In software markets, the sheer number of available applications makes it rather challenging for any given new one to stand out and be noticed by consumers. Moreover, a push towards privacy by regulators and consumers is making it harder to target consumers. As such, firms have to rely on more non-targeted go-to-market strategies. We explore two popular strategies through which developers can catalyze adoption by helping consumers directly or indirectly learn the value of their products—seeding (free full-feature product giveaways to a subset of the consumer base) and time-limited freemium (TLF). Seeding, as a business strategy, existed for a long time. On the other hand, the feasibility to offer market-wide TLF became mainstream more recently, with the advent of the Internet and a plethora of digital tools. Thus, a natural question emerges—if TLF represents nowadays a feasible and easily implementable strategy for software applications, has seeding approach been rendered irrelevant in these markets? In this study, we provide managerial recommendations on when each of these strategies with a free full-feature-consumption component is optimal, based on social and self-learning dynamics, consumer priors, adoption costs, and individual product value depreciation. To that end, under a multi-period parsimonious unifying framework, we show that S becomes dominated as free trials enter the picture. We identify two specific market factors that, when present, can induce seeding to be optimal when consumers initially underestimate true product value—(i) user adoption costs and/or (ii) individual depreciation of value by usage. Moreover, we show that these two factors have a moderating effect on the impact of word-of-mouth (WOM) effects on the optimality of seeding. In the absence of these factors, stronger WOM effects alone cannot give seeding an edge against the other business strategies. However, once either depreciation or adoption costs are accounted for, strong WOM effects increase the relevance of seeding (enlarging its optimality region in the parameter space). Our results remain qualitatively consistent under a battery of robustness checks.

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Don’t Count Non-Targeted Seeding Out Just Yet

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Don’t Count Non-Targeted Seeding Out Just Yet