Revenue to Receipts: Shortening the Post-Acquisition Cash Cycle

⚡ Quick Summary

• “Revenue to receipts” is the lag between when you recognise revenue and when cash actually hits the bank.
• For unlisted assets, long or unpredictable lags can quietly destroy deal returns, especially right after acquisition.
• Post‑acquisition, you need a cash‑first map of the entire revenue process: invoicing, terms, disputes, credits, and collections.
• The goal is to convert revenue into receipts faster without breaking customer relationships or creating high customer concentration risk.
• Model the full path from contract to cash, then run experiments on terms, billing cadence, and collection workflows.
• Tie your improvements back to budget vs actuals in cash and working capital KPIs so value is measurable.
• This article sits under the broader unlisted asset management guide and connects closely to working capital improvements and collections dashboards.
• If you’re short on time, remember this: what you bill doesn’t matter until it becomes cash. Model, measure, and manage the lag aggressively.

💡 Introduction: Why This Topic Matters

Most acquisition models assume a neat, predictable conversion from revenue to cash. Reality, especially for complex unlisted infrastructure or service‑heavy unlisted assets, is messier: billing delays, disputes, credit notes, partial payments and bad debt all stretch the cash cycle. Post‑acquisition, this lag is often worse as systems change and teams adjust. For sponsors and operators, understanding unlisted assets means more than tracking EBITDA – it means knowing how quickly revenue becomes cash you can redeploy. This cluster article dives into that gap. It shows how to build a simple but rigorous “revenue to receipts” model, how to use it for decision‑making, and how it fits alongside your 90‑day cash plan, working capital programmes and budget vs actuals analysis in cash.

🧩 A Simple Framework You Can Use

Use a five‑stage framework: contract → bill → collect → clean up → learn. Contract captures pricing, term structures, and any bespoke clauses that affect timing. Bill covers when and how invoices are generated, including system bottlenecks. Collect focuses on reminders, follow‑up workflows, and dispute handling. Clean up covers, credits, write‑offs, and adjustments. Learn is the feedback loop: you compare forecast vs actual cash cycles and embed improvements in process and systems. This framework plugs into your broader unlisted asset management model and working capital dashboards from AR ageing to collections. It lets finance and operations see exactly where value is leaking, without needing complex statistical models.

🛠️ Step-by-Step Implementation

Step 1: Map Current Revenue-to-Cash Pathways

Start by documenting how revenue actually becomes cash today, not how it’s supposed to work. For each major revenue stream, map the journey: contract signed → service delivered → invoice issued → due date → reminder cadence → dispute handling → cash received. Capture variations by customer type or region, especially where customer concentration is high. This is core to understanding unlisted assets you’ve just acquired: are cash lags structural (industry norms) or fixable (process gaps)? Annotate where manual steps slow things down – spreadsheet billing, approvals, or handoffs between teams. Link each pathway to an average days‑to‑cash number that you can later track in dashboards. The goal is a visual, shared picture that finance, operations, and any financial adviser working with you can all agree on.

Step 2: Build a Baseline Cash-Conversion Model

Next, convert that map into a quantitative model. For each revenue stream, set assumptions for timing: days from delivery to invoice, typical terms, actual average days to pay, dispute rates, and write‑offs. Use templates or AR ageing logic from your working capital toolkit to project when invoices become cash by cohort. This gives you a baseline “revenue to receipts” curve that can be compared across unlisted assets in the portfolio. Make sure the model feeds into your short‑term cash view and budget vs actuals in cash so the impact of slow collections is obvious. Don’t overcomplicate – focus on a small number of drivers that explain most of the lag.

Step 3: Identify Quick Wins and Structural Constraints

With a baseline in place, separate quick wins from hard constraints. Quick wins might include tightening invoice cut‑offs, fixing data issues that cause billing delays, or standardising reminder workflows. Structural constraints might be customer procurement cycles, regulated billing windows for unlisted infrastructure, or long approval chains in key accounts. Model the impact of each potential change on days‑to‑cash and overall working capital. Prioritise by impact vs effort. Be especially cautious where high customer concentration is present – changing terms too aggressively can increase customer concentration risk. Use scenarios to show leadership what happens to cash if you shorten the cycle by 5, 10, or 15 days.

Step 4: Implement Targeted Working Capital Experiments

Now design controlled experiments: change net terms for a specific segment, introduce early‑payment incentives, or adjust reminder frequency. For each experiment, encode assumptions in your model and track actual outcomes via a collections dashboard. Use tooling that links operational changes directly to cash impacts so teams see real results, not just theoretical improvements. Where possible, align these experiments with your broader working capital programme for unlisted assets and with 13‑week cash forecasts. This ensures improvements feed directly into headroom, covenant and deployment decisions. Document each experiment so future teams – or future buyers – can see how you optimised the asset’s cash cycle as part of disciplined unlisted asset management.

Step 5: Industrialise and Embed Learnings

Once successful experiments are identified, turn them into standard practice. Update contract templates, billing policies and system defaults so improvements persist. Add a small set of core metrics – days‑to‑invoice, days‑to‑pay, dispute rate – to your board and investor reporting packs [150]. Align them with budget vs actuals narratives in cash so everyone sees revenue in context of cash conversion, not just P&L. At the portfolio level, compare “revenue to receipts” performance across unlisted assets to spot outliers and share playbooks. Finally, integrate these metrics into any exit readiness work, so future buyers can see a track record of cash cycle improvements, not just headline revenue growth.

📈 Real-World Examples

A mid‑market PE fund acquired a services‑heavy unlisted infrastructure asset where revenue looked strong, but cash was always tight. By building a simple “revenue to receipts” model, they discovered average days‑to‑invoice was 20 days and average days‑to‑pay was 65 – far from the nominal 30‑day terms. Using AR ageing tools, they segmented customers and found that a handful of large, slow‑paying accounts created significant customer concentration risk. The team rolled out standardised billing cadences, early‑payment incentives, and a dedicated collections rhythm. Within six months, days‑to‑cash dropped by 15 days, adding several million dollars to available headroom and supporting parallel working capital improvements. That story later became a core part of the asset’s exit narrative.

⚠️ Common Mistakes to Avoid

Teams often model revenue perfectly but ignore cash timing altogether, assuming receivables will just “wash through.” For unlisted assets, especially those acquired with leverage, this is dangerous. Another mistake is treating all customers the same; in reality, a few large accounts can dominate your cash cycle, especially where high customer concentration is present. Some operators also try to fix everything at once – changing terms, processes, and systems simultaneously – making it hard to isolate what worked. Others don’t connect improvements back to budget vs actuals in cash, so wins are invisible outside finance. The fix: model the lag explicitly, prioritise interventions, measure outcomes via dashboards, and keep stakeholders focused on cash, not just revenue.

❓ FAQs

🚀 Next Steps

Pick one major revenue stream in a recently acquired asset and build a lightweight “revenue to receipts” model around it. Use your existing AR ageing tools, collections dashboards and 13‑week cash forecasts to anchor assumptions. Then identify one or two experiments – faster invoicing, different reminder cadence, or targeted term changes – and run them for 60-90 days. Feed results back into your broader working capital improvement programme and your post‑acquisition cash model. Over time, roll out the same approach across other unlisted assets, building a portfolio‑level view of cash cycle performance. The objective is simple: every dollar of revenue should become cash as fast and predictably as possible.