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Formula-driven 12-month cash flow forecast with operating assumptions, monthly cash balances, management alerts and scenario visibility.
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Thinking about moving from Excel or Google Sheets to a CRM? Don’t migrate the problems with the data.
Before choosing or configuring a CRM, I usually check five things:
• Does every active lead have a clear owner?
• Is there always a defined next action?
• Are follow-up dates consistently tracked?
• Are lead statuses clear and used consistently?
• Could another team member understand the spreadsheet without asking for help?
If several answers are “no”, the problem may not be the CRM yet — it may be the workflow behind it.
A CRM works much better when the sales process is clear before migration starts. I created a free CRM Readiness Self-Check for small teams that want to identify these gaps before investing more time or money in a CRM.
If you’re currently working from spreadsheets and considering a CRM, comment “CHECK” or send me a message and I’ll send it to you.
#CRM #CRMReadiness #SalesOperations #RevOps #WorkflowOptimization #GoogleSheets #Excel #BusinessProcess
AI-powered financial analytics platform built with FastAPI, Pandas, DuckDB, and Gemini API for data analysis, REST APIs, natural-language-to-SQL queries, and automated business insights.
Title: From Sales Metrics to Cash Flow: Financial Model for a Volatile-Revenue Business
Description:
Built an integrated cash flow and P&L model driven by commercial metrics — sales volume, average ticket size, and customer base growth — rather than pre-aggregated financial figures, for a business with uneven or seasonal revenue.
Challenge: The company could see sales and customer numbers, but had no clear view of how much cash that would actually generate, or when — leaving liquidity planning largely reactive.
Approach: Commercial drivers flow through three linked layers — Commercial Metrics → P&L → Cash Flow — entirely on live formulas, so the model recalculates end-to-end when any input changes. Cash conversion is modeled with a collection-lag assumption (a share of revenue collected the same month, the rest the following month), which is what actually creates the liquidity gap between "profitable" and "cash-positive."
What it surfaced (illustrative figures): In the sample run, EBITDA turned positive by month 3, but the cash balance still dipped to a low point before recovering two months later — the gap between accounting profit and available cash that the business needed to plan around. Every driver (orders per customer, ticket size, collection speed) is a single editable input, so the same model reruns instantly for a different scenario.
Outcome: Turned a sales question into a cash planning answer, with the exact month and depth of the liquidity gap visible in advance instead of discovered after the fact.
