You need sales forecasts, but you’re not an analyst. Complex models feel overwhelming, and expensive tools seem unnecessary. This uncertainty prevents you from planning effectively and making informed decisions.
Simple sales forecasting solves this by using past data and basic assumptions. It creates forecasts without complex analysis, which helps you plan effectively and make informed decisions. This approach is accessible to any founder.
This guide provides a low-tech approach to forecasting using past data and a few assumptions, helping you create sales forecasts without complex analysis or expensive tools.
We’ll explore why simple forecasting matters, gathering past data, making assumptions, calculating forecasts, and improving accuracy. By the end, you’ll understand how to create sales forecasts using simple methods.
Key Takeaways
- Gather past data—collect historical sales data to use as foundation
- Make reasonable assumptions—use simple assumptions about growth and trends
- Calculate forecasts—use basic math to project future sales
- Review and adjust—compare forecasts to actuals and improve over time
- Keep it simple—avoid complexity that doesn't add value
Table of Contents
Why Simple Forecasting Matters
Complex forecasting models are intimidating. When you don’t understand the math, you can’t trust the results. This complexity prevents you from forecasting at all.
Simple forecasting matters because it’s accessible. When you use basic methods, you understand the logic and can trust the results. This simplicity enables effective planning.
The reality: Most founders don’t forecast because methods seem too complex. Simple forecasting methods are accessible and effective, which enables better planning and decision-making.
Gathering Past Data
Past data provides the foundation for forecasts. When you gather historical sales data, you have a baseline for projections.
Collect Historical Sales
Gather past sales records:
- Collect monthly or quarterly sales data
- Go back at least 12 months if possible
- Include all revenue sources
- Organize data chronologically
- Build historical dataset
Why this matters: Historical sales show patterns. If you collect past data, you can see trends and patterns. This data provides foundation for forecasts.
Identify Sales Patterns
Look for trends and cycles:
- Identify growth trends
- Spot seasonal patterns
- Notice cyclical changes
- See consistent patterns
- Understand sales behavior
Why this matters: Sales patterns inform forecasts. If you identify patterns, you can project them forward. This identification improves forecast accuracy.
Calculate Averages
Compute basic statistics:
- Calculate average monthly sales
- Find median sales
- Identify best and worst months
- Compute growth rates
- Build statistical foundation
Why this matters: Averages provide baseline. If you calculate averages, you have starting point for forecasts. This calculation enables simple projections.
Document Data Sources
Track where data comes from:
- Note data sources
- Record collection dates
- Document assumptions
- Keep data organized
- Build data documentation
Why this matters: Data documentation ensures accuracy. If you document sources, you can verify data later. This documentation improves reliability.
Pro tip: Use our Dynamic Sales Forecasting Calculator to input historical data and generate forecasts. The calculator handles seasonal patterns and trends automatically, making forecasting easier even with simple data.
Making Assumptions
Assumptions bridge past data to future forecasts. When you make reasonable assumptions, you can project future sales.
Growth Rate Assumptions
Estimate growth expectations:
- Assume growth rate based on past trends
- Consider business stage
- Factor in market conditions
- Make conservative estimates
- Build growth assumptions
Why this matters: Growth rate assumptions drive forecasts. If you assume growth rates, you can project future sales. This assumption enables forward-looking forecasts.
Seasonal Adjustments
Account for seasonal patterns:
- Adjust for known seasonal patterns
- Factor in holiday effects
- Consider industry seasonality
- Make seasonal adjustments
- Build seasonal assumptions
Why this matters: Seasonal adjustments improve accuracy. If you account for seasonality, forecasts are more realistic. This adjustment improves forecast quality.
Market Condition Assumptions
Consider market factors:
- Assume market growth or decline
- Factor in competitive changes
- Consider economic conditions
- Make market assumptions
- Build market awareness
Why this matters: Market condition assumptions add realism. If you consider market factors, forecasts reflect reality. This assumption improves forecast accuracy.
Business Change Assumptions
Account for planned changes:
- Factor in new products or services
- Consider marketing changes
- Assume team or capacity changes
- Make business change assumptions
- Build change awareness
Why this matters: Business change assumptions reflect plans. If you account for planned changes, forecasts include them. This assumption makes forecasts more useful.
Calculating Forecasts
Forecast calculation uses past data and assumptions. When you combine these, you create future projections.
Simple Growth Method
Project using growth rate:
- Start with recent average sales
- Apply growth rate assumption
- Calculate monthly projections
- Extend for forecast period
- Build growth-based forecast
Why this matters: Simple growth method is easy. If you use growth rate, you can calculate forecasts quickly. This method enables accessible forecasting.
Average Plus Trend Method
Combine average and trend:
- Use historical average as base
- Add trend component
- Adjust for seasonality
- Calculate projections
- Build trend-based forecast
Why this matters: Average plus trend method improves accuracy. If you combine average and trend, forecasts are more realistic. This method balances simplicity and accuracy.
Moving Average Method
Use recent period average:
- Calculate average of recent months
- Project forward using average
- Adjust for known changes
- Extend for forecast period
- Build moving average forecast
Why this matters: Moving average method is simple. If you use recent average, you project from current state. This method provides stable forecasts.
Percentage of Previous Period
Use percentage of past period:
- Calculate as percentage of previous period
- Apply growth or decline percentage
- Adjust for seasonality
- Project forward
- Build percentage-based forecast
Why this matters: Percentage method is intuitive. If you use percentage of previous period, forecasts are easy to understand. This method enables accessible forecasting.
Improving Accuracy
Forecast accuracy improves over time. When you compare forecasts to actuals and adjust methods, accuracy increases.
Track Actual Results
Compare forecasts to actuals:
- Record actual sales results
- Compare to forecasts
- Calculate differences
- Identify patterns in errors
- Build accuracy tracking
Why this matters: Tracking actuals shows accuracy. If you compare forecasts to actuals, you see how accurate forecasts are. This tracking enables improvement.
Adjust Assumptions
Refine assumptions based on results:
- Update growth rate assumptions
- Adjust seasonal factors
- Refine market assumptions
- Improve assumption accuracy
- Build assumption refinement
Why this matters: Adjusting assumptions improves forecasts. If you refine assumptions based on results, forecasts get better. This adjustment increases accuracy.
Learn from Errors
Identify and fix forecast errors:
- Analyze forecast errors
- Identify error patterns
- Fix systematic errors
- Improve forecast methods
- Build error learning
Why this matters: Learning from errors improves forecasts. If you identify and fix errors, forecasts get more accurate. This learning enables continuous improvement.
Simplify Further
Remove unnecessary complexity:
- Eliminate assumptions that don’t help
- Simplify calculation methods
- Focus on what matters
- Reduce complexity
- Build simplicity discipline
Why this matters: Simplifying improves usability. If you remove unnecessary complexity, forecasting is easier. This simplification enables consistent forecasting.
Pro tip: Use our Dynamic Sales Forecasting Calculator to automate forecast calculations. Input your historical data and assumptions, and the calculator generates forecasts with seasonal adjustments and trend analysis. Compare forecasts to actuals over time to improve accuracy.
Your Next Steps
Simple sales forecasting enables effective planning. Gather past data, make reasonable assumptions, calculate forecasts using simple methods, then improve accuracy over time.
This Week:
- Collect historical sales data for past 12 months
- Calculate averages and identify patterns
- Make initial growth and seasonal assumptions
- Create first forecast using simple method
This Month:
- Track actual sales results
- Compare forecasts to actuals
- Adjust assumptions based on results
- Refine forecast methods
Going Forward:
- Forecast monthly or quarterly
- Continuously compare forecasts to actuals
- Improve assumptions and methods over time
- Keep forecasting simple and accessible
Need help? Check out our Dynamic Sales Forecasting Calculator for automated forecasting, our Sales Growth Calculator for growth projections, our pipeline forecasting guide for CRM-based forecasts, and our forecast accuracy guide for improving predictions.
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FAQs - Frequently Asked Questions About Sales Forecasting for Non-Analysts: A Simple Method Any Founder Can Use
What historical sales data do you need to start creating a simple sales forecast?
Collect at least 12 months of monthly sales records, including all revenue sources, organized chronologically so you can spot trends, seasonal patterns, and calculate averages.
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Gather monthly or quarterly sales data going back at least 12 months (longer is better for identifying seasonal patterns). Include all revenue sources, organized by time period. From this data, calculate average monthly sales, identify your best and worst months, spot any seasonal patterns (like holiday spikes or summer slowdowns), and compute basic growth rates.
Document where your data comes from and any assumptions you made when collecting it. This documentation helps you verify accuracy later and improves the reliability of your forecasts over time.
What are the four simple forecasting methods a non-analyst founder can use?
The simple growth method, average plus trend method, moving average method, and percentage of previous period method—all use basic math any founder can do in a spreadsheet.
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The simple growth method starts with your recent average sales and applies a growth rate assumption to project forward. The average plus trend method combines your historical average with an identified trend (upward or downward) and seasonal adjustments. The moving average method uses the average of your most recent months as your projection, which provides a stable, smoothed forecast. The percentage of previous period method projects each month as a percentage increase or decrease from the prior period.
All four methods rely on basic arithmetic—no statistical software or analyst expertise required. Start with whichever method feels most intuitive, then compare results across methods to increase confidence in your forecast.
What assumptions do you need to make when building a sales forecast, and how do you keep them reasonable?
You need assumptions about growth rate, seasonal patterns, market conditions, and any planned business changes—keep them conservative and based on past data rather than wishful thinking.
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Four categories of assumptions drive your forecast: growth rate assumptions (based on past trends and business stage, not aspirations), seasonal adjustments (using historical patterns to account for predictable fluctuations), market condition assumptions (considering whether the market is growing, stable, or declining), and business change assumptions (factoring in planned new products, marketing campaigns, or capacity changes).
The key to reasonable assumptions is grounding them in evidence. Use past growth rates as your starting point, be conservative rather than optimistic, and document every assumption explicitly so you can revisit and adjust them when comparing forecasts to actual results.
How do you improve forecast accuracy over time without making the process more complex?
Track actual results against your forecasts each month, identify where predictions were off and why, adjust your assumptions, and remove any complexity that isn't actually improving accuracy.
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Improving accuracy follows a simple cycle: record actual sales results alongside your forecast, calculate the difference, analyze whether errors are random or systematic (consistently over- or under-predicting), and adjust your assumptions accordingly. If your growth rate assumption was too aggressive, dial it back. If you missed a seasonal pattern, add it.
Equally important is simplifying: remove assumptions that don't improve accuracy and eliminate calculation steps that add complexity without adding value. The best forecasting system is one you'll actually use consistently. A simple forecast reviewed monthly beats a complex model that sits unused.
Can you create a useful sales forecast if your business is new and has little historical data?
Yes—use industry benchmarks, competitor data, and your first few months of sales as a starting baseline, then improve rapidly by tracking actual results against your initial projections.
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With limited data, start with whatever you have—even 3-6 months of sales provides a useful starting point. Supplement with industry benchmarks for seasonal patterns and growth rates. Make your initial assumptions conservative, and know that your first forecast will be rough.
The key advantage of simple forecasting is that it improves quickly. After just one quarter of tracking actual results against your forecast, you'll have real data to calibrate your assumptions. Each month of comparison data makes your next forecast more accurate. Start imperfect and improve rapidly rather than waiting for perfect data that may never come.
Why is simple forecasting better for most founders than complex analytical models?
Simple methods are accessible, understandable, and sustainable—if you don't understand the math behind a forecast, you can't trust or improve it, and you're unlikely to keep using it.
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Complex forecasting models fail for most founders for three reasons: they're intimidating (founders avoid doing them), opaque (if you don't understand the logic, you can't evaluate whether the output makes sense), and unsustainable (maintaining complex models requires expertise and time most small businesses don't have).
Simple methods succeed because you understand every step, can spot when something looks wrong, and can actually maintain the process month after month. A basic forecast you update monthly provides far more value than a sophisticated model you built once and never touched again. The goal is consistent, practical planning—not academic precision.
Sources & Additional Information
This guide provides general information about simple sales forecasting. Your specific situation may require different considerations.
For sales forecasting calculations, see our Dynamic Sales Forecasting Calculator.
For sales growth projections, see our Sales Growth Calculator.
Consult with professionals for advice specific to your situation.
