Testing, Testing, 1,2, 3

One of our assignments for class has been to write product assumptions. In that assignment I learned about minimum viable product (MVP). MVP is a proof of concept, the act of “getting something” out there. Some people go as far as defining it as 6-20 customers and most folks agree that by releasing an MVP, you give your company credibility. Yes, it’s important to get a product to market, but I wondered if there were some things companies shouldn’t do when launching a product.

Entrepreneur magazine is a trusty source for a lot of my research given they tailor content to start-ups and small business owners. Today wasn’t the day to let me down. Contributor Cory Levy wrote an article called, Starting Up Wrong: 6 Product Testing Mistakes You Need to Avoid. Spoiler alert, there are 6 things he says to avoid in this process and you’ll have to read the article for the details but here are the headlines:

  1. Letting Bias taint, the process
  2. Data problems
  3. Relying on technology to test
  4. Ignoring the competition
  5. Waiting to ‘sell’ until it’s built
  6. Leaving assumptions untested

While it’s important to get a minimally viable product to market, it’s also important to balance that with testing.

Author: strengthsfocusedintrapreneur

Experienced Human Resources, Engagement, and Leadership professional with a demonstrated history of working in the hospital & health care, and retail industries. Skilled in HR consulting, project management, HR IT systems administration, team leadership, conflict resolution, HR policies, training/facilitating, talent development & placement, and relationship building. I practice very progressive HR methods and utilize strengths based talent strategies. I am committed to the professional development of others and value life long learning.

One thought on “Testing, Testing, 1,2, 3”

  1. Hi Nancy,
    Great post! I was intrigued enough to follow the link. Your blog is full of intrigue, I guess! I think the one that stands out to me is the data section, specifically the idea that you need to have a large enough sample of people that you would get the same data with a group 100 times the original size. Thanks for sharing!
    JOY

    Liked by 1 person

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