Descriptive Analytics vs Predictive Analytics

Descriptive Analytics vs Predictive Analytics. In today’s marketing world, marketers have access to a huge amount of data. Despite the abundance of data, the information isn’t as effective as it could be without systems to organize and interpret it. As marketers design and deploy insight-driven campaigns that target new customers and improve customer retention, predictive analytics and descriptive analytics can provide clarity.

Descriptive Analytics vs Predictive Analytics
Descriptive Analytics vs Predictive Analytics

We will explain the differences between predictive analytics and descriptive analytics in this post. The types of analytics we’ll cover and what they tell us will be discussed. We’ll look at real-world marketing examples of predictive and descriptive analytics. We will also share opportunities to learn about predictive and descriptive analytics that translate into market success.

There are several strategic applications for both predictive and descriptive analytics. There are overlaps between those applications when it comes to marketing, but their roles are unique.

Descriptive Analytics

Analysis of descriptive data focuses on past consumer behavior, such as:

  • History of customer purchases
  • Email or social media campaign effectiveness
  • A website’s click-through rate, time on page, and conversion rate

By using descriptive analytics, companies can find out what’s working, what’s not, and what motivates their customers. Marketers can identify patterns and trends using descriptive analytics by taking numbers and data from the past.

Predictive Analytics

Predictive analytics, on the other hand, determines what is likely to happen in the future, while descriptive analytics explains what has happened. In predictive analytics, current and/or historical data are used in conjunction with statistical techniques — such as (but not limited to) data mining, predictive modeling, and machine learning — to determine whether a particular event will occur in the future.

In addition to these five examples, we also provide a comprehensive overview of predictive analytics in marketing. Examples include:

  • Cluster modeling for segmenting customers and audiences
  • Acquisition of new customers (using identification modeling)
  • Propensity modeling and predictive scoring for lead scoring
  • Recommendations for content and ads (using collaborative filtering)
  • Automated segmentation for personalizing customer experiences

A cohesive organization system and clear questions that need answers make all the difference in descriptive and predictive analytics. The right questions and answers for marketers can be determined by reflecting on the information discovered through descriptive analytics. These answers can then be found using predictive analytics.

Key Difference

Detailed explanations of Predictive Analytics and Descriptive Analytics can be found below:

The descriptive analytics will provide you with a vision of the past and tell you: what has happened? Predictive Analytics tells you what is likely to happen in the future by recognizing the future.

Predictive Analytics uses Statistical Analysis and Forecast techniques to determine the future. Descriptive Analytics uses Data Aggregation and Data Mining techniques to provide knowledge about the past.

A descriptive analysis is used if you want to describe and analyze your organization, whereas a predictive analysis is used if you need to know what will happen in the future and fill in the blanks.

In a descriptive model, you will be able to exploit the past information that is stored in databases and get an accurate report. To identify risks and future outcomes, Predictive models identify patterns in past and transactional data.

An organization can use descriptive analytics to determine where they stand in the market, present facts and figures, and determine where it should focus its efforts. As opposed to predictive analytics, which will allow an organization to forecast the facts and figures about the company in the future, moreover, how it will affect the market in the future.

Descriptive analyses generate accurate reports, but predictive analyses do not always produce accurate results.

Comparison Table

Basis for ComparisonDescriptive AnalyticsPredictive Analytics
DescribesIn the past, what happened. Using stored data.In the future, what might happen? Analyzing past data.
Process InvolvedData Aggregation and Data Mining is involved.Statistics and forecasting techniques are involved.
DefinitionAnalyzing large amounts of data to find useful and important information.Forecasting the company’s future is a very useful part of this process.
Data VolumeA data warehouse is used to store and process large amounts of data. Data from the past is limited.A large amount of past data is analyzed and then advanced techniques are used to predict the future.
ExamplesAn analysis of a company’s performance, sales report, revenue, etc.Analyses of sentiment, credit scores, forecasts for companies, etc.
AccuracyUsing past data provides accurate data in the reports.There is a problem with the results. While it will not tell you what will happen exactly, it will provide you with an idea of what might happen in the future.
ApproachA reactive approach can be taken.Taking this proactive approach is a good thing.

Conclusion: Descriptive Analytics vs Predictive Analytics

The result of this blog shows there is a substantial and important difference between Predictive Analytics and Descriptive Analytics, even though we have only discussed a few characteristics of each.

The market is experiencing an increase in demand for analytics. In today’s world, every organization is talking about Big Data, but it is merely a starting point for creating useful and actionable insights from an organization’s data. Consequently, analytical processes like Predictive Analytics and Descriptive Analytics will aid organizations in identifying their performance, where they stand in the market if they have any flaws, if there are any issues, etc. You can gain insight and foresight into your business by applying these analytical processes.

Here are some important points to keep in mind:

  • The descriptive analysis focuses on presenting data and displaying it on the management site. A predictive model helps to forecast the future, while a statistical model is the focus of prescriptive analysis.
  • In predictive analysis, we analyze what will happen in the future based on past events, but that condition might not occur exactly in the future for the same reason.


Descriptive analytics and predictive analytics are two types of data analytics that are commonly used in business and other fields.

Descriptive analytics is a type of data analysis that focuses on describing historical data and understanding what happened in the past. This type of analytics involves examining past performance and identifying patterns, trends, and correlations in data. Descriptive analytics is typically used to summarize and visualize data, and to identify areas where further analysis is needed.

Predictive analytics, on the other hand, is a type of data analysis that uses statistical and machine learning algorithms to make predictions about future events. This type of analytics involves analyzing historical data to identify patterns and relationships, and then using this information to make predictions about what is likely to happen in the future. Predictive analytics is used to forecast trends, identify risks, and make informed decisions based on data-driven insights.

In summary, descriptive analytics is focused on understanding what happened in the past, while predictive analytics is focused on making predictions about what is likely to happen in the future based on past data. Both types of analytics are important for making informed decisions and driving business success.

Read also: Descriptive Analytics Meaning Examples; PEST analysis of a restaurant; Relationship between logic and education

External resource: Coursera

This post is also available in: English Français (French) Deutsch (German) Español (Spanish) Dansk (Danish) Nederlands (Dutch) Svenska (Swedish)