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Table Figure 1 Revenue operations & AI

The salesforce observability map

Observe work early enough to coach it, and keep late outcomes within their attribution boundary.

Control questionObservable objectCoaching useOutcome boundaryReward riskUnresolved evidence
What work happened?Declared selling behaviorReview sequence and qualityBehavior is not revenueCounting replaces judgementIs the behavior relevant?
What was knowable?Information available at the timeCorrect a process or handoffHindsight is excludedMissing information is punishedWas the information usable?
What result arrived?Outcome and horizonReview conditions and responseExternal drivers remain visibleOutcome bears all blameWhich drivers were controllable?
What should change?Effectiveness purposeAdjust support or supervisionChange is tested laterReward is changed firstWhat evidence would disconfirm it?

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Reference & Evidence

Source: Author's synthetic framework grounded in Cravens, Ingram, LaForge and Young (1993), de Oliveira Santini et al. (2019), and Ghosh and John (2000). The rows are illustrative and do not describe a current salesforce scorecard.

Each line is a claim from the register this journal publishes against, resolved from the register at build time.

  • A The design, and it is a test of someone else's propositions: "the authors develop a conceptual model depicting relationships between salesforce control systems, characteris"tics, performance and effectiveness "as a framework for testing the propositions formulated by Anderson and Oliver (1987)", with "a study of 144 diverse sales organizations" Cravens, Ingram, LaForge & Young. (1993) · CLY93-C1
  • A The finding runs against the intuition: "the results imply a limited role for incen"tive compensation "in salesforce control systems" Cravens, Ingram, LaForge & Young. (1993) · CLY93-C2
  • A And what they call for instead: "they also suggest the need for a proper blend between field sales management and compensation control and identify important avenues for future research" Cravens, Ingram, LaForge & Young. (1993) · CLY93-C3
  • A "this paper conducts a meta-analysis based on 104 studies" of sales control systems de Oliveira Santini, Vieira, Ladeira & Sampaio. (2019) · OSV19-C1
  • A The named relationships are the paper's own list: "significant relationships between behaviour- and outcome-based control systems and the complexity of the products, bonuses, financial performance, sales innovation, organizational support and satisfaction with supervisors" de Oliveira Santini, Vieira, Ladeira & Sampaio. (2019) · OSV19-C2
  • A Each wins somewhere, and the paper says where: "behaviour-based control systems were the most effective mechanism in turbulent markets and for determining financial performance", while "outcome-based control systems were the most efficient instrument for complex products" de Oliveira Santini, Vieira, Ladeira & Sampaio. (2019) · OSV19-C3
  • A "we conducted three experiments to investigate three unresolved predictions involving the incentive-insurance trade-off posited in the model", against a literature where "empirical support remains sketchy" Ghosh & John. (2000) · GJ00-C1
  • A The first prediction, and the condition it needs: "compensation should be less incentive loaded with greater effort-output uncertainty so as to provide additional insurance to a risk-averse agent", supported "but only when risk-averse agents undertook nonverifiable effort" Ghosh & John. (2000) · GJ00-C2
  • A The second prediction failed: "when verifiable effort made incentives moot, as is the case for the second prediction, the model failed to order the data", which is why this is conditional support and not a rule Ghosh & John. (2000) · GJ00-C3

Grades: A, verified against the printed page of the primary source · B, primary source, text layer only · C, authoritative secondary · D, reported.

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