Attrition analytics

Regrettable Attrition Lens - Executive V7
Confidential • FY 2025-26

Regrettable Attrition Lens

Objective: separate value-neutral exits from value-destroying losses using a simple, auditable score.
The Scoring System
0
1
2
3
4
Non-Regrettable
5
6
Watchlist
7
8
9
10
Regrettable High
Score = Performance (0-6) + Tenure (0/2) + Voluntary (0/2)
*Involuntary exits forced to 0
Section A: Exposure Analysis
Non-Regrettable
₹20.29 Cr 81% Exits
Watchlist
₹5.46 Cr 10% Exits
High Regret
₹3.99 Cr 9% Exits
Total Regrettable Exposure: ₹9.45 Cr
32%
of exited compensation mass comes from just 19% of leavers.
Regrettable exits are not random. They are concentrated in value-dense, hard-to-replace roles. The company is disproportionately losing capacity that matters.
Conclusion: Overall attrition is healthy. But ~20% of exits represent concentrated, avoidable damage and deserve focused leadership attention.
Section B - Capacity Loss
Section B

How Much Business Capacity Did We Lose?

We do not claim theoretical revenue losses. We calculate the direct cost of workforce capacity that exited and must be replaced while the business continues to run.
Direct Exposure
₹9.45 Cr
Exited Compensation
● High (3.99)   ● Med (5.46)
×
Rebuild Factor
1.3x
Hiring • Onboarding • Ramp-up
=
Rebuild Capacity Cost
~₹12.3 Cr
Estimated Replacement Value
Interpretation
This is not simply “money lost” — this is proven execution capacity that walked out the door. The organization now incurs a "double spend": paying for the vacancy (lost productivity) and paying the premium to rebuild it (hiring + ramp-up).
Conclusion (Q2): Even after excluding involuntary exits, the organization is being forced to rebuild ~₹10–12 Cr worth of proven execution capacity.
Section C - Concentration
Section C

Where Is The Damage Concentrated?

We analyzed only the 97 high-regret exits (₹9.45 Cr exposure) to locate the source. The data shows clear clustering in specific pockets.
By Business Unit
Business A ₹1.64 Cr
14 Exits
Businesss B ₹1.57 Cr
14 Exits
Business C ₹0.92 Cr
11 Exits
Top 3 BUs ≈ 44% of exposure
By Department
Dept 1 ₹1.86 Cr
24 Exits
Dept 2 ₹1.77 Cr
16 Exits
Dept 3 ₹0.93 Cr
15 Exits
Top 3 Depts ≈ 48% of exposure
By BU Head
BU head 1 ₹2.57 Cr
26 Exits
BU head 2 ₹1.41 Cr
11 Exits
BU head 3 ₹0.89 Cr
7 Exits
Top 2 Heads ≈ 42% of exposure
⚠️

Structural Pattern: Highly Concentrated

This is not a company-wide problem. This is a pocket-level problem. The damage is contained within specific teams and leaders, meaning it requires surgical intervention, not broad policy changes.

Conclusion (Q3): We are dealing with specific, identifiable pockets of capacity destruction. This is controllable and fixable with focused intervention.
Section D - Trend Analysis
Section D

Is this getting better or worse?

Trend analysis of regrettable attrition over the fiscal year.
50%
25%
9.6%
Q1
Apr-Jun
23.3%
Q2
Jul-Sep
16.0%
Q3
Oct-Dec
Spike
55.8%
Q4*
Jan-Mar
FY Quarter Total Exits Regrettable Exits % High Regret Status
FY25 Q1 (Apr-Jun) 177 17 9.6% Stable
FY25 Q2 (Jul-Sep) 133 31 23.3% Elevated
FY25 Q3 (Oct-Dec) 156 25 16.0% Monitor
FY25 Q4* (Jan-Mar) 43 24 55.8% CRITICAL SPIKE
Conclusion (Q4): The quality of attrition is unstable and periodically spikes. This is a controllable system failure, not randomness.
Section E - Tenure Analysis
Section E

Are we losing people at the worst possible time?

Compounding Window Analysis: Identifying when value-destructive exits occur in the employee lifecycle.
0%
< 6 Months
Hiring Failures
Danger Zone
46.4%
6–18 Months
Compounding Window
33.0%
18–36 Months
Return Phase
20.6%
36+ Months
Retention Phase
Tenure Band Regrettable Exits % Share
< 6 months 0 0.0%
6–18 months 45 46.4%
18–36 months 32 33.0%
36+ months 20 20.6%
Conclusion (Q5): Nearly half of value-destructive exits happen in the 6–18 month compounding window. This is controllable with targeted intervention.
Section F - Performance
Section F

What kind of performers are we losing?

Performance rating distribution of regrettable exits. High-performer loss (Rating 4 & 5) represents the most acute value destruction.
Rating 5
Exceptional
3.1%
Rating 4
High Perf
19.6%
Rating 3
Good Perf
56.7%
Missing
Governance Flag
20.6%
(!) Governance Note: 20.6% of regrettable leavers had NO performance rating recorded, indicating a gap in data hygiene.
Performance Rating Regrettable Exits % Share
Rating 5 (Exceptional) 3 3.1%
Rating 4 (High) 19 19.6%
Rating 3 (Good) 55 56.7%
Missing Rating 20 20.6%
Conclusion (Q6): We are losing high performers. We need to drill down further into these cases to understand reasons for attrition for top performers.
Section G - Compensation
Section G

Are we underpaying or misallocating retention money?

Analysis of regrettable attrition by Pay Quartile.
Higher Pay ≠ Lower Regret
0%
Q1
Lowest Paid
4.1%
Q2
Low-Mid
37.1%
Q3
High-Mid
58.8%
Q4
Highest Paid
Pay Quartile total Exits % Share of Regret Regrettable Rate
Q1 (Lowest) 0 0.0% 0.0%
Q2 4 4.1% 3.3%
Q3 36 37.1% 28.1%
Q4 (Highest) 57 58.8% 47.9%
Conclusion (Q7): This is not a low-pay problem. Regrettable exits are concentrated in already expensive roles. Retention spend must be targeted, not sprayed.