One innovative use case I have come across is in the field of e-commerce, where R was used to optimize pricing strategies based on market demand and competitor analysis. The algorithm developed in R analyzed historical sales data, competitor pricing, and customer behavior to suggest optimal pricing levels for maximizing profit while remaining competitive.

In the field of marketing analytics, R has been used to analyze large datasets of customer behavior and preferences. Companies have developed predictive models using R's machine learning capabilities to segment customers, forecast sales, and personalize marketing campaigns to target specific customer segments.

In the finance industry, R has been leveraged to build sophisticated risk models for portfolio management. By utilizing R's extensive statistical libraries and financial packages, companies have been able to analyze historical stock data, simulate different market scenarios, and assess the potential risk and reward profiles of various investment strategies.

One interesting use case I've seen is in the field of image recognition, where R was used to build models capable of identifying objects and patterns in images. By leveraging R's image processing libraries and machine learning algorithms, researchers were able to train models that could accurately classify and tag images based on their content.

Another interesting use case I have encountered is in healthcare, where R was used in the analysis of genomic data to identify genetic markers associated with certain diseases. By combining R's powerful statistical analysis functions with genomic data sets, researchers were able to uncover potential targets for personalized medicine and improve patient outcomes.