Optimal decision making for online referral marketing

• Main Author: GUO, Zhiling
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Dynamic pricing; Word of mouth; Referral marketing; Optimal control; Computer Sciences; E-Commerce
• Online Access: https://ink.library.smu.edu.sg/sis_research/1858; https://ink.library.smu.edu.sg/context/sis_research/article/2857/viewcontent/Optimal_DM_online_referral_av.pdf
• Institution: Singapore Management University

Widely available web 2.0 technologies not only bring rich and interactive user experiences, but also easily help users advertise products or services on their own blogs and social network webpages. Online referral marketing, for example, is a business practice that rewards customers who successfully refer other customers to a website or upon completion of a sale usually via their own social contacts. The referral rewards come in different forms such as shopping vouchers, redeemable points, discounts, prizes, cash payments, etc. We develop an analytical model to evaluate the business potential of incorporating an online referral marketing program into the firm's product selling strategies. Under different demand dynamics, we investigate the optimal decision making including the pricing and referral strategies to maximize the seller's profitability. We find that, under simple decision making environment such as fixed product price and myopic strategy, different demand dynamics yield the same prediction of the referral payment, which turns out to be a static policy. However, under complex market situations, both the optimal product pricing and referral offering critically depend on the demand side dynamics. Under the nonlinear demand dynamics, the referral payment is an all-or-nothing decision throughout the product selling horizon. In contrast, under the linear demand assumption, the referral payment can be partially offered in initial phase of the product introduction. We further offer some managerial insights to guide practical implementation of the online referral marketing strategy.