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=== Business Value ===
As we move into a world of technological advances where more and more data is created and stored digitally, businesses are looking for ways to take advantage of this opportunity and use this information to help generate profits. Predictive analytics can be used and is capable of providing many benefits to a wide range of businesses, including asset management firms, insurance companies, communication companies, and many other firms. Every company that uses project management to achieve its goals benefits immensely from predictive intelligence capabilities. In a study conducted by IDC Analyze the Future, Dan Vasset and Henry D. Morris explain how an asset management firm used predictive analytics to develop a better marketing campaign. They went from a mass marketing approach to a customer-centric approach, where instead of sending the same offer to each customer, they would personalize each offer based on their customer. Predictive analytics was used to predict the likelihood that a possible customer would accept a personalized offer. Due to the marketing campaign and predictive analytics, the firm's acceptance rate skyrocketed, with three times the number of people accepting their personalized offers.<ref>{{Cite journal |last1=Vesset |first1=Dan |last2=Morris |first2=Henry D. |date=June 2011 |title=The Business Value of Predictive Analytics |url=http://nexdimension.net/wp-content/uploads/2013/04/ibm-spss-predictive-analytics-business-value-whitepaper.pdf |journal=White Paper |pages=1–3}}</ref>
Technological advances in predictive analytics<ref>{{cite web |last1=Clay |first1=Halton |title=Predictive Analytics: Definition, Model Types, and Uses |url=https://www.investopedia.com/terms/p/predictive-analytics.asp |website=Investopedia |publisher=Investopedia |access-date=8 January 2024}}</ref> have increased its value to firms. One technological advancement is more powerful computers, and with this predictive analytics has become able to create forecasts on large data sets much faster. With the increased computing power also comes more data and applications, meaning a wider array of inputs to use with predictive analytics. Another technological advance includes a more user-friendly interface, allowing a smaller barrier of entry and less extensive training required for employees to utilize the software and applications effectively. Due to these advancements, many more corporations are adopting predictive analytics and seeing the benefits in employee efficiency and effectiveness, as well as profits. <ref>{{Cite book |last=Stone |first=Paul |title=All Days |date=April 2007 |chapter=Introducing Predictive Analytics: Opportunities |doi=10.2118/106865-MS |chapter-url=https://onepetro.org/SPEDEC/proceedings-abstract/07DEC/All-07DEC/SPE-106865-MS/141862}}</ref> The percentage of projects that fail is fairly high—a whopping 70% of all projects fail to deliver what was promised to customers. The implementation of a management process, however, is shown to reduce the failure rate to 20% or below.<ref>{{cite web |last1=Team Stage |title=Project Management Statistics: Trends and Common Mistakes in 2023 |url=https://teamstage.io/project-management-statistics/ |website=TeamStage |publisher=TeamStage |access-date=8 January 2024}}</ref>
=== Cash-flow Prediction ===
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