What Is Advanced Business Analytics?

A business woman performing advanced analytics on a tablet
A business woman performing advanced analytics on a tablet

When pursuing the utmost success for their organizations, leaders worldwide are turning to advanced business analytics for unique insights that empower data-driven decisions. Leveraging data science beyond traditional business intelligence techniques, advanced data analytics can predict patterns and trends that equip organizations to maintain a competitive edge.

Take Italy-based fashion company Yamamay, for example. With 1,300 shops in 12 countries, the company's data was abundant, inconsistent and inaccurate. Leaders wanted to replace outdated systems with an infrastructure that incorporated advanced business analytics and furthered the company's sustainability goals by switching from on-premises hardware to a cloud-based service.

Through Oracle solutions, the company improved forecasting, enhanced decision-making, sped up responsiveness, and empowered its marketers to personalize promotions. As a result, customer loyalty has increased and the company's sales have increased significantly, nearly doubling in many store locations.

Consider how advanced business analytics can set organizations up for success by looking at the benefits of the technology, exploring several use cases and discovering the techniques of advanced analytics.


Benefits of Advanced Analytics

As the Yamamay case study reveals, there are many benefits to advanced business analytics. Advanced analytics tools combine predictive analytics with prescriptive analytics to gain greater insight into data. Since predictive analytics projects what is most likely to occur in the future and prescriptive analytics suggests actions to affect future outcomes, combining the two can offer many benefits. The combination is often joined with capabilities like artificial intelligence and machine learning to provide even richer insights.

Some of the benefits of advanced business analytics include:

  • Creating a data-driven company culture
  • Predicting future outcomes to gain a competitive edge
  • Making faster, more accurate decisions
  • Improving risk management processes
  • Anticipating problems and opportunities
  • Personalizing the customer experience
  • Improving financial performance

With advanced data analytics, companies and organizations are enhancing each department and business function, ranging from finance and accounting to marketing and advertising.


Advanced Analytics Use Cases

Professionals across all industries leverage advanced business analytics to understand their customers better and improve their products and services. Consider four use cases for advanced analytics that highlight how effective they can be in all types of organizations.


Customer Experience

As organizations seek better to understand the needs and desires of their customers, advanced analytics can provide in-depth information that would otherwise go undiscovered. By identifying customer pain points and revealing opportunities to personalize promotions, advanced data analytics empower improved customer experiences.

For example, Italian olive oil company Fratelli Carli implemented the advanced analytics solution SAS Customer Intelligence 360 to enhance their customers' experiences with the brand. In doing so, the company has preserved its customer-centric approach while making data-driven decisions. Such an approach has cultivated customer loyalty and improved the company's ability to engage consumers in adaptable, personalized ways.


Supply Chain

Errors and disruptions in the supply chain can lead to lost profits, high costs and an overall lack of organizational efficiency. Through advanced data analytics, organizational leaders can have visibility into all aspects of the supply chain and be better equipped to make quick, accurate decisions. Use cases of advanced analytics applications in the supply chain include inventory visibility, deriving root causes of delivery promise failures and reducing lead time.

Mondelez International, Inc., the largest producer of biscuits, chocolate, and candy products in the world, needed an advanced analytics solution for their complex global supply chain. They turned to Tableau and the results were significant. Tableau dashboards provide complete visibility into the company's supply chain, empowering them to maneuver quickly if a supplier is at risk of not having a needed item. Now able to see clearly into their 28,000 suppliers, Mondelez leaders can better increase value from existing suppliers or plan for supplier base reduction.


Risk Management

Advanced analytics are a powerful tool for identifying and detecting threats such as cyberattacks and fraud. The ability to do so in real-time is a game-changer for many organizations formerly lacking insight into various exposures. These powerful data analytics can prevent repetitive losses, reduce costs and quickly flag fraudulent activity.

Canadian sports and entertainment organization Maple Leaf Sports & Entertainment (MLSE) faced potential risks such as interference with ticket sales, social media account takeovers during events and website hacks. By implementing an advanced analytics solution, MLSE reduced its risk factors and saw an 80 percent reduction in the overall time spent investigating non-threat security events and experienced a 90 percent reduction in overall security events.


Description of Advanced Analytics Techniques

Advanced analytics techniques range from data mining to machine learning. Consider six advanced business analytics techniques that successful organizations use to generate positive outcomes for their customers and companies.


Data Mining

This technique includes collecting, storing and processing data to identify patterns and predict future outcomes through descriptive, predictive, and prescriptive modeling. Data mining is helpful in many industries, including retail and health care.


Machine Learning

Machine learning refers to an advanced analytics tool that creates statistical models. The models are based on data patterns, which computational methods detect through supervised, unsupervised, semi-supervised and reinforced learning. Machine learning applications are found in nearly every industry today as they facilitate commonly used features such as image recognition and text generation.


Cohort Analysis

The technique of cohort analysis involves establishing applicable insights through the grouping of users and the study of each group's behavioral patterns. Cohort analysis can provide visibility into marketing and advertising campaigns that will likely succeed and increase customer lifetime value.


Cluster Analysis

The cluster analysis process involves the discovery of similarities and differences in datasets and producing visualizations to present insights. Often used in marketing and insurance, cluster analysis is useful for identifying unique consumer segments.


Retention Analysis

This technique includes studying various cohorts of customers to gain insight into behavioral patterns, consumer influences and retention trends. Retention analysis is useful in marketing and customer service.


Complex Event Analysis/Processing

This technique involves gathering and analyzing event data from multiple sources in real-time to identify relationships and discover patterns. Industries that regularly use complex event analysis include manufacturing, stock market trading and marketing.


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