Predictive Accuracy, Consumer Search, and Personalized Recommendation

Abstract

Firms use predictive technology to attract and direct consumer search through personalized product recommendations. This paper examines the firm’s recommendation strategy by analyzing a key trade-off: accurate recommendations draw high-search-cost consumers into the search process (the “participation-drawing effect”) but may narrow the search intensity of moderate-search-cost consumers (the “search-narrowing effect”). When pricing is inflexible in response to environmental changes, the search-narrowing effect dominates in markets with intermediate predictive accuracy or limited search costs, leading firms to forgo recommendations despite their value in reducing search frictions. However, with pricing flexibility, the no-recommendation strategy is optimal only when both predictive accuracy and search costs are low. Flexible pricing enables firms to capture the surplus from accurate recommendations, strengthening the participation-drawing effect. It also shifts the firm's strategic focus from managing search intensity to managing search participation, increasing the profitability of personalized recommendations. Our findings underscore the dual impacts of personalized recommendations on consumer search behavior and highlight the importance of pricing flexibility in optimizing recommendation strategies. This research provides actionable insights for firms leveraging predictive technologies in customer management.

Keywords: personalized recommendation, directed search, predictive accuracy, pricing flexibility

JEL Classification: M21, M37