Optimizing Lead Scoring Tables for Webinar Attendees | Clay

Optimizing Lead Scoring Tables for Webinar Attendees

hi I’m working on a table based on my webinar attendees (unique export from webinar tool) that I score differently then. After scoring, I push leads in different Lemlist campaign depending on their score I’ve gone for having a view per lead score (sql, warm lead, cold, etc.) on my table but it starts being a bit too heavy to handle because too many information + I have to hide lots of colums, etc. depending on the views what would you recommend to manage this kind of table instead of playing with views?

5 comments

Channeled
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Instead of managing multiple views for different lead scores, consider these more efficient approaches: 1. Use a single formula column to calculate lead scores based on your criteria (webinar attendance data), then use conditional formatting to visually distinguish between SQL, warm leads, and cold leads. 2. Create a dedicated "Lead Category" formula column that automatically assigns labels like "SQL," "Warm," or "Cold" based on your scoring criteria, making it easier to filter and sort. 3. Keep your table structure simpler by focusing on essential columns and using the formula tool to generate comprehensive lead scores that combine multiple data points. Remember that Clay has a column limit of 70 per table (with up to 30-40 being action/integration columns), so a more streamlined approach will help manage this limitation.
Sources: Customize your table view | Documentation | Clay University Lead scoring overview | Documentation | Clay University Table columns | Documentation | Clay University Table columns | Documentation | Clay University Lead Scoring [Clay 101] - Clay University
Léa H.
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conditional formatting is a native formula on clay or else ? where do I find it?
Channeled
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Clay doesn't have a native "conditional formatting" feature in the traditional spreadsheet sense. However, you can achieve similar results using AI formulas, which are credit-free tools that let you format, clean, and reshape your data. To apply conditional logic to your table: 1. Use AI formulas to create a formula column that automatically assigns categories (like "SQL," "Warm," or "Cold") based on your scoring criteria 2. For conditional runs (to control when enrichments run), navigate to the Run Settings of an action and click "Use AI" 3. Define your condition (e.g., "Only run if lead score > 80") 4. Click "Generate Formula" to create the conditional logic AI formulas are powerful for structuring data without using credits, unlike relying on AI integrations for the same tasks.
Sources: AI Formulas + Conditional Runs [Clay 101] - Clay University Conditional statements | Documentation | Clay University AI Formulas + Conditional Runs [Clay 101] - Clay University Conditional Formulas
Léa H.
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so with this method you create columns on the same table? Meaning I have to manage all my colums and lead campaigns from the same unique table right?
Channeled
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Yes, with the AI formula method, you'd be working with a single table containing all your columns. However, you can organize your data more efficiently by: 1. Using formula columns to categorize leads (SQL, warm, cold) based on your scoring criteria 2. Setting up conditional runs so that actions (like pushing to Lemlist) only trigger for specific lead categories 3. Creating a structured scoring system with clear point values for different attributes (like the example with headcount, seniority, and industry) This approach keeps your workflow in one place while using formulas to handle the complexity, rather than creating separate views that become unwieldy.
Sources: AI Formulas + Conditional Runs [Clay 101] - Clay University Lead Scoring [Clay 101] - Clay University Lead scoring overview | Documentation | Clay University