Stop Guessing and Validate What Your Customers Want Presented by: - - PDF document

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Stop Guessing and Validate What Your Customers Want Presented by: - - PDF document

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AT3 Agile Product Development Thursday, June 7th, 2018, 10:00 AM Stop Guessing and Validate What Your Customers Want Presented by: Natalie Warnert CA Technologies Brought to you by: 350 Corporate Way, Suite 400, Orange Park, FL 32073 888 ---

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AT3

Agile Product Development Thursday, June 7th, 2018, 10:00 AM

Stop Guessing and Validate What Your Customers Want

Presented by:

Natalie Warnert

CA Technologies Brought to you by: 350 Corporate Way, Suite 400, Orange Park, FL 32073 888---268---8770 ·· 904---278---0524 - info@techwell.com - https://www.techwell.com/
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Natalie Warnert

CA Technologies

As a developer turned agile consultant, Natalie Warnert deeply understands and embraces the talent and environment it takes to build great products. From building the right product to building the product right, Natalie drives strategy and learning through validation. She has helped various Fortune 500 companies in their agile transformation in the last decade, including Travelers Insurance, Target, Thomson Reuters, and Salesforce. Natalie received her master of arts in
  • rganizational leadership and strategic management from St. Catherine
University and demonstrates continued passion for increasing women’s involvement in the agile and technology community (#WomenInAgile). She chairs the half-day Women in Agile workshop at the Agile Alliance annual conference, which is going on its third successful year. You can read more about Natalie's ideas at www.nataliewarnert.com.
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SLIDE 3 5/26/18 ¡ 1 ¡ @natalie warnert

Stop Guessing and Validate What Your Customers Want!

Natalie Warnert – DevOps West June 7, 2018 @natalie warnert

Natalie Warnert

Sr Agile Consultant Natalie Warnert LLC www.nataliewarnert.com www.womeninagile.com @nataliewarnert

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SLIDE 4 5/26/18 ¡ 2 ¡ @natalie warnert

Answer: What is the point of a product development experiment? What build traps do we fall into? We don’t know what the customer wants and neither do they! OUTCOMES

@natalie warnert

Satisfy Customer Run a business

BALANCING NEEDS

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SLIDE 5 5/26/18 ¡ 3 ¡ @natalie warnert What is an product dev experiment (MVP)?
  • Building just enough to learn and test a hypothesis
  • Learning not optimizing
  • Find a plan that works before running out of
resources ($$)
  • Provide enough value to justify charging (from day 1)

BACKGROUND

Source: Running Lean @natalie warnert *The difference between building the right thing and LEARNING the right thing

THE POINT

What is an product dev experiment (MVP)?
  • Building just enough to learn and test a hypothesis
  • Learning not optimizing
  • Find a plan that works before running out of
resources ($$)
  • Provide enough value to justify charging (from day 1)
Source: Running Lean
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SLIDE 6 5/26/18 ¡ 4 ¡ @natalie warnert most plan A’s

don’t work…

We’re bad at predicting what the customer wants

@natalie warnert

UNDERSTAND THE PROBLEM

  • What is the customer’s problem?
  • Fit into the business model
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SLIDE 7 5/26/18 ¡ 5 ¡ @natalie warnert

CONFIRMATION BIAS

Search for, interpret, favor, recall information that confirms belief or hypothesis
  • Selective memory
  • We are NOT the customer
  • Fake experimentation
  • Correlation is not causation
@natalie warnert Leads to inaccurate conclusions and poor decisions
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SLIDE 8 5/26/18 ¡ 6 ¡ @natalie warnert Solution leads to belief without fact checking (cognitive bias) Actual Facts Belief Solution Source: Agile UX Storytelling: Rebecca Baker @natalie warnert

UNDERSTAND THE PROBLEM

  • What is the customer’s problem?
  • Fit into the business model
What is the customer hiring your product to do?
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SLIDE 9 5/26/18 ¡ 7 ¡ @natalie warnert

DEFINE THE SOLUTION

Smallest possible experiment to speed up learning Build only what is needed (MVP) - generic Pick bold outcomes to validate learning Business outcomes over solution HYPOTHESIS What happens when relevant product recommendations are placed in the cart vs. before the cart? Source: Running Lean @natalie warnert SCIENTIFIC METHOD - HYPOTHESIS If = antecedent; Then = consequent Must be falsifiable otherwise it cannot be meaningfully tested It can never be totally proven (theory)
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SLIDE 10 5/26/18 ¡ 8 ¡ @natalie warnert

HYPOTHESIS

Change it to a question, not a statement: What happens if…? What do you want to learn? Do observations agree or conflict with the predictions derived from the hypothesis? How do you find empirical data? @natalie warnert

You stand to learn the most when the probability of the expected

  • utcome is

50%; that is, when you don’t know what to expect

  • Lean Analytics

“

”

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SLIDE 11 5/26/18 ¡ 9 ¡ @natalie warnert

HYPOTHESIS EXAMPLE Will strangers pay money to stay in our house? First, it needed to demonstrate there was a market for paid room rentals in a personal setting. Second, it needed to attract enough users to its specific platform so that supply and demand could be met in any location.

@natalie warnert A startup can focus on only
  • ne metric. So
you have to decide what that is and ignore everything else – Noah Kagan, AppSumo

“

”

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SLIDE 12 5/26/18 ¡ 10 ¡ @natalie warnert

MEASURE QUALITATIVELY

Get out of the building! What are our customers doing? Continuous feedback loop with customers @natalie warnert MEASURE QUANTITATIVELY More stuff (products/services) More people (adding users) More often (stickiness, reduced churn, repeated use) More money (upselling and maximizing price) More efficiently (reduce the cost of delivering and supporting, customer acquisition) Source: Lean Analytics What is your one metric to rule them all? What are you trying to learn with your hypothesis? Where is that EMPIRICAL data coming from?
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SLIDE 13 5/26/18 ¡ 11 ¡ @natalie warnert but what about

that data we have?!

The customer told us so we can skip that

  • ther stuff…
@natalie warnert

Customers don’t care about your

  • solution. They

care about their problems.

  • Dave McClure,
500 Startups

“

”

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SLIDE 14 5/26/18 ¡ 12 ¡ @natalie warnert

UNDERSTAND THE PROBLEM

  • What is the customer’s problem?
  • Fit into a business model
  • How do you avoid being TOO specific?
@natalie warnert

WE THOUGHT WE KNEW…

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SLIDE 15 5/26/18 ¡ 13 ¡ @natalie warnert

EARLY COMMITMENT TRAP

Assume variability – preserve options (SAFe) Waterfall Fixed: Scope People Time Variable: People Time Scope Agile @natalie warnert Problem/Solution fit Do I have a problem worth solving? Product Market Fit Have I built something people want? Scale How do I accelerate growth and maximize learning?

WHERE TO START

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SLIDE 16 5/26/18 ¡ 14 ¡ @natalie warnert Problem/Solution fit Do I have a problem worth solving? Product Market Fit Have I built something people want? Scale How do I accelerate growth and maximize learning?

WHERE TO START

Ideas are cheap! Acting on them is expensive $$ @natalie warnert Problem/Solution fit Do I have a problem worth solving? Product Market Fit Have I built something people want? Scale How do I accelerate growth and maximize learning?

WHERE TO START

Ideas are cheap! Acting on them is expensive $$ Learning over growth Specificity doesn't scale!
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SLIDE 17 5/26/18 ¡ 15 ¡ @natalie warnert Inception & Strategy Investigation Refinement & Adjustment Development Release UX Runway Source: Natalie Warnert (www.nataliewarnert.com) @natalie warnert Another Hypothesis Example Property listings with professional photos will get more business than market average of those without professional photos. Hosts will sign up for professional photography as a service Source: Lean Analytics, https://www.digitaltrends.com/social-media/airbnb-steps-up- its-game-with-professional-photos/
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SLIDE 18 5/26/18 ¡ 16 ¡ @natalie warnert

BUILD MODEL

Build ¡right ¡ thing ¡ Build ¡ thing ¡ right ¡ Build ¡it ¡ fast ¡ @natalie warnert

LEARNING MODEL

Build ¡right ¡ thing ¡ Build ¡ thing ¡ right ¡ Build ¡it ¡ fast ¡ Learning ¡ Effort ¡ Speed ¡
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SLIDE 19 5/26/18 ¡ 17 ¡ @natalie warnert

WHERE IS THE LEARNING?

Requirements Dev QA Release @natalie warnert

WHERE IS THE LEARNING?

Requirements Dev QA Release Little learning some learning Most learning
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SLIDE 20 5/26/18 ¡ 18 ¡ @natalie warnert CUSTOMER IS NOT ALWAYS RIGHT! Declare assumptions and hypothesis Create an experiment to test hypothesis Run experiment to see what happens Customer feedback (qual and quant) – pivot without reluctance @natalie warnert

WHAT TO AVOID

What is the point of an experiment? Traps:
  • Confirmation bias and fake experiments
  • Premature commitment and fixed scope
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SLIDE 21 5/26/18 ¡ 19 ¡ @natalie warnert

THANKS

FOR COMING

www.nataliewarnert.com @nataliewarnert info@nataliewarnert.com