We are setting up our first automated lead scoring system, but we’re struggling to find the right balance. Should we weigh behavioral actions like webinar attendance higher than demographic data like job title? How do you prevent "score inflation" when a lead repeatedly visits the same pricing page?
3 answers
Finding the sweet spot for lead scoring requires a tight feedback loop with your sales team. In my experience at a SaaS startup, we implemented "point degradation" to solve the inflation issue. If a lead doesn't engage for 30 days, their score automatically drops. For the behavioral vs. demographic split, we found a 60/40 ratio works best. High-intent actions like requesting a demo should always trigger an immediate MQL status, regardless of title. We also used "Negative Scoring" for competitors or students who are just researching, which keeps the CRM clean for the sales reps.
Have you defined a specific "threshold score" that triggers a sales notification, or are you still manually reviewing every lead that passes a certain point?
Start by interviewing your Sales team. Ask them which three attributes or actions most commonly lead to a closed-won deal. Use those as your highest-weighted scoring criteria.
Excellent point, Justin. Marketing often guesses what makes a "good" lead, but Sales knows the reality. Aligning on those criteria is the first step toward true revenue operations success.
Steven, we actually automated the threshold! Once a lead hits 75 points, the automation platform pushes the record into the "Hot Leads" queue in Salesforce. This ensures that our reps follow up within the hour. We also added a "Sync Rule" that notifies the account executive via Slack if a high-value lead from their target account list visits the site, even if their total score is still relatively low. This real-time visibility has significantly improved our conversion rates.