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Care Organization

Quota setting for their Pharma business unit
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Platform Used
Quota Manager

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Company Size
1000-5000

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Location
US

Situation

The Vision Care Business Unit faces challenges in quota setting due to a lack of market data and a diverse product portfolio at various life cycle stages. They seek to automate their manual quota-setting process for improved efficiency and transparency, incorporating local knowledge and enabling flexible scenario modeling.

Outcome

The automation of the quota allocation process reduced processing time by over 90% and improved communication across teams. The system enabled quality evaluation of quotas, scenario simulation, and easier identification of biases linked to geographical performance trends.

Situation

  1. Challenges in Quota Setting for Vision Care Business Unit:
    1. Lack of market data made quantification of potential and market standing unknown.
    2. Lack of market data made quantification of potential and market standing unknown.
    3. Quota-setting strategy dilemma: whether to treat the product as a launch or almost mature, as historical sales history didn’t fit either.
    4. Current methodology relied on statistical parameters, requiring extensive manual work and lacking transparency for the sales team.
  2. Client’s Objectives:
    1. Automate manual quota-setting methodology to save time and increase efficiency.
    2. Achieve high process automation, change agility, and responsiveness.
    3. Incorporate local guidance and knowledge into quota allocation.
    4. Model different factors and evaluate the quality of allocated quotas.
    1. Key Requirements:
      1. High levels of automation and repeatability using statistical parameters.
      2. Agility and flexibility to explore various permutations and combinations of historical sales and growth parameters.
      3. Use historical sales and growth-based parameters, considering local knowledge.
      4. Analytical visualizations and statistical summaries to assess fairness and performance.
      5. Customization of historical periods, with caps and floors to handle outliers.
      6. Define scenarios using different forecast scenarios.
      7. Deployment of integrated quota management platform with backup support and service operations.

Quota Manager Platform Rollout

  1. Data Collection and Ingestion:
    1. Collected data from various sources, primarily from the in-house data warehouse.
    2. Utilized proprietary Data Manager for automated collation, sanitization, and ingestion of data.
  2. Configuration of Quota Manager:
    1. Configured Aurochs Quota Manager to accommodate different scenarios, including historical performance data periods and various sales factors.
    2. Considered factors such as volume growth, sales trends, caps, and floors in quota allocation.
  3. Testing Scenario Configuration:
    1. Developed testing scenarios to assess methodology quality by allocating quotas across different historical periods.
  4. Design of Calculation Workbooks and Reports:
    1. Designed summary calculation workbooks and reports for effective communication of quotas.

Outcome

  1. End-to-end Automation:
    1. Implemented automation of the quota allocation process using statistical parameters, resulting in over 90% reduction in processing time.
  2. Pilot Implementation:
    1. Initially piloted for a small set and later scaled to encompass all teams, roles, and salespeople.
  3. Model Implementation and Comparison:
    1. Developed models based on volume, volume + growth, and existing statistical models with scenarios.
    2. Compared results of different models for effectiveness.
  4. Simplified Process and Communication:
    1. Streamlined quota setting process and improved communication effectiveness in various areas, yielding better results.
  5. Quality Evaluation:
    1. Provided capability to evaluate quota quality at both individual and role levels.
  6. Scenario Simulation:
    1. Enabled scenario simulation for the client’s existing quota allocation methodology.
  7. Bias Identification:
    1. Facilitated easier identification of bias in quotas due to geographical performance trends.

Approach

  1. Evaluation of Existing Process:
    1. Due to lack of market data, scrutinized current process for quota allocation.
  2. Integration of Statistical Process:
    1. Integrated current statistical process into tool for comparison with different models.
    2. Enabled addition of scenarios to statistical process for enhanced analysis.
  3. Model Implementation and Comparison:
    1. Developed models based on volume, volume + growth, and existing statistical models with scenarios.
    2. Compared results of different models for effectiveness.
  4. Utilization of ML-Based Sales Forecasting:
    1. Utilized ML-based sales forecasting trends for mature products with stable history.
    2. Incorporated trends in quota setting process for improved accuracy.
  5. Analytical Visualizations:
    1. Employed post-hoc analytical visualizations to evaluate methodologies for fairness, performance, and outliers.
  6. Comparison with Actual Sales Results:
    1. Compared quality of quotas generated using traditional statistical process and new Aurochs processes with actual sales results.
    2. Assisted client in identifying gap areas in entire process for refinement.

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