Motivational Tricks for New Year’s resolutions

This article by Ayelet Fishbach in the Harvard Business Review is giving us advice right in time to be used on our New Year’s resolutions! He mentions 4 strategies to motivate yourself:

 

  1. Design Goals, not chores: chores are those tasks that you don’t enjoy though you may like the outcome (the very explicit example given in the article is undergoing chemotherapy!!). She suggests to choose tasks with intrinsic motivation rather than extrinsic one, that is the ones that motivate you right when doing it, instead of having to translate the task into a further away goal (like on the chemotherapy example) to remember you why you are doing it. But also motivation works at its best when goals at tangible. 

    “Abstract ambitions—such as “doing your best”—are usually much less effective than something concrete, such as bringing in 10 new customers a month or walking 10,000 steps a day. As a first general rule, then, any objectives you set for yourself or agree to should be specific.”Here is how to express those goals in a SMART way:

  2.  Find effective rewards: If there is no way you find an intrinsic task to reach your goal, if there is no attractive aspect of the task at hand ;-( then you could improve your motivation by offering you a treat.“[..]

    it can be helpful to create external motivators for yourself over the short- to-medium term[..]. You might promise yourself a vacation for finishing a project or buy yourself a gift for losing weight. But be careful to avoid perverse incentives.”
    “Another common trap is to choose incentives that undermine the goal you’ve reached. If a dieter’s prize for losing weight is to eat pizza and cake, he’s likely to undo some of his hard work and reestablish bad habits. If the reward for excelling at work one week is to allow yourself to slack off the next, you could diminish the positive impression you’ve made. Research on what psychologists call balancing shows that goal achievement sometimes licenses people to give in to temptation—which sets them back.”Other kind of external rewards that work quite well are the ones that count on/talk to/ your “loss aversion” bias:
    “Online services such as StickK.com allow users to choose a goal, like “I want to quit smoking,” and then commit to a loss if they don’t achieve it: They have to donate money to an organization or a political party that they despise, for example.”

  3. Sustain progress: don’t slow down after the first “burst of motivation”. There are many tricks that can be used, like:
    “If you break your goal into smaller subgoals—say, weekly instead of quarterly sales targets—there’s less time to succumb to that pesky slump.[…]
    Another mental trick involves focusing on what you’ve already done up to the midpoint of a task and then turning your attention to what you have left to do. My research has found that this shift in perspective can increase motivation.”
  4. Harness the influence of others: This next text really talked to me, did it never happened to you?

    When we witness a colleague speeding through a task that leaves us frustrated, we respond in one of two ways: Either we’re inspired and try to copy that behavior, or we lose motivation on the assumption that we could leave the task to our peer.[…]
    One rule is to never passively watch ambitious, efficient, successful coworkers; there’s too much risk that it will be demotivating. Instead, talk to these peers about what they’re trying to accomplish with their hard work and why they would recommend doing it.[…]
    Listening to what your role models say about their goals can help you find extra inspiration and raise your own sights.”

    Personally I prefer this bit of advice:

    “Interestingly, giving advice rather than asking for it may be an even more effective way to overcome motivational deficits, because it boosts confidence and thereby spurs action.[…] when they [offered their wisdom to others], they laid out concrete plans they could follow themselves, which have been shown to increase drive and achievement. 

So here we are, a little bit more in control of the result of our next New Year’s resolutions. Don’t you think so?  You’ll tell me about it in 2019! 😉

 

The Global View at the European Digital Industry Cup

In October took place the “European Digital Industry Cup” in Croatia. Lovely country, great sailing experience, and above all, great quality of speakers!  They gave us an overview of the global trends in our industry, and in particular I loved the presentation from Claus Kjeldsen, CEO of the Copenhagen Institute for Futures Studies on the “Development of the world scenario in the next 20 years”.

In his talk, he explained that the different uprisings of nationalism’s that we can see all over the world like in the US, Austria, Poland, Turkey or Italy are a consequence of the globalization: now that everybody can be reached, instead of a scenario of all of us joining forces to liberate countries, moving towards spreading open democracies in the world, this is a scenario of fragmentation, the personalization of powers.

I would like to add here Mario Vargas Llosa’s explanation; He calls this movements “the call of the tribe”: he explains that this nationalism mode is a defense system, we feel attacked with all the changes going on, so we put ourselves on a defense mode, and we look for security. it’s easy to just follow somebody and feel like being part of a group.  We feel less lonely and more strong, we feel safer.

They also see the power shifting towards China, as it was a long time ago. And it’s not the economical and political power but also the technological one too. Having technological power in these days means being able to collect more data, to make people dependent on your technology (as we are at the present of Google).

Look at the great influence China is getting in Venezuela?  Not to mention how they cope Africa and are here in Europe? We have an actual economical battle between Ali Baba and FedEx right now here in Liège, Belgium, would you like to guess who will win?

The thing is that China is, as you know, not a democracy. So we had it wrong, our wishes have not come true and there’s no spreading of open democracies. Instead other political organisations are gaining power, a little bit scary for our personal rights.

This mega-trend, foreseen for 10 to 15 years at least, makes us wonder: how we, western democracy powers, will react to this shifting of power?

What could we do to make people think? Any ideas?

The Fourth Age, by Byron Reese

I received a copy of the book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity for evaluation (thanks again!), and I can tell you that the way it has been written has some great advantages.  The subject of where lead the developments of Artificial Intelligence and robots technology is not easy and very controversial with questions like: will all our jobs be taken by computers in the future? Are we facing the destruction of humanity by the future Artificial Intelligence robots? and so on.

Byron Reese presents us not with one possible future, but with different possible outcomes of Artificial Intelligence technology depending on our basic beliefs.  Those basics beliefs are represented by the answers to classical though difficult philosophical questions, like: what are we, what is life for you (that will define if you believe a machine could be alive)? do you believe in a soul? and others alike. He guides your reasoning through these questions in a very easy way: he provides 3 or 4 possible answers for each question and he analyses each of them. That helps to clarify your thoughts, you can see which one resonates better with you. So it’s a multiple choice answer, what makes it much more easy to adhere to one possible answer, you don’t stay forever discussing it..

He uses the same method for each great question you ask yourself over the future society: will robots become conscious? will they take all our jobs? Will they take us over? You’ll have each time 3 or 4 possible choices of answers, and a discussion of the implications of each one. If you wonder, go to his website, he put there a quizz to see how ‘robot-proof’ is your job 😉

In conclusion, he’s not presenting his view of the future, but many options, each of them depending on your core beliefs. I liked it, it’s an easy and straightforward method to help us see the implications of actual technology on our future.

4 Behaviors to find new business ideas

I went to an open event at Vlerick, and Prof. Miguel Meuleman gave us a talk on the Entrepreneurial Mindset. He gave us good tips to find business ideas. Based on the six-year study behind the book The innovator’s DNA, he mentioned these 4 discovery activities that the authors have identified as the ones in which innovative entrepreneurs spend more time:

  1. Questioning
  2. Observing
  3. Experimenting
  4. Networking

 

 

The good news is that anybody can nurture these behaviors.

  • Questionning:

Miguel used a restaurant to illustrate how to question the status-quo: What do you need to have a restaurant? We quickly came up with a list:

  •  capital,
  • a venue,
  • marketing,
  • food,
  • a cook,

Now, question each of the assumptions: how could you have a restaurant without capital? how without a venue? without marketing?… He even gave us an example of a restaurant without food! (If you wonder, it was in a seafood market, where you could buy your own fish and bring it to the restaurant that cooked it for you 🙂

If you analyse your answers, you may see that to overcome one of the traditional assumptions (like needing capital to launch a restaurant) you are using a new trend (like crowdfunding). Our assumptions come from years of doing something in the same way, but then thanks to a technological advance, a new tool, a new trend, those assumptions are not true anymore.

When discovering a new trend we have to reflect on what it means to our business, is it challenging one of the basic assumptions we worked with before? What are the problems that can be solved with this trend? Is it creating new expectations from our customers? (As example of this, KBC is now offering a contact channel through Whatsapp)

  • Observing

Miguel suggests us to look at dating sites like Tinder 🙂 Successful dating sites are designed to attract people, they are  rich in new ideas of presentation.  The idea behind is to look at new startups websites to find good design ideas. When something is easy to use, it usually catches up quickly and soon your customers will want it in your site too.
Observe your customer, think of your customer’ journey.  As an example, Nordstrom innovation lab designed an ipad app to help customers choose their lenses in a very Agile way: they installed the whole team (developers, designers,.. even the SCRUM board!) directly in one shop, and they follow the customer to see how they walked around, what they were looking at, how they finally chose their lenses.  They came with a working prototype within a day, and asked real customers to try it, so they could see immediately the functionalities that were missing, or the ones that were not user friendly.

  • Experimenting

Experimenting is not just trial and error. It has to be designed. Facebook is not the same to everyone, there are many versions of the application running simultaneously, they can quickly see which features work better. To look for ideas, Miguel suggests us to look at the Airbnb site: they share all the experiments that they do with their users.

  • Networking

The idea is to talk to people about the new ideas, to discuss the problems to be solved, to come up with different options of your initial product. See people’s reaction, incorporate their insight to your initial idea.

At the actual pace of change in our society, we should do this regularly, to systematize the process of innovation and don’t become a dinosaur (professionally speaking).

How our economy is shifting towards network-centric players

Managing our hub economy, HBR

I loved this article from the Harvard Business Review: Managing Our Hub Economy,by Marco Iansiti and Karim R. Lakhani, the authors explain in a very clear way what we are already experiencing in the last decade already at the macro-level economy.

The global economy is coalescing around a few digital superpowers. We see unmistakable evidence that a winner-take-all world is emerging in which a small number of “hub firms”—including Alibaba, Alphabet/Google, Amazon, Apple, Baidu, Facebook, Microsoft, and Tencent—occupy central positions. While creating real value for users, these companies are also capturing a disproportionate and expanding share of the value, and that’s shaping our collective economic future. The very same technologies that promised to democratize business are now threatening to make it more monopolistic.

Beyond dominating individual markets, hub firms create and control essential connections in the networks that pervade our economy. Google’s Android and related technologies form “competitive bottlenecks”; that is, they own access to billions of mobile consumers that other product and service providers want to reach. Google can not only exact a toll on transactions but also influence the flow of information and the data collected.

These big ‘Hub’ companies, as these authors call them, are companies that you cannot ignore when you want to do business in many markets today.  The interesting point of this article is that those same companies have a great competitive advantage over traditional companies in a lot of other markets. And each time they dominate in a different market, their competitive advantage grows to capture yet more easily the future next market they’ll wish to enter.

This is flipping, because one of the great advantages we all see on being connected through Internet and being heard by (almost) everybody is the democratisation of power, the opening of opportunities for everybody… and what is really happening is that the same companies that are offering the inter-connections are growing so much that they are not avoidable, so they monopolise the communications channels.

Hub firms don’t compete in a traditional fashion—vying with existing products or services, perhaps with improved features or lower cost. Rather, they take the network-based assets that have already reached scale in one setting and then use them to enter another industry and “re-architect” its competitive structure—transforming it from product-driven to network-driven. They plug adjacent industries into the same competitive bottlenecks they already control.

For example […] Google’s automotive strategy does not simply entail creating an improved car; it leverages technologies and data advantages (many already at scale from billions of mobile consumers and millions of advertisers) to change the structure of the auto industry itself.[…]

If current trends continue, the hub economy will spread across more industries, further concentrating data, value, and power in the hands of a small number of firms employing a tiny fraction of the workforce.[…]

To remain competitive, companies will need to use their assets and capabilities differently, transform their core businesses, develop new revenue opportunities, and identify areas that can be defended from encroaching hub firms and others rushing in from previously disconnected economic sectors. Some companies have started on this path—Comcast, with its new Xfinity platform, is a notable example—but the majority, especially those in traditional sectors, still need to master the implications of network competition.

In this article, the authors encourage the ‘hub’ companies to realize the impact they have on society, the resentment that could rise if their power is not wisely used.

Most importantly, the very same hub firms that are transforming our economy must be part of the solution—and their leaders must step up. As Mark Zuckerberg articulated in his Harvard commencement address in May 2017, “we have a level of wealth inequality that hurts everyone.” Business as usual is not a good option. Witness the public concern about the roles that Facebook and Twitter played in the recent U.S. presidential election, Google’s challenges with global regulatory bodies, criticism of Uber’s culture and operating policies, and complaints that Airbnb’s rental practices are racially discriminatory and harmful to municipal housing stocks, rents, and pricing.

Thoughtful hub strategies will create effective ways to share economic value, manage collective risks, and sustain the networks and communities we all ultimately depend on. If carmakers, major retailers, or media companies continue to go out of business, massive economic and social dislocation will ensue. And with governments and public opinion increasingly attuned to this problem, hub strategies that foster a more stable economy and united society will drive differentiation among the hub firms themselves.[…]

A real opportunity exists for hub firms to truly lead our economy. This will require hubs to fully consider the long-term societal impact of their decisions and to prioritize their ethical responsibilities to the large economic ecosystems that increasingly revolve around them. At the same time, the rest of us—whether in established enterprises or start-ups, in institutions or communities—will need to serve as checks and balances, helping to shape the hub economy by providing critical, informed input and, as needed, pushback.

They explain that with the growing connectivity, we share information at near-zero marginal cost. Thus networks are creating value:

Metcalfe’s law states that a network’s value increases with the number of nodes (connection points) or users—the dynamic we think of as network effects. This means that digital technology is enabling significant growth in value across our economy, particularly as open-network connections allow for the recombination of business offerings[…]

But that value is not much distributed among players to begin with, moreover the bigger the network, the stronger effect of attraction that it will exert, thus exacerbating the differences:

But while value is being created for everyone, value capture is getting more skewed and concentrated. This is because in networks, traffic begets more traffic, and as certain nodes become more heavily used, they attract additional attachments, which further increases their importance. This brings us to the third principle, a lesser-known dynamic originally posited by the physicist Albert-László Barabási: the notion that digital-network formation naturally leads to the emergence of positive feedback loops that create increasingly important, highly connected hubs. As digital networks carry more and more economic transactions, the economic power of network hubs, which connect consumers, firms, and even industries to one another, expands. Once a hub is highly connected (and enjoying increasing returns to scale) in one sector of the economy (such as mobile telecommunications), it will enjoy a crucial advantage as it begins to connect in a new sector (automobiles, for example). This can, in turn, drive more and more markets to tip, and the many players competing in traditionally separate industries get winnowed down to just a few hub firms that capture a growing share of the overall economic value created—a kind of digital domino effect.

They give then some well-known examples of our near past:

Just a few years ago, cell phone manufacturers competed head-to-head for industry leadership in a traditional product market without appreciable network effects. [..] But with the introduction of iOS and Android, the industry began to tip away from its hardware centricity to network structures centered on these multisided platforms. The platforms connected smartphones to a large number of apps and services. Each new app makes the platform it sits on more valuable, creating a powerful network effect that in turn creates a more daunting barrier to entry for new players. Today Motorola, Nokia, BlackBerry, and Palm are out of the mobile phone business, and Google and Apple are extracting the lion’s share of the sector’s value. The value captured by the large majority of complementors—the app developers and third-party manufacturers—is generally modest at best.

The domino effect is now spreading to other sectors and picking up speed. Music has already tipped to Apple, Google, and Spotify. […] On-premise computer and software offerings are losing ground to the cloud services provided by Amazon, Microsoft, Google, and Alibaba. In financial services, the big players are Ant, Paytm, Ingenico, and the independent start-up Wealthfront; in home entertainment, Amazon, Apple, Google, and Netflix dominate.

Where are powerful hub firms likely to emerge next? Health care, industrial products, and agriculture are three contenders. But let’s examine how the digital domino effect could play out in another prime candidate, the automotive sector […].

The authors then describe their analysis of the transformation that is going on in the automotive sector:

As with many other products and services, cars are now connected to digital networks, essentially becoming rolling information and transaction nodes. This connectivity is reshaping the structure of the automotive industry. When cars were merely products, car sales were the main prize. But a new source of value is emerging: the connection to consumers in transit. […] If consumers embrace self-driving vehicles, that one hour of consumer access could be worth hundreds of billions of dollars in the U.S. alone.

Which companies will capitalize on the vast commercial potential of a new hour of free time for the world’s car commuters? Hub firms like Alphabet and Apple are first in line. They already have bottleneck assets like maps and advertising networks at scale, and both are ready to create super-relevant ads pinpointed to the car’s passengers and location. […]

The transformation will also upend a range of connected sectors—including insurance, automotive repairs and maintenance, road construction, law enforcement, and infrastructure—as the digital dominos continue to fall. […]

In conclusion :

To reach the scale required to be competitive, automotive companies that were once fierce rivals may need to join together. […]

Of course, successful collaboration depends on a common, strongly felt commitment. So as traditional enterprises position themselves for a fight, they must understand how the competitive dynamics in their industries have shifted.

I think this analysis is highly accurate and we can expect similar developments in other industries.  They give a good advice to bare in mind when defining the best strategy for the long term.

Embrace difficulties to stay mentally fit and happy!

I just came by this old article from Ian Leslie in The Economist magazine, it’s about a thought: embrace difficulties when they arise, they force us to be more creative and bring more satisfaction when we overcome them.

There are two ideas intertwined here: the first one is that when things come too easy, we don’t savor them enough. In French I would say « Il faut de la pluie pour faire le beau temps ».

This article brought up a memory of my childhood: we had the means to eat good meat every day. Yes, you can argue that having meat every day is not healthy, but having been brought up in Argentina, well, meat (of any kind) was mandatory at the menu! The thing is that I remember a period we ate beef tenderloin, that is a very tender cut of beef meat. Obviously, we appreciated that cut, and for a long period, every dish at home containing beef meat was done with that cut.  On the oven, as a steak, or in a wok, it was always tenderloin.

Believe me, you can get tired of it!  After a while, whenever I went for dinner to friends and they had another cut, I really savored it, even if it was not so tender.

What about not having money limitations? Yes, I’m sure I would go for a ravaging shopping for a while… until I’ll end up having more than what I need, more than what I could wear on a season! And what after that?  Shopping will not taste the same ?

It’s the same on other levels. At work, if there is no challenge, we’d lose interest, emotion.

But not only that, here is the second idea: challenges force us to think, guide our imagination and help us to come up with innovative solutions. And after the exercise, we end up with a sense of satisfaction of having solved the problem that we would not have experienced without the problem in the first place. This sense of satisfaction for having stretched our brain muscle is equivalent to the endorphin’s after a physical exercise!

Our brains respond better to difficulty than we imagine. In schools, teachers and pupils alike often assume that if a concept has been easy to learn, then the lesson has been successful. But numerous studies have now found that when classroom material is made harder to absorb, pupils retain more of it over the long term, and understand it on a deeper level. Robert Bjork, of the University of California, coined the phrase “desirable difficulties” to describe the counter-intuitive notion that learning should be made harder by, for instance, spacing sessions further apart so that students have to make more effort to recall what they learnt last time. Psychologists at Princeton found that students remembered reading material better when it was printed in an ugly font.

So remember next time you encounter a pebble on your way : embrace the opportunity of some brain gymnastic and enjoy life!

Using The Past To Discover What The Customer Will Want Next

I loved the article What’s your best innovation bet? by Melissa Schilling in this summer issue of the Harvard Business Review, as it has always been very hard to guess the future:

Image from Magda Kochanowicz

Melissa Schilling says that “By mapping a technology’s past, you can predict what future customers will want.”  For that she explains her method:

  • 1 – Identify the key dimensions

What she means here is to examine/analyse/determine the different aspects in which the technology has evolved, like on processing speed or on precision just to mention some typical dimensions, and to relate them to the need of users: how much has the technology satisfied that need? She gives a clear example with the recording industry, where the basic dimension for many years was the audio fidelity:

By the mid-1990s, both industries were eager to introduce a next-generation audio format. In 1996 Toshiba, Hitachi, Time Warner, and others formed a consortium to back a new technology, called DVD-Audio, that offered superior fidelity and surround sound. They hoped to do an end run around Sony and Philips, which owned the compact disc standard and extracted a licensing fee for every CD and player sold.

Sony and Philips, however, were not going to go down without a fight. They counterattacked with a new format they had jointly developed, Super Audio CD. Those in the music industry gave a collective groan; manufacturers, distributors, and consumers all stood to lose big if they bet on the wrong format. Nonetheless, Sony launched the first Super Audio players in late 1999; DVD-Audio players hit the market in mid-2000. A costly format war seemed inevitable.

You may be scratching your head at this point, wondering why you’ve never heard about this format war. What happened? MP3 happened. While the consumer electronics giants were pursuing new heights in audio fidelity, an algorithm that slightly depressed fidelity in exchange for reduced audio file size was taking off. Soon after the file-sharing platform Napster launched in 1999, consumers were downloading free music files by the millions, and Napster-like services were sprouting up like weeds.

If you wonder: ”who could have predicted the disruptive arrival of MP3? How could the consumer electronics giants have known that a format on a trajectory of ever-increasing fidelity would be overtaken by a technology with less fidelity?” Well, that’s just the method she’s presenting in this article, which first step is identifying the different dimensions at play.

For example, computers became faster and smaller in tandem; speed was one dimension, size another. Developments in any dimension come with specific costs and benefits and have measurable and changing utility for customers. Identifying the key dimensions of a technology’s progression is the first step in predicting its future.

To determine these dimensions, trace the technology’s evolution to date, starting as far back as possible. Consider what need the technology originally fulfilled, and then for each major change in its form and function, think about what fundamental elements were affected.

Tracing its [the recording industry] history reveals six dimensions that have been central to its development: desynchronization, cost, fidelity, music selection, portability, and customizability. Before the invention of the phonograph, people could hear music or a speech only when and where it was performed. When Thomas Edison and Alexander Graham Bell began working on their phonographs in the late 1800s, their primary objective was to desynchronize the time and place of a performance so that it could be heard anytime, anywhere. Edison’s device—a rotating cylinder covered in foil—was a remarkable achievement, but it was cumbersome, and making copies was difficult. Bell’s wax-covered cardboard cylinders, followed by Emile Berliner’s flat, disc-shaped records and, later, the development of magnetic tape, made it significantly easier to mass-produce recordings, lowering their cost while increasing the fidelity and selection of music available.

For decades, however, players were bulky and not particularly portable. It was not until the 1960s that eight-track tape cartridges dramatically increased the portability of recorded music, as players became common in automobiles. Cassette tapes rose to dominance in the 1970s, further enhancing portability but also offering, for the first time, customizability—the ability to create personalized playlists. Then, in 1982, Sony and Philips introduced the compact disc standard, which offered greater fidelity than cassette tapes and rapidly became the dominant format.

[…] I usually ask teams to agree on three to six key dimensions for their technology.

The recurring dimensions across industries are: ease of use, durability and cost.  To foresee the future, it is worth also to imagine new  dimensions worth exploring. A good tip to come up with those new aspects is to think big, no constraints, what could the customer want in an ideal world.

Folklore has it that Henry Ford once said, “If I had asked people what they wanted, they would have said faster horses.” If any car maker at the time had really probed people about exactly what their dream conveyance would provide, they probably would have said “instantaneous transportation.” Both consumer responses highlight that speed is a high-level dimension valued in transportation, but the latter helps us think more broadly about how it can be achieved. There are only limited ways to make horses go faster—but there are many ways to speed up transportation

  • 2 – Locate your position

For each dimension, examine the value consumers are receiving for actual technology

This will help reveal where the greatest opportunity for improvement lies.

[..] For example, the history of audio formats suggests that the selection of music available has a concave parabolic utility curve: Utility increases as selection expands, but at a decreasing rate, and not indefinitely. When there’s little music to choose from, even a small increase in selection significantly enhances utility. Consider that when the first phonographs appeared, there were few recordings to play on them. As more became available, customers eagerly bought them, and the appeal of owning a player grew. Increasing selection even a little had a powerful impact on utility. Over the ensuing decades, selection grew exponentially, and the utility curve ultimately began to flatten; people still valued new releases, but each new recording added less additional value. Today digital music services like iTunes, Amazon Prime Music, and Spotify offer tens of millions of songs. With this virtually unlimited selection, most customers’ appetites are sated—and we are probably approaching the top of the curve.

Many dimensions have S-shaped curves: Below some threshold of performance there is no utility, but utility increases quickly above that threshold and then maxes out somewhere beyond that.

  • 3 – Determine your focus

Once you know the dimensions along which your firm’s technology has (or can be) improved and where you are on the utility curves for those dimensions, it should be straightforward to identify where the most room for improvement exists. But it’s not enough to know that performance on a given dimension can be enhanced; you need to decide whether it should be. So first assess which of the dimensions you’ve identified are most important to customers. Then assess the cost and difficulty of addressing each dimension.

For example, of the four dimensions that have been central to automobile development—speed, cost, comfort, and safety—which do customers value most, and which are easiest or most cost-effective to address?

[..] Tata Motors’ experience with the Nano is instructive. The Nano was designed as an affordable car for drivers in India, so it needed to be cheap enough to compete with two-wheeled scooters. The manufacturer cut costs in several ways: The Nano had only a two-cylinder engine and few amenities—no radio, electric windows or locks, antilock brakes, power steering, or airbags. Its seats had a simple three-position recline, the windshield had a single wiper, and there was only one rearview mirror. In 2014, after the Nano received zero stars for safety in crash tests, analysts pointed out that adding airbags and making simple adjustments to the frame could significantly improve the car’s safety for less than $100 per vehicle. Tata took this under advisement—and placed its bets on comfort. All 2017 models include air-conditioning and power steering but not airbags.

Once you have identified the dimensions, the author suggests scoring these criteria to help you prioritize where to put the effort of innovation: how much users care about the dimension, room for improvement of the technology, and the cost involved in developing a new product on that dimension.  See this example for blood-sugar monitoring devices:

DIMENSION IMPORTANCE TO
CUSTOMERS (1–5 SCALE)
ROOM FOR
IMPROVEMENT (1–5 SCALE)
EASE OF
IMPROVEMENT (1–5 SCALE)
TOTAL
SCORE
RELIABILITY 5 1 1 7
COMFORT 4 4 3 11
COST 4 2 2 8
EASE OF USE 3 2 3 8

This matrix is very helpful to explicit the need to change a company’s traditional strategy:

It can also help overcome the bias and inertia that tend to keep an organization’s attention locked on technology dimensions that are less important to consumers than they once were.

Depending on your company’s situation (lack of cash, strong market position,..) you can weight some of the scoring to get your ‘personalised score. You can also use this method to analyse your competitors positioning and expected future products.  Knowing their actual market strength and their potential future directions will make you see the best ‘bet’ for your company in an ever evolving industry.

The technology assessment exercise can help companies anticipate competitors’ moves. Because competitors may differ in their capabilities (making particular technology dimensions harder or easier for them to address), or because they may focus on different segments (influencing which dimensions seem most important or have the most room for improvement), they are likely to come up with different rankings for a given set of dimensions.

The great insight of the method presented in this article is not on getting the innovation idea, but more at a strategic level, on where it will be better to put the effort for Your company considering its Actual circumstances at this Present market (evolution of the industry and existing competence).

Perhaps more valuable is the big-picture perspective it can give managers—shedding new light on market dynamics and the larger-scale or longer-term opportunities before them. Only then will they be able to lead innovation in their industries rather than scramble to respond to it.

Big Data and Ethics

BIG Data and Ethics was held a few weeks ago in the new premises of the DigitYser, downtown Brussels.

It was a great Meetup, with interesting speakers and an interested public 😉 It’s always a pleasure when the public can contribute and presentations raise great discussions, and it is more important here on this gathering on ethics, as people still have to position themselves on the different aspects of this topic.

I was particularly surprised when Michael Ekstrand from Boise State University mentioned a use of the recommendations systems that I hadn’t think of: using it as a tool to tackle the intention behaviour gap: ‘I don’t do what I want to do’ (for example not eating while on a diet). Recommenders can be used to help you change your behaviour, giving you nudges as incentive.

Jochanan Eynikel also mentioned the use of technology as a morality enforcer.

Still, there are possible drawbacks:

Another area that was discussed was the ethical fact that Personalisation has a direct negative impact on Insurance as it goes against Risk mitigation (mutualising it among customers). There are sensible domains where a ‘human’ approach should be taken.
How to ensure ethical and moral concerns are taken into account? One approach is through participatory design, that is a framework to get users voices on the subject during the design phase. MIT is strongly pushing participatory design to tackle many basic dilemmas.

Solving and clarifying our human position on these kind of dilemmas is more than relevant when we are talking here about autonomous technology, that is when technology is teachings itself, as driving cars learning from users.
Can we imagine not having human supervision in all domains? How to introduce Ethics so that the system itself can choose the ‘good’ decision and discard the others?

Pierre-Nicolas Schwab presented us the General Data Protection Regulation as “the only thing that the EC can do to force companies to take data privacy into account: fine them if they don’t”:

At the end of the meeting, this question has been raised: “Do data scientist and programmers need an Hippocratic oath?” Like ACM that has a code of conduct, something like ‘don’t harm with your code’.
What’s your opinion on this?

Elections warn about ethical issues in algorithms

I tweeted recently on this article about how Big Data has been used on the last American Presidential campaign.

Concordia Summit, New York 2016

“At Cambridge,” he said, “we were able to form a model to predict the personality of every single adult in the United States of America.” The hall is captivated. According to Nix, the success of Cambridge Analytica’s marketing is based on a combination of three elements: behavioral science using the OCEAN Model, Big Data analysis, and ad targeting. Ad targeting is personalized advertising, aligned as accurately as possible to the personality of an individual consumer.

Nix candidly explains how his company does this. First, Cambridge Analytica buys personal data from a range of different sources, like land registries, automotive data, shopping data, bonus cards, club memberships, what magazines you read, what churches you attend. Nix displays the logos of globally active data brokers like Acxiom and Experian—in the US, almost all personal data is for sale. […] Now Cambridge Analytica aggregates this data with the electoral rolls of the Republican party and online data and calculates a Big Five personality profile. Digital footprints suddenly become real people with fears, needs, interests, and residential addresses.
[…]

Nix shows how psychographically categorized voters can be differently addressed, based on the example of gun rights, the 2nd Amendment: “For a highly neurotic and conscientious audience the threat of a burglary—and the insurance policy of a gun.” An image on the left shows the hand of an intruder smashing a window. The right side shows a man and a child standing in a field at sunset, both holding guns, clearly shooting ducks: “Conversely, for a closed and agreeable audience. People who care about tradition, and habits, and family.”

Now I came across this other article by Peter Diamandis, featuring what we can expect in 4 year’s time for the next future elections’ campaign.

5 Big Tech Trends That Will Make This Election Look Tame

5 Big Tech Trends That Will Make This Election Look Tame

If you think this election is insane, wait until 2020.

I want you to imagine how, in four years’ time, technologies like AI, machine learning, sensors and networks will accelerate.

Political campaigns are about to get hyper-personalized thanks to advances in a few exponential technologies.

Imagine a candidate who now knows everything about you, who can reach you wherever you happen to be looking, and who can use info scraped from social media (and intuited by machine learning algorithms) to speak directly to you and your interests.

[…] For example, imagine I’m walking down the street to my local coffee shop and a photorealistic avatar of the presidential candidate on the bus stop advertisement I pass turns to me and says:

“Hi Peter, I’m running for president. I know you have two five-year-old boys going to kindergarten at XYZ school. Do you know that my policy means that we’ll be cutting tuition in half for you? That means you’ll immediately save $10,000 if you vote for me…”

If you pause and listen, the candidate’s avatar may continue: […] “I’d really appreciate your vote. Every vote and every dollar counts. Do you mind flicking me a $1 sticker to show your support?”

I know, this last article is from the SingularityHub, but even though they tend to be alarming, knowing how fast technology advances, the predictions they advance are not too exaggerated…

In any way, that reminds me how important it is to ACT on the ethical issues of algorithms. Please notice the capital letters to stress on the movement, which is to take action.  There are many issues that need to be identify, to be discussed, to raise awareness upon, to regulate, and on some of them we can already act on at company level.

I talked in May last year at the Data Innovation Summit about the biases that can be (and usually are) replicated by the new algorithms based on data.  Since then I began working on a training program to help identify and correct those bias when designing and using algorithms, and I’m reminded with the above mentioned articles that this cannot be delayed, it’s needed right now.

So if you are interested on getting your people and organization be aware of biases (human biases and digital ones), and be trained to fix these issues, contact me!

EmojiOne

We are creating our future, let’s don’t close our eyes, we can take control and assume our responsibility setting the railings that will guide the path to our future society.

 

AI and Machine Learning in business: use it everywhere!

How One Clothing Company Blends AI and Human Expertise, HBR nov-16

How One Clothing Company Blends AI and Human Expertise, HBR nov-16

Last week Bev from PWI’s group in Linkedin pointed me to a great HBR article: “How One Clothing Company Blends AI and Human Expertise”, by H. James Wilson, Paul Daugherty and Prashant Shukla.

It describes how the company Stitch Fix works, using machine learning insights to assist their designers, and as you will see, they use machine learning at many levels throughout the company.

The company offers a subscription clothing and styling service that delivers apparel to its customers’ doors. But users of the service don’t actually shop for clothes; in fact, Stitch Fix doesn’t even have an online store. Instead, customers fill out style surveys, provide measurements, offer up Pinterest boards, and send in personal notes. Machine learning algorithms digest all of this eclectic and unstructured information. An interface communicates the algorithms’ results along with more-nuanced data, such as the personal notes, to the company’s fashion stylists, who then select five items from a variety of brands to send to the customer. Customers keep what they like and return anything that doesn’t suit them.

The Key factor of success for the company is to be good at recommending clothes that not only will fit the customer and that they’ll like enough to keep them, but better than just ‘like them’, that they like them enough to be happy with their subscription.

Stitch Fix, which lives and dies by the quality of its suggestions, has no choice but to do better [than Amazon and Netflix].

Unlike Amazon and Netflix that recommend directly products to the customers, here they use machine learning methods to provide digested information to their human stylists and designers.

[…] companies can use machines to supercharge the productivity and effectiveness of workers in unprecedented ways […]

Algorithms are for example analysing the measurements to find other clients with same body shape, so they can use the knowledge of what items fitted those other clients: the clothes that those other clients kept. Algorithms are also used to extract information of clients’ taste on styles, from brands preferences and their comments on collections.  Human stylists, using the results of that data analysis and reading the client’s notes, are better equipped to choose clothes that will suit the customers.

Next, it’s time to pick the actual [item of clothe] to be shipped. This is up to the stylist, who takes into account a client’s notes or the occasion for which the client is shopping. In addition, the stylist can include a personal note with the shipment, fostering a relationship, which Stitch Fix hopes will encourage even more useful feedback.

This human-in-the-loop recommendation system uses multiple information streams to help it improve.

See how stylists maintain a human dialog with their clients through the included note. This personalised contact is usually well appreciated by customers and it has a positive effect for the company because it opens the door to receive their feedback to better tailor their next delivery.

The company is testing natural language processing for reading and categorizing notes from clients — whether it received positive or negative feedback, for instance, or whether a client wants a new outfit for a baby shower or for an important business meeting. Stylists help to identify and summarize textual information from clients and catch mistakes in categorization.

The machine learning systems arelearning through experience’ (=adapting with the feedback) as usual, but in a humanly ‘supervised’ way. This supervision allows them to try new algorithms without the risk of losing clients if results are not as good as expected.

Stitch Fix employs more than 2,800 stylists, dispersed across the country, all of them working from home and setting their own hours. In this distributed workforce, stylists are measured by a variety of metrics, including the amount of money a client spends, client satisfaction, and the number of items a client keeps per delivery. But one of the most important factors is the rate at which a stylist puts together a collection of clothes for a client.

Speed is an important factor to satisfy their customers’ demands, and machine learning gives them the needed insight so much quicker than if stylists had to go through all the raw data!

This is where the work interface comes into effect. To enable fast decision making, the screen on which a stylist views recommendations shows the relevant information the company keeps about a client, including apparel and feedback history, measurements, and tolerance for fashion risks — it’s all readily accessible

The interface itself, which shows the information to the stylist, is also adapting through feedback, being tested for better performance.  And you could go again one step further and check for bias on the stylists:

Stitch Fix’s system can vary the information a stylist sees to test for bias. For instance, how might a picture of a client affect a stylist’s choices? Or knowledge about a client’s age? Does it help or hinder to know where a client lives?

By measuring the impact of modified information in the stylist interface, the company is developing a systematic way to measure improvements in human judgment

And there are many other machine learning algorithms throughout the company:

[…]the company has hundreds of algorithms, like a styling algorithm that matches products to clients; an algorithm that matches stylists with clients; an algorithm that calculates how happy a customer is with the service; and one that figures out how much and what kind of inventory the company should buy.

The company is also using the information of the kept and returned items to find fashion trends:

From this seemingly simple data, the team has been able to uncover which trends change with the seasons and which fashions are going out of style.

The data they are collecting is also helping advance research on computer vision systems:

[…] system that can interpret style and extract a kind of style measurement from images of clothes. The system itself would undergo unsupervised learning, taking in a huge number of images and then extracting patterns or features and deciding what kinds of styles are similar to each other. This “auto-styler” could be used to automatically sort inventory and improve selections for customers.

In addition to developing an algorithmic trend-spotter and an auto-styler, Stitch Fix is developing brand new styles — fashions born entirely from data. The company calls them frankenstyles. These new styles are created from a “genetic algorithm,” modeled after the process of natural selection in biological evolution. The company’s genetic algorithm starts with existing styles that are randomly modified over the course of many simulated “generations.” Over time, a sleeve style from one garment and a color or pattern from another, for instance, “evolve” into a whole new shirt.

How does a company using so many machine learning systems look like at employee level? How is it perceived by the employees? This is what they say:

Even with the constant monitoring and algorithms that guide decision making, according to internal surveys, Stitch Fix stylists are mostly satisfied with the work. And this type of work, built around augmented creativity and flexible schedules, will play an important role in the workforce of the future.

Machine learning and AI (artificial intelligence) systems are changing the way companies do business.  They are providing an insight that either could not be grasped before, or that it could, but not at that speed, nor being accessible as a tool to assist each and every employee.

The least that can be said is that this will improve productivity in all sectors and, as today almost everyone has access to the Internet to verify a word, look for a translation, a recipe, check the  weather and countless other uses, the new generation of employees will be assisted by tons of algorithms that will analyse data and deduce, induce or summarize information to assist them in their work and in their decision-making.