by By David Zetland (originally published at One handed economist) Big data helps monopolies, not you Economists say competition in markets rages from “perfect” (no company can charge a price over cost without losing 100% of its customers to another company) to “monopoly” (one company sets prices to maximize profits). Two caveats are important. First, the monopolist doesn’t charge as much as possible but whatever maximizes profits. There might be a lot of trading going on, but also a lot of missing trades. Second, businesses seek monopoly power in different ways, from having a unique product with zero substitutes (pretty rare) to being open for business at a certain time and place (pretty common). Businesses often try to create monopoly power by making it
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by David Zetland (originally published at One handed economist)
Big data helps monopolies, not you
Economists say competition in markets rages from “perfect” (no company can charge a price over cost without losing 100% of its customers to another company) to “monopoly” (one company sets prices to maximize profits).
Two caveats are important. First, the monopolist doesn’t charge as much as possible but whatever maximizes profits. There might be a lot of trading going on, but also a lot of missing trades. Second, businesses seek monopoly power in different ways, from having a unique product with zero substitutes (pretty rare) to being open for business at a certain time and place (pretty common). Businesses often try to create monopoly power by making it hard to compare products with competitors or across boundaries. That’s why they sell the same razor in pink for women and blue for men, change model numbers for the same product in different markets, change package sizes, and so on.
Thus, businesses make it harder to see similarities and differences because confusion for you means larger profits for them.
Flipping this idea over, businesses want to identify similarities and differences among customers to make it easier to charge different prices to different customers. Their goal is not to charge as much as the market will bear but as much as you will bear.
Thus, businesses “price discriminate” (PD) in a quest to get $4 from you and $6 from me for the same product. There are three types of PD, arranged from easiest to hardest to implement:
- “Third degree PD means charging a different price to different consumer groups,” e.g., young vs old or lunch vs dinner.
- “Second-degree PD means pricing according to quantity demanded, e.g., larger quantities are available at a lower unit price.”
- “First degree PD means (FDPD) charging the maximum price each consumer is willing to pay.”
There are many examples of second and third degree PD, but FDPD is harder to pursue. In the past, we got close to FDPD with auctions in which the highest bidder won the good, but auctions take time and still leave money on the table (the winner only needs to outbid the second place bidder).
Now the technology exists to allow routine and widespread FDPD. That technology has arrived with “big data,” and it’s not your friend.
Why?
- Our social media habits reveal our likes, choices, and friends
- Our social graph links us to friends and relatives, allowing data to be cross-checked and refined with weak or strong links to the people around us.
- Our loyalty cards, credit cards, and credit scores can be used to understand our ability- and willingness-to-pay
- Our phones track everywhere we go.
- Personal fitness devices record our heart-rates, stepping speed, etc.
- Data brokers can cheaply buy and combine many datasets and use machine learning (AIs) to create our “digital twins.” Twins may not be too accurate when they are born (here’s one effort), but your actions are constantly being compared to your twin’s predicted action. With time, your twin will will know you better than you do.
Taken together, Big Data means that you will be paying more and getting less for many goods and services. At its most-dystopian extreme, Big Data will direct you to friends, work and romance based on business profit-maximization instead of your own ideals of happiness.
My one-handed conclusion is that big data is more of a curse than a blessing for the average human.
NB: I’ve blogged for years on the weaknesses and threats of social media, but this post also draws on my 25 years of experience in working with data and the many ways we abuse data.