What are the statistical forecasting methods
Sales forecasting is the process of predicting future revenue. Before a business can comfortably make financial and operational decisions, it’s important to know where business — and revenue — is headed. Sales forecasting is immensely important to developing successful strategies for making decisions about everything from investor communications to operations, hiring, and more.
Businesses use varying methods to produce forecasts — to analyze data, measure trends, and gather expert intuition for an accurate expectation of how sales will pan out for the projected future.
What are the types of forecasting methods?
Sales forecasting can be broken into a few major buckets, each using unique models to form sales predictions:
- Opinion (or qualitative)
- Historical (qualitative and quantitative)
- Prescriptive (AI-enabled)
Each sales forecasting method carries intrinsic value that companies utilize to gather projections for sales and ultimately make hard pressed decisions for the future. The more analytically-minded might find themselves immediately wondering which is a statistical method of forecasting. The original forecasting methods relied on the opinions of sellers to predict future deals. Many enterprise selling teams still rely on historical forecasting, which relies on quantitative forecasting methods like stats and data, relying heavily on hard facts regarding historical sales trends to make judgement on what to expect for future sales outcomes. This differs from opinion, or qualitative forecasting, which is not a statistical method of forecasting, but can incorporate those opinions to augment other data.
Statistical forecasting methods are widely used for their sheer objectivity in reading data and using math to identify trends that can be applied to future sales estimates. The ability to isolate moving trends enables many businesses a clean look at the past history of their sales performance and bolsters confidence that future sales cycles will exhibit similar trends.
Let’s dive into statistical methods of sales forecasting.
Statistical methods of sales forecasting
Here are four commonly used statistical methods:
1. Simple moving average (SMA)
Adhering to its promise, a simple moving average method takes the total sales within a period and divides by the total number of units within that period, like days or weeks. The moving average is what populates the forecast timeline. This is a very popular method due to its ability to produce quick, rough estimates.
Of course, simple math produces simple solutions. This approach might provide the quickest results, but it incorporates the least amount of data and applies only basic math to arrive at a projection. For that reason, businesses cannot rely on Simple Moving Average forecasting when making important, impactful decisions about company growth.
2. Exponential smoothing (SES)
Probably the second most used statistical method of sales forecasting takes the moving average method and assigns value based on the age of considered data points, decreasing the weight of a sale the farther in the past it occurred. Businesses use exponential smoothing to reduce and remove random effects that may play a smaller role in the larger, extended view of a time series. This is the approach Excel uses in its forecasting function, for example.
3. Autoregressive integration moving average (ARIMA)
ARIMA uses time series data to predict the future with multiple parameters, allowing for the consideration of market trends, seasonal exceptions, sales cycles, and even error within the datasets used. Mainly used for short term forecasting, ARIMA is often repeated in an effort to scrub out white noise that hinders the accuracy of the forecast.
While helpful in practice, there are limiting factors that can make ARIMA forecasting less accurate. Stable, consistent patterns produce the most accurate results, which makes the ‘scrubbing’ component tedious, especially if those analyzing the data aren’t sure when to consider data clean.
4. Neural network (NN)
Neural networks are the most recent approach to producing sales forecasts, and they can provide more accurate predictions than other, simpler algorithms. Neural networks function much like a human brain, building smart connections between a growing number of data points with machine learning. Collective[i] uses collective data from a growing network of sales organizations, which provides our neural network with a greater amount of buyer behavior insights in which to find connections that might go missed by standalone algorithms.
When machine learning can provide real-time forecasting insights based on a neural network, companies are able to make quick decisions to improve their sales figures, adjust impactful financial and operational decisions, and rest easy knowing that guesswork is no longer part of the sales forecasting. This is exactly the advantage Collective[i] provides modern sales teams.
Improve your forecasting accuracy. Boost your selling potential. Click here to see what the future of sales looks like with Collective[i].