Renewal Capacity Allocation pack
Allocate limited customer-success hours across upcoming renewals to maximize model-estimated retained ARR, modeling intervention effects explicitly under hard constraints.
Renewal Capacity Allocation is an Algenta Decision Pack. It answers one recurring, high-stakes question for a customer-success organization:
Given upcoming renewals, account health, ARR, renewal dates, and the customer-success (CSM) hours you actually have, where should you spend intervention time to maximize expected retained ARR?
The pack does not guess. It takes your renewal book and your capacity, models how additional CSM hours change each account's renewal probability, then runs a constrained optimizer that respects your hard rules. The result is a defensible allocation — hours per account and per CSM — plus the baseline it beats, the accounts it left out and why, and an honest account of every assumption behind the numbers.
Like every Decision Pack, it runs deterministically in your organization's decision runtime: the same inputs always produce the same output, and every figure is versioned for reproducibility. The language model may help you frame a request and explain a result, but it never invents the numbers — the outcome model and solver do.
The decision it frames
Objective
Maximize expected retained ARR across the horizon
Lever
Customer-success intervention hours per account
Hard constraints
Total hours ≤ available capacity; per-CSM hours ≤ per-CSM capacity (when supplied); every strategic account receives at least its minimum guaranteed hours
Uncertainties
Intervention effectiveness (confounded — an assumption unless calibrated); baseline renewal probability (derived from health)
Outcome metrics
Expected retained ARR; uplift vs. baseline; capacity utilization
What you provide
The pack takes one structured request. You can assemble it by hand, from a query, or start from a governed Excel template so business users fill a familiar sheet and the values flow into the same validated contract (see Run decisions from Excel).
Renewal accounts
Each renewing account carries the evidence the decision needs:
arr— annual recurring revenue at risk, in your currency (must be greater than 0).renewal_date— the contract end date inside the horizon.health_score— a 0–100 account-health score, where higher means lower risk.segment—strategicorstandard.csm_id— the owning CSM, used to enforce per-CSM capacity.baseline_renewal_probability(optional) — a pre-validated model output; if absent it is derived from health.
Accounts missing required evidence (ARR ≤ 0, or no renewal date) are excluded rather than guessed, and the data-quality score is downgraded. A missing health score falls back to the segment-median health and lowers confidence. A health score outside 0–100 is rejected outright — the model never silently extrapolates.
Strategic-account rules and horizon
strategic_min_hours— the hard minimum every strategic account is guaranteed (default 4h).max_hours_per_account(optional) — a hard per-account ceiling (defaults to the model's max useful hours).allocation_step_hours— the granularity the optimizer allocates in (default 1h).horizon_days— the renewal window in scope (default 90 days).
Intervention-effect assumptions (pack config)
This is the honest core of the pack, supplied as configuration so it is visible and auditable rather than hidden inside a model:
effectiveness— the fraction of an account's renewal-probability gap that intervention can close at saturation (default 0.35).saturation_hours— hours at which roughly 63% of the achievable uplift is realized — the shape of diminishing returns (default 8h).max_useful_hours_per_account— the point beyond which more hours add essentially nothing (default 16h).max_renewal_probability— a cap on the estimated renewal probability (default 0.99).segment_modifiers— per-segment multipliers on achievable uplift.source_of_estimateandcalibration_status— where the effectiveness figure came from (default_prior,expert, orcalibrated) and whether it has been calibrated against realized renewals. Each assumption also carries aconfidencelevel.
How the outcome is modeled — honestly
The tempting causal story is "more CSM hours → higher renewal probability." That relationship is confounded: at-risk accounts already receive more attention, so a naive correlation overstates how much intervention actually helps. The pack refuses to launder that confounding into a false promise. Instead it models intervention effectiveness explicitly, and every part of the model is a stated, sourced assumption:
Baseline renewal probability — a fixed, documented curve maps a 0–100 health score into a probability (roughly 0.15 at health 0, 0.95 at health 100). This is an estimated parameter, not tenant-calibrated by default.
Intervention response curve — a saturating curve where uplift grows with hours but with strictly diminishing returns, so each additional hour is worth less than the one before.
Max-useful-hours cap — hours beyond
max_useful_hours_per_accountcontribute nothing, and the estimated probability is capped atmax_renewal_probability.Segment modifiers — per-segment priors on how much uplift is achievable.
Uplift is applied to the gap to certainty and scaled by effectiveness and the segment modifier, so intervention (or zero hours) can only hold or raise an account's renewal probability — never lower it.
What the pack returns
The result object is host-independent — renderers turn it into Adaptive Cards, Excel sheets, or an API response. It contains:
Recommended hours by account and CSM — with each account's baseline vs. model-estimated renewal probability, baseline vs. estimated expected retained ARR, the uplift, whether it was selected, and a plain-language reason.
Expected retained ARR and the uplift vs. baseline.
A baseline comparison — the explicit do-nothing scenario the recommendation is measured against.
Accounts not selected, and why — typically "lower marginal retained-ARR gain than funded accounts."
Hard-constraint status — total capacity and strategic minimums, each marked satisfied or not, with remaining slack.
Capacity utilization — hours used over hours available.
Sensitivity to intervention effectiveness — whether the allocation is stable, the threshold (if any) at which it flips, and the estimated uplift at half the assumed effectiveness.
Alternatives — ranked feasible scenarios, including a "concentrate on highest-ARR accounts" strategy that is shown and checked against the strategic-minimum guarantee.
Provenance separation — observed facts, customer assumptions, estimated parameters, and simulated outcomes kept explicitly apart so nothing blurs together.
A representative recommendation
When there is no feasible solution
Hard constraints are genuinely enforced, so the pack will tell you when they cannot all be met rather than fabricating an answer. It returns a first-class no_feasible_solution result — with the binding constraint and concrete suggested relaxations — when, for example:
the strategic minimums together exceed total capacity;
a single CSM cannot cover the strategic minimums of the accounts they own;
a strategic minimum is larger than the per-account ceiling, so no account could ever receive it; or
required evidence (ARR, renewal date, or capacity) is missing.
Assemble and run the decision
Submit to the decision service
Send the request to the Renewal Capacity Allocation pack through the governed decision endpoint. The service validates the contract, runs the outcome model and solver deterministically, and records the model and pack versions with the result. See Drive a governed decision through the API for the request mechanics.
Next steps
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