Forecasting results are directly shaped by the data they’re built upon. For reliable and meaningful forecasts, RunSmart relies on historical data from your QuickBooks Online account or other accounting software you use. The more historical data available, the more effectively RunSmart can identify patterns, trends, and seasonal variations to create reliable projections.
Data Requirements for Optimal Forecasting
While RunSmart can work with a minimum of 2 consecutive years of historical data, we recommend you use more whenever possible for the most reliable results. A larger dataset provides a more complete picture of your business’s performance over time, allowing RunSmart to detect consistent patterns that might not be visible in shorter timeframes. This helps create forecasts that more closely mirror actual business cycles and variations.
Why Your Forecast May Look Flat or Repetitive
RunSmart identifies patterns in your financial data to make projections. If your forecast appears flat (with repeating numbers or a straight line), it’s often because the data lacks significant trends or identifiable seasonal patterns. Here’s why this might happen:
Inconsistent or Highly Variable Data: If your historical revenue or expense data fluctuates greatly month-to-month without a discernible pattern, RunSmart is indicating that there are no clear and detectable trends. This can cause the forecast to default to a repeating average, as it lacks the necessary stability in the data to make more dynamic predictions.
Lack of Significant Trend or Seasonality: Some historical revenue or expense line items may not show any distinct upward or downward trend, seasonality, or correlation with sales. In such cases, RunSmart may produce a flat forecast, as there’s no statistical basis for projecting changes over time. This is particularly common with one-off expenses or revenue spikes that don’t repeat consistently.
Unusual or Outlier Events: Occasionally, single outlier events (e.g., an unusually high expense one month or a sudden revenue spike) can distort the pattern recognition process. RunSmart works to ignore or smooth out these anomalies, but in some rare cases, they may still impact the overall trend and create unexpected forecast results.
Improving Forecast Accuracy
To achieve the best forecast results, consider the following tips:
Ensure Consistent Data Entry: Regularly update your QuickBooks Online records with complete and accurate monthly data for all revenue and expense items, so RunSmart has a solid foundation of financial data to analyze. We suggest using a professional bookkeeper if you are not comfortable or capable of doing this yourself.
Extend the Data Range: Whenever possible, include more than 24 consecutive months of historical data. If your business has more data to work with, you can add them from the project page and select more years to include in the analysis of your forecast. This helps RunSmart capture longer-term trends that may be obscured in shorter datasets.
By understanding how data quality and consistency impact forecasting, you can ensure that your forecasts provide the most useful insights for your small business.