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Posts Tagged ‘A/B Testing

A Core Formula for Agile B2C Statrups

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Colleague Chris Sterling drew my attention to a Pivotal Labs talk by Nathaniel Talbott on Experiment-Driven Development (EDD). It is a forward-looking think piece, focused on development helping the business make decisions based on actual A/B Testing data. Basically, EDD to the business is like TDD to development.

Between this talk and a recent discussion with Columbia’s Yechiam Yemini on his Principle of Innovation and Entrepreneurship course, a core “formula” for Agile B2C startups emerges:

  1. Identify a business process P
  2. Create a minimum viable Internet service S to support P
  3. Apply EDD to S on just about any feature decision of significance

This core formula can be easily refined and extended. For example:

  • Criteria for choosing P could/should be established
  • Other kinds of testing (in addition to or instead of A/B testing) could be done
  • customer development layer could be added to the formula
  • Many others…

By following this formula a startup can implement the Agile Triangle depicted below in a meaningful manner. Value is validated – it is determined based on real customer feedback rather than through conjectures, speculations or ego trips.

Figure 1 – The Agile Triangle (based on Figure 1-3 in Jim Highsmith‘s Agile Project Management: Creating Innovative Products)

The quip “The voice of the people is the voice of God” has long been a tenet of musicians. The “formula” described above enables the Agile B2C startup to capture the voice of the people and thoughtfully act on it to accomplish business results.

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The Changing Nature of Innovation: Part I — New Forms of Experimentation

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Colleague Christian Sarkar drew my attention to two recent Harvard Business Review (HBR) articles that shed light on the way(s) innovation is being approached nowadays. To the best of my knowledge, none of the two articles has been written by an author who is associated with the Agile movement. Both, if you ask me, would have resonated big time with the authors of the Agile Manifesto.

The February 2009 HBR article How to Design Smart Business Experiments focuses on data-driven decisions as distinct from decisions taken based on “intuition”:

Every day, managers in your organization take steps to implement new ideas without having any real evidence to back them up. They fiddle with offerings, try out distribution approaches, and alter how work gets done, usually acting on little more than gut feel or seeming common sense—”I’ll bet this” or “I think that.” Even more disturbing, some wrap their decisions in the language of science, creating an illusion of evidence. Their so-called experiments aren’t worthy of the name, because they lack investigative rigor. It’s likely that the resulting guesses will be wrong and, worst of all, that very little will have been learned in the process.

It doesn’t have to be this way. Thanks to new, broadly available software and given some straightforward investments to build capabilities, managers can now base consequential decisions on scientifically valid experiments. Of course, the scientific method is not new, nor is its application in business. The R&D centers of firms ranging from biscuit bakers to drug makers have always relied on it, as have direct-mail marketers tracking response rates to different permutations of their pitches. To apply it outside such settings, however, has until recently been a major undertaking. Any foray into the randomized testing of management ideas—that is, the random assignment of subjects to test and control groups—meant employing or engaging a PhD in statistics or perhaps a “design of experiments” expert (sometimes seen in advanced TQM programs). Now, a quantitatively trained MBA can oversee the process, assisted by software that will help determine what kind of samples are necessary, which sites to use for testing and controls, and whether any changes resulting from experiments are statistically significant.

On the heels of this essay on how one could attain and utilize experimentally validated data, the October 2009 HBR article How GE is Disrupting Itself discusses what is already happening in the form of Reverse Innovation:

  • The model that GE and other industrial manufacturers have followed for decades – developing high-end products at home and adapting them for other markets around the world – won’t suffice as growth slows in rich nations.
  • To tap opportunities in emerging markets and pioneer value segments in wealthy countries, companies must learn reverse innovation: developing products in countries like China and India and then distributing them globally.
  • While multinationals need both approaches, there are deep conflicts between the two. But those conflicts can be overcome.
  • If GE doesn’t master reverse innovation, the emerging giants could destroy the company.

It does not really matter whether you are a “shoe string and prayer” start-up spending $500 on A/B testing through Web 2.0 technology or a Fortune 500 company investing $1B in the development and introduction of a new car in rural India in order to “pioneer value segments in wealthy countries.” Either way, your experimentation is affordable in the context of the end-result you have in mind.

Fast forward to Agile methods. The chunking of work to two-week segments makes experimentation affordable – you cancel an unsuccessful iteration as needed and move on to work on the next one. Furthermore, you can make the go/no-go decision with respect to an iteration based on statistically significant “real time” user response. This closed-loop operational nimbleness and affordability , in conjunction with a mindset that considers a “failure” of an iteration as a valuable lesson to learn from, facilitates experimentation. Innovation simply follows.