LeadCrunch Blog

7 Ways AI Changes B2B Marketing

Posted by AJ Agrawal on Jun 28, 2017 8:00:00 AM



You may not think artificial intelligence and B2B marketing are a match made in heaven. But AI is an incredibly flexible field that’s about to jump out of its initial stages into growth. With new changes in AI, you can improve lead generation, establish a marketing database, track behaviors and more

Now’s the time to be asking questions about the possibilities of AI, which has enormous potential for B2B marketing, facilitating the matching of businesses and sales prospects. A loose definition of artificial intelligence is that it’s a set of algorithms that approximates the way humans perform tasks. For instance, someone might code for a way to tie every time in tic-tac-toe. The new subfield of artificial intelligence that’s quickly gaining traction is machine learning.

Machine learning is unique because it involves software that can modify its own code. That means that the computer can learn from trial and error in a similar way to humans. There’s more: Machine learning is built to handle the tasks that humans find intuitive. These intuitive tasks are usually much harder for machines to process or perform.

Starting to see the possibilities yet? A study by SiriusDecisions on the changing of B2B marketing predicts a rise from 8–12 percent in 2010 to a majority in the near future. They cite the fact that B2B automation is already growing exponentially, with 11 times more automation in B2B organizations today than there was in 2010.

Here are some of the ways AI will make a splash in the B2B marketing community by helping B2B companies beat key marketing challenges:

1. High-volume lead generation

You’ll want to start by seeking out some data-collection companies. You need data on your customers and people who are potentially trying to buy from you in order to discern the higher quality leads. It is important to note that there’s a distinction between software used for data collection purposes and that which has artificial intelligence backing it. Both iterations go hand in hand in some cases, so having the actual data in a suitable form to work with is helpful.

2. High-quality lead generation

There’s potential for methods in machine learning to actually match or generate leads for a particular business. In this context, a lead means a prospect that can potentially become a customer. How to distinguish a lead from a mere business acquaintance appears to be the perfect challenge for machine learning to tackle.

Imagine the benefits. You’ll be able to tell the difference between mere inquirers and people who are genuinely able, willing, and prepared to buy. But how can a machine accomplish a task like this? It’s precisely because that task appears to be intuitive that it’s an appropriate place to apply machine-learning methods.

Let’s get into the juicier details. How does AI actually choose between people? We’ll start with the basics. Machine learning operates in a similar way to how humans learn. It uses trial and error over a vast number of sequences before getting to a passable level.

Thus, just as people can learn to judge other people, AI can do the same through machine learning. To get an idea of how this works, look at this recently published article in the Wall Street Journal on how AI aids in hiring, an area where data are crucial.

3. Generating perceived product value

The people heading your marketing efforts will be able to make the best use of AI and machine learning. Assuming you have a product or service that you want to highlight, you’ll need to find the right ways to frame that product.

Being able to correctly frame your product requires a structured process. You have to begin by surveying people through research and testing how much they would pay for your product or service. But instead of software to interpret the data, you’ll want to recruit someone to do the surveying and lead the research efforts. For instance, you might have some luck with CMG.

4. Competing against others for similar leads

Recently, Google’s DeepMind machine-learning program AlphaGo crushed one of the best Go players during an exhibition, beating him 4 to 1 in a 5-set match. The moment was special because AlphaGo is one of the hardest games for computers to pick up and learn, and you can’t make value-based predictions based on position in the same way you can with chess engines.

The reason I bring up AlphaGo here is because it provides a nice metaphor for competition with other firms. The rules of the game of marketing are just a bit more complex, and depend on variables that change with much more unpredictability.

You eventually want to qualify how your competitors decide to go for their leads, and then use measurements of those qualities in a decisive manner when receiving results from interested clients. AI can optimize the leads most receptive or most likely to do business. The methodology is short, sweet and simple:

  • Capture the response
  • Answer their questions
  • Elaborate and qualify based on the questions
  • Contact sales with the information
  • Close the deal

5. Generating public enthusiasm

Don’t forget that we’re in the age of social media as well as the information age. There are numerous platforms you can take advantage of, many of which even have their own tools to help you run and optimize a campaign. Google, Facebook, Instagram and Reddit represent huge opportunities for the business community.

Besides automatic mailers after a potential lead has signed up, there are variety of applications of AI to reliably garner public enthusiasm. The biggest application right now is a fusion between machine learning and human understanding. This is because, while machines are now able to perform an incredible variety of tasks, their mastery over language is limited.

But even if we set that aside, machine-learning companies are changing the way marketing managers generate hype and analyze trends. Chatbots are becoming increasingly realistic in their ability to communicate well. There’s even a Tinder chatbot for guys that automates matches and flirting. Businesses should consider taking advantage of the ability of machine learning to personalize. 

If there’s already AI tech out there that can flirt successfully, imagine the possibilities for integrating it with the existing big-data analytics that social media companies already offer.

6. Marketing to a lengthening sales cycle

Let’s talk about the anatomy of a sales cycle. The term sounds intuitive, but is actually surprising complex.  Basically, a sales cycle is how you get from point A to point B when selling a product to someone. A lengthening sales cycle becomes a problem because each step of the cycle will become more complex and harder to manage. It’s like a game of Tetris, where the blocks keep falling down faster and faster as time progresses.

If you’ve been keeping up at this point, you’ll notice that many of the previous ways I mentioned are all part of the sales cycle. That means that AI has the ability to optimize each part of that cycle, which takes a load off the shoulders of any team.

7, Marketing to a growing consumer base

At the end of the day, it’s going to be a numbers game as your business expands. But if you expand too fast, you might not be able to deal with all of the changes required by your consumer base. For instance, customer service has been transformed, as artificial intelligence and voice recognition have combined to smooth out phone calls and complaints to companies.

And this doesn’t just apply to phones. Online messaging services are also becoming very common for customer service. Furthermore, the appropriate advancements in AI and machine learning are arriving at the same time. Just check out DigitalGenius, a company specializing in using machine learning to automate specialized responses to customers.





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