Customer equity management in a professional player’s agency: how much is worth my current player’s base?

Revista de Psicología del Deporte. 2016, Vol 25, Suppl 1, pp. 11-14 ISSN: 1132-239X ISSNe: 1988-5636 Universitat de les Illes Balears Universitat Autònoma de Barcelona Correspondencia: Gabriel Cepeda Carrión, Department of Business Management and Marketing, Universidad de Sevilla, Av. Ramón y Cajal, 1, 41018, Sevilla, Spain (E- mail: [email protected]) * Departamento de Administración de Empresas y Marketing. Universidad de Sevilla Acknowledgements: This research was carried out, thanks to a research programme supported by the Spanish Ministry of Economy and Competitiveness (Reference: ECO2013- 49352-EXP). Artículo invitado con revisión. Customer equity management in a professional player’s agency: how much is worth my current player’s base? Gabriel Cepeda Carrión*, David Martin Ruiz* and Francisco Javier Rondán Cataluña* CUSTOMER EQUITY MANAGEMENT IN A PROFESSIONAL PLAYER’S AGENCY: HOW MUCH IS WORTH MY CURRENT PLAYER’S BASE? KEYWORDS: Basketball, Management, Agent companies, Customer equity. ABSTRACT: TTraditionally, basketball players’ agents and players’ agents companies in general take decisions about their represented players based on reports, statistics or even premonitions. The future and potential economic revenues that are obtained from the players are more a desire than a variable to take decisions. A methodology to assess and cluster players from a basketball player agent is provided in this paper. Particularly, when, how much and how to invest in basketball players’ portfolios according to their current and potential value and economic revenues. Data were obtained from one of the most important international basketball players’ agents firm. They represent mainly players and coaches from all around the world, mainly the European and American Professional Championships. Traditionally, basketball players’ agents and players’ agents companies in general take decision about their represented players based on reports, statistics or even premonitions. The future and potential economic revenues that are obtained from the players are more a desire than a variable to take decisions. Our study tries to analyze the composition of the basketball players’ portfolio in an agent company. In light of this analysis, we draw conclusion to manage correctly this kind of companies’ players portfolio. We contribute to the literature on agency theory and customer equity management in several ways. First, we provide a methodology to assess and cluster players from a basketball player agent’s portfolio. Particularly, when, how much and how to invest in basketball players according to their current and potential value, and economic revenues. In addition, the long-term survival of these agents depends on the future value of their athletes’ portfolio. In this sense, we show that the basketball-player`s agents should tend to sort their client's portfolio according to the player’s lifecycle. Hence, these firms need a wide base of young and potential good players as much as some stars than provide the money, image and differentiation of the firm. Likewise, representing well-known players facilitates the attraction of young and potential stars. Second, we illustrate the need of relationship marketing to carry out CRM strategies – account retention and development – and as they work in the business of sport. On this regard, Customer Equity Management (CEM) aims to optimize the recruiting and retaining of customers, guiding the marketing investments decisions and analyzing their performance according to the impact which they have on the firm’s customer equity value (Blattberg, Getz and Thomas, 2001). Thus, CEM serves as a basis for evaluating the return on marketing strategies, as it offers those in charge of marketing essential information about the financial result of their decisions. Managing customer relationships implies that customers should not be equally treated, since they differ in terms of profitability (Doyle, 2000). Relationship marketing is a feature of customer relationship management (CRM) that focuses on client loyalty and long-term client engagement rather than shorter-term perspectives. Therefore, managers should make differentiated efforts to acquire, retain, and develop the customers with higher profitability potential, in order to maximize customer equity. Customer equity is defined as “the total discounted lifetime values summed over all of the firm’s current and potential customers” (Rust, Lemon and Zeithaml, 2004), p.110). Similarly, customer lifetime value (CLV) has been defined as “the net profit a company accrues from transactions with a given customer during the time that the customer has a relationship with the company” (Rust, Lemon and Zeithalm, 2004), p.113); the idea is to consider each customer as a financial asset, where its lifetime value represent the discounted value of the entire stream of purchases that the customer would make over a lifetime of patronage. All these ideas of relationship marketing, CRM, and CLV are especially relevant in the context of this study. This is because the clients of players’ agencies are sportsmen and sportswomen who could be engaged to the same agency for all their careers, from their childhood to the end of their professional careers. In addition, agencies have to make a big effort to retain the players who become popular or even stars since they are the main objective of the competition. The quality of the relationship Year 2009 (221) Year 2010 (252) Year 2011 (243) Year 2012 (230) N % N % N % N % Prospects 130 - 74 - 30 - 0 - Active accounts: Base 166 75.1 201 79.8 190 78.2 182 79.1 Active accounts: Pros 49 22.2 41 16.3 45 18.5 41 17.8 Active accounts: Stars 6 2.7 10 3.9 8 3.3 7 3.1 Just Lost Accounts 0 - -25 (11.3*) -53 (21.0*) -43 (17.7*) Long Lost Accounts 0 - 0 - -25 - -78 - Total 351 100.0 351 100.0 351 100.0 351 100.0 between the player and the agency would be one of the most relevant issues of the player’s decision to continue or leave the agency. Although some studies have already been conducted in the area of marketing performance measurement (Gupta, 1988), it was not until the early 2000’s that RMI concepts began to appear in research articles (Verhoef and Lemon, 2013; Homburg, Artz, and Wieseke, 2012). Following this literature, we analyze the interplay player-agent based on the stage of the player lifecycle (from prospect to star) and how to solve the potential drawbacks that the agency literature exhibits. Furthermore, this study helps agents to improve the allocation of time and money among players by detecting who and how much is worthy to invest in. From it emerges the need of managing costs and income by player account. Method Procedure Our dataset has been obtained from one of the most important multinational athlete agency. This company is a global athlete representation and sports marketing firm with offices in worldwide. It was founded in 2002 and represents more than 500 basketball and football players in more than 20 countries. In this study, we only analyzed 351 basketball players and coaches from all around the world, some of them enrolled in teams which play in the Euroleague and NBA Championships. We have collected data regarding the agency’s income from each represented player during the years 2009-2012, along with the total costs developed by the agents. First, we use this information to identify how many segments there are in the customer base according to their potential and profitability, combining the income and estimated career length of each player. Next, we use this information to estimate customer transitions along the years, building a loyalty matrix which includes loyalty/churn rates, and player development rates. We predict the customer lifetime value by multiplying this matrix along with each player’s margin. Finally, the player’s base is the sum of all the player’s lifetime values that are represented by the agency at the time (this is, end of 2012, 2011, 2010 and 2009). Data analysis To do so, the first step was to debug data. For example, players and coaches who yielded no revenues were removed from database. We assessed the variable “potential revenues” by multiplying current revenues by remaining years of active career of each principal. Minimum life-span was considered 1 year for players over 35 and coaches over 65. The second step was to segment the remaining active clients/players’ portfolio. A Bayesian two-step cluster method was used to identify three clusters of clients/players –which we labeled as “stars” (star player), “pros” (professional player account), and “base” (active base account) using potential revenues as segmentation base. Results Our results have clearly identified three clusters in terms of potential profitability, which we labeled as “stars”, “pros”, and “base”. Essentially, they represent the different steps the player goes through along its career if the player is successful (actually very few reach the “star” category). Our findings reveal one player-customer starts being a base, some of them will be promoted to pro, and only a few will climb to stars step. The most common transition is from base to pros, then some of them promote from pros to stars and some of them move to base in the final of their careers. This player lifetime value is very close to player’s sport career. In this regard, findings support the customer pyramid present on other industries (Zeithaml, Rust and Lemon, 2001). In our particular case, we have discovered that the agency is good at developing “base” players, but is not doing so well in retaining “pros”, which account for a great proportion of their income. In order to label all players/coaches (accounts) in the dataset, three other categories of client/players were also identified: “prospects”, “just-lost” and “long-lost” accounts. Prospects are accounts that have not been acquired yet by the company. Justlost represent those accounts which left the company in the previous year (t-1), and include both retired players and those who switch to a competitor. Finally, long-lost are accounts who left the company more than one year ago (t-2), and are not gained back. According to Table 1, the company has 221 active accounts in year 2009, which were incremented to 252 in year 2010. Then, the number of active accounts diminished to 241 in year 2011 and to 230 in year 2012 respectively because the number of accounts which left the company could not be replaced by the number of new accounts which were hired during those years. Although the number of players has grown since the beginning of its activity, the total value of the agency’s customer base has diminished recently, reflecting the trend of signing lower contracts for the industry, due to the global economic crisis and the lack of strong sponsorships for the teams. Revista de Psicología del Deporte. 2016, Vol 25, Suppl 1, pp. 11-14 12 Gabriel Cepeda Carrión, David Martin Ruiz and Francisco Javier Rondán Cataluña *Note: just lost accounts percentage are calculated over previous year number of accounts Table 1. Segmentation of Accounts Additionally, this segmentation of accounts shows there is a classic pyramid where most of the clients are on the pyramid base according to their actual profitability; thus, “base” represent between 75% and 80% of total active accounts, but each account individually generate only between 3.300 and 4.300 euros/year for the company. Base accounts’ average potential income rises up to 136.000 euros. The next tier is “pros”, which represent between16% and 22% of total active accounts, but each of these clients generate between 32,000 and 36.000 euros/year, ten times more than base accounts. In terms of potential income, this amount may rise up to 643.000 euros/year for the company. On the top of the pyramid we find “stars”, which are only 6-10 accounts (3-4% of total active accounts) but generate individually between 95.000 and 125.000 euros/year for the agency. Stars are top players with top contracts in their teams. In terms of potential income, it may rise up to 1.75 million euros a year. Next, we need to build transition matrices for each pair of years considered in the study (2009-2010; 2010-2011, and 2011- 2012). After analyzing three transition periods, we have obtained quite unequal results. We can state that the first year was the best for the company, and then its value declined from 8M$ to 4M$, and from 4M$ to 2M$ approximately. We speculate there are reasons coming from the industry and the impact of the economic crisis (new and renewals contracts are signed for less money than before); but also there are reasons coming from the agency itself. Then, we have projected a “weighted transition” matrix (see Table 2) with data from the three available periods in order to present a fully-informed data analysis of the customer equity. We weighted with a Wi= 0.5 the more recent period (2011-2012) and with a Wi= 0.25 the previous ones (2009-2010 and 2010-2011). We obtained the following results, which we think can represent a better estimation of the customer equity of the agency. Thus, a 72.7% of base accounts were retained; a 6.4% were promoted to pros and a 1.1% to stars, whereas the defection rate at this tier reached 19.8% on average. We can conclude that the agency is doing relatively well with base players. On the other hand, the retention rate of pros accounts reached 54.3%, with a 29.4% of accounts dropped to the base tier, but only a 2.9% were raised to stars level, leaving a total of 13.4% of lost accounts at this level. However, the results in terms of income are dramatic, mainly because of the accounts that were no longer professionals but base, but also because of the lost accounts at this level, which could not be leveraged by those accounts which remained at the professional level. Finally, although almost no stars have left the agency (1.6%), five were no longer classified as stars, two as base accounts (probably because these players were at the end of its sport career), and three as professional accounts (signed a lower contract). The consequence in terms of economic impact is again quite significant. This is not a good result either, but it was counter-balanced by new stars promoted from the base account level. As we display in Table 3, star accounts are worth “only” 125.358 euros, while pros and base accounts reach 30.488 euros and -2.770 euros respectively, using a 10 year period and a 15% discount factor. Pros and star accounts have reduced their lifetime value considerably, reflecting a bad year for the agency in the medium run. Base accounts are still not profitable, despite the improvement in the promotion to higher levels. Customer equity of the agency reaches 2.634.348 euros (see Table 4 next). According to this results, it is salient the impact that retention rates has on the customer equity of the agency. The agency still allocated the same amount of money to each account, but it was not successful retaining star and pros accounts, which is essential at this business. Taking this information, we have projected this data in order to make a revenue prediction. At the end of the projection (year 2021), only 35 active accounts would remain from the initial set (1 stars, 7 pros, and 27 base accounts), while 261 accounts would have been lost in the process, and therefore, should be replaced by new accounts recruitment. Again, these figures are not sustainable if the agency wants to remain in business in the long run. Discussion and conclusions We believe this study provides a very useful tool for managers of agents’ firms to make decisions about their clients (represented players). The actual and prospective investments on returns that are obtained from the players become a relevant variable to take decisions. The managers could use this methodology to assess and cluster players from an agent company. Concretely, when, Customer equity management in a professional player’s agency: how much is worth my current player’s base? 13 Revista de Psicología del Deporte. 2016, Vol 25, Suppl 1, pp. 11-14 Table 2. Transition Matrix (Weighted) Cluster 2013 Cluster 2012 BASE PROS STARS LOST Total PROSPECTS 90.0% 10.0% 0.0% 0.0% 100% BASE 72.7% 6.4% 1.1% 19.8% 100% PROS 29.4% 54.3% 2.9% 13.4% 100% STARS 19.7% 28.5% 50.2% 1.6% 100% Table 3. Customer Lifetime Value (Weighted) Segments Accounts Margins Marketing Costs CLV STARS 7 125,936 10,712 € 125,36 PROS 41 35,796 10,712 € 30,49 BASE 182 4,293 10,712 -€ 2,77 LOST 43 0 0 € 0 how much and how to invest in players’ portfolios according to their current and potential value and incomes. Therefore, this methodology could avoid making decisions about the future of agents’ firms based on emotions, premonitions, and feelings. The economic returns acquired from the players are often more a wish than a decisional variable. In this research, we make available a procedure to assess and classify players from a basketball player agent’s portfolio. In addition, we show the need of developing relationship marketing to perform CRM strategies and as they operate in the business of sports. Furthermore, this study facilitates agents work to enhance the distribution of time and money amongst players by distinguishing who and how much is worthy to invest in. This study does not focus only at the professional level, but also treats the concerns of base level players. A managerial implication arisen from this study is to manifest the necessity of investing much more marketing efforts in stars than in pros, and of course than in base players, according to their importance for business success. To reinforce relationship marketing policies targeting younger pros and stars are crucial to strengthen links between them and companies. Perhaps to lose a star sometimes is unavoidable, but the percentages of lost in the professional group should be kept near zero because of two motives: a) the loss of current incomes, b) the loss of future incomes of prospective stars. Every study, no matter how well it is conducted, has some limitations. In this case, we had no access to data about the costs generated by each sportsman individually. In addition, we acknowledge that the population of the study is limited to basketball players, hindering so the generalization of results. We believe that combining player’s performance in the court with their lifetime value represent a very interesting line for future research. Gabriel Cepeda Carrión, David Martin Ruiz and Francisco Javier Rondán Cataluña 14 Revista de Psicología del Deporte. 2016, Vol 25, Suppl 1, pp. 11-14 Years 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Margin € 2.755.384 € 2.363.008 € 1.995.794 € 1.670.082 € 1.389.518 € 1.151.929 € 952.793 € 786.944 € 649.366 € 535.526 Marketing Costs € 2.017.691 € 1.655.876 € 1.360.802 € 1.119.296 € 921.175 € 758.399 € 624.532 € 514.372 € 423.683 € 349.006 Net Margins € 737.693 € 707.132 € 634.992 € 550.785 € 468.343 € 393.531 € 328.261 € 272.572 € 225.683 € 186.520 Discount Factor (15%) 0,87 0,76 0,66 0,57 0,5 0,43 0,38 0,33 0,28 0,25 Discounted Net Margins € 641.472 € 534.693 € 417.518 € 314.913 € 232.849 € 170.134 € 123.406 € 89.104 € 64.153 € 46.105 Customer Equity € 641.472 € 1.176.165 € 1.593.683 € 1.908.596 € 2.141.446 € 2.311.580 € 2.434.986 € 2.524.090 € 2.588.243 € 2.634.348 Table 4. Customer Equity (Weighted) GESTIÓN DE CLIENTES EN UNA AGENCIA DE DEPORTISTAS PROFESIONALES: ¿CUÁNTO VALE TU CARTERA DE CLIENTES? PALABRAS CLAVES: Baloncesto, Administración de empresas, agencias de deportistas, gestión de cartera de clientes RESUMEN: Tradicionalmente, las agencias de deportistas profesionales y las de baloncesto en particular, toman decisiones sobre sus representados basándose en informes, estadísticas, e incluso corazonadas. Los potenciales ingresos futuros que se obtienen de los jugadores son más un deseo que una variable para tomar decisiones. Presentamos en este articulo una metodología para valorar y segmentar jugadores de una agencia de jugadores de baloncesto profesionales. Concretamente, tratamos de responder a las preguntas de cuándo, cuánto y cómo invertir en la cartera de jugadores profesionales atendiendo a su valor económico actual y a sus potenciales ingresos económicos futuros. Los datos fueron obtenidos de una de las más importantes agencia de jugadores de baloncesto a nivel internacional. Esta empresa representa principalmente a jugadores y entrenadores de todo el mundo, fundamentalmente de ligas profesionales europeas y norteamericanas. References Blattberg, R. C., Getz, G. and Thomas, J. S., 2001. Customer equity: Building and managing relationships as valuable assets. Harvard, MA: Harvard Business Press. Cook, William A and R. S. Talluri (2004), “How the pursuit of ROMI is changing Marketing Management” Journal of Advertising Research, (September), 244-254. Doyle, P. (2000). Value-based marketing. Journal of Strategic Marketing, 8(4), 299-311. Gupta, S. (1988). Impact of Sales Promotions on When, What, and How much to Buy. Journal of Marketing Research, XXV, 342-55. Homburg, C., Artz, M. and Wieseke, J. (2012). Marketing performance measurement systems: does comprehensiveness really improve performance? Journal of Marketing, 76(3), 56-77. Martínez, J. A. and Martínez, L. (2011). A stakeholder assessment of basketball player evaluation metrics. Journal of Human Sport and Exercise, 6(1), 153-183. Verhoef, P.C. and Lemon, K. N. (2013). Successful customer value management: key lessons and emerging trends. European Management Journal, 31, 1-15. Zeithaml, V. A., Rust, R. T. and Lemon, K. N. (2001). The customer pyramid: creating and serving profitable customers. California Management Review, 43(4), 118-142.