# Patent

## Title
Identification of businesses with potential to achieve superior revenue growth and financial performance

## Abstract

A method of identifying target businesses with above-average potential to achieve a selected financial performance target, in accordance with the principles of one embodiment of this invention comprises identifying a plurality of model businesses which have already achieved the selected financial performance target; identifying financial statistics common to the identified model businesses; and screening target businesses for the financial statistics common to the identified businesses. The financial statistics preferably include: at least one of historic annual sales, annual sales growth rates, and time since found to exceed selected annual sales thresholds, gross margins, SG&D, R & D expense, EBIDTA, cash flow from operations, market capitalization.

## Background
CROSS REFERENCE TO RELATED APPLICATION
This application is a continuation-in-part application of application Ser. No. 10/994,212, filed Nov. 19, 2004, the disclosure of which is incorporated herein by reference, and which claims the benefit of U.S. Provisional Application No. 60/524,414, filed on Nov. 21, 2003.


BACKGROUND OF THE INVENTION
This invention relates to methods of identifying businesses with potential to achieve superior financial performance, and to the use of such methods in the selection of investments, the management of investment portfolios, the allocation of resources between business units, and the management of businesses.
From about 1980 to the present, of the 21,581 publically listed U.S. companies, there were 8,626 IPOs across all sectors. Of these companies 4,425 are active today, with the difference having been acquired or merged, or having gone out of business. Of these 4,425 companies, 548 achieved over $1 billion revenue since 1980. Of these 548 companies, 451 went public with revenues less than $1 billion. The ability to identify factors that enabled the growth of these top businesses would be useful both in selecting companies for investment, ranking businesses, allocation of resources between businesses; and in managing a business to achieve financial goals. This set defines America's highest growth companies; those companies who went from founding to $1 billion in annual revenue. The set is defined as publically listed companies, active as of November 2004, with the headquarters location and ownership in the United States.
SUMMARY OF THE INVENTION
Generally, this invention relates to methods of identifying businesses with potential to achieve superior growth and financial performance, and to the use of such methods in the selection of investments, the management of investment portfolios, the allocation of resources between business units, and the management of businesses. Various aspects have been identified as important to the success of businesses, including financial modeling, revenue modeling aligned to marquee customers, number and role of alliances, number and profile of board members and value proposition waves and the number of essentials required to build a scalable business. As a result, quantitative processes can be derived from this pattern to give key indications as to future potential. A focus of the methods of this invention was on the growth lifecycle of the top 548 companies that reached $1 billion in annual revenue.
One preferred embodiment of this invention provides a method of identifying businesses with above-average revenue growth potential by selecting from a plurality of companies with annual revenues of at least $30 million, those companies with revenue growth rates that are within the growth rates of the three trajectories identified.
Another preferred embodiment of this invention provides a method of evaluating at least one target business by identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; comparing the at least one financial statistic common to the model businesses with at least one financial statistic of the at least one target business; and evaluating the at least one target business based upon its comparison with the model businesses.
Another preferred embodiment of this invention provides a method of investing in businesses comprising preferentially investing in target businesses selected by identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; comparing the at least one financial statistic common to the model businesses with at least one financial statistic of the at least one target business; and evaluating the at least one target business based its comparison with the model businesses.
Another preferred embodiment of this invention provides a method of allocating resources among several target businesses within an enterprise by preferentially allocating resources to target businesses in the enterprise selected by identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; comparing the at least one financial statistic common to the model businesses with at least one financial statistic of the at least one target business; and evaluating the at least one target business based its comparison with the model businesses.
Another preferred embodiment of this invention provides a method of ranking at least two target businesses based upon outlook for financial performance by identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; and ranking each of the target businesses based upon the at least one financial statistic
Another preferred embodiment of this invention provides a method of managing a target business to achieve a desired financial performance, the method comprising: identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; managing the operation of the target business to match the at least one financial statistic common to the model businesses.
Another preferred embodiment of this invention provides a method of investing by preferentially investing in businesses with above-average growth potential by selecting from a plurality of companies with annual revenues of at least $30 million, those companies whose gross margins exceed 55%, whose EBITDA exceed 20%, and who had a positive case flow since annual revenues exceeded 50 million.
Another preferred embodiment of this invention provides method of allocating resources between businesses by preferentially allocating resources in businesses with above-average growth potential by selecting from a plurality of companies with annual revenues of at least $30 million, those companies whose gross margins exceed 55%, whose EBITDA exceed 20%, and who had a positive case flow since annual revenues exceeded 50 million.
Another preferred embodiment of this invention provides a method of identifying target businesses with above-average potential to achieve a selected financial performance target by identifying a plurality of model businesses which have already achieved the selected financial performance target; identifying financial statistics common to the identified model businesses; screening target businesses for the financial statistics common to the identified businesses.
Another preferred embodiment of this invention provides a method of identifying a business with above average growth potential by one or more of filtering out businesses that have less than a threshold revenues; filtering out businesses that did not reach a threshold revenue within a specified window of time; filtering out businesses that did have a minimum growth rate as specified times; filtering out businesses that did not maintain gross margins of at a specified rate; filtering out businesses that did not maintain EBITA at a specified rate; filtering out businesses that did not maintain specified positive cash flow; filtering out businesses whose market capitalization to annual revenue ratio is not greater than 3 after annual revenues first exceeded $50,000,000; filtering out businesses whose market capitalization to annual revenue ratio is greater than 4 after annual revenues first exceeded $50,000,000; filtering out businesses that did not have an employee of a customer on its board of directors; filtering out businesses that did not have a customer as a investor; filtering out businesses that did not recruit at least one officer from a competitor; filtering out businesses that do not have the fastest growing sales in at least one category; filtering out businesses that do not have as a customer a publicly traded company that allows its name to be used in connection in connection with promotion of the businesses' products or services; filtering out businesses that do not have as a customer a publicly traded company that allows its name to be used in connection with promotion of the businesses' products or services; and has an officer, director or employee that sits on the board of the business; filtering out businesses that do not have a co-selling agreement with a publicly traded entity; filtering out businesses that do not have a joint selling or joint marketing arrangement with a publicly traded entity that has an officer, director, or employee that sits on the board of the business; filtering out businesses that do not have as a director an officer, director, or employee from a company that purchases the businesses' products or services, or an officer, director or employee from a company that jointly sells a product or service with the business' product or service; filtering out businesses that do not have as a director an officer, director, or employee from a company that purchases the businesses' products or services, and an officer, director or employee from a company that jointly sells a product or service with the business' product or service.
Another preferred embodiment of this invention provides a method of comparing two or more businesses to determine the business with the greatest potential for growth by evaluating each of the companies under consideration and scoring them based upon; scoring the founding team based on a plurality of specified factors; scoring the engineering team based upon a plurality of specified factors; scoring the technology discontinuity/value proposition based upon a plurality of specified factors; scoring the management team based upon a plurality of specified factors; scoring the investors based upon a plurality of specified factors; scoring the strategic alliances based upon a plurality of specified factors; scoring the market segment based upon a plurality of specified factors; scoring the marquee customers based upon a plurality of specified factors; and selecting the company with the greatest cumulative score.
Another preferred embodiment of this invention provides a method of comparing two or more businesses to determine the business with the greatest potential for growth by evaluating each of the companies under consideration and scoring them based upon: scoring the founding team based on a plurality of specified factors; scoring the engineering team based upon a plurality of specified factors; scoring the technology discontinuity/value proposition based upon a plurality of specified factors; scoring the management team based upon a plurality of specified factors; scoring the investors based upon a plurality of specified factors; scoring the strategic alliances based upon a plurality of specified factors; scoring the market segment based upon a plurality of specified factors; scoring the marquee customers based upon a plurality of specified factors; and selecting companies with scores in excess of a predetermined threshold.
Another preferred embodiment of this invention provides a method of investing by preferentially investing in companies with above-average prospects for growth, identified by the methods of the evaluation methods of this invention.
Another preferred embodiment of this invention provides a method of managing an investment portfolio by preferentially investing in businesses with above-average growth potential selected in accordance with the evaluation methods of this invention.
Another preferred embodiment of this invention provides a method of managing a business for above-average growth potential, by one or more of the following active management steps: achieving threshold revenues; achieving threshold revenues within a specified window of time; achieving minimum growth rates at specified times; maintaining gross margins at a specified rate; maintaining EBITA at a specified rate; maintaining specified positive cash flow; maintaining market capitalization to annual revenue ratios above specified minimums; attracting customers as investors; recruiting officers from competitors; leading sales growth in at least one category; attracting as customers publicly traded that allows their name to be used in connection in connection with promotion of the businesses' products or services; attracting as board members employees of customers who are publicly traded companies that allows their name to be used in connection with promotion of the businesses' products or services; entering into co-selling agreement with a publicly traded entities; entering into joint selling or joint marketing arrangements with a publicly traded entity that have an officer, director, or employee who is a board member; having an officer, director, or employee from a customer and/or a joint selling partner, on the board.
Another preferred embodiment of this invention is the success based methodology that is the basis for this patent. The methodology is based on a pyramid problem solving structure that synthesizes financial models and the essentials required to achieve this performance into a set of quantitative measures. The methodology is success based on identifying the pattern across the companies who went from founding to $1 B revenue. The process of normalizing the financials and histories enables the patterns identified in this patent.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a graph showing the disposition of businesses after initial public offering between 1980 and 2000;
FIG. 2A is a graph showing the annual revenue since inception for business not achieving $1 billion revenue vs. businesses exhibiting superior revenue growth (those who achieved $1 billion revenue), each of which has exponential revenue growth;
FIG. 2B is a graph showing three components of the pattern of exponential growth;
FIG. 2C is a graph showing the exponential revenue curves for the 451 companies that went public since 1980 with annual revenues of less than $1 billion, generated by overlaying the revenue curves at the inflection point;
FIG. 3A is a graph showing the three revenue trajectories of selected businesses that achieved $1 billion in annual revenues;
FIG. 3B is a graph of companies who bypassed the inflection point to breakout to $1 billion (i.e., did not follow the 3 trajectories);
FIG. 3C is a graph showing the revenue min-max for the 3 trajectories;
FIG. 3D is a graph showing the revenue min-max for the 3 trajectories followed by three graphs showing equations for the min-max for Four-Year Trajectory;
FIG. 3E is a graph showing the revenue min-max for the 3 trajectories followed by three graphs showing the equations for the min-max for Six-Year Trajectory;
FIG. 3F is a graph showing the revenue min-max for the 3 trajectories followed by three graphs showing the equations for the min-max for Twelve-Year Trajectory;
FIG. 3G is a graph showing the revenue growth rates for businesses following the min-max of the Four-Year, Six-Year Trajectories;
FIG. 3H shows the process for forecasting revenue growth rates and to screen that companies identified to be on a trajectory, are screened for performance to stay on trajectory;
FIG. 4A is a chart showing the distribution of the 451 companies that went public since 1980 with less than $1 billion in annual revenue by sector;
FIG. 4B is a graph of gross margins for information technology businesses in each of the three annual revenue trajectories shown in FIG. 3;
FIG. 4C is a graph of EBIDTA for businesses in each of the three annual revenue trajectories shown in FIG. 3;
FIG. 4D is a graph of cash flow for businesses in each of the three annual revenue trajectories shown in FIG. 3;
FIG. 4E is a graph of gross margins and EBITDA at year 1 for Four-Year, Six-Year, and Twelve-Year Trajectory businesses in different industries;
FIG. 5 is a graph illustrating revenue, market capitalization, and market capitalization to revenue for businesses with less than $100 million in annual revenue, with about $400 million in annual revenue, and more than $1 billion in annual revenue;
FIG. 6A is a graph of the Four-Year Trajectory for reaching $1 billion in annual revenue;
FIG. 6B is a graph of the Six-Year Trajectory for reaching $1 billion in annual revenue;
FIG. 6C is a graph of the Twelve-Year Trajectory for reaching $1 billion in annual revenue;
FIG. 6D is an graph of a first exemplary business on the Twelve-Year Trajectory;
FIG. 6E is an graph of a first exemplary business on the Twelve-Year Trajectory;
FIG. 7 is a graph of 8 attributes (excluding financials which is a ninth attribute) for reaching $1 billion in annual revenue, illustrating the phases of revenue growth;
FIG. 8 is a schematic diagram of 9 attributes of businesses that reached $1 billion in annual revenues;
FIG. 9 is a schematic diagram of R&D attributes of businesses that reached $1 billion in annual revenue;
FIG. 10A is a diagram of value proposition platforms relating to one of the R&D attributes of businesses that reached $1 billion in annual revenue;
FIG. 10B is a diagram showing how the value proposition platforms apply to exemplary business;
FIG. 11 is a diagram of sales attributes of businesses that reached $1 billion in annual revenue;
FIG. 12A is a diagram of sales growth drivers of businesses that reached $1 billion in annual revenue;
FIG. 12B is a graph illustrating the mathematical formulas which identify the key metrics which determine revenue growth rate;
FIG. 13A is a diagram of marquee customers, relating to one of the sales attributes of businesses that reached $1 billion in annual revenue;
FIG. 13B is a table of the role and types of marquee customers;
FIG. 13C is a graph illustrating the role of marquee customers;
FIG. 13D is a table comparing small customers with marquee customers;
FIG. 13E is a table illustrating examples of asymmetrical alliances;
FIG. 13F is a table illustrating the role of asymmetrical alliances;
FIG. 14 is a schematic diagram of cash management attributes of businesses that reached $1 billion in annual revenue;
FIG. 15 is a diagram of some of the drivers of scalable cash management of businesses that reached $1 billion in annual revenue;
FIG. 16A is a diagram of the compositions of boards of directors of businesses that reached $1 billion on each of 4 year, 6 year, and 12 year trajectories;
FIG. 16B is a diagram of the typical composition of boards of directors of businesses that reached $1 billion in annual revenue;
FIG. 17 is a diagram of the composition of the boards of directors of businesses that did not reach $1 billion in annual revenue versus 6 year trajectory companies;
FIG. 18 is a diagram of R&D, sales, and cash management attributes for managing a business in accordance with the principles various embodiments of this invention;
FIG. 19 is a schematic diagram of a screening process in accordance with the principles of the various embodiments of this invention;
FIG. 20 is a diagram of a scorecard for comparing two businesses;
FIG. 21 is a chart of financial statistics (the mean P&L for the information technology sector) of exemplary actual companies that followed the Four-Year Trajectory to $1 billion in annual revenue;
FIG. 22 is a chart of financial statistics (the mean P&L for the information technology sector) of exemplary actual companies that followed the Six-Year Trajectory to $1 billion in annual revenue;
FIG. 23 is a chart of financial statistics (the mean P&L for the information technology sector) of exemplary actual companies that followed the Twelve-Year Trajectory to $1 billion in annual revenue;
FIG. 24 is a diagram of a sample scorecard for evaluating businesses in accordance with some embodiments of the present invention;
FIGS. 25A-25C are diagrams relating to score cards for comparing businesses;
FIG. 25A is a diagram of a possible application of a scorecard in comparing businesses in accordance with some embodiments of this invention;
FIG. 25B is a first example of an actual comparison between two companies using score cards in accordance with the principles of some embodiments of this invention;
FIG. 25C is a second example of an actual comparison between two companies using score cards in accordance with the principles of some embodiments of this invention;
FIG. 26A is a diagram of a possible application of a scorecard in evaluating a business in accordance with some embodiments of this invention;
FIG. 26B is an example of a scorecard used in evaluating a business as part of a due diligence review process;
FIG. 27 is a schematic diagram illustrating the time from inception to the Inflection Point for a sample of the companies from the information technology sector;
FIG. 28 is a schematic diagram of the due diligence process;
FIG. 29A is a table for evaluating a business in accordance with embodiments of the present invention;
FIG. 29B is a table for evaluating a business in accordance with embodiments of the present invention;
FIG. 30 is a example of using a score card in the evaluation of the of a business;
FIG. 31 is a table of financial statistics for Four-Year; Six-Year, and Twelve-Year Trajectory businesses;
FIG. 32 is a table showing a comparison between a target business with the Four-Year Trajectory;
FIG. 33 is a schematic diagram of a management process in accordance with some embodiments of the present invention;
FIG. 34A is a an example of a comparison between the six year trajectory with planned performance of a business, as might be done for management purposes;
FIG. 34B is an example of a report card on the same business in the comparison in FIG. 34A, as might be done for management purposes;
FIG. 35 is a schematic diagram of a success-based methodology in accordance with the principles of some embodiments of this invention;
FIG. 36 is a schematic diagram illustrating a screening process in accordance with the principles of some embodiments of this invention;
FIG. 37 is a chart illustrating the normalization process for a exemplary business in accordance with the principles of some embodiments of this invention;
FIG. 38 is a graph of normalized market capitalization at $1 billion in annual revenue;
FIG. 39 is a graph of examples of revenue growth of selected exemplary businesses; and
FIG. 40 is a flow chart illustrating one embodiment of a computerized method of identifying target businesses with above-average potential to achieve a selected financial performance target.



DETAILED DESCRIPTION OF THE INVENTION
Various embodiments of this invention relate to evaluating and/or comparing businesses. As used herein the term “business” can mean a standalone company, such as a corporation or limited liability company, but it can also mean a division or business unit of a larger entity. The methods of evaluating and comparing businesses can be applied to investment decisions (e.g., whether or not to invest); portfolio management (e.g. whether or not to add, retain, or remove a particular business from an investment portfolio); and due diligence in business acquisitions and divestitures. The methods of evaluating and comparing business can also be used in the management of businesses.
As illustrated in FIG. 1, from about 1980 to the present, of the 21,581 publicly listed U.S. companies, there were 8,626 IPOs across all sectors since 1980. Of these companies 4,425 are active today, with the difference having been acquired, merged or gone out of business. Of these 4,425, active companies, 548 achieved over $1 billion revenue since 1980. Of these 548 companies, 451 IPO with revenue less than $1 billion. As illustrated in FIG. 2, of the businesses that achieve above-average growth, and particular, those which achieve $1 billion revenues, these businesses demonstrate highly variable incubation periods, and different growth rates, but each demonstrated exponential revenue growth.
FIG. 2A demonstrates the revenue growth pattern identified broke into three parts. The first graph, a traditional view, highlights that growth had a basic pattern; variable time from founding to an inflection point (knee of the curve), followed by a variable exponential growth rate. FIG. 2B shows that growth of the most successful high growth companies had two parts, time to $30-$70 million revenue was highly variable with the mean of 5.3 years at $50 million+/−20 million FIG. 27, and a definitive range of exponential growth rates to $1 billion annual revenue.
More specifically, the inventor has discovered that, as shown in FIG. 3A, revenue growth for above-average growth companies, i.e., those that reach $1 billion in annual revenue, is characterized by two things: First, the time to reach approximately $50 million in annual revenue (the “Inflection Point”), which for these companies has a mean of 5.3 years from inception (year that the company was founded). (See FIG. 27). Second, from the Inflection Point, the time to reach $1 billion in annual revenue (for those companies that will reach $1 billion in annual revenue) typically takes one of three trajectories. A first group reaches $1 billion in annual revenue in 3.5±0.5 years (the “Four-Year Trajectory”); a second group reaches $1 billion in 6±1 years (the “Six-Year Trajectory”); and a third group reaches $1 billion in 12±2 years (the “Twelve-Year Trajectory”). Companies in each of these trajectories are defined by distinctly different revenue growth rates, as shown in FIG. 3A. There was a group of a few companies who were exceptions; those companies who bypassed the inflection point (FIG. 3B) with no common pattern to $1 billion. These companies broke out by growing from <$20m to >$80m in one year.
The revenue trajectories are characterized by minimum and maximum curves which are the ranges for fastest and slowest companies in each of the trajectories. Bach trajectory minimum-maximum curve is characterized by definitive data points and an exponential equation with high R squared. FIGS. 3C, 3D, 3E and 3F are the specific data and equations for each of the 4, 6 and 12 year trajectories. The growth rates for each of the min-max curves are included in FIG. 3C. Within each of the growth trajectories, company growth can vary as long as they grow within the min-max limits. The inventor identified the growth rates ranges, for the 4 and 6 year trajectories, on a per year basis as indicated in FIG. 4G. The growth rates for the 12 year trajectory are too highly variable; therefore the companies must grow between the min-max trajectory bounds of the 12 year trajectory to any growth rate range.
FIGS. 21, 22, and 23 show the mean P&Ls for information technology companies in each of the three trajectories. The P&Ls highlight that companies were cash flow positive early and scaled. This performance was driven by achieving exponential revenue growth, achieving attractive and sustainable gross margins, R&D/SG&A spending limited to achieve over 10% EBITDA. Free Cash Flow from operations was therefore best in class. This is in contrast to the tech bubble of the early 2000's when prevailing wisdom was to be cash flow negative in order to scale.
A first indicator of businesses with above-average growth potential is consistent revenue growth rates aligned to one of the 3 trajectories. The inventor determined that the companies being screened have growth rates that align to each of the 3 trajectories. The process in FIG. 3H indicates that companies can be screened to be identified to be on one of the 3 trajectories based on revenue level and growth rate. The growth rate determines which trajectory the company is on. Based upon the company's position between the min-max curves of each trajectory, the net weighted average growth rate forecasts the expected position. If the company growth rate causes the company's revenue to drop below the minimum revenue curve, the company is screened out of the sample.
For each sector, companies can be screened for building quality financial performance. For the information technology sector, as a sector example, these companies had positive cash flow. In each of the Four-Year, Six-Year or Twelve-Year Trajectories, the above-average growth businesses (e.g., those that eventually reached $1 billion in annual revenue) consistently achieved at least 55% gross margins. See FIG. 4B. In each of the Four-Year, Six-Year, or Twelve-Year Trajectories, the above-average companies managed selling, general and administrative (SG&A) costs, and research and development (R&D) costs to maintain earnings before interest, taxes, depreciation and amortization of (EBITDA) of at least 20%. See FIG. 4C. Finally, in each of the Four-Year, Six-Year, or Twelve-Year Trajectories, the companies managed to maintain positive cash flow from operations as early as $25 million in annual revenues. See FIG. 4D.
Another indicator of companies with higher than average growth potential can be the ratio of market capitalization to revenue. As shown in FIG. 5, businesses with higher than average growth potential (e.g., those that eventually reached $1 billion in annual revenue) had market capitalization to revenue ratios greater than 3, and more preferably at least about 4 or more. In comparison, businesses with $400 million in annual revenues had market capitalization to revenue ratios of less than about 3 and companies with less than $100 million in annual revenue had market capitalization to revenue rations of less than about 2. This chart demonstrates that around $60-100 million revenue, companies on the right trajectory are recognized by the investment community for their potential. The above P&L models, of the companies who made it to $1 billion are more highly rewarded. The data was a select group of companies from the early 1990s (equivalent to where the NASDAQ is today). The valuation of these companies was roughly proportional to the NASDAQ index. These winning companies had distinctively higher growth rates and consistent EBITDA performance that was superior to other companies. This superior financial performance drove the higher valuations.
The four growth phases that characterize a company with above-average growth potential are illustrated in FIG. 6A for a 4 year trajectory company. As shown in FIG. 6A, these phases can be characterized as “Business Development”, “Inflection”, “Break Out”, and “Segment Leader”. (FIGS. 6B and 6C show the curves exist for the 6 and 12 year trajectories, in which they remain the same). The Business Development phase is the founding year to the inflection phase. The growth in the 4 year example is about 3×. During the inflection phase, the growth is about 5×. This phase is where the business system is formed and the company determines which trajectory it has the option for. The Breakout phase is the phase to $400 million in annual revenue. At this point companies can fail to break through to become a market Segment Leader as their product line does not diversify or scale.
For the mean 4 year trajectory business shown in FIG. 6A, during the Business Development Phase, annual revenues ranged from $8 to $30 million, with a 360% CAGR. During the Inflection Phase, annual revenues ranged from $30 to $70 million, with a 518% CAGR. This particular business was additionally recognized as one of the top high growth startups. In the Breakout Phase, annual revenue grew from $ $254, average CGAR is 220%. Finally, in the Segment Leader Phase, annual revenue grew from $254-$720 million to $1 billion, with about 138% CAGR. The mean revenue curves with growth rates aligned by phase for the six and twelve year trajectories are in FIGS. 6B and 6C respectively. The application of these phases to two real world examples is shown in FIGS. 6D and 6E.
FIG. 7 shows important attributes for growth at each phase. In the Business Development Phase, a founder or founder team is in place, a core engineering team is in place, the business acquires a marquee customer, the business acquires a strategic alliance, the business acquires a smart investor, and the business develops a technology platform and value proposition. In the Inflection Phase, the business has $50 million revenue with at least about 55% growth rate; an acquisition to extend product line; multiple strategic alliances; a top management team; and makes an IPO.
As shown in FIG. 7, in the third Breakout Phase, the business has either a scalable growth platform or it switches to the next market wave. The business also has exponential revenue growth. The revenue growth is driven by product line extensions, marketing, marquee customers, alliances, international expansion, and acquisitions.
The common features of businesses in the final Segment Leader phase are one or more of the following: an explosion into broad market solutions; capturing top position as industry explodes; establishment as the fastest growing company in category; aggressive branding; recruiting top executives from competitors; marquee customers and alliances; multiple acquisitions to broaden/fortify product line; and scaling international, distribution, service/support operations.
Across the four phases of company development, at least nine attributes can be identified. These indicators are in place by the inflection phase and scale across the remaining phases. FIGS. 6C and 6D illustrate two case studies followed by the typical profile of the most successful companies.
As shown in FIG. 8, three scaling essentials, which can be visualized as the sides of triangle, are: scalable R&D, scalable sales, and scalable cash management. The attributes of scalable R&D include a founder or founding team that can shape an opportunity; an innovative core engineering team, and a technology discontinuity aligned with value proposition discontinuity. The attributes of scalable sales include a marquee customer, asymmetric alliances, and targeting or shaping a high-growth market segment. Finally, the attributes of scalable cash management include a high net margin business model, involved smart investors and directors; and a management team that can execute in the context of a growth strategy.
Scalable Research and Development
As shown in FIG. 9, the attributes of scalable research and development include (i) a founder or founding team who can shape an opportunity; (ii) an innovative core engineering team; and (iii) a technology discontinuity aligned with a value proposition discontinuity. Indicators of a founder/founding team that can shape an opportunity include: a compelling technology/architecture vision liked to a compelling value proposition; a demonstrated track record for innovation, development, and business skills; distinctive management skills; team building skills, achieving R&D innovation on time, and business acumen. Indicators of an innovative core engineering team include: a small, highly effective team (e.g., 10 to 15 engineers) at innovation, development, and on time delivery; creation of sustainable intellectual property and architecture/technology lead; close links to technical community, alliances, and customers. Indicators of a technology discontinuity value proposition discontinuity alignment include a sustainable technology discontinuity enabling compelling customer centric value proposition; a foundation for compelling business economics, e.g. sustainable gross margins of 55-90%, and significant lifecycle revenue, and marquee customer. Various types of value proposition platforms are illustrated in FIG. 10A.
While the focus of innovation varies dramatically across industries, all innovation sectors share a common need for applied R&D scalability enabling exponential business growth. Scalable applied R&D is applied R&D that is highly linked to business functions, linked to the customer, remains flexible in its problem solving approach and solutions, and enables a sustainable business model with an expansive value proposition.
Scalable R&D links product development with true customer needs and are willing to iterate in collaboration with the customer. Scalable applied R&D principles require the pursuit of a business model that is attractive, potentially expansive, and can become a primary driver of scalable growth. Scalable applied R&D is therefore composed of 5 key elements: targeting a scalable opportunity, pursuing a scalable solution or architecture, implementing a flexible R&D process, linking product development with marquee customers, and enabling an attractive business model with sustainable margins typically over 50%.
Unscalable research and development occurs when the research and development function fails to leverage other business functions during concept screening and development, or the core engineering team believes its own hype and is not linked to customers. Unscalable research and development also occurs when the team encounters difficulty making the leap from bench to production and has no clear path to scale production at low risk. The time to first meaningful revenue is too long funding drying up. The pursued innovation does not offer access to market adjacencies. Unscalable research and development also occurs when management fails to screen opportunities to identify business models that solve immediate problems and have the potential to transform into broader, categorical, solutions. Business tactics do not effectively accommodate a range of time horizons. Challenges are encountered balancing investment between short and long term in order to scale. Unscalable research and development occurs when development efforts focus on the wrong market and/or fail to reap immediate potential due to pursuit of blue-sky opportunities. The team is unwilling or unable to solicit and react to feedback from key customers. Unscalable research and development occurs when the product offering does not enable attractive and sustainable gross margins.
The common approach to applied R&D development typically involves a generally inflexible R&D process characterized by long laboratory and prototyping efforts before product launch, development schedules based more on time gating than problem resolution, and little linkage to other business functions that would enable a business model that meets true market needs. There are many situations that illustrate that this approach often leads to performance that does not capture the full potential.
In contrast, companies that maintained a scalable R&D approach, was markedly more successful. By adopting the scalable R&D approach, a company can dramatically boost its revenues, and achieve best-in-class ratios. Achieving this best-in-class level of performance requires several fundamental changes; namely redefining the process for targeting opportunities, broadening the scope or scale of a conceivable solution and breaking down the barriers between R&D and customers. The scalable R&D philosophy emphasizes continuous problem solving and iteration based on market feedback instead of simple internal focus. By taking a scalable approach companies realize growth faster.
The decisions made during the scalable R&D process focuses the value proposition into one of 3 waves. (1) First wave shapers create a new architecture or fill an architectural gap. Delivering new architectures requires competing with competitors with outdated technology. (2) Second to market who deliver a better value proposition or fill an application niche. (3) Third, fourth, fifth to market, winning companies out execute the first and second wave players.
FIG. 10B illustrates the application of these attributes to several real world businesses.
Scalable Sales
FIG. 11 illustrates the attributes of scalable sales to execute exponential revenue growth. These attributes include a marquee customer, asymmetric alliances, and targeting or shaping a high-growth market segment. Indicators of scalable sales am secure marquee customers, the ability to leverage asymmetric alliances, and targeting/shaping high growth market segments. Indicators of marquee customers include agreements with marquee customers who “believe” and are committed to quickly shape product/application and business; marquee customers who are highly committed to success of the company, and who serve as a reference account based on wide scale deployment, intellectual and managerial capital commitment, key investor, and strategic alliance partner; customer agreements that establish market credibility and attractive lifecycle revenue. In general a marquee customer is a well known and respected customer, usually established and publicly traded, that either recommends the businesses products to others, serves as a reference for the business, or allows its name to be used in and advertising or marketing of the business. Indicators of marquee customers are shown in FIG. 13A. The indicators of the leveraging of asymmetric alliances include engaging potential alliance partners early; securing a “big brother” alliance to provide marquee customer credibility through joint sales and marketing or joint commercialization; and creating and leveraging alliance network to capture full value. The indicators of targeting/shaping high growth market segments include high growth (greater than 80%) or emerging market or opportunity for the number 2 or number 3 positions in a large, high growth sustainable market; the existence of attractive adjacent segments for future growth; and a simple market entry strategy that can create a beachhead. Key sales growth drivers are indicated in FIG. 12. The mathematical formulas to identify the key metrics which determine revenue growth rate are identified in FIG. 12B. These attributes are the 7 degrees of freedom. For example, for eBay, the number of transactions, commission/transaction, number of auction areas and countries covered are the key degrees of freedom which align to eBay's revenue growth.
In order to gain market credibility, establishing deep relationships with marquee customers is critical. A marquee customer is a customer who is committed to the business success. They demonstrate this commitment through 4 initiatives: (1) Drive product requirements by testing product in large scale, operating environment and defining the required interfaces for customer systems. (2) Wide scale deployment. (3) Determine customer centric value proposition to be used with other marquee customers. (4) Serve as customer and market reference, often termed a “lighthouse” customer
A marquee customer can provide credibility. An established and leading reference commits to the start-up's product over established products by other vendors. A marquee customer can also provide investment. A marquee customer may provide either funding or a significant revenue stream. A marquee customer can be a distribution/integration partner. A marquee customer may serve as a strategic distribution channel or an integration partner. Finally, a marquee customer can provide a product platform. A marquee customer may provide critical technology or infrastructure to either enhance time to market or crate significant barrier to entry.
Marquee customers can be identified by their track record and by particular leaders. A select set of companies have a history and culture of sponsoring the adoption of new technology. Having board members or management team members with a broad network to target the right subset of customers creates quick insight to narrow the options. On the flipside, sponsors in marquee customers have a broad network to qualify and screen for opportunities. An introduction through one of these advisors is a differentiated approach to gain mindshare. It is not always necessary to give the product away to secure a marquee customer. Finding a set of sponsors with passion to solve a key problem and linking the value proposition creates a value equation. Three levels of sponsors are often required: (1) Technology decision makers must endorse the approach. They can serve as blockers or enablers. (2) The business decision maker serves as the project sponsor. (3) CEO/COO/CIOs serve as the approver and endorser. As mentioned above, value propositions that fill critical architectural, capability or services bottlenecks and therefore can demonstrate high value to marquee customers, do not need to be commoditized. Yes, marquee customers want a pricing advantage for the value they bring, but quality relationships are built on win-win equations. The right customers for the right solution want companies to succeed and in order to evolve their solution and provide ongoing support.
Asymmetric alliance include joint development, licenses, product bundling and distribution, and co-marketing on industry platform.
Technology giants increasingly must rely on significantly smaller partners to speed time to market for their innovative products and services. As one executive at a global technology company explained, “If I'm not in the game of partnering with leading edge technology start-ups, then I'm out of the game in leading edge technology. I'll be reading about innovation from Gartner and then I'm behind the curve.” Indeed, 70% of alliances announced by the 10 largest global technology companies in the second quarter of 2002 were with partners less than one-tenth their size. Established technology companies tie up with company's fractions of their size to increase revenues in their current business by solving mission-critical challenges or to place bets on the future and shape emerging competitive spaces.
For their part, alliances are attractive to the smaller company primarily for scale. Smaller companies gain access to much needed scale across functions by partnering with technology giants. Smaller, often-niche companies access scale benefits in innovation and product development through access to their partner's platform and range of products and services. In marketing and sales, scale benefits typically result in lower cost access to high impact, “marquee” customers.
While more often than not these partnerships fail to deliver against in-going expectations of the companies, Over 80% of the technology companies who scaled to over $1 billion in revenue were successful at one or more asymmetric alliances. The asymmetric alliance relationship typically provided a win-win relationship. For the larger partner, the majority of alliances had a platform of innovation integration; a level of integration between the two products/architectures so that the customer was offered a more compelling value proposition than if he bought the products separately. In the software industry, this often takes the form of porting software or operating systems to a de facto industry platform. Filling a critical capability gap provides the large partner with a broader solution set for customers, speeds up account development time or creates product line leverage for additional sales.
For the smaller partner, the alliance partnership provides: (1) Drive product requirements by testing product in large scale, operating environment and defining the required interfaces for customer systems. (2) Market credibility and access to a pipeline of quality and marquee customers. (3) Refinement of customer centric value proposition to be used with other marquee customers. (4) Serve as a market reference, often termed a “Big Brother” alliance partner.
Once partners have agreed to an alliance, remaining high enough on the agenda of the Big Brother to capture value from execution is difficult. For starters, by their nature, smaller companies have limited resources and lots of distractions (management, engineering, etc). Managing these alliances requires significant time and attention. As a senior executive of a successful smaller company explained, “I'm in contact with the senior executives of our alliance partner no less than once a month. At the same time, I'm in contact at least 2-3 times per week with the operational people.” Moreover, senior executives at the larger company need to be kept abreast of the impact of the partnership in order to maintain their commitment and run interference vis a vis potential “blocking executives” within their organization. One smaller company CEO explained their successful approach to gaining traction as, “We make sure that we have an intense focus on what value we deliver to the business owner in our alliance partner. We ensure that we serve him so well that other parts of his own company aren't as responsive.” His “Big Brother” counterpart explained his approach like this, “Bring us value and we'll darken the skies with our fabulous customers and assets.”
Large company executives, both marquee customer and alliance partners, must be constantly vigilant to identify and attract the most promising companies both to solve critical bottlenecks in their current pipeline and to ensure they are well positioned vis a vis their competitors. On the other hand, scaling companies that are forced to “bootstrap” more than ever, must overcome significant hurdles to attracting, developing and executing relationships that deliver sustainable value. Relatively few CEOs of high growth companies have succeeded using alliances. As a result, many executives and leading investors are skeptical of the prospects of succeeding in partnerships between small-high growth companies and industry giants. One of the tricky counterintuitive elements here is to plan for failure and mediocrity . . . even the smartest will discover that despite best efforts not all of these will work and in some cases the combined DNA of the two companies will in some cases turns out to be toxic. It takes real skill and focus to navigate a growth relationship. Board governance is a critical differentiator of scaling companies. A board analysis of 50 successful companies versus failures highlighted that alliance partners and customers representatives on boards are a critical ingredient to success. Of America's fastest growing tech companies, 60% of boards had alliance partners versus less than 20% for failures. Failing companies were investor dominated.
While the initial customer development process incorporated in Elements 2 and 3 ensures that marquee customers and asymmetric alliances help shape the business, the potential to grow exponential revenue growth with attractive margins is primarily based on the lifecycle revenue of marquee customers. Gross margins, while ultimately bounded by market structure, can be strongly influenced by sound operational performance focused on marquee customers, and a sales force that stresses the full corporate value proposition. From the onset, the entire organization must focus on achieving three financial goals to replicate the behavior exhibited by companies that have been able to historically achieve scalable growth: (1) must be able to execute to consistent gross margins and sustain those margins as the business scales from $100M to $1B. Margins are typically in the 40s for hardware, 50s for semiconductors, 80s for software businesses (FIG. 4E). Must create a cash flow positive business model with marquee customers. (2) Must grow marquee customer revenues to over $50M over 8 years. Revenue growth must be exponential. This can be achieved through deeper relationships, more sophisticated selling and a broader product line offering. The company, as it broadens its offering, must drive its strategy to solve a portfolio of problems based on its core value proposition. (3) Top performing management teams must manage effectiveness of SG&A and R&D to the gross margin achieved in order to achieve the required EBITDA and cash flow from operations. Many teams find it easy to overspend based on a rationale that it is required for revenue growth.
Scalable sales relies upon performance-based leadership across four key areas: execution in the context of a scalable business strategy, iterative problem solving, a sales culture to leverage marquee customers and asymmetric alliance brothers to gain market credibility and cross-functional performance metrics (such as on time delivery of new products). In a scalable sales approach the process is iteratively linked with marquee customers and asymmetric alliance partners as the foundation for a strategy to exponentially scale a cash flow positive business model.
Scalable Cash Management
As illustrated in FIG. 14, the attributes of scalable cash management are a scalable business model and high net margin business model; involved smart investors and board management team who can execute in the context of a growth strategy. The indicators of a scalable business model include a business model that generates a profit and loss cash flow and a business model that scales. Some drivers of scalable cash management as shown in FIG. 15. The indicators of involved and smart investors and board include high degree of connections to create leverage, create strategic alliances, secure marquee customers, and proactively shape the market; a board that effectively manages/supports management team; and a board that is composed of members with complementary skills/perspectives including marquee customers, alliances, and management expertise. The boards of companies that did not reach $1 billion in sales were dominated by investors. In contrast, 19% of 4 year trajectory companies had both customers and alliance partners on their boards. FIG. 16A compares the board compositions of Four-Year, Six-Year, and Twelve-year businesses. FIG. 16B identifies the average size and composition of boards for companies who made it to $1B revenue. FIG. 17 compares board compositions for Six-Year Trajectory businesses with businesses that failed to reach $1 billion in annual revenue. Indicators of a management team who can execute in the context of a growth strategy include a team highly skilled at managing revenue and cash flow aligned to compelling business model; sustainable gross margins in excess of 55%; early positive cash flow from operations; and successful growth/scaling the organization; and, finally a management team that is capable of developing and closing marquee customer, strategic alliance and investor deals.
Identifying Scalable Opportunities
Identifying scalable opportunities requires due diligence in three key areas: R&D, sales, and financial management. Within each of these areas, specific criteria should be examined to assess the scalability potential of any given opportunity.
R & D Due Diligence
Due diligence in the R&D area focuses on understanding the viability, potential and defensibility of the technology and solution form that is being investigated. To provide a truly scalable opportunity, a technology and/or solution must offer a distinct architectural advantage relative to existing alternatives. Further, it must represent an innovation that has considerable foreseeable potential to drive performance improvement in a given area of application and provide in-roads to a variety of other potential applications.
Sales Due Diligence
Due diligence in the sales area focuses on the scalability of the opportunity associated with any given innovation must be examined by assessing the level of demand for that innovation in the marketplace, the likely duration of the need, and the overall scope of the accessible market. Truly scalable opportunities satisfy latent demand, create completely new market segments and provide access to significant revenue pools (in excess of $1 billion).
Financial Due Diligence
Due diligence in the financial area focuses on three additional criteria to be satisfied for an opportunity to be considered scalable. Scalable opportunities must provide gross margins, at the outset, in excess of 50% to facilitate sales efforts and continued investment in R&D. The value proposition of the business must be increasingly distinctive over time, effectively raising barriers to competitive entry rather than enabling fast followers. Finally, the business model pursued to bring a given opportunity to market must be consistent with the overall strategy of the organization, not just a plan put forward by R&D in isolation.
Evaluating opportunities against these criteria can be made more efficient by pursuing the process in an iterative fashion involving three major screening phases—a preliminary scan, a value creation assessment, and a final investment decision, each getting progressively more rigorous (FIG. 19). By asking questions of increasing specificity only after an opportunity has passed more fundamental criteria, the effort required to achieve analytical rigor in the evaluation can be focused only on worthy opportunities. However, it is important to note that even after the initial decision to invest; the potential of each opportunity should be continuously revisited as progress is made toward the creation of scalable business model.
The definition of a scalable solution or architecture is linked directly to the innovation strategy which can take on any one of three forms depending on the evolving market structure. (1) First wave innovators create a new architecture or fill an architectural gap. (2) Second-But-Better companies either deliver a better value proposition, or fill an application niche. (3) Third Wave third, fourth, fifth to market, winning companies who out execute the first and second wave players.
A top tier core engineering team is essential to pursuing a scalable solution or architecture. The team is typically a small, highly effective team (10-15 engineers) at innovation, development and on-time delivery. They create sustainable intellectual property and architecture/technology lead. A differentiating factor how closely they are linked to the technical community, alliances and customers. The ability of the core engineering team to conceptualize algorithms and designs was their core technical strength.
Getting the scalable solution may require skills/capabilities a company doesn't have. These can be obtained thru acquisition or developed in house.
Successful pursuit of scalable opportunities requires a flexible R&D organization and product development process. Achieving R&D flexibility requires focus on information flows within a cross-functional development team and careful coordination of the efforts of all sub-teams. To do this, companies must focus on improving the quality, timing, and synthesis of information throughout the development cycle. Rather than rigidly following a sequence of tasks on a pre-scheduled timeline, they must engage in continuous problem solving, synthesizing results and adapting the workflow as needed. This process keeps product options open longer and enables the organization to act on new customer and market information later in the process. Decisions are then made only when all the options are fully understood, not at fixed gate meetings. As a result, companies that create a flexible R&D environment make better decisions at each step along the way and are better able to assess the potential of opportunities and achieve scalable growth.
Another critical step in the development of a scalable business opportunity is iterating the product/solution in collaboration with marquee customers early in the development process. A marquee customer is an industry respected corporation who is committed to the success of the product and the company. Companies will work with marquee customers if they have a robust initial product, are confident they can serve and service marquee customer and are capable of quickly iterating product design to meet customer requirements. Trial deployments with smaller customers can be an interim step if the product is premature and still in the in development phase or companies are unsure of their ability to support product in live customer environment. Coupling marquee customers to the product development process provides three major benefits: (1) Marquee customers are coupled into the product development process. As such they provide product requirements. Tactically, this feedback can be as simple as marquee customers requiring key capabilities in order to make a major commitment. Marquee customers can test and validate the scaling of the product. Finally, required interfaces to become compatible with installed systems can be required. (2) Determine customer centric value proposition to be used with other marquee customers. Highly committed marquee customers will share their value proposition in terms of benefits and economic terms so that the value proposition transforms from a technology-based to a business-based value proposition. (3) Serve as industry reference to fill the pipeline with other marquee customers and to shape the market.
Building a relationship with a marquee customer initiates a virtuous cycle that leads to the development of asymmetric alliances that create broader solutions and in turn attract more marquee customers.
While the initial financial screening process ensures that a given opportunity has the potential to provide attractive margins, the pursued business model must be sustainable. Gross margins, while ultimately bounded by market structure, can be strongly influenced by sound operational performance, and a sales force that stresses the full corporate value proposition. From the onset, the entire organization must focus on achieving three financial goals to replicate the behavior exhibited by companies that have been able to historically achieve scalable growth: (1) there must be able to execute to consistent gross margins and sustain those margins as the business scales from $100M to $1B. Margins are typically in the 40s for hardware, 50s for semiconductors, 80s for software businesses. (2) Beyond $50M revenue, companies must achieve an EBITDA of 20% (of revenue). If the gross margin is higher, management teams can afford to self-invest in R&D and incremental SG&A. As a result, the business must create a cash flow positive business model around $25 million revenue which scales proportionately to sales.
Top performing management teams must manage effectiveness of SG&A and R&D to the gross margin achieved in order to achieve the required EBITDA and cash flow from operations. Many teams find it easy to overspend based on a rationale that it is required for revenue growth.
Today most companies manage the R&D process as a functional silo with a serial gating process—project cost, time to development, and project sales are typically the leadership performance indicators. In contrast, scalable R&D relies upon performance-based leadership across four key areas: execution in the context of a scalable business strategy, iterative problem solving, a culture of efficiency and functional integration, and cross-functional performance metrics. In a scalable R&D approach the R&D process is iteratively linked with marquee customers and a strategy to exponentially scale a cash flow positive business model.
Scalability is not just a process; it is a philosophy that must be embraced by an organization to succeed. Companies that have successfully adapted to this environment have successfully embraced fundamental changes; redefining the process for targeting opportunities, broadening the scope or scale of a conceivable solution and breaking down the barriers between R&D and customers. The scalable R&D philosophy emphasizes continuous problem solving and iteration based on market feedback instead of simple internal focus. Companies who succeed will have an undeniable edge, as they respond to customer and market changes more effectively and rapidly than their competitors.
Innovation, as a critical enabler of growth, occurs in different elements of the value chain in different industry sectors. While all industries innovate to various degrees, the true focus of innovation tends to vary considerably by sector. Analysis of select innovation sectors, indicates that there are 3 fundamental innovation types; product R&D, process innovation and innovative customer access. Indicators of unscalable sales include a siloed sales function. A transactional sales force, looking for the next deal often demonstrates a “dine and dash” approach. The sales team fails to understand critical customer requirements required to secure a major commitment. This customer feedback often requires the teamwork and support of management and the engineering team. A management team that believes its own hype or is inwardly focused on the technology can be a major reason why team is unwilling or unable to solicit and react to feedback from key customers. The sales approach may be unscalable, such as where the sales team finds an initial customer who is not a surrogate for a larger community of needs of other customers and who cannot evangelize and is respected by the broader market. Picking the wrong customer drives the business into a “rathole” as the business focuses on meeting unique customer requirements. As a result, the time to first meaningful revenues is too long.
Another indicator of unscalable sales is an inflexible sales model. It is not always clear what the sales process or account development cycle will look like. The sales model rarely is obvious from the start. Employing a large sales force before figuring out the sales model leads is often the approach used to meet high expectations. Management fails to screen opportunities to identify sales models that solve immediate problems and have the potential to transform into broader, categorical, solutions,
Still another indicator of unscalable sales is failing to solve a real problem in a distinctive way. Decision makers, at every level, really only make a few key decisions each quarter. Forming a relationship with a new company with a new product is a big deal. One board member expressed it this way, “we are looking for a CEO who can form executive relationships. Our engineers get the RFP but we do not understand the customer requirements and what is driving these requirements. We lose when other vendors help the customer develop the RFP!”. Selling into customers with tight budgets requires a real focus to understand what the customer priorities are and how they prioritize investment decisions. The CIO of a leading Fortune 100 enterprise “With our tight budgets, we are only investing in the most critical priorities. We always have to more with less, so if vendors can help us solve critical problems with a resourceful approach, it influences our prioritization.”.
Avoiding these pitfalls requires focused targeting and screening for scalable revenue opportunities and the pursuit of scalable solutions in a flexible manner. In addition, successful companies link product development with true customer needs and are willing to iterate in collaboration with the customer. Scalable sales principles require the pursuit of a business model that is attractive, potentially expansive, and can become a primary driver of scalable growth. Scalable sales is therefore composed of 5 key elements: (1) targeting a scalable opportunity; (2) pursuing marquee customers; (3) implementing an asymmetric alliance; (4) linking product development, marquee customers and alliance partners, and (5) enabling an attractive business model with sustainable margins typically over 50%.
The common approach to sales development typically involves a generally inflexible R&D and serial process characterized by substantial R&D and prototyping efforts before product launch followed by sales scanning to qualify initial customers. Much to management's surprise, the sales cycle can often exceed the time required to develop the product. There are many situations that illustrate that this approach often leads to performance that does not capture the full potential. Achieving a best-in-class level of performance requires several fundamental changes; namely redefining the process for targeting opportunities, broadening the scope or scale of a conceivable solution and breaking down the barriers between R&D and customers. The scalable sales philosophy emphasizes continuous problem solving and iteration based on market feedback instead of simple internal focus. By taking a scalable approach companies realize growth faster.
Due Diligence Process
A benchmark-based process that gives clear indicators against which one can judge/evaluate the growth progress of a company. The process is based on two tools; financial benchmarks and essentials report card. The tools are based on the success pattern of winning companies. The due diligence process can be applied to a single company (standalone or business unit) being investigated or the sum of the report cards can create a process for a “dashboard” for an investment portfolio. The end result is the ability to identify companies who have a higher probability of success based upon the essentials scorecard. End products that can be generated are: (1) a financial assessment compared to the trajectory of winning companies; (2) a definitive score based on an essentials assessment as compared to winning companies; and (3) identification of areas where companies are deficient or have core strengths. The benefits are a standard evaluation tool based on quantifiable metrics against winning companies. This is in contrast to the qualitative process, based on a high degree of intuitive skills, presently in widespread use.
The steps used to apply this method can include: (1) Financial modeling of the most successful synthesized successful company histories into an essential report card. Compared with low growth equivalent companies founded in the same year, going after the same space. (2) Identifying scalable opportunities requires due diligence in three key areas: R&D, sales, and financial management. Within each of these areas, specific criteria should be examined to assess the scalability potential of any given opportunity. See, e.g. FIG. 29. (3) Due diligence area focuses on understanding the viability, potential and defensibility of the technology and solution form that is being investigated. To provide a truly scalable opportunity, a technology and/or solution must offer a distinct architectural advantage relative to existing alternatives. Further, it must represent an innovation that has considerable foreseeable potential to drive performance improvement in a given area of application and provide in-roads to a variety of other potential applications.
For example, Due diligence in the sales area focuses on the scalability of the opportunity associated with any given innovation must be examined by assessing the level of demand for that innovation in the marketplace, the likely duration of the need, and the overall scope of the accessible market. Truly scalable opportunities satisfy latent demand, create completely new market segments and provide access to significant revenue pools (in excess of $1 billion).
The Due Diligence Process involves filling in a report card to determine a score. The score is determined from the success pattern of winning companies. Winning companies performed average or above in all 8 indicators (the ninth category of financials being considered separately). The score can be calculated on a scale of 1-5 based on a detailed report card. The score ranking determines the investment action/decision. Exemplary criteria are shown in FIG. 24, and an example of an application of these criteria is shown in FIG. 26A.
An example of a report card filled in for two comparative companies is shown in FIG. 26B. An investment team could use this actual card to score the investment, leading to the appropriate action.
Exemplary criteria to complete the FIG. 26B report card are shown in FIGS. 29A and 29B. Evaluating opportunities against these criteria can be made more efficient by pursuing the process in an iterative fashion involving three major screening phases—a preliminary scan, a value creation assessment, and a final investment decision, each getting progressively more rigorous. By asking questions of increasing specificity only after an opportunity has passed more fundamental criteria, the effort required to achieve analytical rigor in the evaluation can be focused only on worthy opportunities. However, it is important to note that even after the initial decision to invest; the potential of each opportunity should be continuously revisited as progress is made toward the creation of scalable business model.
Companies can be evaluated against the financial trajectory of winning companies. At the inflection point, the financial trajectory is broad, yet defined. For companies at this phase, the goal is to ensure they have the right revenue growth and EBITDA performance. The growth rate in following years will determine the 4, 6, or 12 year trajectory. Depending on the tech sector, while gross margins varied, the EBITDA performance averaged 23% for companies the 1st year after the inflection point (revenue varied depending on the trajectory—see financial P&Ls). See, e.g. the data in FIG. 31 and the comparison in FIG. 32.
Due diligence in the financial area focuses on three additional criteria to be satisfied for an opportunity to be considered scalable. Scalable opportunities must provide gross margins, at the outset, in excess of 50% to facilitate sales efforts and continued investment in R&D. The value proposition of the business must be increasingly distinctive over time, effectively raising barriers to competitive entry rather than enabling fast followers. Finally, the business model pursued to bring a given opportunity to market must be consistent with the overall strategy of the organization, not just a plan put forward by R&D in isolation.
Depending on the score of specific essentials, a dashboard can be created for an investment portfolio. The companies can be grouped according to investment class. See, e.g. FIG. 30.
Investment Tools
Investors can screen for companies who are on trajectory to $1 billion in annual revenue. This screening process enables investors to spot companies early. The following steps of the process involves comparing these companies to the mean P&L models, comparing sub sector performance and comparing company valuations to the historical trends. Valuation models are calculated based on the value of winning companies normalized by the Russell 2000 index. When investors have the opportunity to interview management teams, prior to investment, the Due Diligence Process (described above) can be applied.
Management/Corporate Business Development Process
A corporate business development process based on financial metrics and an essentials report card that can evaluate new divisions against the growth pattern of America's most successful tech companies. As shown in FIG. 33, the process provides a unique feedback loop to management to diagnose, initiate corrective actions and track progress of business building initiatives.
Considering the nine attributes identified financial metrics which identifies success vs. failure; growth rates; gross margins; EBITDA, R&D costs, SG&A costs, and the report card process can be used to measure and track progress, and provide corporate guidelines for business development process. For example, a high growth division of a $700M company had a projected plan that tracked to the 6 year trajectory. Comparing their plan to the benchmark highlighted that the 3rd year revenue projection was higher than the mean while SG&A was below the mean. See FIG. 34A. In addition, returns were below average. When management was asked to fill in the report card, the score indicated that the essentials required for top line growth were below average. This score puts in question the revenue forecast and the underlying business plan. See FIG. 34B.
In managing a company for above-average growth, the three areas should be controlled: scalable research and development; scalable sales; and scalable cash management. With respect to sealable research and development, the company should target and invest in scalable initiatives. The company should pursue a scalable solution of architecture. The company should link product development with marquee customers. With respect to scalable sales, the company should target and invest in scalable value propositions. The company should build deep relationships with marquee customers to achieve market credibility. The company should create asymmetric alliance relationships to gain market leverage. The company should link customers and alliances with the management team. With respect to scalable cash management, the company should shape a sustainable business model. The company should achieve a 10-20% EBITDA to achieve positive free cash flow (FCF) which scales proportionately with sales. The company should have a highly connected board, adding customer's alliances and CEO.
Report Card Analysis
The inventor derived a report card by comparing companies founded in the same year and going after the same space. Typically one made it to $1 billion in annual revenue while the other struggled. The report card template formed the bases for the analysis of the performance required. The comparatives of the nine attributes essentials are highlighted in FIG. 24A, and their application is illustrated in FIG. 25.
For example, the inventor compared companies who were founded in the same year and going after the same space (market). See, e.g., FIGS. 25B and 25C. What differentiated management teams was their ability to create and execute a strategy. A strategy was defined by the external shaping essentials; value proposition, ability to secure marquee customers, leverage asymmetric alliances and a board that was highly connected.
The value proposition is an important attribute, affecting the ability to secure marquee customers, asymmetric alliances, drives the business model and shapes the product line direction. The value proposition tended to fall into one of three waves; wave one—shaping a new architecture or filling architectural gaps, wave two—second but better or filling application niches, wave 3-3rd,4th,5th to market requiring the entrant to out execute the incumbents (FIG. 10A). The value proposition required different emphasis on the essentials, depending on the value proposition. See, e.g., FIG. 10B
A marquee customer was a customer who would become a lighthouse customer. Almost all of the most successful companies had marquee customers. A marquee customer uniquely provided four key assets; help shape a value proposition, product requirements, and leverage assets to test the product and serve as an industry reference (a lighthouse customer. Marquee customers often drove the basic sales from customer segments. Marquee customers came in four types depending on the lifecycle of the product. For example, in the case of Sun Microsystems, Kodak was an early marquee customer; gave an initial order, invested in the company and took a board seat. While all marquee customers did not invest, they did align to the 4 points mentioned. In contrast, many failing or struggling companies has small customers who were not marquee, large customers who did not provide the value add or only had a few customers and did not scale. While data is not publicly available, sampling indicates that marquee customers committed to a deep relationship over time; global scale deployment and a full product line deployment. For example, one F50 marquee customer had these purchase patterns; growth from $1 million initially to over $50 million and a full product line deployment (e.g., Cisco). See, e.g., FIGS. 13A-13D.
Marquee customers helped to get these companies an asymmetric alliance or “Big Brother” alliance partner. Over 80% of the companies had a Big Brother to give them market credibility and access to marquee customers. What distinguished these alliances, over 80% were innovation based; integrated products to offer a more complete solution to customers (e.g. Microsoft ported DOS to the IBM PC). See, e.g., FIGS. 13E and 13F.
The average board of winning companies was balanced between investors and other external board members. What distinguished winning companies on the 4 year trajectory was the presence of alliance board members. Over 60% of 4 year trajectory companies had a board member from an alliance partner. In contrast, failed or low growth companies had boards that were investor centric. See, e.g., FIGS. 16A, 16B, 17, and 28.
Operation
A method of evaluating at least one target business in accordance with a preferred embodiment of this invention comprises identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; comparing the at least one financial statistic common to the model businesses with at least one financial statistic of the at least one target business; and evaluating the at least one target business based its comparison with the model businesses. These model businesses preferably have at least $400 million in annual revenue, and the target businesses preferably have at least $30 million in annual revenue. The target businesses preferably also have at least 100% annual sales growth, and are preferably less than about seven years old.
A variety of financial statistics can be used for comparison, including one or more of historic annual sales, annual sales growth rates, and time since found to exceed selected annual sales thresholds, gross margins, SG&A, R&D expense, EBIDTA, cash flow from operations, market capitalization.
A method of investing in businesses in accordance with the principles of one embodiment of the present invention comprises preferentially investing in target businesses selected by a method of identifying businesses with above-average revenue growth potential by selecting from a plurality of companies with annual revenues of at least $30 million, those companies with revenue growth rates that are within the growth rates of the three trajectories identified (reference FIGS. 3A-3H). An example of companies on trajectory reference FIG. 39.
A method of allocating resources among several target businesses comprising an enterprise in accordance with the principles of one embodiment of the present invention comprises preferentially allocating resources to target businesses in the enterprise selected by identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; comparing the at least one financial statistic common to the model businesses with at least one financial statistic of the at least one target business; evaluating the at least one target business based its comparison with the model businesses. The model businesses and the target businesses and financial statistics used are preferably the same as described above.
A method of ranking at least two target businesses based upon outlook for financial performance in accordance with the principles of one embodiment of this invention comprises identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; and ranking each of the target businesses based upon the at least one financial, managing the finances of the target business to match the financial statistic common to the model businesses. The model businesses and the target businesses and financial statistics used are preferably the same as described above.
A method of managing a target business to achieve a desired financial performance in accordance with the principles of one embodiment of this invention comprises identifying a plurality of model businesses that have achieved a desired financial performance; identifying at least one financial statistic common to the model businesses; managing the finances of the target business to match the financial statistic common to the model businesses. The model businesses and the target businesses and financial statistics used are preferably the same as described above.
A method of identifying businesses with above-average growth potential in accordance with the principles of one embodiment of this invention comprises selecting from a plurality of companies with annual revenues of at least $25 million, those companies whose gross margins exceed 55%, whose EBITDA exceed 20%, and who had a positive case flow since annual revenues exceeded 50 million.
A method of investing comprising preferentially investing in businesses with above-average growth potential in accordance with the principles of one embodiment of this invention comprises selecting from a plurality of companies with annual revenues of at least $25 million, those companies whose revenue growth adheres to the process in FIGS. 3A-3H and whose revenue and revenue growth screen for above average revenue growth; for the information technology sector-gross margins exceed 55%, whose EBITDA exceed 20%, and who had a positive case flow since annual revenues exceeded $30 million.
A method of allocating resources between businesses in accordance with the principles of one embodiment of this invention comprises preferentially allocating resources in businesses with above-average growth potential by selecting from a plurality of companies with annual revenues of at least $25 million, those companies whose gross margins exceed 55%, whose EBITDA exceed 20%, and who had a positive case flow since annual revenues exceeded $30 million.
A method of managing a business for above-average growth potential in accordance with the principles of one embodiment of this invention comprises maintaining gross margins in excess of 55%, EBITDA in excess of 20%, and a positive case flow after annual revenues exceeded $30 million.
A method of identifying target businesses with above-average potential to achieve a selected financial performance target, in accordance with the principles of one embodiment of this invention comprises identifying a plurality of model businesses which have already achieved the selected financial performance target; identifying financial statistics common to the identified model businesses; and screening target businesses for the financial statistics common to the identified businesses. The financial statistics preferably include: at least one of historic annual sales, annual sales growth rates, and time since found to exceed selected annual sales thresholds, gross margins, SG&A, R&D expense, EBIDTA, cash flow from operations, market capitalization.
A method of identifying a businesses with above average growth potential in accordance with the principles of one embodiment of this invention comprises filtering out businesses that have less than about $50,000,000 in annual revenues, and more preferably filtering out businesses that did not reach $50,000,000 within seven years from founding, and still more preferably filtering our businesses that did not reach $50,000,000 within about five years from founding.
In one embodiment, the method can further include filtering out businesses that in the year in which annual revenues first exceeded $30,000,000, did not have an annual revenue growth rate of at least about 30%, and more preferably filtering out businesses that in the year in which annual revenues first exceeded $30,000,000, did not have an annual revenue growth rate of at least about 100%. In another preferred embodiment the method can include filtering out businesses that in the year in which annual revenues first exceeded $50,000,000, did not have an annual revenue growth rate of at least about 30%, and more preferably filtering out businesses that in the year in which annual revenues first exceeded $50,000,000, did not have an annual revenue growth rate of at least about 100%.
In some embodiments the method includes filtering out businesses that did not maintain gross margins of at least fifty-five percent after annual revenues first exceeded $50,000,000, and more preferably filtering out businesses that did not maintain gross margins of at least sixty percent after annual revenues first exceeded $50,000,000.
In some embodiments the method also includes filtering out businesses that did not maintain EBITA of at least 20% after annual revenues first exceeded $50,000,000.
In some embodiments the method also includes filtering out businesses that did not maintain positive cash flow after annual revenues first exceeded $50,000,000, and more preferably filtering out businesses that did not maintain positive cash flow of at least 10% of annual revenues after annual revenues first exceeded $50,000,000.
In some embodiments, the method comprises filtering out businesses that did not maintain gross margins of at least fifty-five percent, EBITA of at least 20%, and positive cash flow after annual revenues first exceeded $50,000,000, and more preferably filtering out businesses that did not maintain gross margins of at least sixty percent after annual revenues first exceeded $50,000,000 and filtering out businesses that did not maintain positive cash flow of at least 10% of annual revenues after annual revenues first exceeded $50,000,000. In some of the preferred embodiments businesses are further filtered out depending based upon market capitalization to annual revenue ratio, for example businesses whose ratio is less than 3, or more preferably 4 or 5.
In some embodiments, the method comprises filtering out businesses that did not have an employee of a customer on its board of directors and/or a customer as an investor. In some preferred embodiments the method comprises filtering out businesses that did not recruit at least one officer from a competitor. In some preferred embodiments the method comprises filtering out businesses that do not have the fastest growing sales in at least one category. In some preferred embodiments the method comprises filtering out businesses that do not have a marquee customer, for example a publicly traded company that allows its name to be used in connection in connection with promotion of the businesses' products or services, and more preferably filtering out businesses that do not have a marquee customer with a representative on the board of directors. In some preferred embodiments the method includes filtering out businesses that do not have a co-selling agreement with a publicly traded entity, and more preferably filtering out businesses that do not have a joint selling or joint marketing arrangement with a publicly traded entity that has an officer, director, or employee that sits on the board of the business.
In some embodiments the method further comprises filtering out businesses that do not have as a director an officer, director, or employee from a company that purchases the businesses' products or services, or an officer, director or employee from a company that jointly sells a product or service with the business' product or service, or businesses that do not have as a director an officer, director, or employee from a company that purchases the businesses' products or services, and an officer, director or employee from a company that jointly sells a product or service with the business' product or service.
A method of comparing two or more businesses to determine the business with the greatest potential for growth, in accordance with the principles of one embodiment of this invention comprises evaluating each of the companies under consideration and scoring them based upon; scoring the founding team based on a plurality of specified factors; scoring the engineering team based upon a plurality of specified factors; scoring the technology discontinuity/value proposition based upon a plurality of specified factors; scoring the management team based upon a plurality of specified factors; scoring the investors based upon a plurality of specified factors; scoring the strategic alliances based upon a plurality of specified factors; scoring the market segment based upon a plurality of specified factors; scoring the marquee customers based upon a plurality of specified factors; and selecting the company with the greater cumulative score.
A method of comparing two or more businesses to determine the business with the greatest potential for growth in accordance with the principles of one embodiment of this invention comprises evaluating each of the companies under consideration and scoring them based upon: scoring the founding team based on a plurality of specified factors; scoring the engineering team based upon a plurality of specified factors; scoring the technology discontinuity/value proposition based upon a plurality of specified factors; scoring the management team based upon a plurality of specified factors; scoring the investors based upon a plurality of specified factors; scoring the strategic alliances based upon a plurality of specified factors; scoring the market segment based upon a plurality of specified factors; scoring the marquee customers based upon a plurality of specified factors; and selecting companies with scores in excess of a predetermined threshold.
The methods can also be used as part of a method of investing. In accordance with this aspect of the invention, investments would preferentially be made in companies with above-average prospects for growth, identified by the method various evaluation methods of this invention. The methods can also be used as a part of a method of managing an investment portfolio. In accordance with this aspect of the invention, the portfolio is preferentially investing in businesses with above-average growth potential selected in accordance with the various evaluation methods of this invention. The methods can also be applied to the active management of a business.
In another aspect of the present disclosure, a computerized method is provided for identifying target businesses with above-average potential to achieve a selected financial performance target of $1 Billion in revenue within a predetermined timeframe. The computerized method comprises using a computer having a software program that is configured to identify financial statistics on revenue growth data for businesses that have achieved a financial performance target of $30 to $70 million in annual revenue, and to determine minimum and maximum curves for screening the identified businesses. The financial statistics on revenue growth data for various businesses may be acquired by financial statistics providers such as Standard and Poor's Compustat Database, for example, or from Thomson Reuters. Standard and Poor's also provides software programs that are capable of performing the step of screening financial data of various businesses to identify those businesses that have achieved a financial performance target of $30 to $70 million in annual revenue. Such software may include Standard and Poor's Backtester software, combined with customized formulae and statistical algorithms which are programmed into the Compustat Database and the Backtesting software. The computer and software program for performing the computerized method are further configured to determine revenue growth trajectory curves from the revenue growth rate data for the identified businesses, from which an inflection point in the revenue growth rate curve may be identified where annual revenue is in the range of $30 million to $70 million. The computer and software program for performing the computerized method are further configured to determine an exponential equation defining a maximum curve associated with a mean 4 year trajectory of revenue growth to $1 billion in revenue. (See FIG. 3D for mean 4 year timeframe and equation defining Max Rev 4 year bound of 4 year mean). The computer and software program for performing the computerized method are further configured to determine an exponential equation defining a minimum curve for a mean 12 year trajectory of revenue growth to $1 billion in revenue. (See FIG. 3F for mean 12 year timeframe and equation defining Max. Rev 12 year bound of 12 year mean). Exemplary software programs that are capable of determining an exponential equation defining the above minimum and maximum curves may include Excel spreadsheets, Standard and Poor's Compustat Databases and Backtester Software, or other suitable software programs.
The computer and software program for performing the computerized method are further configured to screen for target businesses and select those businesses, based on the revenue level and revenue growth rate data for each business, to determine which target businesses have revenue growth rate trajectory curves that fall within the bounds of the above minimum and maximum curves for achieving a forecasted revenue growth to $1 Billion in revenue within a timeframe of between 4 and 12 years from the time of the inflection point in the revenue growth trajectory curve of the identified businesses. Specifically, the computerized method screens businesses that have at least $30,000,000 in annual revenues based on the revenue growth data for the identified businesses, wherein screening includes identifying an inflection point in the deter wined revenue growth rate trajectory curve from which the business's revenue can be normalized, and selecting target businesses that have revenue growth rate trajectory curves that fall within the bounds of the above minimum and maximum curves. The computer and software program for performing the computerized method are further configured to output a list of the target businesses selected by the above screening. One example of a software program configured to perform the above screening may include the combination of Standard and Poor's Backtester software, which may be programmed and combined with customized formulae and statistical algorithms, and a customized database built on Oracle's Enterprise Class Database software, where the customized formulae are programmed into the Backtesting software and/or Compustat Database. The computerized method may further include the step of selecting one or more of the identified target businesses for inclusion in an investment fund. Furthermore, the computerized method may further include the step of segregating identified target businesses whose revenue growth rate trajectory curves fall within a trajectory range for achieving a revenue growth to at least $1 Billion in a mean of 4 years from the inflection point in the revenue growth rate trajectory curve of the identified target business, or whose revenue growth rate trajectory curves fall within a trajectory range for achieving a revenue growth to at least $1 Billion in a mean of 6 years from the inflection point in the revenue growth rate trajectory curve of the identified target business. This provides for ranking businesses into first, second and third trajectory ranges associated with exponential growth curves corresponding to 4, 6 and 12 year means of revenue growth to $1 Billion. It should be noted that while the above embodiment screens publically traded companies, the method may be utilized to screen a plurality of privately owned companies, where financial data is available for such private companies. Additionally, the above method may be utilized to identified and/or rank target businesses having revenue growth rate curves that fall between the bounds of exponential equations defining a minimum curve associated with a trajectory of revenue growth to $10 billion and a maximum curve associated with a trajectory of revenue growth to $10 billion, within a given timeframe from the time of the identified inflection point in the revenue growth trajectory curve of the identified businesses.
Referring to FIG. 40, one preferred embodiment of a computerized method of identifying target businesses with above-average potential to achieve a selected financial performance target of $1 Billion in revenue within a predetermined timeframe is shown. The method comprises acquiring data relating to financial statistics on revenue growth data for various publically traded businesses from data source provider at step 300. The method further includes the step of screening the financial statistical data on publically traded businesses, using a computer and software, to determine which businesses have annual sales above a select financial performance target of a minimum annual sales at 310. Upon completion of the above screening, the method identifies a set of businesses from the various publically traded businesses that have achieved the performance target of $30 million to $70 million in annual revenue (conclusion of step 310). Using a computer, the method then determines at step 320 a maximum curve associated with a mean 4 year trajectory of revenue growth to $1 billion, defined by an equation Y=86.45 x2−206.31x+139.23, where y=revenue and x=time in years. (See FIG. 3D for mean 4 year timeframe and equation defining Max Rev 4 year bound of 4 year mean). The method also determines a minimum curve associated with a mean 12 year trajectory of revenue growth to $1 billion, defined by an equation Y=25.313 e0.1926X, where y=revenue and x=time in years. (See FIG. 3F for mean 12 year timeframe and equation defining Max. Rev 12 year bound of 12 year mean). Using a computer and software, the method identifies at step 330 an inflection point in a trajectory curve of the revenue growth rate for each identified business within the set, from which the identified business' revenue growth data is normalized for comparison to the maximum and minimum curve equations. The method then performs step 340, using a computer and software such as Standard and Poor's Backtester software that may be programmed and combined with customized formulae and statistical algorithms, to select from the set of identified businesses those businesses having revenue growth rate trajectory curves falling between the maximum and minimum curves, for inclusion in a list of target businesses. Lastly, the method outputs a list of target businesses (conclusion of step 340), which target businesses are identified as being forecasted to achieve a revenue growth to $1 Billion within a time frame of between 4 years and 12 years from the time of the inflection point in the revenue growth rate trajectory curve for the target business.







TABLE I







List of 4, 6, 12 year companies considered in financial analysis selected from the list of


companies considered based on: revenue at IPO less than $170 million and followed pure


4, 6 or 12 year revenue trajectory from the inflection point to $1 Billion in revenue:















Year @






$1B



Company
Ticker
Description
revenue















4 Year Companies





1
NVR INC
NVR
OPERATIVE BUILDERS
1988


2
MANPOWER INC/WI
MAN
HELP SUPPLY SERVICES
1989


3
ORACLE CORP
ORCL
PREPACKAGED SOFTWARE
1990


4
CISCO SYSTEMS INC
CSCO
COMPUTER COMMUNICATION
1994





EQUIP



5
TIME WARNER INC
TWX
MOTION PIC, VIDEOTAPE PRODTN 
1996


6
BUDGET GROUP INC -CL A
BDGPA
AUTO RENT & LEASE, NO DRIVERS
1997


7
PEOPLESOFT INC
PSFT
PREPACKAGED SOFTWARE
1998


8
INTEGRATED ELECTRICAL SVCS
IES
ELECTRICAL WORK
1999


9
BROADCOM CORP -CL A
BRCM
SEMICONDUCTOR, RELATED DEVICE
2000


10
E TRADE FINANCIAL CORP
ET
SECURITY BROKERS & DEALERS
2000


11
IDT CORP
IDT.C
PHONE COMM EX
2000





RADIOTELEPHONE



12
SIEBEL SYSTEMS INC
SEBL
PREPACKAGED SOFTWARE
2000


13
UNITEDGLOBALCOM INC -CL A
UCOMA
COMMUNICATIONS SERVICES, NEC
2000


14
VERITAS SOFTWARE CO
VRTSE
PREPACKAGED SOFTWARE
2000


15
EARTHLINK INC
ELNK
CMP PROGRAMMING, DATA
2001





PROCESS



16
NVIDIA CORP
NVDA
SEMICONDUCTOR, RELATED DEVICE
2001


17
XO COMMUNICATIONS INC -CL A
3XOCM
PHONE COMM EX
2001





RADIOTELEPHONE



18
EBAY INC
EBAY
CMP PROGRAMMING, DATA
2002





PROCESS



19
KING PHARMACEUTICALS INC
KG
PHARMACEUTICAL PREPARATIONS
2002


20
VERISIGN INC
VRSN
PREPACKAGED SOFTWARE
2002



6 Year Companies





1
APPLE COMPUTER INC
AAPL
ELECTRONIC COMPUTERS
1984


2
LIZ CLAIBORNE INC
LIZ
WOMENS, MISSES, JRS OUTERWEAR
1987


3
SUN MICROSYSTEMS INC
SUNW
ELECTRONIC COMPUTERS
1988


4
MICROSOFT CORP
MSFT
PREPACKAGED SOFTWARE
1990


5
PACIFICARE HEALTH SYSTEMS
PHS
HOSPITAL & MEDICAL SVC PLANS
1991


6
NOVELL INC
NOVL
CMP PROGRAMMING, DATA
1993





PROCESS



7
STAPLES INC
SPLS
MISC SHOPPING GOODS STORES
1993


8
TECH DATA CORP
TECO
COMPUTERS & SOFTWARE-WHSL
1993


9
SOLECTRON CORP
SLR
PRINTED CIRCUIT BOARDS
1994


10
ATMEL CORP
ATML
SEMICONDUCTOR, RELATED DEVICE
1996


11
EXPRESS SCRIPTS INC
ESRX
INS AGENTS, BROKERS & SERVICE
1997


12
FLEXTRONICS INTERNATIONAL
FLEX
PRINTED CIRCUIT BOARDS
1997


13
D R HORTON INC
DHI
OPERATIVE BUILDERS
1998


14
EQUITY RESIDENTIAL
EQR
REAL ESTATE INVESTMENT TRUST
1998


15
STARBUCKS CORP
SBUX
MISC FOOD PREPS, KINDRED PDS
1998


16
TOMMY HILFIGER CORP
TOM
MEN, YTH, BOYS FRNSH, WRK CLTHG
1998


17
US CELLULAR CORP
USM
RADIOTELEPHONE
1998





COMMUNICATION



18
BLYTH INC
BTH
MISC MANUFACTURNG INDUSTRIES
1999


19
LITHIA MOTORS INC -CL A
LAD
AUTO DEALERS, GAS STATIONS
1999


20
RENT-A-CENTER INC
RCII
EQUIP RENTAL & LEASING, NEC
1999


21
EGL INC
EAGL
ARRANGE TRANS-FREIGHT, CARGO
2000


22
PETCO ANIMAL SUPPLIES INC
PETC
RETAIL STORES
2000


23
WATSON PHARMACEUTICALS INC
WPI
PHARMACEUTICAL PREPARATIONS
2001


24
WESTERN WIRELESS CORP -CL A
WWCA
RADIOTELEPHONE
2001





COMMUNICATION



25
CENTRAL PARKING CORP
CPC
AUTO REPAIR, SERVICES, PARKING
2002


26
ANDRX CORP
ADRX
DRUGS AND PROPRIETARY-WHSL
2003


27
CAREER EDUCATION CORP
CECO
EDUCATIONAL SERVICES
2003


28
MEDIACOM COMMUNICATIONS
MCCC
CABLE AND OTHER PAY TV SVCS
2003



CORP





29
MEDIMMUNE INC
MEDI
BIOLOGICAL PDS, EX DIAGNSTICS
2003



12 Year Companies





1
MAGNA INTERNATIONAL -CL A
MGA
MOTOR VEHICLE PART, ACCESSORY
1988


2
DELL INC
DELL
ELECTRONIC COMPUTERS
1992


3
HUNT (JB) TRANSPRT SVCS INC
JBHT
TRUCKING, EXCEPT LOCAL
1993


4
FIFTH THIRD BANCORP
FITB
COMMERCIAL BANKS
1994


5
BARRICK GOLD CORP
ABX
GOLD AND SILVER ORES
1995


6
BB&T CORP
BBT
COMMERCIAL BANKS
1995


7
BRINKER INTL INC
EAT
EATING PLACES
1995


8
CABLEVISION SYS CORP -CL A
CVC
CABLE AND OTHER PAY TV SVCS
1995


9
LSI LOGIC CORP
LSI
SEMICONDUCTOR, RELATED DEVICE
1995


10
MICHAELS STORES INC
MIK
HOBBY, TOY, AND GAME SHOPS
1995


11
SUN HEALTHCARE GROUP INC
SUNH
SKILLED NURSING CARE FAC
1995


12
AIRGAS INC
ARG
INDUSTRIAL MACH & EQ-WHSL
1996


13
FRANKLIN RESOURCES INC
BEN
INVESTMENT ADVICE
1996


14
SYNOVUS FINANCIAL CP
SNV
COMMERCIAL BANKS
1996


15
TELEPHONE & DATA
TDS
RADIOTELEPHONE
1996





COMMUNICATION



16
WASTE MANAGEMENT INC
WMI
REFUSE SYSTEMS
1996


17
BED BATH & BEYOND INC
BBBY
HOME FURNITURE & EQUIP STORE
1997


18
BELO CORP -SER A COM
BLC
NEWSPAPER: PUBG, PUBG & PRINT
1997


19
CBRL GROUP INC
CBRL
EATING PLACES
1997


20
CINTAS CORP
CTAS
MEN, YTH, BOYS FRNSH, WRK CLTHG
1997


21
KLA-TENCOR CORP
KLAC
OPTICAL INSTRUMENTS & LENSES
1997


22
NEIGHBORCARE INC
NCRX
SKILLED NURSING CARE FAC
1997


23
PIER 1 IMPORTS INC/DE
PIR
HOME FURNITURE & EQUIP STORE
1997


24
AMERICAN PWR CNVRSION
APCC
ELECTRICAL INDL APPARATUS
1998


25
BMC SOFTWARE INC
BMC
PREPACKAGED SOFTWARE
1998


26
DREYER'S GRAND ICE CRM HLDGS
DRYR
ICE CREAM & FROZEN DESSERTS
1998


27
ELECTRONIC ARTS INC
ERTS
PREPACKAGED SOFTWARE
1998


28
EXPEDITORS INTL WASH INC
EXPD
ARRANGE TRANS-FREIGHT, CARGO
1998


29
FIDELITY NATIONAL FINL INC
FNF
TITLE INSURANCE
1998


30
FISERV INC
FISV
CMP PROCESSING, DATA PREP SVC
1998


31
GENENTECH INC
DNA
PHARMACEUTICAL PREPARATIONS
1998


32
HIBERNIA CORP -CL A
HIB
COMMERCIAL BANKS
1998


33
LEGG MASON INC
LM
SECURITY BROKERS & DEALERS
1998


34
PAYCHEX INC
PAYX
ACCOUNT, AUDIT, BOOKKEEP SVCS
1998


35
SIERRA HEALTH SERVICES
SIE
HOSPITAL & MEDICAL SVC PLANS
1998


36
SUNGARD DATA SYSTEMS INC
SDS
PREPACKAGED SOFTWARE
1998


37
TOLL BROTHERS INC
TOL
OPERATIVE BUILDERS
1998


38
WILLIAMS-SONOMA INC
WSM
HOME FURNITURE & EQUIP STORE
1998


39
ADOBE SYSTEMS INC.
ADBE
PREPACKAGED SOFTWARE
1999


40
MASONITE INTERNATIONAL CORP
MHM
MILLWORK, VENEER, PLYWOOD
1999


41
MENS WEARHOUSE INC
MW
APPAREL AND ACCESSORY
1999





STORES



42
PRICE (T. ROWE) GROUP
TROW
INVESTMENT ADVICE
1999


43
TALISMAN ENERGY INC
TLM
CRUDE PETROLEUM & NATURAL GS
1999


44
WERNER ENTERPRISES INC
WERN
TRUCKING, EXCEPT LOCAL
1999


45
WORLD FUEL SERVICES CORP
INT
PETROLEUM, EX BULK STATN-WHSL
1999


46
XILINX INC
XLNX
SEMICONDUCTOR, RELATED DEVICE
1999


47
ACXIOM CORP
ACXM
CMP PROCESSING, DATA PREP SVC
2000


48
AMERN EAGLE OUTFITTERS INC
AEOS
FAMILY CLOTHING STORES
2000


49
BIOMET INC
BMET
ORTHO, PROSTH, SURG APPL, SUPLY
2000


50
CLAIRES STORES INC
CLE
APPAREL AND ACCESSORY
2000





STORES



51
D&K HEALTHCARE RESOURCES
DKHR
DRUGS AND PROPRIETARY-WHSL
2000



INC





52
HOVNANIAN ENTRPRS INC -CL A
HOV
OPERATIVE BUILDERS
2000


53
INVACARE CORP
IVC
ORTHO, PROSTH, SURG APPL, SUPLY
2000


54
MARKEL CORP
MKL
SURETY INSURANCE
2000


55
MBIA INC
MBI
SURETY INSURANCE
2000


56
NATIONAL COMMERCE FINANCIAL
NCF
COMMERCIAL BANKS
2000


57
NORTH FORK BANCORPORATION
NFB
COMMERCIAL BANKS
2000


58
REGIS CORP/MN
RGS
PERSONAL SERVICES
2000


59
TIMBERLAND CO -CL A
TBL
FOOTWEAR, EXCEPT RUBBER
2000


60
MAXIM INTEGRATED PRODUCTS
MXIM
SEMICONDUCTOR, RELATED DEVICE
2001


61
O REILLY AUTOMOTIVE INC
ORLY
AUTO AND HOME SUPPLY STORES
2001


62
PRIDE INTERNATIONAL INC
PDE
DRILLING OIL AND GAS WELLS
2001


63
SHAW GROUP INC
SGR
ENGINEERING SERVICES
2001


64
SYMANTEC CORP
SYMC
PREPACKAGED SOFTWARE
2001


65
TRENWICK GROUP LTD
TWKGQ
FIRE, MARINE, CASUALTY INS
2001


66
VARCO INTERNATIONAL INC
VRC
OIL & GAS FIELD MACHY, EQUIP
2001


67
WESTCORP
WES
SAVINGS INSTN, FED CHARTERED
2001


68
ALPHARMA INC -CL A
ALO
PHARMACEUTICAL PREPARATIONS
2002


69
APOLLO GROUP INC -CL A
APOL
EDUCATIONAL SERVICES
2002


70
COMMERCE BANCORP INC/NJ
CBH
COMMERCIAL BANKS
2002


71
GENERAL GROWTH PPTYS INC
GGP
REAL ESTATE INVESTMENT TRUST
2002


72
GUITAR CENTER INC
GTRC
HOME FURNITURE & EQUIP STORE
2002


73
M/I HOMES INC
MHO
OPERATIVE BUILDERS
2002


74
PRIORITY HLTHCARE CP -CL B
PHCC
DRUGS AND PROPRIETARY-WHSL
2002


75
RADIAN GROUP INC
RDN
SURETY INSURANCE
2002


76
THOR INDUSTRIES INC
THO
MISC TRANSPORTATION EQUIP
2002


77
AMBAC FINANCIAL GP
ABK
SURETY INSURANCE
2003


78
LANDRYS RESTAURANTS INC
LNY
EATING PLACES
2003


79
LINCARE HOLDINGS INC
LNCR
MISC HEALTH & ALLIED SVC, NEC
2003


80
NEWFIELD EXPLORATION CO
NFX
CRUDE PETROLEUM & NATURAL GS
2003


81
PACIFIC SUNWEAR CALIF INC
PSUN
FAMILY CLOTHING STORES
2003


82
PENN NATIONAL GAMING INC
PENN
MISC AMUSEMENT & REC SERVICE
2003


83
PIONEER NATURAL RESOURCES
PXD
CRUDE PETROLEUM & NATURAL GS
2003



CO





84
SCP POOL CORP
POOL
MISC DURABLE GOODS-WHSL
2003


85
SYNOPSYS INC
SNPS
PREPACKAGED SOFTWARE
2003


86
TOTAL SYSTEM SERVICES INC
TSS
BUSINESS SERVICES, NEC
2003


87
XTO ENERGY INC
XTO
CRUDE PETROLEUM & NATURAL GS
2003



Breakout Companies





1
REEBOK INTERNATIONAL LTD
RBK
RUBBER AND PLASTICS FOOTWEAR
1987


2
AMERICA WEST HLDG CP -CL B
AWA
AIR TRANSPORT, SCHEDULED
1990


3
CONSECO INC
CNO
ACCIDENT & HEALTH INSURANCE
1991


4
UNITEDHEALTH GROUP INC
UNH
HOSPITAL & MEDICAL SVC PLANS
1992


5
MCI INC
MCA
PHONE COMM EX
1993





RADIOTELEPHONE



6
POLARIS INDS INC
PII
MISC TRANSPORTATION EQUIP
1995


7
CAREMARK RX INC
CMX
DRUG & PROPRIETARY STORES
1996


8
WYNDHAM INTERNATIONAL INC
WBR
HOTELS, MOTELS, TOURIST COURTS
1998


9
AMAZON.COM INC
AMZN
CATALOG, MAIL-ORDER HOUSES
1999


10
HOLLYWOOD ENTMT CORP
HLYW
VIDEO TAPE RENTAL
1999


11
US ONCOLOGY INC
USON
DRUGS AND PROPRIETARY-WHSL
1999


12
PRIMUS TELECOMM GROUP INC
PRTL
PHONE COMM EX
2000





RADIOTELEPHONE



13
QUANTA SERVICES INC
PWR
ELECTRICAL WORK
2000


14
YAHOO INC
YHOO
CMP PROGRAMMING, DATA
2000





PROCESS



15
AMERICAN TOWER CORP
AMT
COMMUNICATIONS SERVICES, NEC
2001


16
FELCOR LODGING TR INC
FCH
REAL ESTATE INVESTMENT TRUST
2001


17
MERITAGE CORP
MTH
OPERATIVE BUILDERS
2002


18
INTRAWEST CORP
IDR
MISC AMUSEMENT & REC SERVICE
2003


19
NEXTEL PARTNERS INC
NXTP
RADIOTELEPHONE
2003





COMMUNICATION



20
PETROKAZAKHSTAN INC
PKZ
CRUDE PETROLEUM & NATURAL GS
2003


21
TAKE-TWO INTERACTIVE SFTWR
TTWO
PREPACKAGED SOFTWARE
2003

## Claims

1. A computerized method comprising:
identifying a plurality of businesses which have achieved a selected financial performance target of $30 million to $70 million in annual revenue;
identifying financial revenue growth rate statistics for each of the plurality of businesses to screen for exponential revenue growth companies, based upon data for the identified businesses obtained from financial databases;
determining exponential revenue growth trajectory curves from the revenue growth rate data for the identified businesses using a computer, to identify an inflection point in the revenue growth trajectory curve for each business that is indicative of where annual revenue is in the range of $30 million to $70 million;
determining an exponential equation defining a maximum curve for a mean 4 year trajectory of revenue growth to $1 billion in revenue, and an exponential equation defining a minimum curve for a mean 12 year trajectory of revenue growth to $1 billion in revenue, using a computer; and
screening target businesses that have at least $30,000,000 in annual revenues based on the revenue growth rate data for the identified businesses, wherein screening comprises identifying an inflection point in the determined revenue growth rate trajectory curve from which the business's revenue growth can be normalized, and selecting target businesses, based on the revenue level and revenue growth rate data for each business, by using a computer to determine which of the identified businesses have revenue growth rate trajectory curves that fall within the bounds of the minimum and maximum curves for achieving a forecasted revenue growth to $1 Billion in revenue within a predetermined timeframe of between 4 and 12 years from the time of the inflection point in the businesses' revenue growth trajectory curve; and
outputting a list of the target businesses selected by the above screening as having above-average potential to achieve a selected financial performance target of $1 Billion in revenue within the predetermined timeframe.

2. The computerized method of claim 1, further comprising the step of selecting one or more of the identified target businesses for inclusion in an investment fund.

3. The method according to claim 2 wherein each target business has at least 30% annual sales growth.

4. The method according to claim 3 wherein the each target business is less than seven years old.

5. The method according to claim 1 wherein the plurality of businesses are comprised of privately owned businesses.

6. The method according to claim 1 wherein the financial statistics include at least one of historic annual sales, annual sales growth rates, time since found to exceed selected annual sales thresholds, gross margins, SG&A, R&D expense, EBIDTA, cash flow from operations, market capitalization.

7. The method of claim 1 wherein the step of selecting target businesses includes identifying those target businesses whose trajectory curves determined from revenue growth rate data fall between the maximum curve for a mean 4 year trajectory of revenue growth to $1 billion in revenue, and the minimum curve for a mean 12 year trajectory of revenue growth to $1 billion in revenue, wherein the maximum curve for a mean 4 year trajectory of revenue growth to $1 billion in revenue is defined by the equation:


Y=86.45x2−206.31x+139.23, where y=revenue and x=time in years,

and the minimum curve for a mean 12 year trajectory of revenue growth to $1 billion in revenue is defined by the equation:


Y=25.313e0.1926X, where y=revenue and x=time in years.

8. The method of claim 7 wherein the inflection point in the trajectory curve of a business's revenue growth rate is a point where the business's revenue is in the range of $30 million to $70 million in sales that provides for normalizing various business's revenue trajectory curves relative to each other, and wherein the method further includes the step of normalizing the trajectory curve for the business's revenue growth rate, for comparing the business trajectory curve to the minimum and maximum curves irrespective of time.

9. The method of claim 8 further comprising the step of selecting those identified target businesses for inclusion in various types of investment funds.

10. The method of claim 1 further comprising the step of determining an exponential equation defining a maximum curve for a mean 4 year trajectory of revenue growth to $10 billion in revenue, and an exponential equation defining a minimum curve for a mean 12 year trajectory of revenue growth to $10 billion in revenue, using a computer.

11. The method of claim 1 wherein the equation defining the maximum curve for a mean 4 year trajectory is an equation selected from the group consisting of an exponential equation, a logarithmic equation, a geometric-progression equation, and combinations thereof, where said equations are configured to yield a curve of compounded revenue growth from $30 million to $1 Billion within 4 years.

12. The method of claim 11 wherein the equation defining a second minimum revenue growth the minimum curve for a mean 12 year trajectory is an equation selected from the group consisting of an exponential equation, a logarithmic equation, a geometric-progression equation, and combinations thereof, where said equation is configured to yield a curve of compounded revenue growth from $30 million to $1 Billion within 12 years.

13. The method of claim 1 further comprising the step of segregating those identified target businesses whose revenue growth rate trajectory curves fall within a trajectory range for achieving a revenue of at least $1 Billion in a mean of 4 years from the inflection point in the revenue growth rate trajectory curve of the identified target business.

14. The method of claim 1 further comprising the step of segregating those identified target businesses whose revenue growth rate trajectory curves fall within a trajectory range for achieving a revenue of at least $1 Billion in a mean of 6 years from the inflection point in the revenue growth rate trajectory curve of the identified target business.

15. The method according to claim 1 further comprising the step of filtering out businesses that in the year in which annual revenues first exceeded $30,000,000, did not have an annual revenue growth rate of at least about 50%.

16. A system for identifying target businesses system comprising one or more computers configured to:
receive and/or provide data identifying a plurality of businesses which have achieved a selected financial performance target of $30 million to $70 million in annual revenue;
receive and/or provide data identifying financial statistics on revenue growth rate data for the identified plurality of businesses;
determine exponential revenue growth rate trajectory curves from the revenue growth rate data for the identified businesses, to identify an inflection point in the revenue growth trajectory curve indicative of where annual revenue is in the range of $30 million to $70 million;
determine a maximum curve for a mean 4 year trajectory of revenue growth to $1 billion, using an equation Y=86.45 x2−206.31x+139.23, and a minimum curve for a mean 12 year trajectory of revenue growth to $1 billion, using an equation Y=25.313e0.1926X, where y=revenue and x=time in years;
screen and identify target businesses whose revenue growth rate trajectory curves determined from revenue growth rate data fall within the bounds between the maximum curve for a mean 4 year trajectory and the minimum curve for a mean 12 year trajectory, and select those target businesses for inclusion in a list of target businesses, which are forecasted to have revenue growth to $1 Billion in revenue within a predetermined timeframe of between 4 and 12 years from the time of the inflection point in the businesses' revenue growth trajectory curve; and
output the list of selected target businesses as having above-average potential to achieve a selected financial performance target of $1 Billion in revenue within the predetermined timeframe.

17. The system of claim 16 wherein the one or more computers are further configured to segregate those identified target businesses whose revenue growth rate trajectory curves fall within a trajectory range for achieving a revenue growth to at least $1 Billion in a mean of 4 years from the inflection point in the revenue growth rate trajectory curve of the identified target business.

18. The system of claim 16 wherein the one or more computers are further configured to segregate those identified target businesses whose revenue growth rate trajectory curves fall within a trajectory range for achieving a revenue growth to at least $1 Billion in a mean of 6 years from the inflection point in the revenue growth rate trajectory curve of the identified target business.

19. The system of claim 16 wherein the one or more computers are further configured to segregate those identified target businesses whose revenue growth rate trajectory curves fall within a trajectory range for achieving a revenue growth to at least $1 Billion in a mean of 12 years from the inflection point in the revenue growth rate trajectory curve of the identified target business.

20. A computerized method of identifying target businesses, the method comprising:
acquiring data relating to financial statistics on revenue growth data for various publically traded businesses from data source provider;
screening the financial statistical data on publically traded businesses, using a computer and software, to determine which businesses have annual sales above a select financial performance target of $30 million to $70 million in annual revenue;
using a computer to determine a maximum curve for a mean 4 year trajectory of revenue growth to $1 billion, defined by an equation Y=86.45 x2−206.31x+139.23, and a minimum curve for a mean 12 year trajectory of revenue growth to $1 billion, defined by an equation Y=25.313e0.1926X, where y=revenue and x=time in years;
using a computer and software to identify an inflection point in a trajectory curve of the revenue growth rate for each identified business within the set, from which the identified business' revenue growth data is normalized for comparison to the maximum and minimum curve equations;
selecting from the set of identified businesses, using a computer and software, those businesses having revenue growth rate trajectory curves falling between the maximum and minimum curves, for inclusion in a list of target businesses, and
outputting a list of the target businesses, which are identified as being forecasted to achieve a revenue growth to $1 Billion within a predetermined time frame of between 4 years and 12 years from the time of the inflection point in the revenue growth rate trajectory curve for the target business, and identifying the listed target businesses as having above-average potential to achieve a selected financial performance target of $1 Billion in revenue within the predetermined timeframe.