Building on last week’s roundup, we have focused again on the top trends as we enter into 2019: Artificial Intelligence (AI), Machine Learning (ML), and Data Analytics. We found that B2B eCommerce will continue to grow through 2021 and could benefit from AI and ML predictions. Also, we take a look at Prescriptive analytics and how they build on predictive analytics to help marketers understand and unlock answers to future outcomes. Lastly, we look at the effects of how AI and ML are applied across multiple functional areas in organizations including the supply chain.
How AI and ML are Boosting B2B eCommerce 5 Important Artificial Intelligence Predictions
by Indrajeet Deshpande, Community Contributor for MarTech Advisor
With the impact of artificial intelligence (AI) and machine learning (ML) visible on almost every facet of business, let’s look at how AI and ML are helping to boost B2B eCommerce. According to Forrester, the US B2B eCommerce market will grow to be a $1.2 trillion market by 2021, but what five key areas should marketers be looking to focus on?
Prescription for Business Success—a New Breed of Analytics
by Brian J. Dooley, Author, Analyst, and Journalist—IT, featured on TDWI.org
Prescriptive analytics builds on predictive analytics by evaluating and recommending actions through heuristics (rules), machine learning, and other AI constructs. In very basic situations, this can be reasonably straightforward. However, as actions and potential consequences grow in complexity, it can become extraordinarily difficult.
Data Science for Marketers: Predictive vs. Prescriptive Analytics
By George Karapalidis, Data Scientist for Vertical Leap, featured on business2community.com
How much would you like to know what your customers are up to and what kind of nudge they need to convert? Your past data holds answers to likely future outcomes, and predictive intelligence helps you unlock those. While the concept of advanced forecasting isn’t new—the airline industry has been analyzing their flight data for years to minimize late arrivals and optimize routes—this trend is now starting to reach its full potential in the marketing industry.
The Case for Machine Learning in Supply Chain Planning
by Shaun Phillips, an expert in Machine Learning and the Product Manager for QAD, featured on Supply & Demand Chain Executive
The case for adopting machine learning techniques is logical. It is faster, cheaper, more accurate and is less human input for higher quality output. Machine Learning is an application of artificial intelligence in which systems can analyze large volumes of data and make predictions based on correlation and causation without being explicitly programmed. It has been a significant technology breakthrough and has been applied across multiple functional areas in organizations including the supply chain.
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