Author

Melanie Carey
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Senior Solutions Architect

Melanie Carey has worked at SAS for over 18 years. She started out as a consultant assisting customers on their Activity Based Costing models and Strategic Performance initiatives. She then worked on cutting edge initiatives like Social Media Analytics and Launch Revenue Optimization in the Emerging Technologies group. She has created numerous demo's for the field and has taken the lead for the Visual Analytics Interactive Reports available on sas.com. Melanie currently works within Cloud and Information Services as the technical lead for SAS product trials.

Advanced Analytics | Analytics | Data for Good
Melanie Carey 0
Take customer care to the next level with automated prediction in SAS Visual Analytics

What is automated prediction? Automated prediction, in less than a minute, runs several analytic models (such as decision trees, gradient boosting, and logistic and linear regression) on a specific variable of your choice. Most of the remaining variables in your dataset are automatically analyzed as factors that might influence your specified variable. They are called underlying factors. SAS then chooses the one model (champion model) that most accurately predicts your target variable. The model prediction and the underlying factors are then displayed. You can adjust the values of the underlying factors to determine how the model prediction changes with each adjustment.

Advanced Analytics | Analytics | Data for Good | Data Visualization
Melanie Carey 0
Are your hospital resources at risk of hitting capacity?

How have healthcare providers and governmental agencies predicted the fast-changing, potentially exponential increase in the need for medical services and equipment through the various stages of the COVID-19 pandemic? Mathematical techniques that attempt to model and understand the likely spread of the disease have been instrumental. The SEIR model is

Advanced Analytics | Analytics | Data for Good
Melanie Carey 0
Speed up your COVID-19 research with text analysis: step-by-step

Adverse outcomes, and the rapid spread of COVID-19, have accelerated research on all aspects of the disease. You may have found it overwhelming, and very time-consuming, to find relevant and specialized insights in all the scientific literature out there. To aid researchers in quickly identifying relevant literature about key topics

Analytics | Artificial Intelligence | Data Visualization | Machine Learning | Programming Tips
Melanie Carey 0
How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 3

In the second of three posts on using automated analysis with SAS Visual Analytics, we used the automated analysis object to get a better understanding of our variable of interest, X-Sell and Up-sell Flag, and how it is influenced by other variables in our dataset. In this third and final

Analytics | Artificial Intelligence | Data Visualization | Machine Learning | Programming Tips
Melanie Carey 0
How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 2

In the first of three posts on using automated analysis with SAS Visual Analytics, we explored a typical visualization designed to give telco customer care workers guidance on customers most receptive to upgrade their plans. While the analysis provided some insight, it lacked analytical depth -- and that increases the risk of  wasting time, energy and

Analytics | Artificial Intelligence | Data Visualization | Machine Learning | Programming Tips
Melanie Carey 0
How SAS Visual Analytics' automated analysis takes customer care to the next level - Part 1

You're the operations director for a major telco's contact center. Your customer-care workers enjoy solving problems. Turning irate callers into fans makes their day. They also hate flying blind. They've been begging you for deeper insight into customer data to better serve their callers. They want to know which customers