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Predictive Analytics

What is predictive analytics?

According to Gartner,
"Predictive analytics is a form of advanced analytics that examines data or content to answer the question, “What is likely to happen?” It is characterized by techniques such as regression analysis, multivariate statistics, pattern matching, predictive modeling and forecasting. Predictive models score the propensity for customers to respond to a marketing campaign by analyzing historical patterns, relationships and behaviors."

It analyzes the historical data, trends, and external factors with the help of artificial intelligence (AI), machine learning (ML), and predictive models.

Why is predictive analytics important?

Understanding what went wrong is a must for every business. Identifying what might go wrong or perform well equips businesses with operational strategies to avoid or achieve the future outcome that has been predicted. Organizations can benefit in multiple ways, such as monetary returns and expenses, efforts, workforce management, financial stability, and more.

How does predictive analytics work?

Identifying the use cases

The underlying operation of predictive analytics is determined by the use cases that need to be forecasted. Like predicting the seasonal flu cases next year, product sales in a specific month, next year's revenue, forecasting flood damages based on historical data, etc. Specifying the use case for prediction helps to determine the ideal or best suited method of predictive analytics model that should be used.

Data management

Businesses use decades of data to get the best predictions. If they need to pull in huge datasets, it should be organized, prepared, and enriched for accurate predictions. Before identifying the suitable predictive models, datasets need to be organized in a data warehouse.

Prediction models

Using various tools and techniques, data scientists develop predictive analytics models based on the use cases and problems that need to be solved. Some common predictive analytics models are Machine Learning, regression models, and decision tree models.

Benefits of predictive analytics

Fraud detection

As predictive analytics can operate with real-time data, it is helpful for businesses to identify behavioral patterns and abnormalities that indicate threat or fraudulent activities.

Risk reduction

Organizations can use predictive analytics to formulate risk mitigation strategies. Predominantly, the banking, financial services, and insurance companies rely on predictive analytics to screen individuals and businesses.

Marketing strategy optimization:

Marketers can leverage predictive analytics to identify whether the campaign will perform well or not based on the past performance data. It hugely helps them save money and efforts. Predictive analytics provides insights on a deeper level, such as seasonal or time-based engagement patterns, demographic-based performance patterns, purchasing patterns, and more. It can provide you with an action plan tailored to the specific segments and preferences.

Production efficiency enhancement

With predictive analytics, businesses can optimize production processes, streamline supply chains, and anticipate customer preferences. This proactive approach minimizes inefficiencies, reduces downtime, and improves overall product performance, leading to better decision-making and enhanced operational effectiveness.

Sharing and collaboration

Sharing and collaboration are integral to an organization's data literacy. Modern BI platforms provide sharing options such as presentations, PDFs, and portals.

What is predictive modeling?

Predictive modeling is a mathematics technique used to predict the future outcomes, activities, and trends based on the given inputs and using historical data.

The role of predictive models in predictive analytics

Companies use predictive models to understand market risks, financial projections, customer behavior, and sales forecast. It helps organizations take precautionary measures beforehand.

Types of predictive modeling

Classification model:

Classification model is one of the commonly used predictive models that uses historical data and produces a broad analysis on the questions that gives a yes or no answer.

Clustering model:

The Clustering model uses a machine learning algorithm that categorizes data points into groups or clusters based on their similarities. This model is commonly used for segmentation and pattern recognition in various fields, such as marketing, biology, and image analysis.

Outliers model:

Within a dataset, an outliers model helps to find unusual or abnormal activity of individual instances or correlated to other data groups. Financial companies use this model the most to identify the unusual transactions in their customer's account.

Time series model:

A time series model helps to understand and predict patterns over time by analyzing a sequence of data points collected. Time series models help identify trends, seasonality, and other temporal patterns within the data.

Decision tree:

A decision tree represents decisions and their possible consequences in a tree-like structure. Each node in the tree represents a decision, and each branch represents the outcome of that decision.

Predictive analytics industry use cases

Retail and Ecommerce:

Predictive Analytics helps retail and ecommerce industries optimize their inventory based on customer demands, identify new market opportunities, and find seasonal sales trends. It enables organizations to understand customer preferences, enhance product usability, and strategize their pricing accordingly.

Example: An online retailer analyzes past purchase data and customer preferences to predict upcoming trends. Based on these predictions, the retailer adjusts its inventory, highlighting and promoting products likely to be popular in the upcoming season. This strategy boosts sales and reduces excess stock.

Healthcare:

Predictive analytics is applied in healthcare for patient outcomes prediction, disease prevention, and resource optimization. It aids in identifying high-risk patients, optimizing hospital bed utilization, and improving overall patient care by predicting possible health issues.

Example:A healthcare provider manages patient health by analyzing electronic health records and lifestyle data; the system predicts patients at risk of readmission. Healthcare professionals then provide targeted interventions, reducing readmission rates and improving overall patient outcomes.

Finance and Banking:

In finance and banking, predictive analytics is utilized for credit scoring, fraud detection, and risk management. By analyzing historical data, financial institutions can make decisions regarding lending, investments, and identifying fraudulent activities.

Example:Banks constantly monitor transactions and flag any unusual patterns, such as sudden large withdrawals or international transactions. This real-time analysis helps prevent fraudulent activities.

Manufacturing and supply chain:

Industries involved in manufacturing benefit from predictive analytics by optimizing production schedules, predicting equipment failures, and improving supply chain efficiency. This helps in reducing downtime, minimizing costs, and ensuring the timely delivery of products.

(AI) Example:A manufacturing plant forecasts equipment failures by monitoring machinery sensor data; the system predicts when equipment is likely to malfunction. This allows the plant to schedule maintenance, reduce downtime, and ensure continuous production.

What is the future of predictive analytics?

Retail and Ecommerce:

• Ease of use: As technology advances, the traditional techniques of analyzing data are also evolving. Professionals who have minimal knowledge of statistics are starting to use predictive analytics models with the help of AI and ML models.
• Forecasting real-time data:Real-time data provides insights that can turn out into a crucial business decision. Forecasting real-time data adds more power to organizations to take precautionary measures and informed decisions.
• Accurate Predictions:By harnessing the AI and ML models, businesses can generate accurate predictions. The AI layer in data preparation tools enriches the quality of data with minimal technical effort.

How do I get started with predictive analytics tools?

Modern BI platforms have in-built predictive analytics capabilities that help you predict your data effortlessly. A powerful data analytics software like Zoho Analytics have AI assisted predictive analytics interfaces where you can make predictions in a few clicks.

Zoho Analytics has a conversational analytics assistant, Ask Zia. You can predict your data by just asking questions to the Ask Zia assistant. It caters to the professionals from the non-statistics background to do predictive analytics by choosing the right prediction model by analyzing the data. Get started with your predictive analytics journey now.

Sign up with Zoho Analytics for a free 15-day trial, start integrating all your data sources, and extract actionable insights from them.