In the consumer goods industry, unique brand positionings are assumed to be the road to success. They document product distinctiveness and so justify high prices. However, as products are getting more and more interchangeable, brand positionings must rely—at least partially—on supporting advertisements. Here, especially ads with visual content (e.g. photos, video clips) are able to connect brands with desirable emotions and values. Recently, besides TV, cinema, newspaper, also search engines, social networks, photo-, video-sharing platforms are used to spread such ads. In this paper, we demonstrate, how deep learning based on such ads can be used to predict uniqueness of brand positionings. A sample application to the German Pils beer market is used for demonstration.

在消费品行业，独特的品牌定位被认为是成功之路。他们记录了产品的独特性，因此证明了高价是合理的。但是，随着产品的互换性越来越高，品牌定位必须（至少部分地）依赖于支持广告。在这里，尤其是具有视觉内容（例如照片，视频剪辑）的广告能够使品牌具有理想的情感和价值。最近，除了电视，电影院，报纸，还使用搜索引擎，社交网络，照片，视频共享平台来传播此类广告。在本文中，我们演示了如何基于此类广告进行深度学习来预测品牌定位的独特性。以德国比尔啤酒市场的示例应用程序为例。