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Features To Look For In A Predictive Analysis Platform

Predictive analysis refers to predicting future outcomes based on the study of historical and present data. It has several advantages for businesses, including gaining a competitive advantage, optimizing processes, finding increased opportunities for revenue growth, reducing risks, and analyzing the customer base.

This process utilizes several techniques, one of which is models. Predictive analytics models use a data set to determine outcomes. The data involved is historical, present, and future. However, collecting and manually preparing the data is time-consuming, leading to delays in your project.

The best option is to use a predictive analytics platform that helps speed up the data preparation process by automating it. Once you feed the software, your data sources begin the automatic data restructuring, cleansing, encoding, and imputation.

Using a platform guarantees accurate predictions and reduces the chances of errors, which are usually rampant in manual predictions. It also optimizes your models, enhancing their accuracy and value over time.

But what are some features to look for in such platforms, and which use cases are they helpful? Continue reading to find out.

Features to look for in an analytics platform

These are some features a platform should have to enable accurate and actionable predictions (on which you can act in the future).

Connects to various databases

The software should enable you to connect with various databases through code-free integration and the absence of database manipulation (extracting a particular data set and applying its findings to an entirely different collection of data).

Once you connect to the database, the drag-and-drop interface lets you create a data set that can be analyzed by machine learning (ML). Manually doing it is tiring as the process involves various steps such as data exploration, collection, discovery, and problem formulation.

Data Enrichment

It refers to improving a particular data set by filling the missing parts or supplementing them with new information. It helps companies better understand their customers and gain valuable insights into their buying behavior.

However, enriching the data is a significant challenge for companies because of its volume and finding data that is relevant. Using software simplifies the process of collecting relevant global data to arrive at relevant results at a faster pace.

Improves your output

Using a platform for predictive analytics improves your output as it optimizes your models while giving importance to exclusive features as required. By automating them, you save time in dealing with complicated parts of the predictive process, including feature selection, encoding, and feature engineering.


The first time you use this prediction software, it will deliver the results quickly. However, AI-backed optimization will continuously analyze your data, increasing the accuracy of the prediction with time. You can also use the live dashboard to monitor the performance and take the required action.

In which cases can it be used?

Conversion rate modeling

It helps you predict conversion rates and identify the prospects with the highest chances of conversion into a sales opportunity. It enhances audience segmentation and predictive lead scoring and prioritizes inbound leads.

Customer win-back

It helps you win back inactive customers by developing a win-back strategy. You can use the platform to analyze your customer base and send a message which renews their interest. The software helps you identify your top customers and develop a strategy to increase their engagement.

Customer demand

You can use the software to predict customer demand by mining millions of customer data rows and adding external data to datasets. It helps you make SKU-level predictions and lets you immediately see which products the customers require.

A predictive analytics platform containing the above features benefits you in plenty of ways. It improves the accuracy of your predictions, increases the output, enriches the data, and reduces the potential for errors. Better predictions help you understand your customers better, thus increasing your overall sales and revenues.

Also Read: 10 Latest Trends In Big Data Analytics

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