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Compare AI Software Firms: Revenue, Margins, Retention

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Compare AI Software Firms: Revenue, Margins, Retention

This guide walks you through comparing AI software companies by three financial and operational metrics: revenue growth, profit margins, and customer retention. You’ll learn where to find the data, how to calculate each metric, and how to weigh them together to make a more informed decision—whether you’re an investor, a buyer, or a strategist.

What You’ll Need

  • Access to financial statements (10-K, 10-Q, annual reports) for public companies, or financial disclosures for private ones.
  • A spreadsheet tool (Excel, Google Sheets) to organize data and calculate metrics.
  • For private companies: pitch decks, press releases, or third-party databases like Crunchbase or PitchBook (if available).
  • Customer churn data—often found in earnings calls, investor presentations, or customer success reports.

Step-by-Step

  1. Identify your comparison set. Choose 3–5 AI software companies that are similar in size, target market, or product category. Avoid mixing a pre-revenue startup with a mature public company—the metrics won’t be comparable.
  2. Gather revenue data for at least 3–5 years. For public companies, pull annual revenue from income statements (e.g., from SEC filings). For private companies, use disclosed revenue or estimates from credible sources. Record the figures in a spreadsheet with columns for each fiscal year.
  3. Calculate revenue growth rate. For each year, use the formula: (Current Year Revenue – Previous Year Revenue) / Previous Year Revenue × 100. This gives you a percentage. For example, if revenue went from $100M to $130M, growth is 30%. Compute this for each company and each year to see trends, not just a single snapshot.
  4. Determine profit margins. The most common margins are gross margin and operating margin. Gross margin = (Revenue – Cost of Revenue) / Revenue × 100. Operating margin = Operating Income / Revenue × 100. Pull these from the income statement. For AI software, gross margins are often high (70–90%) because the marginal cost of serving one more customer is low. Operating margins vary widely; high growth often comes with negative operating margins due to heavy R&D and sales spend.
  5. Assess customer retention. Look for two metrics: net revenue retention (NRR) and gross revenue retention (GRR). NRR measures the revenue retained from existing customers including upsells and cross-sells, minus churn. GRR only accounts for churn and downgrades, ignoring expansion. Both are usually reported as percentages. NRR above 100% means existing customers are spending more over time. GRR of 90%+ is strong for SaaS. If not disclosed, estimate churn by looking at customer count changes and average revenue per customer, but be aware this is less accurate.
  6. Normalize the data. Adjust for company size. A $10M company growing 100% is not directly comparable to a $1B company growing 30%. Use a simple normalization: divide growth rate by revenue (e.g., growth / revenue in billions) to get a growth efficiency ratio. Similarly, compare margins relative to revenue scale.
  7. Create a scoring matrix. Assign weights to each metric based on your priorities. For example, if you care about long-term viability, weight retention 40%, growth 35%, and margins 25%. Score each company from 1 to 5 for each metric (5 being best), then multiply by the weight and sum to get a total score. This forces a structured comparison.
  8. Analyze trends, not just points. Look at whether growth is accelerating or decelerating. Check if margins are improving as the company scales (economies of scale). See if retention is stable or slipping. A company with declining growth but rising margins might be maturing; one with high growth but falling retention is a red flag.
  9. Consider qualitative context. Read the management discussion in annual reports or earnings call transcripts. Look for mentions of AI model costs, customer concentration, or changes in pricing. These factors directly affect future margins and retention.
  10. Document your sources and assumptions. For each number you use, note where it came from and any estimates you made. This makes your comparison reproducible and defensible.

Common Mistakes

  • Comparing growth rates without considering base size. A 50% growth on $1M is $500K; 20% on $100M is $20M. Always look at absolute dollar changes too.
  • Using gross margin as a proxy for profitability. Gross margin ignores R&D, sales, and administrative costs. AI companies often have high gross margins but still lose money overall.
  • Ignoring the difference between NRR and GRR. A company can have 95% GRR but 120% NRR, meaning it loses customers but makes up for it with expansions. Both are important, but they tell different stories.
  • Relying on a single year’s data. One good year can be an anomaly. Always look at multi-year trends to smooth out volatility.
  • Not adjusting for seasonality. Some AI companies have heavy Q4 sales. Annual data avoids this, but if you use quarterly data, compare year-over-year, not quarter-over-quarter.
  • Assuming all revenue is recurring. Some AI companies sell one-time licenses or professional services. Check the revenue breakdown; a high growth rate driven by one-time deals is less valuable than recurring subscription revenue.

Tips

  • Use a consistent time frame. Always compare the same fiscal years. If one company’s fiscal year ends in December and another in June, align the periods as best you can.
  • Look for cohort-based retention data. Some companies disclose retention by customer cohort (e.g., customers acquired in 2020 vs. 2021). This shows whether newer customers are stickier than older ones.
  • Check the definition of “customer.â€� Some companies count a large enterprise as one customer; others count each seat or account. This affects retention percentages dramatically.
  • Use free tools to speed up data collection. For public companies, SEC EDGAR, Yahoo Finance, or Google Finance can give you historical financials. For private companies, look for funding announcements that often include revenue figures.
  • Weight metrics according to your goal. If you’re a short-term investor, growth might matter most. If you’re a long-term buyer, retention and margins might be more important.
  • Create a visual dashboard. Plot growth vs. retention on a scatter plot to spot outliers. Companies in the top-right quadrant (high growth, high retention) are usually the strongest.

Troubleshooting

  • Data is not available for private companies. If revenue isn’t disclosed, use funding amounts and estimated valuations as a rough proxy, but state clearly that these are estimates. Alternatively, compare only public companies.
  • Revenue growth is negative for some companies. That’s fine—include it. A negative growth rate with high retention might indicate a company that is shrinking but stable. Just make sure your scoring handles negative numbers correctly (e.g., assign a 1 for any negative growth).
  • Retention data is not disclosed. Estimate churn by looking at customer count changes from year to year. If that’s not available, use a proxy like the percentage of revenue from existing customers (if mentioned in earnings calls). If you can’t estimate, note it as “N/Aâ€� and exclude that metric from your scoring for that company.
  • Margins are negative for all companies. This is common in early-stage AI. Instead of comparing absolute margins, compare the trend—are margins improving? Also consider gross margin, which is usually positive.
  • Companies have different fiscal year ends. Use the most recent complete fiscal year for each company, and note the actual dates. For quarterly comparisons, use the same quarter (e.g., Q1 2024 vs. Q1 2023).

Frequently Asked Questions

What is a good revenue growth rate for an AI software company?

It depends on size. For early-stage companies (under $10M ARR), growth above 50% is strong. For mid-size ($10M–$100M), 30–50% is healthy. For large public companies, 15–25% is considered good. Always compare within similar revenue ranges.

What is the difference between gross margin and operating margin?

Gross margin only subtracts the direct cost of delivering the software (hosting, support, etc.). Operating margin also subtracts R&D, sales, marketing, and administrative costs. Operating margin is a better measure of overall profitability.

Why is net revenue retention (NRR) more important than gross retention?

NRR includes expansion revenue from existing customers, which is a key growth driver for SaaS. A high NRR (above 110%) means you can grow even without acquiring new customers. Gross retention only shows how many customers you keep, not how much they spend.

Can I compare a company with negative operating margin to one with positive?

Yes, but you need to consider growth. A company with negative margins but high growth might be investing for the future. A company with negative margins and low growth is a red flag. Look at the trend over time.

What if a company reports “adjusted� EBITDA instead of operating income?

Adjusted EBITDA often excludes stock-based compensation and one-time costs. It can be useful, but it’s not standardized. Prefer GAAP operating income for comparability. If you use adjusted figures, note the adjustments.

How do I handle companies that have both software and services revenue?

Separate the segments if possible. Software typically has higher margins and retention. Services (consulting, implementation) have lower margins and are less recurring. Compare the software segments separately to get a fair picture.

Related

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