Analyzing Historical Sales Performance? Don’t Skip the Safeguards

Looking at past sales numbers can reveal powerful insights—growth trends, seasonality, customer behaviors, and more. But without the right safeguards, that analysis can mislead more than it informs.

Here are key protections to apply when analyzing historical sales performance:

🔍 Data Quality: Make sure the data is clean—remove duplicates, correct errors, and ensure consistency in formats and units across time periods.

📅 Context Is Key: Were there promotions, stockouts, or one-off events (like COVID or a system migration)? These can skew trends if not accounted for.

📈 Meaningful Metrics: Don’t just track revenue. Dig into margins, conversion rates, CAC, and customer retention. These tell the real story behind your numbers.

📊 Seasonality & Comparability: Compare the same periods year over year. Q2 vs. Q4 may not be apples to apples.

📁 Documentation: Keep track of your assumptions and data sources. Transparency builds trust—and allows for reproducibility.

🔐 Compliance & Privacy: If customer data is involved, ensure compliance with GDPR, CCPA, and other regulations.

🤝 Cross-functional Input: Sales numbers alone can’t explain everything. Collaborate with marketing, ops, and finance for the full picture.

Strong analysis is built on strong foundations. Skip the safeguards, and you risk making decisions on faulty ground.

📌 What safeguards do you always apply before diving into sales data?