4 AI Trends Changing B2B Marketing Forever (And What You Need to Know to Stay Relevant)

It was just over five years ago that many of our heart strings were pulled by the Oscar-nominated, science-fiction romance film Her. The film set slightly in the future, tells a story of a heartbroken man, Theodore, who develops a personal relationship with his artificially intelligent virtual assistant, “Samantha”. As Samantha learns and develops needs, desires and emotions, we witness the evolving nature and risks of modern technology. 

What only a short while ago seemed to be a futuristic narrative has quickly become a mainstream technology. And the benefits of artificial intelligence have been gaining popularity in the world of B2B by helping to solve common marketing challenges. 

Machine learning, and predictive, and intent – oh my!

According to SiriusDecisions’ 2018 Global CMO Study, 31% of organizations are prioritizing AI implementation in the next two years. But a recent Everstring and Heinz Marketing study found that B2B marketers are generally unclear of what AI means and only 13% of marketers felt very confident in their knowledge of AI.

Artificial intelligence (AI) is a term casually thrown around and often applied inconsistently when talking about B2B marketing technology. Part of the confusion surrounding AI is due to the fact that it is quite difficult to arrive at a universal definition of intelligence as a whole, since intelligence is highly dependent on context.

Artificial intelligence was defined by an article in TheStreet as:

 “...Software that is able to use and analyze data, algorithms and programming to perform actions, anticipate problems and learn to adapt to a variety of circumstances with and without supervision”

Emerj defines AI as:

“Artificial intelligence is an entity (or collective set of cooperative entities), able to receive inputs from the environment, interpret and learn from such inputs, and exhibit related and flexible behaviors and actions that help the entity achieve a particular goal or objective over a period of time.”

To break those two definitions down to layman’s terms, AI can be simply explained as using software to do things that humans need intelligence to do. When we use software to solve problems or apply logic, we call this artificial intelligence.

Often times, the term “AI” is used interchangeably with machine learning – but this is incorrect. AI is the broader category of autonomous machine intelligence, and machine learning is a specific component of AI. In other words, machine learning is AI but not all AI is machine learning.

As a B2B marketer, we can use AI powered tools to overcome blind spots, better understand our audience and automate repetitive tasks. Leveraging AI solutions, marketers are able to digest more information, simplify decision making and automate serving up next best action content.

How so? Read on for the deets on four AI solutions that are driving innovation for B2B marketers.

4 AI Solutions that are Driving Innovation for B2B Marketers

1. Chatbots and Virtual Assistants

Much like “Samantha” in Her was able to learn over time through interactions with Theordore, we are seeing virtual assistants and chatbots leverage machine learning to improve engagement with B2B prospects and customers.

Virtual assistants like Conversica are improving conversions via email by using AI to qualify leads and eliminate leaks in your funnel. Conversica’s software enables marketers to create their own virtual assistant who will engage in two-way email conversation to follow-up with 100% of your leads by sending emails that sound like they are coming from a real person. The software will continue to follow-up with your buyers – and by listening for intent – the virtual assistant will interpret buyer replies and respond appropriately to help you accomplish your conversion goals.

Use cases for Conversica include follow-up with early stage inbound leads, reviving leads that have become unresponsive to outreach from your sales team and helping to drive attendance for online or offline events. 

Intelligent chatbots like Drift enable B2B marketers to reduce friction in the sales process and improve customer experience by quickly connecting with visitors, in real-time, through chat playbooks. Playbooks are designed to give visitors what they are looking for – whether they want to talk to a sales or support representative, book a meeting, or be directed to the right resources for their needs – immediately.

Get your team set up with a Drift chatbot here

One important distinction Drift makes about a well-designed chatbot is that they aren’t meant to trick your visitors into thinking they are talking to a human. Rather, a good chatbot uses AI to understand exactly what your visitors are asking and better answer their questions. Drift advocates that conversational AI is about ease of engagement and giving people the information they are looking for as fast as possible.

In a world where instant gratification has become the norm, chatbots and virtual assistants work around the clock, 24/7, to help B2B marketers provide a cost-effective level of on-demand service that people have learned to expect.

While many B2B companies are investing in chatbots and virtual assistants for reductions in operational costs, recent research conducted by Forrester has found that the improved customer experience delivered by AI assistants can boost revenue to achieve a greater impact on bottom-line profits than just cost-savings alone.

What’s next in AI assistants? Sentiment analysis. 

Forward-thinking AI companies like Affectiva are looking at how to detect emotion in recorded speech by observing changes in voice volume, tone and pace. This technology can be used by AI assistants to adapt their approach when detecting anger or frustration in a text conversation. B2B marketers can use this technology to deploy assistants that are able to recognize and learn which responses elicit positive reactions and repeat those tactics.

2. Prospect Sourcing, Intent and Predictive

Predictive solutions, providers of intent data and AI supported scoring models for prospect prioritization are gaining traction in the market for their ability to help marketers efficiently understand buyer fit, inform segmentation and enable more precise targeting. AI solutions are also being used for look-alike modeling to source new prospects that share relevant characteristics with their best customers.

Intent is the most widely adopted AI use-case and is at the core of today’s most popular AI innovations for B2B marketers. Intent data is what CMO and co-founder of Idio, Andrew Davies defines as “exactly what it sounds like: data that reveals a buyer’s (or group of buyers’) intent to do something”. B2B marketers look for intent or buying signals to identify what a prospect is interested and when they are most likely to buy.  

AI tools help to collect and make sense of buying signals that come from both first and third party sources. First party intent sources are generated from buyer activity on your own website – such as content downloads, page visits or information explicitly provided to you through form questions. Third party sources are obtained from vendors that collect intent data from buyer activity on publisher websites and are used to augment and append your first party data.

These intent and predictive tools are helping B2B marketing teams analyze their existing database using AI to identify which prospects and customers are most likely to convert and have the highest revenue impact. Teams using these tools can then prioritize marketing and sales efforts accordingly. 

3. Deep Personalization

Thanks to B2C innovators like Amazon, Netflix and Spotify, expectations around content personalization have skyrocketed. People expect curated content that meets their needs and preferences – and B2B experiences are no exception to these expectations.

Today’s AI solutions analyze massive amounts of data to personalize messaging for the needs of industry segments, organizations and individuals. AI personalization works by gathering information at a scale that would not be feasible by human research alone to deliver a message tailored to the specific needs of an individual.

According to an article posted on MIT Sloan Management Review:

Once insights about individual customers are in hand, companies can also deploy a deep level of personalization in their marketing approach. AI products can evaluate all relevant content created by a marketing team, such as web pages, blog posts, and emails, and indicate the ideal material to display to a customer at each point in their journey. When prospects click on a link in a marketing email in this scenario, they are shown a dynamic landing page specifically designed to appeal to them.

As a marketer, we are privy to collecting vast amounts of data about our audiences, to not leverage that data to better accommodate the interests and needs of our prospects and customers is poor etiquette and lazy. If someone gives you insights about them or their industry, it is rude not to use them. 

But be warned, too much personalization can backfire. Stay away from useless or unwanted personalization. A 2019 Forrester study found that more than half of the consumers they surveyed are actively pulling back from providing personal data due to skepticism or distrust. Make sure you understand when your efforts are working for the interest of empathizing and connecting with your audience’s challenges – and when you’ve crossed the line to meaningless, irrelevant and creepy over-personalization.

4. AI Powered Content Marketing

AI is transforming the future of content marketing. From auto-tagging content for personalized content experiences to AI-powered content creation – the world of content marketing will forever be changed by AI.

AI tools are replacing the need to manually tag content. Using Natural Language Processing (NLP) an AI tool can sift through thousands of content pieces to learn your organization’s unique content taxonomy and auto-tag future posts. This frees up your content team to focus on creating more content while also improving the accuracy of your content tags. With an improved content taxonomy, your AI tool can also serve up better content recommendations and more personalized content experiences across your digital marketing mix.

While we are still a little ways off from relying on AI to create engaging blog posts from scratch, natural language generation is growing in ability to support short-form B2B content creation. AI tools are being used for email subject lines and calls-to-action to help marketers improve clicks and conversions. AI is also being used for creating derivative content from core assets. For example, you can use an AI tool to turn your lengthy ebook into a number of short, simple blog posts.

Technical and time consuming SEO tasks are also being alleviated by AI solutions. Peter Mikeal, Head of Marketing Strategy at NC.-based Small Footprint, told CMS Wire, “historically, a lot of marketing strategies are very manual such as identifying keywords to optimize blogs or articles for SEO and then creating backlinks. As machine learning matures, all of these tedious and time-consuming SEO tasks when creating marketing content will be automated with fewer errors along the way.” And with the rise of voice search, AI can play a major role in helping marketers analyze voice data and optimize content for voice search.

Key Considerations

For the resource constrained marketing departments (isn’t that all of us?), AI solutions can help overcome time and money limitations and enhance B2B marketing capabilities by acting as a force multiplier. AI solutions enable marketers to analyze extremely large data sets for actionable insights, enable individualization at scale, create efficiencies to free up time for your team to spend on more strategic tasks and improve the overall effectiveness of modern marketing teams.

However, despite the many advantages of AI, there are still limitations. Artificial intelligence is only as good as the data you are using and therefore, AI is not appropriate in situations where there is insufficient or poor quality data. Privacy concerns also need to be taken into consideration as any breach of privacy can have a major backlash and negatively impact your brand.

To help safe-guard against AI gone wrong, ensure you organization has the following two bases covered:

1. Culture of experimentation: When innovating with AI, make sure you always test out the common-sense response of your new solutions. Creating a culture of Agile program activation will help make sure your team is testing and optimizing new tools while also minimizing risks of inaccurate AI outcomes.

2. Head-space strategy: Leveraging AI to take over repetitive, mundane, daily-grind tasks will allow you to free up resources. However, organizations have a tendency to fill newfound time with more repetitive and mundane tasks. Time savings from AI should be used to roll out a head space strategy. By taking some of the grunt work off your plate, make sure you are leveling up your teams to take on higher-value work. A strategic time investment in improving your organization’s customer empathy is a smart way to shift resources. Use your newly acquired time to invest in elevating your team’s understanding of your customers at a human level by dedicating resources to empathy marketing. 

Just as Samantha in Her continued to evolve and learn, today’s modern AI solutions are only getting smarter and more proficient at solving our marketing challenges. Rising buyer expectations for more personalized and compelling engagements paired with an overwhelming set of data to inform our marketing strategies have given way to a need for AI solutions in B2B marketing. The old-fashioned methods used to analyze data, make decisions and build campaigns will no longer be enough to stay relevant.

What are your predictions about the future of AI in B2B Marketing? Leave a comment below.