AI Lead Scoring for Business Success

Lead scoring is long overdue for an upgrade. Static, rules-based lead scoring methods have always left something to be desired. In fact, a 2021 study by Openprise showed that only 35% of marketers were confident in their ability to score leads accurately.

But recent developments in AI lead scoring aim to solve many of the problems past lead scoring models faced: reliance on surface-level insights, rigid scoring models that don’t adapt, and incomplete data, just to name a few.

Curious about using lead scoring with AI? This guide will tell you everything you need to know, including benefits, top AI lead scoring tools, and use cases.

What Is AI Lead Scoring?

AI lead scoring is the use of artificial intelligence (AI) to evaluate a lead's fit and purchase intent. Instead of relying on gut feeling or manual rules, AI analyzes large volumes of data from multiple sources to determine how valuable a lead is to the sales team.

Examples of AI lead scoring include:

• Scoring leads based on how similar they are to past converted leads.
• Automatically adjusting lead score thresholds based on past conversions.
• Combining first and third-party data from multiple sources.
• Analyzing call transcripts to detect buying signals.
• Flagging leads who stop engaging or show churn risk behavior.
• Recommending what actions to take next.

Lead scoring with AI improves on manual and rule-based methods by removing guesswork and human bias. Rather than following a set of rules, it continually refines its predictions as new data comes in. Best of all, when implemented correctly, AI lead scoring can help reduce time spent on unqualified leads and improve conversion rates.

How AI Transformed Lead Scoring

The idea of ranking and scoring leads first gained traction around the turn of the century with the explosion of internet-based marketing. The original scoring models involved a lot of manual work and were prone to bias and inconsistencies. But as technology develops, lead scoring is gradually becoming easier to execute and more accurate in its predictions.

Today, many marketing platforms are incorporating AI into their lead scoring software. This shift from static to dynamic scoring has made lead scoring less labor-intensive and easier to connect to sales pipeline growth. To better understand what a shift AI lead scoring represents, let’s take a quick look at how lead scoring has evolved.

Manual Lead Scoring

Manual lead scoring involves assigning a value, or score, to certain characteristics or actions, then totaling a lead’s score based on this system. Businesses started using this type of lead scoring in the early 2000s as a way to manage the increased volume and varying quality of leads that were being generated from digital marketing.

But while manual lead scoring is helpful in creating a structure for prioritizing leads, these systems are not data-driven; they rely on assumptions and biases. They are also time-consuming, labor-intensive, and cannot adapt to rapidly changing buyer behavior. For example, sales teams might decide that viewing a pricing page is a more serious intent signal than opening an email, even if there is no data to prove that assumption is correct.

Key characteristics:

• Uses fixed rules to assign scores.
• Scores rarely change unless manually updated.
• Relies on human judgment or assumptions.
• Difficult to scale and maintain as lead volume increases.

Example

Sarah, a freelance marketing consultant, receives an inquiry from a local real estate agency interested in SEO and email marketing. She opens her manual lead scoring spreadsheet and scores the lead across five categories:

• Industry fit (real estate): +10
• Requested services match her offerings: +10
• Budget is flexible at $2,000/month: +10
• Small team of 6 (ideal client size): +5
• Ready to start next month: +5

The lead earns a total of 40 out of 50 possible points, placing them in her "high-priority" range. Based on this, Sarah tags the lead in her CRM and immediately schedules a discovery call.

Automated Lead Scoring via CRM

Automated lead scoring is the process of using software to automatically assign scores to leads based on predefined rules or behaviors. This helps businesses prioritize the most promising prospects without manual input. Lead scoring started to evolve as marketing automation platforms and CRMs like Salesforce, Marketo, and HubSpot became more powerful and widespread. This was a necessary evolution in an increasingly data-rich environment.

While still rule-based, automated lead scoring was faster, more consistent, and easier to scale than manual lead scoring. But it still struggled to adapt to complex or unique buyer behaviors. Key characteristics:

• Pulls data from tools like HubSpot or Salesforce to score leads.
• Scores change automatically based on lead behavior.
• Reduces bias by applying the same rules to every lead.
• Saves time and scales easily with growing lead volumes.
• Still follows rules-based logic.

Example

Jake runs a small web design agency with a team of five. He uses a CRM with automated lead scoring to prioritize incoming leads.

When a new inquiry comes in from a bakery chain looking for a website redesign, the system assigns scores based on preset rules:

• +10 for being in the food & beverage industry (a preferred niche),
• +10 for selecting “full website build” as a service
• +10 for indicating a budget over $3,000
• +5 for wanting to start within 2 weeks.

The lead automatically gets a score of 35 and is tagged as “high priority.” The CRM immediately adds the contact to Jake’s “Hot Leads” pipeline and triggers a follow-up email offering available time slots for a discovery call.

Predictive Lead Scoring

Predictive lead scoring uses algorithms to analyze large datasets to identify the attributes and actions that most often lead to conversions. It emerged in the mid-2010s as advancements in machine learning and data analytics made it possible to go beyond rule-based models. Companies like Salesforce and HubSpot began integrating predictive capabilities into their software. This began the transition toward more objective and data-driven lead scoring models.

These systems were an improvement because they continuously learn and refine themselves. This helped them remove bias from the equation and adapt to changing behaviors.

Key characteristics:

• Uses algorithms to identify patterns and score leads.
• Scores leads based on likelihood to convert, not just fixed criteria.
• Models improve over time as new data is collected.
• Models are tailored to specific sales outcomes and customer journeys.
• Often outperforms manual and rule-based systems in prioritizing high-quality leads.

Example

Maya runs a small digital ad agency specializing in social media campaigns for local service businesses. Her CRM uses predictive lead scoring, which means it analyzes historical data to identify patterns in leads that tend to convert.

When a new inquiry comes in from a home cleaning service, the system looks at factors like business size, website behavior, and email engagement, things Maya hasn’t manually scored. It sees that this lead viewed three pricing pages, opened two emails, and matches the profile of past clients who converted quickly. Based on the CRM’s machine learning model, the lead is given a predictive score of 88 out of 100, higher than average, and automatically pushed to the top of Maya’s pipeline with a recommendation to follow up within 24 hours.

AI Lead Scoring

AI lead scoring leverages advanced techniques like natural language processing, machine learning, and real-time behavioral analysis to deliver even more nuanced insights. It evolved from predictive lead scoring as artificial intelligence technologies became more accessible and integrated into CRM and marketing platforms. Products like Salesforce Einstein and HubSpot AI were some of the first major players to take AI lead scoring into the mainstream.

AI systems pull from a broader range of data sources and continuously adapt to changes in buyer behavior. It’s similar to predictive lead scoring, but it uses a lot more data from a wider range of signals. It also works in real time, and gets smarter over time as it learns your potential clients’ conversion behavior.

Key characteristics:

• Leverages complex algorithms to assess and score leads.
• Updates scores in real-time as new data or behaviors emerge.
• Can analyze hundreds of variables simultaneously.
• Pulls data from multiple sources.
• Adapts scoring logic to your specific business goals and conversion history.
• Goes beyond predictive analytics to recommend what action to take.
• Scales easily as lead volume increases.

Example

Lena owns a boutique HR consulting firm that works with startups and growing teams. She uses a CRM with AI-powered lead scoring, which continuously learns from her past deals, client types, and sales cycles.

When a new lead signs up through her website, the AI reviews dozens of data points, including company size, LinkedIn activity, job title, email engagement, and website clicks. It recognizes that leads from tech startups with 10-50 employees, who view her pricing page twice and respond to an initial email, are highly likely to convert.

The AI assigns this new lead a score of 92 out of 100, along with an insight: “High engagement + profile matches converted clients.” Lena gets an instant notification and a prompt suggesting the best time to call based on the lead’s past behavior.

6 Key Benefits of AI Lead Scoring

While manual lead scoring systems were helpful in creating a structure for scoring leads, those systems were not scalable or entirely reliable. AI lead scoring systems offer a more intelligent solution that not only predicts who is going to convert, but why they will convert and what you can do to move them along.

Let’s take a close look at some of the benefits provided by AI lead scoring:

1. Higher Accuracy

Lead scoring with AI leads to higher accuracy because it relies on actual data from past conversions to remove guesswork and bias from the equation. Instead of relying on what sales or marketing teams think makes a good lead, AI can analyze past conversion data to find out what patterns and characteristics were common among your best customers. This data-driven approach leads to more accurate predictions and better sales outcomes.

2. Adaptive and Self-Improving

AI models automatically adjust and improve their algorithms as they collect more and newer data. While manual or rules-based scoring stays static, AI tracks what worked and what didn’t, so it can improve its understanding of what makes a good lead. For example, if buyer behavior shifts or a new marketing channel starts producing better leads, the AI can detect those trends and adapt the scoring accordingly, without human intervention.

3. Predictive Insights

AI makes predictions by analyzing large volumes of data from multiple sources to learn which traits and actions correlate with successful conversions. It uses this data to build a predictive model that estimates each lead's likelihood to convert. Using machine learning, the model continuously updates and improves as new data becomes available. This helps AI lead scoring models get smarter and make more accurate predictions over time.

4. Efficiency at Scale

AI can process massive amounts of data and automatically assign scores without slowing down or making mistakes. This allows sales and marketing teams to handle increased lead volume without a proportional increase in workload.

5. Uncovers Hidden Signals

AI can spot patterns that wouldn’t be obvious to a human. It goes beyond surface-level insights like job title and company size to find subtle signals that strongly correlate with conversions. For example, AI might analyze the past two years' worth of conversion data and learn that leads from a PPC campaign who watch a demo video and open three follow-up emails within a week are most likely to become customers. This kind of complex analysis wouldn’t be possible in any other way.

6. Integrated with CRM Systems

Most AI lead scoring systems will integrate natively, or through a third-party software, with your CRM platform. This allows your AI model to pull data from your CRM and also update lead records in real-time. Marketing and sales can then use this data to create visual CRM dashboards and instantly see which leads are most likely to convert without adding extra steps or complexity.

4 Best CRMs with AI Lead Scoring

Since the explosion of AI in late 2022, AI lead scoring has quickly become a standard feature in modern CRMs. There are currently dozens of platforms that offer this capability. But really, only a handful of players offer native or integrated AI lead scoring capabilities that would be helpful for a small business.

Let’s take a close look at four of the top CRMs for small businesses:

1. HubSpot

HubSpot is a great CRM for growing businesses. Many of its features were built with scalability in mind, and the AI leads scoring feature is no exception. Since it’s built directly into HubSpot’s CRM, there’s no need for complex setup or third-party integrations. This makes it accessible even for teams without technical expertise. And the HubSpot AI lead scoring model will continuously adapt to new data, allowing it to scale with you as your business grows and expands into new markets.

2. Salesforce

Salesforce’s Einstein AI fully integrates with the Salesforce ecosystem, making it a great option for businesses that are already using the platform. It offers deeper customization and more advanced modeling compared to HubSpot AI lead scoring and other tools on this list.

But while still accessible, it can be more complex to set up and manage; there’s definitely more of a learning curve with this tool. Salesforce Einstein AI is known for its accuracy but often requires more data volume and technical expertise to manage.

3. Zoho

Zoho’s Zia AI lead scoring is a great entry point for small businesses who want to explore AI without the cost and extra features included with HubSpot and Salesforce. Zoho offers predictive insights without requiring complex setup or a large volume of data. It is especially practical for small businesses thanks to its seamless integration with other Zoho tools and its low cost of entry.

4. Freshsales

The Freshsales AI engine, Freddy AI, is an ideal choice for growing teams that need both speed and accuracy. Freshsales is easier to use than some other tools like HubSpot or Salesforce, which makes it a great option for non-technical teams. It integrates easily with other Freshworks products, and it's perfect for teams who are already familiar with the ecosystem.

When AI Lead Scoring Makes Sense

AI lead scoring represents a huge shift in the accuracy and adaptability of lead scoring.

But that doesn’t mean it’s right for everybody. New businesses or smaller businesses may still prefer automated or even manual lead scoring if they don’t have the volume, the data, or the budget to justify an investment in AI or predictive models.

Here’s an overview of when to use each type of lead scoring method.

Quick Recommendations:

• Just starting out? Use manual or simple rule-based scoring in your CRM.
• Managing a growing pipeline? Use rule-based or consider predictive if your CRM supports it.
• Scaling or segmenting multiple funnels? Go for AI-powered lead scoring to create custom, adaptive scores at scale.

Final Thoughts

Whether you're looking for ways to integrate AI into your sales process, or you need a more accurate way to score leads, AI lead scoring may be the answer you're looking for. It offers an exciting way to do more with less while also offering measurable improvements in sales outcomes. But it's important to evaluate CRMs based not just on AI features, but on how well they fit your business overall.

At Softailed, we want to take the weight off your shoulders. Our CRM Comparison tool lets you compare CRM software side by side. Check it out today and see which tool works best for you.