Are Voice Assistant Apps Trending? #amazonalexa, #googleassistant

Imagine yourself relaxing in your bedroom and watching a San Francisco 49ers game. You want to order a pizza, but you are too lazy to open your laptop or mobile phone to place an order. In that case, you ask Alexa; “Alexa, open Dominos and place an order.” What are the other instances you feel like, you don’t want to use your phone to perform particular tasks?

I use Alexa or voice assistant for the following things:

  • Track my schedule
  • News updates
  • Weather updates
  • Schedule a meeting
  • Set reminders or alarms
  • Read audio books
  • Maintain todo lists

Voice is the most natural form of interaction. Conversational systems allow humans to talk to the computer by using voice as a mode of command instead of using the text.

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How should we define MVP?

A million dollar question every product manager faces, “how should I define Minimum Viable Product (MVP)?” As a PM, I always like to think about how can I design my product so that I can make it Most Valuable Product with minimum efforts?

I face following questions when I think about MVP every time:

  • There is a higher bar for quality now than ever before. It means I can not compromise on quality and performance. If that is the case how should I consider quality, performance and all functional requirements in a given timeframe?
  • Dictionary meaning of ‘Viable means: capable of working successfully; feasible. But what does ‘Viable’ means in MVP? Is its development cost, is it possible to build the product or is it usage by customers?
  • What are the essential requirements should I consider?
  • Is there a way I can capture customer feedback before I build MVP?

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Machine Learning/AI/Big Data Products – Whether to Ship or to Learn is the Question?

Google displays personalized search suggestions, Netflix populates recommendations for shows, and Amazon provides products you may like widget are notable examples of machine learning or big data products. These product features will not show accurate results until algorithm understand end-user pattern. Therefore, the fundamental question for machine learning/AI/Big Data product managers is whether to ship or to learn?

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