  Discontinuing ad creatives at an appropriate time is one of the most
important ad operations that can have a significant impact on sales. Such
operational support for ineffective ads has been less explored than that for
effective ads. After pre-analyzing 1,000,000 real-world ad creatives, we found
that there are two types of discontinuation: short-term (i.e., cut-out) and
long-term (i.e., wear-out). In this paper, we propose a practical prediction
framework for the discontinuation of ad creatives with a hazard function-based
loss function inspired by survival analysis. Our framework predicts the
discontinuations with a multi-modal deep neural network that takes as input the
ad creative (e.g., text, categorical, image, numerical features). To improve
the prediction performance for the two different types of discontinuations and
for the ad creatives that contribute to sales, we introduce two new techniques:
(1) a two-term estimation technique with multi-task learning and (2) a
click-through rate-weighting technique for the loss function. We evaluated our
framework using the large-scale ad creative dataset, including 10 billion scale
impressions. In terms of the concordance index (short: 0.896, long: 0.939, and
overall: 0.792), our framework achieved significantly better performance than
the conventional method (0.531). Additionally, we confirmed that our framework
(i) demonstrated the same degree of discontinuation effect as manual operations
for short-term cases, and (ii) accurately predicted the ad discontinuation
order, which is important for long-running ad creatives for long-term cases.
