Artificial Intelligence (AI) is getting a lot of attention, but AI isn’t a single technology. Instead, it’s an umbrella term for various approaches that can train machines to solve problems in ways that mimic human intelligence.
Machine learning (ML) is one of these approaches, and predictive analytics are used in machine learning algorithms. Businesses use these algorithms to predict outcomes based on historical data and make smarter decisions. Both machine learning and predictive analytics involve collecting and analyzing data from past events to make better decisions about the future.
For example, experts believe that AI makes self-driving cars safer than human drivers. It will also allow computers to better diagnose medical conditions than doctors by scanning through piles of data at lightning speed and finding patterns invisible to humans.
Machine learning and predictive analytics have become popular buzzwords—so much so that many people use them interchangeably when discussing applications such as chatbots, drones, autonomous vehicles, and more.
What is predictive analytics?
Predictive analytics refers to using data and statistical analysis to predict future outcomes. Data scientists and statisticians use various mathematical models and algorithms to find patterns in historical data that can predict future events.
By using predictive analytics software, companies can understand how customer behavior will likely change in response to business strategies or other variables.
Functions of predictive analytics
For predictive analytics models to be accurate, they must have a large sample size and capture relevant data points.
Common functions of predictive analytics include
- Recommendation engines: These use algorithms to make product suggestions to platform users. This function generates suggestions for what consumers might want next, given their current interests and purchases over time.
- Predictive modeling and scoring systems: These scores predict risks and customer segments using information like recent purchases and demographics.
- Prescriptive analytics: These model what will happen in specific scenarios. Companies can use these models to predict what will happen if they raise prices, for example.
- Forecasting analytics: This indicates future values based on historical data sets. For example, forecasting may be applied to cash flow management or predicting seasonal sales changes.
- Predictive maintenance: Predictive maintenance uses analytic methods and software tools to detect issues before equipment breaks down. This can prevent downtime, reduce costs and improve overall equipment reliability.
Benefits of predictive analytics
The major benefit of predictive analytics is that it helps you make sense of data, which can help improve your business processes, and overall performance. Let’s take a look at some ways in which predictive analytics can benefit your business.
- Help businesses identify patterns that they might otherwise miss, including market trends and areas of profitability.
- Helps companies to make accurate predictions about future events or outcomes.
- Increases the efficiency of a business’s internal operations.
- Maximizes resources by reducing the likelihood of mistakes in decision-making processes.
- Predictive analysis can lower costs through more efficient hiring practices, improve fraud detection and reduce risks.
Use cases of predictive analytics
Predictive analytics are used in many industries, including healthcare, finance, manufacturing, and telecommunications, to make better business decisions. Some of these use cases include:
Many organizations rely on predictive techniques for fraud detection purposes. By analyzing high volumes of transactional information—whether in financial or nonfinancial sectors such as healthcare or retail—businesses will be able to identify patterns that suggest fraudulent activity.
Organizations use predictive models and algorithms to cluster large customers based on their predicted future behavior. This helps companies target those most likely to convert sales. Marketers can spend more time communicating with the most engaged customers, thus improving overall returns by reducing marketing spending directed toward unlikely prospects.
Predict demand and optimize pricing
Rather than guessing how many consumers will want to purchase particular items at varying prices, organizations can employ predictive models and algorithms to project demand and adjust prices accordingly.
Perform predictive maintenance
Big data and predictive analytics can help maintain enterprise assets with minimal downtime. From airlines to manufacturing, predictive models predict impending failure on machines and take action before a failure occurs.
Companies use predictive models and algorithms to automate talent acquisition processes and hire top candidates more quickly.
What is machine learning?
Machine learning is a collection of algorithms that teach computers to learn from data without being explicitly programmed. It’s a branch of artificial intelligence that lets computers make their own decisions by detecting patterns and correlations in massive amounts of data. This process starts with a large amount of data that has been tagged and collected in a specific way.
Functions of machine learning
Machine learning allow computers to continuously improve their predictive accuracy by performing computational analyses, discovering patterns, and creating predictive models.
Common functions of machine learning include
- Training models: A training model is a set of labeled samples used for teaching an algorithm how to perform specific tasks.
- Classification: Classification involves assigning inputs into predefined categories or classes according to their characteristics.
- Preparing models: Preparing models includes dividing datasets into train/test sets, then normalizing and preprocessing datasets.
- Regression: Regression analysis helps make predictions about future behavior based on current trends or data about previous behaviors of individuals or groups.
- Detecting anomalies: Detecting anomalies is an integral part of business intelligence solutions as it allows businesses to identify unusual activity and prevent potential security breaches or fraud.
Benefits of machine learning
Machine learning can help automate processes, eliminate repetitive manual tasks, and optimize performance. The result is a powerful tool that can save enterprises time and money. ML also:
- Reduces workload of employees by making routine work less tedious, thus freeing up staff to work on higher-value projects.
- Eliminates human error from jobs that would otherwise need to be monitored closely.
- Optimizes internal processes such as inventory management and supply chain management so you can manage risk in your business more effectively.
- Gathers data and presents it in actionable ways so companies can make better decisions based on information, not guesswork.
- Accelerates research and development by helping organizations prototype new ideas quickly and with fewer resources.
- Uncovers patterns hidden in data so companies can develop strategies for satisfying consumer demand.
Use cases of machine learning
Predictive modeling is being applied to a range of fields, including finance (fraud detection), customer acquisition (targeted advertising), supply chain management (shipping optimization), and sales (predictive lead scoring).
With process automation, companies automate manual tasks that could be done faster and often more reliably than people can do them. That frees up employees to handle tasks requiring human interaction.
A machine-based, dynamic pricing strategy involves searching for optimal prices based on revenue goals and factors like availability, competitors’ actions, seasonality trends, weather forecasts, and even news events. Prices might vary over time based on changes with any of those factors.
The rise of AI in healthcare has led to improved medical treatment through advanced research methods and interpretative tools that collect genetic information about patients’ cells along with metadata related to their health conditions, lifestyle choices, or medical histories.
Predictive analytics vs. machine learning: Differences
Key differences between predictive analytics and machine learning are outlined in the table below.
|Predictive Analytics||Machine Learning|
|Relies on statistical probabilities||Relies on data insights|
|Seeks to answer the question of what will happen next||Focuses on uncovering patterns from past behaviors that can be applied to future activities|
|Uses historical data to make future predictions for decision-making||Uses trained data and algorithms to automate future processes|
|Builds on descriptive analytics and diagnostic analytics||Trained via supervised and unsupervised learning|
|Identifies actionable insights||Identifies anomalies|
|Based on neural networks and decision trees||Based on systems of rules|
|Provides recommendations for human intervention||Drives automated decisions rather than offering recommendations|
|Used for predicting unknown values||Used for classification, prediction, clustering, and regression|
Predictive analytics vs. machine learning: Similarities
Predictive analytics and machine learning share distinct similarities. These techniques use statistical modeling, mathematics, data mining, and pattern recognition to make predictions about future events.
Due to their predictive nature, both have been used in various business applications such as forecasting customer behavior, targeted marketing campaigns, sales forecasting, and fraud detection.
Other common similarities:
- Both techniques use extensive large datasets to determine future outcomes.
- A data analyst with training in mathematics, statistics, computer science, data management, and pattern recognition performs the tasks.
- Because they involve human decision-making, there is always a possibility of human error, which can impact model reliability and accuracy.
- They require decision-makers to continually run tests until they find their sweet spot for optimal results.
- Performance improvement and lead time rely on increasing granularity within available data.
Benefits to business
Regardless of similarities or differences, both predictive analytics and machine learning help businesses gain valuable insights by looking at volumes of data.
They can help enterprises to understand customer behavior better by analyzing current trends and market dynamics that have implications for future revenue. These are some exciting predictions.
By using these two tools together, organizations can optimize time and resources while minimizing risks to their business. When combined with other tools in your data science toolkit, your company’s ability to make sense of the data it collects will continue to grow exponentially over time.
No matter what industry sector you work in, having actionable intel on hand will be essential for making important business decisions down the road. Plus, as all industries are adopting more advanced technologies, effective information management is becoming critical to staying ahead of competitors.
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