“Thought-worlds consist of tacit and taken-for-granted assumptions about what is important, relevant, or necessary” (2014, McQuarrie)
“You have got to start with the customer experience and work backwards toward the technology ” (1997, Jobs).
A study of unsuccessful product concepts found that developers in “leap before you look” thought-worlds take a solution to the market, not a product (1987, Dougherty). Without a comprehensive understanding of customers’ thought-worlds, unproductive surprises crop up in late stages of concept commercialization.
Unproductive surprises include:
- Investment of scarce resources … people, time, money … in development of unnecessary benefits
- Expensive rework when necessary benefits surface in the late stages
- Value proposition fails to attract profitable customers
Customers’ Uncertainties About A Product Concept
Customers’ Thought-World … Key Uncertainty
Technical … How do we fit this concept into our product platform?
Manufacturing …. What shift in production systems do we need?
Field … Will end users adopt our new product incorporating this concept?
Planning … What is the market forecast for our new product?
Conducting hypothesis experiments in customers’ thought-worlds provides the data developers need for dealing with customers’ uncertainties … and for validating assumptions.
Don’t Think Solution … Think Hypothesis
Frequently developers treat a product concept as a solution and take a running leap into the market. They implicitly assume that customers think the same way the developers do about the problem the concept is designed to solve.
Implicit assumptions are dangerous if they are “load-bearing” (2002, Dewar). In new product development systems, failure to validate load-bearing assumptions in the front end leads to significant changes and expense in a development’s late stages.
In a lean product development system, before beginning commercial development developers treat a product concept as a hypothesis , an educated guess (2015, Blank). Hypotheses require experimentation and data to validate or invalidate their assumptions .
Some load-bearing assumptions in the front end are:
- Who are the right customers?
- What’s their distinctive value proposition?
- How much will they pay to capture the distinctive value?
Validating a product hypothesis’ assumptions
As one of the successful developers interviewed by Dougherty said:
“You have to get into the ears and minds of users. If you can’t explain the product (hypothesis) in 30 seconds, you’re dead (1992, Dougherty)
Between 25-50 interviews with prospective customers are recommended to gather data for validating assumptions about product concepts (1993, Griffen; 2002, Castellion; 2014, Blank; 2012, Maurya). The individuals interviewed are decision makers within the product concept’s value chain. They are chosen randomly from a sample of two hundred or more decision makers.
Good data gathered through elicitation methods during 25-50 interviews provides the clues to validating load-bearing assumptions. The central limit theorem of statistics (2013, Wheelan) makes it possible to generalize from these clues and validate assumptions with a strong probability that they represent the thinking of all decision makers in the value chain.
Interviews can be either face-to-face or by phone. I prefer to interview by phone, using my elicitation skills and my practitioner experience as a technology manager and marketing manager to gather good data. Also, decision makers are more open about their thought-world uncertainties than they are in face-to-face interviews.
(1987 ) Dougherty, D., New Products in Old Organizations Ph.D Thesis, MIT
(1993) Griffin, A., and Hauser, J., Voice of the Customer Marketing Science 12: 1-27
(1992) Dougherty, D., Interpretive Barriers to Successful Product Innovation Organization Science 3 #2 p. 193
(1997) Jobs, S. , World Wide Developers Conference, https://goo.gl/pX3on2 accessed 5/23/15
(2002) Castellion, G., Telephoning Your Way to Compelling Value Propositions The PDMA ToolBook for New Product Development, John Wiley & Sons, NY, NY
(2003) Dewar, J., Assumption-Based Planning A tool for reducing avoidable surprises Cambridge University Press Cambridge UK
(2012) Maurya, A., Running Lean 2nd Edition O’Reilly,Sebastopol, CA
(2013) Wheelan, C., Naked Statistics W. W Norton & Company, NY, NY
(2014) McQuarrie, E., Customer Visits: Building a Better Market Focus Rutledge
(2015) Blank, S., Why Build, Measure, Learn isn’t just throwing things against the wall to see if they work http://goo.gl/pOe74r accessed 5/11/15