This detailed Case Study and Market Simulation workflow follows the overview called: CS-101 Rise of the Microbrew.
Part 05 of the Market Simulation models the USA Beer Industry from the early 1950’s to the 1980’s when mass marketing was used to develop and shape the brands of the major beers.
As breweries created increasingly generic beers, advertising shifted away from informing consumers about the product – whether the beer was sweet, bitter, or malty. Instead, beer brands were linked to such things as popular sports and beautiful women.
In the early 1950s, Anheuser-Busch spent $0.51 cents per barrel on media adertising, but by the late 1980s, it was spending nearly 10 times this level. Major breweries now spend around 20% of their revenues on marketing.
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 Mass Marketing of beer and the creation of Brands as a form of Horizontal Differentiation.
The video contains a brief presentation covering the 4th economic phase of the beer industry along with an explanation of the KNIME workflow. More detail can be found in the explanation below.
#1 Iteration Counter
By the 1950’s, huge investments in Pasteurization and Refrigeration had wiped out all but the largest breweries (see Rise of the Microbrew Part 04). But while all Beer was now readily available and of exceptionally high quality, the industry was back to the problem of having little to differentiate each Product.
Mass Marketing changes that by introducing “Horizontal” Differentiation through the development of Brands.
The first branch of the Market Simulation workflow that models this phenomenon creates a loop iteration counter. This iterates through 20 iterations approximating 20 years of marketing investment. At each iteration, the value of each beer brand increases. Each brand also becomes more unique over time with decreasing conformity to the other beer brands.
Click on an icon to see and scroll through the enlarged version of the images.
#2 Brand Value
Over the 20 iterations (20 years) the value of each Brand steadily increases from $0.00 to a Mean value of $1.00 per serving. The Standard Deviation (SD) of Brand Value follows this trend by being fixed to 20% of the Mean. After 20 iterations, the SD increases from $0.00 to $0.20.
Developing a Brand is not cheap, and the Cost of marketing the Brand is assumed to be equal to the Mean value of the Brand. That is, the marketing Cost also increases from $0.00 to $1.00 per serving over the 20 iterations.
#3 Brand Correlation
While the Brands are becoming more valuable, they are also becoming less similar. In a Market Simulation, the “Differentiation Horizontal” node defines similarity as the degree of “Conformity” a Feature Variation has to the norm.
The Brand Correlation branch of the workflow drops the Conformity between Brands from 1.0 (perfect correlation) to 0.0 (uncorrelated).
Finally, the “Matrix Distribution” node combines the Brand Value (Mean + Standard Deviation) with the Correlation between Brands to create two part-worth Customer Distributions. These two Distributions define the Willingness To Pay (WTP) that Customers have for each Brand.
#4 Product Generator
The two beer Products in the Market are made up of Features:
- Product 01 = Beer + Pasteurization + Product_01.Brand
- Product 02 = Beer + Pasteurization + Product_02.Brand
The “Beer” Feature is identical for both Products as USA lagers are virtually indistinguishable. This workflow branch creates a single Customer Distribution containing the part-worth value of “Beer”.
The “Pasteurization” Feature is also identical for both Products as all unpasteurized beer has been pushed out of the Market (see Rise of the Microbrew Part 04). This workflow branch creates another single Customer Distribution containing the part-worth value of “Pasteurization”.
The only differentiating Feature for beer is the Brand of each, which has been growing more valuable, more unique, and more costly over the past 20 iterations. The two part-worth Customer Distributions for Brand were created by the upstream branches of the workflow.
The “Product Generator” node combines all of these Features to define the overall Willingness To Pay (WTP) that Customers have for each beer Product.
#5 Profit Maximization
The vendors of both beer Products now engage in a competitive battle to generate as much Profit as possible. Each vendor sets a Profit Maximizing Price that is responsive to both the Willingness To Pay (WTP) value of their Product, as well as the Price of their Competitor.
The “Price War” node is used to simulate this competitive battle.
The Price War node runs multiple internal Price experiments on behalf of each Competitor. The Price of the first Product is adjusted until a Profit Maximization point is reached. Then the Price of the second Product reacts until it finds its own Profit Maximization point. This back-and-forth battle continues until an equilibrium point for the overall Market is found.
If “Value Creation” is defined as the extra value a Customer would pay for a Product minus the Cost of providing that value, then Branding often provides no additional value.
In this Market Simulation, for example, the Brands provided and additional average value to Customers of $1.00 per serving. However, the Cost of providing that value was also $1.00. If Value Creation = Value – Cost, then $0.00 value was created.
And yet, the investment into beer Brands made an enormous difference in the Revenue and Profitability generated by each brewer!
Because Branding had the effect of re-ordering the preferences that Customers had for each Product. In 1950 a Customer was indifferent towards both Miller and Budweiser. But by 1980 the Customer perceived the two Products as being vastly different.
This Market Simulation illustrated that, while marketing and branding Costs were rising rapidly from 1950 to 1980, the Profit Maximizing Prices of the Products were rising faster. This was not due to any sort of collusion or abuse of market power, but was true even if all Competitors were engaged in a constant Price War to find their Profit Maximizing Prices.
And although the size of the Market was relatively stagnant, with no new Customers entering the Market, each brewer found that Branding helped them collect more money from each existing Customer.
Market Simulation can explain how, in spite of the generic lager Products and the “Price War” level of competition, the Revenue and Profitability of all beer rose rapidly during this time.