Search Engine,Inc.: Growth Versus Exit Strategy for a Mature Tech Start-Up

Abstract

This case is about an information technology start-up that has grown into a global tech company in 10 years with revenue growth of 35.4% from 2008 to 2011. However, during that same period, four major competitors were acquired by integrated search providers like Microsoft and Oracle. These firms integrate enterprise search into broader information services like data mining, analytic, and predictive services. This case enables students to role-play as a consulting team hired by Search Engine, Inc.’s Board to assess the company’s strengths and weaknesses relative to its changing competitive environment and to evaluate whether industry consolidation should be viewed as a threat to its long-term viability or as an opportunity for further growth. If the team recommends selling the company, it needs to identify a potential buyer who would benefit the most from synergistic effects of acquiring their technology, talents, and clients. If the team recommends a growth strategy, it has to specify whether the company should grow within the specialized search market or compete in the integrated search market and whether to finance such growth with private equity or with an IPO. Whether a growth or exit strategy is recommended, the team needs to come up with a fair market valuation.

Keywords

SWOT analysis growth or exit strategy enterprise search engine IT start-up valuation

Introduction

Search Engine, Inc. (SEI) is a privately owned tech start-up spun off in 2000 from research done at Carnegie Mellon University (CMU) in Pittsburgh. By 2011, SEI has grown into a global tech company with 120 employees and $24 million in revenues from about 140 customers. SEI’s cofounders, Professor Stuart Green and Dr. Andre Pascal, are confident that their enterprise search engine is among the best in the specialized segment of the enterprise search industry and that their customers value the implementation expertise and postsale support services they offer. Revenues grew at a compounded annual growth rate of 35.4% from 2008 to 2011. However, during that same period, four of their major competitors were acquired by larger, diversified public corporations like Microsoft, Hewlett-Packard (HP), and Oracle in the United States and Dassault in France. These firms integrate enterprise search into the broader content management and analytic services they offer. SEI’s Board members are wondering if they should view this storm of acquisitions as a threat to their long-term viability or as an opportunity for further growth within the specialized search market, if not into the integrated search market. Although the company preferred to finance future growth plans by raising more private equity, some members wondered what it would take to prepare the company for an IPO. If they decide this is a threat, they have to identify potential buyers who would benefit the most from synergistic effects of acquiring their technology, talents, and clients. In either case, they need to come up with a fair market valuation of their business. They hired your investment bank to help them answer these questions, to assess SEI’s strengths and weaknesses relative to its changing competitive environment, and to spell out the trade-offs between a growth strategy versus an exit strategy.¹

Background

Search Engines: Internet, Metasearch, and Enterprise

The founders of SEI launched their metasearch engines (www.sei.com and www.clustering.com) on the Internet to showcase their time-saving clustering innovation but later evolved into a specialized enterprise search vendor focused on applying their clustering and search technology to vertical niches such as customer service, R&D, and supply chain. Before we explore SEI’s history and product evolution, it is necessary that we have some understanding of how metasearch and enterprise search engines differ from an Internet search engine like Google that we are all familiar with.

Internet search

An Internet search engine is a software system that associates search words entered by a user in a query box with websites that contain the words and then uses an algorithm to rank and present the results from the most to the least relevant. Individual information seekers, content creators, consumers, entrepreneurs, and enterprises derive value from search engines in the form of time saving, raised product awareness, price transparency, productivity gains, entertainment, innovation, and new business models (McKinsey & Company, 2011). Among the earliest search engines to be launched were Lycos and Alta Vista in 1994, Yahoo in 1995, and Google in 1997. Today, the industry is dominated by large firms (Google and Microsoft) because of the high cost of building databases by following links from site to site, indexing new pages found, and then updating the databases over time and of the higher cost of continually adding innovative features that promise user convenience besides strictly web search.² Initially, these databases were built by humans, but as the number of websites on the Internet increased exponentially, it became more economical to use “search robots or crawlers”—software programs that crawl websites and capture their contents for indexing. Google and Microsoft earn revenues by (a) licensing their search engine technologies or making their databases available to other search engine companies who add on special features and (b) selling search queries to businesses whose small advertisements are displayed with search results.

Metasearch

Instead of “crawling” the web to build, maintain, and update large databases like Google and Microsoft do, a metasearch engine sends search queries to several search engines all at once through a process called federation (Sherman, 2005). Because different search engines use different ranking algorithms, a metasearch engine applies its own algorithms for “deduplication” or removing duplicate results and for ranking the aggregated search results according to their own rules. By using a metasearch engine, information seekers who use multiple search engines save a lot of time by not having to sift through multiple lists of search results with different relevance rankings, and manually removing duplicates.

Enterprise search

Enterprise search is the application of metasearch technology to make content from within an organization’s databases, document management systems, e-mail, file systems, and intranet searchable by different user groups who need the information to support decisions or to boost productivity, among others. Metasearch occurs within the enterprise when a user’s search query is sent to multiple content repositories, each returning search results which can be accessed from the user’s browser. Many enterprise search systems integrate structured data (e.g., financial reports) and unstructured data (e.g., customer e-mail). An enterprise search engine indexes multiple content sources, develops multiple interfaces (e.g., executive portal, staff intranet, supplier extranet) to suit different users, and ensures security so that the search engine only allows authorized users to access restricted information (Smart Logic, 2015).

The Enterprise Search Market

Forrester Research classifies enterprise search vendors into three categories: detached, specialized, or integrated. On the lower end of the cost spectrum, detached search vendors like Google, Fabasoft, and ISYS offer ease-of-use and ease-of-installation by way of add-on search appliances to enterprise content management systems. Google’s search appliance displays results in a user-friendly manner and has the best search algorithm for accuracy and context matching, but according to Katey Wood, an analyst at market research and consulting firm Enterprise Strategy Group, “the Google appliance isn’t the most cost-effective means of enterprise search because it’s only so scalable. When you reach your installation’s limit you have to buy another box.” Other critics do not like the fact that its hardware version changes frequently and needs to be tested and certified with current and past versions. Fabasoft offers a search appliance that is focused on document and content management. ISYS offers document-centric enterprise search as an appliance or add-on to a content management system like Microsoft’s SharePoint. On the higher end of the cost spectrum, integrated search vendors like HP, IBM, and Microsoft provide search tools as part of broader information management services such as content management, data mining, warehousing, content analytics, and predictive analytic. Specialized search vendors are enterprise search vendors that target specific subsections of the enterprise (e.g., customer service, R&D) or vertical markets (i.e., supply chain).

History of SEI

Founders’ Academic Roots

With bachelors and master’s degrees in information engineering from the University of Illinois, Stuart Green became Nobel Laureate Herbert Simon’s advisee in the Computer Science PhD program of CMU.³ Since earning his PhD in 1991, he published 50 journal articles and book chapters in computer, natural, and social sciences and was an acting editor of the journal, Machine Learning. As a member of CMU’s Computer Science Faculty, Professor Green was always looking for top students in universities around the world to do research with him. Professor Gaudin, a colleague at the University of Paris, recommended Andre Pascal, who was interested in doing research at a foreign university to fulfill his French civil service requirement. Of great interest to Professor Green was Dr. Pascal’s multidisciplinary background: B.S. in philosophy from the Sorbonne, an M.S. in cognitive science, and an imminent PhD in pure mathematics from the University of Paris. When he joined CMU’s Computer Science Department as a visiting scientist in the Fall 1998, Professor Green got him started on a research project with “fail-fast” instructions. This meant that if they did not see a path to achieve a 10 times improvement fairly quickly, they were to drop the project and move on to the next one. The next project led Professor Green and Dr. Pascal, later working with a graduate student, Dave Richards, to develop an algorithm for text clustering that grouped search results into distinct categories while examining each cluster to determine an appropriate label for it. Clustering yielded a quick overview of the main topics, enabled easy access to valuable but low ranked search results, and grouped related documents for joint consideration. It saved time and improved the ability to find and discover valuable information within search results. For example, a search for “diabetes” would divide the results into the following clusters: type 1 diabetes, type 2 diabetes, teen diabetes, research, treatment, complications, and so on. This reduced search time significantly as the user could focus only on results that were most relevant to their search needs. The metasearch websites (SEI.com and clustering.com) that Professor Green and Dr. Pascal eventually developed used this research breakthrough. They developed federation and deduplication in parallel, originally as a way to demonstrate the power of clustering on web search results from multiple search engines.

As dot-com businesses started to proliferate in the late 1990s, Dr. Pascal kept asking Professor Green the question: “When are we going to start our own business?” In the spring of 2000, Professor Green finally agreed to be a cofounder of the business along with Dr. Pascal and Dave Richards. With the clustering method as proprietary technology and working out of their respective homes, they hired a lawyer to file incorporation papers. SEI was finally incorporated in June 2000 with a very lean staff. Professor Green became President and Dr. Pascal became Chief Scientist. Dr. Pascal and Dave Richards focused on developing a marketable product out of their clustering innovation and then extending it. SEI’s standard clustering technology (Clustering 1.0) organized search results into topical folders on the fly, without any preprocessing of source documents.

There were skeptics who thought it was a bad time to launch an information technology (IT) firm soon after the dot-com bust of March 2000. However, Timothy Slevin, a senior vice president at Parker Hunter who tracks the IT industry thought otherwise, arguing that

If you look at Google and at Alta Vista prior to that, there do tend to be generational advances in search engine technology … Searching the Internet is kind of a need that’s not going away and it’s not getting any smaller. (Kolber, 2001)

Early Stage Financing

Inventors and entrepreneurs typically require a relatively small amount of capital to prove a specific concept for a potentially profitable business opportunity that still has to be developed and proven. This is called preseed funding (Azevedo, 2017). The funded work may involve product development as opposed to pure research, but it rarely involves initial marketing. It can be said that CMU provided SEI its preseed funding, as the initial research which led to the clustering algorithm was developed by the cofounders with university salary or stipend, support staff, and facilities from September 1998 to June 2000. As a CMU spin-off, the cofounders, therefore, agreed to grant CMU the right to 20% of “future proceeds,” a vague phrase that was revisited later when the company first sought venture capital funding. A start-up then needs seed stage funding to assemble its core personnel, prepare an initial business plan, or conduct a market study. The cofounders pooled $70,000 from their own savings and contributions from their families.

Early stage funding to further develop the product line and initiate marketing came from taxpayers. On June 1, 2000, Innovation Works, a Pittsburgh incubator provided $100,000 in convertible loans in the first stage of seed funding, and on July 4, 2002, additional funds of $500,000. Innovation Works is part of a statewide network, Ben Franklin Technology Partners, which brings together a team of former CEOs, technology industry veterans, and early stage experts to provide business expertise and capital into high-potential companies with the greatest likelihood for economic impact in Pennsylvania. Since its seed fund began in 1999, Innovation Works has become the single largest investor in seed-stage companies in Southwest Pennsylvania, having invested over $60 million in over 168 technology start-ups as of 2014. Those companies have been a magnet not only for attracting over $1.43 billion of later stage capital to the region but also for creating thousands of new jobs.

With many years of experience in writing research grant proposals, Professor Green also tried to raise federal funding for their project from the National Science Foundation through CMU.⁴ That academic proposal was not funded, but he later succeeded in getting a Small Business Innovation Research Grant from National Science Foundation to SEI. On January 29, 2001, SEI received $100,000 as Phase 1 funding and a total of $860,000 in subsequent Small Business Innovation Research funding phases. With this funding, they rented space in a former dentist’s office next to a park in Squirrel Hill, the same Pittsburgh neighborhood where the cofounders lived, and hired their first employees.

Product Evolution and Marketing Strategy

In the early years, the company decided that the best way to develop market awareness was to showcase the technology, which they did at CMU even before starting SEI. They offered metasearch of the web at SEI.com, but that corporate site increasingly became needed to support their enterprise business. The founders also felt the need for a consumer search engine that aspired to reach a wider audience. So, the company launched Clustering.com in 2004 as a pure consumer service website supported by ads as was done at other web search engines. Different tabs offered metasearch for news, jobs (in partnership with Indeed.com), U.S. government information, and blogs. Customized tabs allowed users to select sources and create personalized tabs. It had free toolbars for Internet Explorer and Mozilla Firefox and a search plugin for these two browsers.

A New York City start-up was SEI’s first non-paying customer in 2001. The first paying customer was a California start-up, Aurigin, which used SEI’s clustering technology as part of its database of patent records that its clients search to find commercial opportunities. Aurigin’s clients used to spend half of the time searching the database and the other half using the information. According to Lynne Saunders, then Aurigin’s vice president for corporate marketing, SEI’s clustering method would reduce the amount of time it takes to search the database by 20% to 30% (Kolber, 2001). In the following years, SEI’s client list grew as it won several blind requests for proposal from government as well as corporate enterprises.

SEI’s cofounders initially marketed their metasearch and clustering technologies as add-on software to companies that have database-intensive products. However, as they interacted with enterprise clients in their first 2 years, they learned that there were functional gaps in the search system offered by better known and much larger vendors. To fill the need for a better search solution, they decided to collect customer requirements and identify where existing solutions were failing. Having CMU alumni with strong backgrounds in information retrieval already on their staff, it was easy for the company to rapidly develop a full-featured enterprise search platform from scratch, incorporating its innovations in deduplication, federation, clustering, scaling, high performance, and easy customization. Thus, SEI evolved from a company that provided its innovations as add-on features to the search engines that their clients already used to a full-scale enterprise search solution.

SEI’s enterprise search solution was marketed under the name “Momentum Enterprise Search Platform.” It was first offered with low capability and limited security but successively upgraded to new versions with higher capability and multilevel security. In 2007, SEI introduced a mobile version of Momentum that extended enterprise search to all mobile devices. It also introduced a mobile search to clustering.com. A year later, the company launched its Remix Clustering technology (Clustering 2.0) on clustering.com. With one more click after viewing the initial clustered results, Remix Clustering allowed the user to see submerged or secondary topics that were not generated in the initial clustering. It worked with user feedback, clustering the same search results again but explicitly ignoring the topics that the user has already seen. It helped users find new topics and gain more insights related to their search queries, thus taking the searcher’s productivity to a new level (Scardilli, 2008). When the cofounders applied for their first patent in 2007, they described the purpose and the features of the remix clustering system and method as follows:

An increase in information available to a user of computing technologies has a tendency to increase the number of topics that are similarly related. Given the large amount of information that is now available, it is increasingly likely that a first set of search results generated in response to an initial search query will contain information that is not of interest to the user. What is needed in the art is a technique to enable a search query to be conducted by taking advantage of linguistic feedback. Furthermore, what is needed is a technique to enable the presentation of search results to be refined in a manner based on what is not of interest to a user, either intrinsically or because the user has already seen and evaluated certain information and next wants to see more or different information.

By October 2009, SEI marketed Momentum Enterprise Search Platform 7.5 as a comprehensive enterprise search platform for information optimization providing secure access to all of the information that professionals need inside or outside their enterprise regardless of application, format, or location. It introduces significant enhancements in extensibility, scalability via on-demand index management, and performance via triple data crawled, and identified documents that were not indexed due to corrupt file types. It marked SEI’s most significant step in extending the power of Momentum for enterprise clients and OEM partners looking to build and deliver search-centric applications. It allowed enterprise clients to add value to existing applications and develop new solutions that take advantage of massive internal information resources. The Momentum Platform provides an easy-to-use solution for locating the information and expertise needed for better productivity and performance throughout the organization and an advanced platform for developing search-based applications. Momentum 7.5 was made available in two versions: (a) a stand-alone search solution and (b) an embedded OEM⁵ version for partner applications. Independent researchers have given Momentum high marks in the areas of connectivity, security, federation, interface flexibility, and relevance.

The company’s own revenues funded its early expansion. In 2004, SEI considered seeking venture capital but decided to put this off when it entered a big deal with America Online. It was not until its 8 years of operation that SEI first accessed venture capital. On March 17, 2008, North Atlantic Capital of Portland, Maine provided a $4 million Series A loan, payable over 5 years at 12% annual amortization. In May 2010, Clustering was acquired by Yippy, Inc., an Internet start-up based in Fort Myers, FL, for $5.5 million as part of their plan to provide parents, educational institutions, and government entities a much safer alternative for all web-based activities (Pittsburgh Business Times, 2010). These two major cash inflows financed SEI’s expansion in marketing, sales, and customer support.

Client and Geographic Diversification

By the end of 2011, SEI had about 140 clients, $24 million in annual revenues, with a 3-year compounded annual growth rate of 35.4%. It has clients in the United States as well as in foreign countries, the later accounting for about 15% of revenues. About 70% of revenues are from corporate clients and 30% from government entities. SEI’s corporate clients span various industries: life sciences, manufacturing, electronics, consumer goods, and financial services. Among the big corporate clients were Airbus, Procter & Gamble, Bupa, Organon, John Deere (Deere & Co.), Verizon Wireless, Lowe’s, Fidelity (FMR LLC), and LexisNexis. Among the government clients were the governments of the United States, Israel, and New Zealand as well as U.S. government agencies like the U.S. Air Force, U.S. Navy, Social Security Administration, Defense Intelligence Agency, and the National Library of Medicine.

SEI’s growth can be attributed to a high level of satisfaction among its big customers. In 2005, SEI introduced a government search tab at http://gov.clustering.com to search political- and government-related information. It also made large public documents such as the 2006 federal budget, “The 9/11 Commission Report,” and “The Iraq Study Group Report” searchable to the public. In the Fall 2005, the U.S. General Services Administration awarded SEI, in partnership with Microsoft’s MSN search, the contract to do a makeover of the official Web portal of the U.S. government (FirstGov.gov), displacing AT&T and Fast Search & Transfer. The portal had many challenges: high costs, frozen implementation, poor ranking, and subpar ratings by users. After finding out from the government staff what their challenges and goals were, SEI developed a vision of what the service could look like and demonstrated it at www.gov.clustering.com, which they liked very much and greatly influenced their choice. The new portal (usa.gov) was launched in October 2008. Less than a year after the makeover, USA.gov was awarded the Pioneer Award from Federal Computer Week and received high ratings from academic evaluators of e-government services. From then on, SEI won many other federal government contracts, among which are Kids.gov, GobiernoUSA.gov, and Consumer.gov (sister sites of usa.gov designed for specific audiences), and the Military HOMEFRONT portal of the U.S. Department of Defense (www.militaryhomefront.dod.mil).

In January 2007, the National Library of Medicine (NLM), the world’s largest biomedical library, awarded SEI a contract to enhance searching on its website: www.nlm.nih.gov and NLM’s consumer health websites: MedlinePlus (http://medlineplus.gov and http://medlineplus.gov/spanish). These websites aggregate medical and health information from government agencies and other authoritative organizations. SEI licensed the Momentum software to NLM and will train its staff, but NLM will do the design and implementation itself. The NLM rated proposals from SEI and other vendors based on a scoring criterion for the prototypes or concept demo sites that they created for review by NLM. Joyce Backus, head of the NLM’s reference and web services section, said, “SEI offered the best search results, based on the content and metadata of the NLM Web documents.” When key words are entered on the search box, the user is taken to the search results page. On the left side are hierarchical lists of topics and collections that can be expanded and collapsed and used to navigate. On the right side, above the results list is a box with a “spotlight tab” that can show relevant content directly (without leaving the search results page) from a medical encyclopedia or compiled drug information or news reports. Reviewing the categorized display in the demo, industry expert Janice McCallum, President of GrandView Insight, Inc., commented that

search results are easier to interpret because they are displayed in a more visually attractive and meaningful way. The display makes it simple for users to quickly scan the page to see what types of information are available for their specific need. The categorized results are especially helpful when searches are carried out on very general topics, terms for which there are multiple meanings, or topics for which there is a wide range of information available … the most relevant results really stand out in the display and users can quickly find related information. (Hane, 2007)

In a 2007 press release, Professor Green, then CEO of SEI, said:

By marrying NLM’s expertise in selecting, creating, and updating authoritative health information with ours in delivering superior search experiences, citizens will soon be very well served by the enhanced NLM, surpassing anything else out there. SEI’s clustering technology creates its categories on-the-fly from the search results, using terms in the title, snippet, and any other available textual description (including metadata) in the search results themselves. Technologies that provide more of a “guided- navigation” approach rely on controlled vocabularies and predetermined folders of content. The current implementation of MedlinePlus presents categories shown by folders, but there’s no depth beneath each folder

Organon, a global manufacturer of prescription drugs distributed in over 100 countries, selected SEI’s Momentum 5.5 enterprise search platform in 2007 to increase the productivity and efficiency of its more than 11,000 pharmaceutical professionals. According to Arnold Verstraten, Organon’s Manager of Web Applications and eCollaboration, Momentum was chosen after an exhaustive evaluation of many enterprise search platforms and vendors because it yielded precise and intuitive results that are essential to Organon’s core therapeutic fields of fertility, gynecology, and anesthesia, without trade-offs in security, scalability, stability, and fault tolerance. SEI was also credited for its proactive and committed attitude during the pilot phase. Organon provided an interactive demonstration of the Momentum deployment at the European Text Analytics Conference held in Amsterdam on April 27, 2007, giving SEI complimentary marketing to an international audience.

Competitors and Product Differentiation

By the first quarter of 2012, SEI had further differentiated its product lines to address the needs of different market segments. Aside from the full-suite Momentum Platform 7.5, it was marketing Momentum Platform for Government, Momentum for OEM and Embedded Applications, Customer Experience Optimization (CXO) for Customer Service, CXO for Sales and Account Management, and CXO Suite. The following product descriptions are adapted from the SEI, Inc. profile cited in HighBeam Research (2011).

Momentum Platform for Government. This provides capabilities to improve information access, reuse, and collaboration across the full range of government activities. From internal knowledge portals that enhance agency performance to intelligence analysis, military operations, and public-facing websites, Momentum helps government agencies fulfill their missions and deliver value to taxpayers.

Momentum for OEM and Embedded Applications. This provides advanced search and discovery capabilities that enhance value and market acceptance for information-centric applications. It enabled product managers and chief technology officers to focus on developing their core applications while leveraging the power of Momentum to deliver the search, clustering, navigation and contextual intelligence capabilities to differentiate their products in the market. Momentum’s modular design and flexible licensing terms allowed OEM partners to select the components and features that best suit their market needs.

CXO for Customer Service. This gives customer service agents a single workspace to access information from all of the different systems they needed to use, ensuring that they had the right information and making it much easier to deliver outstanding service. The problem it addressed is poor customer service, a leading cause of customer defection. This product saves a customer service agent a lot of time from not having to search through different applications and repositories to locate product information, customer account history, support incidents, and troubleshooting procedures, among others.

CXO for Sales and Account Management. This enables all sales and account management professionals to have complete, up-to-the-minute information about their customers as well as the right case studies, brochures, and product information needed to address customer needs, tailor service offerings, and drive revenue and long-term relationships.

CXO Suite. This is a search-based application that connected sales, account management, and customer service professionals with all of the information they needed to engage customers and create an outstanding experience that encourages loyalty, long-term relationships, and repeat business. It provides a unified workspace for sales, account management, and customer service professionals, giving them the knowledge and up-to-date information about customers, products, and other topics they needed for top performance. CXO presents relevant information from all of important enterprise systems—including customer relation management (CRM), content management system (CMS), e-mail, support ticketing, wikis, supply chain, and more—in an easy-to-use interface that gave them the visibility and insight needed to resolve customer issues, maximize sales opportunities, and build strong long-term relationships.

SEI competes in the specialized search segment with Attivio, Cambridge Semantics, Coveo, Endeca (acquired by Oracle), Exalead (acquired by Dassault), Sinequa, and Recommind. All the specialized search vendors provide access to data across different data sources but differ in how they do it. Some companies provide an index that spans data sources; others use analytics to find related data from multiple sources without having to build an index. SEI, Attivio, Coveo, Oracle’s Endeca, and Recommind crawl various structured and unstructured data sources and build an index much like Google and other web search engines. The following discussion is based on product reviews cited in Smalley (2012). Attivio, Coveo, Exalead, Endeca, and Sinequa all have advanced Unified Information Access capabilities. However, Seth Grimes, an industry analyst, consultant, and organizer of the Sentiment Analysis Symposium, asserts that “The market for search solutions that are adapted to particular verticals or business problems or types of data or styles of results delivery is going to remain much larger than the market for generalized unified access.”

Attivio focuses on custom business intelligence applications. Its Active Intelligence Engine combines the power of enterprise search, Business Intelligence, and big data for strategic applications and solutions that can be deployed on premises or in the cloud. Coveo focuses on content management and customer intelligence applications. Its Unified Indexing Platform consolidates information silos securely across enterprise-, social-, and cloud-based systems. Cambridge Semantics and Exalead both use semantic analysis—a basic form of artificial intelligence that infers some degree of meaning from data. Cambridge Semantics uses Semantic Web technology to integrate structured and unstructured data for custom Business Intelligence applications. Exalead uses semantic analytics to unify data for custom search applications, particularly in engineering. Sinequa combines semantics and indexing. Its Unified Information Access platform uses natural language processing and semantic analytics to sort and combine data from multiple sources and then builds an index for search-based applications, principally for Business Intelligence. Endeca’s faceted index classifies data in multiple ways and provides highly navigable data views for business intelligence, particularly e-commerce. Its “Guided Search” is the most effective way for customers to dynamically explore retailer’s storefront and find relevant and desired items quickly. Exalead provides data discovery solutions to search, reveal, and manage enterprise information assets for faster, smarter decision-making, real-time unified data access, and improved productivity. Sinequa ES is a mature and complete software solution to cope with the information explosion and the resulting complexity of finding the right information at the right time within the many information silos of large enterprises or administrations. Sinequa ES V8 provides a simple and secure solution to accessing relevant data from all enterprise sources. Recommind focuses on search and information management applications, principally in e-discovery and e-governance.

SEI’s interface innovations rest upon a solid core of engineering. The company provides document-oriented clustering of results, principally for supply chain, R&D, and big data applications. Its enterprise search engine, Momentum, has a competitive edge in providing great flexibility and adaptability for complex IT environments and being user-friendly, as it can be administered through a web browser.

Momentum excels in capturing and delivering quality information across the broadest range of data sources, no matter what format it is or where it resides. The software automates the discovery of data and helps employees navigate it with a single view across the enterprise, providing valuable insights that drive better decision-making for solving all operational challenges. Momentum was engineered to be one of the most flexible and scalable platforms. According to the cofounders themselves, it has two major differentiators, which are critical for the success of ambitious search deployments: (a) great flexibility and adaptability for complex enterprise IT environments and various scaling requirements and (b) configurability and deployability to give the user a better experience. For those with an IT background, Exhibit A in appendix provides excerpts from an interview with Professor Green conducted by Arnold (2003) in which he describes the features that make the Momentum enterprise search platform distinctive.

In Beyond Search, SEI was one of a handful of companies pegged as “up and coming vendors to watch,” noting that unlike many search systems, SEI delivers performance, scalability, stability … complementing the sophisticated on the-fly text processing functions that make content access and exploration easy, fast, and enjoyable (Arnold, 2008). The Pittsburgh Technology Council picked it as the “IT Company of the Year” twice (2009 and 2011). In its 2011 review of enterprise search vendors, Butler Analytics (2012) ranked SEI #3 in terms of interoperability and support for diverse data source, trailing behind Endeca and Autonomy, but ahead of Coveo and ISYS, as well as in presentation of search results, trailing behind Endeca and ISYS, but ahead of Autonomy, #4 in Search sophistication, trailing behind Endeca, Lucid Imagination, and Autonomy, but ahead of Exalead.⁶ However, Forrester Research credited SEI’s Momentum platform as the most robust and powerful engine on the market and named it the “top-scored enterprise-search product” in September 2011. That same year, Technology Marketing Corporation, a global integrated media company, named SEI’s CXO as the Customer Interactions Solutions Product of the Year.

Big Data and Consolidation in the IT Industry

The International Data Corporation, a leading global provider of market intelligence for the IT market, estimated that 2.5 quintillion bytes of data are created every day from a variety of sources including sensors, social media, and billions of mobile devices around the world. This makes it difficult for businesses to navigate and analyze big data to improve competitiveness, efficiency, and profitability. International Data Corporation projected the market for big data technology and services to grow at an annual rate of nearly 40% and to reach $16.9 billion by 2015.

Big data technology and services may be divided into four categories: decision support and automation interfaces, analytics and discovery, data organization and management, and infrastructure (see Exhibit B in appendix) for what each category includes. Among the big data service providers reviewed by International Data Corporation are Amazon.com, Dell, HP, IBM, Microsoft, Oracle, SAP, and SAS. Some of these companies are also integrated enterprise search vendors, that is, as part of various management services they provide to enterprise clients, they either developed search engines in-house or acquired specialized enterprise search vendors. For example, in 2008, Microsoft agreed to buy Fast Search & Transfer ASA, a Norwegian maker of software for finding documents, e-mail, and other data stored on corporate networks and websites. The intention was to integrate it into its SharePoint content management platform (Guth, 2008). At the time of this acquisition, Professor Green was confident that:

Microsoft’s SharePoint is not good enough for the complex problems of an enterprise, especially cross- repository search that needs to harmonize disparate security models. Although the integration of Fast into Microsoft’s SharePoint may get them more customers, those customers are very Microsoft-centric in their software purchases and weren’t great sales prospects for SEI anyway. However, some Board members were concerned that Microsoft could provide the search engine to its customers at a much lower price that a smaller company like SEI would find hard to match.

In the next few years, more enterprise search engine vendors were acquired by integrated search vendors and big data technology and service providers. On June 8, 2010, Dassault Systèmes, a world leader in 3D and Product Lifecycle Management solutions, announced its acquisition of another French company, Exalead, a leading provider of search platforms and search-based applications for consumer and business users. On August 18, 2011, HP announced its acquisition of Autonomy, the second largest British software maker. Two months later, Oracle, which engineers hardware and software to work together in the cloud and in enterprise data centers, announced that it was acquiring Endeca Technologies, Inc., a privately held enterprise search company based in Cambridge, MA. Endeca had made it through troubled times in 2001 and then again in 2008 to 2009. Founder and CEO, Steve Papa, had previously made public that an IPO was the ultimate plan for the business, but he feared that going through another downturn might threaten the business. During an event in Boston hosted by The Capital Network, one of its venture capital investors, Papa said,

I did not want to go through another economic downturn. All I needed was one hiccup and a freeze-up of the capital markets, and I can’t get the cash I need to run the business. Public markets are fairly hostile when there’s a lot of uncertainty.

When Endeca accepted Oracle’s offer, Papa confirmed this concern about uncertainty in the economy, saying,

A business selling to enterprises like Endeca would need enough smooth sailing in the economy that we could go public with enough confidence to project forward numbers. So the rationale was, we could do all the right things over the next three years, and not necessarily have a better outcome than what was being presented to us. (Alspach, 2012)

Management and Talent Recruitment

In the beginning, Professor Green and Dr. Pascal liked the team aspect of a small start-up, where everyone was pulling in the same direction toward the same goal. The cofounders also enjoyed a lot of freedom in charting the direction of the company. However, by choosing not to chase venture capital early on, talent recruitment, and retention became a challenge. According to one interview with the Pittsburgh Business Times (Spencer, 2013), Dr. Pascal recalled, “It made hiring more difficult because there was no validation that can come with outside investors. It was the Green and Pascal show.” As an incentive, employees were given stock options, initially valuing the company at $50,000 and keeping the exercise price low. Fred Johnson was hired as chief financial officer in 2007 to lead the effort of raising the company’s first round of outside capital.

During the 9 years that he served as SEI’s CEO, Professor Green was voted “Entrepreneur of the Year” by Inc. magazine and also by Ernst & Young (for the North Central Region in 2007). As the client list grew, Professor Green felt the need to hire experienced business executives to help him take the business to the next level, while he continued to serve as chairman of the Board. In the fall of 2007, a recruiter from Washington, DC, who helped SEI hire a vice president for sales in the past, was hired to do a search for a new CEO. In June 2008, after 9 months of not finding a good match, Robert Mills, a member of SEI’s Advisory Board who had significant experience in the software industry agreed to serve as CEO. The following year he hired John Jones as vice president for strategic alliances and Stephen Smith as the new chief financial officer to replace Fred Johnson who retired. In 2011, due to the growth in sales of SEI’s CXO Suite, Dan Brown was hired as Director of Customer Experience Operations responsible for helping customers extrapolate the business value they receive from implementing this software across their organization.

As more and more enterprise search engine vendors were acquired by integrated search vendors and big data technology and service providers, some of SEI’s Board members started to wonder whether they should raise more private equity capital to further grow revenues by diversifying into content management and predictive analytics, thus competing in the integrated enterprise search space. Other Board members argued that going public was the best way to tap unlimited sources of funding and wondered what it would take to make SEI ready for an IPO. They needed to find out what the Securities and Exchange Commission’s (SEC) minimum revenue requirement was and the ramifications of the Sarbanes–Oxley Act. Existing shareholders have to weigh the dilution of equity, the loss of privacy as far as management’s strategic direction, disclosure of which the SEC requires, and the pressure that comes from stock analysts’ expectations of revenue and profit changes quarter after quarter. Other Board members expressed preference for an exit strategy or a liquidity event, that is, to sell the company to a big public firm as their major competitors have done. Some Board members did not want SEI to become the “last man standing without a suitor”. SEI was the largest pure play in the search engine space, and it was “clean,” there were no patent disputes and other legal and accounting issues.⁷ SEI’s Board members eventually reached a consensus that they needed to hire an investment banker that could guide them toward choosing the option that was best for the company. Thomas Weisel Partners (later acquired by Stifel Financial Corporation) was chosen after interviewing a few investment bankers. Having been an advisor to SEI prior to becoming SEI’s CEO in 2008, Robert Mills was aware that SEI has over 10 years of experience and innovation in data navigation and visualization technologies for both structured and unstructured data, and its ability to index and search data across multiple repositories is a distinguishing capability, applicable to all industries and clients. He had the vision that:

Businesses need a faster and more accurate way to discover and navigate big data for analysis. SEI’s technology will make it easier for business users to get value from all of their data and content.

Management Issues

Professor Green and Mr. Mills, as chairman of the Board and CEO, respectively, directed Thomas Weisel Partners to assess SEI’s strengths and weaknesses and recommend whether they should diversify and compete with integrated enterprise search companies or exit by being acquired by one. Should a growth strategy be recommended the investment bank will have to identify possible sources of private equity or look into the possibility and ramifications of an IPO? Should an exit strategy be recommended, the investment bank needs to identify possible buyers. There were speculations in the search engine industry that Symantec and Interwoven, which both had long-standing OEM partnerships with SEI (4 years in the case of Symantec) would be interested in buying it out. Although, there has not been any OEM partnership with IBM, it was making a big push toward big data and was also a possible buyer. Mr. Mills provided Thomas Weasel Partners a brief overview of these three possible buyers (Exhibit C in appendix). The challenge was to identify the one who would reap the most synergistic effects from acquiring SEI’s technology, talents, and client base. Finally, a valuation of SEI as a tech company is needed no matter what the recommendation is. Will SEI’s enterprise value in the eyes of private equity investors differ from that of stock market investors in an IPO and from that of a public company wanting to buy it? As chairman of the Board, Professor Green emphasized that a measure of cash flow like earnings before interest, taxes, depreciation, and amortization (EBITDA) is not as important a driver of value for privately held tech companies as the growth of their revenues.

Consulting Team’s Discussion Questions

Thomas Weasel Partners has provided your team with initial research notes on valuation of tech companies, in general, and recent acquisition prices and other information for SEI’s peers, in particular (Exhibit D in appendix). You have been charged with doing an analysis of SEI’s opportunities and threats as well as presenting a set of recommendations pertaining to the following questions:

What strategy is best for SEI: growth or exit?

If a growth strategy is recommended, should it continue to grow within the specialized search market segment or move into the integrated search market segment? Should this growth be funded by private equity or by an IPO? How will valuation vary across these two strategies?

If an exit strategy is recommended, who is the best possible buyer? What range of offer prices would be appropriate for SEI’s technology, tech talent, and customer base?

Footnotes

Appendix

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biography

Adora D. Holstein is a professor of economics at Robert Morris University near Pittsburgh, PA. She earned her PhD in economics from the Pennsylvania State University in April 1987, and BS business administration and accountancy and master’s in Asian studies from the University of the Philippines.

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