Winning the Hardware Software Game Winning the Hardware-Software Game - 2nd Edition

Using Game Theory to Optimize the Pace of New Technology Adoption
  • How do you encourage speedier adoption of your product or service?
  • How do you increase the value your product or service creates for your customers?
  • How do you extract more of the value created by your product or service for yourself?

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algorithm

  • Two Potential Market Outcomes

    Complementary Infrastructure Requirement

    Benefits of Self-Driving Cars

    Costs of Self-Driving Cars

    Winners

    Losers

    System Evolution

     

    Driverless (autonomous) vehicles is one of the hottest topics being discussed in the news lately. Some writers have been touting the enormous benefits adoption of driverless cars will bring, emphasizing the utopian scenario associated with the new technology. Others have noted the large industries dislocations their adoption will create, emphasizing the dystopian scenario. This analysis is my attempt to better understand what the market for driverless cars will entail.

     

    Two Potential Market Outcomes

    There have been two general market scenarios bandied about in discussions of autonomous vehicles:

    • Personal Self-Driving Cars (PSDC): In this scenario people generally own their own vehicles, but instead of people doing the driving, the vehicles drive themselves. This market outcome would yield a vehicle environment that looks relatively similar to the one that exists today, except that cars would have no drivers.
    • Shared Self-Driving Cars (SSDC): In this scenario people don’t own their own vehicles. Instead, third-party providers of transportation services own fleets of driverless vehicles, which people hail when they need to go somewhere. In other words, the SSDC scenario conflates autonomous vehicle with peer-to-peer (or sharing) technologies. This market outcome would yield a vehicle environment that is radically different from the one that exists today.
  • Are you getting as much value from your Big Data or IoT analyses as you can? There’s a very good chance you’re not. And it might not be for lack of trying. There are three, big contributors that are likely to be preventing you from being able to extract as much value as you could from your data:

    1. You dive right into the data without first creating a roadmap;
    2. You don’t understand the context and limitations of your data; and/or
    3. Your analyses are too complex.
  • Price discrimination may be defined as selling the same thing to different people for different prices. Price discrimination can take many forms, such as volume discounts, price premiums, or market segmentation. Suppliers regularly use many different forms of price discrimination, which people generally don’t object to.

    Some suppliers use dynamic pricing, a sub-category of price discrimination, in which prices change over time with market conditions. Consumers have been used to the fact that prices for airline tickets and hotel rooms change constantly, and that different people end up paying different prices for a seat on the plane. While uncomfortable with the practice, consumers have generally come to accept this type of dynamic pricing (what choice do they have?).

    Over the past few years, dynamic pricing has become more widely used by sellers as a means of supplementing shrinking margins in an increasingly competitive world. As more information becomes easily available in digital form, pricing algorithms used to support dynamic pricing systems have been able to draw upon more and more information to hone prices and increase profits.

    A more controversial sub-category of dynamic pricing is personalized pricing, which uses personal information on each customer to tailor prices specifically to that customer.

    This analysis examines the different types of price discrimination, how they increase profits, why they are becoming increasingly prevalent, and some emergent issues surrounding their use.

  • The Yelp Controversy

    Yelp is a social media site that hosts information about local businesses and lets customers post ratings and reviews about their experiences with those businesses. Yelp uses a proprietary algorithm to sort through posted reviews and filter out those that might be fake or inauthentic. Reviews that do not pass muster are relegated to a separate folder and not used to calculate businesses’ average ratings scores, which are prominently displayed to users.

    Yelp generates revenues to support its business primarily through sales of ads to local businesses that appear on Yelp’s website.

    Business owners have alleged that in an attempt to coerce (extort) businesses into advertising on Yelp’s site, or in retaliation for ceasing to advertise on its site, Yelp manipulates those businesses’ reviews by either

      i.  Filtering out good reviews and relegating them to the “reviews not recommended” folder, thus hiding them and lowering businesses’ average star ratings,

     ii.  Highlighting bad reviews of the businesses, and/or

    iii.  Creating fake bad reviews.

    Business owners allege that Yelp’s manipulations of the businesses’ reviews have resulted in lost sales for their businesses.

    Yelp vehemently denies accusations that it extorts business owners or manipulates the filtering of reviews.

    This analysis examines the Yelp Ratings Game. First, I describe the shift in customer relations that the advent of social media has engendered. Next, I describe how the Yelp site works. Then I discuss possible reactions by business owners to having ratings and reviews by users about their businesses posted online and perhaps manipulated to the business owners’ detriment. Finally, I discuss some other interesting issues related to the Game.