Customer Profitability Analytics enables banks to analyze customer, account, product, and transaction data and apply costing models to determine a bank-wide view of profitability. Applying predictive analytics, they can model future behavior and derive a lifetime value for each customer.
Hear a panel of HR leaders engage in a conversation about how they are using predictive and prescriptive analytics applied to talent to drive better business results. Join us to explore how you can be more effective in designing pilots, demonstrating the ROI for talent analytics, and translating talent data into talent insight.
In today's economic downturn, organizations are looking for ways to improve the way they do business to keep ahead of the competition and improve revenue. Increasingly, organizations are finding that the benefits of BI can be complemented when combined with predictive analysis.
Published By: Mintigo
Published Date: Sep 05, 2018
One of the most common use cases for AI in B2B is to make predictions about which accounts are most likely to buy and which leads are most likely to convert. However, use cases for AI are being extended beyond predictive account and lead scoring to include decision-making and process automation as well. Download this SiriusDecisions technology perspective on Predictive Analytics and Artificial Intelligence Technology to learn more.
This paper will cover:
The benefits, evolution and capabilities of AI technology solutions for B2B organizations
The core and extended capability groups of AI
The business priorities supported by AI
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The 21st century marks the rise of artificial intelligence (AI) and machine learning capabilities for mass consumption. A staggering surge of machine learning has been applied for myriad of uses from self-driving cars to curing cancer. AI and machine learning have only recently entered the world of cybersecurity, but its occurring just in time. According to Gartner Research, the total market for all security will surpass $100B in 2019. Companies are looking to spend on innovation to secure against cyberthreats. As a result, more tech startups today tout AI to secure funding; and more established vendors now claim to embed machine learning in their products. Yet, the hype around AI and machine learning what they are and how they work has created confusion in the marketplace. How do you make sense of the claims? Can you test for yourself to know the truth? Cylance leads the cybersecurity world of AI. The company spearheaded an innovation revolution by replacing legacy antivirus software with predictive, preventative solutions and services that protect the endpoint and the organization. Cylance stops zero-day threats and the most sophisticated known and unknown attacks. Read more in this analytical white paper.
Tax fraud is already prevalent, and fraudsters are more sophisticated and automated than ever. To get ahead of the game in detecting fraud
and protecting revenue, tax agencies need to leverage more advanced and predictive analytics. Legacy processes, systems, and attitudes
need not stand in the way. To explore the challenges, opportunities, and value of tax fraud analytics, IIA spoke with Deborah Pianko, a
Government Fraud Solutions Architect within the SAS Security Intelligence practice.
This paper will outline the value and methods involved in data mining across both quantitative and qualitative data. In addition, it will describe the data transformations necessary before doing such work, and the tools that are particularly valuable for mining mixed data types.
Learn what criteria distinguished certain companies as top performers within the SMB sector, the factors to consider when assessing your organization's BI competency and the required actions to achieve best-in-class performance.
This paper defines predictive analytics, then details ways this type of analytics can be applied to marketing, risk, operations and more. It also includes information relevant to a wide variety of industries - from manufacturing to hospitals.
The solution to operationalizing analytic s involves the effective combination of a Decision Management approach with a robust, modern analytic technology platform. This paper discusses both how to use a focus on decisions to ensure the right problem gets solved and what such an analytic technology platform looks like.
This paper will discuss the barriers to data-driven decision making for midsized businesses, and how experts and non-experts alike can use SAS Visual Analytics to unlock the value of data including big data to increase revenue, cut operational costs and better manage their business.
Driving the right interaction at the right time.
This paper examines the need to uncover and understand the
dynamic roles and changing needs of individuals, the importance of synchronizing interactions with these changing needs, and how predictive analytics can drive customer intimacy by facilitating 1:1 interactions.