Predictive analytics is a solution that comprises the collection and analysis of data through quantitative means to draw conclusions and insights, and ultimately predict future events. An increasing number of organizations are turning to predictive analytics to make better decisions and improve performance.
In this session we’ll explore predictive areas such as benchmarking, performance measurement, and commerce insight. We’ll look at practical ways to use these services and solutions to improve sourcing & procurement, sales & marketing, procure-to-pay, and order-to-cash processes.
Come to this session to find out how predictive analytics can be a game-changer and ultimately the best tool in your tool box.
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Predictive analytics is a solution that comprises the collection and analysis of data through quantitative means to draw conclusions and insights, and ultimately predict future events. An increasing number of organizations are turning to predictive analytics to make better decisions and improve performance. In this session we’ll explore predictive areas such as benchmarking, performance measurement, and commerce insight. We’ll look at practical ways to use these services and solutions to improve sourcing & procurement, sales & marketing, procure-to-pay, and order-to-cash processes. Come to this session to find out howpredictive analytics can be a game-changer and ultimately the best tool in your tool box. ------------------------
The insights delivered by standard business intelligence and reporting are not readily actionable; they must be translated to action by way of human judgment. Metrics, reports, dashboards, and other retrospective analyses are important components of enterprise business intelligence, but their execution is ad hoc in that it is not clear a priori what kind of actions or decisions will be recommended, if any. Predictive analytics is specifically designed to generate conclusive action imperatives. Predictive analytics delivers a powerful aggregate win by driving millions of operational decisions, such as whether to pay an invoice to term or accept an early pay discount; to recruit a supplier; to nurture an at-risk customer. To identify fraudulent payments. Predictive models drive decisions. Whether to raise or lower commodity prices. One of the best known predictive applications which almost all of us have gone through is the credit card scoring. When you apply for a credit card, the retail bank gets your credit score from a credit bureau. The scoring model used by the credit bureau uses our credit history, loan applications, and other data to predict our risk level. This result enables the retail bankers to accept or deny the our application. Predictive Analytics has a wide range of impact and use cases in business and our personal lives. For this conference, we’re going to focus on B2B commerce-related processes.
explore predictive areas such as benchmarking, performance measurement, the commerce graph, and the commerce index. We’ll look at practical ways to use these technologies to improve sourcing & procurement, sales & marketing, procure-to-pay, and order-to-cash processes. This new technology has been proven effective for both enterprise and emerging businesses worldwide. Come to this session to find out if predictive analytics could be a game-changer and ultimately the best tool in your tool box.
Eric Siegel, Ph.D., Founder, Predictive Analytics World, Author, Predictive AnalyticsSir Francis Bacon, an 18th-century founder of the modern scientific method, famously argued that“Knowledge is power.”1 With all due respect to this great scientist, in the 21st century, “knowledge isprofit” for those firms that deploy big data predictive analytics solutions to reduce risks, make smartdecisions, and create differentiated, more personal customer experiences. The answers are in thedata — but only if companies look for them.
Predictive analytics uses algorithms to find patterns in data that might predict similar outcomesin the future. A common example of predictive analytics is to find a model that will predict whichcustomers are likely to churn. For example, telecommunications firms can use customer data suchas calls made, minutes used, number of texts sent, average bill amount, and hundreds of othervariables to find models that will predict which customers are likely to change mobile carriers. Ifa carrier can predict the reasons why customers are likely to churn, it can try to take preemptiveaction to avoid this undesirable outcome. But this isn’t a one-time operation; firms must reruntheir analysis on new data to make sure the models are still effective and to respond to changes incustomer desires and competitors. Many firms analyze data weekly or even continuously.
The Ariba Network is the world’s largest Business Network, allowing Buyers and Sellers to become more efficient across their Buying, Selling, and Cash Management ProcessesAs those of us whowork in procurement and finance are well aware, our departments can add tremendous value to the bottom line. In an average large enterprise, every dollar/euro saved in procurement equates to 5x that amount of revenue delivered when it comes to net income (source: Ardent Partners). Best in Class companies have over 85% of their spend under management (meaning spend that procurement manages directly or heavily influences), compared to only 54% amongst the others. Imagine the bottom line impact these Best in Class organizations are having on their companies.
Achieved 80%Procurement Processes Improved – greater understanding of methods of procurementDeeper Awareness & Campaigning of the Ariba tool being the Financial System of Record for Spend ManagementAP Processes Enhanced to Standardize all Suppliers on Ariba processes