How AI and Machine Learning Can Boost B2B Email Outreach

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No one likes to leave money on the table, but many B2B marketers do it again and again with their email messaging.

Every time a customer, existing or prospective, opens an email, it’s an opportunity. It’s your chance to cross- or upsell a current user, or to engage a prospect and move them along in their journey toward purchase. Yet too many emails are dead ends. The recipient might open the message, but the content fails to resonate enough to prompt the next step. The opportunity is lost.

One of the biggest reasons marketers miss these opportunities is a tendency to rely on gut instinct and trial and error. Combine that with the fact that what will engage one user can alienate another, and you end up with email outreach that is hit-and-miss at best.

The good news is that Artificial Intelligence (AI) and Machine Learning (ML) can help, and many marketers are already leveraging these newer technologies to great effect. For example, AI and ML can help automate and improve timing, testing, and segmentation. They can turn massive amounts of raw data into useful insights, helping to refine lead generation and scoring, even predicting lifetime value. And they can help power hyper-personalization by taking behavior and context into account. All of this combined can help propel B2B marketers closer to the dream — ‘mass reach with one-to-one precision’.

Let’s break down what AI and ML are and how they can improve some specific aspects of your marketing outreach.

AI and ML Defined

Artificial Intelligence and Machine Learning are complex technologies, too complex to go into much detail here. But simply put, AI refers to systems that automate processes in ways that mimic human intelligence. For instance, Amazon Echo’s ability to understand speech and perform actions based on spoken commands is AI in the sense that a machine is mimicking the human brain’s ability to do the same.

Machine Learning, meanwhile, is a subset of AI that applies algorithms to analyze data and make decisions, applying the results of those decisions to learn to make better choices in the future. Machine Learning is the process by which a machine builds intelligence.

ML for Lead Generation and Scoring

Since machines can analyze more data, and faster, than humans could ever hope to, one obvious application for ML is to look at a pool of potential customers, compare them to past and current customers, then use analysis of that data to predict how the prospects in the pool will respond to certain messages.

The data will sometimes reveal patterns you might have been unaware of, like the fact your product appeals primarily to certain industries or that customers tend to be clustered in one geographic region. Imagine, for example, that for some reason your product is very popular with mid-sized retail and e-commerce companies based in the Midwest. ML can scan business descriptions for companies who fit those criteria, help identify the appropriate personas within those companies, and score them based on similarities and differences to existing customers. And it can do it incredibly fast, even with massive data sets.

ML for Content Delivery

Once you’ve identified and scored a new pool of potential customers, the next step is to serve them the right messages. As a marketer, you might have a hunch as to which content will work best, but, again, the data might show something different.

AI can categorize your content, help organize it thematically, and Machine Learning can quickly learn which themes resonate with which customers. ML can then help you customize and test your outreach — your email content, yes, but also display ads, landing pages, and even personalized sequences in your drip campaigns — all based on lead scoring and associated content themes. In this way, ML can help you create a unique, personal journey for each unique lead. And it will learn from the results to continuously improve.

Don’t Be Intimidated

Artificial Intelligence and Machine Learning have been the subjects of a great deal of hype lately. Marketers are wise to maintain a bit of healthy skepticism. But if you look closer, the practical applications of the technologies, especially for B2B marketing, aren’t difficult to see. AI and ML are helping businesses identify and score better-qualified leads, while driving one-to-one personalization at scale. If you start small and are willing to experiment, you’ll likely find there is an AI and/or ML solution that will help you reach the right customers, with the right message, at the right time. And isn’t that the goal of every marketer?

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