Unveiling consumers’ brand post content preferences: a best-worst scaling approach

While research has examined how social media brand post characteristics drive consumer engagement, the underlying content preferences of consumers remain underexplored. This study investigates these preferences, their heterogeneity, and contextual factors shaping preference variation using best-worst scaling. Data were collected via an online survey. The sample comprised 515 consumers in Germany, Austria, and Switzerland. BWS responses were analyzed using counting analysis combined with multinomial and mixed logit models to quantify relative preferences and heterogeneity, latent class analysis to identify distinct consumer segments, and a random forest machine learning algorithm to assess contextual influences on segment membership. Analyses revealed a clear hierarchy: shopping-related use value was most preferred, followed by daily inspirations and brand/product information, while social value ranked lowest. Four distinct consumer segments emerged: Information Hunters, Entertainment Enthusiasts, Shopping and Inspiration Seekers, and Brand Post Omnivores. Age, personality traits, and the primary social media platform were strong predictors of segment membership. The results enrich our understanding of the motivational mechanisms underlying consumer brand post engagement and offer guidance for designing user-centric brand post content, helping organizations align their strategies with user preferences to enhance both interaction quality and content effectiveness. • Best-worst scaling is used as novel method to gauge brand post content preferences. • Consumers prioritize shopping value with direct buying, discounts, and promotions. • Consumers seek daily inspiration from posts with fresh, unexpected ideas. • The study reveals heterogenous preferences, identifying four consumer segments. • Brands should tailor brand posts to match consumer content preferences.