Economic and market conditions, customer demographics, pricing and marketing activities can all affect your organization. Use econometric, time series and forecasting techniques to understand those factors and improve your strategic planning.
Analyze the impact of promotions and events.
The time series and econometric capabilities of SAS/ETS software provide users with several mechanisms for determining promotional lift. The depth and flexibility of the SAS modeling environment can accommodate any business scenario. Determining the effectiveness of promotions and events enables you to better allocate marketing dollars in the future.
Model customer choices and price elasticities.
Maximize your marketing efforts by understanding which product features are important to a particular audience. Modeling customer choices based on their attributes helps improve strategy by predicting customers' decisions. Understanding these choices and the factors that influence them enables you to adjust marketing strategies or fees to target the right population.
Measure and predict marketing investment activities.
Understand which key business drivers are effecting consumer demand. Model customer demand based on marketing or media mix activities that measure the impact of pricing, advertising, in-store merchandising, store distribution, sales promotions and competitive activities. Using simulation and optimization tools, you can maximize investments to drive profitable volume growth.
Provide the information needed to make better staffing decisions.
Forecast demand for services so you can maximize staff resources. Automatically account for seasonal fluctuations and trends, and select the best method for generating the demand forecasts. Efficient staff allocations mean customers' needs can be met with no wasted resources.
Model risk factors and predict economic outcomes.
Copula methods and compound distribution modeling let you model multivariate dimensions of risk factors. These are valuable in risk management applications where many correlated risk factors must be modeled but where the risk factors are non-normally distributed. SAS/ETS can fit probability distributions for the severity (magnitude) of random events, such as the distribution of insurance claims.
Screenshots & Demos
- High-performance econometrics
- Cross-sectional econometric methods
- Time series analysis
- Panel data econometrics
- Time series data tools
- Data acquisition tools