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Manager Effectiveness Report Across Teams
Send engagement, attrition, promotion and review data for your managers, and get a comparison that separates a hard team from one that needs support.
River joins four signals that already sit in four separate systems and are almost never compared side by side. Internal promotion rate and attrition come from the HRIS, an engagement score comes from your pulse survey platform, and review specificity is the share of claims in a manager's own team reviews that are checkable rather than vague. Each manager gets a composite built from all four, plus a baseline computed from the other managers in the comparison, not an org-wide average that already contains their own number.
Most manager-effectiveness frameworks add a new measurement layer instead: a quarterly 360, a self-assessment, a tracker counting 1:1 frequency. None of them join data a company already has sitting in the HRIS, the engagement platform and the review record. In the worked example below, two managers at the same company had the two worst promotion rates in their group, and read alone that number makes them look like the same problem. Joined with the other three signals, one of them turns out fine.
This is built for whoever owns several managers at once and needs to know which ones are actually developing people, not just running comfortable teams. Picture a VP with five direct-report managers, an HR business partner preparing for calibration, or a chief of staff deciding where coaching budget goes next. The engagement number carries real weight in the mix: Gallup finds managers account for at least seventy percent of the variance in team engagement scores across a business.
Why a promotion-rate ranking cannot tell you who needs support
A manager with the lowest promotion rate on the team is not the weakest manager. Some teams are structurally harder to promote out of: a senior specialist group with no higher title to reach, a function built eighteen months ago where every seat is still new, a team two years past the last reorg. A raw promotion-rate ranking cannot tell 'this team has nowhere higher to go' apart from 'this manager is not developing anyone'. Most scorecards stop at the raw number, because the other three signals sit in systems nobody has joined to it.
At Marloe Logistics, five managers reporting to one VP were compared over a trailing two years. Renata Voss's Fulfillment Ops team promoted one of eleven people, nine percent, second worst of the five. Dario Alba's Support team promoted zero of eight, the worst outright. Read alone, the two numbers say the same thing. Joined with the rest, they split hard: Voss's team lost only one of eleven and scored 81 on engagement, both the best of the five. Alba's team lost three of eight, scored 59 on engagement, and wrote reviews only 19 percent checkable.
The composite turns that split into one number. Voss scored 61.3, five points above the 55.5 average of the other four managers, so her low promotion rate reads exactly like a senior, well-retained team with limited headroom. Alba scored 35.1, twenty-seven points below the 62.1 average of the other four, with every one of the four signals pointing the same direction. That gap decides whether a manager gets a headroom conversation or a support plan, and it stays invisible until engagement, attrition, promotions and reviews sit in the same table.
How it works
Send the exports
Engagement scores, attrition and promotion records, and review submissions, by manager, whatever format they arrived in.
Signals get normalized
Promotion rate, retention, engagement and review specificity each scaled to the same 0 to 100 basis.
Composite and baseline build
Each manager's composite compared against a baseline computed from the other managers, not one that includes them.
You get the specifics
A sheet with every manager side by side, and a document naming who is flagged and why.
What you get
- A comparison sheet across every manager: promotion rate, attrition, engagement and review specificity, side by side
- A composite per manager, weighted evenly across the four, so no single signal carries the whole verdict
- Each manager's baseline built from the other managers, never an average that already includes their own score
- A document naming which managers are flagged and, signal by signal, what is actually driving each one
- A check on context you supply, because a claimed reason should match the other signals, not override them
Common questions
Why not just rank managers by one clear metric, like retention?
Because one metric alone cannot distinguish a hard team from a bad manager. A manager can have excellent retention because the team is comfortable and under-challenged, or poor promotion numbers because the team is senior and has nowhere higher to go. Retention, promotion, engagement and review specificity each rule out a different wrong explanation, and only joining them tells you which manager actually needs a different conversation.
What is review specificity, and where does it come from?
It is the share of claims in a manager's own team's reviews that are observations, something checkable, rather than conclusions, a judgement with nothing behind it. It is the same split a performance review's evidence ledger produces for one review, aggregated instead across every review that manager wrote this cycle.
Why compute a manager's baseline excluding themselves?
Because an org-wide average already contains the outlier you are trying to measure against it, which quietly shrinks the gap you are looking for. A manager with a genuinely poor composite pulls the average down toward their own number. Comparing them against the other managers in the set instead keeps the baseline honest.
I already know one team is harder than the others. Does this account for that?
You can say so in the optional context field, and the tool checks the claim against the other three signals rather than accepting it outright. A senior team with genuinely limited promotion headroom should still show strong retention and engagement. If the other signals do not back up the story, that mismatch gets reported, not smoothed over.
How far back should the data go?
Two years trailing is the sweet spot. Shorter than that and a single promotion cycle or one resignation swings the numbers around; a manager with one departure on a six-person team already shows seventeen percent attrition. Longer than three years and a reorg or a role change usually makes the comparison meaningless.
Does a low composite mean the manager should be managed out?
No. It means the manager and their VP have a specific, evidenced conversation to have, built from which signal is actually driving the number down, rather than a general sense that something feels off. What happens next, coaching, a support plan, reassigning part of the role, is a judgement call this tool does not make.
Where does the promotion-rate half of this connect to a wider promotion process?
If a manager's flagged promotion rate turns out to be real and not a headroom story, the fix usually runs through the same levelling and promotion process that catches people overlooked for advancement. This comparison names the manager, but a real promotion case still has to be built person by person.
Manager Effectiveness Report Across Teams
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