What is the Difference Between Predictive And Descriptive Analytics?

technology, analytics, business, predictive and descriptive analytics

If you are a business owner or manager, you have probably heard of the terms predictive and descriptive analytics. But what do they mean and how can they help you improve your decision making, performance and profitability?

Predictive analytics

Predictive analytics is a powerful tool that can help businesses make informed decisions by forecasting future outcomes based on historical data.

By analyzing patterns and trends in past data, predictive analytics can help answer questions like “What will happen if I launch this new product?” or “How likely is this customer to churn?” or “What is the optimal price for this service?”

What can predictive analytics do?

  • Identify opportunities and risks: You can use predictive analytics to discover new markets, segments, products or services that can increase your revenue or reduce your costs. You can also use it to detect potential threats, such as fraud, cyberattacks or customer attrition, and take preventive actions.
  • Optimize resources and processes: You can use predictive analytics to allocate your resources more efficiently, such as inventory, staff, equipment or budget. You can also use it to improve your processes, such as marketing, sales, operations or logistics, by finding the best strategies, tactics or parameters.
  • Enhance customer experience and loyalty: You can use predictive analytics to understand your customers better, such as their preferences, needs, behaviors or sentiments. You can also use it to personalize your offers, recommendations or interactions with them, and increase their satisfaction, retention and advocacy.

Techniques used in predictive analytics

There are several techniques used in predictive analytics, including:

  • Regression analysis: a statistical technique used to identify the relationship between a dependent variable and one or more independent variables. It is commonly used to forecast future trends based on historical data.
  • Decision trees: type of machine learning algorithm that can be used for both classification and regression problems. They work by recursively splitting the data into subsets based on the most important features.
  • Neural networks: type of machine learning algorithm that are modeled after the structure of the human brain. They can be used for both classification and regression problems.
  • Time series analysis: is a statistical technique used to analyze time-dependent data. It is commonly used to forecast future trends based on historical data.
  • Clustering: technique used to group similar data points together based on their characteristics. It is commonly used for segmentation and anomaly detection.
  • Random forests: Random forests are an ensemble learning method that combines multiple decision trees to improve accuracy and reduce overfitting.

Tools for performing predictive analytics

SAP Predictive Analytics: provides predictive modeling and machine learning capabilities. It can be used to build predictive models, perform data mining, and create visualizations.

H2O.ai: an open-source platform for machine learning and predictive analytics. It provides several algorithms for predictive modeling, including deep learning, gradient boosting, and generalized linear models.

SAS Advanced Analytics: is a tool that provides predictive modeling, data mining, and machine learning capabilities. It includes several algorithms for predictive modeling, such as decision trees, neural networks, and regression analysis.

TIBCO Statistica: a tool that provides advanced analytics capabilities, including predictive modeling, data mining, and machine learning. It includes several algorithms for predictive modeling, such as decision trees, neural networks, and regression analysis.

Oracle DataScience: is a cloud-based platform that provides machine learning and predictive analytics capabilities. It includes several algorithms for predictive modeling, such as decision trees, neural networks, and regression analysis.

DataRobot: a cloud-based platform that provides automated machine learning capabilities for predictive analytics. It includes several algorithms for predictive modeling, such as decision trees, neural networks, and regression analysis.

Q Research: Q Research is a tool that provides predictive modeling and machine learning capabilities. It includes several algorithms for predictive modeling, such as decision trees, neural networks, and regression analysis.

RapidMiner: is a tool that provides machine learning and predictive analytics capabilities. It includes several algorithms for predictive modeling, such as decision trees, neural networks, and regression analysis .

Descriptive Analytics

Descriptive analytics is a type of data analytics that is used to summarize what has happened in the past. It involves analyzing historical data to identify patterns and trends, and using visualizations and reports to communicate the insights gained from the analysis.

Descriptive analytics can help businesses answer questions like “How many sales did I make last month?” or “What are the most popular products in my store?” or “How satisfied are my customers with my service?”

What can descriptive analytics do?

  • Monitor performance and progress: You can use descriptive analytics to track your key performance indicators (KPIs), such as revenue, profit, market share or customer satisfaction. You can also use it to measure your progress towards your goals and objectives, and compare it with your benchmarks or competitors.
  • Understand patterns and trends: You can use descriptive analytics to explore your data and find meaningful patterns or trends that can explain what has happened in the past. You can also use it to validate your hypotheses or assumptions, and test the impact of your actions or interventions.
  • Communicate and report results: You can use descriptive analytics to present your data in a clear and compelling way, using charts, graphs, dashboards or stories. You can also use it to communicate and report your results to your stakeholders, such as customers, employees, partners or investors.

Common techniques used in descriptive analytics

  1. Measures of central tendency: Measures of central tendency, such as mean, median, and mode, are used to describe the central value of a dataset.
  2. Measures of dispersion: Used to describe the spread of a dataset. Measures of dispersion include range, variance, and standard deviation.
  3. Data visualization: Data visualization techniques, such as charts and graphs, are used to communicate insights gained from the analysis.
  4. Exploratory data analysis: Exploratory data analysis is a technique used to analyze data and identify patterns and relationships.
  5. Statistical analysis: Statistical analysis techniques, such as hypothesis testing and regression analysis, are used to identify relationships between variables.

Tools available for performing descriptive analytics

Microsoft Excel: a widely used tool that can be used for simple descriptive analytics. It has powerful statistical and data visualization capabilities. Pivot tables are a particularly useful feature for summarizing and analyzing large data sets.

Tableau: is a popular business intelligence tool that can be used for descriptive analytics. It provides a wide range of visualization options and allows users to create interactive dashboards.

Google Analytics: a web analytics tool that can be used for descriptive analytics. It provides insights into website traffic, user behavior, and other metrics.

Python: Python is a popular programming language that can be used for data analysis and visualization. It provides several libraries, such as Pandas and Matplotlib, that can be used for descriptive analytics.

R: R is another popular programming language that can be used for data analysis and visualization. It provides several libraries, such as ggplot and dplyr, that can be used for descriptive analytics .

Conclusion

As you can see, predictive and descriptive analytics are complementary and synergistic. By combining them, you can gain a holistic view of your business situation, from past to future, and make smarter decisions that can drive your growth and success.


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