Humankind goes to Mars, produced the first artificial heart, and the team at Leadfeeder is forever lost on the quest to invent the world’s first automated Lead generation software for B2B companies. Or, so we like to do in our spare time. You know, as a hobby. In a world oversimplified by hashtags and acronyms like ABM, SEO, CLV, there’s no shortage of buzzwords and phrases to keep tabs on. And, AI in sales and marketing has been a trending topic of conversation…again. For the last couple of years, people have been predicting the onset of a Fourth Industrial Revolution — a movement propelled by the advent of AI and Machine Learning. Both technologies show no signs of slowing down. And from fashion to heavy equipment and construction, every industry has been forced to adapt; assessing and reinventing processes to avoid becoming obsolete. Generally speaking, B2C markets have had an easier go at incorporating machine learning and AI. AI in B2B businesses, on the other hand, has been slower to gain traction — despite the benefits. This is now changing. According to a Salesforce study, B2B marketers acknowledge AI as the technology they’re most likely to implement in 2020. This is certainly interesting, as only 10 percent of B2B marketers are using it today. In honor of the new decade, I thought the only logical course of action to take a look into where today’s B2B technology lies. The case for AI and machine learning in B2B marketing and sales From a marketing standpoint, there’s overlap in the way B2B and B2C companies do business. They’re both reliant on not only the leads they’re generating but their qualified lead volume. If the end goal is to drive more revenue, success is a direct factor of whether what you’re selling is aligned with the people most likely to buy. Other vanity metrics and KPIs are irrelevant. AI in sales and marketing is relevant to both sides of the coin for better understanding a customer base. However, it’s even more so the case for B2B companies operating off longer and more complex buying cycles. Machine learning can provide deep customer insights and help accelerate a customer’s decision-making process. Deepak Agarwal, VP Artificial Intelligence at LinkedIn, says, > “At LinkedIn, AI is like oxygen. We’ve been using it for over a decade to create the member experiences that people value most on our platform. It adds structure to the rich and valuable data that we have – and crucially, it helps to keep our site safe.” In this sense, AI acts as a marketing team force multiplier with thousands of machines analyzing millions of megabytes worth of data. And it never gets tired. Free ebook The Ultimate Guide To Website Visitor Identification Discover how to identify, qualify and connect with leads generated from your analytics. Download Now AI improves lead quality: sourcing and scoring Everyone is interested in generating more leads. It’s the driving force beyond most marketing programs playing at a numbers game. The more interested prospects you have to engage with, the greater your potential for making a sale — unless the prospects aren’t relevant to your business in the first place. Outside of simply generating leads, another crucial element of B2B sales and marketing is lead scoring. Of all the leads generated in a month, how many actually translate to sales? With limited time and resources, the end goal of better understanding lead source quality and scoring is a more efficient, automated sales process. Teams can prioritize leads most worth the effort. AI comes into play here with its ability to help B2B marketers identify upfront which customers are most likely to buy. When you consider that the average number of peopleinvolved in a B2B purchase is 6.8, this is an otherwise cumbersome process. Using AI in the lead scoring process gives companies the ability to account for behavior across numerous stakeholders. Predictive analytics bridges the gap between massive amounts of customer data and what to do with it. AI can monitor trends and patterns — making it easier for marketers to focus on the efforts that matter rather than trying to fit a one-size-fits-all approach for every lead sent their way. AI provides useful customer insights Building and optimizing a lead generation funnel starts with understanding your customer’s journey. You want to meet them where they are at in every sales stage, with relevant content. What are their pain points? What kind of solution are they after? How do they talk about the issues they encounter? Outside of vaguely mapped personas and generalizations, AI is transforming our ideas around what a customer wants with the help of machine learning. Social listening, combined with AI detection, is a broader example of this. It can help dig into the specific language used across social platforms to pinpoint trends and common keywords. Some AI-based companies are developing software to recognize voice patterns. For those selling over the phone, this could help gauge a prospect’s level of interest to better determine where to make efforts around following up. AI can help with personalization Think about this: 57 percent of buyers (B2B included) will depend on suppliers to anticipate their needs. Generic pitches just won’t cut it for B2B marketing in 2020 and beyond. And honestly, presenting information to your potential customers in a way that’s both timely and suited to their needs isn’t too much to ask. Especially with how much data you’re collecting. The science of big data in B2B marketing is all about centralization. To get the most out of customer data, companies will need to move away from siloed marketing and sales systems. They’ll need to aggregate and use machine learning to detect patterns across the whole. This is how recently popularized account-based marketing (ABM) approaches thrive. Once you have teams pulling insights from a single source, they can work in tandem to create better targeted, personalized messages, addressing specific business needs and use cases. A more personalized buying experience for B2B customers translates into shorter sales cycles. Trust is established early on, research time is minimized, and the focus lies on implementation. Using AI to create an enhanced buying experience As a B2B business, knowing where your customers are in the sales funnel allows you to determine which strategies to use when and where. And the proof around personalization is already out there in the form of higher conversion rates. Amazon, for example, accounts for 35 percent of its revenue with highly targeted cross-sells and upsells. There’s power in talking to a customer like they’re the only one in the room, listening and translating what you’ve heard into actionable next steps. Using AI to inform content creation Personalized marketing for B2B companies can also manifest itself in the quality of content created. The level of the relevant subject matter aside, AI can be used to assist in the content creation process by automating and optimizing basic tasks. Think in terms of creating more engaging subject lines and calls-to-actions or pinpointing the best times to send content and get in front of prospects using predictive analytics. Improving digital advertising with AI What you spend on digital ads in B2B marketing is directly tied to relevancy. When you’re connecting with audiences to take action, you’re getting the most bang for your buck. AI is revolutionizing this space by taking the guesswork out of targeting and optimization. Marketers can use this technology to show people the ads most relevant to them — reducing ad cost as a result. AI can help nurture customer relationships One of the biggest pain points in B2B marketing is customer relationship management, pre- and post-sale. Marketing teams struggle to effectively respond to customer emails. Even though increasing customer relations by a mere 5 percent can lead to a 25 percent - 95 percent increase in revenue. However, emerging AI software solutions are making it possible for companies to engage in two-way conversations — without the involvement of a real person. This kind of technology analyzes customer responses to determine intent and develop responses that keep conversations active. And companies like Epson are already reaping the rewards of this approach. They’ve seen a customer response rate increase of 240 percent, with a qualified lead increase of 75 percent. Chatbots are another, more widely used application of AI among B2B companies. When incorporated across customer-facing channels, they can be automated to share FAQs and push relevant sales-minded content as needed. Free ebook The Ultimate Guide To Website Visitor Identification Discover how to identify, qualify and connect with leads generated from your analytics. Download Now Final thoughts: How AI and machine learning are changing B2B marketing in 2020 If the past two years have revolved around hype and exploration around emerging AI and machine learning technologies, the year ahead will be one of implementation. Maybe you’re a marketer or sales person wondering how to use AI or machine learning. What it is. How it might make you look better in front of your boss. The pros and cons of the future. And, more importantly, how you can do your job faster and easier to get more B2B leads. Rather than simply collecting data, B2B marketers are recognizing the need for actionable insights. And, eehhh why? 2020 will be the year that marketers hunker down on automation and personalization to pinpoint where to spend their time and who to spend it on. With the level of bureaucracy companies operate under and complexity behind established data systems, attempts to adopt emerging technologies won’t be perfected across the board. I get it. Welcoming robot overlords powered by AI and machine learning into your B2B business sounds scary. But considering the world’s got the first robot citizen, Sophia, I predict the adoption of AI and machine learning in the B2B industry will start to speed up a little bit.