Prediction analytics is used by business domains such as supply chain, marketing, sales, and finance to anticipate consumer behavior.
Table of contents
- Predictive Analytics in ABM
- Steps on Implementing Predictive Analytics
- Final Thoughts
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.
Step 3: Model Operation and Observation
The last step is to validate the data to create accurate strategies that aim to garner optimal results and performance to achieve business goals.
In the B2B business world, ABM and predictive analytics can change the way traditional marketing is done. In this adventurous journey, data meets precision and marketing meets science so that marketers can embark on the successful journey of turning potential brands into permanent clients.
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