Question 1

What are predictive analytics tools and software?

Accepted Answer

Predictive analytics software is all about making business outcomes predictable. Data scientists and data analysts can do this by using data mining and predictive modeling to analyze historical data. By better understanding the past, businesses can gain insights into the future. Predictive analytics is a step further than general business intelligence, which companies use to pull actionable insights from their data sets. Instead, users can develop machine learning algorithms and predictive models to help forecast and achieve business-critical numbers. 
 The reason businesses can hit those critical numbers and become more predictive is due to the boom of big data. Companies can harness their data like never before. By recording and owning more and more historical and real-time data, data scientists have larger sample sizes to work with, meaning they can be much more accurate. Additionally, companies investing in predictive analytics without ensuring that their data is accurate, clean, and accessible will ultimately be wasting their time. However, those who can wrangle their data properly will create a significant competitive edge and hold an advantage in the market.

Question 2

Benefits of using predictive analytics tools

Accepted Answer

Accurately predict and forecast revenue numbers based on a wide range of variables
Understand and account for customer churn and retention
Predict employee churn based on historical factors for turnover
Make more precise, data-driven decisions in all departments based on available data
Determine both risks and opportunities that were otherwise hidden within company data

Question 3

Why use predictive analytics solutions?

Accepted Answer

There are a number of applications for predictive analytics software and reasons businesses should adopt them, but they all boil down to understanding what has happened in the past, what could happen in the future, and what should be done to ensure positive business outcomes. These are considered descriptive analytics, predictive analytics, and prescriptive analytics. 
 Descriptive Analytics (understanding the past) — Descriptive analytics deals with understanding what has happened in the past and how it has influenced where a business is in the present. This means undergoing data mining on a company’s historical data. This type of analysis can be obtained by using business intelligence tools, big data analytics, or time-series data. Regardless of how it is attained, providing descriptive analytics is a key foundation of predictive analytics and creating data-driven decision-making processes. It requires thorough data preparation and organizing the data for easy descriptive analysis. 
 Predictive Analytics (knowing what is possible) — Predictive analytics allows users and businesses to know and anticipate potential outcomes. Building predictive models based on descriptive analysis can ensure that businesses do not make the same mistake twice. It can also provide more accurate forecasting and planning, which helps to optimize efficiency. Ultimately, this analysis makes the unknown known. 
 Prescriptive Analytics (so now what?) — The final step and ultimate reason for using predictive analytics tools is to make clear actions based on the suggestions and recommendations of the predictive models. This is where machine learning and deep learning functionality come into play. Some predictive analytics solutions can provide actionable insights without human intervention. For example, it can provide a short list of sales accounts that should close quickly based on several variables. Becoming prescriptive takes analytics a step further and is the ultimate reason for adopting advanced, predictive analytics.

How Many Predictive Analytics Software Products Does G2 Track?

Category Stats (Sep 2026)

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G2 Grid® for Predictive Analytics Software

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Best Predictive Analytics Tools and Software - Page 25