Predictive Lead Scoring in Sales: In-depth Guide (2023)
If you‘ve ever felt like your sales team was wasting time and effort on low-quality leads, you‘re not alone. Sales reps following up on leads that will never convert is an inefficient process that far too many companies still struggle with today.
The solution is implementing a data-driven system that accurately predicts the likelihood of a lead converting based on historical patterns. Let‘s explore what predictive lead scoring is, why it‘s become essential for sales teams, and how to make the most of this transformative technology.
What Makes a Lead "Hot" or "Cold"?
Before we dive into the details, it helps to understand what makes a lead more promising in the first place. While some factors are obvious, like lead source or demographic data, others are more subtle and require analysis to surface.
For example, through mining historical sales data, we might uncover that leads from Twitter convert at a 58% higher rate than those from Facebook ads. Or that small business owners in the technology services industry have bought our offering at a 72% higher clip than other sectors.
The goal of predictive lead scoring is to automatically surface these types of insights to determine the "hotness" of each lead. Rather than relying on superficial traits or gut instinct, these data-driven models provide an objective score rooted in statistical patterns and trends.
Why is Predictive Lead Scoring Critical for Sales Teams?
The benefits of implementing a predictive lead scoring system are immense:
- Higher conversion rates – Focusing efforts only on hot leads nearly guarantees better results. According to Aberdeen Group, companies using predictive scoring convert leads at a 73% higher rate on average.
- Improved productivity – Reps waste less time engaging with cold prospects and have more capacity for profitable deals. CSO Insights found that predictable lead scoring increased sales productivity by over 25%.
- Personalized engagement – With context about each lead, reps can craft targeted messaging and offers likely to resonate. According to Salesforce, personalized outreach boosts conversion rates by an average of 800%.
- Consistent processes – Scores provide an unbiased, uniform approach rather than gut feelings. McKinsey states that fact-based lead qualification makes sales processes 7x more consistent.
- Accelerated growth – Higher conversion rates lead directly to faster sales cycles and revenue growth. Forbes reports that qualified lead scoring can improve revenue results by 10-20%.
In today‘s competitive climate, predictive lead scoring is no longer a nice-to-have but an essential foundation for sales success.
How are Lead Scoring Models Developed?
Now that you know why predictive scoring is so valuable, let‘s explore how these systems actually work under the hood. The process involves three core steps:
1. Compiling Historical Sales Data
The algorithms require extensive training data in order to identify meaningful patterns and correlations. The more quality sales records available, covering both won and lost deals, the better the model will perform. Most experts recommend at least 500 historical deals to work with initially.
2. Applying Machine Learning Algorithms
With a solid data foundation, data scientists test different ML algorithms to see which approach best predicts outcomes. Common models include regression analysis, decision trees, random forests, and neural networks. The best performer is selected based on measures of precision and recall.
3. Dynamically Updating Scores
As new data comes in, like changes to records or lead actions, the model automatically re-assesses them and updates lead scores accordingly. This allows scores to stay dynamic and reflect the most recent indicators of potential.
Here is an example distribution of lead scores from a predictive model:
| Lead Score | Category | % of Leads |
|---|---|---|
| 90-100 | Hot | 20% |
| 70-89 | Warm | 30% |
| 1-69 | Cold | 50% |
As you can see, predictive models provide a nuanced view of lead potential versus binary hot/cold labels. The scores allow reps to precisely prioritize their efforts based on each lead‘s relative potential.
How Should Companies Implement Predictive Lead Scoring?
Follow these best practices to ensure a successful rollout of predictive lead scoring:
- Garner executive support – Educate leadership on the revenue benefits and get buy-in across sales, marketing and data teams.
- Audit existing data – Assess the completeness and quality of required sales and CRM data before modeling begins.
- Map integration points – Plan how scoring data will flow across sales tools like CRM, email, and sales engagement platforms.
- Test rigorously – Split data to evaluate model performance on a holdout set before deployment. Monitor for accuracy.
- Roll out thoughtfully – Slowly ramp up usage of scores until reps are accustomed to factoring them into workflows.
- Optimize with feedback – Continuously gather sales rep input on lead prioritization effectiveness to refine the model.
With the right preparation and executive sponsorship, predictive lead scoring can transform sales efficiency and results. But this is only scratching the surface of how companies can leverage data to guide sales teams. Let‘s examine some other high-impact applications:
- Next-Best-Action Recommendations – Models prescribe the optimal sales follow-ups based on each lead‘s profile and stage.
- Predictive Content Recommendation – Automatically serve personalized content to leads based on models of engagement.
- Lookalike Modeling – Identify prospects that statistically resemble your best customers to expand Total Addressable Market.
- Churn Prediction – Flag at-risk customers based on patterns so account managers can proactively retain them.
The possibilities are truly endless when combining sales data with the sophistication of AI. Companies owe it to their teams to unlock these tools and leverage them for growth.
Comparing Lead Scoring Vendors
Many excellent vendors have emerged in the predictive lead scoring space. Here is an overview of key players:
Salesforce Einstein
The CRM leader‘s data science offering. Perfect for existing Salesforce customers with CRM data pre-loaded. Provides actionable insights and sales guidance.
6sense
Combines scoring with intent and external signals for a complete view of accounts. Excels at B2B use cases and integrated D&A workflows.
Infer
Empowers accurate targeting, routing and nurturing through robust segmentation and lookalike capabilities. Plays well with leading MAPs and CRMs.
Lattice Engines
Focused on helping B2B companies identify in-market accounts. Integrates with Bombora for intent data and CabinetM for peer insights.
MadKudu
Specializes in leveraging web visitor signals and behavior to prioritize incoming sales inquiries and leads. Seamless integration with existing sales stacks.
I‘m happy to discuss the detailed capabilities of each vendor and recommend the best fit depending on your specific data infrastructure, use cases, and objectives. Predictive scoring can deliver incredible value, so choosing the right platform is a crucial catalyst for sales success.
Lead Prioritization is the Future
As leads grow more complex and sales cycles elongate, buying signals are only set to multiply. Predictive analytics will grow more sophisticated at incorporating new data sources and machine learning techniques over time.
Lead scoring represents just the tip of the iceberg for how AI can guide human sales reps to be more effective. I‘m excited to see these technologies continue maturing to unlock their full potential. With the right foundations and approach, predictive lead scoring can transform any sales team overnight.
