Definition
Half-life is an analytics feature in Salesforce that helps users measure, visualize, and understand their business data. It provides tools for building reports, dashboards, or data explorations that turn raw data into actionable insights.
Real-World Example
Consider a scenario where a data analyst at MarketPulse is working with Half-life to uncover trends and patterns hidden in their CRM data. By configuring Half-life, they create visualizations that tell a clear story about business performance. The executive team uses these insights to adjust strategy mid-quarter and the company exceeds its revenue target by 12%.
Why Half-life Matters
Half-life in Salesforce Analytics refers to a decay metric used to measure how quickly a data point, engagement signal, or score loses relevance over time. In predictive scoring models like Einstein Lead Scoring, half-life calculations help determine how much weight to give recent activities versus older ones. For example, a website visit from yesterday should influence a lead score more heavily than one from three months ago. Understanding half-life helps organizations calibrate their analytics models so that time-sensitive data drives decisions appropriately rather than treating all historical data as equally valuable.
As organizations accumulate larger datasets and more complex scoring models, properly configuring half-life parameters becomes essential for maintaining prediction accuracy. An organization with millions of engagement records needs its scoring models to naturally decay older signals so that current buying intent rises to the top. Without appropriate half-life settings, stale data pollutes predictions - a lead who was highly engaged a year ago but has gone silent might still show a high score, wasting sales rep effort on dead leads. Conversely, setting the half-life too short causes the model to ignore valuable historical patterns. Finding the right balance requires ongoing analysis and calibration specific to each organization's sales cycle length.
How Organizations Use Half-life
- Quantum Marketing Solutions — Quantum Marketing Solutions configured their Einstein Lead Scoring half-life to 45 days, matching their typical B2B sales cycle length. This ensures that leads who visited the pricing page 2 weeks ago score higher than those who visited 4 months ago, directing sales reps to the most currently engaged prospects.
- VelocityTrack SaaS — VelocityTrack SaaS uses half-life decay in their customer health scoring model to weight recent product usage more heavily than historical usage. A customer who logged in daily last month but hasn't logged in for 3 weeks sees their health score decline, triggering a proactive outreach from the customer success team before churn occurs.
- Atlas Retail Analytics — Atlas Retail Analytics applies half-life calculations to their marketing engagement scores to determine email campaign targeting. Contacts whose last engagement was within the half-life window receive promotional offers, while those beyond the threshold are moved to re-engagement campaigns with different messaging strategies.