The Lean Launchpad at Stanford—Class 2: Business Model Hypotheses
By now the nine teams in our Stanford Lean LaunchPad Class were formed, In the four days between team formation and this class session we tasked them to:
- Write down their initial hypotheses for the 9 components of their company’s business model (who are the customers? what’s the product? what distribution channel? etc.)
- Come up with ways to test each of the 9 business model canvas hypotheses
- Decide what constitutes a pass/fail signal for the test. At what point would you say that your hypotheses wasn’t even close to correct?
- Consider if their business worth pursuing? (Give us an estimate of market size)
- Start their team’s blog/wiki/journal to record their progress during for the class
The Nine Teams Present
Each week every team presented a 10 minute summary of what they had done and what they learned that week. As each team presented, the teaching team would ask questions and give suggestions (at times pointed ones) for things the students missed or might want to consider next week. (These presentations counted for 30% of their grade. We graded them on a scale of 1-5, posted our grades and comments to a shared Google doc, and had our Teaching Assistant aggregate the grades and feedback to pass on to the teams.)
Our first team up was Autonomow. Their business was a robot lawn mower. Off to a running start, they not only wrote down their initial business model hypotheses but they immediately got out of the building and began interviewing prospective customers to test their three most critical assumptions in any business:
Value Proposition, Customer Segment and Channel. Their hypotheses when they first left the campus were:
- Value Proposition: Labor costs in mowing and weeding applications are significant, and autonomous implementation would solve the problem.
- Customer Segment: Owners/administrators of large green spaces (golf courses, universities, etc.) would buy an autonomous mower. Organic farmers would buy if the Return On Investment (ROI) is less than 1 year.
- Channel: Mowing and agricultural equipment dealers
All teams kept a blog – almost like a diary – to record everything they did. Reading the Autonomow blog for the first week, you could already see their first hypotheses starting to shift: “For mowing applications, we talked to the Stanford Ground Maintenance, Stanford Golf Course supervisor for grass maintenance, a Toro distributor, and an early adopter of an autonomous lawn mower. For weeding applications, we spoke with both small and large farms. In order from smallest (40 acres) to largest (8000+ acres): Paloutzian Farms, Rainbow Orchards, Rincon Farms, REFCO Farms, White Farms, and Bolthouse Farms.”
“We got some very interesting feedback, and overall interest in both systems,” reported the team. “Both hypotheses (mowing and weeding) passed, but with some reservations (especially from those whose jobs they would replace!) We also got good feedback from Toro with respect to another hypothesis – selling through distributor vs. selling direct to the consumer.”
The Autonomow team summarized their findings in their first 10 minute, weekly Lesson Learned presentation to the class.
Our feedback: be careful they didn’t make this a robotics science project and instead make sure they spent more time outside the building.
If you can’t see the slide deck above, click here.
Autonomow team members:
Lee Redden (MSME Robotics, Jun 2011) Research in haptic devices, autonomous systems and surgical robots, BSME (U Nebraska at Lincoln), Family Farms in Nebraska
Joe Bingold (MBA, Jun 2011) Head of Product Development for Naval Nuclear Propulsion Plant Control Systems, US Navy, MSME (Naval PGS), BSEE (MIT), P.E. in Control Systems
Fred Ford (MSME, Mar 2011) Senior Eng for Mechanical Systems on Military Satellites, BS Aerospace Eng (U of Michigan)
Uwe Vogt (MBA, Jun 2011) Technical Director & Co-Owner, Sideo Germany (Sub. Vogt Holding), PhD Mechanical Engineering (FAU, Germany), MS Engineering (ETH Zurich, Switzerland
Our next team up was Personal Libraries which proposed to help researchers manage, share and reference the thousands of papers in their personal libraries. “We increase a researcher’s productivity with a personal reference management system that eliminates tedious tasks associated with discovering, organizing and citing their industry readings,” wrote the team. What was unique about this team was … Next Page »