Field workforce management software reports become manageable only when every exception carries a cause. One compliance percentage usually hides four separate problems: blocked access, wrong schedules, undeclared leave, and real non-compliance. Each has a different owner and a different fix, so the breakdown matters more than the headline number.
The report arrives every Monday. Attendance compliance, 68 percent. For an operations head running a few thousand deployed people through a manpower agency, that figure starts a familiar sequence: a call with the agency, a reminder about discipline, a promise to follow up, then the same number the following week.
There is a better response, and the operators who get results use it. Refuse the number. Ask what it is made of.
That is not pedantry. In field operations, one compliance percentage is usually three or four unrelated problems stacked into a single figure, and the fixes have nothing in common. One group needs a helpdesk ticket. One needs a corrected schedule. One needs an advisory to the supervisor. One needs a memo from the agency. Send the same reminder to all four and only the last group hears anything relevant.
Attendance rate and usage rate are not the same number
Most field reports lead with attendance rate: how many people timed in today, divided by headcount. Useful for payroll. Almost useless for judging whether your system is being used.
Usage rate answers a different question. It counts everyone who reported something at all through the system: a time in, a declared rest day, a filed leave, even a recorded absence against a scheduled shift. An absence recorded against a schedule is data. It tells you where someone was supposed to be and that they were not there. A silent user tells you nothing.
Take an illustrative deployment of 1,200 merchandisers and promoters. On a given day, 900 timed in, 150 declared a rest day, 60 were on approved leave, and 40 were marked absent against a schedule. Fifty people produced nothing at all. Report attendance rate and adoption looks like 75 percent, which invites a conversation about discipline that will not go anywhere. Report usage rate and adoption is 95.8 percent, with a real gap of 50 people. Those 50 are the only names anyone needs to work on.
The distinction matters commercially, not just semantically. Head office reads a low number as a failing rollout and starts questioning the investment. The agency reads it as an accusation and gets defensive. Both reactions come from a metric that was measuring the wrong thing.
The four causes behind an inactive field user
Define inactive strictly: no schedule, no time in, no declaration, nothing. Once that group is isolated, the count alone still cannot be managed. Every inactive line needs a cause, and in a decade of Philippine deployments the causes fall into four buckets that almost never overlap.
Access and technical. The account was created but never activated. The device runs an old app version, or the load ran out, or the phone was replaced and nobody re-enrolled it. These people cannot report even when they want to. They belong in a support queue with a ticket number, not in a compliance discussion. Left unlabelled, they quietly cap your usage rate at a ceiling no amount of follow-up will lift.
Administrative and schedule. Someone moved from an 8 to 5 shift to a 9 to 6 shift and the change was never approved or uploaded. The login window no longer matches the person, so the app blocks the time in and the report shows nothing. In multi-store programs the same failure appears as a missing store assignment: the person is real, the store is real, the mapping between them was never completed. This is master-data work owned by coordinators, and it is the cause most often mistaken for laziness. Automated scheduling removes most of this bucket before it reaches a report.
Undeclared rest day or leave. The person is legitimately off. Nobody filed it in the system, so a normal day off appears as a hole in the data. The fix is an advisory and a process correction with the immediate supervisor, usually delivered once and then held by habit. Companies that skip this step spend months chasing absences that were never absences.
Non-compliance. The person could have reported, had a working account and a valid schedule, and did not. This is the only bucket that deserves a memo, and it is almost always the smallest of the four. Escalating it through the agency works precisely because the other three causes have already been stripped out and the case is clean.
The operational discipline behind this is simple to state and rare to see: which inactive users do we troubleshoot, which do we correct, which do we advise, and which do we memo. Ask the agency or the internal coordinator to make that breakdown a standing column in the weekly report rather than a special request. A number you can act on beats a number you can only forward.
A one minute store visit still counts as coverage
Once attendance holds, the next report is store coverage against a journey plan. The trap is that most systems will happily record a visit that never happened in any meaningful sense.
A merchandiser arrives at a mall branch, checks in, waits a minute, checks out, and walks away. The dashboard counts one store visited. The shelf was not checked, the display was not photographed, the price tags were not verified, the competitor spread was not noted. Coverage looks healthy while execution quietly does not exist. Anyone who has run field teams in the Philippines has seen this pattern, usually discovered when sell-out data contradicts a perfect coverage report.
Four controls close the gap, and none of them require a new system:
Require the call procedure forms before check-out is permitted. If the opening checklist, display audit, and stock count are mandatory fields, a one minute visit becomes impossible by construction rather than by supervision.
Geofence both ends of the visit. Check-in inside the store perimeter is common. Check-out inside the same perimeter is what stops a person from logging out from the parking lot or the next town.
Require a reason code and a photo for every unplanned visit and every schedule change. A screenshot of the store contact’s message or the supervisor’s approval takes ten seconds to attach and removes the argument entirely at month end.
Put dwell time on the manager dashboard. Average and total minutes per store, sorted ascending, will show you within a week which routes are real and which are drive-bys.
The denominator trap that turns deviations into compliance
Here is a measurement failure that survives in a surprising number of deployments. A promoter plans five stores and visits six, the sixth being an unplanned deviation. The system adds that deviation to both the actual count and the target count, so productivity reports 100 percent. Plan against six, delivered six.
The arithmetic is defensible. The management value is gone. The whole point of a journey plan is that the plan was set deliberately, by someone weighing store value, travel time, and coverage frequency. If any visit can be added to the target after the fact, the plan stops constraining anything and compliance becomes a number that can never fall below 100.
Report three figures separately instead of one. Completion against the original uploaded plan, which is the only number that tests planning discipline. Deviation count with reasons, which tells you whether the plan is wrong or the execution is loose. Coverage of the assigned store universe over the cycle, which catches the stores nobody visits in either version.
A rising deviation rate is not automatically bad. In distribution management and appliance retail, a supervisor who reroutes toward a store with a stockout is doing the job. What you cannot afford is a deviation rate that is invisible because it was absorbed into the compliance percentage.
Why adoption has to come before dashboards
The sequencing question comes up in every rollout. The instinct is to build the analytics first, because dashboards are what the executive committee asked for. The order that works runs the other way.
Phase one is attendance and system usage, held above a defined threshold for at least two consecutive weeks. Phase two is journey plan and visit compliance, which only produces meaningful output once the people in it report daily. Phase three is in-store data capture: sell-out, display execution, inventory counts, and competitor pricing, which is where trade marketing execution becomes measurable. Dashboards built on 60 percent adoption produce numbers nobody will defend in a meeting, and the credibility lost there is expensive to recover.
One caution on inheriting confidence from phase one. A strong attendance usage rate proves that each person is correctly mapped to one location, because otherwise the time in would fail. It proves nothing about whether the full store universe is assigned correctly for multi-store journeys. Reconcile the store master list against actual assignments before journey plans go live, or phase two will fail for reasons that look like adoption problems and are actually data problems.
Scale makes the discipline pay off rather than making it optional. Megasoft gets secure attendance and coverage data from 1,500 merchandisers and coordinators, and volume like that only stays clean when the exception handling is defined rather than improvised. Coverage gains follow the same rule. Chooks-to-Go digitized 90 percent of their field reports and doubled store coverage from 10 to 20 stores per day per field employee, a result that is only believable because the underlying visit records were gated rather than self-reported.
What to audit this quarter in your field workforce management software
Eight checks, each doable inside a normal week.
- Split your weekly report into attendance rate and usage rate, and stop quoting the two interchangeably in management meetings.
- Define inactive in writing: no schedule, no time in, no declaration. Circulate the definition to the agency and to your coordinators so the count means the same thing to everyone.
- Add a cause column to every inactive line and require it in the standing report format, not on request.
- Assign one owner per cause. Support queue for technical, coordinator for schedule and mapping, supervisor for undeclared leave, agency or HR for non-compliance.
- Switch on form gating so check-out is blocked until the required visit forms are complete.
- Pull dwell time for one week, sort ascending, and review the bottom 20 records with the area supervisor.
- Reconcile the store master list against current assignments before uploading journey plans, and record the match rate.
- Freeze new dashboard requests until usage holds above your threshold for two consecutive weeks.
None of these need a procurement cycle. They need someone senior enough to insist that a number arrives with its reason attached.
Where Tarkie fits
Tarkie was built for exactly this order of operations. Geo-tagged attendance with declared rest days and leaves gives you a usage rate rather than a bare attendance count. Required forms can gate check-out so a visit is not closed until the call procedure is done, and deviation reason codes with photo validation keep unplanned visits accountable instead of hidden. Journey plans upload directly, and dwell time, deviation rate, and coverage against plan land in the manager dashboard rather than in a consolidated spreadsheet someone builds on Friday. Teams running store audits and in-store execution usually start with Trade Check for this reason.
We have run this sequence across more than 200 implementations since 2014, and the pattern holds: the deployments that succeed are the ones where somebody refused to accept a percentage without its breakdown.
Get the reasons attached to your numbers
If your weekly field report still arrives as a single percentage with no cause column, the fix belongs in the system rather than in a supervisor’s memory. Tarkie handles the usage-versus-attendance split, form-gated check-outs, and deviation tracking as standard behavior, on Philippine field teams from 50 people to several thousand. Tell us what your Monday report looks like, and we will show you the same report with the reasons attached: go.tarkie.com/inquire
Frequently asked questions
What is a good usage rate for field workforce management software?
Most Philippine deployments should target 95 percent usage, measured as everyone who reported anything through the system, including declared rest days, approved leaves, and absences recorded against a schedule. Attendance rate alone will read far lower and misrepresent adoption. The remaining gap should be small enough to work name by name.
What does inactive mean in a field attendance report?
Inactive means a user produced nothing at all on that day: no schedule, no time in, and no declaration of leave or rest day. It is different from absent, because an absence recorded against a schedule is still usable data. Only the inactive group represents a genuine visibility gap.
How do you stop field staff from checking in and out of a store without doing anything?
Make the required visit forms mandatory before check-out is allowed, so the call procedure has to be completed before the visit can close. Geofence the check-out as well as the check-in, and review dwell time per store weekly. Together these three controls remove the one minute visit as an option.
What is a journey plan or PJP in field sales?
A journey plan, often called a permanent journey plan or PJP, is the pre-set schedule of which stores a field employee visits, on which days, and how often. It becomes a compliance measure once actual visits are recorded against it. The plan only works as a control when unplanned visits are tracked separately instead of added to the target.
Should we build dashboards before or after field team adoption?
After. Reports built on partial adoption produce numbers that get disputed in the first management meeting, and rebuilding trust in the data takes longer than the rollout itself. Hold usage above your threshold for two consecutive weeks, then move to visit compliance and in-store data capture.