This research investigates the effect of TV advertising on website traffic and sales for new and growing e-commerce and online services brands. We created a Python-based extraction and transformation pipeline to join data from four different sources and to establish one of the most comprehensive datasets in this field. It contains approximately 300,000 TV ad airings in 2016/17 of around 800 unique creatives and associated website-tracking and brand awareness data of 23 consumer-facing digital-native brands. We draw on signaling and information processing theory to assess whether companies can boost online consumer activity by aligning creative cues in TV commercials with consumers’ awareness of their brand. Our empirical analyses reveal that informative and imagery-heavy ads are more powerful for lesser-known brands, while better-known brands achieve a higher uplift with emotional commercials. This provides compelling insights for marketing practitioners on how to conceive their TV ads to maximize online response. Our work further shows that e-commerce and media players can generate a competitive advantage by integrating disparate data sources and conducting in-depth analytics on the types of ads that achieve the highest impact for their brands as well as by developing and employing the associated information systems infrastructure to do so efficiently.