Tag: Announcement

Introducing New Ping! Design

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I am happy to introduce a new, more streamlined design for Ping! >> Loyalty Science in Practice. It’s been nearly ten years since I designed the look and feel of Ping! 1.0. The redesign was long overdue. I hope you like the new look. Ping! will still have the same great content, bringing cutting-edge research insights to help your business effectively build and maintain customer loyalty. Thank you for being a loyal reader!

Note: If you still see the old design on some of the pages, you may need to clear your browser cache.

Looking for a YouTube API Programmer

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I am looking for a programmer familiar with YouTube API to work with me on one of my research projects. The project’s intent is to find out what makes some branded messages (i.e., branded viral videos on YouTube) more popular than others. Thanks to a research grant provided by Empower MediaMarketing, I will be able to pay for the work. Below is a description of what I would like the program to be able to extract from YouTube. If you think you can do this job or if you know someone who can, please contact me. Thank you!

  • What I’m looking for: a program that will connect with YouTube data API to extract information related to a video and a user or a channel.
  • Programming language: YouTube supports JAVA, .NET, PHP, and Python. It doesn’t matter to me what language is used, as long as I can implement it on my end (or through remote access to a central server) to extract data.
  • Types of data to be collected:
  1. Basic initial user/channel information (collected twice, once at the beginning and once at the end, for each channel): Joining time, Location (Hometown, country), Channel views, Total upload views, Number of video uploads, Number of channel comments, Number of subscribers, Number of subscriptions
  2. More sophisticated subscriber information (collected once at the beginning for each channel): Number of the company’s subscribers who are friends with each other or who subscribe to each other, Number of subscribers for each of the company’s subscribers, Number of friends for each of the company’s subscribers, Number of subscriptions for each of the company’s subscribers, The number of common subscription for each pair of the company’s subscribers, The number of common friends for each pair of the company’s subscribers. Alternatively, this can be done by extracting each of the channel subscriber’s subscribers, friends, and subscriptions list and run the calculation later, if data storage is not a problem.
  3. Initial Video Properties (collected once at the beginning for each video): Date posted, Video length, Listed categories, Tags, Descriptions, Initial number of views at the time of sampling
  4. Daily Video Properties (collected daily for each video): Number of views, Number of text comments, Number of video comments, Number of ratings, Average ratings, Number of times favorite
  5. End Video Properties (collected once at the end for each video): Top 10 referral links listed for the video and the number of views associated with each link

I would like to start the project as soon as possible. So please contact me soon if you are interested or can recommend someone. Thanks!