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Net Promotor Score is an important business measurement process where customers are surveyed and asked to rate their likelihood of recommending the company's products and/or services. In many applications, customers are asked to respond on an 11-point ordinal scale of 0 to 10. In developing the score, the data are reformulated into a labelled 3 class scale (0-6: Detractor, 7-8: Passive and 9-10: Promoter). [1] Many companies that choose to use Net Promoter Score as a core management metric integrate the measurement into all phases of the company and seek every opportunity to assess company performance in terms of likelihood to promote the company. In addition to a variety of survey opportunities, the ability to score comments in survey, social media and blogs with promoter rating may provide an additional valuable source of business insight. Even on a three-point scale, Net Promoter is an ordinal classification problem. A number of successful algorithms, that develop ordinal classifiers have been developed. [2] None of the top performing classifiers can be used for applications like text classification or image classification, since they don't employ deep learning. Any appropriate strategy must utilize the ordering information of classes without imposing a strong continuous assumption or fixed spacing assumption on the ordinal classes. In this paper, we use a novel Deep Learning methodology called OHPLnet (Ordinal Hyperplane Loss Network) that is specifically designed for data with ordinal classes. [3] The algorithm is used to develop predictions of the eleven classes, that may be used in the standard Net Promoter Score generation process. 

The problem of ordinal classification occurs in a large and growing number of areas. Some of the most common source and applications of ordinal data include rating scales, medical classification scales, socio-economic scales, meaningful groupings of continuous data, facial emotional intensity, facial age estimation, etc. The problem of predicting ordinal classes is typically addressed by either performing n-1 binary classification for n ordinal classes or treating ordinal classes as continuous values for regression. However, the first strategy doesn't fully utilize the ordering information of classes and the second strategy imposes a strong continuous assumption to ordinal classes. In this paper, we propose a novel loss function called Ordinal Hyperplane Loss (OHPL) that is particularly designed for data with ordinal classes. The proposal of OHPL is a significant advancement in predicting ordinal class data, since it enables deep learning techniques to be applied to the ordinal classification problem on both structured and unstructured data. By minimizing OHPL, a deep neural network learns to map data to an optimal space where the distance between points and their class centroids are minimized while a nontrivial ordinal relationship among classes are maintained. Experimental results show that deep neural network with OHPL not only outperforms the state-of-the-art alternatives on classification accuracy but also scales well to large ordinal classification problems.