Last Updated: June 22, 2018
Twice a month, we bring you headlines from around the industry to keep you informed of the latest trends and conversations. Whether you are in IT, finance, sales or marketing, we are your go-to source for how the intersection of predictive analytics, machine learning and AI is transforming business planning.
Before taking on any analytics initiative, almost all executives will want to know the same thing: “What’s the ROI? Can you guarantee value from this project?” While there are no certainties in the world of business, especially before any data or technical infrastructure assessments can be made, there are a few steps executives can take to get an idea of the cost and value of their prospective analytics initiatives. Read more in Predictive Analytics World to explore some of the questions your team should answer before proposing a large analytics project.
Retailers: Adopt Artificial Intelligence Now
To be successful in the fast-moving retail world, executives must know how to react in real-time when there is a change in consumer habits. More than half of retail customers say they want a “totally personalized experience,” but delivering relevant touch points is where some retailers run into trouble. In fact, 42% of retailers say they don’t know enough to effectively engage key consumer segments. These relevant consumer experiences are achieved by properly leveraging the power of AI, including machine learning and predictive analytics. See how tech-forward retail giants like Wal-Mart and Amazon are using AI to gain more consumer data, optimize inventory and shape the future of retail experiences here.
The amount of data in the world will continue to grow at a rapid pace as consumers generate mountains of new information each day and companies continue to find new ways to leverage these insights. Marketing and sales use this data to spend resources effectively on high value prospects; retail uses it to optimize inventory to match consumer trends; manufacturing uses it to optimize their supply chains, etc. With all these advancements and use cases in each department and industry, data and predictive analytics tasks are no longer solely the job of the IT department. Instead, these initiatives are processes that must be taken on as a team and shared across departments. See inside for the four steps that can lead your team to success with predictive analytics, here.
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