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The term "Gamification" is an emerging paradigm that aims to employ game mechanics and game thinking to change behavior. Gamification offers several effective ways to motivate users into action such as challenges, levels and rewards. However, an open research problem is discovering the set of gamification features that consistently result in a higher probability of success for a given task, game or application. The objective of this paper is to bridge this knowledge gap by quantifying the gamification features that are consistently found in successful applications. Knowledge gained from this work will inform designers about the gamification features that lead to higher chances of an application's success, and the gamification features that do not significantly impact the success of an application. The case study presented in this work leverages demographic heterogeneity and scale of applications existing within mobile platforms to evaluate the impact of gamification features on the success or failure of those applications. The successful game design features identified have the potential to be embedded into interactive gamification platforms across various fields such as healthcare, education, military and marketing, in order to maintain or enhance user engagement.

This work investigates the “must have” and “deal breaker” product feature preferences expressed by users of online platforms (e.g., customer review websites or social media networks) in order to inform designers of product features that should be investigated during the next iteration of a product’s launch. Existing design literature highlights the risks of aggregating group preferences, and suggest that design teams should instead, focus on maximizing enterprise value by optimizing the attributes of a product. However, design knowledge about products and product attributes are influenced by market information, which is dynamic and difficult to acquire. The use of online product review platforms has emerged in the design community as a viable source of product data acquisition and demand model prediction. However, as the heterogeneity of product preferences increases, so does the complexity of understanding which product attributes should be optimized by the design team to maximize enterprise value. These challenges are exacerbated in product preference acquisition techniques that rely on mining online data, as the customer is typically unknown to the designer, which limits the amount of follow up data available to be mined. By quantifying the degree of “must have” and “deal breaker” product preferences expressed online, designers will be able to understand what product-features should be omitted from next generation product design optimization models (i.e., “deal breaker” features) and what product features should be considered (i.e., “must have” features). A case study involving customer electronics mined from online customer review websites is used to demonstrate the validity of the proposed methodology.

In this paper, AR based Home Decor App is implemented which uses FAST corner detection. The Proposed System implements Home Decor App for furniture units using concept of Augmented Reality, which changes world of E-commerce by providing best solution to the customers while purchasing online products. The customer buys the product after virtual trial, which is possible through the concept of Augmented Reality. This is just completely new experience to online customers

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