Time series forecasting methods
At its core, forecasting sales is the art of prediction. Most seasoned sales professionals have a toolkit of techniques they use to gather predictions about upcoming sales cycles — and many include both quantitative and qualitative sales forecasting methods.
While these two types of forecasting each carry their own benefits and outlook goals, quantitative forecasting methods are widely considered critical for leveraging hard data to produce datamaps of projected sales outcomes. Diving even deeper, quantitative forecasting methods don’t all look the same. Take time series forecasting, for example, which is used to plot out a course of sales over a period of time.
Since time series forecasting is one of the most prevalent models used by sales teams, let’s take a closer look at how it works — and why modern sales teams are turning towards machine learning tools to make time series forecasting a thing of the past.
What are time series forecasting methods?
Among sales forecasters, time series forecasting methods have become popular and useful ways to leverage historical sales data to predict future sales outcomes. Time series forecasting aims to predict what sales will look like in a given future period based on data from a similar, previous period.
These are the most common time series models used to produce quantitative sales forecasts.
Simple moving average (SMA)
Because of the simplicity, simple moving average methods are used very frequently to build projections for future sales outcomes with some simple division. Taking the total amounts of sales, or dollars, earned within a given period of time and dividing by the total number of units results in an average for that time period.
As forecasters work to develop a time series that showcases expected sales, the moving averages of the sales/time will plot out the expected sales journey. Amateur forecasters can even use time series forecasting methods in Excel, which provides a more accessible platform for predicting sales. This is mostly used for quick forecasts to capture preliminary ideas of what could be coming. To get more accurate predictions, sales forecasters will use more complex methods with more inputs.
Exponential smoothing (SES)
Another frequently used time series forecasting method is exponential smoothing. This method builds on results from simple moving averages, but uses predetermined logic to assign value to considered data points based on their age. The farther out each data point gets, the less value it has in the final forecast representation. SES might be employed to reduce the value of random effects, like market demand and larger, unique purchases.
Autoregressive integrated moving average (ARIMA)
Autoregressive integrated moving average (ARIMA) forecasting involves the use of special parameters when reviewing and determining a time series forecast. These special parameters can include:
- Market trends
- Seasonal sales/exceptions
- Shifting sales cycles
- Possible errors
Because these parameters can often be specific, many businesses utilize ARIMA methodology to produce predictions for short term forecasts. When repeated, ARIMA forecasting works to clean data output of errors and white noise that can affect the accuracy of the forecast.
Scrubbing data with ARIMA can be a tedious effort, especially for forecasters who aren’t sure when to consider data clean.
There’s no real way to predict the future with 100% accuracy. Sure, the root of quantitative forecasting methods is cold, hard data, but no historical data can tell the full story of a sales cycle. Factors like world events, popular trends, growing/shrinking demand, and even future competition all play an inevitable role in the final outcome of any sales cycle. Time series forecasting with machine learning tools can enable businesses to compile data, assign value with automated processes, and consider external factors like expert opinion, market trends, world events. With machine learning, forecasting platforms use artificial intelligence to consider a larger breadth of factors to achieve the most accurate predictions for sales outcomes possible.
We agree with Forrester that Ai-enabled, prescriptive forecasting is the future of sales. Collective[i]’s sales forecasting platform harnesses real-time market intelligence to develop daily forecasting predictions and AI-enabled next and best actions. By automating data collection and attribution, our software eliminates the burden of wasted time and resources spent on data management, while also leveraging data from a growing neural network to gather a full spectrum of inputs that can impact sales deals. With daily recommendations, businesses using Collective[i] can tap into an adaptive data environment to pivot sales tactics, increase engagement with qualified leads, and close more deals.
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