Fill the form, and we'll contact you! X

Thank you!!x

Message sent

Contact us@

19.6.2018 - 10:15 ID BBN Guest writer

Introduction to Predictive Marketing Analytics

Predictive Marketing is the process, tools and rules for applying predictive analytics to making marketing and sales decisions. The objective of Predictive Marketing is to anticipate which marketing and sales actions will most likely lead to the desired customer behaviour and to carry out those actions.

Predictive Marketing is not a replacement for more traditional marketing approaches. Marketing and sales decisions will continue to be based mostly on product releases, inspirational ideas, what competitors and peers are doing, and what has worked well in the past. What Predictive Marketing will do is supplement traditional marketing with a new, more analytical method of sorting and prioritising marketing and sales actions.

One of the key aspects of Predictive Marketing is Predictive Marketing Analytics (PMA), which involves using historical customer data to predict future outcomes and trends. For example, PMA can tell you which accounts to target for churn prevention and which leads are most likely to become customers and are, thus, most worth pursuing. PMA is typically and predominantly done using computer algorithms (e.g., Machine Learning).


Graphic 1: Predictive Marketing Analytics Process 

During the last five years, the majority of B2C companies have started utilising Predictive Marketing Analytics. But PMA is really nothing new. The individual methods and formulas have been around for almost fifty years. So what is behind its recent growth in popularity? The explanation is that three critical enablers have simultaneously matured to the point at which PMA has become easily accessible to almost any company.

  1. Marketing Clouds and CRM-centric sales generate a massive amount of behavioural data. These include sales interactions, digital customer engagement, social media, loyalty (e.g. Net Promoter System), support, etc.

  2. Data extraction has become easier and cheaper. Just a few years ago, it took weeks or even months to extract, transform, and load data for analytics. Modern martech applications make data extraction simple and easy to automate using data integration tools like Frends.

  3. Computing costs have plummeted. Machine learning requires a massive amount of computing power. This used to be very expensive. With cloud computing more or less following Moore’s law, the cost of storing and processing data has fallen drastically.  

Predictive Marketing Analytics isn’t an absolute science. Like traditional marketing, it is some combination of art, intuition, and science. But it does provide companies with the ability to more reliably forecast customer behaviour.

More on the topic:

About the writer: 

Matti Airas is a consultant in predictive data-driven marketing and customer experience. He has previously worked for the customer experience feedback analysis company Etuma and for Nokia in the U.S. His passion is figuring out how to use data to solve business problems.

Matti enjoys writing, podcasts (especially on U.S. politics), golf, long walks with his wife and Jack Russell Terrier, and any kind of skiing.


Artificial Intelligence
Data Management
Data-Driven Decision Making
Marketing Ecosystem
Marketing Technology
Predictive Marketing
blog comments powered by Disqus
About me

The ID BBN guest writer is a selected expert in his/her own field. The guest writers address interesting and current phenomena.

More from the writer