The Sales Learning Curve

The Sales Learning Curve

Reprint: R0607J

When a company launches a new product into a new market, the temptation is to immediately ramp up sales force capacity to gain customers as quickly as possible. But hiring a full sales force too early just causes the firm to burn through cash and fail to meet revenue expectations. Before it can sell an innovative product efficiently, the entire organization needs to learn how customers will acquire and use it, a process the authors call the sales learning curve.

The concept of a learning curve is well understood in manufacturing. Employees transfer knowledge and experience back and forth between the production line and purchasing, manufacturing, engineering, planning, and operations. The sales learning curve unfolds similarly through the give-and-take between the company—marketing, sales, product support, and product development—and its customers. As customers adopt the product, the firm modifies both the offering and the processes associated with making and selling it.

Progress along the manufacturing curve is measured by tracking cost per unit: The more a firm learns about the manufacturing process, the more efficient it becomes, and the lower the unit cost goes. Progress along the sales learning curve is measured in an analogous way: The more a company learns about the sales process, the more efficient it becomes at selling, and the higher the sales yield.

As the sales yield increases, the sales learning process unfolds in three distinct phases—initiation, transition, and execution. Each phase requires a different size—and kind—of sales force and represents a different stage in a company’s production, marketing, and sales strategies. Adjusting those strategies as the firm progresses along the sales learning curve allows managers to plan resource allocation more accurately, set appropriate expectations, avoid disastrous cash shortfalls, and reduce both the time and money required to turn a profit.

When your company launches a new product, do you rush to beef up your sales force capacity? If so, consider the dangers of this impulse: most new products aren’t quite ready for prime time, because they don’t have the features consumers want—or they don’t yet work the way they should. So selling them becomes an uphill battle: you burn through cash only to generate disappointing revenues.

How to avoid this scenario? Give your company time to climb the sales learning curve—finding out how early customers acquire and use your new offering and then modifying your product and sales tactics based on what you’ve learned.

In particular, Leslie and Holloway recommend matching your sales force’s size and skills to where you’re situated on the sales learning curve. For example, after beta-testing the product, assign just a few salespeople to learn as much as they can about customers’ response to your product. Use these insights to perfect the offering and your go-to-market strategy. Expand your sales force only after you see sales accelerate.

By taking time to climb the sales learning curve, you start generating profits on your new offering sooner—and you sweeten the odds of scoring a success in the marketplace.

The Idea in Practice

To climb the sales learning curve, the authors suggest these steps:

In launching any new offering, you’ll face numerous challenges—including identifying and resolving service issues and developing a repeatable sales model. Adding sales capacity too early in the launch prevents you from addressing these challenges. Example: 

Software maker Scalix developed a Linux-based e-mail system that initially garnered enthusiastic responses from CIOs. Scalix expanded its sales force quickly and moved to sell directly to CIOs at large companies. But it encountered several problems. CIOs weren’t the primary decision makers on e-mail systems. IT department managers were—and they didn’t want the headache of switching to Linux. Moreover, the product needed further trials with customers to function properly. Scalix revised its sales approach—but wasted scarce resources in the meantime.

The sales learning process unfolds in three phases—each requiring a different size sales force with different skills:

1. Initiation: You’ve beta-tested the product and have few potential customers. Hire three to four salespeople to learn how customers will use the product and to support other parts of the company in refining the offering as well as marketing and selling strategies. Look for salespeople who:

Example: 

Scalix’s overhauled sales strategy involved hitting the Linux evangelist and early adopter community first, with particular emphasis on smaller targets in the higher education and public sectors (where Linux acceptance was strongest). It hired two in-house telesales reps to drum up leads. With lower-priced salespeople and a compressed sales cycle, the new model offered much better economics than the original approach. And in 2004 and 2005, Scalix was named one of Red Herring’s 100 top private companies of North America.

2. Transition: You’ve acquired a critical mass of customers, and sales are accelerating. Keep your initial sales team focused on learning. Add sales reps who can operate effectively within an evolving sales model but who don’t necessarily have the analytical and communication skills the initial team required.

3. Execution: You’ve developed your formula for success and put your sales force’s support requirements in place. Now bring in traditional salespeople—and arm them with a territory, sales plan, price book, and marketing materials to take orders.

When a company launches a new product, the temptation is to immediately ramp up sales force capacity to acquire customers as quickly as possible. Yet in our 25 years of experience with start-ups and new-product introductions, we’ve found that hiring a full sales force too fast just leads the company to burn through cash and fail to meet revenue expectations. Before it can sell the product efficiently, the entire organization needs to learn how customers will acquire and use it, a process we call the sales learning curve.

Hiring a full sales force too fast just leads the company to burn through cash and fail to meet revenue expectations.

The concept of a learning curve is well understood in manufacturing. Employees transfer knowledge and experience back and forth between a production line and the purchasing, manufacturing, engineering, planning, and operations departments. Over time, the entire process becomes more effective: The more times a process is repeated, the more efficient it becomes and the lower its cost.

Start-ups and existing companies launching new products follow a sales learning curve that’s analogous to the manufacturing learning curve but one that unfolds through the give-and-take between the company and its customers. As customers adopt and use the product, the organization modifies both the offering and the processes associated with making and selling it. (See the exhibit “Learning Processes for Manufacturing and Sales.”) A large sales staff hinders more than it helps a company climb the curve. Instead of following conventional sales wisdom, the firm should focus first on organizing itself so it can learn from customers and respond to them.

An innovative product does not arise fully formed from a flawless development process. It emerges from the give-and-take between all the departments involved in its creation—purchasing, manufacturing, engineering, operations, planning, and the factory floor. The same is true for the sales strategy that will bring that product to market. It, too, is an iterative process, one that involves all the departments that face customers.

It’s important not to confuse the organization’s sales learning curve with a salesperson’s individual learning curve. Most companies expect sales reps to go from new employees to fully productive salespeople during their first months on the job, as they learn more about the product, the customers, the market, and the competition. The sales learning curve we are describing is separate from, and independent of, the individual learning curve and more comprehensive, involving all customer-facing parts of the organization: marketing, sales, product support, and product development. The improvements in sales yield that result from this organizational learning process affect all of the sales representatives, both new and experienced.

In this article, we will look at the sales learning curve as a framework for helping managers and investors develop thoughtful launch strategies, plan resource allocation more accurately, set appropriate expectations, avoid disastrous cash shortfalls, and reduce both the time and money required to achieve a profit. But first, let’s consider some of the unexpected problems start-ups and established companies run into when they launch a new product—problems that could be anticipated if the sales function were viewed as a learning process.

Twenty-five years ago, the major risk in creating a company (or in launching a brand new product from an established company) was the feasibility of the technology. Managers believed: “If we can build it, they will come.” Today, the product development cycle is more predictable, thanks to the greater availability of subcomponents and robust development tools. So the biggest risk for most companies has shifted from getting the product to work to getting it to market. Entrepreneurs increasingly must ask: “When we build it, will they come?”

But if start-ups apply conventional sales wisdom to new-product launches and add sales capacity too quickly, the result is often disappointing revenue growth and a cash shortfall. (For an example of how that works, see the sidebar “How Big a Sales Force?”) That’s because the conventional wisdom fails to address a number of challenges involved in creating markets for unfamiliar products: the time required to educate customers about the offering and learn how they will use it, the inevitable design modifications needed to deliver a robust product that will fully satisfy customers, the identification and resolution of service issues, the development of a repeatable sales model, the selection of appropriate market positioning, and the design of effective sales incentives. Here’s a case in point.

At the time of a successful beta test, young companies typically carry a large fixed cost, consisting of engineering and G&A personnel. In an effort to cover those costs and become cash positive as quickly as possible, they want to ramp up sales quickly. Conventional wisdom suggests that the only way to drive sales is by hiring sales reps as early as possible. To figure out exactly how many reps to hire and when, most sales leaders use a capacity-planning model.

The basic formula for calculating the number of salespeople needed to reach the break-even cash flow point is to divide the total fixed costs by the marginal contribution of an average sales rep (revenue minus cost of goods sold minus total cost per rep). The first step is to calculate total cost per rep.

Say a software company pays its average sales rep $200,000 per year (base plus commission), and a sales rep incurs $50,000 per year in T&E expenses plus $80,000 in administrative and operating expenses. That totals $330,000 in direct costs per rep. To that must be added indirect costs (such as sales support and management personnel), which typically adds as much as 50% of the direct cost per rep, in this case $165,000. That brings the total cost per rep to $495,000 per year.

The next, and most challenging, step is to figure out what annual revenue it’s reasonable to expect an average rep to produce once she’s come fully up to speed—the “standard quota.” To do so, most sales leaders rely on their past experience or the experience of other salespeople selling similar products. That works fine for mature products. Suppose, for instance, that our software company hires a star sales vice president from a Fortune 100 software firm. Based on his previous experience, he might expect a fully effective sales rep (FESR) to produce about $2.5 million in revenue per year, and so that’s the figure he starts with as the standard quota.

Then to account for attrition and uneven performance among reps, he prudently reduces that figure by about 20% to $2 million in average revenue per rep. The software business has high incremental margins of around 90%, yielding an expected contribution of $1.8 million per rep. So, subtracting the total cost per rep of $495,000 from the expected yield, the sales VP arrives at a marginal contribution for an average rep of around $1.3 million per year.

Our company’s fixed costs (engineering and G&A) are running around $12 million per year, so the sales head concludes that he needs about nine or ten sales reps to reach the break-even point. He’d get these reps on board as soon as possible and promise executive management that they’d reach break-even within six to 12 months, the typical time required to train and deploy new sales reps.

But let’s say that our company is a start-up, and the sales VP, in an effort to be conservative about selling a totally new product in a new market, reduces his standard quota from $2.5 million to $1.5 million. That drops the marginal contribution per rep to $585,000. Plugging in this lower marginal contribution would lead him to conclude he needs more like 21 sales reps.

The problem with applying the above methodology to start-ups or new-category product launches, though, is that it doesn’t take into account the organization’s sales learning curve. Our start-up would be swamped if all 21 reps were hired from the outset, when the product was still being refined, the go-to-market strategy had yet to be settled, and revenues were coming in far more slowly than they ever would for a mature product. Cash flow would be strained, sending the company further into the red and disappointing investors.

So what’s the right way to approach capacity planning for a new-product launch? Start out with very low assumptions about expected revenue per salesperson, and increase these expectations gradually, quarter by quarter. Anticipate that during the initiation phase, reps will not generate enough revenue to cover their total costs. Given that the marginal contribution per rep will therefore be negative in this phase, hiring more people will merely deteriorate your cash position. Only when you see the productivity of existing reps approaching the point where they cover their total costs should you consider expanding the sales force. Unfortunately, there’s no magic formula for predicting when that point will arrive. But closely tracking your sales yield, and tying your planning assumptions to the different stages on the sales learning curve, will prevent you from wasting critical funds in the early stages.

Scalix, a software company that develops e-mail and calendaring programs hosted on Linux, is one start-up that struggled to get its sales model right. The company’s founders recognized that the underlying infrastructure of market-leading e-mail systems such as Microsoft Exchange was originally designed for work groups and had never been upgraded to efficiently support large organizations. They believed that the disruptive nature of the Linux operating system created a rare opportunity for a new supplier to enter the mature e-mail market with a solution that was more secure, reliable, scalable, and cost effective. Specifically, they aimed to cut total cost of ownership by 50% to 60%.

Scalix launched its product, based on Hewlett-Packard’s OpenMail system, in July 2003. In early interviews, CIOs responded enthusiastically to the promised cost savings, so the company decided to expand its sales capacity quickly. Its initial strategy was simple: Recruit a high-powered sales leader with enterprise experience and sell directly to CIOs at large companies.

However, as Scalix moved deeper into the sales cycle at large corporations, the company encountered a number of unexpected problems. First, it became evident that the CIO was not the primary decision maker for purchasing e-mail systems. In many cases, the operations team one level down—the people who would be responsible for keeping the system up and running on a daily basis—rejected Scalix’s solution. These department managers didn’t want the headache of moving their Windows-based Exchange administrators over to Linux. And the Exchange administrators themselves viewed moving to Scalix as a career detour.

The second problem was closely related to the first. Scalix discovered that many large companies needed to get more comfortable with Linux before they would run e-mail on it. While a small group of people trained on Linux existed in most organizations in Scalix’s sales pipeline, they were not working on e-mail. Early adopters, such as Amazon and eBay, were running only customer-facing applications on Linux.

The third problem was of an entirely different order and perhaps the thorniest. Scalix learned its product was not quite ready for prime time. CEO Glenn Winokur explained: “You come out, and you think you have a market-ready product. Then you discover that you really don’t. You’re 90% of the way there, but there’s another 10% you have to iterate on with customers. From the time we came out and through all of 2003, we iteratively worked on pilots and trials with customers and learned the full extent of customers’ requirements for enterprise-class e-mail.”

“You come out, and you think you have a market-ready product. Then you discover that you really don’t.”

Scalix faced such an uphill battle selling directly to large enterprises that after a few sales to small public sector accounts, the company corrected its course. In mid-2004, it overhauled its go-to-market strategy to hit the Linux evangelist and early adopter community first, with a particular emphasis on smaller targets in the higher education and public sectors, where Linux acceptance was strongest. To execute this new strategy, Scalix hired two in-house telesales representatives to drum up leads. With lower-priced salespeople and a compressed sales cycle, the new model offered much better economics than the original field sales approach. The revised sales strategy is working well. Scalix was named one of Red Herring’s 100 top private companies of North America in 2004 and 2005.

Such adjustments aren’t unusual when companies interact with real customers deploying a product to do real work and in the process learn how to better meet their customers’ needs. But Scalix could have saved scarce resources and learned important lessons far more quickly had it delayed hiring a traditional sales force and focused all customer-facing departments on learning from the beginning.

Established companies often make many of the same mistakes in launching a new product that start-ups do. They hire experienced sales talent far enough in advance of the launch to allow them to come up to speed (based on conventional sales wisdom), and then they sit back and wait for the sales team members to deliver their expected quotas.

In late 2001, Veritas Software, now part of Symantec, was a large software company that sold three major software products through an international sales force of more than 2,000 field employees. It was very good at selling successive releases of its existing products, but the company’s track record in new categories was spottier. Although its signature file and disk management software was competitive in its features, many customers preferred a bundled hardware and software solution like those from EMC and Network Appliance. So Veritas decided to launch a new class of products, a set of software applications preconfigured to run on servers from vendors like Dell, Compaq, HP, and IBM. This bundled solution would offer the cost advantages of buying commodity PC server hardware from existing vendors together with a complete plug-and-play software package from Veritas.

The company’s initial go-to-market strategy was to create an overlap sales force that would work closely with the regular software sales force. Both sales teams received commissions on the new product. However, a number of problems arose shortly after the launch. Veritas had expected the product to be completely developed and ready to go, but it was not yet either fully reliable or fully functional. This frustrated the regular sales team, which was used to selling mature products. Reps were compensated for sales of the new product but not enough to make up for the extra time and effort required. In addition, the new bundled offering was seen as a potential threat to the company’s traditional hardware partners like Sun Microsystems, making them less likely to cooperate with Veritas at the field sales level. Understandably, savvy regular salespeople never enthusiastically supported the new product. Veritas abandoned the initiative a little over a year after its introduction because the revenue remained substantially below expectations.

Had Veritas better understood what was involved in the sales learning curve for this new venture, it could have anticipated and made provisions for these problems.

Every business goes through a unique learning process, and each industry, company, and product has a different set of drivers. As the Scalix and Veritas examples show, the product will probably not have exactly the right features or work exactly the way it should at the outset. The sales and marketing processes may not be focused initially on the right customers. To traverse the learning curve, product development, marketing, and sales must resolve a host of complicated questions. The product developers, for instance, need to correctly determine which features would make the product valuable to customers. They need to make it easy to use, reliable, and efficient to service. Marketers need to correctly analyze the product’s position relative to its competition. They need to segment its market. They need to develop packaging. The sales team needs to determine the number and type of distribution channels, develop a sales model, work up a sales pitch. The broad range of issues that all three departments must resolve to launch a successful product is summarized in the exhibit “What Goes into a Comprehensive New-Product Strategy.” When you look at the length of the list, it becomes easy to see that launching a new product involves far more than ramping up a large sales force.

Launching a completely new product into a new market is not just a matter of hiring an army of salespeople and letting them loose once the product is created. Product developers, marketers, and sales staff need to resolve a host of issues:

Gaining that knowledge, of course, doesn’t happen all at once. Nor does it happen in a vacuum. It develops gradually: The company makes initial assumptions, which are modified iteratively as feedback comes in from early customers. The modified offering reaches even more customers, whose further feedback hones the product, the message, and the sales efforts—accelerating the company’s progress along the learning curve. This process cannot be short-circuited by sending out an army of salespeople in an effort to gather more feedback more quickly: Many problems are discovered sequentially, revealing themselves only after some preceding issue has been discovered and addressed. Eventually, the company learns enough to reach a level of steady sales.

Companies have long measured their progress along the manufacturing learning curve by tracking costs per unit—the more they learn about the manufacturing process, the more efficient it becomes, and the lower the unit cost goes. Progress along the sales learning curve is measured in an analogous way: The more a company learns about its product, market, and sales process, the more efficient it becomes at selling, and the higher the sales yield. “Sales yield” is defined as the average annual sales revenue per full-time, fully trained and effective sales representative. Typically, sales yield for a new product starts out slowly, accelerates for a while, and then flattens out as the product matures, in a classic S-shape curve.

The steepness of the curve—a measure of how rapidly product revenues reach the break-even point and then achieve targeted levels—varies substantially from product to product. For example, when the Palm Pilot was introduced, it created a whole new product category. The sales learning process was long and complicated, resulting in a sales yield curve that looked like the first curve in the exhibit “How Steep a Curve?” By contrast, the competing Handspring, a follow-on product in a now-established market, was launched with a “better, faster, cheaper” strategy, and its learning process was far quicker. Accordingly, its sales curve is shifted to the left and far steeper, resembling the second curve in the exhibit. For many new-product launches, the sales yield never reaches expected levels, or even the break-even point, resulting in cash shortfalls and premature death for promising products.

Progress along the sales learning curve is measured by tracking sales yield over time. As you might expect, the curve for launching a totally new product into a new market is much longer and flatter initially than one for introducing a variation of an existing product into an established market. Clearly, the longer the learning curve, the greater the revenue gap—that is, the longer it takes for sales yield to reach targeted quota levels. Setting expectations appropriately, therefore, can make the difference between pioneering a new market and aborting too soon.

Tracking sales yield can be more challenging than measuring manufacturing production volumes and costs because revenue-generating activities tend to be less predictable than production. Still, applying the concept of a sales learning curve allows you to understand where in the learning process your launch is, so that you can deploy your sales force, marketing efforts, engineering support, and management time appropriately.

The way to shift the sales learning curve to the left, and reach the break-even point and profitability more quickly, is to track sales yield over time and adjust your go-to-market strategy as you move along the curve. That’s because the sales learning process unfolds in three distinct phases—the initiation phase, the transition phase, and the execution phase—as the exhibit “Ramping Up the Learning Curve” shows. Each phase requires a different size—and kind—of sales force, and represents a different stage in your production, marketing, and sales strategies. The gateways from one stage to the next correspond to two markers of profitability level—the break-even point and some targeted level of steady sales, which we call the “traction point.”

The go-to-market strategy for a new product should unfold in three parts. The staffing and financial resources needed in the first stage, before the product is profitable, are very different from those needed in the second stage, when the product is being refined, and different again from those needed after most issues have been resolved and sales have reached a sustainable, predictable level.

This phase begins when the product is ready to hit the market, that is, when it has been beta tested, and lasts until the break-even point—that is, when sales yield reaches a point where revenue per sales rep equals the fully loaded cost per sales rep. Typically, during this time, few customers will be willing to consider buying the product, and those that do will require significant incentives.

It’s both unrealistic and potentially dysfunctional to assign large sales quotas in the initiation phase. The members of the sales team should be encouraged to focus instead on learning as much as they can about how customers will use the product so they can support engineering, product marketing, and marketing communications in perfecting both the offering itself and the go-to-market strategy and programs. A heavily commission-based pay plan is not only unlikely to achieve sales objectives but can inhibit learning.

It’s also inefficient to hire too many sales reps in this phase. A small sales force not only keeps costs down but is more effective in supporting other parts of the company. Typically, three to four salespeople are enough to start the learning process and to make sure that the problems encountered aren’t just the result of a bad hire.

The types of skills needed during this phase differ from those needed to sell more mature products. They include a facility for communicating with many parts of the organization, a tolerance of ambiguity, a deep interest in the product technology, and a talent for bringing customers together with various functional teams within the company. Salespeople must be resourceful, able to develop their own sales models and collateral materials as needed. We think of this kind of person as the “renaissance rep.”

Toward the end of the initiation phase, companies generally have acquired a critical mass of customers, and sales are beginning to accelerate. Once the sales yield equals the fully loaded expense per sales rep, it’s safe to assume you’ve moved into the transition phase. This second phase lasts until the sales yield reaches a point where company management can see that the product has achieved real traction in the market.

What constitutes “traction” varies from company to company and product to product. Still, we’ve found that a useful rule of thumb is to consider a sales yield of twice the fully loaded cost per sales rep as the end of the transition phase. By this point, the company should have a pretty good idea of what to expect in terms of steady state sales yield for the product.

In the transition phase, sales management should focus on developing a repeatable sales model, refining market positioning, and adding sales capacity at a rate commensurate with the rise in the slope of the curve. The original renaissance reps should continue to stay focused on learning. The people hired at this stage—we call them “enlightened reps”—should be comfortable contributing to a still-evolving sales model but do not need to have the analytical and communication skills of the renaissance reps.

Once sales management is confident that the product has achieved traction and is entering the execution phase, sales reps can be hired as rapidly as the company’s management and financial constraints will allow. In this phase, when the formula for success has been developed and all of the support requirements for sales reps are in place, the company needs more traditional salespeople—what’s known in the industry as “coin-operated reps”—who require nothing more than a territory, a sales plan, a price book, and marketing materials to bring in orders.

Product marketing and marketing communications should ideally be the center of learning activities during the initiation phase. Marketing leadership is responsible for bridging the gap between customers, sales reps, and the engineering organization. Everyone on the marketing team has to be knowledgeable about the product technology, able to understand customers and their needs, and proficient at communicating with renaissance reps. But success in this role requires more than just an understanding of the languages of these disparate groups and individuals in the company. It requires substantial credibility to convince customers, sales, and engineering that their needs will be accurately communicated to the other parties. Marketing must hold the product itself to high standards of completeness, correctness, and fit.

Once beta testing is complete, the company must decide when to launch a marketing campaign. There can be pressure to start the campaign early to support the sales effort, but that’s an expensive use of scarce cash resources. What’s more, it can distract the organization from its primary learning goal and set false expectations among the sales and engineering groups. Worse, a premature marketing campaign can set false expectations in the marketplace that will be difficult to correct.

As the company tweaks the product and learns more about the ways customers use it in the first two stages, the positioning will evolve. Marketing communications must develop a nimble launch schedule that can adjust to the requirements of each stage by preparing collateral materials that can be easily modified and by working with media and other resources so they can respond quickly once the final product and sales strategies have been completed.

Keeping the product development engineering team intact at least through the initiation phase is essential to the success of a new-product launch. This is a significant challenge for both company executives and engineering management: After beta testing is done (and sometimes even before), the engineering organization typically turns to the next product. Cleaning up and making sure that existing products are complete and correct is not the most glamorous phase of product development. What’s more, the reward systems in most R&D organizations encourage engineers to go on to the next challenge. But when companies allow those most intimately knowledgeable about a new product to move on, they slow down learning. New engineers have to be trained, and it takes them longer to make design modifications, stretching out the time required to ramp up sales yield.

Engineering management can take several steps to provide the right incentives and foster the right culture to support the learning curve. The first is to introduce an organizational measure that reflects the importance of staying focused during launch. Companies measure manufacturing success in terms of how long it takes to achieve scale production volumes, and they typically tie engineering to this metric as well. To move along the learning curve faster, though, companies need instead to focus engineering, as well as marketing and sales, on the time it takes to reach the break-even and then sustainable profitable levels of sales yield. In addition, engineers who stay involved throughout the entire learning process need to be rewarded by an assignment to another big project.

It is unlikely that incentives will be established to keep engineers involved with the product once it hits the market without the direct and daily involvement of general management. Indeed, during the first two phases, the whole management team should be focused on the customer frontier, the source of learning. CEOs and division managers must direct the efforts of those who drive the learning and participate in all crucial product and organizational decisions concerning functionality, target markets, sales channels, and marketing strategy.

Too often, executives don’t get actively involved in sales strategy until a revenue gap appears and cash becomes scarce. But it is during the early stages of the learning curve that top management can have maximum impact on the ultimate outcome of the venture. Executives need to take responsibility for projecting the shape of the sales learning curve based on realistic inputs and for ensuring that all learning opportunities are identified. Then, the shape of the curve should drive the design of revenue, expense, and hiring plans across the entire organization. The longer the initiation stage, the longer the learning period and investment phase will be, and the lower the revenue expectations should be set. The most important role of all for senior executives is setting realistic expectations—guided by the curve—for investors, the board, and employees.

High-tech companies routinely “price on the learning curve”—that is, they deliberately set selling prices low on the early manufacturing runs to stimulate volume that will underwrite their efforts to gain enough experience to lower costs and ultimately reap higher profits. Like the manufacturing learning curve, the sales learning curve permits you to see all the aspects of a set of activities—in this case, the go-to-market process—through a new lens and to plan appropriately. Applying the sales learning curve as a strategic construct allows management and investors to share a common language in understanding this phase of the business. And successful management of the sales learning curve allows companies to reduce the time to profitability and the cost of breaking even, increasing the success rate of start-ups and new-category product launches.

The Sales Learning Curve

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