Optimal Recommendation and Long-tail Provision Strategies for Content Monetization

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1 14 47th Hawa Internatonal Conference on System Scence Optmal Recommendaton and Long-tal Provson Strateges for Content Monetzaton Tng-Ka Hwang Department of Journalsm Mng Chuan Unversty, Tawan Yung-Mng L Insttute of Informaton Management Natonal Chao Tung Unversty, Tawan yml@mal.nctu.edu.tw Abstract Ths paper examnes the optmal strateges for prcng, contents varety supply and recommendaton system nvestment by dgtal contents provders. Wth the fast development of dgtalzaton technology and socal partcpaton n recent years, the ways to create and access nformaton contents become dverse wth greater convenence and much lower cost. How to attract more customers of dfferent segments and rase sales revenue becomes the most essental ssue for content provders as the long tal phenomenon becomes sgnfcant. From the supply sde, ncreasng and mantanng a wde varety of content can attract more users. From the demand sde, adaptng sutable recommender systems s consdered as an effectve mplementaton for content sale promoton. However, they both requre the provders to make more efforts on nformaton acquston and balancng the budget allocated on varous types of recommender systems, whch leads to dfferentated changes of sales patterns. In ths paper, we propose an economc model to capture the technologcal and market factors affectng the categorzaton of sales pattern and develop the proper busness strateges of content provson and content recommendaton for supportng the operatons of dgtal content provders. 1. Introducton Due to the fast development of dgtalzaton technology and nternet, the ways to access nformaton contents become dverse wth greater convenence and much lower cost. Wthout the restrctons of physcal space, delvery and nventory, consumers are more self-conscous to evaluate and obtan the expanded varetes of dgtal contents. Under the nteractve network structure of web., user partcpaton and content provson become nterdependent. Content provders are therefore encouragng or supportng users to create valuable user-generated content [17, 3]. As consumers become wth hgher dfferentated preferences and have more freedom of content access, the consumpton patterns become dversfed. Ths nduces the sgnfcance of the long-tal phenomenon, whch descrbes a knd of customer demand pattern for personalzed servce or nche products (tal-products). Anderson [] clams that n a market wth an amount of product varetes, the aggregaton of small nche markets can compete aganst a market of mass common/popular ht-products. In order to attract more customers of dfferent segments and rase sales revenue, ncreasng and mantanng a wde varety of products becomes an mportant busness strategy for content provders [4]. In the supply sde, besdes contnuously provdng professonal qualty content to ft the common demand, t often gets along wth crossover cooperaton or utlzaton of valuable user-generated content to offer customers more varetes of content produced at lower cost [8]. Users expect a wde varety and mass amount of content envronment, but they have to make more efforts for nformaton acquston. The products sold n the nche market are generally not easy to be found by the publc. Therefore, besdes provdng a greater varety of content, t s necessary for content provders to help users to reduce the search costs of the product nformaton. Adaptng sutable recommender systems s consdered as an effectve mplementaton [5, 6, 18]. In addton, t was shown that recommendaton systems can reshape the customers purchasng pattern [4]. As llustrated n the Fgure 1, the x-axs denotes the popularty rank of content varetes and the y-axs represents the popularty. The content wth hgher popularty has a smaller rank, and the sales pattern of content s segmented to ht products and nche products. Wth recommender systems, the shape of content sale dstrbuton s changed and the sale s mproved [5]. However, as shown n the Fgure, for a saturated market, the substtuton effect may occur between ht products and nche products. Such effect may also lead to the reducton n the varetes of content [11, 1]. Snce dfferent types of dgtal content (e.g. move, /14 $ IEEE DOI 1.119/HICSS

2 musc, news, or app software) have some dstnct characterstcs, and a ht product market and a nche market can be coexst, only focusng on the development of ht market but gnorng provson talend products may nstead result n the shrnk of proft [1]. sale amount Fgure 1. Promoton effect of recommendaton systems for an unsaturated market sale amount ht products nche products x= x=m popularty rank of content ht products sales pattern wth recommenders sales pattern wth recommenders nche products orgnal sales pattern orgnal sales pattern x= x=m popularty rank of content Fgure. Substtuton effect of recommendaton systems for a saturated market Recommender systems usually adopt user s profle nformaton to suggest sutable (ht or nche) products to approprate consumers [1]. Theoretcally, recommender systems are bascally dvded to contentbased and collaboratve flterng-based systems. In practce, a content-based system asssts customers to dscover products of personal preference, whch can be vewed as a knd of personalzed recommender, whch help ncreasng sales of nche products [5, 11]. For the collaboratve flterng-based system, t captures the ntentons of followng socal behavors for some customers, whch s an extenson of socal recommender applcaton wth the promoton effect of ht products [4]. For example, the lstng the most popular tems on a webpage can be treated as a knd of socal recommender applcaton. To mprove the proftablty, from the supply sde, content provders have to consder the ssues of optmal strateges for provsonng product varetes and balancng the effect of professonal content and user-generated content for dfferent customer segments. From the demand sde, the budget allocaton on varous types of recommender systems leads to dfferentated changes of sales patterns. In addton, the mpact of changng the customers purchasng pattern can be further explored to develop the content provson and prcng strateges [7]. Indeed, dgtal content s a knd of content-based dgtal nformaton goods. The key characterstcs of these nformaton goods are wth low or zero reproducton margnal cost and dstrbuton cost. Snce the features of dgtal content are dfferent from the tradtonal ndustral goods, t s napproprate for content provders to follow the tradtonal cost-based prcng strategy [14]. To capture the approprate categorzaton of sales pattern and develop the proper busness strateges, the potentally complex relatonshps between the demand and supply sde factors should be clarfed. In ths paper, we propose an economc model based on the ntegrated vews of the supply and demand sdes for dgtal content busness operatons. The busness strateges ncludng content prcng, provson (volume of varetes), and recommendaton (socal recommender and personalzed recommender) are analyzed. The results derved from the proposed model can provde useful nsghts for supportng the content provder n developng approprate busness models of proftablty enhancement. The rest of ths paper s organzed as follows. In secton, we revew prevous lterature related to the current research. In secton 3, we propose an economc model for dgtal content busness operatons and examne the busness strateges (content prcng, provson, and recommendaton) under dfferent market structures. Fnally, secton 4 concludes our fndngs and dscusses future research drectons.. Lterature Revew Ths secton revews the lterature related to ths research, ncludng long tal theory, recommendaton mechansm, and content prcng strateges..1. Long-tal Theory Lots of researches have been conducted on examnng the mportance of long- tal phenomenon for onlne commerce and ts nfluence from both the supply (producers/retalers) and demand (consumers) 1317

3 sdes. In partcular, the long-tal phenomenon has mpacts on affectng the varetes of products and change of demand dstrbuton [4-6]. Zhou & Duan [8] studed the mpact of product varety on long-tal effects. They found the nfluences of onlne user revews are decreased by the greater number of product varetes. In the onlne and transparency envronment, t s easy for customers to fnd out a lower-prced product offered by a competng company. To avod prce comparsons and enhance consumer repeat patronage, Clemons & Nunes [9] suggested resonatng wth customers to offer what they really need. Porcel et al. [4] ponted out that provson of nche products can help to ncrease consumer satsfacton and create customer loyalty. Nevertheless, Elberse [1] found that the proftablty from the long-tal s not guaranteed, and long-tal and ht-product markets can coexst for varous consumpton patterns. Lee et al. [18] noted the mpact of electronc word-of-mouth (e-wom) for onlne commerce would ether promote the long-tal phenomenon or facltate the sales of the ht products based on dfferent types of e-wom mechansm. To promote consumer demand dstrbuton and mprove the proftablty, many studes have shfted the focus on long-tal phenomenon to both long-tal and ht-product market wth adopton of recommendaton mechansm... Recommendaton Mechansm Adopton of recommendaton mechansm s a key factor affectng the demand of dgtal content market. For example, Zhong & Mchahelles [7] used emprcal analyss to show that wthout the actvaton of recommendaton features, Google Play app market s strongly domnated by ht products. Fleder & Hosanagar [11] adopted a smulaton whch ndcates that recommendatons on ht products can affect the dversty of sales. Yn et al. [5] ponted out that successful recommendaton of nche tems to the nterest of rght users can expand the nche market and also boost ht-product sales. Varous types of recommender systems have been developed to deal wth long-tal phenomenon of makng personalzed recommendatons [, 5], or to rase the concentraton of ht-product sales for socal recommendatons [11, ]. To provde a better user experence for consumers wth dfferentated preferences, Jambor & Wang [13] ponted out the mportance of developng a recommender system wth flexble frameworks and multple goals. Furthermore, Oestrecher-Snger & Sundararajan [1] showed that ndvdual product demand and product sales pattern can be reflected by recommendatons..3. Content Prcng Prcng s another mportant ssue of busness strategy for dgtal content enterprses. Lang & Vragov [17] compared prcng schemes and profts for dstrbutng dgtal content over clent-server and decentralzed networks and showed that decentralzed networks are more proftable by ntroducng ncentves to users for content dstrbutng. Chellappa & Kumar [7] examned prcng and customer retenton strateges of onlne retalers wth free product-augmentng servces. They showed the mplementaton of effcent search servce, whch cannot be replcated easly by other competng retalers, can help onlne retalers to rase prces. Kannan, Pope, & Jan [15] mplemented a demand model for content provders. Through examnng optmal prcng strateges for the bundlng of dgtal and prntng content formats, they fnd bundle dscounts can ncrease proftablty. As dgtal content s ted wth dgtal devce, Yu, Hu, & Fan [6] analyzed the effect of content and devce prcng polcy on frm profts. In addton, the mpact of product dfferentaton on prcng strategy s nvestgated. Bhargava and Choudhary [3] found that for nformaton goods, prce dfferentaton strategy s not optmal when the hghest qualty product has the best value-to-cost ratos. As the varance n the utlty of non-prce-related features s greater than that n the utlty of prce [19] and t becomes nconvenent and costly for settng dfferentated prces as the number of content varetes largely ncreases, many onlne content provders choose the approach of unform prcng. In practce, a popular example s Apple s Tunes musc store sold dgtal content at a sngle prce of 99 cents for songs n the early phase of the servce. By keepng a unform and compettve prce, the Tunes musc store brought the prncples of smplcty and easy-tounderstand for consumers. It nduces the ncreasng of consumers purchasng actvtes when people were just gettng used to buy the on-lne dgtal content. Therefore, a unform prce strategy s adopted n the proposed model. 3. The Model We consder a content provder whch retals or rents dgtal content (e.g. news, musc, and move). There are totally n varetes of dgtal content avalable on the onlne channel or e-store. The sale amounts of varous varetes are heterogeneous and followng a power-law-lke dstrbuton. Each varety of content has a popularty rank x, x n1 m (the content wth the hghest popularty has rank ). Denote the unt prce of a content varety as p. The demand functon of 1318

4 content x s descrbed as x x p, where s a demand base coeffcent; s the network nfluence parameter, whch can be mproved by socal recommender systems and s the content ftness parameter, whch can be enhanced by personalzed recommender systems. The demand of a typcal content varety ncreases as the recommender systems are exploted. Thus, x/, x/. As the ncreases, the sale of ht content wll ncrease more and the content sale dstrbuton becomes more asymmetrc ( x/ x ). As the decreases, the sale of nche content wll ncrease and the content sale dstrbuton becomes more symmetrc and balanced. Parameter s used to represent the sgnfcance level of sale decreasng on the content rankng ( x/ x ). Therefore, the shape of sale dstrbuton becomes more asymmetrc as becomes larger. The total content sales are obtaned as m= m xdx and the total revenue collected s. The cost structure of a content provder ncludes two parts. The frst part s the cost of content provson. We assume the content provson cost Cm ( ) s majorly determned by the dgtal content creaton or acquston and neglects the margnal cost. Due to the extra effort of content qualty and marketng, the average cost for a varety of content wth a hgher popularty s not smaller than the average cost for a content varety wth a lower popularty wrtten as p m ( Cm ( )/ m ). The second part s the technology nvestment requred to develop the search tools or recommender systems to help customers fnd desrable content and enhance the content sale. The technology cost ncurred n the development of a recommender system s denoted as R(, ). A recommender system ncludes the technques for utlzng the power of socal nfluence ( ) and mnng the ndvdual preference ( ). Therefore, we have the propertes R(, )/ and R(, )/. The proft functon of the content provder s wrtten as (, ) ( ) p m R C m, where 1 1 m m m pm. (1) The optmal content prce s obtaned by solvng / p. We have p m 1. () The resultng proft functon can be rewrtten as m m R(, ) C( m). (3) 4 1 Accordng to (), to generate non-negatve revenue, the maxmal number of content varetes provded s 1/ m 1 /. Besdes, we also have the max followng observaton: Result 1. The optmal content prce decreases wth the number of content varetes. However the mpact of the number of content varetes on the revenue generaton s non-monotonc Optmal Content Provson In the subsecton, we analyze the ssue of content provson decson - determnng the optmal tal length. One man beneft of the nche contents s the lower provson cost, compared wth the ht contents. The lower cost could be attrbuted to the lower acquston cost (e.g. old, delayed content or user generated content) or the lower effort of promoton. Assume the content wth a popularty rank hgher than (.e. x ) s classfed as a ht content, whle the one wth a popularty rank lower than (.e. x ) s classfed as a nche content. The average provson cost for ht and nche contents s c 1 and c respectvely c c ). The total cost of content provson s ( 1 Cm ( ) mn m, c m c, where 1 ( m ), f ( m ) ( m ). The problem of, f ( m ) decdng the optmal number of content provson can be formulated as max m m R, mn m, c 1 m c m 4 1 (4) The optmal content provson s gven by solvng 1319

5 1 m m c, where 4 1 c c c 1 f m f m (5) For 1 the optmal content provson has a closed form expressed as 4 1c m. (6) 3 Result. The optmal number of content varetes ncreases wth the ftness of customer preference. Result reveals that the monetzaton of long tal content are determned by the lower content creaton (e.g. user-generated content) and acquston (.e. old or delayed content) and the effectveness of the developed recommendaton system wth accurate preference analyss ( ). Notce that the rato of ht content provson s measured by / m. For the sale dstrbuton ( 1 ), the nche content should be provded ( / m 1 ) when the condton 1 34 ˆ c c 1 holds Optmal Recommender Development The content provder can develop a recommender system to promote the content sale (.e. ncreasng and/or decreasng ). For example, developng a socal recommender, utlzng the power of socal nfluence and nformaton cascade, can enhance the content value perceved and the correspondng purchasng wllngness of a user. Owng to the network effect, a socal recommender system wll partcularly beneft the sale of ht content. On the other hand, the content provder can develop a personalzed recommender system to promote the sale of nche content. In the subsecton, we wll analyze the approprate budget allocaton strategy for developng these two types of recommender systems. The development cost for the recommender system s formulated as R(, ) 1, where s the ntal natural level of content demand curve, wthout any support of a personalzed recommender system. The total avalable budget for recommendaton technology nvestment s I. The optmal nvestment problem of the recommender system development can be formulated as 1 m 1 m max, 4 1 st.. I Cm (7) Substtutng wth I / and solvng 1 /, we have the optmal desgn levels of socal and personalzed recommendaton: I m (8) m The portfolo share of the budget allocated for socal recommender and personalzed recommender development respectvely s m 1 m 1, 1, S P mi mi, 1. (9) S P Examnng (8) and (9), we have the followng fndngs Result 3. As the number of content varetes ncreases, the optmal effectveness level of the socal recommender decreases but the optmal effectveness level of the personalzed recommender ncreases. However the porton of the budget allocated for personalzed recommender wll decrease as the total budget enlarges Competng Content Provders We extend the model to consder a market wth two competng content provders A and B. The content prcng, recommendaton, and provson of one provder can affect not only ts own sale but also ts opponent s sale. The proft functon of a content provder A, B s formulated as: m j j j p x p p dx R(, ) C( m ), (1) where j A, B., s the negatve effect on the demand on a provder from the socal and personalzed recommendaton strateges of ts opponent respectvely. represents the opponent s prce effect., 13

6 Solvng / p A, B, the best response prcng functon of provder to provder j s derved as:, 1 m j p j p j 1, j A, B. (11) Further solvng the equatons expressed n (11) smultaneously, we have p m j 1 4 j j j m j 1. (1) 4 For the symmetrc settng ( =, =, m m = m ), the equlbrum prce and proft can be obtaned as: j 1 1 m p m m 1,, (13) where 1 and 1. Result 4. The equlbrum content prce ncreases wth the prce ( ) and personalzaton effect ( ) but decreases wth the socal nfluence effect ( ) from the opponent. We further consder the scenaro that the number of content varetes s a strategc decson varable. The proft-maxmzng problem of content provder s rewrtten as: max m m m 4 mj 1 1, (14) The bet response content provson strategy of content provder s gven by solvng / m or the equaton j j c m m 1 4 m m c j 1 m j 1 (15) The symmetrc content provson can be obtaned by solvng the equaton (4) wth the settng of m m m. j For 1 the equlbrum optmal content provson can be expressed as a closed form: m c (16) Result 5. In a competng market, the number of content varetes ncreases when the socal nfluence effect from the opponent on the sale becomes less sgnfcant.. 4. Concluson In ths paper, we study the provson (volume of varetes) and recommendaton strateges (socal and personalzed recommender desgn) for mprovng the sale and proftablty of the dgtal content wth asymmetrc popularty dstrbuton. From the supply sde strategy, the decson of the optmal number of content varetes s analyzed. From the demand sde strategy, the problem of budget allocaton on varous types of recommender systems s examned. The dstnct economc roles of the two popular recommendaton approaches are formulated and the mpact of the network (socal nfluence) and content (varetes and preference ftness) factors on developng the prcng strateges under dfferent scenaros of market nteractons are also analyzed. We fnd that (1) content prce wll decrease wth the number of content varety and () the optmal of content varetes ncreases wth the ftness of customer preference. Besdes, (3) the number of content varetes ncreases, the optmal effectveness level of the socal. 131

7 (personalzed) recommender decreases (ncreases). However, (4) the porton of budget allocated for personalzed recommender development wll decrease as the total budget enlarges. Fnally, we also observe that (5) n a market wth competng provders, the equlbrum content prce and varety amount ncreases wth the prce and personalzaton effect but decreases wth the socal nfluence effect from the opponent. The results can be used to make sense of some ndustry advancements. For examples, wth more choces on products but lack of experence and nformaton, customers are usually choosng on prce. In order to attract customers to buy more varetes of content and rase sales revenue, ncreasng a wder varety of products and mantanng a lower unt prce of a content varety becomes an mportant busness strategy for content provders. As consumers become wth hgher dfferentated preferences, the effectveness of the developed recommendaton system wth accurate preference analyss and more content varety provson are essental. Ths prncple s experenced by Tunes, Amazon, Netflx and other onlne content provders. There are several studes whch can be further extended. Frst, n the research, we focus on the quantty decson ssue of the content provson. The qualty ssue can be further modeled and examned. A natural queston s how to balance the effect of professonal content and user-generated content on mprovng the proftablty. Second, from the perspectve of modelng, the current demand s aggregately measured wthout consderng the purchase choce dynamcs of customers. How the aggregated demand s formed from the choce dynamcs can be further studed. Thrd, besdes the usage based (per download or vew) prcng scheme, the mpact of nonlnear prcng scheme on the resultng content provson and recommendaton strateges can also be compared. Lastly, dfferent types of dgtal content (e.g. move, musc, news, or app software) have some dstnct characterstcs whch may affects the provson and marketng strateges and ther mpact can be explored. 5. Acknowledgements The authors are grateful for the fnancal support of Natonal Scence Councl of Tawan (NSC H-9-54). 6. References [1] G. Adomavcus and A. Tuzhln, "Toward the Next Generaton of Recommender Systems: A Survey of the State-of-the-Art and Possble Extensons", IEEE Transactons on Knowledge and Data Engneerng, 5, 17(6), pp [] C. Anderson, The Long Tal: Why the Future of Busness Is Sellng More for Less, Hyperon, New York, 6. [3] H. K. Bhargava and V. Choudhary, "Informaton Goods and Vertcal Dfferentaton", Journal of Management Informaton Systems, 1, 18(), pp [4] E. Brynjolfsson, Y. Hu, and D. Smester, "Goodbye Pareto Prncple, Hello Long Tal: The Effect of Search Costs on the Concentraton of Product Sales", Management Scence, 11, 57(8), pp [5] E. Brynjolfsson, Y. Hu, and M. D. Smth, "From Nches to Rches: Anatomy of the Long Tal", MIT Sloan Management Revew, 6, 47(4), pp [6] E. Brynjolfsson, Y. Hu, and M. D. Smth, "Long Tals vs. Superstars: The Effect of Informaton Technology on Product Varety and Sales Concentraton Patterns", Informaton Systems Research, 1, 1(4), pp [7] R. K. Chellappa and K. R. Kumar, "Examnng the Role of "Free" Product-Augmentng Onlne Servces n Prcng and Customer Retenton Strateges", Journal of Management Informaton Systems, 5, (1), pp [8] E. K. Clemons and K. R Lang, "The Decouplng of Value Creaton from Revenue: A Strategc Analyss of the Markets for Pure Informaton Goods", Informaton Technology and Management, 3, 4(-3), pp [9] E. K. Clemons and P. F. Nunes, "Carryng Your Long Tal: Delghtng Your Consumers and Managng Your Operatons", Decson Support Systems, 11, 51(4), pp [1] A. Elberse, "Should You Invest n the Long Tal?", Harvard Busness Revew, 8, 86, pp [11] D. Fleder and K. Hosanagar, "Blockbuster Culture's Next Rse or Fall: The Impact of Recommender Systems on Sales Dversty", Management Scence, 9, 55(5), pp [1] O. Hnz and J. Eckert, "The Impact of Search and Recommendaton Systems on Sales n Electronc Commerce", Busness & Informaton Systems Engneerng, 1, (), pp [13] T. Jambor and J. Wang, "Optmzng Multple Objectves n Collaboratve Flterng", n Proceedngs of the Fourth ACM Conference on Recommender System, 1, Barcelona, Span. [14] R. Jones and H. Mendelson, "Informaton Goods vs. Industral Goods: Cost Structure and Competton", Management Scence, 11, 57(1), pp [15] P. K. Kannan, B. K. Pope, and S. Jan, "Prcng Dgtal Content Product Lnes: A Model and Applcaton for the Natonal Academes Press", Marketng Scence, 9, 8(4), pp [16] J. J. Laffont and J. Trole, Competton n Telecommuncatons, MIT press, 1. [17] K. R. Lang and R. Vragov, "A Prcng Mechansm for Dgtal Content Dstrbuton over Computer Networks", 13

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