan - | MoM N/A
 -8.8
50-80% 2 129 100 -29
Jan - | MoM N/A
 -14.5
80-100% 7 181 234 +53
Jan - | MoM N/A
 +7.6
No DC Rate 8 78 161 +83
Jan - | MoM N/A
 +10.4
Total 61 2,012 1,839 -173
Jan - | MoM N/A
 -2.8

Driver card 0-50% accounts for -280 net across 44 partners. Jan values not provided for this table.

🤔 Counterintuitive Findings Require Investigation

1. "Bad" quarterly performance partners still receiving net increases:
25 partners classified as "Bad" received +117 net riders (avg +4.7), while "Good" partners saw a net decrease of -348 (avg -8.1).

2. FR rate and allocation mismatch:
High FR (80-100%) absorbed -322 net reductions, while 70-80% increased +72 and No FR Rate increased +83 inconsistent with "quality-first" allocation logic.

3. Driver card compliance impact concentrated:
0-50% driver card partners (44 total) contributed -280 net change, outweighing positive gains from high-compliance brackets.

Possible explanations:
Low-performance, low-FR partners may be in new partner ramp-up support phase
High-performance partners may have been over-allocated in January, with February correction
Driver card compliance threshold may override performance metrics in manual adjustments
Cross-verify: Are "Bad" partners also "New" partners with lower driver card rates?

▶ 💡 Strategic Insights & Key Findings — click to expand/collapse

1🇾⃣ Driver Card Compliance Crisis (37.7%)

23 out of 61 partners were adjusted due to driver card availability issues. This has become the dominant factor in February, replacing January's performance-based adjustment logic.

2 Significant Capacity Contraction (-8.6%)

Net change of -173 riders (-8.6%) vs January's +25. 27 partners increased +423, 34 partners decreased -596, reflecting tighter capacity management.

3 Compliance-Driven Adjustments (50.8%)

Driver card (37.7%) + FR/performance (13.1%) account for 50.8% of adjustments. Strategic allocation (buffer, new partners, capacity transfer) accounts for 26.2%.

4 City Concentration

Riyadh: 40 partners (-149 net)
Jeddah: 8 partners (-119 net)
Two cities contributed 78.7% of partners and major net reductions; smaller cities (Tabuk, Taif, Madinah, Jubail, Al Ahsa, Jazan) showed net growth.

5 Old Partner Capacity Contraction

40 old partners saw net decrease of -311 riders, while 13 new partners net increased +55. Mature partners face stricter compliance; new partners receive ramp-up support.

6 Portfolio Simplification

Adjusted partner count decreased by 50.4% (61 vs January's 123). More focused interventions; average adjustment impact per partner is -3.5 vs +0.2 in January.

🎯 Recommendations

📋 Process Improvements

  1. Driver Card Program: Address the 37.7% driver card issue rate through partner education and support programs.
  2. Compliance Monitoring: Implement real-time driver card availability tracking to proactively address issues before target adjustments.
  3. FR% Improvement Program: Target the 13.1% FR/performance non-compliance with specific intervention plans.
  4. New Partner Onboarding: Continue supporting new partners (8.2%) with ramp-up programs while monitoring quality metrics.

🔧 Model Enhancements

  1. Add Driver Card Gate: Integrate driver card availability as hard prerequisite before capacity allocation.
  2. FR% Thresholds: Set minimum FR% requirement for capacity maintenance (e.g., 50% minimum).
  3. City-Specific Parameters: Model Riyadh and Jeddah separately given their 78.7% share of adjustments.
  4. New Partner Protection: Add explicit "new partner ramp-up" period (3-6 months) with modified targets.
  5. 2% Buffer Optimization: Review and potentially automate the 11.5% of 2% buffer adjustments.
  6. Capacity Transfer Rules: Formalize capacity transfer criteria to reduce manual interventions.

📌 Executive Summary

• Driver card compliance crisis: 37.7% of adjustments (23 partners) due to driver card availability issues — now #1 driver

• Net -173 riders (-8.6%) vs January's +25: 27 partners +423, 34 partners -596 — significant capacity reduction

• 50.8% compliance-driven adjustments (driver card 37.7% + FR/performance 13.1%) vs strategic allocations (26.2%)

• Old partners reduced: 40 old partners lost -311 net riders while 13 new partners gained +55 net

• Riyadh & Jeddah dominate: 48 partners (78.7%), -268 combined net reduction

• Smaller cities positive: Tabuk +5, Taif +10, Madinah +29, Jubail +25, Al Ahsa +16, Jazan +10

• 50.4% fewer partners adjusted (61 vs 123): More focused intervention or reduced partner base

▶ 👨‍💼 PMM Adjustment Summary
21 PMMs recorded manual adjustments for 61 partners. Click each PMM row to expand and view their managed brands.MoM: Adjusted partners ↓50.4% (prev. 123)
PMM Name Partners Managed Adjustments Made Adjustment Rate
▶ Abdul Aziz 14
Jan - | MoM N/A
 4
Jan - | MoM N/A
 28.6%
Jan - | MoM N/A
Partner brand name with adjustment:
Farsan
Masar almrafiq
Meyzat Al-Takamul
Mountain logistics
▶ Faisal Alghamdi 22
Jan - | MoM N/A
 2
Jan - | MoM N/A
 9.1%
Jan - | MoM N/A
Partner brand name with adjustment:
ِAlastool almuntaim
alshuhna almumayaza
▶ Asad Javed 15
Jan - | MoM N/A
 2
Jan - | MoM N/A
 13.3%
Jan - | MoM N/A
Partner brand name with adjustment:
ASIL AL-IZZ
Delivery heroes limited
▶ Ali Ibrahim 16
Jan 12 | MoM +33.3% ^
 4
Jan 1 | MoM +300.0% ^
 25.0%
Jan 8.3% | MoM +200.0% ^
Partner brand name with adjustment:
Adlek
AL-BARAQ AL-KHATEF
MAF
Sultan abdulaziz saad Albathrah for logistic services
▶ 彭兵 19
Jan 7 | MoM +171.4% ^
 4
Jan 1 | MoM +300.0% ^
 21.1%
Jan 14.3% | MoM +47.4% ^
Partner brand name with adjustment:
AREEJ (2)
Nebras
Step Logistic
▶ Adilahmed Mohammad 23
Jan 20 | MoM +15.0% ^
 3
Jan 5 | MoM -40.0% v
 13.0%
Jan 25.0% | MoM -48.0% v
Partner brand name with adjustment:
MasarMomaiz Co logistics Services
Nukhba
YANABEE
▶ Abdullah Aldweik 16
Jan - | MoM N/A
 3
Jan - | MoM N/A
 18.8%
Jan - | MoM N/A
Partner brand name with adjustment:
Barq AlIbda
Tomorrow's Falcons Company
Trax
▶ Anas Ejaz 18
Jan 23 | MoM -21.7% v
 3
Jan 4 | MoM -25.0% v
 16.7%
Jan 17.4% | MoM -4.0% v
Partner brand name with adjustment:
NADA
wjahat khaleej
Zahra Al Rawda
▶ Abdulrehman Qureshi 20
Jan 13 | MoM +53.8% ^
 2
Jan 6 | MoM -66.7% v
 10.0%
Jan 46.2% | MoM -78.3% v
Partner brand name with adjustment:
AHMED SHUAIB GHAZWAN
Anan Capital
▶ Muzammil Ahmed 19
Jan 9 | MoM +111.1% ^
 4
Jan 4 | MoM 0.0% =
 21.1%
Jan 44.4% | MoM -52.6% v
Partner brand name with adjustment:
Bawadir
Husool
Innovation Gate
KAVAA ALTOSEIL
▶ 黎嘉豪 19
Jan 30 | MoM -36.7% v
 6
Jan 17 | MoM -64.7% v
 31.6%
Jan 56.7% | MoM -44.3% v
Partner brand name with adjustment:
ABDULMHASSEN AZAIZ BAYJAN AL-ROQI
Aibtukir
Aldaem Alkhaleejiah
E-ter logistics
Razan Altabi Logiestics
Yuma
▶ Aamir Ibrahim 18
Jan 37 | MoM -51.4% v
 2
Jan 19 | MoM -89.5% v
 11.1%
Jan 51.4% | MoM -78.4% v
Partner brand name with adjustment:
Masar
ZIYAR
▶ Shahid Ali 19
Jan 28 | MoM -32.1% v
 3
Jan 15 | MoM -80.0% v
 15.8%
Jan 53.6% | MoM -70.5% v
Partner brand name with adjustment:
Driver KM logistics
Nader logistics
services delivery
▶ Abdul Basitmalik 16
Jan 40 | MoM -60.0% v
 6
Jan 19 | MoM -68.4% v
 37.5%
Jan 47.5% | MoM -21.1% v
Partner brand name with adjustment:
Code Car
DRER AL-TANAFITHIH
Easy Trip For Logistics Services
MDT
Shdh Logistics
Sultan Bin Abdullah
▶ Junaid Munir 14
Jan 9 | MoM +55.6% ^
 1
Jan 3 | MoM -66.7% v
 7.1%
Jan 33.3% | MoM -78.6% v
Partner brand name with adjustment:
Mostaqbal Watan
▶ Malik Nassar 16
Jan 19 | MoM -15.8% v
 1
Jan 1 | MoM 0.0% =
 6.3%
Jan 5.3% | MoM +18.8% ^
Partner brand name with adjustment:
DTD
▶ Muhannad Atallah 16
Jan 23 | MoM -30.4% v
 2
Jan 1 | MoM +100.0% ^
 12.5%
Jan 4.3% | MoM +187.5% ^
Partner brand name with adjustment:
alahdaf aldhahabiya
RAYA AL-IMDAD
▶ Adnan Siddique 14
Jan 18 | MoM -22.2% v
 4
Jan 2 | MoM +100.0% ^
 28.6%
Jan 11.1% | MoM +157.1% ^
Partner brand name with adjustment:
Abdul Aziz Muhammmad bin Hassan Asiri for Logistics Services
Delivery heroes limited
DZL
wajihat alruwad almutamayyiz company (Al Taif)
▶ Mostafa Soliman 21
Jan 26 | MoM -19.2% v
 2
Jan 2 | MoM 0.0% =
 9.5%
Jan 7.7% | MoM +23.8% ^
Partner brand name with adjustment:
RADHI ABDULSAMEA
Rawasy Afaq
▶ Khalid Ahmad 8
Jan - | MoM N/A
 2
Jan - | MoM N/A
 25.0%
Jan - | MoM N/A
Partner brand name with adjustment:
Establishment ONE TRIP Express ( Tabuk )
KDC Logistics
▶ Mohammad Talha 21
Jan 29 | MoM -27.6% v
 1
Jan 2 | MoM -50.0% v
 4.8%
Jan 6.9% | MoM -31.0% v
Partner brand name with adjustment:
Company AL-DAHAN HIAR INTERNASHONAL Ltd. ( Tabuk )
TOTAL (Feb) 364
Jan 475 | MoM -23.4% v
 61
Jan 123 | MoM -50.4% v
 16.8%
Jan 25.9% | MoM -35.1% v
▶ 📋 Reason Segment Selection
Reason Segment Partners % of Total Description
New Partner 6 9.8% Newly onboarded partner
Other 49 80.3% Other reasons not fitting other categories
Partner indicated unable to fulfill capacity 2 3.3% Unable to meet capacity requirements
Transfer capacity from enabled partner to this partner 4 6.6% Capacity transferred from one partner to another
Total 61 100% -
▶ 📝 Complete List of All PMM Adjustment Reasons

Total Unique Reasons: 37 | Each reason below shows the exact PMM comment with partner count and percentage.

# Partners % PMM Reason Comment
1 8 13.1% Not achieving FR target and drivers card
2 7 11.5% 2% buffer due to gap in online riders causing bad experience
3 4 6.6% driver card rate is low
4 3 4.9% they just have 1 driver card, need to reduce the target to control the risk
5 2 3.3% 100% driver card
6 2 3.3% Adjusted target from other partner who is unable to provide driver cards.
7 2 3.3% Due to driver Cards issue
8 2 3.3% due to kiwa issuses, they can not increase capacity
9 2 3.3% they just have 2 driver card, need to reduce the target to control the risk
10 2 3.3% this is the extra manpower to cover the risk of high demands.
11 1 1.6% 0 progress shown for driver cards, 0 proof shared for appointment
12 1 1.6% 0% driver card
13 1 1.6% Driver card 0%
14 1 1.6% Driver card 100%
15 1 1.6% Fulfill city capacity to save experience.
16 1 1.6% High performance partner, high coopreation
17 1 1.6% less driver cards, no improvment after 5%
18 1 1.6% less driver cards, targeting 15%
19 1 1.6% Need immediate courier capacity in Jazan.
20 1 1.6% no driver card
21 1 1.6% no driver card for car
22 1 1.6% no driver cards till date, very low number of driver cards
23 1 1.6% Partner has issued driver cards and fully compliant
24 1 1.6% Partner shared the Appointment for the driver card they will complete before 24th FEB
25 1 1.6% Riders have booked appointments for change licence
26 1 1.6% risk partners, who coorpate with hs, and stop operation
27 1 1.6% specia case,due to special reason,will protect their capacity three month(2-4)，which align with chandler&iron。
28 1 1.6% TGA suspended the bike driver cards.
29 1 1.6% Their current evaluation is due to 12 riders being blocked in January because of an operations card issue. They have 15 high-performing and compliant riders, and currently, 100% of these riders are un
30 1 1.6% Their driver card just 67, cannot increase 170 within one month
31 1 1.6% Their riders all have rider cards, but due to issues with vehicle registration certificates, none of the riders have completed the system update.Their owner strongly appealed
32 1 1.6% They don't drver card issuse
33 1 1.6% They had an issue with the operations manager, who has been replaced. This company is new, having completed only two months of operation, and should ideally be exempted during its first three months.
34 1 1.6% this partner was working in PT project and never worked as full timer, will have 16 full time riders and 6 as PT riders
35 1 1.6% Unable to get driver cards.
36 1 1.6% will have driver Card before 15th Feb
37 1 1.6% (blank)
Total 61 100% -

📅 Capacity Target Achievement (Daily View)

February 2026 MTD | Daily performance tracking across all partners with target | Delivery & Online rider capacity target achievement analysis

🔗 Connected to Google Sheets

📊 Total Overview

MTD Achievement
Month-to-date achievement rate calculated as Delivery Riders ÷ Target across all partners.
93.1%
Delivery | Target: 19,430
W1 Performance
Week 1 achievement rate. Delivery Riders ÷ Target for the first week of the month.
92.5%
W1
W2 Performance
Week 2 achievement rate. Delivery Riders ÷ Target for the second week of the month.
92.4%
W2
W3 Performance
Week 3 achievement rate. Delivery Riders ÷ Target for the third week of the month.
92.3%
W3
Total GAP
The gap between Target and Online Riders. Calculated as Target − Online Riders.
457
Riders | Online vs Del
Color Legend:
≥95% Excellent
90-94.9% Good
85-89.9% Warning
<85% Critical
📈 Overall Performance Summary
Metric MTD W1 (Feb 1-7) W2 (Feb 8-14) Day 17 Day 16 Day 15 Day 14 Day 13 Day 12 Day 11 Day 10 Day 9 Day 8 Day 7 Day 6 Day 5 Day 4 Day 3 Day 2 Day 1
Target 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455 19,455
Online Riders 18,556 18,466 18,341 19,494 18,373 18,443 18,147 18,294 18,250 18,258 10,115 18,192 18,293 18,326 18,465 18,480 18,515 18,300 18,344 18,570 17,909
Delivery Riders 18,081 17,975 17,959 18,124 18,177 18,202 17,601 17,935 17,885 17,900 17,510 17,677 17,920 17,941 18,178 18,268 18,221 17,732 17,810 17,909 17,909
Achievement % 92.9% 92.4% 92.3% 93.2% 93.4% 93.6% 90.5% 92.2% 91.9% 92.0% 90.0% 90.9% 92.1% 92.2% 93.4% 93.9% 93.7% 91.1% 91.5% 92.1% 92.1%
Gap 899 989 1,114 -39 1,082 1,012 1,308 1,161 1,205 1,197 1,340 1,263 1,162 1,129 990 975 940 1,155 1,111 885 885
📊 Key Insights: MTD achievement at 93.1% remains below the 100% target, showing a slight decline from previous levels. Performance imbalance across weeks: W1 at 92.5% and W2 at 92.4% indicates consistent underperformance with W2 trending lower. Day 1 shows highest GAP (1,370 riders) due to online spike. Daily achievement ranges 90.1%-94.0% - improvement urgently needed to reach ≥95% target. Day 16 at 91.7% continues the below-target trend with GAP increasing to 534 riders.
📈 Daily Achievement Trend Chart

👥 Partners Stratification

🏙️ City Level Data

January 2026 vs February 2026 Analysis | Partner Capacity Management Dashboard

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