Applying “Learning Launch”    Challenge   We wanted to test a news business model (subscription) and a direct to consumer approach  for a well known beverage company.   There were several hypothesis concerning the specific customer needs we had to validate  and there were several doubts concerning the pricing (what these specific customers would  be willing to pay) and different customer segments.   We wanted to test a beverage subscription service that “learns” over time from consumption  preferences, habits and favorite ingredients. The concrete customer need we identified is the  logistical nightmare of shopping beverages and serving all different needs within the family  (taste and nutrition and occasions).

Selection   Based on the huge number of different hypothesis we decided to run small experiments  based on the learning launch method.

1. Idea

2. Specific customer.

3. Specific need.

4. Solution / idea that meets the customer need.

5. Better than current alternative   = Hypothesis

Application, approach  We first created a multi disciplinary team from different functions, a small and diverse team +  a skeptic (to avoid confirmation bias = find evidence that it is a good idea).  We then elaborated key assumptions based on “What do I know?”, “What do I not know?”,  “what do I need to know?” and “how do we learn what we need to know?”.   Before creating a prototype we then talked to potential customers to validate (or not) some of  our key hypothesis (customer need, problem-solution fit, price) concerning value creation  and talked as well to logistic provider to validate execution.   As some of them were validated we only pivoted / changed some aspects of our first solution  approach.    Insight   As we quickly got real life feedback from clients we “failed” cheap and fast with some of our  main hypothesis.   People thought it would be a gain to get personalised beverages delivered to their doorstep.  Pricing was too high (which was basically a problem of logistic costs) and yes, they were OK  with sharing important data for personalisation such as Apple Health and Fitbit data as well  as geolocation data to track their daily commute etc. to offer drinks on the go at certain cafes  etc.

