Marketers spend billions of dollars on advertisements but to what end? At the
purchase time, if customers cannot recognize a brand for which they saw an ad,
the money spent on the ad is essentially wasted. Despite its importance in
marketing, until now, there has been no study on the memorability of ads in the
ML literature. Most studies have been conducted on short-term recall (<5 mins)
on specific content types like object and action videos. On the other hand, the
advertising industry only cares about long-term memorability (a few hours or
longer), and advertisements are almost always highly multimodal, depicting a
story through its different modalities (text, images, and videos). With this
motivation, we conduct the first large scale memorability study consisting of
1203 participants and 2205 ads covering 276 brands. Running statistical tests
over different participant subpopulations and ad-types, we find many
interesting insights into what makes an ad memorable - both content and human
factors. For example, we find that brands which use commercials with fast
moving scenes are more memorable than those with slower scenes (p=8e-10) and
that people who use ad-blockers remember lower number of ads than those who
don't (p=5e-3). Further, with the motivation of simulating the memorability of
marketing materials for a particular audience, ultimately helping create one,
we present a novel model, Sharingan, trained to leverage real-world knowledge
of LLMs and visual knowledge of visual encoders to predict the memorability of
a content. We test our model on all the prominent memorability datasets in
literature (both images and videos) and achieve state of the art across all of
them. We conduct extensive ablation studies across memory types, modality,
brand, and architectural choices to find insights into what drives memory