Advanced Analytics

Advanced analytics enables you to discover patterns and trends in structured and unstructured data, and use this insight to predict the outcomes of future events and interactions. With advanced analytics, your organization can adjust its plans and strategies to become more competitive, minimize potential risk and make the best decisions in real time.

It is a grouping of analytic techniques used to predict future outcomes. Advanced analytics can include:


Predictive analytics
What will happen next if our customers continue to purchase as they have in the past?
What is likely to happen to our sales if the current trends continue?

Simulation
What if we introduce a new product into the market, how are our competitors likely to react?
What if we change our pricing strategy, how will that impact our customer loyalty and market penetration?

Optimization
How can we achieve the best loading plan for a truck?
What are the best drug research projects to invest in to maximize our profits, minimize our cash outflow in the next 24 months and coincide with our expiring drug patents?

Other applications to Advanced analytics
Product Rationalization
Product Affinity Analysis
Market Basket Analysis
Customer Churn Modeling
Customer Segmentation
Sales Forecasting

Advanced analytics are based on mathematical principals and started as descriptive statistics, which are basically used to sum and count past occurrences for what has happened in the past, which is useful in a reactive, course correction manner. Advanced analytics allows you to anticipate possible future outcomes and either capitalize on them or adjust now to impact the future.

The traditional technique for building a predictive model is based on hypothesis testing which more of a statistical approach. Data mining is a technique for building predictive models where the data is visually explored and used to determine which predictive model to use to “fit” the data.