Pekka Koskinen is the CEO & Founder of Leadfeeder, a lead generation software.
Did you know that the number of marketers adopting AI technology grew by 44% between 2017 and 2018? At Leadfeeder, we use machine learning to filter ISPs and nonrelevant hostnames out of the lead data we provide to customers. LinkedIn’s VP of artificial intelligence, Deepak Agarwal, has even gone on record declaring that “at LinkedIn, AI is like oxygen.”
Compared to the collective and growing enthusiasm, however, AI’s actual implementation has been relatively low. Only a handful — less than one in five, according to a Demandbase report — of B2B organizations apply AI and machine learning to their sales and marketing.
However, that’s beginning to change. According to Demandbase, “A full two-thirds are currently planning, evaluating, or implementing AI for marketing or sales.”
With the fog of hype surrounding AI, real-life use cases — especially for B2B businesses — have been clouded by fermenting skepticism and exaggerated predictions. This raises the question: How exactly are AI and machine learning being applied to B2B marketing?
AI is revamping lead generation (without stressing sales teams).
Lead generation is the lifeblood of any business. However, considering that most leads (73%, according to MarketingSherpa) are not sales-ready, identifying leads to prioritize and pursue can place undue stress on the backs of marketers and sales reps. Small teams can’t afford to invest attention in leads at opposite ends of the sales readiness spectrum. Doing so would soar lead acquisition costs, bleed profitability and harm productivity.
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Unfortunately, practices for qualifying and scoring leads, like BANT (budget, authority, need, timing), force teams to hand-pick the wheat from the chaff. Data is analyzed manually, and reps are forced to apply personal judgment and sporadic research to score leads based on multiple personas. Not only is this an unrealistic workload, but it also produces inaccurate and misleading data.
Taking a giant stride toward sales automation, there are AI tools that can streamline the lead qualifying process with predictive lead scoring based on accurate data and attributes. Marketing and sales teams can not only pinpoint but also prioritize which leads to close and nurture in the future without upping headcount.
Thanks to predictive lead scoring, Segment, a leader in API integration software, was able to predict and identify the 16% of its leads that accounted for 80% of its total revenue — all without allocating additional employees or adjusting its budget.
AI is empowering hyper-personalization.
It’s no secret that the modern B2B buyer has become conditioned to a personalized buying experience. Personalization is no longer a plus; it has become a mandatory expectation, and AI-powered technology and software can help B2B businesses meet it.
With social listening tools like Awario, marketers can monitor their real-time reputation, initiate conversations with customers and even personalize lead generation with keywords/mention tracking spans across multiple social networks. With others — like Pathfinder, a content insight software — B2B brands can tailor content campaigns, read customer intent signals and better educate their audience.
Pair these tools together, and browsing habits, firmographic data and content consumption patterns — among other attributes — can be analyzed in real time and at scale, producing hyper-personalized recommendations for every buyer.
AI is nurturing pre- and post-sale customer relationships.
According to Drift, RapidMiner — a leading data science platform provider — turned from its chatbot to the AI-powered LeadBot in order to help balance customer support with lead generation.
The bot was able to quickly direct customers to resources and can resolve many customer inquiries. If it can’t, it will pass the complex issues to the human team for further investigation. This has allowed RapidMiner to net more than 4,000 leads to date and generate over $1 million in sales.
B2B buyers are beginning to expect the same on-demand shopping experience found in B2C shopping. AI is bridging that gap in expectation.
AI and machine learning are changing B2B marketing.
Adopting emerging technology like AI without succumbing to hype and ending up with a product that fails to add value is challenging. Here are the steps and thought processes I use when reviewing the purchase of new tech:
1. Start with your biggest bottlenecks and problems. Pick problems that are hindering growth, limiting ROI or slowing down your workforce. Take the example of a growing company whose smaller sales and marketing teams are struggling to qualify, score, prioritize and personalize to high-quality inbound leads. AI-based automated lead scoring and personalization tech can boost efficiency and deliver more sales-ready leads without straining the existing marketing and sales staff.
2. Invest in customer experience and retention. Look for AI software solutions that can record, analyze, transcribe and infer customer insights from sales calls with prospects. Teams using such tools can keep their pulse on competitors in their market and learn how to better engage and retain their customers.
3. Improve your measurements and analytics. You can outmaneuver your competitors if you have better data and insights than them. Look for measurement and sentiment-driven AI technologies that can assist with prospecting calls and messaging. With knowledge of the emotional reaction to a pitch or particular style of messaging, both marketers and sales reps can identify what works and double down on messaging that resonates with their buyers.
4. Start small. If you’re confident that a certain solution can solve existing problems or improve processes, do your research and look for a vendor with proven case studies or referrals, then gradually test and monitor performance to gauge a tool’s actual potential.
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