A definition you can quote, the time-decay formula with an illustrative worked example, why email opens mislead, and how engagement differs from buying intent.
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Quick answer
An engagement score summarises how much and how recently a contact or account has interacted with you, weighting each interaction by its significance and by how long ago it happened.
It is a temperature reading, not a forecast. Its best use is comparative and directional: which accounts are cooling, which contact inside an account is actually responding, and whether a relationship that used to be active still is.
Engagement score = Σ (interaction weight × decay factor), where the decay factor for exponential decay is 0.5 raised to the power of (age in days ÷ half-life in days).
Set by significance. A reply, a meeting attended, or a request for pricing represents real effort by the contact. A page view represents less. An email open represents very little, for reasons covered below.
The number of days after which an interaction counts for half as much. Thirty days suits a fast cycle; ninety suits a long evaluation. The virtue of expressing decay as a half-life is that anybody can reason about it without touching the maths.
Contact-level scores roll up to an account. Report both the total and the number of distinct contacts contributing, because those two numbers describe very different situations.
These weights are invented to demonstrate the calculation and are not recommendations.
Half-life: 30 days. Weights: reply to an email 20, meeting attended 30, pricing page viewed 5.
A contact replied to an email 10 days ago. Decay factor = 0.5 raised to (10 ÷ 30) = 0.79, so the contribution is 20 × 0.79 = 15.8.
They attended a meeting 45 days ago. Decay factor = 0.5 raised to (45 ÷ 30) = 0.35, so the contribution is 30 × 0.35 = 10.5.
They viewed the pricing page twice, 5 days ago and 60 days ago. Decay factors are 0.89 and 0.25, giving 4.5 and 1.3.
Total engagement score = 15.8 + 10.5 + 4.5 + 1.3 = 32.1. The meeting was the most significant single interaction by weight, yet the recent reply now contributes more, which is exactly the behaviour decay is there to produce. Note also that the denominator in each decay calculation is the half-life, not the observation window, so extending how far back you look does not change what recent activity is worth.
Three decisions, all of them practical. Which open deals are going quiet, which customer accounts are cooling ahead of a renewal, and which contact inside an account is worth investing in when several are listed.
None of those require the score to be precise. They require it to be comparable across accounts and honest about recency, which a decayed weighted sum manages well.
The score becomes a measure of tenure. Long-standing contacts float to the top and recent activity is buried under years of accumulated small interactions.
Privacy features in some mail clients pre-fetch images and register opens the recipient never performed, and security scanners can do the same. A score built largely on opens is partly a measurement of your contacts' email software.
The most engaged contacts are often the least likely buyers: analysts, students, competitors, and enthusiasts who read everything and purchase nothing. Read engagement beside fit or the score will mislead consistently.
An account with a high score driven entirely by one person is single-threaded. The number looks reassuring and the deal is one role change away from silence.
A steady mid score is usually less interesting than a high score that has halved in six weeks. Keep a short history and look at the slope.
A useful engagement model weights two-way interactions highest, decays with a stated half-life, reports breadth alongside total, keeps enough history to show direction, and is read next to fit rather than alone. Representatives use it to decide who to call and can explain what the number means.
A weak one accumulates without decay, treats every interaction as equivalent, rests heavily on opens, and produces a leaderboard of people who will never buy. Its usual symptom is a team that has stopped looking at it while the dashboard continues to report it faithfully.
| Score | Question it answers | Where it misleads alone |
|---|---|---|
| Engagement score | How warm is this relationship right now | Enthusiasts with no budget score very highly |
| Fit score | Would this be a good customer if they bought | Says nothing about timing or interest |
| Intent score | Are they researching the category at all | Approximate, and usually company-level only |
| Lead score | How should we rank this lead overall | Hides which component drove the total |
| Health score | Is this customer likely to renew | Applies to customers, not to prospects |
Show the score on the contact and account record with a short history so direction is visible, and list the interactions that contributed. A representative deciding who to call this morning needs to see that an account dropped from high to moderate over three weeks, and which three interactions are holding it up. A bare number, however well calculated, does not survive contact with a busy day.
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The most engaged contacts in the database never buy anything.
Engagement measures interaction, not purchasing capacity or authority. Read it alongside fit, so that a highly engaged contact in an unsuitable organisation is recognised as an audience member rather than a prospect. Content enthusiasts inflate engagement scores and are frequently the least likely people to hold a budget.Engagement read with fit
Scores never fall, so long-standing contacts always look warm.
Apply exponential decay with a half-life matched to your sales cycle, and state it explicitly so everyone knows what the number means. Without decay, an engagement score becomes a measure of how long somebody has been on the list, which is exactly what it was supposed to replace.Explicit half-life
Email opens dominate the score and the numbers do not match reality.
Weight opens very low or exclude them, and instead weight replies, meetings attended, and requests for information. Privacy features in some mail clients can register opens that never happened, which means a model built on them is partly measuring the recipient's software rather than their interest.Two-way signals weighted highest
An account looks engaged, and it turns out one person accounts for everything.
Aggregate engagement across the buying group and report how many distinct contacts are contributing. One enthusiastic individual and four silent colleagues is a single-threaded deal wearing a healthy score, and it will stall the moment that person changes role or goes quiet.Engagement breadth, not just depth
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