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CHAPTER 9
Extract More Revenue and Margins from Your Teams and Resources

The third element of a Revenue Operating System is to leverage digital technology and advanced analytics to create value by improving the performance and financial contribution of revenue teams. These technologies can accelerate sales growth and extract more revenue, margin, and value from your revenue teams by:

Optimizing the allocation of revenue team resources to realize a greater share of your target markets
Developing and retaining high-performing selling talent
Focusing your selling teams on the best account opportunities
Ensuring more sales reps achieve quota targets
Generating more value in terms of price realization, share of wallet, and margin in sales transaction
Expanding customer contract value, lifetime value, and annualized recurring revenues

One major opportunity, when it comes to getting more revenue from your teams, is to focus on a better system for recruiting, ramping, and retaining top sales talent—to make sure, in short, that you're building a strong, capable team. Doing so will improve revenue growth and sales quota attainment while reducing cost to sell. Ninety percent of the executives we spoke with agree that their sales reps are their biggest growth asset. Yet, in our experience, few manage their sales reps as valuable assets. Most don't realize how poorly those assets are performing on a financial basis or the true cost of attrition on sales, margins, and costs. If the average CFO evaluated their salespeople as financial assets, they would conclude that sales reps are expensive (between 10–40% of firm spending), that they require significant maintenance and upkeep (training and management), yet underperform (most fail to achieve quotas) – and have a useful life of less than two years. Why? Because of the fragmented way most organizations recruit, develop, measure, manage, and motivate their reps. In most cases, no single person is responsible for measuring, managing, or improving the performance of the talent pipeline. The financial consequences of these disconnects on your revenue goals, selling costs, and margins can be severe. A 5% increase in sales rep attrition across your sales team can increase selling costs 4–6% and reduce total revenue attainment by 2–3% overall, according to research by Blue Ridge Partners.¹³⁷ In addition to the negative impact on selling costs, lost opportunity, and revenue goal attainment – the revolving door of salespeople can damage customer relationships.

“If you don't invest in training and enabling your people, you wind up spending a significant chunk of resources dealing with rep churn, finding new reps, and ramping them,” according to Frank Jules, President of AT&T Business.¹²⁰ “In the end, very few of them will develop into the top talent you need to outperform the competition.”

Another way to create value is to use analytics to “shrink the bullseye” and “cut the long tail” of customers. Doing so can multiply the impact of every selling interaction and individual in your business. The reason for this is, while every business leader understands the 80/20 rule when it comes to targeting customers, most don't actually apply it. In our experience, most organizations tend to target too many customers and develop too few of them. For many businesses the “customer curve” remains too long and sellers continue to chase “tail accounts” that are unprofitable to pursue. There are good reasons for these tendencies – the optimism of sellers, the desire to realize more market potential, and the pressure to generate the most revenue growth from scarce selling resources. But they are also rooted in bad habits like not challenging entrenched belief systems. Another bad habit is using “gut feeling” assumptions to size and rank opportunities instead of data. A third is relying too much on historical sales data instead of predictive insights about the future when planning.

A system based on facts can change this. The secret is to convert the customer engagement data you already have into insights. Sellers don't need more data. They need actionable insights that inform account priorities, resource allocation decisions, and the level of effort to apply to specific target customers. For example, it is possible to develop highly accurate targeting models that more skillfully predict which customers are going to buy from you soonest, at the highest price, and with the least selling effort using your existing CRM and transaction data. “When we compare customer assignments based on data-derived propensity to buy models with those based on the estimates of sales teams and local market leaders, we typically see improvement of 20% or more in conversion, sales quota attainment, and account development,” reports Jim Quallen, a Managing Director of Blue Ridge Partners who has helped dozens of B2B sales organizations deploy such models.

Using your data assets to better align and allocate your selling resources with market opportunity is another way you can create more revenues and value.

Recent advances in sales performance management software, analytics, and modeling approaches can dramatically simplify complex territory definition and quota design problems. They can also make the process of planning, managing, and updating territories and seller quotas faster, less labor intensive, and more accurate.

Not only that, but recent advances in analytics, modeling, and sales performance management tools provide the opportunity to dramatically improve the territory and quota planning process, the quality and impact of its outputs, and the resources, labor, time, and effort involved in managing it. These advanced modeling techniques offer the potential to improve the accuracy, effectiveness, and predictability of territory and quota plans. For example, businesses that digitize their territory alignment process increase revenue up to 15% through better resource allocation, tight alignment between sales territories and the go-to-market strategy, improved sales productivity, and goal attainment, according to research by the SMA.¹³⁸

Building Block #7: Talent Development - Attract, Develop, and Retain Commercial Talent

Frontline sales managers can use sales enablement and readiness technologies and AI to significantly improve cross-selling, account penetration, and the performance of the “B and C players” on their revenue teams. They can now use these tools to better evaluate, educate, and focus sellers. Sales enablement and readiness tools can automate the evaluation and development of sales talent. Analytics can create measures of seller performance based on activity and behavior. They can also improve the coverage and penetration of key accounts using customer engagement and seller activity data. This can create significant value for a variety of reasons:

Most (57%) sales leaders regard visibility into seller activity and performance as their top sales productivity challenge.⁹
Even more (62%) lack confidence in their organization's ability to cross-sell, upsell, or expand key account relationships, according to Miller Heiman.³⁷
Sales teams are not very effective at coaching and guidance, according to a survey of sales managers and performance professionals.⁹
Managers have largely failed to leverage the potential of advanced analytics to improve accountability and reinforcement. The survey identified visibility into adoption, behavior change, and use of sales skills and tools in selling situations as the biggest opportunity to improve sales performance.

This doesn't have to be the case. There are a dozen commonsense ways that organizations can improve the process of attracting, recruiting, developing, and retaining top sales talent. These include connecting training and development systems into a closed-loop process and using AI to better support training, establish better measures of seller performance, and simplify the seller experience. What's key is managing this effort as one enterprise process and one closed-loop system. An effective first step is to assign an executive to manage and measure the performance of the process of recruiting, ramping, and retaining sales talent across the company. Improving seller attribution, seller satisfaction, and the cycle time to ramp new sales reps even a few percentage points can lead to large improvements in margins, costs, and revenue attainment.

Sales managers and performance leaders (from sales operations, sales enablement, and learning and development teams) at growth-oriented businesses are developing new management capabilities and skills as they struggle to manage, enable, and motivate remote selling teams. In response, sales managers are increasing their adoption of sales enablement technologies and sales analytics to generate the engagement, speed, and productivity essential to being productive in a virtual setting and adapting to the new buying reality.

In particular, they are putting in place systems and programs that leverage data to create value. These include:

Establishing integrated coaching, skill development, and reinforcement processes. Remote learning is now a fact of life because face-to-face training delivery is not a practical option as most organizations have cut travel (84%) while encouraging employees to work at home. In response, frontline sales managers are looking at integrated learning and development solutions like MindTickle, Allego, and Ambition to enable an integrated sales development approach that is both highly efficient and a “closed-loop” in that it connects training to behavior to performance. These solutions blend video role plays with AI evaluation and guided selling to connect development and reinforcement to in-the-moment selling activities. These solutions incorporate conversational intelligence and activity tracking to measure how training translates into actual customer engagement and account success.
Improving your visibility into revenue team performance. The lack of visibility into pipeline activity, seller actions, buyer engagement, and account health are setbacks to remote selling productivity. Sales engagement platforms like Outreach.io give frontline sales managers management tools and dashboards built on individual client and account engagement data that allow them to see pipeline health and activity of any rep, and to drill down as needed to coach, guide, and assist reps in advancing opportunities. Sales automation platforms like Gainsight and Totango give account leads and service managers visibility into account health by aggregating, analyzing, and organizing customer data from many legacy systems to provide real-time signals of churn, service issues, and upselling opportunities.
Creating measures that close the loop between seller performance, training effectiveness, and customer outcomes. Sales leaders are starting to use advanced analytics to derive new measures that more accurately quantify the collective engagement, energy, and customer experience their teams are creating within accounts. They are drawing upon sales analytics solutions to track the behaviors and activities that define team success. At the same time, they are building toward innovative, data-driven customer engagement measures and incentives that are tied to opportunity potential, account health, and seller performance on a scale of 1–10. Integrated learning and development solutions like MindTickle, Allego, and QStream use AI to evaluate selling skills and performance based on activity analysis, conversational intelligence, and performance against standards.
Making one-to-one coaching at scale part of an integrated learning and development process. Historically, sales managers have been limited by their span of control and free time for sales call monitoring or ride-alongs, so they are only able to monitor sales calls and actively coach a limited number of reps. The ability to record sales conversations and compare them to selling outcomes and best practices by integrating with CRM, training, and enablement systems can create a real-time, closed-loop information flow. This gives a sales manager the ability to actively be there at critical points in the actual sales conversation such as common objections, competitive mentions, or signals of attrition. It also lets them understand what training has been adopted, and whether it is successful at changing customer behavior. Continuous, real-time, and individualized coaching allows sales managers to actively manage and develop many more reps. It also accelerates how quickly new reps ramp to full productivity. For example, ChowNow, a leading online ordering platform for restaurants, was able to knit together the systems that support their Sales Development Reps (SDRs) to eliminate manual work, share best practices, and provide real-time coaching in the moments that matter. ChowNow had to double the number of SDRs to exploit exploding demand in the online ordering system market in the wake of the pandemic. Their Growth Operations team unified and enhanced their sales and marketing technology portfolio to make it faster and easier to ramp and support so many new reps. They used AI-enabled tools from Revenue.io to connect the dots across different systems to automate manual work, create best practices libraries, and enable one-to-one coaching from anywhere, at scale. By aligning tools that support the rep coaching and engagement, ChowNow was able to ramp reps to productivity faster (in 60% less time), promote them sooner, and retain them longer (rep attrition dropped by 75%). “Conversation AI is our lifeline right now,” according to Stephanie Sullivan, director of Growth Operations at ChowNow.⁴² “Sales managers have to listen to calls as part of the job – it's nonnegotiable, because it's by far the best way to train. And we have seen dramatically faster rep ramp time for our SDRs as a result.”

Creating a Closed-Loop System for Sales Rep Education, Readiness, Reinforcement, and Measurement

Most organizations have deployed sales training, learning, and development systems and methods. Many organizations have had unsatisfactory outcomes. The sales managers and performance professionals we surveyed rated sales coaching and guidance as one of the most difficult things to do. And managers have largely failed to leverage the potential of advanced analytics to improve accountability and reinforcement. The same survey identified visibility into adoption, behavior change, and use of sales skills and tools in selling situations as the biggest opportunity to improve sales performance.

These circumstances have many sales operations and training leaders reevaluating their sales learning and development investments, resources, and strategies in light of the pandemic's persistent impact.

To develop their teams faster and better, sales leaders are looking for ways to dramatically increase the historically low return they are getting on their investments in sales technology, data, and people assets while leveraging limited resources. Unfortunately, most of the survey respondents told us the sales enablement and readiness technologies in which they have invested were not providing them with their hoped-for levels of visibility, engagement, speed, and sales rep.

“In my experience building digital sales platforms at Hitachi and Cisco, sales training effectiveness and reinforcement can be significantly improved by giving sales reps the right tools, knowledge, and reinforcement at the right time in their sales context,” shares Jeff McKittrick of WalkMe.

The pressure to quickly reskill sales teams has been amplified by the massive shift to remote selling and a hot job market. Sales leaders are being forced to train dozens, hundreds, or thousands of sales reps in new selling tactics and tools to help them engage more effectively in virtual channels and adapt to the faster operating cadence of digital sales. Because of the rate of change, most are trying to do this training in real time rather than pulling large sales teams out for classroom-based training and role playing as they would have done in the past.

“You can have the best technology, but if you don't have the right people leveraging that technology and you don't have the right people anticipating the future needs of your client base, your growth is not going to be sustainable,” says Jennifer Mauldin, Chief Customer Officer of Inmar Intelligence.¹²⁵ “What's driven our success from our inception is that we're a learning organization.”

There is enormous opportunity to develop your sales organizations faster while enforcing the right behaviors in execution by taking a more integrated and virtual approach to sales development, enablement, and measurement – because there are so many threads to weave together between these functions and systems. The technologies and programs that underlie sales development, management, and measurement are usually highly fragmented and spread across HR, training, sales operations, and sales development because most organizations buy and deploy such systems and assets in a siloed fashion. As a result:

Learning management systems are disconnected from the sales enablement systems where sales playbooks reside;
The sales analytics and AI used to measure sales activity, adoption, and behavior are not linked to sales readiness and training; and
Feedback and reinforcement suffer because very little of this information is available to sales managers to support grading, coaching, reinforcement, and performance measurement.

“There's no good business or technical reason that learning management, readiness, enablement and advanced sales analytics need to be separate and disintegrated,” according to Jeff McKittrick, VP of Sales Execution at WalkMe. “All are in common use across most sales organizations. But they exist largely in silos because the functional experts that buy, manage, and deploy the systems and assets that underlie sales development have no financial incentive to connect the dots across them.”

Sales leaders must make linking sales readiness to sales effectiveness a priority. They can no longer afford to automate only “part” of the learning and development process. The urgency and cost of rapidly reskilling sales forces to prepare them for virtual selling will compel organizations to treat them as one system. This will provide sales leaders, managers, and effectiveness executives an end-to-end approach to building skills that can measurably lead to sales and profit outcomes.

Don't waste the crisis. The pressure it creates is an excellent platform to drive change by insisting these expensive and important growth assets work together and do so effectively.

To move at the speed of the market, learning and development organizations must find ways to combine their systems to make them faster, more virtual, and accountable.

By connecting these dots, you can create an integrated sales development approach that is both highly efficient but also a “closed-loop” in that it connects training to behavior to performance (please see Figure 9.1). For example:

Training and skill development can be assigned and executed virtually by taking advantage of video role play and pitch management systems. These allow reps to practice and develop role play, product demo, and “situation-specific” presentation skills remotely using video platforms.
These video role plays can be inspected and graded consistently and at scale using a mix of AI-assisted scoring and human assessment by peers and managers.
The best of these can be catalogued and connected to a master content library of plays and best practices that can be targeted by process stage, persona, product, industry, or pain point.
These plays can be tagged for deployment in real time using advanced AI and guided selling tools that recommend the next best sales play or most relevant content, based on buyer engagement, response data, and the context of the selling situation.
Managers can track and measure how this effort and training translate into actual customer engagement and account success by building data-driven KPI that quantify outcomes based on actual seller activity, customer engagement, and success.
The entire process should fuel a feedback loop that continually reinforces skills, refines plays, adapts to customer needs, and tightens the connection between training and results.

FIGURE 9.1 The Top Ways Customer Behavior Is Impacting Remote Selling Productivity

Not every organization can take all these steps at once. But most of the underlying technologies and assets are in place in most large and complex sales organizations. Connecting any of these building blocks together will represent a significant step forward compared to the status quo or waiting for the next opportunity to conduct traditional classroom training (which could be 12 months away). For example, Scott Kelley, SVP of Sales, Customer Success, and Revenue Operations at GHX, is looking to technology as a way to enable continuous learning at scale. His team is implementing best-in-class revenue enablement strategies to deliver content and training to all teams that interact with customers. “We've used a revenue enablement tool to expand and standardize our training capabilities,” says Kelley.¹²³ “It's allowed us to tap into deep subject matter expertise across the company to create team and function-specific trainings.” Kelley has built a system to streamline the targeting and customization of training, an environment for teams to practice, get feedback and improve, a process for knowledge and skill certification, and ultimately a team that serves as trusted advisers to customers. These same pillars of training are applied to onboarding new team members as well as newly acquired companies to ensure a quick ramp up and increased speed to value.

The business objective of taking an integrated learning and development approach should be to improve your sales readiness and performance in four specific ways:

Reinforcement in the context of day-to-day sales activities with real-time, micro-learning, and easy-to-use training guides;
Accountability with automated and AI-assisted evaluation, grading, and feedback and behavior-based KPI;
Scalability by integrating to the existing sales process workflow and sales enablement assets; and
Cost-effective delivery by using techniques like virtual role playing that allow reps to practice remotely and get feedback and coaching quickly.

Building Block #8: Revenue Optimization – Allocate People, Time, and Effort Against Opportunities

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.³²

What Is Territory Planning and How Does It Unlock and Accelerate Revenue Growth?

Territory planning is the process of creating a workable plan for targeting the right customers, establishing goals for income, and ensuring sales growth over time. It involves making sure your sales team is targeting the right customers. A territory plan documents a structured approach to a defined set of customers, such as named accounts, in a geographic territory, or industry, in vertical or horizontal markets, or in market segments. Territory planning is an ongoing activity that maintains account and territory alignment across functional groups on your revenue team such as sales, sales support, account management teams, and channel partners. An effective sales territory plan can make your team more productive, improve customer coverage, increase overall sales, and reduce costs. On the other hand, unbalanced territory plans and constant changes in territory division can hurt productivity as well as the working relationships between clients and account managers, and those among various sellers.

This is because a wide range of AI tools are now available to create algorithmically derived customer response models to help take the guesswork and gut feeling 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:

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 feelings at best. Sales performance management solutions like Varicent, 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 2–7%, without any change in total resources or sales strategy.¹⁴⁰ 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.¹³⁹
Organizations are using automated workflow processes to get efficiency gains of two to three times when compared with counterparts using manual or spreadsheet-driven processes.¹⁴¹ Data-driven automation can help streamline the planning cycle from 60 to 35 days 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.¹³⁹

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 to improve payment accuracy and resolution.
Algorithmic segmentation, targeting, and coverage modeling. Sales reps spend 7% of their time prioritizing leads and opportunities.²⁰ 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.
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 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 takes 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.¹⁴²
Selling time optimization. A range of sales automation technologies are now able to automate, assist, or eliminate a range of low value-added tasks that nibble away at sales rep time, productivity, and motivation. Sales automation software, including DialSource, People.ai, and Seamless.ai, effectively automate tasks including CRM data entry, data management, finding information, list building, and repetitive selling tasks like dialing, screening calls, and capturing contact information. Digital adoption software like WalkMe makes it faster and easier to find and use mission critical sales tools that provide competitive intelligence, selling content, automate RFPs, record sales calls, and recommend next best-selling actions.
Using advanced analytics to improve the accuracy, predictability, and quality of plan inputs. Professors Leonard Lodish and V (Paddy) Padmanabhan have taught “Leading the Effective Sales Force” 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 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 maximizes firm profits.
John Gleason, EVP and Chief Sales Officer for Ryder Systems, 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.¹¹⁹ “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 by focusing 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.”

Digitizing the Process of Planning, Managing, and Optimizing Sales Territories and Quotas

As businesses grow and evolve their go-to-market approach to better develop the market, they add new selling channels, products, and market segments into their coverage model and selling strategy. These additions lead to more data inputs, seller roles, and functional stakeholders to include in the territory and quota planning process. As a result, there are more variables to analyze, more scenarios to consider, and more trade-offs to balance.

As a rule of thumb, the average B2B organization can take 60–70 days to plan and design territories when dealing with, on average, five different organizations (e.g. marketing, product, finance, human resources, and sales) and a limited number of data inputs to defining territory boundaries (e.g. historical baseline data, corporate growth targets, sales forecasts, and staffing budgets). Adding more stakeholders and data to the process will make your plans better and more aligned with the go-to-market process, which is important. Still, more stakeholders and data inputs can also significantly increase the amount of time it takes to achieve a consensus about territory definitions, particularly if an organization is managing the entire process on a spreadsheet.

For example, increasing the number of organizations involved in the territory and quota planning from three to six or more can double the amount of time it takes to plan, design, and align sales territories with other elements of the go-to-market mix. Adding more data inputs to the process – such as estimates of seller productivity and profitability, customer scoring and ranking data, workload estimates, and customer journey metrics – can dramatically increase the work and time involved in curating data, analyzing different scenarios, and modeling trade-offs between cost, coverage, control, and growth.

“Today, the majority of organizations manage the territory and quota planning process using spreadsheets or home-grown systems to manage these increasingly complex processes,” says Michael Smith. “As a consequence, most of these (two-thirds) fail to finish planning before the sales period starts, which means many revenue teams start off the year without a fact-based upon assignments.”

Replacing spreadsheet-based planning can have significant benefits. Companies that digitize their territory alignment process increase revenue, shrink the planning cycles, and improve the overall achievement of sellers.

Recent advances in Sales Performance Management (SPM) solutions and tools provide the opportunity to dramatically streamline and improve the territory and quota planning process, the quality and impact of its outputs, and the resources, labor, time, and effort involved in managing it. There are five primary ways new technologies can streamline, automate, and enhance the territory and quota planning process.

Digitizing the planning and design of sales territories and sales quota assignments.
Helping to visualize, communicate, and align those plans across the enterprise.
Helping sales managers, sellers, and incentive professionals to manage sales territories and quotas.
Building dashboards for ongoing evaluation, reporting, and stakeholder decision support.
Streamlining and speeding up the process of making mid-period adjustments and plan reviews

There are a variety of tools and advanced technologies that can enhance and improve the process for planning, designing, managing, and evaluating territory and quota plans. SPM software is a fast-growing technology that tackles salesperson assignments and territories, quota management, incentive compensation administration, and sales and incentive performance reporting. Current solutions provide sales leaders and their operations teams management greater control, richer insight, and faster decision making. “The highest performing organizations are using advanced analytics and AI to move information across traditional working silos to find ways to generate more revenues, improve operating efficiency and manage risks like sales rep churn,” according to Marc Altshuller, CEO of Varicent.¹⁴⁴ “The next generation of sales performance management software allows revenue operations and sales leaders to integrate the plan, operate and pay process to create smarter and more balanced territory plans and quotas that can adapt quickly to shifting market realities.”

Sales organizations are increasingly using SPM solutions to recalibrate forecasts, recast seller performance expectations, and use analytics to better manage sales territories and quotas in rapidly changing environments. Some of the more impactful ways to digitize territory and quota planning include:

Automating the process workflow, including data entry, analysis, and collaboration with stakeholders. “Organizations that have automated workflow processes are seeing efficiency gains of two to three times when compared to counterparts using manual or spreadsheet-driven processes,” according to Michael Smith, who has helped over 300 B2B organizations unlock more growth from existing selling assets in the last decade.
Develop scenarios to test different resource allocations, sales assignments, roles, and territory configurations to find ways of managing more accounts with fewer reps and modeling the financial impact including forecasting costs, outcomes, and commissions based on potential changes. “The number of possible ways you can structure territories and allocate seller time effort and activities is nearly infinite in multi-channel organizations with many selling roles, segments, and products in their portfolio,” according to Cam Tipping, whose SABRE simulation tools are being used by business schools and global revenue teams to model and simulate their territory plans using real-world scenarios. “Sales performance management solutions and business simulations make it much easier for operations teams, territory managers and individual sellers to explore the potential of the many different ways they can allocate time, effort, and while fine tuning their day-to-day mix of customer, product, and activity priorities.”
Seller performance management offers advanced analytics that provide more accurate and empirical measures of seller productivity, performance, and profitability. These are important design parameters for bottom-up calculations of seller workload, capacity, and sales quota assignments. They also make it easier to track performance of sellers within a selling period. Today, only a fraction of organizations (6%) are able to give their sales teams access to the daily reports of progress and status, or near real-time data, they need for day-to-day sales territory management and decision support.⁴²
Automating quota management, including mapping visualizations and dashboards for ongoing evaluation and decision support, helps make quota tracking “active” and provide sales managers the KPIs they need to actively manage performance of their territory teams and individual reps against those goals.
Dashboards that automate the collection and reporting of key metrics required for actively managing quota attainment and providing decision support in real time. For example, 57% of companies use dashboards to monitor territory performance and opportunity metrics to monitor territory performance and health; this was cited as the biggest gap in territory management, alongside a need to move from “gut feeling” and anecdotal management feedback to more data-driven KPIs to guide decisions and adjustments.¹⁴¹

Building Block #9: Revenue Enhancement – Increase Revenue Yield with Better Packaging, Pricing, and Personalizing Offers

Sales teams can use analytics to improve the 4Ps of selling by optimizing pricing dynamically based on willingness to pay, and by personalizing products and proposals to deliver and capture more value from sales transactions. For example, more disciplined and algorithmic pricing offers up to five times the profit potential of cost and growth initiatives because it can expand margins by 3–10% with existing resources and improve earnings multiples with limited investment. Pricing is the most efficient way to improve a firm's profitability, according to Professors John Zhang and Jagmohan Raju of Wharton Business School in their book, Smart Pricing.¹⁰² Improving a firm's price by 1% effective price increase without changing anything else normally will increase profitability by over 10%, according to Wharton Research Data Services. Your organization can get two to five times the profit leverage from top-line price optimization than it can from efforts aimed at reducing costs.

Another factor impacting margins and selling effectiveness is rising customer demands for more relevant and personalized content. This dynamic has been exacerbated by the dramatic shift to virtual and digital channels accelerating the use of digital media and the collaborative content, videos, and assets needed to fuel them. Eighty percent of firms are increasing the use of digital media to fuel remote selling and the content creation needed to support owned digital channels.⁵

Advanced analytics can improve all aspects of pricing, proposal development, and solution packaging through personalization, automation, configuration, and optimization. These include:

Pricing optimization and innovation. Research from Wharton Business School shows that pricing is the most critical profit driver in business, offering two to five times the profit potential relative to growing sales and reducing fixed and variable costs. A data-driven pricing strategy can yield profit margin expansion of 3–10% with existing resources through pricing optimization and innovation.¹⁰² Analytics leaders like PROS, Vendavo, and Zilliant are helping businesses across industries to use advanced analytics to expand profits and create firm value by evolving their processes, systems, and teams four ways:
1. Governance of pricing policies across the enterprise to stop millions of dollars of margin leakage.
2. Automating, simplifying, and speeding the execution of pricing strategy in terms of price setting, discounting, exceptions, and adjustments due to competitive, exogenous, and market factors.
3. Executing sophisticated dynamic and data-driven pricing strategies that expand demand, capture more value, and maximize customer lifetime value, including dynamic, personalized, and demand-based pricing.
4. Driving innovation through pricing by enabling new business models with advanced pricing models that better package and realize value including SaaS, subscription, premium services, sharing, and fractional ownership models.
Enabling data-driven personalized proposals, presentations, and offerings. Pricing optimization solutions like PROS, Zilliant, and Vendavo are blending customer engagement data with information from CRM and other legacy systems to create more compliant, optimal, and, in some cases, dynamic pricing. CPQ solutions like Qorus, RFPIO, Apptus, and DealHub are using data from CRM systems to make it fast and easy to assemble, create, and personalize proposals, RFP responses, and client materials.
Proposal and presentation automation and personalization. Customers want more personalized and relevant content. Sales managers believe meeting this need is the best way they can improve sales productivity and effectiveness. Helping your sellers to deliver more dynamic and personalized presentations, proposals, and microsites is a big opportunity to differentiate and improve performance. Configure, Price, Quote (CPQ) solutions providers like DealHub and Apptus are helping revenue teams to meet this need with dynamic personalized presentations, content, microsites, slide decks, and pricing. Qorus and RFPIO are automating the process of preparing and personalizing RFPs and proposals. This is an opportunity, because only 37% of sales reps use CPQ solutions like these today.²⁰
Creating content that is ready for digital and virtual channels. 4D sales reps will need channel-ready content that meets buyers' high expectations for relevance, personalization, visualization, and collaboration in a remote setting situation. To meet the needs of modern buyers, virtual sales reps will need to rely more heavily on virtual, video, and collaboration platforms. This means personalized, immersive video and Augmented Reality (AR) and Virtual Reality (VR) content will become more important as online buyers place a premium on the quality and context of content. Organizations that sell tangible or experiential offerings are looking to 3D content creation platforms like Kaon Interactive, Spatial, and Unity to create the channel-ready content that delivers a more immersive experience in digital, mobile, and VR platforms.

Advancing the Science of Personalization: How Pandora Is Using AI and Machine Learning to Enable One-to-One Personalization at Scale

The quest to deliver one-to-one personalization at scale is a huge business need and fast becoming a fundamental component of the go-to-market system. Businesses that are able to exploit the power of AI and ML to better understand customer behavior and personalize their customer experience will outperform their competition.

Advanced personalization and recommendation systems are the brains behind every customer-facing technology in your business – including voice-enabled devices, configurable offerings, sales enablement systems, dynamic pricing, response management systems, and personalized ads.

Ninety percent of organizations are using AI to improve their customer journeys, revolutionize how they interact with customers, and deliver them more compelling experiences.³⁶ Seventy-five percent of Netflix users select films recommended to them by the company's machine learning algorithms.¹⁴⁵ To realize this potential, businesses in every industry are pouring money into the talent, tools, and data to exploit the potential of AI to grow revenues, profits, and firm value. AI hiring grew 32% in the past year.¹⁴⁴

Despite all the hype and expectations around the ways AI will transform business, the reality is that mainstream adoption of AI in customer engagement is still in its early days. The runway for innovation is long, and there are myriad ways that AI-enabled personalization can improve margins, conversion rates, and firm value.

One of the biggest obstacles preventing businesses from realizing the full potential of AI in commercial applications is the inability of executives to prioritize, direct, and allocate resources to the most profitable AI applications. “Most of the success and failures to harness the power of AI to transform business lies in management understanding of how to apply, deploy and direct these powerful tools” according to Kartik Hosanagar, Professor of Technology, Digital Business, and Marketing at the Wharton School and author of the influential book A Human's Guide to Machine Intelligence.¹⁴⁷ Another factor delaying the business impact of AI is that it takes an enormous amount of learning data to develop robust ML models.

Few businesses have the level of experience, learning data, and technical acumen that Pandora does when it comes to developing AI-driven personalization and recommendation systems. Pandora literally jump-started the development of their recommendation systems with the Music Genome project 20 years ago, and has since had access to one of the largest learning data sets and the best data scientists in the world.

“Pandora was born 20 years ago from what was called the USIC Genome Project which became the first music recommender system for consumers,” according to Scott Wong, VP of Machine Learning for Foundation, Search, and Voice Science at SiriusXM and Pandora.¹⁴⁸ His team builds reusable machine learning systems that power recommendations and discovery across Sirius XM, Pandora, and other products. Over the last two years, he worked to integrate the best science data and technology from Pandora, Sirius XM, and a powerful long-term platform. “The initiative started with a team of professional musicologists sitting down and listening to music tracks day in and day out annotating over 450 different attributes about those tracks. They looked at things like the time signature, harmonics, vocals, genre, time periods, instrumentation language, and tons of different dimensions about what is really under the hood in any given song.”

That foundation of data gave Pandora what Wong calls a great “cold start” scenario for building a recommendation engine before they even had access to customer interactions and other learning data.

When Pandora did start to access listener data to refine their algorithm, their progress and the quality of their prediction models improved dramatically. “The music genome project really enabled us with the first round of content-based strategies for the recommender system. But after people started using the product and actually listening, it provided us the implicit and explicit feedback that's really the source of digital gold when it comes to machine learning. Listener feedback data really enables the development of advanced and predictive recommender systems,” says Wong.

Content-based recommender systems like this are valuable because they are very good at identifying and recommending highly relevant content, songs, or products regardless of preconceived notions, popularity, or other influences, according to Hosanagar's book. That's hugely important in a world where every business is trying to differentiate its products, services, and brands through personalized and highly contextual experiences.

From that foundation, Pandora evolved their approach to combine other data signals and modeling techniques, such as collaborative filtering, which was popularized by Amazon, to take their predictive models to the next level. This lets them take advantage of both explicit signals from their millions of users (such as thumbs-up ratings) and implicit signals (e.g. how long they listen, what actions they take).

“What's happened over time is we started to combine techniques all at the same time,” says Wong. “That flywheel, once you get it going, yields extremely powerful models for predicting and recommending. We started to incorporate other categories of strategies into our recommender systems in addition to the content-based strategy we started with. We employed user-based strategies, which look at whether people like you also like this other thing and collaborative filtering strategies that simultaneously look at which listeners are similar, and which content is similar. All of these can help you explain and learn based on what people do and discover the hidden similarities between content and preferences.

“Our access to millions of listeners is helping us build the next generation of recommender strategies. As an example, we've evolved our models to the point where we can take the best of what humans have – the best eye for analyzing the genes underlying music – and scale it up to a much, much larger catalog of songs. We call the combination using humans as quality machines for scale. One way we've been able to accomplish that is to develop a machine listening system where a model listens directly to the audio of untagged songs in our back catalog and new songs when they come in against the label training data we have from the Music Genome Project. Using this approach, we can predict a lot of those attributes about new music that none of the humans internally have ever listened to. And so collectively when you put all these different techniques together we can power algorithmic radio stations that draw from 30 million different sources instead of two million that are human analyzed. In our business, it lets individual people personalize their own stations to their own tastes, and in aggregate it helps us track what is most successful on which stations.”

Wong's team builds reusable machine learning systems that power recommendations and discovery across Sirius XM, Pandora, and other products. Over the last two years, he worked to integrate the best science data and technology from Pandora and Sirius XM into a powerful long-term platform. Today Pandora uses these insights in all aspects of their business. Applications include helping individual people personalize their own stations to their own tastes, tracking what is most successful on which stations, and understanding where, when, and how to upsell a customer.

Most of the recommender engine fundamentals that Pandora has developed are universally applicable to the personalization problems every business faces. Hence, the learning is transferable to industries without such a great head start or access to data science.

For example, one of the big applications of AI and ML at Pandora is to figure out how to upsell a customer on a subscription. In this use case, Wong's team is using their advanced models to answer difficult questions that are common to every business – Who should we target? When is the right time to reach them? And in what context? Pandora's models are able to dig into very granular contextual variables from the promotion type, content, product fit, channel of engagement, and presentation of an offer.

“We've built machine learning models to maximize the effectiveness of how we engage with users in the upsell process. We call that smart conversations. Our models look at all aspects of user behavior – what they are doing in the app at any point in time? Who are the right people to target for upsell? When is the right time to engage them? Should we be including any kind of promotions at the same time? Our models maximize the effectiveness of those interventions by determining the right subjects, the right topics, the best artists for that person. We use recommendations inside the content itself.”

Pandora's approach is unique and in some ways more personalized than those taken by peers like Spotify, because Pandora's models are built on a strongly contextual user experience with inputs that are very explicit in terms of the specific albums, artists, and content users prefer. Such sensitivity to user input generates really powerful feedback data for developing models.

Wong paints a long runway and wide array of applications for AI-enabled recommender systems going forward. Pandora continues to push the envelope on the next generation of recommender strategies by incorporating search and voice signals. Search provides early signals on trends and event-driven actions that make recommendations richer. Advances in voice science allow Pandora to explore thematic queries for even deeper insights into user preferences, and the combination of Sirius XM's audio and broadcast capabilities and Pandora's digital streaming data sources gives Wong's team a real edge in evolving their models by cross pollinating listener experiences across millions of listeners, stations, and inputs. In its final state, Wong envisions a one-to-one relationship between the user and the algorithm that adapts to exactly who they are and what they like.

“Just like digital technologies have fundamentally transformed business over the past twenty years, AI is set to do the same over the next 20 years,” advised Professor Hosanagar. “AI is no longer just for engineers and data scientists. It's for everyone. Professionals can no longer afford to have a poor understanding of something so fundamental to business and society today.”

To help managers who don't have Pandora's experience and acumen to catch up and apply AI to enable personalization in selling, Hosanagar and the team at Analytics@Wharton worked with experts like Scott Wong to create an online executive curriculum – AI For Business Specialization – to help managers understand how to apply AI and ML to grow their businesses.¹⁴⁹ To build this curriculum, Hosanagar and his team worked with leaders at Pandora and Google to offer executives the benefit of millions of hours of modeling, analysis, and learning, giving them a leg up in the market.

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Table of Contents for CHAPTER 9: Extract More Revenue and Margins from Your Teams and Resources

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Building Block #7: Talent Development - Attract, Develop, and Retain Commercial Talent

Building Block #8: Revenue Optimization – Allocate People, Time, and Effort Against Opportunities

Building Block #9: Revenue Enhancement – Increase Revenue Yield with Better Packaging, Pricing, and Personalizing Offers

Table of Contents for
CHAPTER 9: Extract More Revenue and Margins from Your Teams and Resources