TL;DR: A B2B logistics company in Leeds with £4.6M in revenue and 180 active accounts was assessing client health at a fortnightly meeting where three people reported from gut instinct. Last year, 25 accounts churned. The commercial team anticipated 7. The other 18 were surprises. When we mapped the data retroactively, churned accounts had been declining for an average of 4.2 months across four systems (WMS, TMS, Sage, Salesforce) that nobody was looking at simultaneously. We built a four-stage agent that aggregates signals weekly, calculates a weighted health score per account, alerts account managers when scores cross below threshold, and learns which signals most strongly predict churn. The fortnightly 2-hour meeting became a 30-minute weekly dashboard review. Early declines caught at month 1-2 instead of month 4. Agent cost: £260/month.
The Fortnightly Review
Every other Monday, Tom pulls up a spreadsheet. 180 rows. One per client. He's colour-coded it: green, amber, red. The colours are based on his judgment, last updated at the previous review (or whenever he remembered something concerning in between, which is a data freshness policy governed entirely by the frequency with which concerning things happen to cross his mind).
He and the two account managers go through the list.
"Barrett & Sons has been quiet. Volume's down a bit. Probably seasonal."
"Crossfield Group raised a complaint about a late delivery last week. I've spoken to them, I think it's fine."
"Northway hasn't replied to my last two emails. Could be nothing."
Three people assessing 180 accounts by combining data they've each checked separately (WMS reports for one, Sage revenue trends for another, Salesforce complaints for the third) and reporting at a fortnightly meeting from memory and instinct. Two hours. Every other Monday. 156 hours per year of gut-instinct synthesis.
Volume declines show up in the WMS. Complaint patterns show up in Salesforce. Revenue erosion shows up in Sage. Delivery failures show up in the TMS. But nobody is looking at all four simultaneously for the same account. The signals exist in separate systems. The synthesis happens in Tom's head, once a fortnight, for 180 accounts.
Last year, 25 clients left. Tom's team identified 7 as at-risk before the cancellation notice arrived. The other 18 were, in Tom's assessment, "not on our radar." Eighteen accounts showing declining volume for an average of 4.2 months before cancellation. The signals were in four systems. Nobody connected them because the dots were in four different places and one person's inbox, and connecting dots across four systems for 180 accounts fortnightly from memory is the sort of task that sounds plausible in a meeting and impossible in practice.
The Company
B2B logistics and fulfilment company in Leeds. Forty-two employees. £4.6M annual revenue. 180 active client accounts across warehousing, fulfilment, and distribution services.
Tom is the commercial director. His team: two account managers, each responsible for roughly 60 accounts, with Tom overseeing all 180 and managing the largest directly. Average annual contract value: £25,600. Long-term relationships. Warehousing and fulfilment contracts typically run 12-24 months. When a client leaves, the replacement sales cycle takes 3-6 months and costs roughly five times what retention would have cost.
The commercial team's loaded cost: £195,000 per year. An estimated 25-30% goes to manual health assessment: reviewing complaint logs, checking volume trends, scanning email threads, and preparing for the fortnightly review. That's £48,750 to £58,500 per year in monitoring work.
Churn from the prior 12 months: 14% rate. 25 accounts. £640,000 in revenue. Of those 25, an estimated 40-50% were saveable (service issues that could have been addressed if flagged earlier, not pricing undercuts or closures). Saveable revenue: £256,000 to £320,000 per year.
What the Spreadsheet Couldn't Show
The spreadsheet captured status. Green, amber, red. What it couldn't capture was trajectory.
An account showing green at the last review and green at this review might be stable. Or it might have been declining steadily between reviews, invisible because nobody checked between fortnights. Tom's colour-coding was a snapshot taken every 14 days. The accounts that churned were declining continuously. A fortnightly snapshot catches the accounts that fall off a cliff. It misses the accounts that slide gradually, which is what 18 of 25 were doing.
The 4.2-month average decline followed a consistent pattern: order volumes down 15-30%, complaint frequency ticking up from zero per quarter to two or three, email response times lengthening, QBR engagement dropping from substantive discussions to polite nods, payment terms stretching from net-30 to net-40 to net-45.
Each signal, taken alone, was explainable. A reasonable person looking at any single signal would say "normal variation." A reasonable person looking at all five for the same account over the same four months would say something quite different. But nobody was looking at all five because the signals lived in four systems and the only aggregation mechanism was one person's instinct, exercised fortnightly.
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