sas success stories

A telecommunications company had a great success with SAS Analytics. They analyzed customer usage data like call duration, data usage, etc. This helped them to design more targeted and cost - effective service plans, resulting in increased customer loyalty and a boost in revenue.

Sure. One success story is a large retail company using SAS Analytics to optimize its inventory management. By analyzing sales data over time and across different stores, they were able to reduce overstocking and understocking, which led to significant cost savings and increased customer satisfaction.

In the healthcare sector, there's a great SAS success story. A hospital system used SAS to analyze patient data. SAS helped in predicting patient readmission rates. They could identify the factors that were likely to lead to a patient being readmitted, such as certain comorbidities or lack of follow - up care. By intervening early and providing better post - discharge care plans, they significantly reduced readmission rates, saving costs and improving patient outcomes.

A manufacturing company used SAS to improve quality control. SAS analyzed production data to identify the root causes of defects. They could then take corrective actions, resulting in a significant reduction in defective products.

They influence potential customers by showing real - world examples of success. When a potential customer sees that other companies in their industry have achieved great results with SAS, they are more likely to consider it for their own business.

From sas customer success stories, we learn about the versatility of SAS products. They are often tailored to meet specific industry needs. For instance, in the retail industry, SAS has enabled companies to analyze customer buying patterns more effectively, which in turn helps in inventory management and marketing strategies. This shows that SAS can adapt to different business requirements and drive success.

One key element is its flexibility. SAS can be adapted to different types of data and business requirements. Another is its reliability. It has been proven to handle large - scale data analysis tasks without crashing.

Sure. One success story could be a company that was using SAS for data analysis but found it costly and less flexible in some aspects. They decided to migrate to R. With R, they were able to use a vast array of open - source packages for data manipulation, visualization, and advanced analytics. For example, they used ggplot2 for creating beautiful and highly customizable visualizations that were much easier to produce compared to SAS graphics. Also, R's community support allowed their data scientists to quickly find solutions to any problems they faced during the transition, leading to a more efficient and innovative data analysis process.

The key elements include the ease of learning and code readability. R's syntax is often considered more straightforward and easier to understand compared to SAS. This makes it quicker for new users or those migrating from SAS to pick up. Also, the availability of advanced analytics libraries in R is important. Packages like tidyr for data cleaning and reshaping are very powerful. Moreover, the ability to customize and extend R's functionality easily through user - created packages is a major factor in a success story.

Sure. The SAS success story often involves its powerful analytics capabilities. For example, in many business scenarios, SAS helps companies analyze large amounts of data quickly and accurately. It enables them to make better decisions regarding market trends, customer behavior, and resource allocation.