Lead scoring is supposed to be all about the numbers — a quantitative approach to determining where a lead stands in their buying journey and pinpointing exactly the right moment to hand them off from marketing to sales. But for years, lead scoring has been a hidden source of subjectivity within marketing and sales teams.
Now, AI makes it possible to use historical data, rather than preconceived assumptions, to capture and quantify a hyper-precise snapshot of each prospect’s stage in the buying process. This novel approach, also called predictive lead scoring, is a true game-changer for sales and marketing teams.
Let us explore why this paradigm shift is so important, and the benefits AI brings to the lead scoring landscape (and beyond).
Where traditional lead scoring falls short
Traditionally, lead scoring involves assigning a set number of points to a lead, based on certain characteristics (like demographic or firmographic data) or digital behaviors (like visiting a pricing page or downloading a gated piece of content). The higher the score, the closer the lead is to making a purchase — and after they cross a predetermined threshold of points, they are handed off to sales for one-on-one conversations.
The problem with traditional lead scoring is the subjective nature of its underlying model. In many cases, sales and marketing leaders sit in a room, map out all the possible data points and online behaviors they can conceive, and assign a relative number of points to each — based on their collective beliefs about which behaviors are most likely to indicate likelihood to purchase.
This approach relies on firsthand experience and human intuition, which are both valuable, but it does open the door for bias and subjectivity to enter the scene.
For example, marketing and sales leadership at a B2B software company may have the perception that leads with senior-level job titles (like Directors, VPs, or C-levels) tend to convert at higher rates and set up their scoring model to assign higher points to these individuals.
However, this could mean the scoring system undervalues champions with “lower” titles, such as managers or individual contributors. These may be the would-be customers who are actively seeking solutions and influencing purchase decisions, or even those with purchasing authority at smaller companies.
In other words, not the type of leads you want to under-serve, even if their junior titles do not catch the attention of the people in charge of setting the lead scores.
How AI lead scoring course-corrects for bias and subjectivity
Rather than pre-determining criteria by committee, AI-powered lead scoring relies on your company’s historical data. These powerful models can aggregate and analyze volumes of demographic, firmographic, technographic, and intent or behavioral data from your closed-won and closed-lost deals. They understand exactly who these leads were and how they behaved prior to converting (or failing to convert).
By slicing and dicing all this real-world data in mere moments, these complex algorithms have the capacity to identify hidden patterns and nuances within your datasets. This allows them to identify which combinations of characteristics and behaviors truly correlate with conversion, rather than relying on predefined assumptions.
Continuing our SaaS example from above, an AI lead scoring model would account for job title — but it might determine that engagement metrics like online webinar attendance and content downloads are stronger indicators of lead quality than job title alone. And it may even detect that managerial-level leads tend to convert at a higher rate than C-suite leads. The AI lead scoring engine would use this real-world data to calibrate the relative importance of each specific job title, along with content engagement, building a more nuanced and accurate assessment of lead potential.
Do not get us wrong: human oversight, experience, and intuition will always be valuable, and your manual lead scoring model still has a vital role to play (more on that below). But AI brings a previously unimaginable level of clarity and data-driven decision-making to the lead scoring process.
The benefits of AI lead scoring
In this way, AI-powered lead scoring delivers clear benefits over the traditional, manual approach:
- By analyzing historical data, AI shifts your lead scoring model’s foundation from subjective assumptions to real-world information.
- Rather than treating each datapoint or behavior as an isolated incident, AI provides a more nuanced score by considering the full picture of a prospect’s history of engagement and behavioral patterns.
- AI delivers predictive lead scoring that anticipates how likely each lead is to convert, based on their similarities, shared characteristics, and common behaviors with your past closed-won deals.
- This predictive lead scoring helps teams improve lead prioritization and ROI by focusing their time and efforts on the leads most likely to convert.
- An AI lead scoring engine will continually improve over time by evaluating its own performance and refining its underlying model — unlike traditional lead scoring, which requires teams to manually measure, evaluate, and adjust models at recurring intervals.
Despite these clear benefits, it is important to note that the technology powering AI lead scoring is still new — and some AI tools use a “black box” approach that prevents you from seeing and understanding exactly how the scoring model works. While AI capabilities evolve, you will want to maintain your traditional lead scoring system so you can evaluate how the different models compare and make sure your AI lead scoring engine is performing as expected.
Before the score: AI fills your funnel by identifying and targeting new leads
Of course, you don’t need to wait for a lead to enter your CRM and await a score to harness the power of AI for demand generation. You can use AI tools to fill your funnel in the first place by intelligently identifying and targeting high-quality leads.
Identifying quality leads
With AI capabilities, it’s become much more achievable to identify new leads who fit squarely within your ideal customer profile (ICP). Instead of casting a wider net and generating a larger volume of leads who may not be a good fit for your business, AI tools make it possible to laser-focus on identifying and contacting high-quality leads with a greater likelihood of conversion.
For example, AI models can aggregate and analyze intent data, which includes online behaviors that signal a prospect’s interest in your product or service. These can include website visits, content downloads, email opens and clicks and other online interactions. By layering intent data with static information like demographics and technographics, AI tools can identify quality leads that have indicated some likelihood of purchase in the future.
Targeting quality leads with improved personalization
Once you’ve identified your new leads, AI can help you segment and target effectively with personalized outreach across all types of marketing campaigns, from account-based marketing (ABM) and content syndication to display adsand PPC.
AI-driven personalization analyzes user data to understand preferences and characteristics and segments audiences accordingly. Generative AI tools like ChatGPT and DALL-E help marketers deliver tailored content to each segment by creating personalized versions of content that address more nuanced pain points and challenges.
While human oversight is still needed to ensure all messaging is accurate and on-brand, these gen AI tools can significantly speed up the content creation process by generating tailored use cases or customizing headlines and visuals for a specific audience.
For example, here’s how this could play out in an AI-assisted content syndication campaign:
- Industry-specific personalization: A B2B software company can use AI to customize variations of a syndicated blog post about data security for the healthcare industry (highlighting specific issues around compliance) and the finance sector (discussing risk management and fraud protection).
- Role-specific personalization: A syndicated eBook on digital transformation can be tailored with AI to emphasize strategic planning and ROI for C-level executives, while IT professionals would be targeted with a version that dives deeper into implementation and best practices.
- Technographic personalization: For a company selling marketing automation software, AI can help generate customized landing pages that highlight integration capabilities with Salesforce or Hubspot—and then serve the appropriate page to visitors whose companies use that specific solution.
- Account-based personalization: In an ABM content syndication campaign, you can use gen AI tools to quickly create personalized landing pages with content, case studies, and messaging designed to resonate with each target company’s industry and corporate culture.
By reaching highly segmented leads with personalized content that resonates, you can improve engagement and help move them more swiftly along their buying journey.
Harnessing the power of AI for demand generation
In a time when demand gen marketers are repeatedly asked to do more with less, these kinds of AI capabilities offer a solution. Better data, improved segmentation, personalized content, and higher-quality leads: there’s a reason 92% of marketing leaders are already using AI and automation techniques, according to a study from global consultancy Algomarketing.
Ready to join them? Check it out on how to use ChatGPT for effective B2B lead generation — it’s full of tips and tactics that you can start to put into practice today.
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