Event Makers: The Hidden Cash Gap in Spreadsheets

Fifty Excel files. Zero real visibility. How an event agency discovered too late that “compliant” numbers concealed a €41K gap

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Paolo Rinaldi has managed Event Makers, a B2B corporate event agency in Verona, since 2018. When describing his financial control system, the answer is immediate: “Excel.” One spreadsheet for monthly budget, one for costs per event, one for margin per client, one for projected cash flow, one for year-over-year comparison. Fifty interconnected files, manually updated once a month by an external commercialista (Italian CPA and business advisor).

In September 2025, Paolo discovered that available cash in the bank account was eighty-two thousand euros, not the one hundred twenty-three thousand his budget Excel indicated. The forty-one thousand euro difference was not a transcription error. It was the sum of client receivables blocked by payment delays that Excel showed as “collectible within thirty days” but which in reality had been stuck for ninety days, plus a fifteen-thousand-euro wire transfer for a June event that the client had disputed for “non-conforming service” and would probably never arrive.

The problem was not the commercialista’s dishonesty or Paolo’s incompetence. The problem was that Excel, by its nature, shows what it’s told to show. It doesn’t automatically explore whether receivables entered as “collectible” are actually collected. It doesn’t investigate whether clients who always paid punctually have started delaying. It doesn’t cross-reference accounting balance with actual bank balance to highlight discrepancies. Excel is a passive tool. It waits for someone to manually update the data, and shows the result. Nothing more.

The Invisible Information Gap

Event Makers closed 2023 with four hundred ten thousand euros in revenue and an operating loss of two hundred nine thousand euros. In 2024 revenue dropped to three hundred fifteen thousand euros, the loss reduced to forty-six thousand euros. In 2025 revenue further declined to two hundred one thousand euros with an operating loss of forty-two thousand euros. The numbers seem to tell a story of gradual improvement: losses are reducing, the situation is under control.

But these aggregate numbers hide critical dynamics that Excel doesn’t spontaneously highlight. Personnel costs, which in 2023 represented thirty-six percent of revenue, rose to fifty-four point seven percent in 2025. This means that today over half of revenue is absorbed by salaries before considering any other costs. The benchmark for event agencies indicates a maximum sustainable incidence of forty percent.

Gross margin per event, which in 2023 averaged twenty-eight percent, dropped to nineteen percent in 2025. This erosion occurred gradually, event after event, without any automatic alert highlighting it. Audio-visual suppliers increased prices by fifteen percent, location costs grew by twelve percent, but client pricing remained unchanged because “the market won’t accept increases.” The result is insufficient margin to cover fixed costs.

Client portfolio concentration reached critical levels. Two clients represent sixty-eight percent of total revenue. When one of these two communicated in October 2025 the decision to reduce the 2026 event budget by fifty percent, Paolo suddenly realized that sixty thousand euros of projected revenue had evaporated. But this dependence was already present in 2024. Simply, no one was monitoring it with the necessary frequency.

What Excel Cannot Do

Excel’s fundamental problem is not lack of functionality. It’s possible to build complex spreadsheets with advanced formulas, automated macros, and dynamic charts. The problem is that Excel requires someone to actively decide what to search for, which data to cross-reference, which anomalies to investigate. And it requires that someone have the time and expertise to do this every day.

Paolo Rinaldi is not a professional CFO. He’s an entrepreneur who manages events, coordinates suppliers, maintains relationships with corporate clients. He doesn’t have the skills or time to build predictive dashboards, program automatic alerts when critical indicators exceed thresholds, or train machine learning models on client behavioral patterns. His commercialista updates Excel spreadsheets once a month with accounting data, but doesn’t have daily visibility on bank movements, actual payment delays, or granular margin composition by client.

The information gap is this: the difference between aggregate numbers that are correct from an accounting standpoint, and operational intelligence that identifies problems when they’re still solvable. Excel provides the former. An integrated financial intelligence platform provides the latter.

The Dashboard That Explores Instead of Waiting

The fundamental characteristic distinguishing an integrated platform like Mentally.ai Copilot from Excel is that it doesn’t wait for someone to tell it what to search for. It automatically explores data in depth, cross-references five different sources in real-time, identifies anomalous patterns through machine learning trained on three hundred thousand invoices from Italian SMEs.

The automatic Cassetto Fiscale (Italian Tax Drawer, the Revenue Agency’s digital document repository) scheduled every night downloads all active and passive invoices, receipts, tax documents. This data is instantly compared with actual bank movements. If an invoice shows as “collected” in the accounting system but the corresponding wire transfer is not present in the bank account, the dashboard immediately signals the discrepancy. Paolo would have discovered the forty-one thousand euro difference not in September when he manually checked, but the very day the discrepancy was created.

Predictive cash flow analyzes the actual payment behavior of each individual client. If client X has always paid at sixty days and suddenly starts paying at ninety, the algorithm identifies the trend and automatically updates liquidity forecasts. If client Y has reduced budgets by twenty percent in the last ninety days, the platform signals the risk of further reductions. These patterns are not visible looking at aggregate monthly numbers on Excel.

What-if scenarios allow investigating the impact of strategic decisions before making them. If Event Makers had access to this tool, Paolo could have simulated: “What happens if the main client reduces budget by fifty percent?” The answer would have been immediate: insufficient gross margin in three months, need to cut one employee or acquire thirty-five thousand euros in new revenue. With four months advance notice, both options were viable.

The Cost of Delayed Control

The difference between discovering a problem today and discovering it in four months is the difference between a manageable difficulty and an irreversible crisis. Event Makers discovered the liquidity discrepancy in September. If it had discovered it in May, it could have negotiated payment terms with suppliers, activated a receivables factoring at contained discount, or downsized the structure gradually. In September, with urgent payments to face and bank credit line already utilized at eighty-five percent, remaining options were drastically reduced.

Excel is not a wrong tool. It’s an inadequate tool for managing the financial complexity of a modern SME. An integrated platform that automates complete control, cross-references multi-source data in real-time, and uses machine learning to identify hidden patterns is no longer a luxury for large companies. It’s the minimum operational standard to satisfy the continuous monitoring obligations required by regulations on adeguati assetti (adequate organizational arrangements, per Italian Corporate Code).

Paolo today is completely rethinking his financial control system. Not because Excel provided wrong data, but because Excel showed only what it was asked to show. And no one had asked it to investigate whether receivables “to be collected” were actually collectible, whether historic clients were silently reducing budgets, whether margin per event was eroding month after month. The crisis was hidden in the spreadsheets, invisible until it exploded.

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Replace Excel with Predictive Intelligence

Event Makers discovered a forty-one thousand euro liquidity difference when it was too late for preventive interventions. Your company can avoid the same mistake by replacing manual spreadsheets with an integrated platform that automatically explores data instead of waiting for monthly updates.

Mentally.ai Copilot eliminates the information gap between aggregate accounting numbers and daily operational reality. Automatic Cassetto Fiscale synchronized every night, instant invoice-bank movement comparison, ML predictive cash flow that identifies payment delays before they become critical, what-if scenarios to validate strategic decisions.

For service SMEs €3M-€30M (~$3.2M-$32M USD) revenue:

• Real-time dashboard updated every 6h (vs monthly Excel)
• Automatic liquidity discrepancy alerts (vs manual discovery)
• ML client patterns: budget reduction identified 90 days earlier
• Complete continuous control compliant with CCII art. 2086 (Italian Corporate Crisis Code)

→ Trial: €1 for 15 days complete access
→ Plan: €99/month for 5 companies + unlimited users

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Disclaimer: Case study based on real data from Italian SME in events sector. Company name, location, personal names and numerical values modified for privacy while maintaining proportions and trends unchanged.

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