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Economics, Monetization and the “New Order” Automobile Industry

Premise: How will the traditional car industry create and extract customer value in the future when the source of that value is no longer the vehicle itself?

An increasingly digital economy is overwhelming every industry, and no business, from the largest legacy institutions to plucky start-ups, is safe. To survive, businesses must embrace digital transformation. For those who haven’t, we have already seen what happens to them. Once long-standing giants in advertising, marketing, commerce, entertainment, transportation and hospitality industries have either shuttered their doors for good, or are on the fringes of the industries they once dominated (see Figure 1).Economics, Monetization and the “New Order” Automobile Industry

Figure 1: Digital Transformation Causing Industry Disruption and Disintermediation

These industries all have something in common.They have seen both new and established competitors leverage digital technologies to drive superior customer, product, service and operational insights that have disrupted business models and disintermediated customer relationships. For those businesses who feel they have not yet been impacted, it’s only a matter of time before digital transformation hits you (see Figure 2).

Economics, Monetization and the “New Order” Automobile Industry

Figure 2: More Industries Under Attack

Which is probably why the former vice chairman of GM, Bob Lutz, stated “the automobile industry has no future.”

To quote Mr. Lutz:

“Our daily travel will migrate to standardized passenger modules as the demolition of the traditional auto industry accelerates. Within five years, people will start selling their cars for scrap or trade them in for autonomous passenger modules as self-driving cars take over transportation. Within 20 years, human-driven vehicles will be legislated off highways. Companies like Lyft, Uber, Google, and other technology companies will take charge of an industry now centered in Detroit, Germany, and Japan.”

And market projections support Mr. Lutz’s position (see Figure 3).

Economics, Monetization and the “New Order” Automobile Industry

Figure 3: Automobile Industry Transformation from “Cars as Product” to “Cars as Service”

However, I’m not quite as negative as Lutz. The automobile industry can maintain its relevance by embracing digital transformation to find new sources of customer, product, service and operational value. And the economics of these “superior customer, product service and operational insights” will be the key to creating new monetization opportunities.

The New #Economics of #Transportation

As usual, economics will ultimately be the basis for digital transformation and uncovering new monetization opportunities. This affords us the opportunity to revisit an important concept from our college economics classes: Economic Utility

“Utility” refers to the total satisfaction a customer receives from consuming a good or service. The economic utility of a good or service will directly influence the demand, and therefore price, of that good or service. The standard unit of measurement that microeconomics uses to measure economic utility is called the “util.”

For example, I may go to the supermarket with $100 to spend, along with a phantom 100 utils representing 100% of the value I expect to receive from my purchases. Let’s say that $67 of my $100 is spent on necessities (meat, bread, milk, produce). However, although 67% of the $100 is spent on necessities, the number of utils assigned to those purchases may only be 40 for me. The remaining 33% of the money is spent on Snicker bars, Halo Tops, Cap’n Crunch, and other unnecessary but delightful goodies. But the utils or value that I receive from these purchases totals 60.

Utils provide a rough numerical measure of consumer value for a product or service.  However, there are problems with the utility concept in execution:

  • Utils are hard to measure; that is, they must be measured or quantified in an indirect manner
  • The value of utils are different for every individual consumer

But, that’s where Big Data and Data Science jump in!

Using Big Data and Data Science to Determine Consumer Utils

So how can industry leaders use the economic utility concept and utils to guide their industry’s digital transformation? It starts by understanding and quantifying each individual consumer’s utils or value associated with travel.

Step 1:  Understanding the Individual – the Power of One

What we learned from the economic utility concept is that utils differ by individual consumer; that each individual consumer has different preferences, propensities, tendencies, inclinations, biases, interests, passions, associations, and affiliations (see Figure 4).

Economics, Monetization and the “New Order” Automobile Industry

Figure 4: Big Data Is about Monetizing the Power of One

Understanding and quantifying these individual behaviors and tendencies is critical to driving the organization’s monetization efforts. From the blog “Becoming Netflix Intelligent: Something Every Company Can Do!” we get this guidance about how a leading analytics company like Netflix leverages individual behaviors and tendencies to guide their monetization efforts:

The secret to Netflix’s success comes from the power of combining detailed viewer behavioral data and detailed show/program characteristics data with machine learning to make predictions about what shows what viewers might want to watch. This is a recipe that every company can and should follow!

Applying the “Netflix Analytics Recipe” to the automobile industry, we could leverage each individual consumer’s behavioral insights, tendencies, and preferences to place value or utils the following types of questions:

  • What in-car services are most valued by what customers based upon their usage patterns?
  • How do I get more customers to try new services based upon their individual propensities?
  • Which vehicle types (e.g., sedan, SUV, station wagon) are preferred in what situations?
  • When is transportation demand the highest and what are the likely destinations based upon day of the week and holidays?
  • How much of a premium is a customer likely to pay to get to their destination faster, and in what situations?
  • How much of a premium is a customer likely to pay for a more luxurious ride, and in what situations?
  • How long is a customer willing to wait for their transportation given their situation?

Being able to assign economic utility or value to each consumer in each of the above situations (use cases) is key to creating new monetization opportunities. However, answering these questions requires organizations to start capturing data AND building out analytic insights at the level of the individual consumer.

But, how can you hope to monetize each individual’s economic utility if you cannot capture the data and create the analytic insights at the level of the individual consumer?

Step 2:  Understanding (Envisioning) the Role of Data Science (AI | ML | DL)

Data science is the organization’s data monetization engine. It is through data science that we will derive the economic utility or value for each individual consumer within each unique transportation scenario, or use case. It is also through data science that we will drive new sources of customer, product, service, and operational value.

Artificial Intelligence, Machine Learning and Deep Learning (AI | ML | DL) is critical to helping organizations to predict outcomes and prescribe actions for each individual consumer in each of the key monetization scenarios (use case). Business executives need to invest the time to understand and envision what AI | ML | DL can do in each of the different consumer scenarios (use cases) which includes:

  • AI (Artificial Intelligence) is the theory and development of computer systems able to perform tasks normally requiring human intelligence (e.g. visual perception, speech recognition, translation between languages, etc.).
  • ML (Machine Learning) is a sub-field of AI that provides systems the ability to learn and improve by itself from experience without being explicitly programmed.
  • DL (Deep Learning) is a type of ML built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network.” A DL framework is SW that accelerates the development and deployment of these models.

Understanding how AI | ML | DL help organizations envision how to leverage their data is critical to deriving and driving new sources of customer, product, service, and operational value.

Economics Driving Business Disruption and Customer Disintermediation

When looking at the big picture, let’s be sure not to overcomplicate the data monetization and digital transformation processes. The real digital transformation process starts by identifying, validating, and prioritizing the consumer’s key business and operational scenarios.

Once the data has been captured, the incumbent automobile manufacturers must combine their discoveries with their foundation of institutional knowledge. Then starts the creative work. Companies must think “outside the vehicle box” to combine that institutional knowledge with new sources of consumer behavioral and engagement data to create a more holistic view and understanding of each individual consumer.

With that holistic view captured, the organization can then apply modern data science concepts with AI | ML | DL to derive and drive new customer, product, service and operational monetization opportunities. If they don’t, a competitor may perfect the analytics necessary to extract more economic utility out of the market and seize market share.

The automobile industry should not be intimidated by digital transformation. The recipe is there. It just requires a new mindset and approach for understanding and quantifying the economic utility (value) for each individual consumer and key transportation scenario of use case.

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