Implementing Predictive Analytics in Your ABM Toolkit

November 24, 2023 by SalesMark Global

Prediction analytics is used by business domains such as supply chain, marketing, sales, and finance to anticipate consumer behavior.

Table of contents

Introduction

The business landscape is a fiercely competitive industry; thus, to move ahead, B2B marketers have moved from traditional marketing to account-based marketing (ABM). Thus, with ABM, you can focus on specific leads through personalized marketing with the help of personalized messages targeting specific accounts. However, the whole process can be conducted with the help of predictive analytics, which gives you insights into the future and enables you to improve your ABM initiatives.

In this blog, you will learn how to implement predictive analytics in your ABM toolkit.

1. Predictive Analytics in ABM

Using advanced-level analytical techniques, predictive marketing has changed how brands are attracted, converted, and retained as customers. By marketers, it often refers to the practice of harnessing the power of technology to forecast future customer needs and behaviors.

However, marketers might often consider that ABM and predictive analytics are two different strategies. But the fact is that leveraging predictive ABM can transform the way B2B companies used to do business. Thus, for a successful ABM program, you might require deep knowledge of the target’s business, each contact’s role, peers, and informing relationships in their organization.

2. Steps on Implementing Predictive Analytics

When marketers are aware of the future, they can strategize ABM programs properly. Thus, with the help of predictive analytics, you can be aware of your company’s future with the help of new and updated data. Here are three steps to implement predictive analytics for a better ABM program:

Step 1: Data Collection and Defining Project

The project goal, data sets, and scope need to be defined in the first step. This is where marketers and researchers need both primary and secondary data to collect web traffic, insights, and already existing research, like offline forms and databases.

Step 2: Statistical Approach and Predictive Modeling

Now the collected data needs to be analyzed using predictive and statistical tools to conclude. This validates the presuppositions through a multi-level approach model.