Aurochs Deployed Quota Management For The Leading Vision Care Organizations

The challenge

The Vision Care Business Unit faced several challenges in quota setting:

Lack of market data made quantification of potential and market standing unknown.
A quota-setting strategy dilemma: whether to treat the product as a launch or as almost mature, as the historical sales history fit neither.
The current methodology relied on statistical parameters, requiring extensive manual work and lacking transparency for the sales team.

Automate the manual quota-setting methodology to save time and increase efficiency.
Achieve high process automation, change agility, and responsiveness.
Incorporate local guidance and knowledge into quota allocation.
Model different factors and evaluate the quality of allocated quotas.

High levels of automation and repeatability using statistical parameters.
Agility and flexibility to explore various permutations and combinations of historical sales and growth parameters.
Use of historical sales and growth-based parameters, considering local knowledge.
Analytical visualizations and statistical summaries to assess fairness and performance.
Customization of historical periods, with caps and floors to handle outliers.
Define scenarios using different forecast scenarios.
Deployment of an integrated quota management platform with backup support and service operations.

Data collection and ingestion: collected data from various sources, primarily the in-house data warehouse, using the proprietary Data Manager for automated collation, sanitization, and ingestion.
Configuration of Quota Manager: configured Aurochs Quota Manager to accommodate different scenarios, including historical performance data periods and various sales factors such as volume growth, sales trends, caps, and floors.
Testing scenario configuration: developed testing scenarios to assess methodology quality by allocating quotas across different historical periods.
Calculation workbooks and reports: designed summary calculation workbooks and reports for effective communication of quotas.

Evaluation of existing process: due to a lack of market data, scrutinized the current quota allocation process.
Integration of statistical process: integrated the current statistical process into the tool for comparison with different models, enabling the addition of scenarios for enhanced analysis.
Model implementation and comparison: developed models based on volume, volume + growth, and existing statistical models with scenarios, then compared results for effectiveness.
ML-based sales forecasting: utilized ML-based sales forecasting trends for mature products with stable history, incorporating trends into the quota-setting process for improved accuracy.
Analytical visualizations: employed post-hoc analytical visualizations to evaluate methodologies for fairness, performance, and outliers.
Comparison with actual sales results: compared the quality of quotas from the traditional statistical process and the new Aurochs processes against actual sales results, helping the client identify gap areas for refinement.

End-to-end automation: automated the quota allocation process using statistical parameters, resulting in over 90% reduction in processing time.
Pilot implementation: initially piloted for a small set, then scaled to encompass all teams, roles, and salespeople.
Model comparison: developed and compared models based on volume, volume + growth, and existing statistical models with scenarios for effectiveness.
Simplified process and communication: streamlined the quota-setting process and improved communication effectiveness, yielding better results.
Quality evaluation: provided the capability to evaluate quota quality at both individual and role levels.
Scenario simulation: enabled scenario simulation for the client's existing quota allocation methodology.
Bias identification: facilitated easier identification of bias in quotas due to geographical performance trends.