Tips for would-be founders, technical or non-technical, before rolling up their sleeves and develop their products! From various ways of "pretotyping" to accurately gauge target customer's response, lean method, minimum viable product, feature selection, planning a product with robust data cycle, coping with delays, and guiding a team of rockstar engineers to build the right product and build the product right. Some personal experienced shared at the end as case studies.

1. Prototyping & Product Development
2018/07/18 @ Be Young! Beyond! Startup Bootcamp
Albert Y. C. Chen, Ph.D.
Vice President
Viscovery
albert@viscovery.com
slideshare.net/albertycchen

2. Albert Y. C. Chen, Ph.D. 陳彥呈 博⼠士
• Currently
VP of R&D, Viscovery
Adjunct Faculty, Taiwan AI Academy
Reviewer, MOST, MOEA AI programs
Consultant, Nexus Frontier Tech, UK
Consultant, Cinnamon AI, Japan
Mentor, Hack NTU, Make NTU, GIS
• Previously
2015–2017：Chief Scientist, Viscovery
2015–2015：Principal Scientist, Nervve Technologies, NY
2013–2014：Computer Vision Scientist, Tandent Vision Science, CA
2011–2012：R&D Staff, GE Global Research, NY
• Education
Ph.D. in CS (Computer Vision & Machine Learning), SUNY-Buffalo
B.S. in CS, National Tsing-Hua University

3. Before we build anything
• "Listen to what customers want" v.s. "I know what my
customers would want"
• Faster horse-pulled carriage v.s. automobile?
• Zune v.s. iPod

4. Beware! Flops are common;
visionaries are rare
Apple Newton, 1993
Microsoft Bob, 1995
New Coke, 1985

5. What do customers want?
Never do questionnaires
• Response bias: survey result easily influenced by
phrasing of questions, demeanor of researcher, the way
the experiment is conducted, etc.
• Acquiescence bias: not thinking and agreeing with
every question.
• Demand characteristics: participants altering their
responses simply because they are part of an
experiment.
• Question order bias.
• Social desirability bias: the participant only want to be a
kind person and not be mean.

6. What do customers want?
Pretotype with videos
• Demo videos as if there's an actual working product, e.g.,
Google glass, Magic Leap AR glasses.

7. What do customers want?
Pretotype with just an interface
• Provide an interface, do everything manually first to gauge
the initial level of interest (ILI).

8. What do customers want?
Pretotype with hacked dummies

9. Product planning
Categorize features into three buckets:
• A gamechanger. People will want to buy your product
because of this feature.
• A showstopper. People won’t buy your product if you’re
missing this feature, but adding it won’t generate demand.
• A distraction. This feature will make no measurable
impact on adoption.
Empirically, successful products have one to three
gamechanging features, dozens of features that neutralize
showstoppers, and very few features that are distractions.
http://www.defmacro.org/2013/09/26/products.html?
utm_source=wanqu.co&utm_campaign=Wanqu+Daily&utm_medium=email

10. Product planning for large
corporations
• Even large corporations have to select limited features for
new products.
• Don't do more showstopper features than the absolute
minimum you can get away with.
• E.g., The original iPhone only had acceptable voice
quality and lacked copy-paste function.

11. Feature scoring example
https://baremetrics.com/blog/feature-framework?utm_source=wanqu.co&utm_campaign=Wanqu+Daily&utm_medium=email

12. Product planning for
startups
• Chicken egg problem:
• No money, no demo. No demo, no money.
• Expectations for working prototype is increasing.
• A successful entrepreneur is able to bootstrap and
deliver.

13. Lean startups

14. Fail early, fail often, fail forward!
Build
Measure
Learn
The lean cycle
FAIL?
Ideate
pivot leap of faith
MVP
Build
Measure
Learn
Start all over again!
MVP

15. The Minimum Viable Product
(MVP)
• To start the process of "learning" as quickly as possible.
http://www.expressiveproductdesign.com

16. Further splitting the prototyping
process for apps/websites

17. Further splitting the prototyping
process for AI-related ideas
• Like "pretotyping", swap out core functionalities step by
step with:
• human in the loop
• general purpose AI on the cloud
• fine-tune and repurpose existing models
• train your own AI from scratch
• reinforce own AI with robust cycle of data.

18. The data cycle for lean AI startups
different data
unique AI
business
advantage
Speed～～
• The faster the data cycle, or the larger the
volume in each cycle, the better the AI

19. Choose your problem carefully. Only
do those with a strong data cycle.
Problem Data Scenario
Data cycle
quality
Face Recognition
user photos from
around the world
users would
correct labels
themselves
★★★★★
Face Recognition
surveillance
cameras in China
police would
need to manually
correct labels
★★★★
Face
beautification
app users
hire add'l labor to
manually inspect
the results
★★
Virtual makeup app users
hire add'l labor to
manually inspect
the results
★★

20. Initial prototype would likely
run into hoards of problems
• Don't spend >2 weeks in hacking together the MVP.
• Instead of following a strict scrum cycle, use a more
relaxed process like "kanban" and just digest all the tasks
ASAP.
• There's going to be delays. There's going to be bugs. It's
OK if the initial prototype (for demo purposes) to be
incomplete, fragile, or breaks down (that is why we need
investment to perfect the product). Just make sure that the
"game changer" feature wows.

21. Follow-up iterations are
going to be challenging
• Refrain from continuously adding more features to make
the uses happy; the costs of the running addition of
features is underestimated.
• Stories from some local favorites of how adding
features killed the product.

22. Managing a team of rock-
star engineers
• Rock-star engineers are self-motivated. When their
directions are aligned with the company's, their productivity
are amazing.
• What to do when they propose things that they want to do,
but the PM/CEO deems inappropriate/uncessary?
• Successful entrepreneurs are great at convincing:
• co-founders to join the cause,
• the team to sprint in the same direction,
• customers that they desperately need the product,
• investors to invest immediately or would regret forever.

23. Amazing things started by
engineers as their side projects

24. Amazing things started by
engineers as their side projects

25. Regardless, timing is the
most important
(Bradford Cross, TedX, 2015/03)
2000
.com bubble
2002 2004 2006 2008 2010 2012 2014 2016
housing bubblebroadband connectivity > 50%

26. Falcon 9
takeoff
Falcon 9
decelerate
Falcon 9
vertical
touchdown
When things don't work out,
how many more times
would you try again?

27. Sometimes, it's not the product...
It's the way that you're selling the product.

28. Sometimes, it's not the product...
It's the way that you're selling the product.

29. Sometimes, it's not the product...
It's the way that you're selling the product.

30. Here's to the Crazy Ones