Predictive analytics (PA) and machine learning (ML) are powerful tools for uncovering insights in large volumes of data. Many organizations use machine learning for personalizing consumers' website experiences and predictive analytics for forecasting outcomes of campaigns. This quick guide will review the definitions of both predictive analytics and machine learning, how they differ, and use case examples.
What is machine learning?
It wasn't so long ago that the idea of "machine learning" was something out of science fiction. But now it is everywhere. Organizations are using machine learning to explore their large volumes of data and to automate processes. Machine learning involves training algorithms, neural networks, or processing computers to analyze data and output findings at scale. This output can include recommendations, automated text, or flagged outliers.
A misconception of machine learning is that this technology will replace humans in analytics and statistics. But in reality, ML needs human influence and expertise for it to work correctly, including abstract and creative thinking. Additionally, many tasks that use ML were simply not possible or sustainable as manual efforts.
Examples and use cases of machine learning
Machine learning examples include:
- Uncovering patterns in market research
- Flagging errors in transactions or data entry
- Predictive text message suggestions on our phones and automated subtitles on videos
- Personalized shopper experience based on browsing history
- Signal anomalies in medical research
Those features were born from machine learning. Once designed and trained by data experts, ML can further help users of all technical abilities dig into complex data analysis. New data analytics platforms use machine learning to allow users to query their data using natural language processing.
What is predictive analytics?
Predictive analytics involves advanced statistics, including descriptive analytics, statistical modeling and large volumes of data. Predictive analytics can include machine learning to analyze data quickly and efficiently. Like machine learning, predictive analytics doesn't replace the human element. Instead, PA supports data teams by reducing errors and uncovering significant insights.
Examples of predictive analytics
Predictive analytics uses models to understand what is going on in specific processes and calculate what could happen when variables change. These advanced mathematical models can result in beneficial insights, such as:
- What-if analyses to predict changes in sales quotas
- Forecasting based on historical data to predict seasonal trends in supply
- Segmentation and cohort analysis for analyzing customer behavior
- Identifying potential at-risk students in schools
Benefits and challenges of predictive analytics and machine learning in business
By having sound forecasting using predictive analytics, enterprise businesses can reduce the risk of decision-making. Leaders can use data to back up their decisions and identify clearer probabilities of outcomes.
For example, predictive analytics might lead to more efficient marketing campaigns by identifying trends in customer behavior. Machine learning might automatically monitor transactions of your customers and output anomalies or a new direction. In both cases, automation can lead to analysts making better-informed recommendations.
The challenge of these powerful tools is that predictive analytics and machine learning require human expertise and resources. They also require monitoring and benchmarking. You or your data teams should ask yourselves the following questions:
- When will you know your model is working correctly?
- How will you determine your neural network or machine learning output is accurate?
Choosing those preferred outcomes at the beginning of the process will set you up for success.
For more information on how organizations can use these processes, check out five more scenarios and resources for advanced analytics and machine learning.