Close length
Median fell from 13 to 7 working days (IQR 5–9). The gain concentrates in customers whose bank reconciliation was on the critical path — for those it was the single change that moved everything downstream.
Customers
Every figure here is measured from production data rather than reported in a testimonial, and every one shows where it sits in the distribution. A median with no spread sets an expectation half of customers will not meet.
Send a few figures and we will tell you which of these you would plausibly see.
Customers
Median across customers who ran the relevant workflow for at least two quarters, with the interquartile range in brackets.
Median fell from 13 to 7 working days (IQR 5–9). The gain concentrates in customers whose bank reconciliation was on the critical path — for those it was the single change that moved everything downstream.
91% at week 20 (IQR 78–95%). Vendor concentration is the strongest predictor: customers whose top 50 vendors cover most volume land above the upper quartile within twelve weeks.
93% (IQR 86–96%). The remaining 7% is judgement — short payments, unexplained deductions, batched wires against many invoices — and it stays with a person by design.
Median improvement of 9 days (IQR 4–14). Almost entirely driven by earlier invoicing and by collections being worked systematically rather than by customers paying faster.
Median fell from 5 days to under 1 (IQR 0.5–2). This is the outcome multi-entity customers most consistently report as the reason the engagement paid for itself.
From a median of 3 untied balance sheet accounts to 0. Unglamorous, and the finding auditors respond to most directly.
The interquartile ranges above are wide, and that is the honest part. A customer at the lower quartile on bill coding is at 78% rather than 91% — still useful, and a materially different business case.
Four factors explain most of the variance, and all four are knowable before you buy. Vendor concentration, document quality, whether coding rules are written down, and how much history exists for the pattern to be learned from.
A customer strong on all four typically lands above the upper quartile within twelve weeks. A customer weak on two of them sits below the median indefinitely, and no amount of model improvement changes that, because the information required is not present in their systems.
Two patterns account for nearly every disappointing outcome we have had, and both were visible in advance.
One customer’s expense coding plateaued in the low sixties because receipts arrived as photographs of crumpled paper, frequently illegible. That is not a model problem and no tuning fixed it. The honest fix was changing how receipts were captured, which was a policy decision rather than a software one.
Another customer’s allocation workflow escalated constantly because four people in the business described the allocation rule differently. Automating it would have encoded one person’s version permanently, so we stopped and told them the governance decision came first.
We now screen for both during scoping and will decline the work rather than take it and underperform. That costs us engagements and it costs us fewer than the alternative does.
From production data, not from customer self-reporting. Straight-through means the transaction completed with no human interaction and was not reversed within 90 days — an approval click does not count, which is why these figures are ten to fifteen points below what a looser definition would produce.
Before figures are measured during the first month rather than recalled. Where a customer could not supply a reliable baseline, they are excluded from that metric rather than estimated.
The sample skews toward services businesses and software companies, which is what our customer base looks like. It under-represents distribution and manufacturing and we would expect different results there.
Is one run on your own data. Send several hundred historical transactions with known outcomes and we will score against them and show you where it fails, before you commit to anything. A vendor unwilling to be measured that way before a purchase is telling you something.
Questions
Our medians describe our customers. Only your data describes you, and we will score against it before you commit.