Talking Code

Sven Lenaerts joins us to share his expertise on product design and user experience. This conversation includes some thoughts on MVPs, when to hire a designer, and what a product person really does.

Here's what to listen for:

• 00:46 What do you do as a product designer and UI/UX designer?
• 04:18 What should I figure out before I talk to a designer or developer about building a product?
• 07:01 What things can I do inexpensively that are lower-risk to see if building an app is the right solution for the problem I am trying to solve?
• 12:35 What are the biggest mistakes people make when trying to define their Minimum Viable Product (MVP)?
• 15:29 Should you do much experimentation before the product goes out into the wild?
• 17:53 What do you do when clients are including features in their MVP that they shouldn’t be?
• 21:22 How do you formulate conversations to make sure the vision of the product is realized? What do those conversations look like?
• 27:13 Does your vision document have a narrative start/finish to it?
• 31:17 What “expensive mistakes” can be made when building products?
• 34:58 Are there any qualities that would distinguish a good product person from a bad one?
• 38:38 What can I do if I want to stretch my skills and be a little risky without feeling like I’m putting a client at risk?
• 44:32 What do you do when you find yourself becoming cynical about product ideas and features?
• 50:06 What can I do to help a UI/UX design person do their best work?

Mubashar Iqbal, the #1 product maker on ProductHunt, tells us about how he launches products that people use in weeks, not months.

Here's what to listen for:

• 00:57 - What is Mubashar’s Background?
• 02:11 - What does being the most featured maker on Product Hunt mean?
• 03:25 - What helped you to become #1 and have so many products features on Product Hunt?
• 05:22 - What does the “featured” product distinction mean?
• 06:27 - What was important in different products that ended up making them get featured?
• 07:40 - What is it that you do to ensure not overbuilding?
• 12:13 - What is your feature-building process?
• 14:13 - Where do you draw the line between building a feature now vs later?
• 17:38 - How do you make yourself comfortable with pushing products out when they’re ready to be pushed out?
• 19:43 - How do you handle requests for features?
• 26:01 - How has the adding features metric changed since you originally launched the project?
• 27:00 - Do you use quantitative data in addition to doing qualitative customer development?
• 28:06 - What kind of long-term success have you seen with your products and what has made a difference between the ones that are ones that are successful over time and the ones that go wayside?
• 31:50 - Is there an example of an app that you built in the past where you built way too much?

Jonathan Cornelissen tells us about DataCamp, the need for data scientists, and how to become one yourself. We also learn about some popular languages and libraries for analyzing data.

Here's what to listen for:

• 00:43 What is the story behind DataCamp?
• 02:06 What is data science?
• 02:52 What kind of xdata is out there that can be analyzed?
• 04:46 Do I need a scientific or statistical background to work with data science?
• 05:26 Does DataCamp help establish a theoretical background?
• 06:21 Do only big companies need data science?
• 07:16 What is big data?
• 07:58 Can the term big data be used interchangeably with data science?
• 09:08 Do you need a “billion dollar budget” to build a data science team? What kind of people do I need to build that kind of team?
• 12:08 What is behind the shortage of data scientists?
• 12:48 What can a startup do to incorporate data science into their team?
• 13:45 What is meant by data savvy?
• 14:10 What do you do with the data once it’s collected?
• 14:50 What is cohort analysis?
• 15:42 Once users are segmented, what could you do at that point?
• 16:21 Are correlations the primary sort of analysis?
• 17:14 Are people trying to make causative claims out of correlative data?
• 18:23 What are some other examples of techniques in addition to correlation?
• 18:55 Are there any other interesting algorithms out there that people are using?
• 20:07 Are these analyses run offline or real-time?
• 20:37 What is the Spark framework?
• 21:10 What is the R language?
• 24:09 Where does R fit in in a company?
• 24:47 Is R being run by a human or is there also a sense of R running on the server to serve up recommendations?
• 25:30 Is R still evolving as a language?
• 25:58 Is there anything people should try to learn before trying to tackle R as a language?
• 26:52 Why learn a language like R?
• 28:27 Does R allow you the ability to communicate the insights that you’re getting from the data that you’ve analyzed to build a narrative to help the non-technical people on your team?
• 29:19 Is visualizing the data that we get back important to our understanding of that data? Why?
• 29:57 Does DataCamp help people visualize data?
• 30:51 Aside from R, what other tools are out there that a data scientist would use?
• 31:23 What is Hadoop?
• 33:09 What is the concept of MapReduce?
• 33:42 What is the mark of a good data scientist?
• 35:30 Why do you need domain expertise?
• 38:30 How are people becoming aware of data science? Where do these people start?

Jeff Patton, author of User Story Mapping, teaches us how to map user stories by focusing on the user's journey to an outcome. He shares his opinion on the notorious "MVP" and how he helped Gary Levitt build his MVP with Mad Mimi.

Here's what to listen for:

• 00:49 What is a user story?
• 02:07 What does a user story look like?
• 02:57 When people refer to user stories do they mean the documentation around the conversations they’ve had?
• 03:44 Why is just having stories written down in a document not sufficient?
• 05:47 What is a good user story template?
• 09:17 What was the motivation for writing User Story Mapping?
• 11:44 Is the concept of a “map” about the narrative of a user’s journey?
• 17:36 How did Jeff help Gary from Mad Mimi get clarity on what he was doing?
• 20:54 Why were things taking longer for Gary when he came to you?
• 23:31 What does Jeff’s road mapping process look like?
• 26:47 What was it about Jeff and Gary’s conversation that took him from having a giant backlog to organizing user stories?
• 29:36 What is your definition of a “minimum viable product” (MVP)?
• 34:41 Why do you want to build something “less than minimal” before building the MVP?
• 39:34 Why is so difficult to put a time estimate on when software will be done?
• 44:53 What is meant by “scope doesn’t creep, understanding grows”?

Sarah Doody, a UX designer, consultant, and writer, tells us how to build products with great user experiences. We will hear why user experience is far more important than design.

Here's what to listen for:

• 00:46 - What is Sarah’s background in UX (User Experience)?
• 03:09 - What is the distinction between the experience and the interface?
• 05:53 - How do you create the ideal team at a startup?
• 10:53 - What is the distinction between experiential design and visual design?
• 13:14 - Which comes first: experiential design or visual design?
• 14:34 - What’s the process for evaluating the UX of an app?
• 19:01 - How do you get customers to use your product the way that it’s intended to be used?
• 20:22 - What mistakes do you see in the UX of an app once you are past the onboarding flow?
• 24:26 - How does user experience get compromised?
• 29:10 - How can you get back on track?
• 33:00 - How is product development like storytelling?
• 37:39 - What is a storyboard?
• 41:30 - Is a storyboard like a comic strip?
• 43:16 - How do you evaluate whether a developer is good or not at UX design?