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Seeded marketing campaigns (SMCs) have become part of the marketing mix for many fast-moving consumer goods (FMCG) companies. In addition to making large investments in advertising and sales promotions, these firms now encourage seed agents or microinfluencers to discuss brands with friends and acquaintances to create further value. It is thus critical to understand how an FMCG seeding program interacts with traditional marketing tools when estimating the effectiveness of such efforts. However, the issue is still underexplored. The authors present the first empirical analysis of this question using a rich data set collected on four brands from various European FMCG markets. They combine advertising and sales promotion data from FMCG brand managers with sales and retail variables from market research companies as well as firm-created word-of-mouth variables from SMC agencies. The authors analyze the data using several approaches, confronting challenges of endogeneity and multicollinearity. They consistently find that firm-created word of mouth through SMC programs interacts negatively with all tested forms of advertising but positively with promotional activities. This phenomenon has significant implications for understanding the utility of SMCs and how they should be managed. The analysis implies that SMCs may increase total sales by approximately 3%–18% throughout the campaigns.

Consumer-generated communication processes have drawn increasing attention of marketers and researchers. However, an underresearched issue is that interpersonal communications are not always brand specific. Thus, a person can adopt a brand either as a result of communication with adopters of that brand (within-brand influence) or as a result of an interaction with adopters of competing brands (cross-brand influence). This study shows that the interplay between within- and cross-brand influence can have a substantial effect on the growth of markets under competition. The authors develop a model that explicitly represents these two influences and focus on the case of two otherwise identical competing brands with different entry times. As a result of within-brand influence, current customers create an interaction-based advantage for the first entrant, which grows with time. Thus, we illustrate how customers should be viewed as market assets who yield increasing returns during the diffusion process. Conversely, cross-brand influence enables a market follower to enjoy a shorter takeoff time. Given the combination of both, the authors predict the “dual pattern,” characterized by a fast takeoff for a follower, followed by a widening gap in favor of the first entrant, all else being equal. They show that such a pattern dominated the market growth of the cellular industry in Western Europe. They explain the reasons behind this dual pattern, rule out straightforward alternative explanations, and discuss the managerial implications.

Using data on a large number of innovative products in the consumer electronics industry, the authors find that between one-third and one-half of the sales cases involved the following pattern: an initial peak, then a trough of sufficient depth and duration to exclude random fluctuations, and eventually sales levels that exceeded the initial peak. This newly identified pattern, which the authors call a “saddle,” is explained by the dual-market phenomenon that differentiates between early market adopters and main market adopters as two separate markets. If these two segments—the early market and the main market—adopt at different rates, and if this difference is pronounced, then the overall sales to the two markets will exhibit a temporary decline at the intermediate stage. The authors employ both empirical analysis and cellular automata, an individual-level, complex system modeling technique for generating and analyzing data, to investigate the conditions under which a saddle occurs. The model highlights the importance of cross-market communication in determining the existence of a saddle. At low levels of this parameter, more than 50% of the cases of new product growth involved a saddle. This percentage gradually decreased as the parameter increased, and at values close to the within-market parameters, the proportion of saddle occurrences dropped below 5%.