Are You A Good Fit for A Career As A SAS Predictive Modeler?

SAS is one of the most widely used data management and analytics platforms globally. The company’s main product is made atop a data quality platform that lets users improve, integrate, and govern enterprise data. SAS Data Management can consume data from legacy systems and Hadoop, create rules, and reuse them. SAS Visual Analytics lets users visually explore data to automatically highlight key relationships, outliers, and clusters. Users can also take benefit of advanced visualizations and analyzed auto charting.

Predictive modeling is a process that forecasts outcomes and probabilities through data mining. Each model comprises a specific number of predictors, variables that help determine and influence future results. A statistical model is formulated once all the data has been collected for the required number of relevant predictors.

In SAS Predictive modeling, the model is chosen based on a detection theory that tries to guess the probability/possibility of an outcome given a specific amount of input data, say, for example, if provided an email sent through predictive modeling, we determine how likely it is that it is spam.

How Do SAS Predictive Modeling Models Work?

SAS Predictive Modeler models have their strengths and weaknesses and are best used for specific uses. One of the most significant benefits applicable to all models is reusable and adjusted to have standard business rules, and a model can be reusable and trained using algorithms. But how do these SAS Predictive Modeler models work?

The analytical models run one or more algorithms on the data set on which the prediction will be carried out. It is a repetitive process because it affects training the model. Sometimes, multiple measures are used on the same data set before a business objective is found. It is important to note that SAS Predictive Modeler models work through an iterative process.

It begins with pre-processing, then data is mined to comprehend business objectives and data preparation. Once preparation is complete, data is modeled, evaluated, and finally deployed. Once the process is completed, it is repeated.

Data algorithms play a huge role in this study because they are utilized in data mining and statistical analysis to help define trends and patterns in data. Several algorithms are built into the analytics model incorporated to perform specific functions. These algorithms include time-series, association, regression, clustering, decision trees, outlier detection, and neural network algorithms.

Each algorithm serves a specific function. For example, outlier detection algorithms see the anomalies in a dataset, while regression algorithms predict constant variables based on other variables present in the dataset.

Benefits of SAS Predictive Modeler for Businesses

In the new digital economy, one customer may encounter brands via multi-channels at different touch-points throughout their shopping journey. While each channel provides opportunities for businesses to serve and attract customers and prospects, the need for companies to consistently offer a better and better experience to their customers also increases.

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Here are some of the areas where machine learning and SAS Predictive Modeler will prove to have significant impacts:

• Budgeting: Companies can use SAS Predictive Modeler to forecast more accurately their budgeting needs, instead of having to guess and depend on the old models of "what-if." As a result, collaborations between departments can be enhanced for better team-works.
• Customers’ Insight: As mentioned above, advanced analytics can help businesses produce actionable customer insights predicting future actions by consumers. Companies can utilize this information to create better products/services tailored specifically to their customers. Similarly, companies can also apply this principle to increase the conversion rates and improve customers’ loyalty, reward program, and more.
• Cost Reduction: With the customer lifecycle becoming shorter and more complex, adopting SAS Predictive Modeler and machine learning technology will help companies have more effective marketing campaigns, reducing expenses while generating more revenue.
• Gaining Perspective: Business organizations can implement SAS Predictive Modeler to gain insights into the future success of their new products and services. This is especially useful when there is insufficient historical data available to forecast or when the past is not indicative of the future. SAS Predictive Modeler assists companies in making informed decisions in the absence of experience.

Conclusion

SAS Predictive Modeler provides immense benefits and helps companies generate more accurate forecasts for business outcomes. However, each business is distinct and will need various tools for different areas of analytics. Additionally, many companies also currently encounter other difficulties when it comes to applying machine learning and SAS Predictive Modeler across their businesses.