Leverage data insights to predict with precision.
Success in sales begins with understanding the past and setting a course for the future, and IBM SPSS Statistics helps you achieve that by predicting trends and identifying opportunities. Using advanced forecasting techniques, it enables you to analyze historical sales data, evaluate key trends, and anticipate future outcomes with precision. Whether you are in retail, e-commerce, telecommunications, manufacturing, or education, SPSS Statistics provides the insights needed to set realistic sales targets, plan inventory, and optimize resource allocation. By transforming complex datasets into accurate, actionable forecasts, it helps drive sustainable growth and achieve your business goals with confidence. This capability also empowers long-term growth by improving efficiency, customer satisfaction, and market positioning.
Let’s see how Time Series Modeler can forecast bandwidth utilization for a broadband operator.
Applications
Analyze time series data to identify recurring patterns, seasonal variations, and hidden cycles that influence business dynamics. Spectral analysis helps break down complex time series into frequency components, revealing underlying periodicities. This method is particularly useful for industries like telecommunications and retail, where understanding cycles—such as daily traffic patterns or seasonal shopping trends—can improve resource planning, optimize marketing efforts, and anticipate customer behavior with precision.
Leverage historical data to predict future values with enhanced accuracy. Autoregression models establish relationships between past values of a time series and its future behavior, allowing for precise short-term forecasts. This is essential for industries such as travel and telecommunications, where understanding lagged relationships—like how past bookings affect future trends or how network usage evolves—drives better scheduling and infrastructure optimization.
Achieve accurate predictions with the Expert Modeler as it evaluates data and automatically selects the most suitable forecasting method, including exponential smoothing or ARIMA. This simplifies complex forecasting tasks for businesses in sectors like retail, education, and e-commerce, allowing them to focus on interpreting insights and crafting strategies without manual trial-and-error processes.