  Uplift modeling has been used effectively in fields such as marketing and
customer retention, to target those customers who are more likely to respond
due to the campaign or treatment. Essentially, it is a machine learning
technique that predicts the gain from performing some action with respect to
not taking it. A popular class of uplift models is the transformation approach
that redefines the target variable with the original treatment indicator. These
transformation approaches only need to train and predict the difference in
outcomes directly. The main drawback of these approaches is that in general it
does not use the information in the treatment indicator beyond the construction
of the transformed outcome and usually is not efficient. In this paper, we
design a novel transformed outcome for the case of the binary target variable
and unlock the full value of the samples with zero outcome. From a practical
perspective, our new approach is flexible and easy to use. Experimental results
on synthetic and real-world datasets obviously show that our new approach
outperforms the traditional one. At present, our new approach has already been
applied to precision marketing in a China nation-wide financial holdings group.
