Lead scoring is a technique used within CRM systems to analyze lead attributes and behavior to assign each lead with a numerical score that represents their likeliness to convert. This allows sales and marketing teams to prioritize outreach and resources to contacts most ready to buy. In this article, we’ll look at different approaches to lead scoring as well as factors that should be considered when building a lead scoring model within lead management software.

Understanding Lead Scoring Criteria

When setting up lead scoring criteria in your lead management software, the goal is to analyze factors that provide insight into a lead’s purchase readiness and propensity. Common criteria used for lead scoring include:

Web behavior and engagement

Things like the number of page visits, time spent on the site, and content downloaded can provide clues about a lead’s interest level. Leads who are more engaged should score higher.

Marketing campaign activity

Leads who respond to promotional emails, open invoices or proposals, and attend webinars are typically more sales-ready than those who ignore campaigns. Their level of engagement can influence their score.

Nurture program progression

By tracking a lead’s movement through automated email campaigns, webinars, ebooks, and other content in a marketing nurture program, their stage completion gives insights into purchase intent. Leads further in nurture tracks should score higher.

Firmographic data

Attributes like company size, industry, location, job role, etc that marketing qualified leads provide can be evaluated to understand if they match the buyer persona. Relevant leads score higher.

Social profiles quality

The presence of leads on relevant social networks, number of connections, engagement levels, and profile completeness can be used to gauge credibility and purchase signals. This gives the sales and marketing teams a solid framework of criteria to use intelligent data from within their lead management software to derive purchase signals for scoring leads. The right combination of these factors ensures that leads likely to buy soon are prioritized for outreach.

Setting up lead scoring models

There are different approaches to setting up the lead scoring model and algorithms within your CRM:

Weighted factor model

This involves assigning weighted scores to criteria e.g. web behavior (30%), nurture progression (25%), social profile (15%), etc based on relative importance. Scores are added to get the final lead score.

Complex algorithmic model

Advanced algorithms can analyze combinations of factors, apply fuzzy logic considering exceptions, and derive highly accurate predictive scores. This requires data science expertise.

Stages based model

Instead of scores, leads are classified into stages like Suspect, Prospect, Engaged, etc. based on criteria fulfilled. Movement across stages indicates increasing purchase likelihood.

Customizable model

Some CRM software offers flexible model customization, allowing the addition or removal of criteria or varying the scoring methodology based on business needs without coding. This ensures the model remains relevant. The best approach depends on your resources, technical skills, data availability, and business priorities. Over time, models should be optimized based on conversion insights from past leads.

Testing and optimizing lead scoring performance

Once the scoring model is set up in the lead management software, it’s important to test its accuracy in prioritizing converting leads:

• Analyze score distribution of past converted vs non-converted leads to check if the model effectively separates them
• Continuously refine the model by checking score accuracy on newly converted leads and including their attributes in model recalibration
• Segment leads into probability of conversion categories based on scores to see if outreach strategies need adjustment
• Ensure sales reps find high-scored leads more qualifying than low-scored ones through feedback
• Recalibrate the model periodically or as business/market changes to maintain lead scoring relevancy
• Continuous optimization is crucial to leverage lead scoring as an effective prioritization tool within the lead management software, helping sales teams focus efforts on sales-ready prospects.

Common Challenges Businesses Face Setting Up Lead-Scoring Models

Below are some common challenges businesses may face when setting up and optimizing lead-scoring models: Lack of data: Lead scoring requires robust data on leads’ activities and attributes. Businesses just starting out may not have enough historical data to derive meaningful scoring criteria. Complex attribution: It can be difficult to accurately attribute lead conversions to specific marketing and sales efforts since multiple touches are often involved over time. This impacts scoring model calibration. Subjectivity bias: Sales and marketing teams may assign subjective importance to certain criteria based on preferences instead of data-driven insights. This reduces score accuracy. Dynamic markets: As markets change rapidly, criteria that predicted conversions in the past may no longer be relevant. Models need frequent recalibration to reflect shifting buying behaviors. Siloed data: Issues around data integration if information resides across different legacy systems instead of a centralized CRM platform, affecting the completeness of scoring criteria. Resourcing constraints: Setting up data-driven lead scoring models requires data science skills which may not always be available internally, requiring external expertise and budget. Lack of testing: Insufficient focus on model evaluation through post-conversion analysis leads to the persistence of inaccuracies in prioritizing leads. Resistance to change: Sales teams, accustomed to certain ways of working, may find it difficult to adjust strategies based on quantitative scoring instead of inherent biases. Lack of customization: “Out-of-the-box” scoring solutions from CRM vendors rarely fit unique business processes requiring further enhancement effort. Over-optimization: Frequent tweaking of models based on minor outcomes can deteriorate overall accuracy instead of continuous improvement.

Conclusion

When implemented effectively, lead scoring powered by data within a CRM software helps sales and marketing teams gauge prospect purchase signals to focus engagement resources only on qualified possibilities. With testing and recalibration, lead scoring models evolve to identify converting leads with higher accuracy over time. This enhances overall pipeline volume and velocity for organizations. With the right CRM solution and lead scoring methodology, businesses can streamline their demand generation and sales processes significantly.