These days, marketers can’t read about their profession without getting bombarded with wild claims about how AI is going to disrupt everything they do. And with the sales and marketing functions evolving so rapidly in recent years, marketers in particular must embrace an entrepreneurial spirit and constantly explore new technologies in order to give their team a competitive edge. That mindset shift, along with new consumer trends — such as self-driving cars and intelligent voice-first products like Amazon’s Alexa and Apple’s Siri — are bringing the possibilities of AI to the forefront of business-to-business marketing technology discussions.
But all of this begs the question, “Which AI claims are hype and which are reality?”
In order to know what a new technology like AI can bring to the table, it’s important to fully understand the problems you’re trying to solve. When it comes to the current state of AI solutions for marketing and sales, today’s reality is less futuristic robots or automating every single marketing workflow, and more about how data can answer one important baseline go-to-market question: who to sell and/or market to. There’s a wealth of intelligence that predictive analytics and machine learning bring to the task of answering these questions — and that’s the crux of where AI is delivering value today.
Forward-thinking enterprise sales teams are saving tons of time by simply using predictive solutions to improve the way they filter and prioritize inbound leads. Companies with the “champagne” problem of an overwhelming volume of incoming prospects are using predictive analytics and AI to automatically research and qualify leads who looks like their company’s ideal customer. For example, Shoretel’s market development team found that predictive scores could tell them not just which prospects are the best fit, but also which ones are showing current buying behavior. As a result, the telco leader’s sales reps can instantly understand who their best prospects are and determine where their time should be spent — insight which has resulted in 8X greater conversions. Now it takes just 12 calls to uncover one marketing-qualified lead (MQL) vs. the 100 calls it took before the company adopted predictive analytics.
A related use case we’re seeing quite often is lead routing. Rather than traditional approaches of distributing new leads based on geographical regions or a simple round robin without much consideration to actual lead quality, companies are starting to leverage AI to route leads more intelligently. Businesses use predictive scores to decide which leads should be put into marketing nurture tracks until they show more potential, which should be sent to sales development reps for further research, and which should be aggressively followed up on by account executives right away.
Another real-world use case for predictive analytics is business expansion. As more teams adopt account-based sales and marketing strategies to move upmarket, a common question is “how do we find customers who will spend more with us?” But with outbound prospecting, it’s tough to focus on the right target accounts vs. trying to boil the ocean.
