Artificial intelligence and machine learning are proving to be very useful in just about every business function in the enterprise, and marketing is no exception. AI is already impacting marketing, and it’s going to further shape the future of how business is done and how relationships are forged between companies and their clients.
As I wrote recently in MarTechToday, most AI in marketing applications are focused on B2C use cases, many of which we’re very familiar with as consumers ourselves. Most of us know that the ads that show up on Facebook, on banners or on Google are targeting individual users directly based on past behavior, demographic data, location information and more — a process that couldn’t be done at scale without the aid of AI.
How B2B marketing can benefit from machine learning
For companies that sell to businesses, communication between salespeople and marketing teams is critical. A day in the life of a salesperson is often chock-full of tasks that could be seen as marketing-related. Educating and training customers, following up via email and scoring leads are all possible gray areas at the intersection of marketing and sales — and, as it turns out, AI applications in marketing are aiming to tackle a lot of these critical functions.
I decided to analyze two particularly important examples of how AI is being used in marketing today to improve processes, make suggestions and find solutions in a business-to-business context.
Use Case 1: Boosting lead generation
For decades, B2B lead generation has been a process of hours of human research into different companies and categorization of people with purchasing influence within each company. In this instance, the value of AI lies in the machine’s ability to identify and generate B2B leads that grow your company’s database.
LeadGenius does so by picking out the top decision-makers with buyer roles in each company, giving you a direct contact to several potential new clients. Once the potential clients have been targeted, the tool generates a target list and highlights key data points to help you segment your audience and develop personalized messages.
[Read the full article on MarTech Today.]
Some opinions expressed in this article may be those of a guest author and not necessarily Marketing Land. Staff authors are listed here.
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Author: Daniel Faggella
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