Delayed vendor payments every off-season until a working capital overhaul fixed the cycle.
This $9M retailer had a business that ran on seasonal peaks. About 60 to 65% of their annual revenue came in during just 4 to 5 months. Once that window closed, revenue dropped sharply, sometimes by as much as 55%. That kind of seasonality is manageable, but only if you plan for it. They weren’t planning. They were reacting. Every slow season, the same cash crunch showed up and the same fires had to be put out.
The real problem wasn’t the slow season. It was that nothing was set up to handle it.
No forecast meant no early warning. By the time cash fell below safe operating levels, it was already too late to do anything about it.
The timing mismatch between payables and receivables kept working capital constantly under pressure. And with inventory purchases driven by fear rather than data, the problem compounded itself every cycle.
Every time things got tight, the response was the same. Delay a vendor. Borrow short term. Survive the season. Repeat.
The cash flow issues weren’t caused by one thing, so the fix couldn’t be either. We broke the engagement into three focused phases, each targeting a different layer of the problem.
Step 1
Locked meeting dates upfront, set a pre-read process one week before every meeting, and defined who owned what across the CEO, CFO, and board chair. Meetings had agendas, minutes, and action-item tracking so nothing fell through the cracks between sessions.
Step 2
Set a weekly leadership meeting rhythm, defined the month-end close window with a hard target of the 15th and no later than the 21st, and built KPI dashboards with named owners across sales, marketing, and finance.
Step 3
Modeled weekly inflow targets against operating outflows of roughly $197K per week excluding rent and $250K including rent. Rent scenarios, funding needs, and runway were visible in one place and updated on a consistent basis.
Step 4
Pressure-tested unit-level labor costs, revenue targets, and cash break-even assumptions at the location level. Set the FY2026 budget deadline at January 31 with year-end financials due February 15 and assigned clear ownership across the leadership team.
Phase 1
The business had no visibility into what was coming. We built a detailed weekly cash flow model that mapped revenue inflows by season, payables schedule, payroll cycles, and inventory purchase commitments all in one place. For the first time, liquidity gaps were visible 6 to 8 weeks before they hit. That alone shifted the entire mindset from reactive to proactive.
Phase 2
On the receivables side, we introduced a 2% early payment incentive and put a structured collections follow-up process in place. On the payables side, we negotiated extended terms with key vendors, moving from Net 30 to Net 45 and Net 60. Then we built a payment prioritization system to make sure outflows were structured, not scrambled.
Phase 3
We ran a full SKU velocity analysis to identify what was actually selling and what was just sitting. Reorders on slow-moving items were cut back, excess stock was liquidated, and the purchasing cadence was rebuilt around the forecast model rather than gut feel.
Step 4
Pressure-tested unit-level labor costs, revenue targets, and cash break-even assumptions at the location level. Set the FY2026 budget deadline at January 31 with year-end financials due February 15 and assigned clear ownership across the leadership team.
The real test came at the end of the first off-peak cycle under the new system.
Cash hit its seasonal low but stayed above the minimum operating threshold. No vendor payments were delayed. No short-term borrowing. Marketing budget going into peak season was fully intact.
For a business that had firefighted through every slow season for years, this was the shift. Not because everything was perfect, but because the outcome was planned for the first time.
The business stopped reacting to cash problems. It started anticipating them.
Of customers recently chose a financial product from a provider other than their main bank.
Of revenues at risk between now and 2025 if card-issuing banks are slow to invest in next-gen payment options.
The share of US banks’ working hours which could be impacted by technologies like generative AI.
the average premium that commercial payments clients would be willing to pay their provider for value-added services.
The numbers only tell part of the story. Once the system was in place, the way the business operated day to day completely changed. Leadership went from putting out fires every slow season to actually running the business with visibility and control. The stress of not knowing what was coming next went away, and decisions that used to feel risky started feeling straightforward.
A 13-week forecast gave 6 to 8 weeks of advance warning on cash gaps.
Cutting $200K from excess inventory required looking at data they already had.
Moving from Net 30 to Net 60 added more breathing room than any revenue initiative.
One structured forecasting model replaced years of reactive scrambling.
Seasonal Retail, Consumer Goods
~$9M Annual Revenue
One Full Slow Season Cycle
Crisis Mode
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The cash flow improved fast. But the bigger change was how the business started operating day to day.
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