As with any machine, digital technology provides us with different mechanisms to control outcomes. Some of these are inherent to the configuration of the machine. Others are levers that create immediate change. In a car, for example, you can now modulate your drive mode and speed to get more fuel economy on long trips. Shift torque ratios and gears to get more horsepower in a race. Stiffen your suspension and center of gravity to get more agility around curves. Achieving these different performance outcomes involves adjusting variables like torque, fuel and oxygen intake, transmission ratios, and suspension stiffness. These actions used to be done with a clutch, gas pedal, and a gear shifter. More and more these are managed by a computer.

For a computer – you might want battery life for long plane flights, processing power for data management, high-pixel screens for gaming and VR experiences. This requires adjusting variables like energy output, memory usage, processing resources, and screen configuration. The dials and sliders that control these variables are easy to find on the control panel on a modern computer.

Your Revenue Operating System is no different. We've already shown you how to build the right configuration. Now we will discuss how you can tune the dials to maximize performance.

Selling systems can generate vastly different outcomes based on how variables like channel mix, customer treatment types, coverage ratios, selling effort, and product emphasis are set up. For example, a pharmaceutical company was able to drive $25 million in marginal sales contribution – an 8% increase – by changing the size, deployment, and product emphasis of their sales force, according to research conducted by Professor Leonard Lodish of Wharton.⁴⁹ We've seen other organizations dramatically adjust the sales performance by shifting the key parameters such as calling patterns, customer targeting, and product emphasis. These efforts can result in rapid revenue growth and better profit contributions – without adding resources and costs at the same rate.

There are three ways you can use advanced analytics to create business impact:

1. Digitize your planning processes to improve agility in deploying your resources. Digitizing the process of planning, managing, and optimizing territory boundaries, seller targets, and quota assignments will make those processes faster, less expensive, and more data driven. Not only will this streamline key commercial processes, but it will also provide more timely visibility into the performance of people and programs against goals. The ability to make mid-period adjustments and conduct more frequent plan reviews will drive greater accountability.
2. Use analytics to make better predictions, forecasts, and investment decisions. Taking advantage of advanced analytics and AI capabilities – and the massive new sales data sets available to every business – can help you significantly improve the accuracy, predictability, and quality of your plan inputs. The ability of analytics to consolidate data and cross-pollinating it provides better value-added analysis. This translates into better and more accurate planning inputs. Better estimates of seller capacity and future sales. Better predictions about how customers will respond. More accurate sizing of potential opportunities.
3. Adopt advanced modeling techniques to evaluate more scenarios and build consensus. Advanced models and algorithms make it easier to develop, evaluate, and optimize many different scenarios. This is especially important because there are so many variables to consider when managing your growth engine. In a sales context, scenario planning and modeling of the key factors and assumptions that predict sales. These variables include the different ways competitors will respond, the ways in which seasons will change demand, or how well territory “boundaries” will line up with opportunity. Getting these estimates as accurate as possible is important if you want to realize the most growth from your scarce selling resources. Likewise, assumptions about sales rep tenure, how fast revenues will be booked, the channels customers want to use, and the best way to staff those channels have a big impact on your top and bottom line. The ability to simulate quick adjustments to any or all of these variables will help you better prioritize and work through difficult decisions with trade-offs. It will also allow you to pivot your system to shifting market opportunities. Models can speed up the analysis of planning inputs, such as breaking out historic revenue by-products, channels, industries, and geography. They make it faster to adjust the allocation of effort based on what will deliver sales at the lowest cost, highest profits, and greatest top-line revenue.

Digitize Planning Processes to Improve Agility in Deploying Your Resources

You can improve your return on selling assets by digitizing the sales territory and quota planning processes.

The process of designing, planning, managing, and optimizing sales territory boundaries and seller quota assignments has several dysfunctional elements. First, the process itself is complex, lengthy, and prone to error. It's a labor-intensive undertaking for most sales organizations, even in the best of circumstances. Second, the process is slow and fails to keep up with rapid changes in competition, demand, and innovation. Third, it involves many inputs and a near infinite range of possible scenarios that overwhelm any spreadsheet-based approach. Finally, it requires stakeholders from sales, marketing, product, finance, and operations teams to jointly balance factors against sometimes contradictory and/or competing objectives.

To optimally plan, design, and refine territory boundaries and seller quota assignments, we must ensure that they are precise, accurate, fair, profitable, and attainable. Ensuring all of this involves collecting, analyzing, and modeling up to fifty or more qualitative and quantitative data inputs from inside and outside the company.

Recent changes in the economy, buying behavior, and selling models have amplified the complexity of allocating growth resources to opportunity. Managers have to make more frequent adjustments to sales territories and quota assignments to respond to market conditions, customer preferences, and competitors that are changing faster than ever. The frequency of these changes will only increase as selling teams become more and more digital, data driven, distributed, and diverse.

At the same time, a revolution in AI has introduced new solutions and modeling tools that make it faster and simpler to evaluate the growing set of variables involved in allocating commercial resources and investment. Developments in advanced analytics provide the opportunity to improve the Territory and Quota Planning (TQP) process. These improvements affect the quality and impact of the outputs of the TQP process and the resources, labor, time, and effort involved in managing it. Advanced modeling techniques offer the potential to improve the effectiveness and predictability of territory and quota plans. Here are some examples of the positive impact that digitizing your core planning processes can have:

• Generating more revenue growth from existing sales assets. Companies that digitize their TQP process increase the revenue they can generate with existing resources. The revenue increase comes from four places: better resource allocation; tighter alignment between sales territories and your go-to-market strategy; improved sales productivity; and improved goal attainment.
• The agility to reach the market faster. Organizations that use automated workflow processes are many times more efficient than their counterparts who use manual or spreadsheet-driven processes. Data-driven automation can help take weeks out of the TQP cycle. One way to obtain these results is by automating the collection and analysis of many data inputs. Another advantage of automated tools is better collaboration. They make it faster and easier for your sales, operations, marketing, compensation, finance, and strategy teams to work together to align territories and quotas.

Digitizing your processes is a win-win. It can help you improve sales achievement while reducing selling costs. Organizations that use automated technology for territory design have higher sales achievement with lower selling costs. A digitized process makes it faster and easier to match territories with revenue and profit growth opportunities and reduce overall selling channel costs.

Unfortunately, most organizations are not digitizing these processes. Most still cling to outdated approaches to the territory and quota planning process. Fewer than 20% of selling organizations have a data-driven, quantified understanding of the total market opportunity and untapped customer potential, according to a survey of 870 B2B executives worldwide by Bain & Company.¹⁵⁰

A big reason for this is you can't digitize a process you have not yet systematized. The fact is most managers say they are not very good at territory and quota planning in the first place. Only 36% of sales executives and performance professionals say they are effective at territory design.¹³⁸ Most of them (79%) feel they have inadequate off-cycle and midyear territory evaluation practices.¹⁵¹

Why is this? One big reason is too many companies still do this process manually. In the digital age, spreadsheets are still the primary tool for most organizations when looking to manage sales quota and territory planning. As a result, as we've already noted, most organizations largely fail to finish planning before an upcoming sales period starts.

Another reason is the sheer complexity of planning. “The number of variables and permutations involved in modern territory and quota planning have increased dramatically,” reports Michael Smith of Blue Ridge Partners. “This additional rigor will yield more precision, higher goal attainment and greater opportunity realization. But businesses that still use spreadsheets to manage their sales quotas and territory planning, they fail to get their updates completed and accepted by the field two-thirds of the time.”

Use Analytics to Make Better Predictions, Forecasts, and Investment Decisions

Corporate leaders struggle with any long-term growth formula because so many growth plans are based on guesses, forecasts, and hunches on which growth investments will work. Growth plans tend toward these uncertainties because managers rarely agree on these three fundamental things: the most essential questions about their growth strategy, the true economic rationale for evaluating strategic growth investment, and the fundamental “math of growth.”

This is an area where advanced analytics can create a lot of value. Analytics give managers the horsepower, processing power, and facts they need to better assess the trade-offs between conflicting corporate agendas and perspectives. They also make it easier to agree on and align all aspects of the go-to-market model – from sales force strategy to market segmentation to product portfolio, go-to-market, and sales incentive strategies.

Growth strategy, at its core, seeks to allocate business resources in the way that realizes the greatest revenues and profits from the market. While there is no one perfect growth plan for everyone, there is a balance that is probably best for your specific company. A common understanding of both the assumptions and expectations of the plan is required on some level, or else execution will suffer.

“It's important to remember that defining, sizing, balancing, and optimization of growth resource allocation depends on a number of interrelated factors,” reminds Cam Tipping, whose SABRE strategy simulation is used in 70 top MBA programs to teach growth strategy. “These factors are always in conflict. Cost vs. customer service. Sales capacity vs. coverage. Seller balance and fairness vs. revenue maximization. Seller satisfaction vs. short-term revenue growth. Sales rep location, skill and expertise vs. market need. This leads to trade-off decisions. There is no right answer. Each organization has its own priorities, methods, or ‘algorithms’ for balancing these trade-offs to arrive at territory definition and quota assignments that create the most value for the enterprise – in terms of short- and long-term growth, profitability, and firm value.”

To be more specific, academic research tells us that there are seven interrelated decision factors that inform the development of growth strategies and plans.¹⁰⁶ These factors include the selling channel designs and go-to-market strategies that every selling system runs on. All of these are defined in the glossary. All of these will require a different mix of qualitative inputs (e.g. judgment); quantitative inputs (e.g. historical data); objective inputs (facts); or subjective data inputs (guesses). Selling channel designs and go-to-market strategies also vary based on how they are derived. For example, some organizations use a top-down approach to divide markets into segments and rep assignments that are uniform and rational. Others use a bottom-up approach that factors in more local market input and the unique capabilities and skills of individual sellers. Neither is perfect. So the best management teams try to use both to get the most accurate plans in place. Mixing and matching these approaches is another way that models and automation can really help. (See Figure 10.1.)

Given this interrelationship between go-to-market variables, it's important to align all of the components of the go-to-market strategy. These seven variables must work in concert with the prescribed territory boundaries and sales quota assignments that generate revenue and yield from resources. Multiple strategic and tactical objectives are in tension and require active balancing. Coordination among key stakeholders in other functions will ensure that your territory and quota plan aligns with relevant strategies, such as channel strategy, product portfolio strategy, market segmentation, and incentives.

At a high level, you need to balance four fundamental trade-offs when optimizing the growth formula for your company.

• The trade-off between high levels of control over the sales force and market coverage. Too much control may limit sales freedom and miss opportunities. Too little control can result in undisciplined selling, overlaps, and disputes.
• The trade-off between cost and the customer experience. Too much focus on optimizing cost can hurt the lifetime value and quality of important client relationships. Too little focus on costs can waste effort and erode margin.
• The trade-offs between maximizing opportunity and the retention of your sales force. Overly aggressive goals can be unrealistic and increase stress and attrition on the sales team. On the other hand, relaxing growth goals can leave value and revenues on the table and reduce your competitiveness and productivity.
• As organizations move to multichannel, digital, and virtual selling models – they need to rebalance selling team activities, roles, and priorities. For example, engaging customers through direct and digital channels requires a much different type, mix, and sequence of customer engagement and different customer treatment types. The productivity, workload, and capacity of virtual sales reps will be different from traditional field sellers or key account managers. Modern engagement models need to factor in the level of digital engagement, reductions in sales travel due to remote selling. You must also factor in the increased speed and frequency of response that digitally enabled customers have come to expect when you establish activity-based productivity measures.

To fully realize the growth potential of new territory designs, interaction patterns, and customer priorities, it is likely you will need to reengineer their selling architecture. This means adjusting your territories, incentives, engagement models, roles, and customer engagement cadences to generate higher returns from your revenue teams. If you try to manage all these variables using desktop productivity tools like spreadsheets, you will quickly become overwhelmed with the volume of variables and scenarios to consider.

This is an area where advanced analytics, AI algorithms, and models can really help. These tools can help you assess many different scenarios and inform better optimization and resource allocation decisions quickly. For example, these tools will help you evaluate many different scenarios faster, as well as allow you to manage many more variables in reconfiguring selling architecture. They can also support you in optimally matching selling resources with specific market opportunities. In addition, using modeling tools can speed up the development and evaluation of a wide variety of trade-off decisions throughout the planning and optimization process, including but not limited to:

• Sales resource allocation: Optimizing the additional revenues and profits associated with increasing staffing in a market. Optimizing in this way involves experimentation with different levels and mixes of staffing based on assumptions about sales response function, rep productivity, the marginal cost of incremental sales, and demand elasticity.
• Sales force emphasis: Optimizing the incremental profit and revenue contribution vs. level of effort, mix of calling, and products sold. There are thousands of rep, customer, and personal combinations to consider. Still, selling performance, resource requirements, and margins can change dramatically based on these variables. So, exploring many options makes sense if it can be done quickly and affordably.
• Optimizing territory assignments: Optimizing sales potential vs. the mix of customers and accounts in a given territory can have a positive impact on sales productivity, total profits, and revenue growth and risk. You can experiment with a variety of different combinations to achieve the optimal territory definitions and assignments for your organization. Advanced models can process and optimize territories to balance a variety of critical variables. Some of the most important ones to consider are: Making sure quotas are equal vs. equitable. The optimal combination of carrots (incentives and lifts) and sticks (gates and penalties) to motivate sellers. Emphasizing selling new as opposed to existing products to maximize margins and customer lifetime value. The best division of time spent with new and existing clients to optimize top-line growth and cost to sell. The degree you should use measures of activity and behavior as opposed to outcome-based metrics to motivate sellers to make the right decisions where to allocate their efforts.
• Breaking down baseline revenues and revenue forecasts by product, channel, industry, and geographic mixes: This involves understanding how changes in market coverage will affect your market share, revenue attainment, and cost to sell.
• Optimizing top-down opportunity allocation. Top-down quota planning breaks down the total revenue opportunity of a company into smaller units that can be assigned to individual sales reps or teams. There are a variety of ways a business can break down that opportunity based on cost to sell, sales force size, product emphasis, staffing levels, and sales force focus.

Adopt Advanced Modeling Techniques to Evaluate More Scenarios and Build Consensus

Advanced analytics and, particularly, the use of advanced modeling techniques can help your team plan, manage, and measure the performance of your growth strategy. They allow you to better and more accurately create, test, and improve plans.

Professors Leonard Lodish and V “Paddy” Padmanabhan have taught the “Leading the Effective Sales Force” curriculum to a generation of growth leaders over the past decade at Wharton and INSEAD.¹⁴³ They believe it is no longer enough to rely on history or “rules of thumb” in making sales force allocation decisions. The precise sales performance data available to sales managers is increasingly able to help them rationally decide on sales force size, territory boundaries, and call frequencies for each account and prospect.

“Decision science has evolved beyond simple extrapolations of historic performance or management ‘rules of thumb’ about key TQP planning parameters, such as seller workload estimates, the sales response function, opportunity potential or seller productivity,” relates Cam Tipping. “Advanced models and business simulations are empowering sales managers and key stakeholders in product, marketing, and senior leadership to develop much more accurate and nuanced planning assumptions based on quantitative facts and qualitative management judgements that reflect the true drivers of sales performance and customer response which yield much more effective planning outputs. This includes a better understanding about how sales assignments were derived, and why they are in the collective best interest all parties involved.”

Using advanced sales analytics and modeling techniques to derive more accurate and predictive planning parameters is an emerging best practice. Data inputs from many different data sources no longer solely rely on extrapolations of historical baseline data derived from simple assumptions. Some data inputs can be derived by modeling sales response functions, sales profitability, customer value modeling, signals of customer intent and readiness to buy, and “win probability.”

A fact-based business case for sales resource allocation is now possible. Investment in markets can be empirically determined by developing a sales response model for the markets you serve. Such a model looks at demand and supply information, competitive spending, and the relationship between sales staffing and revenue performance. Other models can calculate incremental profit and revenue contribution of incremental effort, as well as calling patterns and product emphasis.

The ability of analytics to make planning insights more predictive helps sales organizations unlock the potential in their go-to-market approach, which can drive growth, improve yields, and generate the greatest return on growth investment, according to Marc Altshuller, Founder and CEO of Varicent. “The most advanced organizations are using AI to find the predictive elements in their unique data to identify customer opportunities and seller performance issues,” shares Altshuller. “For example, it's possible to identify and predict which sellers have the highest probability of hitting their quotas or churning, including the ability to drill down into the detail on the headwinds, tailwinds, traits, and behaviors that explain why they are at risk and what drives their performance. A leadership team can use this granular and predictive data to decide on the thresholds of revenue and thresholds of churn risk they can tolerate in their plan.”¹⁴⁴

Seventy-nine percent of sales teams currently use or are planning to use sales analytics technology.¹⁴ It is possible to calculate much more accurate inputs for several critical data inputs to the planning, management, and measurement of growth resources. Your growth strategy can be improved significantly with advanced modeling and analytics techniques that use the following inputs:

• Estimates of market potential and opportunity: By incorporating internal sales baselines over three years or more with external measures of economic activity, demand, buying behavior, and market trends to more accurately quantify and forecast market opportunity or the total addressable market. Improving your estimates with facts can greatly enhance the TQP process because most sales and marketing executives are not using data to support their growth strategy and resource allocation decisions. For example, two-thirds of CMOs could not demonstrate the contribution of marketing to firm sales and profits with the data they have, according to a survey of 500 Global CMOs in the Forbes Marketing Accountability Report.¹⁵
• Seller profitability and performance: By correlating rep activities and outcomes with profit contribution, sales quota attainment, and productivity metrics such as calling volume and conversion rates.
• Customer and account priorities: By calculating account potential by combining internal sales baseline data with external firmographic, technographic, and demographic information as well as usage, adoption, and buying intent to determine customer buying potential, penetration, and lifetime value.
• Sales workload estimates: By building rep workload and capacity estimates using a wide range of scenarios based on different customer engagement models – the number, mix, nature, and frequency of customer engagement, different levels of treatment by customer type, and the mix of products presented to those customers.
• Sales forecasts: By integrating pipeline health and opportunity metrics from CRM with customer engagement, intelligence, intent, and win-probability data from first-party data drawn from customer-facing systems and third-party data sources.
• The sales response function: This is the relationship between sales and marketing investments and actions and the contribution they generate in terms of revenues, profits, and business objectives. This is perhaps the biggest assumption underlying all of your growth plans. According to academic research, the optimization of all aspects of sales operations depends on the behavior of response functions, either explicitly or implicitly.¹⁵²

  Management's ability to accurately estimate the effects of sellers' efforts on business outcomes is critical to help them make effective and optimal decisions about how to allocate sales resources.

  The science of sales response calibration has evolved from simple estimates of the response function (the relationship between effort and results) to far more sophisticated and accurate approaches based on advanced modeling techniques, according to research by Professor Lodish.¹⁵³ Most managers allocate their salespeople to markets based on their judgments or simplistic linear “extrapolations” or “rules of thumb” (e.g. if one rep generates $1 million in new sales in a given market then two reps will generate $2 million). Advanced analytics make it practical to create far more objective and complex data-based econometric techniques. These techniques are based on more sophisticated modeling approaches like regression analysis, maximum simulated likelihood, and hierarchical Bayesian analytics. A good place to learn about these techniques is in the “Customer Analytics for Growth Using Machine Learning, AI, and Big Data” executive education course at Wharton developed by Professor Raghu Iyengar.¹⁵⁴

  Advanced modeling techniques can create much more accurate response functions that represent how sales will vary with selling efforts in different scenarios. For example, a model should be able to tell you how much effort is required to maximize sales on a company, territory, or rep level. It should also allow you to see how those predictions will change based on external factors such as competitive, market, and environmental influences. These models are much more accurate, transparent, and measurable than the simplistic historic extrapolation, linear relationships, or “rules of thumb” that managers have used as the basis of estimating the response function.

  An emerging best practice for solving the problem of allocating scarce resources is to conduct an econometrics modeling analysis. Econometrics offers a set of equations describing the behavior of customers and markets. Such models are useful because they better predict how sales will increase as you add resources to a given region or market. This is because customers don't respond in a linear fashion. More complex S-shaped (or convex-concave) response functions better predict customer response because they are most common in nature and factor in the concept of diminishing returns.¹⁵²

Algorithmic models and planning simulations can help managers maximize their return on growth assets a number of ways. They can help you refine these critical assumptions and inputs to your plan. They can set up better tests to see if those assumptions are close to the mark. They can see how those assumptions change many periods into the future. Models also allow you to evaluate hundreds or thousands of different allocation scenarios. This is important because there are literally millions of different ways you can go to market, and small differences in your approach can make a big impact. Finally, models help you incorporate input and perspectives of more people on your team faster and with less labor.

Algorithmic models and simulation tools can be a true force multiplier when it comes to sales strategy. They can help you build resource allocation strategies that increase sales while reducing costs, stress test the allocations and assumptions behind those strategies, and bring those strategies to market faster.

This is an important point because at its core, growth strategy is fundamentally the strategic allocation of growth resources and investment. Those allocation decisions are based on dozens of assumptions and predictions about the future. Most of those assumptions, predictions, and scenarios are based on gut feeling, institutional belief systems, and unchallenged assumptions about the value, responsiveness, and attainability of customers and markets.

Executives continue to struggle when they make the critical growth trade-offs, allocation, and risk investment decisions required to adapt to a dynamic, rapidly changing marketplace. This is because their huge investments in marketing analytics are better at improving marketing tactics than they are at informing strategic growth decisions. The A/B testing that your digital marketing teams routinely use to track performance are great at optimizing marketing tactics, campaigns, and little decisions. The problem is they don't tell you how to run your selling system better. As Elissa Fink, former CMO of Tableau, puts it, “There is no A/B test for the big strategy and trade-off decisions the CMO needs to make, where you are either all-in or out.”¹⁵⁵

The more you use data to inform and test your long-term growth plans – the smarter you become about your business, your market, and your decisions.

The Power of Simulations to “War Game” Scenarios, Pressure Test Plans, and Building a Common Purpose

AI-driven simulation-based tools can provide you with a faster, more collaborative way to generate your territory, product launch, account-based marketing, and business unit growth plans. Using simulation tools lets your leadership team “war game” more scenarios. Almost every profession uses simulations to develop complex plans and strategies and to enhance skills and talent. Lawyers use simulations to game different strategies for mock trials. Doctors in training use simulations to practice surgery without the risk of hurting patients. Pilots train on flight simulators so they don't crash when they make mistakes. Simulations are gaining popularity as a sales strategy and planning tool. There are a variety of big benefits associated with using these simulation-based tools, particularly when compared to the traditional top-down approaches to strategy development that most companies use:

• First, a simulation compresses time in testing go-to-market strategies. Most growth strategies will not bear fruit or fail until several sales periods after they are deployed, which means managers and planners can only guess at what will happen in years two, three, and four. You either guess right, or you guess wrong—and everyone has their own best guess. Simulations let groups of people play out different growth scenarios years out into the future. For example, the SABRE business tool mentioned earlier allows teams to “war game” different resource allocation strategies seven periods into the future. This is a huge advantage. “The SABRE simulation is one of the most highly ranked courses in the Wharton MBA program for a reason,” according to Professor Dave Reibstein, who has taught the class for several years. “Simulation tools are extremely effective at teaching, testing, and proving out different growth strategies and management perspectives.”
• Second, AI-driven simulations can manage millions of scenarios and possible resource allocations to find the best combination to maximize growth. They allow managers from sales, marketing, service, and product groups to collectively test and balance combinations. From which products the sales force should emphasize to which customers to target and prioritize in calling, to the different ways of treating those customers: all of these factors combine to help you generate the greatest profit and growth contribution and ROI, and quota attainability.
• A third benefit is “crowdsourcing” the market knowledge and experience of your team. Simulation tools can incorporate dozens, even hundreds, of field leaders into the planning process. This means using simulations makes it possible to combine bottom-up, local market knowledge and performance insights with top-down focus on realizing the greatest profit, revenue, and opportunity share. The best of both worlds. “Simulations are one of the fastest ways to build a team consensus,” reports Cam Tipping. “A simulation tool brings together different points of view and lets you test different approaches. In the end, you get better consensus understanding of the customer response function, and a better sense of how to manage the product portfolio strategy.”
• A fourth benefit of simulations is that they allow planners the opportunity to “pressure test” and adapt plans to deal with rapidly changing and different competitive and market scenarios.
• A fifth advantage is speed. Simulations effectively accelerate the time between strategy development, tactical planning, buy-in, communication, and implementation by revenue teams. This is important because most organizations that use spreadsheets and manual planning approaches cannot get those plans updated and agreed upon by seller before the selling year actually begins. That's effectively starting a journey without a plan.
• A final advantage is risk management. All future plans are risky. And while it's nice to say things “take risk and fail fast,” most managers really don't want to take big risks with their careers. “Simulations let you guess and screw up without costing you your career,” advised Professor Reibstein. “Few of my students have taken big risks on their jobs. But all of them have crashed a plan in a flight simulator game.” Experience is the best teacher. One of the biggest advantages of simulations is they let you try different things without consequences. And they give you years of experience in a week.”

The Power of Models to Algorithmically Balance and Tune Your Revenue Engine

Sales modeling is both an art and science. This is true even in the most data-driven organizations.

But the speed and complexity of modern selling has made modeling an essential tool for tuning and running your revenue operation system. For example, modeling is increasingly critical to sales resource allocation due to rapidly changing customer behavior, shorter product life cycles, and the complexity of omnichannel selling.

Managing a selling system requires constantly monitoring and rebalancing the seven interrelated inputs against corporate growth goals and resource constraints. For example, you will need to change the size, segmentation, and emphasis of your selling channels as buyers move to online channels or require higher levels of support. When demand shifts or new markets emerge, you will need to reset your sales territories and market segments. And adjusting the way you engage and treat customers based on their changing needs and potential is a never-ending exercise.