Case studies

What actually changed, with numbers

Three deployments written up properly: the situation, what we configured, and what came out of it. Anonymised at the customers' request — we do not publish named references without written permission, and we would rather show you real detail without a logo than a logo without detail.

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Productive hours per paid hour
Unexplained time

Composite of the three deployments below, shown to illustrate the shape of the change. The per-study figures are in each write-up.

BPO · 320 seats

A contact centre found nine per cent of paid hours it could not account for

9% Unaccounted paid hours found

1 week → 0 Manual audit collation

2 Handover points fixed

The situation

A two-site outsourcing provider was billing clients on rostered hours while its own delivery managers suspected adherence was slipping. Attendance lived in a spreadsheet maintained by a team leader at each site, and client audits took a week of manual collation.

What we configured

BeActive was rolled out to one site first, with shifts and rotations configured against the real roster. Idle thresholds were set high — this is phone work with long listening periods — and away-from-system time was classified as productive where it matched scheduled breaks.

What came out of it

The gap between paid and productive hours came out at nine per cent, concentrated in two specific shift handovers rather than spread across staff. Fixing the handover process recovered most of it. Client audit packs are now a scheduled export.

Drawn from the figure in this write-up: the nine per cent gap, expressed the other way round.

It was not a people problem, which is what everyone assumed. It was two handovers where nobody owned the queue.

Agency · 25 people

An agency discovered it was billing half the hours on one retainer and double on another

2 Retainers repriced

11h Weekly admin removed

3 mo Data before decisions

The situation

A creative agency ran eleven retainers on reconstructed Friday timesheets. Margin was falling year on year with no clear cause, and account leads had no evidence for scope conversations.

What we configured

Time attribution was enabled per client, and the team spent two minutes a day allocating their tracked hours instead of an hour every Friday reconstructing them. Three months of real data were collected before any commercial decisions were made.

What came out of it

Two retainers were consuming roughly half the billed hours; one was consuming nearly double. Two were repriced at renewal with the hours data on the table, one was restructured, and Friday timesheet admin disappeared.

Over-serviced retainer
Under-serviced retainer

Hours actually delivered against hours billed, indexed to 100. Two of the eleven retainers.

The number that changed the conversation was not productivity. It was hours per account, which we had never actually measured.

Software · 18 engineers

A product team stopped planning sprints against days that did not exist

4.5h Real daily focus time

8h What planning assumed

2 Recurring meetings cut

The situation

Sprint estimates missed consistently and nobody could explain why. Planning assumed eight-hour days; the engineering manager suspected meetings and support interrupts were the cause but had no way to quantify either.

What we configured

Activity monitoring was enabled at team level only, with individual comparison deliberately off the table and stated as such to the team. Meeting time was classified through the calendar integration, and support work was attributed as a separate project.

What came out of it

Available focus time averaged four and a half hours per engineer per day, not eight. Sprint capacity was re-based on the real figure and estimate accuracy improved materially within two sprints. Two recurring meetings were cut.

Focus hours per engineer per day

What sprint planning assumed against what the team actually had.

We told the team up front that nobody would be ranked on this. That is the only reason it worked.

Figures are taken from each customer's own BeActive reporting over the period described. Company names, sites and individuals have been removed at their request.

The pattern

What these three have in common

Three very different businesses, and the same three things made the difference in each of them.

The problem was structural, not personal

In all three cases the assumption going in was "people are not working hard enough". In all three the actual cause was a process: a handover, an unmeasured retainer, a meeting load. Nobody needed disciplining.

They collected data before deciding anything

Between one and three months of measurement before any commercial or staffing decision. Acting on week one is how organisations reach confident wrong conclusions.

They told the team the truth up front

Announced, explained, everyone given their own dashboard, and explicit about what it would not be used for. The engineering team was told outright that nobody would be ranked — the manager credits that for it working at all.

We are looking for the next three

If you deploy BeActive and are willing to have the results written up — anonymised or named, your call — we will do the analysis with you and share it back whether the numbers flatter us or not.

We do the analysis with you, on your own numbers
You see the write-up and approve it before anything is published
Named or anonymised is entirely your decision
You keep the analysis whether we publish or not

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