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Tools · General business

Incentive Design

Design pay that actually changes behavior — grounded in how motivation works.

The method

Variable-pay / incentive design (expectancy theory + line of sight)

The company pays out a bonus pool every quarter and cannot point to a single behavior it changed. Or worse — it changed behavior precisely as designed: the sales team sandbagged pipeline into next quarter because the plan paid them to. Incentives always work; the question is whether they work on what you intended.

Edward Lawler's Rewarding Excellence is built on the expectancy logic that has anchored pay-for-performance research for decades: an incentive motivates only when the whole chain holds — the person believes effort improves the measured performance, believes the measured performance triggers the reward, and values the reward enough to care. Lawler's name for the first link is line of sight, and his claim is that it is where most plans die: a warehouse team paid on company profit has no line of sight, so the plan is an expense, not an incentive. His Strategic Pay walks the design menu — merit pay, individual incentives, gainsharing, profit sharing — and insists that the choice is a strategy-fit decision, and that process (who participates in design, how openly it's communicated) determines whether an identical structure motivates or corrodes.

Gerhart and Rynes's Compensation: Theory, Evidence, and Strategic Implications is the evidence audit, and it corrects folklore in both directions. Against the pay-doesn't-motivate crowd: the research shows money is not a weak motivator, and incentives work through two engines — motivating the people you have, and sorting who joins and stays, an effect designers routinely forget. Against the incentive enthusiasts: rewarded measures get gamed, and the 'risk' knob (how much pay rides on performance) has real costs. And on the famous claim that extrinsic rewards crowd out intrinsic motivation, their reading of the evidence is that it is far less reliable than its reputation — a risk worth checking, not a law of nature. That is the honest posture for a designer: check the expectancy chain, check the gaming surface, and treat crowding-out as a flagged hypothesis rather than a veto.

The books give you the theory and the cautionary tales; here you describe the role, the behaviors, and the budget, and the scheme comes back with every reward trigger scored for line of sight, the effort-to-valence chain checked link by link, and the gaming and crowding-out risks named rather than discovered in production.

The books behind this tool

How it works

Describe a role/team, the behaviors you want more of, and the budget, and it drafts a variable-pay scheme grounded in the compensation corpus — plan type, a line-of-sight-scored reward-trigger map, and an Expectancy-Theory check of the effort→performance→reward→valence chain. Flags weak line-of-sight, broken motivation links, gaming risk, and crowding-out of intrinsic motivation. Distinct from the pay-LEVEL tools (band/midpoint/benchmark) — this designs the incentive SCHEME on top.

You bring

{ role_or_team, target_behaviors, budget? }

You get

{ scheme_summary, plan_structure, reward_triggers[] (measure · line_of_sight · grounded_in), expectancy_check[], gain_sharing_scaffold|null, risks{gaming,crowding_out_intrinsic}, grounded_in, provenance }

Use it for

See it work

example output

Role/team: a 12-person inside-sales team; target behaviors = closing multi-year contracts without sandbagging pipeline; budget ~15% of base as variable pay.

Incentive Design — Inside-Sales Team (multi-year deals)

Scheme summary: A variable-pay plan for 12 inside-sales reps designed to reward multi-year contract value without nudging reps to sandbag pipeline or pull deals forward dishonestly. Budget: ~15% of base as variable.

Plan structure

  • Type: individual bonus (commission) with a multi-year term multiplier.
  • Rationale: inside-sales output is individually attributable and has strong line-of-sight to closed deals, so individual incentives fit; a term multiplier steers behavior toward the strategic goal (contract length) rather than raw bookings. (Grounded in: expectancy theory, line-of-sight / agency canon.)

Reward triggers

Behavior / outcomeMeasureLine of sightNote
Close multi-year contractsTotal contract value × term multiplier (1.0× / 1.5× / 2.0× for 1 / 2 / 3-yr)StrongRep directly controls the ask and the close
Maintain a healthy pipelineQualified pipeline coverage (3× quota)ModeratePartly upstream of marketing; use as a gate, not a bonus
Forecast accuracyCommitted-deal hit rateModerateCounters sandbagging; rep influences but doesn't fully control timing

Expectancy check (Vroom)

  • Effort → performance: intact — disciplined discovery and multi-year framing measurably raises term length.
  • Performance → reward: intact — the multiplier ties term directly to payout.
  • Reward → valence: weak — a 15% variable band may not motivate top reps used to uncapped commission; consider an above-quota accelerator.

Gain-sharing scaffold

Not applicable — individual attribution is clean, so a team gain-share would dilute line-of-sight. (Returned null.)

Risks

  • Gaming: reps may discount heavily to win the 3-yr multiplier → cap discount or score on margin-adjusted TCV. Reps may sandbag the forecast to protect the "commit" hit rate → balance with the pipeline-coverage gate.
  • Crowding-out of intrinsic motivation: heavy commission can erode team selling and customer-first behavior → keep a qualitative component and don't make 100% of variable pay deal-count-driven.

Grounded in: compensation corpus — expectancy theory (Vroom), line-of-sight, agency theory, intrinsic-motivation crowding-out.

Run it on your data

Call it on your own inputs — over the API, or hand it to your AI agent via MCP. Discovery is open; running it is metered.

REST  POST /api/bicycle/incentive-design
MCP   design_incentive_plan

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