Advanced analytics, as the phrase implies, typically goes beyond the scope and capabilities of basic business intelligence, or BI tools. After all, it involves the autonomous (or semi-autonomous) examination of data or business information to achieve a variety of outcomes.
While basic BI and analytics are useful in providing a quick overview of the business, advanced analytics is crucial for forecasting future trends, predicting the possibility of one outcome over another and making recommendations based on those predictions, not to mention enabling better insights and a deeper understanding of the business. In a nutshell, advanced analytics is the foundation for stronger and strategic decision making for any company.
There are several techniques used in advanced analytics, and they each have a role to play. However, a few of them are used more often because they help paint a clearer picture, or give clearer predictions.
This is often the first step, and arguably the most useful technique in advanced analytics. Data mining is the process of examining large amounts of raw data to find and identify relationships, sequences, and even anomalies. Once this is done, data sets can then be created and connections between them analysed.
In other words, data mining turns raw data into useful information – usually about risks and opportunities – that key personnel in a company can then use to make better, more strategic decisions.
This advanced analytics technique is used to examine the behaviour of related groups, or cohorts. These cohorts are typically made up of people who have something in common; for example, they could live in the same area, or they visited a website at least once in a certain period of time.
Cohort analysis is extremely useful because it can help a business examine its customers’ behaviours in the context of the consumer lifecycle. That makes it easier to identify behavioural patterns at various points in the customer journey, from first visit to an online store or newsletter sign-up, to first purchase, and on to repeat visits or repeat purchases.
As you can see, this technique is dynamic and allows you to uncover important insights you otherwise wouldn’t have had.
This technique is more exploratory, and as such is a good starting point for helping businesses understand the data they have. As the name implies, its goal is to sort different data points into clusters, where the data points in one cluster are similar to each other, but different from data points in a different cluster.
In marketing, cluster analysis is typically used to divide large customer bases into smaller, distinct segments. Marketing specialists can use the resulting information to pinpoint those in the business’s customer base who would be more likely to respond to certain promotions or use one service over another.
Cluster analysis is one of the building blocks for machine learning, the next useful technique in advanced analytics. This technique involves source code being built into a computer, allowing it to identify data and build statistical models and predictions around that data, with no need for human intervention.
This is often used in conjunction with data mining, machine learning, and statistical models to predict the likelihood of certain future outcomes. This technique is crucial for decision makers, since it will guide them in choosing one course of action over another based on the predicted outcome or outcomes.