TL;DR
This paper introduces an agent-based probabilistic graph model to simulate and analyze influencer marketing campaigns on social networks, accounting for real-world factors and varying scenarios to optimize influencer selection strategies.
Contribution
It presents a novel agent-based, probabilistic graph model that incorporates real-world factors to evaluate influencer marketing strategies under diverse conditions.
Findings
Celebrity influencers perform worse with non-luxury products.
Nano-influencers are more effective for non-luxury products.
Influencer effectiveness varies with customer interest levels.
Abstract
Social media are extensively used in today's world, and facilitate quick and easy sharing of information, which makes them a good way to advertise products. Influencers of a social media network, owing to their massive popularity, provide a huge potential customer base. However, it is not straightforward to decide which influencers should be selected for an advertizing campaign that can generate high returns with low investment. In this work, we present an agent-based model (ABM) that can simulate the dynamics of influencer advertizing campaigns in a variety of scenarios and can help to discover the best influencer marketing strategy. Our system is a probabilistic graph-based model that provides the additional advantage to incorporate real-world factors such as customers' interest in a product, customer behavior, the willingness to pay, a brand's investment cap, influencers' engagement…
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