A growing number of companies use predictive analytics, causing decision-makers elsewhere to strongly consider following suit. That’s understandable, but it’s vital to understand how the technology works and what it can do before investing in it.
Read more: Top Big Data Tools & Software for 2021
What Is Predictive Analytics and How Does It Work?
Predictive analytics is a type of data analysis that uses machine learning and algorithms to predict future events or trends. Additionally, it’s a statistical method that can take past or current information to make predictions.
Users generally interact with predictive analytics tools via specialized dashboards that generate data streams based on specific questions posed. Someone might wonder, “What percentage of this month’s sales will come from new customers versus returning ones?” or “What’s the likelihood of running out of a certain item this month?” Data analytics can answer those queries and many others.
Predictive Analytics vs. Prescriptive Analytics
As people learn more about predictive analytics, they’ll likely come across content about prescriptive analytics, too. While they have similar names, these technologies are not the same.
Predictive products give details about future events. Prescriptive tools offer solutions based on what the data shows.
Predictive products give details about future events, helping people plan for them. In contrast, prescriptive tools offer solutions based on what the data shows. Users often depend on them to solve known problems.
Types of Predictive Analytics Forecasting Models
Numerous predictive analytics exist to help users make accurate and useful forecasts. Some of the broadly utilized types include:
- Neural networks: Using algorithms to find relationships between datasets
- Regression techniques: Showing relationships between variables
- Decision trees: Displaying the possibilities stemming from each choice made
- Time series models: Generating trend data across a specific period
- Clustering: Grouping data according to shared characteristics
- Outliers: Highlighting data that deviates from the norm
What Are the Benefits of Predictive Analytics?
Many people understandably want to know how predictive analytics could help them before deciding to use such solutions. For starters, predictive tools enable company leaders to move away from gut instinct and assumptions. Relatedly, they cut down on time-intensive decision-making processes and eliminate guesswork.
Predictive tools enable company leaders to move away from gut instinct and assumptions.
Predictive analytics algorithms are well-suited to intensive data processing. Thus, they can
work with larger amounts of information faster than humans could alone. It’s then possible to identify patterns people may otherwise miss.
This technology can also improve the processes associated with product launches, supply chain decisions, and customer outreach efforts, among others. Not surprisingly, having a predictive analytics strategy can also help companies achieve better marketplace performance. One study showed that 63% of business representatives cited their data analytics programs as creating a competitive advantage.
How Do Companies Use Predictive Analytics?
Hospitals can use predictive analytics to determine patient survival rates, allowing the best resource allocation. One study combined algorithms and patient medical record data to assess a person’s likelihood of hospital readmission within 30 days. Hospital administrators found that this analytics application reduced that rate, resulting in improved outcomes.
Marketing teams often depend on predictive analytics to assess how a campaign or new product launch will perform. One company claims 85% accuracy with technology predicting which actors will generate the most profits if placed in lead roles.
Similarly, event planners and tour managers rely on algorithmic predictions when choosing which markets to visit, as well as the audience segments to target. They can also assess how fast a certain date may sell out.
Retailers rely on predictive tools to understand likely sales trends, as well as preventing product depletion or overstock issues. They know factors such as weather and preferences can make sales fluctuate, and want details on the elements that drive or decrease consumer demand.
Hiring managers apply predictive analytics in numerous ways. For example, they may do so to gauge a potential candidate’s suitability. Alternatively, predictions may let them intervene to communicate with disgruntled employees. Then it’s possible to address matters before the workers leave the organization.
Further, analytics could determine how a company’s workforce needs may change in the coming months, letting managers prepare.
Read more: HRIS Trends for 2021: The Future of HR Management
What Are Some Examples of Predictive Analytics Platforms?
Business leaders must examine their organizations’ specific needs before investing in predictive analytics tools. However, some may wish to start their searches by looking at some of the industry-leading offerings. For example, Board offers automated predictive modeling features to speed utilization. SAP Analytics Cloud has tailored fast-start solutions that allow people to start analyzing data in days rather than weeks.
A selling point associated with Alteryx is that it caters to people without coding experience who consider themselves citizen data scientists. Then, at the other end of the spectrum, there’s the KNIME Analytics Platform. It’s geared towards people with formal data science backgrounds. This open-source option lets users build and optimize their models, plus create data visualizations to aid stakeholder comprehension.
Data Quality Matters
No matter when or how a company’s decision-makers want to start using algorithms to predict the future, high quality data is essential for success. If a company feeds incorrect or duplicate information into a powerful tool, the results may not have the accuracy and dependability a business leader needs to feel confident.
Read next: What Is Vector Similarity Search?