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How to Run Sales Forecasting: Approach + Free Template

Sales forecasting

Sales Forecasting

The Sales forecast is a key report for every sales organisation. Building it in the right way is key for revenue visibility, and for predicting future sales accurately, however there's limitations in what it can provide.


Things to bear in mind

There are several factors can influence your forecast, and how you go about building it, and how broad of a team you need involved:


  • Data Availability: The type and amount of data you have can determine the best technique.

  • Complexity of Sales Cycle: More complex sales cycles might need advanced techniques.

  • Market Conditions: External factors like economic changes and competitor activity play a role in the forecasting process.

Factor

Considerations

Data Availability

Historical sales data, CRM data

Complexity of Sales Cycle

Length, stages, and variability

Market Conditions

Economic trends, competitor analysis

For a deep dive into how to manage your sales pipeline effectively, visit our sales pipeline management guide.


Grabbing Historical Data


Analyzing historical data is key for effective sales forecasting, and using past sales data, you can make more informed predictions about future sales performance.


Using CRM Software to run Sales Forecast


Integrating your CRM software into your sales forecasting significantly enhances the accuracy of your predictions and many CRMs will carry the native ability to run sales forecasting in the way we recommend, however if data quality is an issue you might choose to pull data out and run it manually.


Seeing as they track and store historical sales data and can provide an overview of your sales pipeline, it makes sense to keep inside your CRM if you can. By manually analyzing this data however, you can potentially identify trends, patterns, and also potential opportunities.


With CRM software automating the data collection process, it can reduce the likelihood of human error and ensure more reliable information for your forecasts. It also generates detailed reports and dashboards, making it easier to visualize your sales performance metrics.


Benefits of CRM Software Integration:



Excel Formulas, Google Sheets and Graphs


Excel and Google Sheets can be powerful tools for analyzing historical sales data and creating accurate sales forecasts. By using pre-built formulas and creating graphs, you can visualize your data and identify trends that may impact future sales.


Helpful Excel Formulas for Sales Forecasting:


  • SUM: Adds up the total sales for a specific period.

  • AVERAGE: Calculates the average sales over a designated timeframe.

  • TREND: Projects future sales based on historical trends.

  • FORECAST: Estimates future sales using linear regression.


Creating Graphs:


Visualizing your data with graphs helps identify patterns and make more accurate predictions. Common graph types for sales forecasting include:


  • Line Graphs: Display trends over time.

  • Bar Charts: Compare sales performance across different periods.

  • Pie Charts: Show the proportion of sales from different product categories.


By integrating CRM software and using Excel formulas and graphs, you can effectively analyze historical sales data and make informed forecasts. This approach helps you boost your revenue and optimize your sales processes.


The Weighted Pipeline Method

The Weighted Pipeline method is a sales forecasting technique that uses historical data to estimate potential sales more accurately, and is what we would recommend for nearside revenue forecasting. By assigning probabilities to each stage of your sales pipeline, you can get a clearer picture of your future revenue.


Calculating the Probability of Success

In the Weighted Pipeline method, the probability of success increases as leads progress through various stages of the sales pipeline. Historical data helps determine these probabilities, providing a data-driven approach to forecasting.


In an ideal weighted pipeline, you can dynamically assign the probability using your statistical average close rates from each stage. As these fluctuate so will your weightings.


The downside of attaching it to current averages is that in businesses with less stable data, you may find that the fluctuations can vary, and in businesses that have to report externally to investors, you may find that the level of fluctuation to not be tolerable.


In this case, then you can assign a more stable probability, or add in a buffer to the overall probabilities.


Here’s an example of how probabilities might be assigned to different stages in your sales pipeline:

Sales Stage

Probability of Success (%)

Initial Contact

10%

Qualification

25%

Proposal Made

50%

Negotiation

75%

Established Customer

96%


Forecasted Sale Amount Calculation


To get the weighted pipeline value across a stage, you take the total amount of revenue in each stage and multiply it by the probability, to give you a weighted value.

Sales Stage

Probability of Success (%)

Total Pipeline Value ($)

Weighted Pipeline Value ($)

Initial Contact

10%

1,250,000

125,000

Qualification

25%

1,000,000

250,000

Proposal Made

50%

500,000

250,000h

Negotiation

75%

400,000

300,00

Established Customer

96%

300,000

288,000

Finally, to create a monthly forecast, you take the weighted values across each opportunity and assign it to the month in which it is set close (as per the close date)


Free Sales Forecast Template


We've built a really detailed sales forecasting template as part of our free catalogue of revenue tools.


The template is a Google Sheet that you simply make a copy of, however if you want to download as an Excel sheet, simply use the "Download As" function.



Put in your email and you'll get the link straight to your inbox - Simple!


Adding in Statistical Methods


You can use statistical and mathematical models as part of your forecast in an attempt to predict future sales based on historical data, however as this is can miss important decisions and strategy that is already set to happen in the future.


Whilst this method provides objective, data-driven predictions that can be more reliable than qualitative methods, it still misses out things like changes to your marketing budgets and volumes, and team changes like hiring, promotion cycles or opening new territories.


If using these approaches, you should be mindful that whilst it can feel technical to run this type of analysis, it's still a highly flawed approach to forecasting.


If you're going to apply quantitative techniques to a pipeline forecast, you might do any of the following:


  • Time Series Analysis: Uses historical sales data to spot patterns and trends over time.

  • Regression Analysis: Finds relationships between sales and other variables (like marketing spend).

  • Exponential Smoothing: Weighs past observations differently, giving more importance to recent data.

  • Moving Averages: Averages sales data over a specific period to smooth out fluctuations.


Technique

Description

Use Case

Time Series Analysis

Identifies patterns over time

Predicting seasonal trends

Regression Analysis

Analyzes relationships between variables

Impact of marketing campaigns

Exponential Smoothing

Emphasizes recent data

Short-term sales forecasts

Moving Averages

Averages data over time

Reducing volatility in data

Things like CRM software, dedicated sales forecasting solutions, and even pre-built Excel formulas can automate these calculations and for a simplified overview of expected sales, you can use graphs and tables generated by these tools.


If you're looking to build a more advanced bottoms up model of future revenue beyond your active pipeline, you should look to more of a revenue projection model.


Factors Affecting Sales Forecasting

There are several factors can affect the precision of your sales forecasts. These factors can be broadly categorized into internal and external influences.


Internal Influences

Internal factors within your organization can significantly impact the effectiveness of your sales forecasting techniques. Some key internal influences include:


  • Changes in Sales Teams: Modifications in the structure or size of your sales team can affect forecast accuracy. For example, onboarding new account executives or sales development reps can lead to fluctuations in sales performance. Understanding account executive responsibilities can help in adjusting your forecasts accordingly.

  • Sales Management Procedures: Changes in sales management techniques, like introducing new sales methodologies or changing sales quotas, can influence forecast outcomes.

  • Promotions and Campaigns: Running promotions or marketing campaigns can lead to spikes in sales. Including these factors in your forecasting model is essential for reliable predictions.

  • Staff Changes: Changes affecting staff, like reallocations or new hires, can impact sales performance. Monitoring sales performance metrics is vital.


External Influences

External factors outside your organization also play a crucial role in shaping sales forecasts. Key external considerations include:


  • Seasonal Fluctuations: Sales can vary significantly with seasons. For instance, businesses may see higher sales at the end of financial year.

  • Regulatory Changes: New laws or regulations can impact sales.

  • Emergence of Competitors: New competitors entering the market can influence your sales. Understanding competitors' marketing models helps forecast sales more accurately.

  • Macroeconomic Environment: Economic indicators like GDP growth, unemployment rates, and consumer confidence levels can influence sales. Monitoring economic trends is essential for accurate sales forecasts.

  • Trends and Legislation: Trends in consumer behavior and changes in legislation are important external factors. For instance, a shift towards eco-friendly products can impact sales forecasts for businesses in related industries.

Internal Factors

Description

Changes in Sales Teams

Modifications in team structure or size

Sales Management Procedures

New sales methodologies or quotas

Promotions and Campaigns

Marketing efforts affecting sales

Staff Changes

Reallocations or new hires

External Factors

Description

Seasonal Fluctuations

Sales variations with seasons

Emergence of Competitors

New market entrants

Macroeconomic Environment

Economic indicators affecting sales

Trends and Legislation

Consumer behavior trends and legal changes

Understanding both internal and external factors is crucial for improving the accuracy of your sales forecasts. By considering these influences, you can make more informed decisions and better predict future sales outcomes.


Importance of Accurate Forecasting

Accurate sales forecasting is key for making informed decisions about sales activities, future revenue, and overall sales processes. Here we explore the key benefits of precise forecasting, focusing on strategic planning and inventory management.


Strategic Planning Benefits

Accurate sales forecasting plays a big role in strategic planning. By predicting future sales, your business can set realistic sales goals and make informed decisions about resource allocation. This foresight helps in planning marketing campaigns, setting sales targets, and managing budgets effectively.


Accurate forecasting also helps attract investors and potential talent by showing a clear vision of future growth.


Challenges with Sales Forecasting

Whilst a critical part of revenue forecasting, a pipeline forecast comes with significant visibility issues. With pipeline forecasting only taking into account active opportunities, the effectiveness of your sales prospecting is a component that is missing. Reductions in the effectiveness of this area can be hard to spot until overall pipeline volume starts drying up, and so a sales forecast can start to be come a lagging metric of future revenue.


There's also an inherent limitation due to pipeline duration - If your opportunities take 90 day to go from open to close, and your view is limited to opportunities, your view is limited to 90 days into the future. Any further and you'll be trying to look to a time before the opportunities even exist. To solve this problem, you need to look at a different way of looking at future data, using Revenue Projection.


This approach takes into account all of the historic data approaches that you would use across a sales pipeline forecast, and instead takes into account your capacity planning, as well as all of your future strategic changes. Using this approach you can see significantly further into the future.


Wrap Up

Sales forecasting techniques are vital for revenue leadership, helping businesses predict future sales accurately and make informed decisions. By choosing the right technique and understanding the limitations of quantitative methods businesses can gain a more reliable, data-driven forecast.


Accurate sales forecasting is essential for strategic planning, influenced by both internal factors like changes in sales teams, external factors such as market conditions and economic trends. By understanding these influences, businesses can set realistic sales goals, plan more effective marketing campaigns and better manage budgets, ultimately driving revenue growth and operational efficiency.

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