How B2B Marketers Use AI To Focus On Improving Lead Scoring
When used correctly, AI is one of the most powerful tools that marketers have available to help turn leads into conversions.
Over the last year, B2B marketers have relied almost exclusively on digital signals to determine who’s visiting their websites, what the visitor’s level of interest is and their likelihood of acting. With this abundance of signals (often mixed with faulty information), how can companies improve their lead scoring efforts to determine the signals that indicate action?
Demand Gen Report’s latest webinar, Lead Scoring Simplified: Using Data & AI To Focus On Your Best Leads, combines the lead-scoring expertise of Kerry Cunningham, VP, Principal Analyst at Forrester with in-depth insights and best use practices from Deniz Olcay, VP, Product Marketing at Dun & Bradstreet to answer that question with a simple explanation: artificial intelligence (AI).
AI-powered lead scoring is the practice of ranking leads by the propensity to convert and, ideally, turn them into a customer. This form of automation works by accessing a broad range of third-party data sets to find high-value audiences, with specifics into who’s visiting a website and analyzing their actions, Olcay explained in the webinar. When it comes to lead scoring, he explained that marketing teams generally fall into two buckets:
- 5% fall into bucket one, which is the “curse of abundance,” according to Olcay. This typically means they have too many leads to follow up on and not enough resources to identify who’s likely to convert; while
- The other 95% percent fall in bucket two, which consists of marketers who need to rebuild trust with their sales teams due to a low quality of inbound leads.
While AI can solve these problems (and more), it needs some help laying the groundwork. Instead of setting your systems to automate without regulation, organizations need to build a foundation for success. This includes improving filtration capabilities to prevent unqualified leads from slipping through the cracks, as well as identifying intent signals and refining insights into the likelihood of lead action.
Enhancing Lead Scoring Using AI
Cunningham kicked off the session by pointing to a couple strategies companies can take to better prioritize their lead scoring. This included understanding who’s visiting an organization’s website and determining their level of interest, which is identifiable with one simple question: Did they bring their friends?
“If your lead visited on their own, they probably won’t buy something immediately,” Cunningham explained in the webinar. “However, if two or three other people visited, and you have intent data that shows they came from the same company, that likely means they’re all part of an active buying group, which presents a great opportunity.”
If multiple people from the same company are visiting a website, that usually indicates they’re getting ready to act. Unfortunately, those insights aren’t handed to companies on a silver platter; instead, they often filter through a system that Cunningham referred to as the “demand unit waterfall.” He explained that inquiries start at the top, then flow down through automation that qualifies them, passing through a telequality team and the sales team. It’s in this system’s efficiency and accuracy that lead scoring is housed.
However, the reality is that qualified leads often fall to the wayside when going through the filtration process. Cunningham explained that automation filters are generally right about 90% of the time. Within that 10% are false positives or, perhaps more dangerously, false negatives, which run the risk of ignoring a qualified lead and losing them to a competitor. While the idea of losing that chunk is troubling, there are steps companies can take to improve AI’s capabilities. Cunningham explained that organizations can turn to three kinds of signaling to improve filtration performance, including:
- Fit, which indicates how well an account will fit your ideal customer profile;
- Current state, a dynamic signal that shows what’s going inside an account, the technologies it has and where it is in its contract(s); and
- Interest, which leads to intent signaling and identifies if a company’s in-market and looking for new solutions.
Once those signals are identified and applied to leads, Olcay explained that marketing teams can turn to a color-coded lead scoring system to further identify the likelihood of action:
- Red, which probably don’t fit the company’s criteria for qualification and only had a few interactions;
- Yellow, which have the potential to fit a certain criterion because the lead attended a few webinars and opened a couple emails; and
- Green, which are leads that indicated through various actions that they’re ready to engage with a sales rep.
“AI-powered lead scoring can help you is really your effectiveness on the red and the yellow,” said Olcay. “It can help materially reduce bad leads that are entering your sales follow-up sequences while improving the score of yellow leads. This allows companies to effectively route the leads to the sales teams before they fill out a form or raise their hand.”
Throughout the session, the pair dive deeper into these insights and outline steps how AI-driven lead scoring fuels their success, which is backed by real world examples, success statistics, best practices and so much more.
This story premiered on our sister site, DemandGen Report.