Key Benefits of Advanced Analytics
One key benefit of advanced analytics is that it helps to improve the accuracy of business operations. In contrast with intuition or experience-based decision-making approach, the data-driven approach of advanced analytics makes predictions based on facts, and thus directs you to the option with the highest expected benefit.
Detailed level analysis – Make personalized sales/marketing possible
Advanced analytics techniques find patterns from less aggregated datasets, such as at an individual customer level or store level. For example, instead of simply concluding the overall market demand of a customer segment and offering the full audience’s favorite product, it is possible to capture the characteristics of each individual customer. Using an agile architecture, various data sources can be integrated to provide a more complete picture of the customer and allow for a more personalized experience. Good models can significantly increase marketing effectiveness as well as customer satisfaction.
Better performing advanced techniques – Increase prediction accuracy
Predictive analytics requires robust statistical models and algorithms. However, this type of data science has become much more accurate, faster, and achievable due to current data mining techniques. For instance, artificial neural networks greatly improved pattern recognition performance by generating a collection of functions transforming the input variables to approximate the targeted outcome. Deep learning algorithms are applied to create more powerful hierarchies. Mathematical computations and algorithms which allow machines to learn the complex analytical models are becoming more accurate and reliable.
Confidence level – Provide risk level information of a suggested decision
Most commercialized data mining techniques provide a confidence level for the prediction (e.g., 95% confidence interval, false-positive rate or false-negative rate) and models are selected based on performance measures. It benefits business people by conveying the risk level of implementing the recommended action. In some circumstances where there is a high cost for acting on false-positive or false-negative predictions, we may adjust the model’s parameters to avoid high-cost false prediction or choose not to act on low confidence predictions.