jerryking + behavioral_change   8

He Grew Up on a Farm. Now, He Helps Protect Them.
Oct. 3, 2019 | The New York Times | By Norman Mayersohn.

Books: Warren Buffett biography, “Buffett: The Making of an American Capitalist,”

Few livelihoods offer as many paths to failure as agriculture. Throughout history, farmers have been at the mercy of nature — be it weather, pests or crop diseases — even as the survival of people and livestock depended on their success...... Thomas Njeru, is a co-founder and the chief financial officer of Pula, a four-year-old microinsurance firm that serves 1.7 million smallholder farms of 0.6 acres or less in 10 African countries and India. Microinsurance — think of it as an offshoot of the microloan programs that kick-start businesses in impoverished areas — provides protection for low-income individuals who do not have access to conventional coverage....Pula, based in Nairobi, Kenya, partners with government agencies and loan providers to cover the cost of the insurance, which is included in the price of seed and fertilizer; there is no direct charge to the farmer. Among the coverages Pula provides is weather index insurance to cover failures of seed germination, using satellite data to determine whether there has been sufficient rainfall. Longer-term coverage, called yield index insurance, compensates farmers with replacement supplies in the event of a poor harvest......People in Africa don't invest in agriculture because the chance of them losing their money due to the vagaries of the weather is huge.........Pula’s mission is to give farmers confidence by providing risk mitigation. Our solutions protect a farmer’s investment by pairing it with insurance. We build business cases to persuade Fortune 500 companies, seed and fertilizer suppliers, lending institutions, and governments in Africa, that embedded insurance will help deliver better results for both businesses and food security....The sad reality is that farmers are one drought or one disease outbreak away from sliding into absolute poverty......the penetration of agriculture insurance in Africa is less than 1 percent. The reason is that insurance companies’ business models are not set up to serve the unique needs of smallholder farmers......scaling Pula’s business model to the point that insured seed and fertilizer become ubiquitous in the market......The average annual insurance premium per farmer is about $3 to $5. This includes the cost of product development, pricing, underwriting, claim adjustment and, of course, the claim costs. We use artificial intelligence, mobile-based registration systems, remote sensing and automation tools...Agriculture insurance is a cemetery of pilots and trials..
Africa  agriculture  behavioral_change  books  Bottom_of_the_Pyramid  crop_insurance  farming  insurance  Kenya  low-income  microfinance  mobile_applications  poverty  precarious  Pula  seeds  smallholders  start_ups  risks  risk-mitigation  Warren_Buffett  weather 
11 days ago by jerryking
Honesty That Benefits All
November 11, 2013 | NYT | By DOUG STEINER.

Headlines highlight the bad deeds of players in financial markets: insider trading scandals, traders colluding on interest rate manipulation, executives backdate options, etc....One tool of tackling problematic behavior is to rely on behavioral economics (i.e. traditional economics' assumption — that everyone acts rationally when making decisions — is wrong).

Behavioral economists combine the social psychology of human interactions with the thought processes involved in making economic decisions. They predict and explain how people use faulty logic in building a framework for making decisions. Then they figure out how to make people behave properly by inserting new triggers for better behavior..... people can justify lying if it’s “just a little bit.”(e.g. customers underreporting annual miles driven when filling out their car insurance audit forms, or their income when filling out tax returns). ...adding "morality reminders" (e.g. asking customers to sign forms attesting to the accuracy of their reports at the top of a page, instead of the bottom)....can change behavior, ... minor, even imperceptible changes to workflow can significantly affect honesty....human decisions can be influenced with small suggestions — say, a reminder that “over 99 percent of people truthfully answer these questions.” Or a group might be reminded of a collective cause-and-effect. (“You and your colleagues will not be eligible for bonuses if any of you engage in illegal behavior.”)

Employing similar behavioral psychology in financial transactions can discourage bad actions. Some examples:

■ Getting legal advice: .... Showing lawyers the profound influence they have on trading action might dissuade them from endorsing or seeming to endorse questionable decisions.
■ Making the costs clear to clients: Modern technology allows firms to automatically trade against clients who are unaware of the practice or oblivious to it. Clients generally lose money on these trades. Such actions are legal, even if they’re unseemly. This type of behavior has to be defined as immoral within the industry, or it won’t be long before it is made illegal
■ Setting the right tone:

...the financial crisis of 2008 showed that risk perception and reality differed widely. Efforts to use social psychology to change behavior are resulting in two changes at the same time.

The first is a change in the general perception of business risk, and how much risk a firm should assume to make returns to shareholders. The second is more important and more controllable. It involves personal perceptions of how much risk they should take when, say, trading securities, to impress their bosses and presumably get a larger bonus.
Doug_Steiner  behavioural_economics  honesty  financial_markets  financial_services  behavioral_change  risk-assessment  risk-perception  personal_risk  psychology 
january 2014 by jerryking
What Data Can’t Do - NYTimes.com
By DAVID BROOKS
Published: February 18, 2013

there are many things big data does poorly. Let’s note a few in rapid-fire fashion:

* Data struggles with the social. Your brain is pretty bad at math (quick, what’s the square root of 437), but it’s excellent at social cognition. People are really good at mirroring each other’s emotional states, at detecting uncooperative behavior and at assigning value to things through emotion.
* Data struggles with context. Human decisions are embedded in contexts. The human brain has evolved to account for this reality...Data analysis is pretty bad at narrative and emergent thinking.
* Data creates bigger haystacks. This is a point Nassim Taleb, the author of “Antifragile,” has made. As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation.
* Big data has trouble with big (e.g. societal) problems.
* Data favors memes over masterpieces. Data analysis can detect when large numbers of people take an instant liking to some cultural product. But many important (and profitable) products are hated initially because they are unfamiliar. [The unfamiliar has to accomplish behavioural change / bridge cultural divides]
* Data obscures hidden/implicit value judgements. I recently saw an academic book with the excellent title, “ ‘Raw Data’ Is an Oxymoron.” One of the points was that data is never raw; it’s always structured according to somebody’s predispositions and values. The end result looks disinterested, but, in reality, there are value choices all the way through, from construction to interpretation.

This is not to argue that big data isn’t a great tool. It’s just that, like any tool, it’s good at some things and not at others. As the Yale professor Edward Tufte has said, “The world is much more interesting than any one discipline.”
massive_data_sets  David_Brooks  data_driven  decision_making  data  Nassim_Taleb  contrarians  skepticism  new_graduates  contextual  risks  social_cognition  self-deception  correlations  value_judgements  haystacks  narratives  memes  unfamiliarity  naivete  hidden  Edward_Tufte  emotions  antifragility  behavioral_change  new_products  cultural_products  masterpieces  EQ  emotional_intelligence 
february 2013 by jerryking
College come-ons
March 1998 | American Demographics | by Tibbett L. Speer

Transition periods are key times to get consumers to change previous behaviors. In college, there are several - dorm to apartment, apartment to apartment, apartment to first job. The college market is a good-sized target, and it is growing. More than 14 million students are projected to enroll in US higher-education institutions in 1998, according to the National Center for Education Statistics. Best time to get students' attention is during spring break. Goofy games and product give-aways still prevail during break debaucheries. But companies have evolved beyond the beach, tracking students back to campus and even to their parents' homes. For example, Ford Motor Co. solicits recent graduates and soon-to-be graduates with direct mailings sent to their permanent address. The company generates collegiate sales by offering students a $400 cash incentive to purchase a car, or $650 to lease one. Other ways to entice students are discussed, as are the places to do so.
Colleges_&_Universities  students  marketing  transitions  new_graduates  behavioral_change 
july 2012 by jerryking
New Rules for Bringing Innovations to Market
March 2004 | HBR | Bhaskar Chakravorti.

The more networked a market is, the harder it is for an innovation to take hold, writes Bhaskar Chakravorti, who leads Monitor Group's practice on strategies for growth and managing uncertainty through the application of game theory. Chakravorti argues that executives need to rethink the way they bring innovations to market, specifically by orchestrating behavior change across the market, so that a large number of players adopt their offerings and believe they are better off for having done so. He outlines a four-part framework for doing just that: The innovator must reason back from a target endgame, implementing only those strategies that maximize its chances of getting to its goal. It must complement power players, positioning its innovation as an enhancement to their products or services. The innovator must offer coordinated switching incentives to three core groups: the players that add to the innovation's benefits, the players that act as channels to adopters and the adopters themselves. And it must preserve flexibility in case its initial strategy fails.

Chakravorti uses Adobe's introduction of its Acrobat software as an example of an innovator that took into account other players in the network--and succeeded because of it. As more content became available in Acrobat format, more readers were motivated to download the program," he observes. "The flexibility in Acrobat's product structure and the segmentation in the market allowed the pricing elasticity that resulted in the software's widespread adoption."
HBR  innovation  networks  network_effects  rules_of_the_game  commercialization  monetization  product_launches  howto  growth  managing_uncertainty  cloud_computing  endgame  Adobe  uncertainty  switching_costs  jump-start  platforms  orchestration  ecosystems  big_bang  behaviours  behavioral_change  frameworks  sharing_economy  customer_adoption  thinking_backwards  new_categories  early_adopters  distribution_channels  work-back_schedules 
july 2012 by jerryking
Why a Product’s Job Matters
April 18, 2007 | - The Informed Reader - WSJ | by Robin
Moroney. A basic principle of business–knowing what consumers want from
a particular product–is often ignored by corporations. Many businesses
focus on qualities that are largely irrelevant to the consumers’ buying
decisions, such as product prices, or data on customer age, gender and
marital status. Some business-to-business companies slice their markets
by industry; others by size of business. The problem with such
segmentation schemes is that they are static. Customers’ buying
behaviors change far more often than their demographics, psychographics
or attitudes. This leads to situations in which, in the words of the
late business guru Peter Drucker, “the customer rarely buys what the
business thinks it sells him.”
Peter_Drucker  Clayton_Christensen  Scott_Anthony  segmentation  marketing  market_segmentation  static  dynamic  purchase_decisions  hiring-a-product-to-do-a-specific-job  B2B  demographics  psychographics  attitudes  demographic_information  relevance  consumer_behavior  behavioral_change  irrelevance 
january 2010 by jerryking
Google's Banker
May 3, 2004 | Fortune | By Adam Lashinsky.... Valentine also
took a different approach on making investments: He bet on the
racetrack, not the jockey. "... you build great companies by finding
monster markets that are in transition, and you find the people later,"
says Valentine...."But in Moritz, Valentine saw a resemblance to another
precocious go-getter he had observed at close range: Steve Jobs.
"They're both incredibly aggressive questioners," says Valentine. "And
our business is all about figuring out which questions are relevant in
making a decision, because the people who are starting a company (i.e. the founders) don't
have a clue what the answers are."... Valentine's principles: only
targeting businesses with fat margins; avoid capital-intensive
businesses; take measured steps; never underestimate the difficulty of
changing consumer behavior; don't begin a rollout until you're sure the
recipe is working; avoid any business Wall Street is prepared to throw
hundreds of millions of dollars at.
behavioral_change  capital-intensity  consumer_behavior  disequilibriums  Don_Valentine  founders  large_markets  margins  Michael_Moritz  precociousness  questions  rollouts  rules_of_the_game  Sequoia  Steve_Jobs  vc  venture_capital  Wall_Street 
october 2009 by jerryking
Those were the days;
06-25-2004 G & M RoB Magazine article by Doug Steiner on
the behaviour changes occurring in Bay Street among the brokerages.

First Marathon--led by Lawrence Bloomberg--and Gordon Capital, Connacher's secretive institutional boutique, were the Street's two toughest and savviest firms. First Marathon helped pioneer the discount brokerage concept in the early 1980s with Marathon Brown (which TD Bank bought in 1993). Bloomberg also perfected the "eat what you kill" compensation plan of fat bonuses for partners and employees who put together lucrative deals. It changed the payouts of almost every trader and investment banker on Bay Street, Howe Street and Ren Lvesque Boulevard....By 1995, the internet was changing trading forever. Disnat, E*TRADE Canada and other on-line dealers pushed the banks into flat-fee trading. Within three years, commissions for small trades tumbled 70%.

Yet Canada still had five stock exchanges: Vancouver, Alberta, Winnipeg, Toronto and Montreal. TSE president Rowland Fleming urged the exchanges to modernize, and the TSE closed its trading floor in 1997. His pugnacious leadership style helped persuade the dealers to remove both him and their own duplication of costs by consolidating the exchanges.

The culture was changing as well. Watering holes in Toronto, Montreal and Vancouver lost customers. Alcohol was no longer greasing the wheels of fortune. It was being replaced by MBAs, CFAs and hard work.
'80s  Bay_Street  behavioral_change  bourses  brokerage_houses  cultural_change  culture  Doug_Steiner  eat_what_you_kill  Gordon_Capital  hard_work  reminiscing  stockmarkets 
january 2009 by jerryking

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