產(chǎn)品定價(jià)因素分析MBAdissertation：以印度移動(dòng)應(yīng)用程序定價(jià)分析為例

在當(dāng)今快速發(fā)展的經(jīng)濟(jì)中，人們已經(jīng)意識(shí)到能無時(shí)無刻用移動(dòng)應(yīng)用程序通訊的重要性。這是移動(dòng)連接的概念，但它迅速被用作達(dá)到商業(yè)或社會(huì)目的的工具。在尼爾森信息移動(dòng)最新發(fā)表的調(diào)查中顯示，印度有超過2700萬的智能手機(jī)用戶。再加上5000萬的移動(dòng)互聯(lián)網(wǎng)用戶(“移動(dòng)應(yīng)用峰會(huì)2012”,CMAI)；移動(dòng)應(yīng)用的潛力是巨大的。
 

（在印度）每個(gè)月人們下載移動(dòng)應(yīng)用程序的數(shù)量超過1億，與全球地位相當(dāng)?shù)膰蚁啾?，印度的移?dòng)應(yīng)用產(chǎn)業(yè)還正處于萌芽階段。Gartner估計(jì)，印度手機(jī)應(yīng)用市場的銷售額將達(dá)到1.5億美元，預(yù)計(jì)在移動(dòng)應(yīng)用程序跨平臺(tái)穩(wěn)定增長。如果移動(dòng)應(yīng)用程序開發(fā)人員要從這個(gè)市場賺取自己應(yīng)得的市場份額，則需要他們的移動(dòng)應(yīng)用價(jià)格穩(wěn)定在最佳水平，獲得最多的下載和客戶群。
 

從理論上講，有大量的因素可能影響應(yīng)用程序或任何產(chǎn)品的價(jià)格。基于文獻(xiàn)上可用類似的市場定價(jià)，我們已經(jīng)制定了一系列基于變量的假設(shè)。 
 

In today’s fast-paced economy, people have realized the essence of being connected 24x7. The concept of being on the move yet connected be it for business or social purposes is fast becoming the new norm. This is evident from the recent survey published in Nielsen Informate Mobile Insights, suggesting that there are over 27 million smart phone users in India. Add to this another 50 million mobile internet users (“Mobile Apps Summit 2012”, CMAI); the potential for mobile applications is huge.
 

With over 100 million mobile apps being downloaded every month (in India), the mobile application industry here is at a nascent stage as compared to its global counter-parts. Gartner estimates the Indian mobile application market to be $150 million and foresees a steady growth in mobile applications across platforms. For mobile application developers to earn their share from this market requires them to price their mobile application at optimum level to get the most downloads and grow on their customer base.
 

Theoretically, there are a high number of factors which may affect the price of an application or any product to speak of. Based on the literature available on pricing in similar markets, we have formulated a number of hypotheses based on variable identified as in, the demographics (such as Age, Gender and Salary) of existing and potentially new customers, or the option to buy from within the application itself, i.e., in-app purchases. To add to it, the substitute applications in the market to which a customer can switch to and the type of developer of the application, i.e., individual or a firm.
 

Following enlist and discuss all testable hypotheses.
 

可用的替代品———Availability of Substitutes#p#分頁標(biāo)題#e#
 

Price of new products or services is decided well before they have been developed or delivered. For example, Apple always announces the price and sale date well in advance. Even Steve Jobs, during extremely early demo of Apple TV in 2006, pre-announced a price and estimated delivery date (Frommer, 2012). Such practices are required to set a price in order to effectively market company’s product. Therefore, firms often face substantial cost uncertainty when planning to price its new product or service and strive for information to overcome such a situation.

Many companies do not focus on cost implications in great details despite presence of a great competition. However, many other companies invest heavily to accurately estimate the production cost despite relatively limited competition. This elaborates a direct relationship between optimal cost information to price new/existing products or services and competition/substitutes. Understanding the possibility of cooperation and information related to consumer price sensitivity and loyalty play vital role in competitive pricing Sudhir (2001). Further, Consumer’s demand elasticity and competitive reactions are studied as important determinants of pricing behavior (Dickson and Urbany, 1994).
 

It’s a common understanding that consumers tends to switch stores in response to price cuts. Thus, competitor price cuts/offers are to be handled diligently. Vice versa, customers react to price hike by switching away and such reaction patterns propose that one should follow price cuts but do not follow price hikes. However, competitive price change is complicated because of the difficulty to intercept reactions of both consumer and competitor towards pricing initiatives (Dickson and Urbany, 1994). Further, Price war is a common scenario whilst retaliation with the price (Rao et al., 2000). Companies may decide their response to this threat in either way. However, pricing decisions, during price war threat, demands to understand several key aspects such as consumers' price sensitivity, self-ability and competitors' response. Retailers may try to reduce the cost of reacting to a price threat by using principles such as selective price cuts according to consumer segment, geographical area or product line (Rao et al., 2000).
 

Literature review reveals a linear relationship between price of a product or a service and competition of substitute products or services available. This has given us a scope to study this phenomenon and understand related intricacies in mobile applications.
 

Based on this the literature review and other factors associated we suggest our hypothesis as following.Hypothesis 1 (H1): Availability of substitutes has inverse relation with prices of an application i.e. more the number substitutes available lower is the price and vice versa.

 

人口的影響———The Demographics Effect#p#分頁標(biāo)題#e#
 

Demographics is all about who your targeted customer is (Martins, 2012) i.e. higher the proportion of your targeted customer more of success of your product and its price acceptance. Therefore, demographics play an important role in pricing of any product. Demographics involve variables such as Life Stage, Age, Generation, Gender, Income, Race and Culture (Kotler, 2012). Demographic characteristics such as Age, Gender, and Income are major factors influencing consumer’s product knowledge, purchase experience, and overall purchase behavior in most services and products, and are sporadically used for market segmentation (Estelami, 1998). Therefore, we have limit scope of demographics, for our study, to Age, Gender, and Income.
 

Internet plays a vital role for mobile application usage and customer reachability and penetration. Therefore, it could be safely assumed that internet users represent major customer base for such products. Several studies founded that consumers who belong from higher income group could be initial user of internet (Flynn and Goldsmith, 1993; Goldsmith et al., 1998). Another study portrayed gender and income as important demographic factors, where males were found to be more susceptible, than females, for online shopping and also emphasizing age to be a weak factor (Hyokjin et al., 2002). Yet another, amazing fact was revealed by a study which highlighted that internet majorly attracts low income group users (UCLA; 2004, 2005).
 

Internet user’s gender ratio (57.1% male and 45.1% Female), and their income is important to decide for online shopping (UCLA, 2000). Empirical study (Juha, 2006) suggests that male customers are more attracted towards usage based pricing whereas female customers show inclination towards fixed fee. In addition, younger customers are more inclined towards actual usage pricing than older customers that rely on fixed pricing (Juha, 2006). Further, studies (UCLA, 2001) revealed that consumers in age group 16-28, on an average, took 14.9 months to make an online purchase in comparison to consumers in age group 19-24 and 56-65 who took, on an average, 22.3 months and 23.2 months respectively.
 

Pricing is also a variable of customers at different stores, for example income (Hoch et al. 1995) is related to price sensitivity. “Fixed Basket” model of shopping behavior (Bliss 1998, Bell and Latin 1998) also supports that price sensitive customers tend to prefer low price products. This emphasize that a price sensitive customer may not pay for a product until either product’s price is reduced or customer’s buying capacity is increased. Another study cited that real estate business rely heavily on consumer income for pricing (Appell, 1997) suggesting that consumer’s income basis of pricing in other verticals as well. Further, Audit Bureau of Circulations (ABC) has also approved a rule allowing newspaper publishers to price subscribers based on demographics such as senior citizens and students (Anonymous, 1997). In addition, it is commonly observed that lower income groups are provided with highly subsidized products suggesting price differential based on income. Thence, income provides an additional avenue to segment customers.#p#分頁標(biāo)題#e#
 

There are few studies which negating other studies and suggesting that no relationship exists between pricing and demographics (Meyers-Levy and Sternthal, 1991). There could be many reasons for such an observation such as equal ability of customers from various demographics to attain product related information, sample homogeneity leading to no effect of demographics et cetera.
 

Based on this the literature review and other factors associated we suggest our hypothesis as following.Hypothesis 2 (H2): Prices are going to differ across different demographic avenues. Specifically, prices for an application targeted for a male in higher age, and higher income prices would be higher than any other group.
 

應(yīng)用程序的開發(fā)人員———Developer of the App
 

According to literature, developer or the seller reputation plays a major role in taking the pricing decisions of any product. The image and quality expectations which are a reflection of reputation have a direct correlation with the price (Landon et al. 2010).The price premium associated with reputation is more than that of quality improvements. Since in a mobile application download, the user is unaware of the quality and performance beforehand, it relies on company’s reputation in order to pay the price for the expected quality.
 

The role of reputation in competitive market has also been established wherein the product quality is unobservable (Allen, 1984). It states that there exists an equilibrium in which prices are comparable to average cost but are greater than marginal cost. Firms usually offer high quality products at higher price when quality cannot be differentiated easily (Shapiro, 1984).
 

Consumer’s willingness to pay for a product depends on the sensorial traits, the reputation of producer as well as objective variables as studied in wine industry (Benfratello, Luigi, 2009) which shows that the prices are contained in the reputation specification.
 

Mobile application platforms provide users with an application rating facility which gives them a means to measure the reputation of the service and the firm. The effects of retailer reputation on pricing strategy in e-commerce industry has been studied and reveals that better reputation allows the retailers to charge higher prices(luo, Chung, 2010).
 

Online auctions also provide the evidence of the positive relationship between superior seller reputation and higher prices (Johnston, 2003). The study of goodwill, brand equity and prices verified a price premium of 4.8 percent over lower rated products in same category. A study conducted on ebay found that seller’s percent on negative rating significantly impacts the price that users wish to pay (Mike, 2010). Also, analytical buyers and impulse buyers pay greatly different premiums depending on the reputation of the company (Yongseog el al., 2005).#p#分頁標(biāo)題#e#
 

Hence, the reputation of seller seems to have a positive impact on price premium charged across various products in diverse industries. This study aims to find if this relationship holds well in mobile application industry as well.Hypothesis 3 (H3): Application rating has a positive relationship with application’s price.

 

購買應(yīng)用程序期權(quán)———In-App purchase option
 

In-app purchase is the purchase of digital content by a customer inside an application (mobile application in our case). The digital content available for purchase includes subscriptions based on application usage, upgrades offered on the applications, its features, expansion packs to enhance the content/usability of an application and virtual goods like virtual money in gaming applications (Anonymous, 2012).
 

The total market revenue generated by in-app purchases is over 39% of total sales of mobile applications (IHS iSuppli, Feb 2012) and thus is a significant factor in deciding the pricing model for an application. Though the in-app purchases are limited to mostly gamers purchasing majority of in-app subscriptions, upgrades and virtual goods, many application developers of other genres have started realizing the importance of in-app purchases to generate revenues and focusing their efforts here. Time-based content is a case in study which confirms this as the customers are moving to more time based content like news, magazines et cetera. through the in-app purchases made via the mobile applications of the content providers (Shield, 2012). It is estimated by ABI Research that by the end of 2012, the revenue generated from in-app purchases will surpass the revenues generated from pay per download model (ABI Research, 2012). This signifies the importance of choosing in-app purchases as a factor to study how it affects the pricing of a mobile application.
 

Some studies conducted in this field have suggested that many application developers have started following the “freemium strategy”, where in the application with a limited feature-set is provided to the customers. Customers are required to pay for the additional features, expansions and upgrades through the in-app purchases (Müller, Kijl, & Martens, 2011). Some studies point to different models of pricing altogether for in-app purchases, as the pricing of mobile applications will reach a tipping point beyond which a customer will not be willing to buy the product (Kaneshige, 2009.), while Sturdivant suggests a tiered content model to bring in revenues, a level of free application to bring in users which will help us to monetize through another layer of in-app purchases and services (Sturdivant, 2011).
 

We will be further studying the in-app purchase factor to establish whether the price of a mobile application depends on the in-app purchases available for the application or not.#p#分頁標(biāo)題#e#

Hypothesis 4 (H4): Prices of app will depend on the in-app purchases available within the application. Specifically, higher the in-app purchase, lower the price of app.