Operating scope

Country head

Capability

All capabilities

Active handovers

Outcomes written back

Connected work · Agent system

Business vertical

People and workforce

Agent on shift

Screening Agent

Accountable person

Hiring managers

Evidence · hourly

Roster and attendance

Next step

PeopleOS

PeopleOS · screen 04 of 05

Attrition early warning

Where people are leaving, and what those stores have in common.

3 issues · 1 waiting on you

Issues in scope

3 instances across 2 places

Waiting on you

0 already decided in this session

Three largest

AED 4.2m–AED 12.9m

Modelled annual value, three largest only

Running without asking

0 armed and waiting on a threshold

Decision owner

Country head

Returns to Country head, Country HR

Scope

Oman

27 stores · all capabilities

Listen

Leaver rate by store and roleShift pattern and overtimeExit reasons, codedInternal moves and promotions

Process

Store pattern, not individual predictionWhat the leaving stores shareIs it pay, shift, manager or travel

Act

Surface the pattern to the countryRedeployment before exitRefuse to flag individuals

Written backEvery outcome returns to the systems that produced the evidence.

KindOrder

Queue

3 issues · 1 waiting on you

Choose

Attrition in Oman concentrates in 10 stores and the cause is not the same in all of them

Oman

What happened

Coded exit reasons, worked shift patterns and overtime by store separate into distinct groups. Some stores lose people to shift instability, some to travel distance, some to a single role type that is under-resourced everywhere.

Why it matters

A single retention programme across all of them fixes one group and wastes money on the other two. The evidence separates them, but which to address first is a resourcing decision with different costs and different payback.

Already done

Stores are grouped by the pattern their own data shows, with the cost of each intervention modelled separately.

Worth

AED 2.7m to AED 8.3m a yearModelledAttrition concentrated in specific stores with different causes in each group. Value is the intervention matched to the cause rather than applied across all of them.Leaver rate in the concentrated stores

Then what

Chosen intervention costed and scoped to its store groupCountry headReports back 7 Oct

Done

Exit reasons are coded from what people actually said, in the language they said it

Oman

What happened

Exit conversations run in the leaver's language and the reasons are coded consistently rather than written as free text by whoever conducted it.

Why it matters

Free text exit notes are unusable in aggregate, which is why exit notes are hard to act on in aggregate. Coded reasons across a country make the pattern visible in a month rather than never.

Already done

Running across markets, coded to one scheme, with the free text retained alongside.

Worth

AED 1.5m to AED 4.6m a yearModelledExit reasons coded consistently across markets rather than written as free text. Value is the pattern visible in a month rather than never.Exit reasons usable in aggregate

Then what

Running continuously, coded across marketsCountry HRStanding, no return date

Coded consistently across markets, with the original wording retained

Declined

The system declined to flag individuals as likely leavers

Express, City Centre

What happened

The data would support scoring individuals on likelihood of leaving. The read stops at the store and the role.

Why it matters

An individual flight risk score changes how a manager treats that person, usually badly, and it is frequently wrong. Store and role patterns produce every useful intervention without labelling anyone.

Already done

Every retention action names a store, a shift pattern or a role. None names a person.

Exposure

Not a revenue lineAn individual flight risk score changes how a manager treats that person, usually badly, and it is frequently wrong.Retention actions naming a store, not a person

Then what

Refusal held, store and role level onlyGroupStanding, no return date

Override

Nobody. Refused at design

Cost of overriding

An individual flight risk score changes how a manager treats that person, and it is often wrong