Supported by Greenhaven Road Capital, finding value off the beaten path.
Each year we do a brief recap, review, and emphasis on the Nudgestock conference hosted by Rory Sutherland and Ogilvy Change. Here are the notes from, 2018. Here is the post from 2019’s podcast recap. If someone wants more here’s the page of playlists.
Rory Sutherland‘s book Alchemy is great but he’s better in person (virtually) than on the page (physically or digitally).
Sutherland wants people to think in novel ways. It’s not so much cold-hard-logic that rules our lives but warm-fuzzy-feelings. Blocking this is the desire to appear logical in our thinking. However, said Sutherland, “There’s a lateral solution to everything.”
Stories are a great lateral solution. When Rory was on a flight and got a bus instead of an airbridge he was disappointed. Until the pilot told a good story. “Suddenly I reframed the bus from being an inconvenience to being a conveyance. Suddenly I didn’t have to walk past twenty Toblerone stands in order to get out of the airport. Tell a good story and the meaning changes.”
This tool has a name: benign bullshit. It doesn’t do any harm to tell the story of the bus instead of an airbridge and it can have great effects. It’s an asymmetric bet, just like watching any one of Rory’s many talks.
Tricia Wang on “how marketing mistook clicks for customers.”. Wang is the propagator of the term “thick data” a form of naming. She made up this name because she needed something people could understand in meetings. If clicks form big data then talking to customers is thick data.
Thick data and big data are the peanut butter and the jelly to understanding customers. (company culture is the bread). Wang said, “Thick data allows you to see the world with alien eyes, to ask questions and unpack assumptions that might lead you to make the wrong move or miss the mark entirely.”
Big data is backward-looking and numbers can’t quantify tears and smiles. Alice Waters could have counted checks and measured menu items but instead she walked through the dining room. Both big and thick data help and both help more together.
Maths Mathisen spoke about his app, ‘Hold’. If people check their phone often then maybe they should be reminded about how often they do it. A lot of Sutherland’s work and Ogilvy’s ideas are about reframing and changing the meaning. A bus becomes a conveyance. Mathisen wants to do that with how people use their phones.
Robert Frank spoke about ‘the mother of all cognitive illusions’ and he’s the reason for all this. It was his book, The Economic Naturalist that sent Sutherland scurrying along Benign Bullshit Boulevard.
You’ll need to watch for the mother of all cognitive illusions, but even if Frank is wrong it’s right to listen to him. He points out the way we listen, hear, and remember stories as well as the importance of relative comparisons. You may not agree with his conclusions but you will learn about human beings.
Stephanie Johnson spoke on diversity and inclusion. Good organizations tend to argue well and Johnson cited research that “When have a diverse room, people are more willing to play devil’s advocate.” Relatedly, Ben Horowitz pointed out this was the catalyst for casual dress, to deemphasize HiPPO decisions and focus on good ideas.
Richard Wise was hilarious about “making the ‘rational’ benefit irrationally appealing”. Explaining classic advertising campaigns like, Don’t mess with Texas, What happens in Vegas stays in Vegas, and Got Milk, Wise pointed the way lateral ideas work.
The Got Milk campaign was initially not national. It was made for the California dairy industry and the key insight was a bit of thick data. A group of people was asked not to drink milk for a week and report back on how they felt. A week passed, the group returned, and their response was unexpected. It wasn’t the glass of milk people missed but the breakfast cereal (with milk), the coffee (with cream), and the birthday party (ice-cream).
How does an ad for a product work if it doesn’t feature the product? With lateral thinking. The insight for the California dairy farmers was Milk and _____. This is hard, Wise said, “What I like about (this approach) is taking away your pride in your product and being willing to look at where it actually lives in people’s lives.”
Gerd Gigerenzer spoke about how less (data) is more (accurate). “Logic and utility are beautiful mathematical theories but they don’t describe how most of us actually make decisions.”
Gigerenzer goes back and forth with the Thaler and Kahneman camp, but Rory likes them all because he’s focused on things that work in the real world, not things that are statistically significant in a laboratory. Gigerenzer’s chief beef with T&K is that heuristics actually work quite well and the T&K error is that they’re measuring the wrong thing.
Bob Iger’s decision not to buy Twitter is a point for Gigerenzer’s case. Iger said it was a gut call and admits it in the book. Thanks to a run of successes, Iger doesn’t have career risk to admit this. Most people don’t enjoy this buffer. Heuristics work, said Gigerenzer because they “are fit for a world where you need a robust solution because there is no optimal solution.”
Jennie Roper spoke on the mere exposure effect and noted, “When you pick a shampoo, mortgage, or mobile phone provider, most of the content is equal so why do you pick one of the other? It’s familiarity.”
For a business some exposure is good, more is better, and too much is too much. The goal is a bell curve of 9-12 moments, depending on the industry and certain goals. Thanks to digital this can be tracked and honed.
Sir Paul Collier spoke about the future of capitalism and noted that “Capitalism doesn’t work on autopilot.” Instead, there should be some kind of structure, often from the government. Collier wants to keep the rules of bowling but to have someone pull up the gutter bumpers every now and again.
Thanks for reading and enjoy the videos.
2 thoughts on “Nudgestock 2019”
[…] travel has gone from good to bad to bearable. Using ideas that Rory Sutherland evangelizes, we need to keep in mind what metrics matter. During the phase from good to bad, […]
[…] that I use a lot is Gigerenzer’s recognition heuristic. Usually the first answer that comes to mind is the right one. But not always […]