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DATA DRIVEN SALES RESOURCE ALLOCATION: A CXO BLUEPRINT FOR TRANSFORMING THE SALES TERRITORY AND QUOTA PLANNING PROCESS TO ACCELERATE REVENUE GROWTH
This paper explores how a new generation of growth leaders are taking advantage of the data and technology assets within their sales technology portfolios to better focus selling time, attention, resources and investment on the accounts and activities that generate the most revenue, profits, and firm value. It outlines how growth leaders (CXOs) are taking advantage of recent advances in advanced analytics and sales performance management solutions to transform the territory and quota planning process to generate more revenue with existing resources.
This in depth analysis highlights three primary ways business leaders can transform their approach to territory and quota planning to unlock more growth and firm value from existing selling assets and offerings:
- Process automation. Digitizing the process of planning, managing, and optimizing territory boundaries, seller targets and quota assignments will make it faster, less expensive and more data-driven, accountable, and collaborative. This includes streamlining the planning and design of territories and quotas, providing more visibility into performance against goals, and speeding up the process of making mid-period adjustments and plan reviews.
- Improved planning parameters and inputs. Leveraging AI and massive new sales data sets can significantly improve the accuracy, predictability, and quality of plan inputs. This includes curating and combining inputs and data sources into value-added analysis that derives better and more accurate planning inputs, including estimates of seller capacity, productivity and profitability, sales forecasts, and opportunity potential.
- The ability to evaluate more optimization scenarios. Deploying advanced analytics, models, and algorithms makes it easier for managers to develop, evaluate, and optimize many different scenarios and variables to make better optimization and resource allocation decisions. Models can speed up the analysis of a wider variety of trade-off decisions and factors throughout the planning and optimization process – including tuning territory boundaries, cutting historic revenues by product, channel, industry, and geography, and adjusting opportunity allocation based on cost, staffing, and sales force emphasis as well as external influencers such as competition, market factors, and seasonality.