Wintersemester 2019 - Cinefoxx - Executive Summary

Project partner and “problem/question”: initial status of the project and research question

The idea for CINEFOXX was born in 2017. Firstly, the founders have tried to find similar services in the market with the characteristics they thought for their product. However, there was no personalized movie recommendations system with a large movie database that was also fast and independent from the major players in the field. Later on, they have started to write a first business plan that included recommendations for DVDs and Blu-Rays. However, over time they decided to set a focus on streaming in order to leverage the upcoming trend. After having written the business plan a first offline program was written to rate movies and they started to build up a website. Because of personal matters, both the technological progress and the effort invested into the idea slowed down till the beginning of the GATE program.

At that time, they needed support in developing a more complex business model that included both a deeper knowledge of the industry and a better understanding of the potential viewers. Moreover, it is worth to mention that even if the algorithm was ready to use, in order to have a perfect working AI, they needed ratings from people. For this reason, we have planned future activities to reach 10k raters at the lowest cost possible, for example: sending requests on Facebook groups and forums, partnering with universities and film clubs. In addition to these plans, we have actively tried to onboard ratings through university contacts and by developing a social media page.


Since it was crucial to know more about the customer needs and the current situation, we decide first to conduct a survey about the habits and the problems they were facing in choosing movies. The sample was composed of 159 people coming from 13 different countries with different age in order to have data as unbiased as possible. At the same we conducted 19 exploratory interviews to get responses about ratings, selecting movie behaviours, general media consumption and trust in AI. Also, for these interviews, we selected people of different age in order to identify better our target market. 

The second most important issue was to gain knowledge about the industry and the market. We made extensive researches to understand how affiliate marketing and advertising work in order to propose a business model and revenue streams that can be meaningful and realistic. At the same time, we discovered more about the German market, its demographics and the current situation in order to target appropriate numbers and develop the next milestones to hit.


According to our self-conducted survey, 63% (out of 159 Europeans with varying ages) are not satisfied and 58% declare that the main problem is the time wasted in the process of choosing the right movie to watch (around 12 minutes on average). 

In order to address those needs, we offer a website where viewers, after registration, can get personalized recommendations for movies and series. The recommendations are generated by an algorithm based on AI. It feeds on a huge database of movies/series, continuously improves, and requires little user input. Most importantly, the AI considers 30 different categories in order to identify the specific preferences of an individual viewer. Once the recommendation is given, the viewer is redirected to Amazon Prime to purchase and watch the movie/series. In order to make it sustainable, we plan to develop a two-phased business model. In the first three years of the venture, revenues will be generated by advertising and affiliate marketing programs. The former is mainly based on Facebook, Google Ads and Content Marketing. The latter considers Amazon and Lieferando affiliate marketing programs. In the second phase of the business model, we plan to integrate a premium account in our offering. It will provide an ad-free experience and will include many options for customization.

Contact Person

  • Andrei Grecu and Michael Kerzendorfer

Student team

  • Edoardo Bubani Cremonese

  • Barnabas Detky

  • Massimo Kostl

  • Giulia Passaro

Project Partner team

  • Andrei Grecu

  • Michael Kerzendorfer


  • Shtefi Mladenovska 

  • Peter Keinz

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