AI and Machine Learning for B2B Marketing
You may not think artificial intelligence and B2B marketing are a match made in heaven. But artificial intelligence (AI) is an incredibly flexible field that’s about to jump out of its initial stages into major 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 data driven 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? SiriusDecisions released a marketing automation for B2B study predicting a rise from 8-12 percent in 2010 to a majority in the very near future. They cite the fact that B2B automation is already growing exponentially, with 11 times more marketing automation being used by B2B marketing teams 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, and identify potential customers much faster and with greater accuracy:
1. High-volume lead generation
To take advantage of these new marketing technologies, you’ll want to start by seeking out some data-collection companies. You need data on existing customers and people who are potentially interested in buying from you in order to determine high-quality leads and get a clearer picture of your ideal customer profile.
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’re marketing qualified leads, not jumping down time-consuming rabbit holes with prospects who may not be interested. You can tell the difference between mere inquirers and people who are ready, willing and able 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, this recent article from the Wall Street Journal shares the value of using artificial intelligence in employee recruitment, an area where smart data is crucial and cost-effective.
3. Using AI to generate perceived product value
B2B marketers will be able to make excellent use of AI and machine learning in how they position products and services. If you have goods you want to highlight, you need to find the right way to frame those goods to potential customers.
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. This can be done through social media polling and outreach, or email surveys to gather current feedback and insights. 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. How to beat the competition in lead generation campaigns
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 that it provides a nice metaphor for competition with other firms. The rules of the game of B2B 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
- Share lead information with your sales team
- Close the deal
5. Generating enthusiasm with social media
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 B2B community.
Besides automatic mailers after a potential lead has signed up, there are a 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, artificial intelligence has a limited mastery over language.
But even if we set that aside, machine-learning companies are changing the way marketing teams 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 potential for integrating it with the innovative big-data analytics that social media companies already offer.
6. Marketing to a lengthening B2B sales cycle
Let’s talk about the anatomy of a B2B sales cycle. The term sounds intuitive but is actually surprisingly 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. This means that artificial intelligence has the ability to optimize each part of the B2B sales 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. For relationship management, CRM systems have adapted with artificial intelligence and voice recognition to smooth out phone calls and better address written complaints for greater efficiency and satisfaction in responses to customers.
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.