Thirty years ago, I was a product manager for a startup company that created high-performance, multiprocessing minicomputers. Powerful, scalable, and based on open standards, they offered exceptional price/performance. In 1988, Electronics magazine gave its Computer of The Year award to the flagship model
As we introduced the system, we described the “ideal” customer: a medium to large organization that used Unix-based systems, and ran large database applications, especially Oracle applications. We trained the sales force, produced some modest direct mail campaigns, and launched.
Then reality set in. In the first three months, we sold about 45 machines to some 30 different organizations. We gathered data about our new customers and looked for correlations that would help us target prospective customers more precisely. We found nothing — no patterns in terms of size, SIC code, geography, application, and so on. The data were almost random.
We were stumped. So, we decided to interview the key decision maker in each account. We created an interview guide and fanned out to visit customers
After our visits, we dug into our findings. Again, we found no useful patterns in the demographic data. Then we started describing the key decision makers. Who were they? Why did they decide on us?
Most of the decision makers were men in their early thirties who had recently been promoted to a position typically described as VP, Data Processing. They replaced an older person who had held the same position for more than ten years. One of our marketers had a flash of insight: “It’s almost like the decision maker is saying, ‘I’m the new sheriff in town. We’re going to do things my way. This is one of my first big decisions … and we’re going to buy a hot new machine from a startup company. I’m going to make my mark.’”
It turned out to be a very accurate description. Nominally, our customers were buying our machines to run large applications. But psychology was perhaps more important. We estimated that roughly 60% of our customers fit the “new sheriff” profile.
We decided to market specifically to new sheriffs. We trawled through organization profiles and identified those that had a new VP of Data Processing. We sent each new sheriff a fairly intense mail campaign coupled with calls by our local sales rep. The campaign succeeded rather well. From the time of the launch, we grew to $300 million in revenue in about two years.
I didn’t know it at the time, but we were practicing an art that today is called, the “jobs to be done theory of innovation.” (Click here for a good introduction). Developed by Clayton Christensen and his colleagues, the basic idea is that demographic information doesn’t reveal why a person chooses to purchase a new product or service. If we misunderstand the job to be done, our innovations will miss the mark.
Our startup company, for instance, positioned around big machines for big databases. We wanted to offer ever bigger, pricier machines. The new sheriff profile, however, changed our thinking. To get in the door, we needed to make it easy for the new sheriff to buy something on his own authority. So, we introduced an entry-level machine priced just below a typical VP signature limit.
Similarly, think about why men buy pajamas. We might think they simply want to stay warm. But men in America typically don’t buy pajamas until they have a daughter who is three years old. Their motivation is not to stay warm but to preserve their modesty. If we misunderstand that, we’ll produce far too many cozy, warm, flannel pajamas that men will never buy.
In my experience, good marketers and salespeople use the jobs-to-be-done method naturally and intuitively. They’re good observers and naturally ask a basic question: why do people buy these products? They dig into the data but, more importantly, they observe how people behave and ask insightful questions. The management guru, Ted Levitt,was a natural at this. He noted that people don’t buy gasoline for their cars. Rather, they buy the right to continue driving.
The jobs-to-be-done theory suggests that the key to innovation is sociology, not technology. Do you want your company to be more innovative? It’s time to add more marketers and salespeople – and maybe a sociologist and anthropologist – to your development team.
My buddy, Yancey, is a Bitcoin broker. He’s been arranging deals part-time for several years now. About a year ago, he went full time. He seems to be doing fine.
It’s ironic that the Bitcoin needs a broker. In my opinion, the best thing about Bitcoin, and the underlying blockchain, is the potential to disintermediate transactions. By eliminating middlemen, blockchain systems may deliver two major benefits:
Conceivably, the blockchain can produce a world of frictionless commerce where we no longer need trusted intermediaries. It’s ironic that Yancey serves as an intermediary for a technology that aims to eliminate intermediaries.
This suggests the blockchain has not yet reached its full potential. My question for Yancey: will it ever? I chatted with Yancey for about an hour last week. Here are some of the highlights.
Additionally, the structure of the blockchain itself can help prevent fraud. What’s stored in the blockchain can’t be changed. A bad actor could conceivably add to the blockchain but such additions are easy to identify and trace.
Valentine’s cards intrigue me. Suellen and I exchange them every year. Some are sweet. Some are funny. Some are silly. Some are mildly sexy. But really, is there anything new in them? Have we come a long way, baby, or are we just recycling platitudes? And how did people in past centuries address their love, passion, and heartaches? What can we learn from our ancestors?
By happy accident, I discovered a partial answer in the Newberry Library in Chicago. The Newberry holds a copy of The New Academy of Complements published in London in the 17th century. The small book – easily tucked in a pocket — is addressed to “both Sexes” and contains a “variety of Courtly and Civil complements” as well as “Eloquent Letters of Love and Friendship … both amorous and jovial.” Happily, the compliments are compiled by “the most refined Wits of the Age.”
What were the best amorous compliments of 17thcentury England? Here are a few of my favorite selections. *
Complemental Expressions towards Men, Leading to The Art of Courtship.
Sir, Your Goodness is as boundless, as my desires to serve you.
Sir, You are so highly generous, that I am altogether senceless.
Sir, You are so noble in all respects that I have learn’d to love, as well as to admire you.
Sir, Your Vertues are so well known, you cannot think I flatter.
Complements towards Ladies, Gentlewomen, Maids, &c.
Madam, When I see you I am in paradice, it is then that my eyes carve me out a feast of Love.
Madam, Your beauty hath so bereav’d me of my fear, that I do account it far more possible to die, than to forget you.
Madam, Since I want merits to equallize your Vertues, I will for ever mourn for my imperfections.
Madam, You are the Queen of Beauties, your vertues give a commanding power to every mortal.
Madam, Had I a hundred hearts I should want room to entertain your love.
I don’t have room to quote many of the gems contained in the book, but here are a few of the categories. Each category provides several models of what to say or write to address the situation. Who knows – you might need one from time to time.
A Gentleman of good Birth, but small Fortune, to a worthy Lady, after she had given a denial.
The Ingratiating Gentleman to his angry Mistriss.
The Lover to his Mistriss, upon his fear of her entertaining a new Servant.
The Jealous Lover to his beloved. The Answer: A Lady to her Jealous Lover.
A crack’t Virgin to her deceitful Friend, who hath forsook her for the love of a Strumpet.
A Lover to his Mistriss, who had lately entertained another Servant to her bosom, and her bed. The Answer: The Lady to her Lover, in defence of her own Innocency.
As you can see, The Academy provides the words to address (and perhaps remedy) most any situation. Is a bald man bothering you? Here’s how to respond:
Sir…while I could be content to keep my Coaches, my Pages, Lackeys, and Maids, I confess I could never endure the society of a bald pate.
I think the Newberry might find an interesting niche market by publishing high quality Valentine’s cards with extracts from The Academy. I know I would buy some. In the meantime, I hope you can find an appropriate quote for whatever your needs are this Valentine’s Day
* The edition held by the Newberry Library was published in 1671. The selections in this article are drawn from the 1669 edition
A little over two years ago, I wrote an article called Male Chauvinist Machines. At the time, men outnumbered women in artificial intelligence development roles by about eight to one. A more recent report suggests the ratio is now about three to one.
The problem is not just that men outnumber women. Data mining also presents an issue. If machines mine data from the past (what other data is there?), they may well learn to mimic biases from the past. Amazon, for instance, recently found that its AI recruiting system was biased against women. The system mined data from previous hires and learned that resumés with the word “woman” or “women” were less likely to be selected. Assuming that this was the “correct” decision, the system replicated it.
Might men create artificial intelligence systems that encode and perpetuate male chauvinism? It’s possible. It’s also possible that the emergence of AI will mean the “end of men” in high skill, cognitively demanding jobs.
That’s the upshot of a working paper recently published by the National Bureau of Economic Research (NBER) titled, “The ‘End of Men’ and Rise of Women In The High-Skilled Labor Market”.
The paper documents a shift in hiring in the United States since 1980. During that time the probability that a college-educated man would be employed in a
“… cognitive/high wage occupation has fallen. This contrasts starkly with the experience for college-educated women: their probability of working in these occupations rose.”
The shift is not because all the newly created high salary, cognitively demanding jobs are in traditionally female industries. Rather, the shift is “….accounted for by a disproportionate increase in the female share of employment in essentially all good jobs.” There seems to be a pronounced female bias in hiring for cognitive/high wage positions — also known as “good jobs”.
Why would that be? The researchers consider that “…women have a comparative advantage in tasks requiring social and interpersonal skills….” So, if industry is hiring more women into cognitive/high-wage jobs, it may indicate that such jobs are increasingly requiring social skills, not solely technical skills. The researchers specifically state that:
“… our hypothesis is that the importance of social skills has become greater within high-wage/cognitive occupations relative to other occupations and that this … increase[s] the demand for women relative to men in good jobs.”
The authors then present 61 pages on hiring trends, shifting skills, job content requirements, and so on. Let’s just assume for a moment that the authors are correct – that there is indeed a fundamental shift in the good jobs market and an increasing demand for social and interpersonal skills. What does that bode for the future?
We might want to differentiate here between “hard skills” and “soft skills” – the difference, say, between physics and sociology. The job market perceives men to be better at hard skills and women to be better at soft skills. Whether these differences are real or merely perceived is a worthy debate – but the impact on industry hiring patterns is hard to miss.
How will artificial intelligence affect the content of high-wage/cognitive occupations? It’s a fair bet that AI systems will displace hard skills long before they touch soft skills. AI can consume data and detect patterns far more skillfully than humans can. Any process that is algorithmic – including disease diagnosis – is subject to AI displacement. On the other hand, AI is not so good at empathy and emotional support.
If AI is better at hard skills than soft skills, then it will disproportionately displace men in good jobs. Women, by comparison, should find increased demand (proportionately and absolutely) for their skills. This doesn’t prove that the future is female. But the future of good jobs may be.
Daniel Kahneman, the psychologist who won the Nobel prize in economics, reminds us that, “What you see is not all there is.” I thought about Kahneman when I saw the videos and coverage of the teenagers wearing MAGA hats surrounding, and apparently mocking, a Native American activist who was singing a tribal song during a march in Washington, D.C.
The media coverage essentially came in two waves. The first wave concluded that the teenagers were mocking, harassing, and threatening the activist. Here are some headlines from the first wave:
ABC News: “Viral video of Catholic school teens in ‘MAGA’ caps taunting Native Americans draws widespread condemnation; prompts a school investigation.”
Time Magazine: “Kentucky Teens Wearing ‘MAGA’ Hats Taunt Indigenous Peoples March Participants In Viral Video.”
Evening Standard (UK): “Outrage as teens in MAGA hats ‘mock’ Native American Vietnam War veteran.”
The second media wave provided a more nuanced view. Here are some more recent headlines:
New York Times: “Fuller Picture Emerges of Viral Video of Native American Man and Catholic Students.”
The Guardian (UK): “New video sheds more light on students’ confrontation with Native American.”
The Stranger: “I Thought the MAGA Boys Were S**t-Eating Monsters. Then I Watched the Full Video.”
So, who is right and who is wrong? I’m not sure that we can draw any certain conclusions. I certainly do have some opinions but they are all based on very short video clips that are taken out of context.
What lessons can we draw from this? Here are a few: