This is a composite story drawn from a real pattern we've seen across a handful of early customers — a regional building materials distributor in the Tampa metro area. Details are representative of what played out in their first 90 days. We're calling the company Suncoast Supply.
The Starting Point: 580 Invoices, Two People, One Spreadsheet
Suncoast Supply runs a lean finance operation. Two accounts receivable staff handle everything: sending invoices, processing payments, managing disputes, and chasing the overdue pile. Combined annual revenue is in the $45–60M range. Their customer base is about 280 active accounts — general contractors, commercial builders, and a handful of municipal procurement offices.
When they connected to Korrelo, we pulled in their QuickBooks Online data and immediately saw the shape of their AR problem. Of their open invoices on day one, 580 were in the 30-day past-due bucket. That's not unusual for a distributor with net-30 and net-45 terms across a mixed contractor customer base. What was unusual — or at least, felt unusual to their AR manager, Sandra — was that she was treating all 580 with the same level of urgency. The 3-day-overdue invoice from a contractor who pays reliably every 38 days got the same Monday-morning email as the 62-day invoice from a buyer who had gone completely silent.
Their DSO at intake was 58 days. Industry benchmark for building materials distribution typically sits in the 42–50 day range depending on customer mix and payment terms. At 58 days, Suncoast had roughly $2.1M more cash tied up in receivables than a company their size and segment should expect. That's not a rounding error. That's a line of credit they were effectively providing to customers for free.
What the First Two Weeks Looked Like
The first thing Korrelo did was score every open invoice by late-payment probability — not just flagging what was already overdue, but identifying which current invoices had high likelihood of going past due based on the customer's historical pattern. This is the part that surprised Sandra most. She had a mental model of her "problem" customers. The scoring confirmed some of what she knew, but surfaced a dozen accounts she'd classified as "fine" that were quietly drifting.
In the first week, the system sent 94 emails on Suncoast's behalf. These weren't generic reminders — they referenced specific invoice numbers, amounts, and in several cases adjusted tone based on the customer's response history. Contractors who had previously responded to firm language got firm emails. Long-term accounts with a pattern of late-but-reliable payment got softer nudges acknowledging the relationship.
Sandra spent about 90 minutes in week one approving email drafts and reviewing the escalation queue. She had been spending closer to 12–14 hours per week on manual follow-up work. That gap — 14 hours to 1.5 hours — didn't happen because her job got easier. It happened because the system was handling the cases that didn't need her judgment, and surfacing only the cases that did.
The 30-Day Bucket: From 580 to 210
By day 45, the 30-day aging bucket had dropped from 580 open invoices to 312. By the end of the first quarter (90 days), it was at 210. The reduction wasn't dramatic in any single week — it was gradual and consistent, which is actually what healthy AR recovery looks like. Big swings usually mean someone is clearing easy invoices and ignoring the hard ones; steady drops mean the collection process is actually working across the full aging spectrum.
A few things drove the shift:
- Earlier intervention on high-risk accounts. By flagging invoices before they went overdue, Korrelo let Sandra's team reach out before a problem escalated. Several mid-size accounts that would normally drift to 45+ days were resolved in the 15–20 day range.
- Consistent follow-up on the long tail. There's a class of customer who simply needs three or four polite emails before they process payment. Not malicious, just administratively slow. These accounts had been falling through the cracks because Sandra didn't have bandwidth to send four emails to 200 accounts. Automated sequences handled it.
- Smarter escalation. The accounts that went to phone calls were the ones that genuinely needed it — silent accounts, dispute patterns, amounts large enough to warrant a relationship call. Sandra made roughly 8–10 escalation calls per week instead of her previous 25–30, and those calls were better prepared. She had context on last email contact, invoice history, and amount outstanding before she dialed.
DSO at Day 90: 27 Days
At the end of Q1, Suncoast's DSO had dropped from 58 to 27 days. That's 31 days of improvement — roughly $1.3M in cash that moved from receivables to the bank account. Their collection rate (amount collected divided by amount invoiced in the same period) went from 81% to 93%.
It's worth being honest about what this number represents. A 31-day DSO reduction in a single quarter is at the high end of what we've seen. Suncoast's starting point was unusually high for their industry and customer mix — which meant there was more improvement available. Companies starting at 42 days DSO against a 38-day benchmark have less room to move. We're not saying every customer will see a 31-day swing. What we are saying is that the mechanism — earlier identification, consistent follow-up, smarter escalation — produced measurable outcomes on a realistic AR portfolio.
What Sandra Stopped Doing
This part matters as much as the DSO number. AR improvement isn't just a cash story — it's a capacity story. Here's the specific work that came off Sandra's plate:
- The Monday morning sort. Sandra used to spend 2–3 hours each Monday reviewing the aging report, prioritizing outreach, and writing follow-up emails. Now she reviews a prepared escalation list and approves queued drafts — about 40 minutes.
- The reminder email queue. She was writing or adapting 40–60 emails per week. Now she reviews and approves (or edits) 15–20 drafts, mostly for accounts with unusual context or large balances.
- The "why haven't I heard from them" calls. A chunk of her weekly calls were prompted by nothing more than noticing an account had gone quiet. Korrelo tracks response status and triggers escalation automatically, so these calls now come with a prepared brief instead of a cold review of the invoice history.
What she does with that time varies. Some of it has gone to more careful dispute resolution — the work that actually requires judgment. Some of it has gone to onboarding a new group of contractor accounts that Suncoast added in Q2. The point isn't that AR automation creates idle time. It creates capacity for higher-value work.
What This Tells Us About Building Materials AR
Distributor AR has a specific structure that most generic reminder tools don't accommodate well. Payment terms vary by customer and order type. Some accounts have standing POs with net-60 terms; others are one-off transactional buyers on net-30. Dispute patterns are common — short-pays are frequent and often legitimate (damaged goods, quantity discrepancies). And the customer relationship matters: an AR manager who is too aggressive with a reliable $400K-per-year contractor can do real damage.
Korrelo's model accounts for payment terms per customer and per invoice, not a single company-wide average. It also learns dispute patterns — if an account has a history of short-paying and then resolving it within two weeks, the model treats that differently than an account that simply stops responding. This nuance isn't something you can embed in a scheduled reminder tool. It requires actually understanding the customer's payment behavior over time.
If you're managing AR for a distributor, manufacturer, or B2B services company with a varied customer base and mixed payment terms, the pattern Suncoast went through is recognizable. The 30-day bucket fills up, the team chases it uniformly, and most of the energy goes into accounts that would have resolved themselves in another week anyway. The accounts that actually need intervention get the same generic email as everyone else. That's where DSO bleeds — not from the obvious problem accounts, but from the misallocated effort across the full aging stack.