How to Set up Manual Lead Scoring

Lead scoring is one of the most powerful tools in modern marketing and sales, but its success lies in the preparation. The foundation of an effective lead scoring system isn’t just the algorithm itself—it’s the data, processes, and strategies that feed it. From optimizing your CRM data to refining your scoring mix, meticulous preparation ensures that your scoring model accurately prioritizes leads and aligns with your business goals.

This blog post will walk you through optimizing property data for precision, streamlining CRM data for accuracy and efficiency and crafting the ideal scoring mix for actionable insights. By the end of this guide, you’ll have the tools to build a reliable, data-driven lead scoring system that transforms your marketing efforts into conversion-driving strategies.

Chapter I: Optimizing Property Data for Precision

In lead scoring, effective use of property data is foundational. Every CRM holds a wealth of data points—up to 300 contact attributes like name, company, and email—but success depends on selecting and refining these attributes for accurate scoring. Let’s break down the key sources of property data and best practices to reduce errors and ensure data reliability.

Key Sources of Property Data:

1. Form Submissions: The most direct method, where users share information when filling out forms.
2. Manual Entries: Added from sales conversations or interactions, often susceptible to errors.
3. Tracking Codes: Behavioral data gathered from digital interactions, like website visits.
4. Company Data Enrichment: External databases providing detailed company information for a fuller profile.

Reducing Errors in Manual Data

Manual entries are prone to inaccuracies. For example, a sales rep might input a quote before a lead is fully qualified, skewing data. To counteract this, follow these steps:

• Limit Manual Data: Rely more on automated data (sources 1, 3, and 4) to reduce errors.
• Structured Inputs: Use dropdowns over text fields for consistency. For instance, a dropdown for job titles minimizes variations like “Mgr” or “Manager.”

Practical Tips for Data Accuracy:

• Choose Key Attributes: Regularly review and update the attributes critical to your lead scoring.
• Automate Data Collection: Automate as much as possible to minimize manual input.
• Standardize Fields: Use dropdowns to ensure consistency across data entries.

By minimizing human input and standardizing data, you can build a reliable base that empowers precise lead scoring, driving data-driven decisions and marketing outcomes.

Chapter II: Streamlining CRM Data for Accurate Lead Scoring

A well-organized CRM database is essential for lead scoring. Precise definitions of leads and customers, coupled with diligent data cleaning, can significantly improve the quality of your scores.

Step 1: Define Leads and Customers

Clear definitions help focus your scoring:

• Leads: Filter out inactive or irrelevant contacts, like those without recent engagement or a company association. Moving these contacts to an “other” category cleans your active lead pool.
• Customers: Focus only on contacts who have converted to a paid subscription, excluding those who only accessed support services or training.

Step 2: Clean Your Data

A well-defined data cleaning process is crucial:

• Remove Duplicates: Duplicate contacts skew data, so eliminate them.
• Correct Formatting: Standardize fields, like job titles and company names, to reduce variations.

This cleaning process can boost accuracy, as seen in our own case study where data cleaning is fundamental for your scoring algorithm.

Practical Tips:

• Regular Audits: Periodically review and update your CRM to keep data current.
• Standardize Entries: Consistent formatting and entry standards reduce errors.
• Leverage Automated Tools: Use CRM tools for tasks like deduplication and standardization.

By maintaining clean, accurate data, your CRM becomes a more effective tool for lead scoring and for driving strategic decisions.

Chapter III: Crafting the Ideal Scoring Mix

Creating an effective lead scoring system is about combining demographic data, behavioral insights, and product-specific criteria to prioritize leads effectively. Here’s how to build and refine your scoring mix.

Building a Scoring Mix

A robust mix involves three components:

1. Demographic Data: Explicit data like job titles or company size.
2. Behavioral Data: Implicit data on engagement, including recent interactions or time spent on your site.
3. Product-Specific Data: Information like a lead’s specific interests or setup needs. In B2B sales, this ensures higher accuracy by addressing each lead’s unique context.

Steps to Create and Balance the Mix:

1. Select Mix Components: Initially, we created a single scoring property, but this hit CRM limits. A better approach is to split attributes into categories and calculate them separately before combining scores.
2. Weighting the Mix: To address differences in lead types (e.g., inbound vs. outbound), set scoring thresholds and adjust for imbalance. For example, inbound leads may naturally score higher in behavioral data, so we boost other scores for outbound leads to maintain fairness.

Practical Tips for Implementing Your Scoring Mix:

• Refine Regularly: Continuously update your scoring based on performance.
• Automate: Where possible, leverage CRM tools to streamline scoring.
• Collaborate with Sales: Work closely with sales and product teams to ensure your scoring reflects business goals.

Conclusion

Effective lead scoring hinges on quality data, a clean CRM, and a balanced scoring mix. By refining property data, cleaning CRM entries, and structuring your scoring mix carefully, you can transform your lead prioritization strategy. These practices lead to higher conversions and empower your team with actionable, data-driven insights. A dynamic scoring model, evolving with market and business needs, will always keep your marketing competitive and on point.