Engagement Scoring
Accounts with high engagement scores convert at 2-4× the rate of low-score accounts in most SMB pipelines.
💡TL;DR
Engagement scoring prioritizes who gets human time. By weighting recency, frequency, and intensity, teams route reps to the few accounts most likely to book a meeting now. It outperforms static lead scores because it decays fast and reflects live behavior.
Definition
A weighted scoring model that ranks accounts or leads based on recent and intense interactions across web, email, and product signals.
🏢What This Means for SMB Teams
SMBs cannot chase everyone. A live score ensures the top of the queue is always the warmest.
📋Practical Example
A 120-employee industrial machinery manufacturer ($45M revenue) sold through 180 regional distributors. They built engagement scores on CAD downloads, spec-sheet views, webinar attendance, and RFQ starts with a 7-day decay. Before: reps chased ~280 accounts/month, meeting rate 11%, quote-to-win 21%. After 90 days, only the top 60 accounts per week were worked; reps got real-time alerts when RFQs were started. Meetings per rep rose from 2.2 to 4.8 weekly, quote-to-win improved to 29%, and quarterly booked revenue grew by $1.1M with the same headcount.
🔧Implementation Steps
- 1
List the top 8-10 behaviors (downloads, RFQ starts, webinar attendance) and assign point weights for recency, frequency, and intensity.
- 2
Apply time decay (e.g., 20% per week) and cap maximum score to avoid runaway leaders.
- 3
Route accounts above a threshold to reps with a daily limit; auto-nurture the rest.
- 4
Recalculate scores hourly and post the top 20 accounts to Slack/Teams with owner assignments.
- 5
Review weekly which signals correlate with meetings and wins; reweight or drop low-signal events.
❓Frequently Asked Questions
How often should we recalibrate engagement score weights?
Calibrate monthly at first, then quarterly once stable. Look at lift between score bands (top vs. middle) on meetings and wins; if lift shrinks below 1.5×, revisit weights and decay windows.
How do we prevent gaming from repeated page refreshes?
Count unique sessions and cap identical events (e.g., max 2 spec-sheet views per day). Use dwell time and scroll depth so accidental or repeated refreshes don’t inflate scores.
⚡How Optifai Uses This
Optifai recalculates engagement scores continuously and feeds the Action Feed.
📚References
- •
- •
Related Terms
Digital Body Language
Observable online behaviors—scroll depth, repeat visits, content mix—that indicate interest or intent before a form fill or direct outreach.
Signal Detection
Real-time detection of buying intent signals from web behavior, email engagement, and deal stage changes.
Action Feed
A single, prioritized stream of AI-generated tasks (calls, emails, meeting nudges) triggered by real-time buying signals. It replaces scattered alerts and static task lists with one execution rail where reps work top-to-bottom.
Buyer Intent Signals
Behavioral events that indicate purchase intent—pricing-page revisits, competitor comparison views, proposal reopens, trial starts, documentation deep reads, budget page visits, or RFP downloads. These signals are ranked by recency, frequency, and depth to drive prioritization and next best actions.
Experience Revenue Action in Practice
Now that you know the terms, see them in action. Experience signal detection, automated actions, and ROI proof with Optifai.
Learn More