Rise of the Microbrew
This detailed Case Study and Market Simulation workflow follows the overview called: CS-101 Rise of the Microbrew.
Part 06, the last part in this Case Study series, examines the unexpected emergence of microbrews in a market that was dominated by the major breweries.
In 1870 there were over 3,200 breweries in the United States. But by 1980 that number had dropped to only 4 breweries that produced beer sold in bars or on store shelves:
Michael Porter, the noted Harvard academic, voiced the view of many industry experts in his 1980 book, Competitive Strategy. He said the barriers to entry in the United States beer industry were so high that another brewer would never enter the market.
But shortly thereafter microbrews burst onto the market, and the number of breweries quickly rose to over 1,500 by the year 2000. Today, there are over 3,000 breweries in the United States.
This Market Simulation explains how microbreweries were able to successfully survive against such powerful competitors.
In essence, a type of differentiation called “Strange Differentiation” enabled these microbreweries to create a sustainable niche that distinguished them from the major competitors.
This Case Study provides a high-level overview of the workflow without detailed explanation. It assumes you are already somewhat familiar with KNIME and Market Simulation. If not, start by reviewing the Building Blocks and Community Nodes.
Market Simulation of the Strange Differentiation which allows Microbrews to emerge and survive as niche products in the beer market.
The video contains a brief presentation covering the 5th economic phase of the beer industry along with an explanation of the KNIME workflow. More detail can be found in the explanation below.
#1 Major vs Micro
The first branch of the Market Simulation workflow defines the high-level differences between the Major breweries and the tiny Micro-breweries.
According to the 1996 Consumer Reports research report titled “Can you Judge a Beer by its Label?”:
“Beers from true micro-brewers often earned low quality scores with stale, sulfury, medicinal and cooked-vegetable flavors.”
Hence, in this first branch of the Market Simulation, the Quality, Flavor, and Brand value of the Microbrews are all set to be 2/3 (0.667) of the value of the Major breweries. The difference in Quality is set using the “Quality” Feature Variation.
Furthermore, as Microbrews can’t take advantage of the economies-of-scale that the Major breweries enjoy, the Cost of the Microbrews is set to be 20% more than the Cost of the Major breweries ($0.60 versus $0.50 per Feature). The difference in Cost is set using the “Expense” Feature Variation. Later, the Price of the Microbrews will also be set higher than the Price of the Major beers ($3.00 versus $2.00 per serving).
But, importantly for “Strange Differentiation”, the Standard Deviation (SD) of the Microbrews is set to be twice as wide as for the Major breweries (0.4 versus 0.2). This is set using the “Niche” Feature Variation.
Click on an icon to see and scroll through the enlarged version of the images.
#2 Major Products
Two Major beer Products are defined:
The Willingness To Pay (WTP) of Customers for these Major beers is set using the top “Differentiation Vertical” and “Differentiation Horizontal” nodes. The two Products (01 and 02) are set to be Feature Variations of the previously defined Major beers.
Only the Brands differentiate these Major beers – the Quality and Flavor of the Major beers are indistinguishable (see Rise of the Microbrew Part 05).
The Horizontal Differentiation between these two Major beer Products is defined as their mutual correlation. This is set using the “Conformity” value within the Input Feature Variations table. As the Conformity of each is 0.5, the two Major beers will have a correlation of 0.5 between them. That is, Customers who prefer Major.01 will be also inclined to prefer Major.02.
#3 Microbrew Products
Two Microbrew Products are also defined:
The Willingness To Pay (WTP) of Customers for these Microbrews is set using the bottom “Differentiation Vertical” and “Differentiation Horizontal” nodes. As above, the two Products (03 and 04) are set to be Feature Variations of the Micro beers defined upstream.
Unlike the Major beers, the Quality, Flavor, and Brand of the Microbrews are all differentiating qualities.
The Horizontal Differentiation between these two Microbrew Products is also set to have a “Conformity” value and Correlation of 0.5. That is, Customers who have a preference for Microbrews will be inclined to select either Micro.03 or Micro.04 before they select a Major beer Product. This inclination of Customers to perceive similarities between the Microbrews extends to their perceived value of Quality, Flavor, and Brand.
#4 Product Generator
The four beer Products in the Market are made up of Features:
- Major 01 (Price = $2.00) = Quality.Major + Flavor.Major + Brand.Major.01
- Major 02 (Price = $2.00) = Quality.Major + Flavor.Major + Brand.Major.02
- Micro 03 (Price = $3.00) = Quality.Micro.03 + Flavor.Micro.03 + Brand.Micro.03
- Micro 04 (Price = $3.00) = Quality.Micro.04 + Flavor.Micro.04 + Brand.Micro.04
To generate the part-worth Customer Distributions for these Features, all of the Feature definitions first need to be concatenated together and then passed to a “Matrix Distributions” node. The concatenated Correlations between each Feature are also passed to the node.
Then the part-worth Customer Distributions for each of the Features are aggregated together into a Product-level Willingness To Pay (WTP) Matrix. This is done by the Product Generator node.
Finally the “Simulate Market” node will predict the number of Customers which purchase each Product. In this Market Simulation, the Market Share of each Product is predicted to be:
- Major 01 = 47%
- Major 02 = 47%
- Micro 03 = 2.6%
- Micro 04 = 2.6%
These results are roughly inline with the actual market results, with today’s Microbrews capturing about 11% of the $22 billion industry in the USA.
Strange Differentiation relates to the Standard Deviation (SD) among the Customers Willingness To Pay (WTP).
In this case, the Major beer Products are objectively better than the Microbrews in every way. They taste better (with less cooked-vegetable flavors), they are of higher quality (through better management of the fermentation process), and have stronger brands. In other words, the Major beers are Vertically Differentiated from the Microbrews with a higher Mean WTP. This can be seen in the chart above where Budweiser’s curve has been shifted to the right of the Microbrew.
However, Microbrews have Strange Differentiation. That is, a set of outlier Customers that place a high value on unique flavors and a different brand experience. In the chart above, Customers Y and Z meet this criteria and would buy the Microbrew, while all other Customers would buy Budweiser.
Note that the name “Strange Differentiation” is appropriate as the Standard Deviation (SD) of the WTP curves can cause several different types of unusual phenomenon. Product Bundling, for example, will cause the SD to collapse and can disrupt a niche positioning. And when additional Features are added to Products the competitive advantage of those Products can evaporate (think of the myriad of Features offered by smartphones that don’t seem to translate into higher Profitability).
The “Rise of the Microbrew” story illustrates how the economic theory works in practice.
The blue histogram from the Market Simulation in the chart above represents the Customer WTP for a Major beer Product. For a $2.00 beer, Customers would pay an average of $3.00 with some willing to pay up to $4.50.
The red histogram represents the Customer WTP for a Microbrew. The range of values is much wider, with some Customers having a negative Willingness To Pay (you would have to pay these Customers money to drink the beer).
Most Customers would only be willing to pay an average of $2.00 for the more expensive $3.00 Microbrew. But a few Customers would be willing to pay up to $6.00. These are the Customers that sustain the niche market for the Microbrew industry.