Every SaaS estate has apps nobody uses and users who've gone quiet — the hard part is proving which is which. Zluri's usage scoring turns raw activity logs into two distinct numbers built for two distinct questions.
"Usage" sounds like a simple thing to measure until you try to measure it. A user who logs in once and does nothing for the rest of the month looks different from one who's in an app daily — but a login count alone can't tell you that difference, and it definitely can't tell you whether an app is worth its renewal versus whether a specific person still needs it. Zluri splits the problem into two separate scores because they answer two separate questions: is this application earning its place in the stack, and is this user still an active part of it.
Two Scores, Not One
Application Usage Score answers a portfolio-level question — across the whole organization, how much is this app actually being used. It's what feeds renewal decisions, redundant-app consolidation, and license right-sizing.
User Usage Score answers a person-level question — how active is this specific user, in general, based on their activity pattern. It's what feeds deprovisioning candidates, seat reclamation, and access-review prioritization.
Both scores are built the same way structurally: pull raw activity signals (sign-in logs, activity logs), convert each into a factor score, then combine the factors using a weight distribution into one final number.
Application Usage Score: The Three Factors
The Application Usage Score is built from three factors, each capturing a different dimension of "is this app actually being used."
Average Volume (AV) is the frequency dimension — how much activity gets logged per user, per month, for this app. A user who signs in three times in a month with five logged activities contributes a specific AV count, which Zluri converts into a predefined AV score.
Average Activity Days (AD) is the spread dimension — not how much activity, but across how many distinct days. An app used in five short bursts spread across five different days reads differently than the same five actions crammed into one day, even though the volume is identical. AD is what captures that difference.
Percentage of Users (PU) is the adoption dimension — of everyone who could plausibly use this app, how many actually do. If 50 of an organization's 100 users touch the app, that's a straightforward adoption rate, and it's the factor most directly tied to "is this app becoming shelfware."
Volume tells you intensity, activity days tell you consistency, and percentage of users tells you reach. An app can score well on one and poorly on the other two — a small, deeply-used tool and a broadly-adopted, lightly-touched one look nothing alike underneath, even if their raw login counts end up similar.
User Usage Score: The Four Factors
The User Usage Score works the same way in principle, but with four factors instead of three, because it's measuring a person's behavior over time rather than a single app's traction.
The pairing matters more than any single factor. MC and DD describe this month — a snapshot. Mean of MC and Mean of DD describe the trailing three-month pattern — a trend. A user who scores fine on this month's raw count but has a declining three-month mean is a very different signal than one who's steady across all four factors: the first is a user in the middle of going quiet, and a snapshot-only view would miss it entirely.
Weight Distribution: Why the Factors Aren't Equal
Neither score is a simple average of its factors. Each factor — AV, AD, PU for applications; MC, Mean of MC, DD, Mean of DD for users — gets a weight distribution percentage that determines how much it contributes to the final number. A factor with a higher weight moves the score more than one with a lower weight, even if both factors individually look identical between two apps or two users.
The exact weight percentages are Zluri's internal calibration and aren't published as fixed values here — what matters for interpretation is the principle: the final score is a weighted combination, not a flat average, so two apps or two users with the same raw activity numbers won't necessarily land on the same final score if their factor mix differs.
Where the Score Actually Gets Used
A usage score by itself is just a number. It becomes useful the moment it's wired into a decision.
Renewal and consolidation decisions. A low Application Usage Score, especially one driven by a weak Percentage of Users, is the signal that starts a redundant-app or renewal-cancellation conversation — the score gives that conversation a number to point to instead of a hunch.
License reclamation. Even inside a well-adopted app, individual users can carry a low User Usage Score while still holding an active, paid seat. That's the gap between "the app is used" and "this specific license is used," and it's the gap license optimization work is built to close.
Deprovisioning and access-review prioritization. A sustained low User Usage Score — flat or declining across MC, DD, and their three-month means — is exactly the kind of signal that turns a routine access review into a targeted one: instead of reviewing everyone on the same schedule, the users whose usage pattern has actually changed get looked at first.
Offboarding candidates that HR hasn't flagged yet. A user whose usage score drops to near zero without a corresponding employment-status change is worth a second look — it can mean a role change, an extended leave, or a departure that hasn't been recorded yet.
Frequently Asked Questions
Is the Application Usage Score the same thing as an adoption rate? No — adoption rate (Percentage of Users) is one of the three factors that feeds the Application Usage Score, not the whole score. Two apps with identical adoption rates can still end up with different final scores if their Average Volume or Average Activity Days differ.
Why does the User Usage Score need a three-month mean if it already has a current-month count? The current-month factors (MC, DD) show where a user stands right now; the three-month means show the trend behind that snapshot. A user can look fine on this month's count alone while their trailing average is clearly declining — the mean factors are what surface that.
Can a widely-adopted app still get a low Application Usage Score? Yes. Percentage of Users is only one of three factors — an app with strong adoption but weak Average Volume or Average Activity Days (people have it, but barely open it) can still land on a low overall score.
Does a low User Usage Score automatically mean an account should be deprovisioned? No — it's a prioritization signal, not an automatic action. It's what should move a user to the top of a review or deprovisioning queue, not a substitute for the actual review decision.
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