A CXO Blueprint For Transforming the Territory and Quota Planning Process to Accelerate Revenue Growth
Digital technology – notably the advent of advanced analytics and AI – offers tremendous potential to improve the productivity, engagement, speed, and financial contribution of revenue teams. These technologies can accelerate sales growth and create firm value by optimizing the allocation of revenue team resources and multiplying the return on selling assets at B2B companies.
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.
The planning, design, management, evaluation, and optimization of sales territories and quotas is a complex, lengthy, labor intensive, and data-rich process that is ripe for transformation. The TQP process directly impacts an organization’s ability to realize its revenue, profit and share goals because it aligns growth resources, assets, and investment with market opportunity. Managers can simultaneously increase sales and reduce costs while dramatically increasing the speed and quality of planning by applying 21st Century selling principles analytics to transform the sales territory and quota planning process.
Some of the most practical and impactful ways data-driven algorithms can create value is to help managers to better allocate sales resources to the right accounts, territories, and tasks. “Organizations are dramatically improving sales performance by using algorithms to help with the basics of account and lead prioritization and qualification, recommending the content or sales action that will lead to success, and reallocating sales resources to the places they can have the most impact,” reports Professor Lodish of Wharton.
This is because a wide range of AI tools are now available create algorithmically derived customer response models to help take the guesswork and gut feel out of aligning sales resources across geographies, accounts, and business lines.
Sales leaders are taking advantage of advanced analytics to optimize the allocation of sales resources and seller time with data-driven algorithms that increase the return on selling resources in a variety of ways. These include:
1. Automating the Territory Design and Quota Assignment Process – There is a big opportunity to optimize the deployment of sales resources by developing data-driven models that map the response functions by market, territory, and segment. Staffing and allocating sales resources across territories is often done by the seat of the pants or gut feel at best. 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. Sales Performance Management solutions like Xactly, SAP Sales Cloud and Optymize digitize and automate the territory and quota planning process of designing territories. For example, optimizing territory design alone can increase sales by 2 to 7%, without any change in total resources or sales strategy.This is because an optimally designed and well-balanced territory plan can improve seller productivity by 10-20% and save costs according to research by the Alexander Group.
Organizations are using automated workflow processes to get efficiency gains of two to three times when compared to counterparts using manual or spreadsheet-driven processes. Data driven automation can help streamline the planning cycle from 60- to 35-day planning cycles by automating the collection and analysis of many data inputs. It also can improve collaboration across the 6-12 different organizations that need to align territories and quotas with the overall go to market, compensation and corporate growth strategy of the company. Solutions like these can also blend CRM data with customer engagement data from other parts of your business to automate and optimize the development of sales incentives and quotas and improve payment accuracy and resolution.
2. Algorithmic Segmentation, Targeting And Coverage Modeling – Sales reps spend 7% of their time prioritizing leads and opportunities. But a range of solution providers have emerged that support predictive lead scoring and lead prioritization models based on customer engagement data from inside and outside the organization. For example, Sales Engagement Platforms like Xant.ai prioritize daily tasks and plays for sales teams using real time buyer intelligence from billions of sales interactions. Third party data providers like Bombora and TechTarget make those models even better by enriching them with customer intent data that lets them know when a prospect is in the market for a solution.
3. Account Prioritization And Profiling Based On Propensity To Buy, Intent And Potential – Leading organizations are developing highly accurate Propensity to Buy targeting models from their existing CRM and transaction data and third party intent data from providers like 6Sense. These models can more accurately predict which customers are going to buy from you, with the least selling effort, and which ones are not likely to buy or will require too much work to convert. When compared with the estimates of sales teams and local market leaders – these models are usually 20% more accurate at predicting who will buy, and who will not. When combined with human insights about local markets and customer relationships, they become even more predictive and accurate. Propensity to Buy targeting models take less time than human targeting. The targeting also gets smarter over time, starting a cycle of measurable and continuous improvement. Most organizations see near term gains of 20% or more in conversion, sales quota attainment, and account development when they use propensity to buy models to focus their resources, according to research by Blue Ridge Partners.
4. 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.
5. Using Advanced Analytics To Improve The Accuracy, Predictability And Quality Of Plan 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. Professor Leonard Lodish and V (Paddy) Padmanabhan – who have taught Leading the Effective Sales Force to a generation of growth leaders over the past decade at Wharton and INSEAD – believe it is no longer enough to rely on history or rules of thumb in making sales force allocation decisions. The precise historical data available to sales managers is increasingly able to help them to rationally decide on sales force size, territory boundaries and call frequencies for each account and prospect that maximize firm profits.
15 AREAS ANALYTICS AND MODELS CAN IMPROVE TERRITORY AND QUOTA PLANNING, DESIGN AND OPTIMIZATION DESIGN PARAMETERS AND PERFORMANCE OUTCOMES
John Gleason, Executive Vice President and Chief Sales Office for Ryder System sees the use of analytics to optimize sales roles, coverage, cooperation, and territories as the next big opportunity to accelerate growth. “I’m a big believer in trying to grow sales without growing the sales organization or cost to sell,” says Gleason. “The more I can use analytics to make sure our reps aren’t wasting time with prospects they’re not likely going to be successful with, the better. We’ve spent a lot of time in that particular area.” Ryder is using advanced analytics to redefine territories and refine the roles and responsibilities within the sales organization to provide better product expertise, cross selling opportunities, and customer experience.
Another way Gleason has Ryder leveraging analytics is to focus sales reps on the highest-opportunity accounts. “There’s an enormous number of prospects out there—probably 20 million companies that rent trucks, seven million class three through eight leased vehicles, and a hundred thousand or so businesses that need warehousing,” he reports. “That’s a lot for 50 salespeople to call on. Analytics have become increasingly important, because when you have a smaller sales organization generating around $3 Billion, you can’t waste a lot of time knocking on doors, so to speak. We became increasingly data driven. We put in much better processes to understand the characteristics of buyers, their current contract status with other providers, who are the decision makers, and when those decision-makers change.”
Business leaders, sales executives, territory managers, and sales operations professionals can learn more about these best practices by downloading the Data-Driven Sales Resource Allocation report which outlines practical ways to generate more value from your investments in technology, data and content assets.
