Take Five #211: New SBA limit lets asset-heavy business buyers stack 504/7(a) loans, then refi..., and more
Top five must-reads this week in the world of SMB acquisitions and operations
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Take Five #211: New SBA limit lets asset-heavy business buyers stack 504/7(a) loans, then refi…, and more
1. ETA buyers often underestimate how much liquidity protects post-close growth plans
2. New SBA limit lets asset-heavy business buyers stack 504/7(a) loans, then refi to recycle 7(a) capacity for future acquisitions
3. How SMB buyers can use both industry data and historical trends in revenue modeling
What Historical Data Reveals
When paired with market data, historical data provides a clear picture of a business’s past performance. It captures actual metrics, such as revenue trends, customer behavior, and sales activity, offering valuable insights for projections. For U.S. small-to-medium business (SMB) acquisitions, this often includes examining monthly or yearly revenue patterns and breaking them down by product or service line. For instance, a local IT services firm with steady, modest growth can use this data to inform future revenue expectations.
Customer metrics are equally crucial. Retention and churn rates, for example, highlight how stable a company’s revenue base is. Subscription businesses with high retention rates tend to have more predictable income streams than those struggling to retain customers. Other useful metrics include repeat purchase rates, contract renewal percentages, average revenue per account (ARPA), and customer lifetime value (LTV). Together, these figures reveal how consistently customers contribute to revenue over time.
Sales funnel data also plays a key role. Metrics like lead volume, conversion rates, average deal size, and sales cycle length help translate current sales efforts into future revenue. For example, steady conversion rates and consistent deal sizes provide a reliable basis for forecasting. Similarly, analyzing historical changes in pricing or discounts can shed light on how past adjustments affected sales volumes and revenue distribution.
Seasonality trends, observed over a 24–36 month period, add another layer of precision. These insights, often drawn from financial records, CRM systems, invoicing tools, and subscription billing platforms, help businesses account for predictable fluctuations in demand.
When Historical Data Is Most Dependable
Historical data proves most reliable in industries that are stable and mature, where market conditions change gradually. Examples include traditional B2B services, local professional services, or non-tech manufacturing and distribution. Businesses with steady performance, consistent product offerings, and predictable customer profiles provide a solid foundation for accurate forecasts.
Recurring-revenue models, such as those used by SaaS companies, managed IT providers, and commercial maintenance firms, offer particularly strong visibility. These businesses often have detailed records of renewals and churn, making their revenue streams easier to predict. Similarly, SMBs operating in stable geographic or demographic markets - like local service providers with long-standing customer bases - tend to exhibit consistent performance patterns. In these cases, assuming that “the past resembles the near future” is more justifiable, making historical trends a dependable tool for revenue projections.
4. EBITDA can look very different once accrual accounting enters the diligence process
5. “Building a $2.2B Aerospace Business From Scratch”
Bryan Perkins breaks down how Novaria Group compounded through 27 acquisitions by focusing on niche aerospace manufacturers with strong process IP, disciplined underwriting, and long-term execution. The conversation covers capital structure decisions, proprietary sourcing, decentralized operations, and the realities of building durable middle-market businesses over decades instead of deal cycles.
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