In the Top Nine Myths of Revenue & Growth, a nine-part series, AlixPartners spotlights the changing calculus of top-line revenue strategies and suggests ways companies can overcome commonly held revenue and growth myths as they pursue—and achieve—profitable growth.

For companies seeking topline revenue growth, the rules of the game have changed.

Across industries, market dynamics are evolving at an ever-increasing pace as companies derive data-driven insights and apply digital strategies to move quickly and decisively in order to adapt and grow.

With those new rules come new strategies.

Now more than ever, speed to results and rapid execution in sales, marketing, pricing, and profitability are becoming fundamental to remaining competitive. Many commonly held assumptions have been rendered obsolete, yet many companies still fall prey to the myths as they struggle to respond to the competition.

Avoiding the myths and achieving tangible growth.

With the changing rules of the game, how can investors and managers overcome myths and maintain or enhance profitable growth? More important, how can they execute while staying strategically nimble enough to remain responsive to the market and not only survive but also thrive?


To some sales professionals, a lead is a lead is a lead. But in reality, no matter the industry or the product, some prospects are simply low quality, with little chance of conversion into sales, whereas others might turn into sales but contribute little to profitability. And then there are the prize prospects—the ones with a propensity to buy even before they see an offer and who generate lifetime value above and beyond the norm. The question is, how can a sales organization tell one sort of lead from another—before it commits resources to cultivating any of them?

This question of whether a lead is “good” is one that bedevils sales professionals across industries. A key part of go-to-market work involves—or should involve—segmenting customers and prospects according to their likelihood to buy, as well as the revenue and profit opportunity they represent. At their simplest, the segments can be ranked: from least likely to convert to most and from lowest opportunity to highest. A segmentation model can be further refined to distinguish between existing customers, which are ranked by the likelihood that they will renew their business and buy more in the future; and prospects, which are ranked by their propensity to buy and the estimated lifetime value of their business. Such rankings help sales leaders (1) decide where to commit their most-valuable resources and (2) determine the skills those resources need if they’re to succeed.

Organizations that perform these kinds of segmentation and resource allocation in the right ways can position themselves for sustained growth and profitability. Those that chase revenue by pursuing all leads with equal vigor or focus on incomplete metrics (e.g., expected revenue) to prioritize leads can find themselves stuck in a lowprofit, low-growth rut, even if they’re hitting all their sales targets. They can further hinder profitability if different functions pursue conflicting objectives. In such cases, management will struggle to find out which product or service lines are performing well, which ones could benefit from larger investments, and which ones should be wound down or eliminated.

To address such challenges, companies can analyze their past performance and current strategies to guide their lead acquisition efforts going forward by focusing on the most-promising industries, the most-favorable geographies, or the most-likely revenue pools. Using estimates of the profitability and lifetime value of each prospective customer, they can devise a segmentation plan that maximizes the amounts of time and effort that get invested in the most-valuable leads and that de-emphasizes less-profitable prospects. Sales representatives can more narrowly focus their targeting of leads based on historical close rates so as to focus on leads most likely to convert and deliver sustained value to the company. Predictive models can add even more precision to their targeting and help generate revenue forecasts for marketing and sales teams. And gaps between revenue forecasts and actual results can help management decide where to focus remediation efforts.


One operator of for-profit educational institutions recently took this approach to improve both the efficiency and effectiveness of its sales function and its profitability. The schools were facing increasing competition for students, as rising employment, buoyed by a recovering economy, prompted many prospective enrollees to enter the working world rather than continue their schooling. Needing to market itself more aggressively, the education provider saw enrollment costs increase and student enrollment decline, squeezing the schools’ profitability from both ends.

The institution’s profitability challenges mounted as the macroeconomic recovery continued, exposing the flaws of a sales mind-set that viewed all leads as valuable and all students as profitable as long as marketing, admissions, and financial aid generated enrollments. In reality, though, the source of a lead, the degree of competition for students, and the reputation of a particular academic program were the determinants that largely predicted the lifetime profitability of a prospect. The pursuit of leads with comparatively low lifetime values only strained the marketing budget and eroded the institution’s overall profitability over time. Moreover, functional key performance indicators (KPIs) were often conflicting with one another, with the perverse result that maximizing the KPIs of one function could limit overall performance. Only by considering those functions as an interconnected system with interdependent, complementary KPIs would it be possible to optimize the performance of each program as well as the institution as a whole. Otherwise, marketing could, for example, generate more prospective students than the admissions and financial aid offices could effectively segment by predicted lifetime value.

To develop the system, administrators created a holistic model to gauge the profit potential of each lead and program. They first calculated acceptable enrollment costs based on tuition minus variable operating expenses such as those for marketing, call centers, and admissions and financial aid staffs. They factored in retention rates to estimate how long the profits would be generated, and they weighed each program’s reputation and job placement rate. Taken together, the collected information served, in effect, as a lifetime value calculation that enabled the administrators to determine the acceptable cost of customer acquisition. Once the various functions began to factor those variables into the segmentation and ranking of prospective students, the number of leads acquired plunged, leaving only the most potentially valuable ones and reducing the cost of student acquisition by about 9%.


The challenges and remediation efforts described in the foregoing case don’t apply only to educational institutions, of course. Consider how one software-as-a-service company’s highly refined segmentation model helped the company rank its prospects and allocate sales resources effectively. The model scored prospects on the prospects’ growth opportunities and likelihood of upselling. Prospects with the highest growth opportunity—about 10% of the total prospect pool—received intensive, high-touch service from senior sales executives as well as multiple functions including sales, marketing, and solutions design. High-potential prospects—judged not as promising as premier prospects but still with significant potential lifetime value— received medium-touch, phone-based service from business development representatives. Representing about 40% of total prospects, those prospects were midsize companies that had already scored some wins, though not as many as the premier prospects had. At the bottom of the rankings of prospects were early-stage customers with low likelihood of buying and limited growth opportunities. Marketing reached out to them through companywide campaigns and brand-building programs.

Elsewhere, a global manufacturer of power-generation equipment uses analytics powered by artificial intelligence to identify likely win opportunities and, through machine learning, infer why those opportunities represent likely sales successes, resulting in a 10% expansion of the sales pipeline. The approach draws on statistical analysis of past wins and losses in order to prioritize opportunities while they’re still in the pipeline, thereby enabling sales leadership to (1) focus on deals most likely to succeed and (2) identify and correct deal features correlated with low rates of success. The AI program considers a myriad of predictive features such as time of year, geography, product type, price, and profit margin, weighting each factor by its level of contribution to past sales wins. The formula enables sales professionals to (1) determine by how much a given feature increases or decreases the odds of a win and (2) decide whether to prioritize a deal scheduled for closing in, say, September—a month correlated with multiple lost sales—or, rather, October, when the company in the past has scored several important wins.

In addition to producing more-accurate sales forecasts, sales leadership can now investigate why certain deal features lower the odds of success by examination of the answers to certain questions: Is the feature a matter of pricing? Does it have to do with service levels? Is the sales organization offering the feature to the wrong set of prospects? and, are there other barriers to a deal’s closing? Detailed analytics enable sales leaders to answer those questions more accurately and thereby generate guidance and direction for reducing—or even eliminating—barriers to closing.

Sales organizations are nothing without prospects in the pipeline. But prospects are of little value to a company that can’t distinguish between high-value leads and those that drain resources without generating revenue or profit. A rigorous, technology-augmented approach to segmentation can help any organization make the most of its sales resources—and deliver more value to the bottom line.