Case Study: 'Garbage In' Financial Modeling

Suby Joseph

Scenario: A startup is creating a financial model to project future sales for their innovative new fitness tracker. 

Garbage In:

  • Sales Data: Instead of using market research or competitor analysis, the founder uses their personal sales target for the first year as the baseline for future projections. This is unrealistic and doesn't account for broader market trends.
  • Manufacturing Costs: The founder estimates manufacturing costs based on a quick online search for similar products, ignoring potential bulk order discounts or unforeseen material shortages.
  • Marketing Budget: The marketing budget is a guess based on what the founder thinks "sounds good" without considering specific marketing channels or their potential return on investment (ROI).


  • Overly Optimistic Projections: The model generates extremely high sales figures, leading to unrealistic expectations for investor funding and future growth.
  • Hidden Cost Risks: The model underestimates manufacturing costs, potentially leading to financial strain later on when actual costs are higher than anticipated.
  • Inefficient Marketing Spend: The random marketing budget could lead to wasted resources and a failure to reach the target audience effectively.

How to Avoid Garbage In:

  • Reliable Data Sources: Use market research reports, industry data, and competitor analysis to inform your assumptions.
  • Seek Expert Advice: Consult with industry professionals or financial analysts to get a more realistic picture of potential costs and market trends.
  • Data Validation: Double-check all data sources for accuracy and ensure they are relevant to your specific market and product.

By avoiding "garbage in" and using reliable, well-researched data, you can build a financial model that provides a more accurate and valuable picture of your company's future.

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