Nahla Davies
May 26, 2021

3 Ways AI and Machine Learning are Transforming B2B Marketing

AI And Machine Learning Are Transforming B2B Marketing

Artificial intelligence (AI) and machine learning (ML) are becoming rapidly entrenched in all forms of business and life beyond it. Why then, do they have so little implementation in actual B2B marketing? Fewer than 20% of B2B enterprises use these technologies in meaningful ways that transform the way they connect with customers. 

That’s a shame since AI and ML-powered marketing analytics provide B2B organizations insights in minutes. These insights generate higher ROI on digital marketing campaigns. This gives businesses their time back so they can focus on improving their service. Businesses also use analytics to automatically generate the best ways to improve their marketing campaign performance.

It’s time for B2B organizations to introduce AI and ML to their digital marketing. Let’s take a look at the top three ways these developing technologies reduce the chance of being relegated to the spam folder. We’ll also examine how they’re transforming B2B marketing now.

Making Customer Collaboration Easier With Quality Software

There are only so many resources that B2B organizations can dedicate to new inbound marketing strategies. AI and ML-powered tools let marketing team members track customer leads through a sales funnel. This can provide valuable insight into the previous engagements with which they had the greatest amount of success. 

Quality inbound marketing software paints the ideal customer profile for marketing teams to make client collaboration easier. Collaboration software often includes features that control which client images and files your other team members can access. Insights such as information on customer interactions helps B2B organizations discover new ideas. This creates a well-rounded customer profile ready for analysis. It also provides inspiration for future content creation.

B2B organizations wanting to transform their marketing process need to ensure they choose the right collaboration software product for their marketing teams. The software solution of your choice should come with features such as project planning that automatically stores updates in the cloud where it can be easily accessed. Your software should have the ability to assign specific team members to certain projects and tasks. 

The most crucial component of choosing collaboration software is ensuring your team members can seamlessly keep each other updated on how each stage of your marketing campaigns is progressing. Campaign managers should also be able to quickly view each project’s status and invite clients to collaborate on project deliverables.  

Small businesses can continue to adjust their content to keep attracting more customers based on the insight they gain from their inbound marketing software. The next step toward creating a complete inbound marketing strategy is focusing on your website’s content.

Examples of proven team-based collaboration tools include:


Asana is a SaaS mobile and web application that’s designed to help simplify team-based organization and work. The primary standout feature of Asana is the ability to divide work up into tasks. There are different categories for each task as well.


Freshbooks offers a cloud-based project-based collaboration tool that’s a good choice for project managers. This is because it enables managers to easily set team permissions. This means can control which employees access which tasks and projects. It also allows you to invite clients to specific projects to help guide and keep an eye on progress.

Zoho Projects

Zoho Projects is another team collaboration tool that offers a variety of important features. These features include Gantt charts, timesheets, and independent tasks, each of which can be easily customized. 

Creating Easy-to-Understand Behavioral Analysis

The core of an organization’s targeted advertising efforts is its ability to identify trends in customer behavior. AI and ML-powered behavioral analysis can take this identification of trends a step further. They predict what customers are interested in purchasing. This is based on their previous multi-channel touchpoints with an organization. Customer trends that behavioral analytics can predict increase as the sets of data grow larger across an organization’s online and offline channels. 

AI-powered analysis of customer behavior does require increasingly large sets of data. It also requires a deep learning system that can use powerful computational processing to keep alongside said sets of data. 

Behavioral analysis isn’t as simple as collecting more sets of data to better identify customer purchasing trends and behavior. It’s important that B2B organizations successfully marry their data with a highly powerful system of deep learning to enjoy personalization that can improve customer outreach efforts and make them more detailed and multi-layered.

This level of deep personalization in targeted marketing and outreach strategies is only achievable with AI-based predictive analytics and can transform the world of B2B marketing as we currently know it. Behavioral analysis can create nearly endless possibilities for customer personalization with the help of AI-powered deep learning systems that use historical data to tailor customer outreach in real-time. B2B organizations that want to start scaling their digital solutions should consider using AI-powered behavioral analysis to get the most out of their marketing efforts.

Automating eCommerce Design Intelligence

A B2B organization’s e-commerce store makes a powerful first impression on its visitors. Business executives need to make sure that impression is as impactful as possible. Many businesses need to increase website traffic and social media post engagement. But they simply can’t compete with the dozens of daily ads that other competitors are publishing. 

It’s much easier to provide a responsive website experience that customers arrive at via design intelligence tools that are automated. As Toronto-based online marketer Gary Stevens of Hosting Canada points out, many B2B eCommerce platforms now rely on AI-based design intelligence. He says, “rather than taking the time to look through all of the themes and layouts, the software picks for you based on preferences and questions asked. It’s a time-saving and decision mitigating feature feel free to add whatever you want on top of it as well.”

An AI-powered platform should be easy to use to automate components of your e-commerce design intelligence. It should have functionalities like drag-and-drop for non-tech-savvy marketing experts. Many B2B organizations don’t have sufficient resources to simply code their websites from scratch. This makes functionalities like drag-and-drop and customizable templates particularly important.

Design intelligence for your site also needs to factor in the speed and performance of your website. Customers have plenty of other sites they can visit instead. They likely don’t want to deal with dreaded hiccups. Slow load times put a hindrance in a small business’s e-commerce store’s speed and performance. Make sure that the SEO that goes into getting them toward your website guarantees a stable experience. 


The actual implementation of AI and machine learning has been low compared to how much enthusiasm exists around these technologies. B2B organizations that want to transform their marketing need to look toward case studies of how brands apply AI/ML to their marketing. They also need to consider the drawbacks and limitations that still come with them.

There are steps companies B2B organizations should use customer collaboration software to make project management easier. They can also really on behavioral analytics that derives valuable insights from online customer behavior. 

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Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed -- among other intriguing things -- to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.