Predictive Lead Scoring in Sales: In-depth Guide (2024)

Predictive lead scoring has revolutionized how modern sales teams operate by enabling data-driven prioritization of leads most likely to convert. This comprehensive guide explores what predictive lead scoring is, why it has become a critical sales capability, how the top solutions work under the hood to score leads, real-world benefits and limitations, and best practices to maximize value.

What is Predictive Lead Scoring and Why Does it Matter?

Predictive lead scoring uses machine learning algorithms to analyze multiple data points on leads and accounts to automatically assign conversion probability scores to each lead. The scores act as a guide for sales reps on which leads to prioritize following up with first.

Lead scoring provides massive benefits to sales productivity and conversion rates compared to traditional lead qualification methods:

  • 300% higher conversion rates – Companies using lead scoring enjoy conversion rates 3x higher on average than those that don‘t. [Source: G2]

  • Prioritize sales outreach – On average, reps spend only 34% of time actually selling. Lead scoring lets them focus on hottest leads. [Source: Outreach]

  • Increase productivity – SalesLoft customers see sales reps increase contacts made per day by 30% after adopting scoring.

Lead scoring benefits

Benefits of lead scoring [Source: LeanData]

With more leads than ever, lead scoring is an indispensable capability allowing sales teams to scale.

How Predictive Lead Scoring Systems Work

Modern lead scoring solutions combine robust data pipelines with machine learning algorithms to assign optimized scores:

Data Sources

Predictive lead scoring ingests both first-party CRM data as well as third-party signals to maximize accuracy:

  • Firmographic data – Industry, company size, location
  • Technographic data – Technologies used
  • Intent signals – Content downloads, searches, site activity
  • CRM contacts/companies – Profiles, activity history
  • CRM deals – Closed won/lost historical deals

Expanding beyond internal CRM data is key. Solutions like Infer, Everstring, and Demandbase incorporate thousands of external firmographic signals to enhance accuracy.

Machine Learning Models

Various supervised machine learning algorithms are leveraged to analyze these data points and identify correlation patterns with lead conversion events:

  • Logistic regression – Predicts probability of a binary outcome
  • Random forests – Ensemble method combining multiple decision trees
  • Neural networks – Models complex non-linear relationships in data

The algorithms "learn" over time by ingested more data and conversion outcomes. Lead scores update dynamically as both new leads come in and existing leads‘ data changes.

Comparing Top Predictive Lead Scoring Vendors

Many solutions exist for implementing predictive lead scoring. Here is an overview of leading options:

Solution Unique Capability Ideal For
Salesforce Einstein Tight integration across Salesforce platform Existing Salesforce CRM users
InsideSales Neuralytics Leverages dataset of 40M+ sales interactions Mid-market and enterprise B2B
Infer Incorporates thousands of external firmographic signals Companies lacking robust CRM data
LeanData Specializes in matching leads to target accounts Account-based sales & marketing
AbsolutData Custom-tailored models for each client Companies hesitant to share data

Salesforce Einstein Lead Scoring

As the 800-lb gorilla in CRM, Salesforce Einstein Lead Scoring is especially powerful for existing Salesforce users. It draws predictive signals across all Salesforce system activity from marketing to sales.

InsideSales Predictive Lead Scoring

InsideSales Neuralytics predictive engine is extremely accurate thanks to its robust dataset of over 40 million historical sales interactions. Its AI-based scoring helps mid-market and enterprise B2B clients.

Infer Lead Scoring

Infer stands out for its focus on incorporating thousands of external data sources to enrich firmographic and technographic data, ideal for companies lacking comprehensive CRM data.

Best Practices for Lead Scoring Success

Follow these tips to ensure your lead scoring program drives maximum value:

  • Involve sales early – Get feedback to identify key conversion factors to focus on.
  • Motivate adoption – Educate reps on what scores mean to build trust.
  • A/B test – Try different email subject lines and nurture tracks for each score bucket.
  • Review results – Audit scores vs actual outcomes to refine algorithms.
  • Automate workflows – Trigger automatic routing and nurtures based on score thresholds.

LeanData lead scoring distribution

Sample lead scoring distribution [Source: LeanData]

Continuously optimizing the model and workflows based on the results will drive the highest ROI from lead scoring.

Who Should Use Predictive Lead Scoring?

Lead scoring provides the most value for:

  • B2B companies with long, complex sales cycles
  • Companies getting more leads than reps can handle
  • Sales teams already tracking CRM data on enough historical deals

Without sufficient conversion data, the machine learning models will lack enough training data to be accurate.

Industries like high-tech, manufacturing, and financial services with extensive sales processes tend to benefit most from lead scoring capabilities.

Limitations to Consider

While extremely powerful, predictive lead scoring has some limitations to be aware of:

  • Lack of context – Scores alone lack qualitative insight into next steps.
  • Data dependence – Models weaken if offerings or target market pivot.
  • Limited transparency – Scores often seem like "black boxes" unless vendor explains drivers.
  • Ongoing maintenance – Models need continuous monitoring and optimization.

Understanding these constraints upfront sets proper expectations. Emerging innovations in areas like prescriptive analytics help provide more actionable recommendations to sales reps.

Take Action to Increase Sales Productivity

In summary, adopting predictive lead scoring enables more efficient follow-ups leading to dramatic conversion rate increases. Prioritize working with sales leadership to build a business case, select the right software based on your needs, and implement lead scoring tightly integrated with your existing sales workflows. Leverage the advice in this guide to maximize success. Let data and AI accelerate your team‘s sales productivity.