2 Important Predictive Modeling Techniques for 2020 Every Marketer Should Know

Posted by syntelli on January 20th, 2020

The conception of predictive modelling has been used for decades that involve data collection, statistical model formulation, prediction making and finally model revising so that more data are available. Most organization understands that they need to utilize efficient predictive analytical tools for analyzing the massive amount of data and leveraging it into predictive results.

It is only in recent times that the usage of Predictive Modeling Techniques in marketing taken the front seat due to the large volume of available customer data. To make predictions about customers like their prosperity to engage, buy, convert and churn, marketers and data scientists can leverage plenty of available external and internal data. In modern data-driven economy, effective predictive modelling technique is slow, requires many data analysts to handle a larger volume of data to stay competitive. A huge number of data that are spread at different sources inhibits businesses from taking decisions with the speed and focus needed to compete in a sophisticated market.

The challenge for businesses is to turn these available data into business value-specific actions and decisions that optimize and channelizes entire business operations across the company. Modern analytics is based on cutting-edge predictive modelling techniques for analyzing a larger volume of data sources and suggests effective actions that can quickly increase and boost business operations.

Common Predictive Modeling Techniques for Marketers 

There are two most common and popular predictive modeling techniques that are used in marketing by marketers such as:

1. Clustering Models

In the marketing field, this is ideally used to segment customers, identify and group them based on precise attributes. Companies collect and an aggregate large volume of customers’ data from various sources such as social media platforms, CRM platforms, mobile apps and e-commerce sites. These data are then fed to cluster models that enable customers to be segmented depending on their demographic locations and behavioral patterns. If marketers have a clear understanding of their customers at a granular level, customer data can be segmented in many ways. A complete and detailed understanding of customers allows companies to predict the accurate behavior of customers.

2. Propensity Models

These are statistical models that are based on the calculated predictions about future outcomes and events based on the available data. There are different types of propensity models that are used in marketing such as predicted CLTV (customer lifetime value), prosperity to churn and buy. Additionally, it provides a score that shows the possibilities that customer will perform a predicted action. Higher the propensity score, more accurate customer prediction!

Predictive Modelling Techniques can prove to be powerful and beneficial tools for marketers in 2020 if the models are provided with quality data so that it can be analyzed properly. Quality data coupled with predictive modelling technique enables companies to gain customers’ valuable insights so that it can be used to build customized and influential marketing campaigns. What’s more? It can also serve as a game-changer by improving business operations, boosting internal processes and stay superior to competitors.


Predictive modeling techniques can serve as a game-changer by optimizing business operations, improving internal processes and surpassing competitors. Modern Analytics works closely with organizations across a wide range of industries to gather and structure data, analyze it using our cutting-edge technology and algorithms, and rapidly deploy customized, prescriptive solutions unique to each client.

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