Category: Marketing

ORC #1: New or Existing Customers

Marketers have fallen into what I call the “Forever / Repeated Acquisition Trap.” [FAT/RAT] Because of the constant pressure to show growth and the ease of spending money on the digital adtech platforms like Google and Meta, most marketers have chosen the path of least resistance. A lot of money is being spent on acquiring (and in many cases re-acquiring) customers. Every other business is doing it, so it just seems the right thing to do. And every acquired customer is being bought via an auction – with the click sold to the highest bidder. Little wonder that customer acquisition costs have spiralled out of control. Anecdotal evidence is that in the past few years, the CAGR for CAC is 40%. This means that costs are doubling every two years – with little sign of it slowing down. Google and Meta’s share of the aggregate spend has been coming down but newer platforms like Amazon, Tiktok, Snap, Microsoft and Apple are rising rapidly.

While there is some spending on digital branding, most of the focus is on acquisition of new customers. These aggregate into the shiny new numbers that marketers show to their bosses every week in review meetings. There is little or no discussion on existing customers – how did their spending grow, how many churned, how many more are likely to churn, and how close the spend is to the lifetime potential. This is because attracting new customers via new spending is easy, while working with existing customers is hard. And yet no business and brand can be built with a leaky bucket. This is the conundrum marketers face.

As I wrote earlier in Marketing: Disrupted and Simplified: “…Marketers made a big and costly mistake. Seduced by the ease of spending on Google and Facebook, and the excitement of continuously acquiring new customers, they missed deepening relationships with existing customers. It is not just marketers but even CEOs who are at fault. The question that gets asked is: how many new customers did we acquire? There is little discussion about retention and revenue expansion from existing customers. (This is true for B2C and B2B brands.) In a competitive market, the focus shifts to landgrab and leads to an arms race of spending investor money or retained profits to show perpetual growth of website traffic or app installs. The rise of ad revenues of Google and Facebook testify to the marketers’ folly. By not building deep relationships with existing customers and by bombarding them with irrelevant messages, marketers have trained their customers to ignore their communications, thus reducing the efficacy of the only method of bringing existing customers back to their website or app for transactions. Once customers start ignoring the messages, the marketer has little or no choice but to spend 10X more on re-acquiring that same customer via the tech giants. With everyone doing the same, the only winners are the attention sellers (Google and Facebook), who in turn create even more powerful data hoses by giving consumers even more free utilities. The irony is that as marketers did not pay attention to their customer needs, they are paying even more dearly to the attention intermediaries to reach their own ex-customers.”

Marketers need to shift focus and budgets to existing customers, using Earned Growth (revenue growth from existing customers and new revenue from referrals) as the metric. Ensuring existing customers come back for more and bring their friends is the only way to build a sustainable profitable business. This conundrum thus has an obvious solution: flip the funnel, with existing customers at the top and new acquisitions at the bottom (and these ideally coming via referrals rather than ad spending).

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Conundrum: To focus on continuous new customer acquisition or nurturing relationships with existing customers

Insight: Rising CAC is hurting profits; new acquisition is an unwinnable arms race

Solution: Shift to existing customers and Earned Growth as the North Star Metric

Conundrums

I started listing out the Marketing OR Conundrums (ORCs) and came up with this list:

1. New customers or existing customers
2. Adtech or martech
3. All customers or Best customers
4. Pull or push
5. Browse or search
6. Reacquire or reactivate
7. Same experience or differentiate
8. Broadcast or personalise
9. Engagement or conversion
10. Multi-channel or omnichannel
11.  SMS or email
12.  Short-term or long-term
13.  Growth at all costs or profits
14.  Best of breed or full stack
15.  Marketing cloud or modern stack
16.  Data or gut
17.  First-party or zero-party
18. Human or AI
19.  Build or buy
20. Inhouse or agency
21.  Marketplaces or direct
22. Incumbent or attacker

I put this list into ChatGPT and asked for more marketing conundrums. Here’s what I got:

1. Product-centric or customer-centric approach
2. Quantity or quality leads
3. Inbound or outbound marketing
4. Cost-per-acquisition (CPA) or return on investment (ROI)
5. Branding or direct response
6. Local or global marketing
7. Organic or paid growth
8. Compliance or innovation
9. Quality or quantity of content
10. Influencer or celebrity endorsements
11. A/B testing or multivariate testing
12. User-generated content or branded content
13. Guerrilla marketing or traditional advertising
14. Virtual or in-person events
15. Owned, earned or paid media
16. Targeting a specific demographic or targeting a broad audience
17. Product or service differentiation
18. Growth hacking or sustainable growth

I then asked ChatGPT for ten more to get the list to 50.

1. Short-form or long-form content
2. Automation or human touch
3. Mobile-first or desktop-first approach
4. Public relations or advertising
5. Guerrilla or ambush marketing
6. Content or context-based targeting
7. In-app or in-browser notifications
8. Product or market-centric approach
9. Brand or performance marketing
10. Virtual or augmented reality-based marketing

So, plenty of choices to be made. Let’s get started with some of the ones I consider as the most important ones.

Modern Marketing

There are two memorable quotes about marketing.

Peter Drucker: “Because the purpose of business is to create a customer, the business enterprise has two–and only two–basic functions: marketing and innovation. Marketing and innovation produce results; all the rest are costs. Marketing is the distinguishing, unique function of the business.”

Theodore Levitt: “The difference between marketing and selling is more than semantic. Selling focuses on the needs of the seller, marketing on the needs of the buyer. Selling is preoccupied with the seller’s need to convert his product into cash; marketing with the idea of satisfying the needs of the customer by means of the product and the cluster of things associated with creating, delivering, and finally consuming it.”

The soul of a business is marketing. Whether it is B2B or B2C, marketing is about conveying the value provided by the product, which in turn makes a customer decide that an exchange of money in return for the product is a transaction that’s worth doing. A marketer’s role thus becomes to create the conditions for such a trade.

Traditional consumer marketing was about creating the demand via media: memorable ads in print or TV, and then ensuring the distribution for fulfilment of demand. Modern marketing is much more complex with the rise of digital in the past 25 years. Every part of the value chain has changed: from the outreach (marketing via search engines, email, social media, and influencers) to the distribution (marketplaces, direct-to-consumer). Ratings and reviews also now play an important role in the purchase decision.

As Activate explains: “While traditional marketing’s focal point is the company’s product or service, modern marketing is more customer-oriented. Businesses that adopt a modern strategy always put their consumers’ satisfaction above all else, so they’re able to address their audience’s unique wants and needs. Since modern marketing uses a digital platform, it’s more customisable. It can also easily adapt to change, so it has no problem keeping up with the latest marketing trends. Plus, it can be accessed on any device, which means companies have a higher chance of reaching a global audience. All of these factors help build customer loyalty and trust, as well as worldwide brand recognition.”

GeeksforGeeks offers a good explainer on the difference between traditional and modern marketing:

The complexity of modern marketing also raises a lot of conundrums for marketers. Existing customers or new customers? All customers or Best customers? Reacquire or Reactivate? Broadcast or personalise?  SMS or Email? Browse or Search? Growth at all costs or profits? Best of breed or Full stack? Build or buy? Work with an incumbent or join an attacker?

When I asked ChatGPT to list a B2C/D2C marketer’s conundrums, this is what I got:

1. Balancing the need for short-term sales with the importance of building long-term brand loyalty
2. Deciding whether to invest in expensive, high-production-value advertising or to create more cost-effective content
3. Prioritizing product innovation vs focusing on customer service
4. Choosing between a wide range of products and services or focusing on a specific niche
5. Balancing the need to acquire new customers with the importance of retaining existing ones
6. Deciding whether to invest in data-driven, targeted marketing or relying on gut instincts
7. Deciding whether to create a strong social media presence or focus on email marketing
8. Balancing the need for a comprehensive marketing strategy with the need for quick-hit tactics
9. Deciding whether to prioritize search engine optimization (SEO) or paid search advertising
10. Balancing the need for customer acquisition with the need for customer retention and growth.

In this series, I will consider the modern marketer’s ORCs (OR conundrums). Of course, the preferred option is an AND but because of limits on budget, time and other resources, marketing – like life – is about choices and trade-offs. In many cases, the answer may be obvious, but the execution is not so.

Choices

For those who have read Tolkien’s “Lord of the Rings”, the word “orcs” is a familiar term. They were the bad-looking, goblin-like scary warriors of the dark forces. As Wikipedia puts it, “Orcs are a brutish, aggressive, ugly and malevolent race of monsters, contrasting with the benevolent Elves and serving an evil power.”

I chose the word ORC to mean “OR Conundrum” – more like an AND/OR conundrum. As a business leader, my day is about meetings and choices I have to constantly make. Some decisions are trivial, while others are critical; some are inconsequential and reversible, a few are not. In most cases, the leader does not have a map but a compass, and thus the series of paths taken (or not taken) determine the final destination. Success or failure is an outcome of the choices one makes.

Growth or profitability? Emerging markets or Developed markets? Organic growth or acquisitions? Short-term or long-term? Hunting or farming? Best Customers or Rest Customers? Depth or breadth? Do inhouse or outsource? Replace or retrain? Office or hybrid? Pipeline or PO (purchase orders)? MQLs or SQLs? Discount or full-price? Private or public? Centralise or delegate? Hire or reject? Product or service? Family or professional? Point solution or full-stack? Branding or performance? Stay or sell? PE or IPO? Work or family? And so they go on – the OR Conundrums. ORCs. They are the make or break of a business.

Jim Collins wrote about OR in his book, “Built to Last”: “Instead of being oppressed by the “Tyranny of the OR,” highly visionary companies liberate themselves with the “Genius of the AND”—the ability to embrace both extremes of a number of dimensions at the same time. Instead of choosing between A OR B, they figure out a way to have both A AND B.” He added:

We’re not talking about mere balance here. “Balance” implies going to the midpoint, fifty-fifty, half and half. A visionary company doesn’t seek balance between short-term and long-term, for example. It seeks to do very well in the short-term and very well in the long-term. A visionary company doesn’t simply balance between idealism and profitability; it seeks to be highly idealistic and highly profitable. A visionary company doesn’t simply balance between preserving a tightly held core ideology and stimulating vigorous change and movement; it does both to an extreme. In short, a highly visionary company doesn’t want to blend yin and yang into a gray, indistinguishable circle that is neither highly yin nor highly yang; it aims to be distinctly yin and distinctly yang—both at the same time, all the time.

Irrational? Perhaps. Rare? Yes. Difficult? Absolutely. But as F. Scott Fitzgerald pointed out, “The test of a first-rate intelligence is the ability to hold two opposed ideas in the mind at the same time, and still retain the ability to function.” This is exactly what the visionary companies are able to do.

Jim Collins calls upon leaders to reject the “Tyranny of the OR” and embrace the “Genius of the AND”. He writes: “Builders of greatness … embrace both extremes across a number of dimensions at the same time—purpose AND profit, continuity AND change, freedom AND responsibility, discipline AND creativity, humility AND will, empirical analysis AND decisive action, etc.”

As leaders, AND is the preferred choice – have the cake and eat it too. It can work for the big picture, but daily decisions are about trade-offs. And that is where the OR Conundrum needs to be faced and addressed – in business, with family, and with oneself.

In this series, I will focus on the ORCs faced by marketers.

MPM

As consumers own (“mine”) Mu for their attention, data and other brand-incentivised actions, prediction markets can be a good potential redemption (and additional earning) mechanism. It brings out the latent wisdom each of us have in our interpretation of future events. By removing the need for real money, MuCo’s Prediction Markets also eliminate the need for regulation. The use of crypto tokens ensures that MuCo cannot debase or devalue the tokens, and there are rules that govern it. Google helps us search the past, Twitter the present, and Mu-powered Prediction Markets (MPM) can help us with the future.

So, how would such markets work?

Mu mined through actions could provide an entry into the world of MPM. The working of MPM would be very much like prediction markets today which are fuelled by real money. People could launch their own public or private bets and contracts, and invite others to compete against them. A contract needs two parties. MPM would, in the initial days, work as a market maker to ensure liquidity. There should be clearly defined outcomes to determine payoffs. For example, India has many important state elections coming up in 2023. As of now, the reliance for directionality of outcomes is either on journalists who have very limited information or pollsters. MPM could offer a third window to the possible electoral outcomes.

Every individual (who so desires) could be given a score on a dashboard. This “social proof” could help identify the superforecasters among the participants who could then monetise their status – much like influencers do on social media.

This Muconomy would be good for brands and consumers. It would create greater demand for Mu which would benefit the brands who are the dispensers of Mu for attention and data. It would also create value for Mu among consumers who could then monetise the Mu on other platforms. It would thus create a Mu flywheel and get a circular earn-and-redeem economy going. As brands get more attention and hopefully transactions from their existing customers, their AdWaste would be reduced even as loyalty and profitability increase.

MPM could thus take our native instincts of betting and boasting on the future and give an outlet to them.

(I want to thank my colleague, Chirag Patnaik, for some of the ideas discussed above.)

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I want to end this series with a quote to think about from Vitalik Buterin: “The idea behind futarchy was originally proposed by economist Robin Hanson as a futuristic form of government, following the slogan: vote values, but bet beliefs. Under this system, individuals would vote not on whether or not to implement particular policies, but rather on a metric to determine how well their country (or charity or company) is doing, and then prediction markets would be used to pick the policies that best optimize the metric. Given a proposal to approve or reject, two prediction markets would be created each containing one asset, one market corresponding to acceptance of the measure and one to rejection. If the proposal is accepted, then all trades on the rejection market would be reverted, but on the acceptance market after some time everyone would be paid some amount per token based on the futarchy’s chosen success metric, and vice versa if the proposal is rejected. The market is allowed to run for some time, and then at the end the policy with the higher average token price is chosen.”

Play Mu-ney

People love to gamble. NYTimes had a story on the popularity of sports betting recently: “One in five Americans has bet on sports in the past year, according to research from the Pew Research Center. During the first half of 2022, Americans placed an average of nearly $8 billion a month in legal sports bets, compared with under $1 billion a month three years earlier, according to SportsHandle, a trade publication. Some analysts have predicted that figure could climb to $20 billion a month by 2026.” It is estimated that more than 140 million Indians use sports betting sites, with the number more than doubling during cricket tournaments like the Indian Premier League.  Sports betting provides instant gratification and outcomes are known in minutes or hours.

There is a vast world of events beyond sports – politics, economic, financial, weather, and more. While the stock markets offer many sophisticated instruments for traders to bet on global and company news, there are very limited options for betting or predicting future events – and then being able to boast “I told you so.” This is where prediction markets with ‘play money’ (as opposed to real money) can come in.

I have written extensively in the past about Atomic Rewards and Loyalty 2.0 with Mu as the token for attention and data and MuCo as the entity building the Muniverse. From “The MuCo Future”: “MuCo is thus running a 2-sided marketplace between brands and consumers. The challenge such marketplaces face is the ‘cold start’ problem – creating enough demand on both sides to get activity going. To overcome this problem, MuCo may need partners who can help accelerate the process of getting Mu in the hands of consumers. It will also need to create an attractive shop to get consumers to see the value and utility of Mu before brands start coming in with their own offerings. This is where MuCo will need initial capital to bootstrap itself.” From “Muniverse Monetisation”: “MuCo enables brands to build hotlines with existing customers in the upstream (attention and data) and downstream (network and voice) by offering a lubricant in the form of Atomic Rewards (Mu points and tokens), thus enabling exchanges which are not happening today. MuCo ends over-reliance by brands on new customers, and instead enables better and deeper relationships with the existing customers – whose acquisition costs have already been paid. MuCo gives marketers a lever in the form of micro-incentives to offer customers to influence their behaviour for non-monetary actions, just as traditional loyalty programs nudge repeat transactions. MuCo’s pitch to brands: pay customers, not Big Adtech.”

Mu is the incentive offered by brands to their customers for the upstream of a transaction (attention and data) and the downstream (ratings, reviews and referrals). For Mu to be attractive, it also needs multiple places where it can be used. In my essays, I have discussed four options: Gamelets, MuShop,  MuMarket, and MuExchange. Prediction Markets can be the fifth and perhaps most interesting option in the Muniverse.

Superforecasting

Dan Gardner and Philip E. Tetlock write in their book “Superforecasting: The Art and Science of Prediction”: “We are all forecasters. When we think about changing jobs, getting married, buying a home, making an investment, launching a product, or retiring, we decide based on how we expect the future will unfold. These expectations are forecasts. Often we do our own forecasting. But when big events happen—markets crash, wars loom, leaders tremble—we turn to the experts, those in the know…Forecasting is not a “you have it or you don’t” talent. It is a skill that can be cultivated.”

Stephen Desmyter writes in Forbes: “Superforecasting is an addictive thing: it requires deep thought, sending your brain from geopolitics to economics to sociology. It asks you to throw yourself into vastly different situations and to take on the role of states-person, Central Bank chairperson, military strategist. More than anything, though, I find as I leap from one forecast to the next, it requires a profound understanding of human behavior. And maybe this is where the real skill of the Superforecasters lies. Take away the superstructures of state and financial systems, take away received ideas about how people will vote or the likely direction of rates, and you have humans making decisions under pressure. That’s why, I think, the Superforecasters are so often able to get their predictions right. It’s fascinating to read through the comments that the forecasters make below their predictions and to see this same theme emerging again and again – they reduce situations down to their essentials, and then ask how they would behave if they were in the same position.”

ChatGPT in response to “How do prediction markets compare with superforecasters?”: “Superforecasters are individuals who are particularly skilled at making accurate predictions about future events. They tend to be more accurate than the average person, and may use a variety of techniques and strategies to improve their forecasting abilities. Prediction markets, on the other hand, are exchange-traded markets that allow people to buy and sell contracts representing the possible outcomes of future events. The prices of these contracts fluctuate based on supply and demand, and can be used to forecast the likelihood of future events. Both prediction markets and superforecasters can be useful for making predictions about future events, and may be used in different contexts to help organizations make better decisions. However, prediction markets rely on the collective wisdom of many people, while superforecasters are individual experts.”

From the Gartner-Tetlock book: “Teams of ordinary forecasters beat the wisdom of the crowd by about 10%. Prediction markets beat ordinary teams by about 20%. And superteams beat prediction markets by 15% to 30%… How did superteams do so well? By avoiding the extremes of groupthink and Internet flame wars. And by fostering minicultures that encouraged people to challenge each other respectfully, admit ignorance, and request help.”

Just as social media platforms have given rise to influencers, prediction markets could help identify the superforecasters. Money and regulation have limited the spread of prediction markets. How can Mu change the game and enable wider reach for prediction markets?

Working

I asked ChatGPT about how prediction markets work. The response:

1. A company creates a prediction market for a specific event, such as the outcome of an election or the success of a product launch.

2. The company issues contracts representing the possible outcomes of the event. For example, in an election prediction market, there might be contracts representing the victory of each candidate.

3. Participants in the market can buy and sell these contracts. The prices of the contracts fluctuate based on supply and demand, and can be interpreted as the market’s prediction of the probability of each outcome occurring.

4. As the event approaches, the prices of the contracts may change based on new information or changing market conditions.

5. After the event occurs, the contracts are settled based on the actual outcome. Those who hold contracts representing the winning outcome are paid the full value of the contract, while those holding contracts for the losing outcomes receive nothing.

From the introduction of the 2011 book “Prediction Markets: Fundamentals, Designs, and Applications” by Stefan Luckner, Jan Schröder, Christian Slamka, et al: “The basic idea of prediction markets is to trade contracts whose payoff depends on the outcome of uncertain future events. Although the final payoffs of the contracts are unknown during the trading period, rational traders should sell contracts if they consider them to be overvalued and buy contracts if they consider them to be undervalued. Until the outcome is finally known, the trading prices reflect the traders’ aggregated beliefs about the likelihood of the future events. In efficient markets, all the available information is reflected in the trading prices at any time.”

Here is a graphic from the book which explains the working:

There are different types of contracts in prediction markets as this table from a paper on “Prediction Markets for Economic Forecasting” by Erik Snowberg, Justin Wolfers and Eric Zitzewitz shows:

Cultivate Labs writes about the two primary mechanisms to enable trading:

Continuous Double Auction (CDA): A continuous double auction (often abbreviated as CDA) is a mechanism for matching buyers and sellers of a stock. In a CDA, the market maker keeps an order book that tracks bids and asks. If I come along and say that I’d like to buy a share stock A for $5, that is recorded in the order book as a bid for 1 share at $5. On the flip side, if you own a share of stock A and are willing to sell that share for $5, that is recorded as an ask. If the bid & ask for two traders match, like in our example (I want to buy stock A for $5, you want to sell it for $5), then the trade is executed. A continuous double auction is also used in traditional stock markets like the NYSE.

Automated Market Makers: One issue with using a continuous double auction in a prediction market is that liquidity can be a problem. Most prediction markets have far fewer participants than an exchange like the NYSE. If I make a bid for $5 and there is no one out there selling the same stock for $5, then I can’t make my trade. If there’s no one to take the other side of my trade, the market would be said to have low or poor liquidity. To alleviate this problem, platforms use what’s known as an automated market maker. In this setup, the platform acts as the “house,” taking the opposite side of all trades. Doing so ensures that participants are always able to make a trade, effectively creating or “making” the market.

The Logarithmic Market Scoring Rule (LMSR), proposed by Robin Hanson in 2002, has become the de facto market-maker mechanism for prediction markets. As the abstract puts it: “In practice, scoring rules elicit good probability estimates from individuals, while betting markets elicit good consensus estimates from groups. Market scoring rules combine these features, eliciting estimates from individuals or groups, with groups costing no more than individuals. Regarding a bet on one event given another event, only logarithmic versions preserve the probability of the given event. Logarithmic versions also preserve the conditional probabilities of other events, and so preserve conditional independence relations. Given logarithmic rules that elicit relative probabilities of base event pairs, it costs no more to elicit estimates on all combinations of these base events.”

Before we discuss Mu and Prediction Markets, we will take a detour to discuss Superforecasting.

Background – 3

Clay Graubard and Andrew Eaddy (Jun 2022): “Barring belief in preordination, we live in a probabilistic world. Forecasting is something we all do, whether or not we know it. It is a tool, it is a process, and critically, it is a skill to generate information. Thankfully, it is a skill we can improve and get good at. That, in essence, is the core takeaway from the research of Philip E. Tetlock, Barbara Mellers and many others. Their research has shown that if we quantify, record, update, score and practice, we can make accurate predictions on complex questions. We can see at least part way through the “fog of war.”… Prediction markets are not new. Individuals have made bets on the future outcome of events since ancient times. And the coined phrase “the wisdom of crowds” can be traced back to early 20th-century England. Prediction markets are marketplaces where participants trade on future outcomes about particular topics. Think of the stock or crypto markets, but dealing with events. Prediction markets are typically binary, offering two fungible assets for a given market (think “Yes” or “No”). These assets trade between 0% and 100% (think $0 to $1), with the current market price representing the crowd consensus. When a forecasted event occurs, traders who purchased shares of the correct outcome are paid $1 for each share that they owned. Similar to long-established public equities markets, the primary incentive for participants in prediction markets is profit, while the by-product of their forecasting activity is information…Prediction markets have the potential to transform today’s political process by involving people in the conversation and delivering more reliable information about the future.”

Alex Tabarrok (Sep 2022): “Political election markets have proven themselves to be a powerful tool for forecasting elections and are typically more accurate, timely and complete than alternative methods such as polls…Political election markets are also useful to hedgers, traders and other market participants to help them predict and incorporate information about risks into asset prices. Markets similar to political election markets have been used to predict other important events such as the prospects for war or scientific breakthroughs and have been adopted by firms to better estimate sales forecasts and other relevant events.”

Economist (Aug 2018): “Legal barriers have long hampered such attempts at crowdsourcing. In America many prediction markets are considered a form of illegal gambling, or akin to trading in commodities futures that requires a licence. Regulators have allowed such services to operate if they are structured as non-profit “research” initiatives and limit bet sizes and numbers of traders, as IEM and PredictIt do. But because of the legal risk, private investors are reluctant to finance prediction markets.”

Walter Frick (Dec 2022): “Prediction markets have been around in one form or another for decades and have already made inroads into journalism during elections. 2023 will be the year they become a source for other types of stories, simply because there’s now too much activity in the crowd forecasting world to ignore. For almost any question you can think of, there are online crowds making predictions. And if journalists do think of a question that isn’t yet being forecasted, there are platforms where they can pose it themselves…As the economists Justin Wolfers and Eric Zitzewitz explain, prediction markets work because they provide: “1) incentives to seek information; 2) incentives for truthful information revelation; and 3) an algorithm for aggregating diverse opinions…Prediction markets aren’t perfect. They’re only as good as the wisdom of their participants and the information those participants have access to. And, like any market, they can be vulnerable to manipulation without oversight.”

George Calhoun (Nov 2022): “It may be that guessing the weight of an ox, and forecasting the winner of a close political race, present different challenges. It is likely that “the wisdom of crowds” is not a universal principle of human decision-making. It is also likely that the error of group judgments increases with the value-at-risk and the volume of trading, contrary to what standard statistical reasoning would suggest. In fact, the failures of prediction markets in the recent [US mid-term] elections were so consistently wrong that they begin to resemble more traditional sentiment metrics, useful perhaps as contrarian indicators, but dangerous if taken at face value.”

Background – 2

Vitalik Buterin (Feb 2021): “Prediction markets are a subject that has interested me for many years. The idea of allowing anyone in the public to make bets about future events, and using the odds at which these bets are made as a credibly neutral source of predicted probabilities of these events, is a fascinating application of mechanism design.”

Liam Vaughan (May 2022): “The potential of prediction markets is well known to anyone who’s read James Surowiecki’s bestseller, “The Wisdom of Crowds.” Well-designed markets can help draw out knowledge contained within disparate groups, and research shows that when people have money on the line, they make better forecasts…Google, Microsoft Corp., and even the US Department of Defense have used prediction markets internally to guide decisions… The size of these markets had been limited because regulators worried that Wall Street-scale trading could create incentives for investors to meddle with reality.”

Wikipedia: “The ability of the prediction market to aggregate information and make accurate predictions is based on the efficient-market hypothesis, which states that asset prices are fully reflecting all available information. For instance, existing share prices always include all the relevant related information for the stock market to make accurate predictions. James Surowiecki raises three necessary conditions for collective wisdom: diversity of information, independence of decision, and decentralization of organization. In the case of predictive markets, each participant normally has diversified information from others and makes their decision independently. The market itself has a character of decentralization compared to expertise decisions. Because of these reasons, predictive market is generally a valuable source to capture collective wisdom and make accurate predictions. Prediction markets have an advantage over other forms of forecasts due to the following characteristics. Firstly, they can efficiently aggregate a plethora of information, beliefs, and data. Next, they obtain truthful and relevant information through financial and other forms of incentives. Prediction markets can incorporate new information quickly and are difficult to manipulate.”

Economist (Feb 2022): “The line between investing and gambling has always been thin. This is especially true for prediction markets, where punters bet on events ranging from the banal (“will average gas prices be higher this week than last week?”) to the light-hearted (“who will win best actress at the Oscars?”). Prediction markets have something of a cult following among finance types who rave about the value of putting a price on any event, anywhere in the world. Such prices capture insights into the likelihood of something happening by forcing betters to put money where their mouths are. But critics argue such markets will fail to grow beyond a niche group, reducing the value of their predictions in the process.”

John Holden (Dec 2022): “Prediction markets are based upon the Efficient Market Hypothesis, which in the prediction market context is the idea that the price represents the likelihood of an event taking place based on all the relevant information. In the context of a presidential election market, if a contract for Joe Biden to be president in 2024 were trading at $.52, then the market would be suggesting that there is a .52 probability that Biden would be elected. As the election passes, someone will go to $1.00, or at least $.99. Prediction markets are effectively a market of binary option contracts: either an event happens or it does not. The contracts settle on the date of an election (or another specified date for other events.) This is not dissimilar from other types of options contracts, which are purchased for a designated date in the future, reflecting a likelihood that a company’s share price or commodity will be at a certain level.”