Online And Offline Retailing - Current State And Future Strategies

Big data is characterized by large volumes of data that require special storage facilities (volume), need for timely access (velocity), a variety of formats (variety), reliability, and validity (veracity) – (Wedel and Kannan, 2016). The term “big data” applies most directly to data that became available with the advent of the internet, which led to the availability of data on online purchases, clickstream data, online review data, data on individual responses to advertisements, data on the timing of browsing and purchases, data on consumers’ physical locations, data on social network participation, and data on competitors’ prices and online advertising (Bradlow et al., 2017). All of this is in addition to the existing point-of-sale data on offline purchases. Bradlow et al. (2017) and Wedel and Kannan (2016) provided extensive discussions of the use of big data in marketing, in general we will focus on the application of big data to marketing decisions in retailing, including customer relationship management (CRM), product, and marketing mix decisions.

In the closed-form expressions of retailer profits, we fix the values of base utility, mismatch cost, and mismatch cost scaling parameter so that we can analyze the equilibrium in terms of the proportion of CC consumers and revenues from targeted advertising. Our analysis of the conditions in which a symmetric or asymmetric equilibrium holds reveals two symmetric equilibria: one when both retailers use a restricted strategy and another in which both retailers use a flexible strategy and one asymmetric equilibrium when retailer 1 adopts a flexible checkout strategy, and retailer 2 adopts restricted checkout strategy, as we detail in Proposition 1 and 2.

Proposition 1. The asymmetric equilibrium with one retailer choosing the restricted checkout strategy and the competing retailer choosing a flexible checkout strategy holds when the proportion of CC consumers is relatively high (α > α1 ) and revenues due to targeted advertising are in the intermediate range (η1 < η < η2 ).

The result is driven by three key factors that operate in our modeling framework: (i) additional revenues from advertising (sharing of information about registered customers with advertisers could increase profits, (ii) utility due to account registration since a higher allows firms to charge rice’s, (iii) proportion the two types consumers market it impacts total demand obtained retile under each strategy. When offers a flexible checkout strategy, both purchase retailer. Consider a scenario in which revenue targeted parameter intermediate range, and retailers are following As CC increases, one realizes prices catering only foregoing PC consumers. prefer to buy the guest option. So, they will strategy.

Alternately, consider the scenario in which the proportion of CC consumers is high, and advertising in revenue parameter is relatively low. As parameter increases, additional from as well ability to charge higher prices, incentivize retailers to switch to a flexible checkout strategy restricted ven though restricted eliminating demand for PC consumers. The competing retailer continues to able to generate profits also. Thus, an asymmetric could arise equilibrium. By considering two distinct consumer types, our result provides a demand-based (rather than just cost-based) justification and plausible explanation for why horizontally differentiated may adopt an equilibrium.

Proposition 2. (a) The symmetric equilibrium with both retailers choosing a restricted checkout strategy holds when the proportion of CC consumers and the targeted advertising revenue parameter are relatively high (α > α2 and η > η2 ). (b) The symmetric equilibrium with both retailers offering a flexible checkout strategy always holds when the targeted advertising revenue parameter is below a given threshold (η < η1 )

The intuition for this result is as follows: When the proportion of CC consumers is relatively lower, it implies that choosing a restricted checkout strategy ignores a large portion market. Further, when coupled with revenues from targeted advertising being offered flexibly enables online retailers to generate additional demand for PC consumers. Thus, we observe flexible strategies in equilibrium proportion and revenue parameters are lower. In contrast, both these parameters exceed given thresholds, and profits serving only exceed incremental generation choose use equilibrium. Fig. 1, shows regions Case 1. As mentioned previously, about 35% of users cite the account creation process key reason for abandoning implying α = 0.65. We 0.65, FF, FR, RR could exist equilibrium depending on values other (as shown 1). Our results broadly consistent notion competing practice may be any combination of two η (additional advertising) parameter changes. order to understand the above propositions further, calculate comparative statistics retailers’ respect some underlying model analysis presented in Proposition 3.