Be crystal clear when communicating your offer to your customers

That was the final message of last Meetup I attended organized by the Fearless Female Founders group. Madeleine Alvarez, an experienced Business Development Consultant and Trainer, presented us the Personal Model Canvas to help us come with a clear definition of our offer. The aim of her presentation was:

Learn how to package your services and products so that your customers will see their value easier, how to align your offer to the market and how to communicate it better.

Don’t we all want that and flounder to do it? Wasn’t she clear? It’s super important that the person you talk to understands your offer from your first words. After some second, they either classify your talk as ‘interesting’ and keep their attention to your saying, or disengage and just don’t put so much attention to what you say.

The first question is to find the value for our customers of our work, not the definition of it. To help us finding the words that our interlocutor will be interested in, she showed us the Personal Business Model Canvas:

http://businessmodelyou.com/

This model helps to differentiate the services (or product) characteristics we are offering from our customer’s covered needs. These last ones, that are described in this model under “How you help”, are the things we must highlight in our communication.

Then, she urged us to find a sentence that could explain our work to a 10 year’s old child. Have you ever tried that? We all know it’s important to be clear, but when working on technological fields we tend to use technical terms, and even if not in technology, usually there are words only known by the ones in the field. We dared her to do so with her job, and she amazed us!

I teach parents to say please and thank you at work, so that everybody works happily and doing their best:
– Please could you give me a report?
– Thanks for your good report.

We enjoy your presentation, thanks Madeleine!

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).

A Good Management strategy is worth!

I am on the Advisory Council of the Harvard Business Review.  Last week I took part in the selection process to choose one article to be the winner of HBR’s most prominent prize, the McKinsey Award.

The 3 pre-selected articles were very interesting, bringing advice to companies on what their C-Suite executives must focus (one was on strategy, one on globalization and the other on the importance of management practices).  

This last one on management was ‘Why do we undervalue competent management?’ by Raffaella Sadun, Nicholas Bloom and John Van Reenen. They argued that good management practices are not valued enough, they are not considered as a possible competitive advantage because they are supposedly easy to copy, they are usually wrongly relayed after a good business strategy/[other business strategies].

They did an impressive research called ‘World management Survey’ across 34 countries interviewing more than 14.000 companies, and their results show without surprise that good management practices correlate to strong performance.  Although this was never argued, they differ from established knowledge in that it is not so easy to copy that.  Achieving operational excellence depends on people: even knowing what to do, changing mentalities and changing a company’s culture is not immediately done. Moreover, there is a positive spiral effect, because employees working in those good managed firms are motivated, and having a good reputation, those companies also attract more talented employees.

Knowing this, why is that all companies don’t go for it?  They mention different issues at play, but one that I find important to emphasize is the bias on our own evaluation:

False perceptions. our research indicates that a surprisingly large number of managers are unable to objectively judge how badly (or well) their firms are run. (Similar biases show up in other settings. for Example, 70% of students, 80% of drivers, and 90% of university teachers rate themselves as ‘above average’.)

In conclusion, improving your management practices is a good strategic movement.  I encourage you to read the full article, and let’s hope your Board and C-executives will read it too 😉

Useful tips on video communication

Last week we had a lovely time at Le Chatelain Hotel for the Christmas drink organized by the Professional Women International association.  They had invited Viviana Siclari and Bruno Souverbie, who gave us nice tips to do videos.

Very interesting subject, as short videos are being used to do marketing clips to promote a new product, to present your company on internet but also to get a job: some companies ask you to send a small video to do a pre-selection before giving you a real job interview. And some real interviews are done on line, so these tips apply too!

At Waterloo Hills, we do short videos with information on ‘how-to’ going digital: you can subscribe to receive videos, each on a particular subject like how to create you e-shop, or how to take advantage of the data in Internet for improving your business.

And if not for videos, these tips also apply to your video conferences, which are becoming of greater relevance with the globalization of the work-force and also the wide spreading of teleworking.

I collected some of the tips for you, dear reader 🙂

  • Don’t put the camera at a different height of the main character: filming top down to you will minimize you, filming bottom up is not nice either.
  • Don’t change too much the camera angles, it’s disturbing.
  • Look directly to the camera when being informative, assertive, when you are directing a message to the viewer. When looking next to the camera, the effect is like in a movie.
  • Be aware of the decoration: each object in front of the camera has to be considered necessary or at least not disturbing; else it’s distracting the attention. As a bad example, look at this interview with a frame with a clown in the background!
  • Don’t wear stripes or peas. Don’t use a shining material, be also attentive to use a material that does not do noises when you move around (your arms for instance).
  • Use pastel color, so the focus is in your face and not the wall or your clothes. Red is not a good color, it makes people look unhealthy. Light blue and green are more likely to favor you.

 

I hope this advice will help you create wonderful videos, where the message passes along.  Show me yours! I’ll be happy to see the result, I surely be reviewing my professional videos.

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.

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.

Managing techniques to improve employee’s engagement: build a culture of trust

In a very interesting article in last Harvard Business Review “The neuroscience of trust” by Paul J. Zak. describes how trust works, and its relationship with employee engagement. Then presents eight management behaviors that create a culture of trust as the base to improve productivity through employee engagement.

Gallup’s meta-analysis […] shows that high engagement—defined largely as having a strong connection with one’s work and colleagues, feeling like a real contributor, and enjoying ample chances to learn—consistently leads to positive outcomes for both individuals and organizations. The rewards include higher productivity, better-quality products, and increased profitability.

[…]In my research I’ve found that building a culture of trust is what makes a meaningful difference. Employees in high-trust organizations are more productive, have more energy at work, collaborate better with their colleagues, and stay with their employers longer than people working at low-trust companies. They also suffer less chronic stress and are happier with their lives, and these factors fuel stronger performance.

Leaders understand the stakes[… but] they aren’t sure where to start. In this article I provide a science-based framework that will help them.

Paul Zak is a professor of economics, psychology  and management, and founder of the Center for Neuroeconomics Studies. He knew that a brain chemical called oxytocin was responsible in rodents to signal that another animal was safe to approach, and he wonder if it was the same for humans. He initiated a long term research to verify if the same neurological signal was also indicating us that we can trust someone. His  experiments proved that:

Oxytocin appeared to do just one thing—reduce the fear of trusting a stranger.

My group then spent the next 10 years running additional experiments to identify the promoters and inhibitors of oxytocin. This research told us why trust varies across individuals and situations. For example, high stress is a potent oxytocin inhibitor. (Most people intuitively know this: When they are stressed out, they do not interact with others effectively.) We also discovered that oxytocin increases a person’s empathy, a useful trait for social creatures trying to work together. We were starting to develop insights that could be used to design high-trust cultures, but to confirm them, we had to get out of the lab.

So he developed safe experiments to measure oxytoxin and stress levels of employees, and also measured their productivity and creativity.

Through the experiments and the surveys, I identified eight management behaviors that foster trust. These behaviors are measurable and can be managed to improve performance.

  1. Recognize excellence.
    The neuroscience shows that recognition has the largest effect on trust when it occurs immediately after a goal has been met, when it comes from peers, and when it’s tangible, unexpected, personal, and public. Public recognition not only uses the power of the crowd to celebrate successes, but also inspires others to aim for excellence. And it gives top performers a forum for sharing best practices, so others can learn from them.
  2. Induce “challenge stress.”
    When a manager assigns a team a difficult but achievable job, the moderate stress of the task releases neurochemicals, including oxytocin and adrenocorticotropin, that intensify people’s focus and strengthen social connections. When team members need to work together to reach a goal, brain activity coordinates their behaviors efficiently. But this works only if challenges are attainable and have a concrete end point; vague or impossible goals cause people to give up before they even start. Leaders should check in frequently to assess progress and adjust goals that are too easy or out of reach.
    76% of people reported that their best days involved making progress toward goals.
  3. Give people discretion in how they do their work.
    Once employees have been trained, allow them, whenever possible, to manage people and execute projects in their own way. […]
    Autonomy also promotes innovation, because different people try different approaches. Oversight and risk management procedures can help minimize negative deviations while people experiment. And postproject debriefs allow teams to share how positive deviations came about so that others can build on their success.
    […]
  4. Enable job crafting.
    When companies trust employees to choose which projects they’ll work on, people focus their energies on what they care about most.
    […]
  5. Share information broadly.
    Only 40% of employees report that they are well informed about their company’s goals, strategies, and tactics. This uncertainty about the company’s direction leads to chronic stress, which inhibits the release of oxytocin and undermines teamwork. […] Ongoing communication is key: A 2015 study of 2.5 million manager-led teams in 195 countries found that workforce engagement improved when supervisors had some form of daily communication with direct reports.
    […]
  6. Intentionally build relationships.
    […] at work we often get the message that we should focus on completing tasks, not on making friends. Neuroscience experiments by my lab show that when people intentionally build social ties at work, their performance improves. A Google study similarly found that managers who “express interest in and concern for team members’ success and personal well-being” outperform others in the quality and quantity of their work.Yes, even engineers need to socialize. A study of software engineers in Silicon Valley found that those who connected with others and helped them with their projects not only earned the respect and trust of their peers but were also more productive themselves. You can help people build social connections by sponsoring lunches, after-work parties, and team-building activities. It may sound like forced fun, but when people care about one another, they perform better because they don’t want to let their teammates down.
    […]
  7. Facilitate whole-person growth.
    High-trust workplaces help people develop personally as well as professionally. Numerous studies show that acquiring new work skills isn’t enough; if you’re not growing as a human being, your performance will suffer. […]
    Investing in the whole person has a powerful effect on engagement and retention.
  8. Show vulnerability.
    Leaders in high-trust workplaces ask for help from colleagues instead of just telling them to do things. My research team has found that this stimulates oxytocin production in others, increasing their trust and cooperation. Asking for help is a sign of a secure leader—one who engages everyone to reach goals. Jim Whitehurst, CEO of open-source software maker Red Hat, has said, “I found that being very open about the things I did not know actually had the opposite effect than I would have thought. It helped me build credibility.” Asking for help is effective because it taps into the natural human impulse to cooperate with others.

The effect of trust on self-reported work performance was powerful. Respondents whose companies were in the top quartile indicated they had 106% more energy and were 76% more engaged at work than respondents whose firms were in the bottom quartile. They also reported being 50% more productive—which is consistent with our objective measures of productivity from studies we have done with employees at work. Trust had a major impact on employee loyalty as well: Compared with employees at low-trust companies, 50% more of those working at high-trust organizations planned to stay with their employer over the next year, and 88% more said they would recommend their company to family and friends as a place to work.

My team also found that those working in high-trust companies enjoyed their jobs 60% more, were 70% more aligned with their companies’ purpose, and felt 66% closer to their colleagues.

Looking at his conclusions, I see the same values that we foster on Agile/SCRUM management methodology.

[…] you cultivate trust by setting a clear direction, giving people what they need to see it through, and getting out of their way.

It’s not about being easy on your employees or expecting less from them. High-trust companies hold people accountable but without micromanaging them. They treat people like responsible adults.

His research proved that these techniques work, and also that a culture of trust accounts for more joy: “joy on the job comes from doing purpose-driven work with a trusted team”.

Embrace the movement for a happier society raising awareness on the benefits of stopping micro-management and  of trusting people to do their jobs.

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.

Big Data workshop at the First European Celebration of Women in Computing

ECWCThis last Tuesday, I lead the ‘Discover Big Data’ workshop at the First European Celebration of Women in Computing.  There were many parallel sessions that morning and I received some questions about my presentation from the participants that couldn’t divide themselves to attend this workshop 😉

Welcome to the Big Data workshop, we need women in Big Data!

This workshop is called ‘Discover Big Data’ because Big Data is a hyped word. It is being used for anything where data is involved, but it still remains confusing as what it means.

  • You are also in Big Data  if you are dealing with data that has to be processed at great velocity, as is the case for GPS or for mobile phones.
  • You are in Big Data if you cross information that come on a variety of formats, like your customer’s transactions and your customer’s emails, or if you go to the social networks, like Facebook or  Twitter.  You can discover what are the topics being discussed, what is being said about your company or  who is talking about your product.
  • You are in Big Data  if you’re exploiting one of the many big available datasets like weather information, official administration records like property records or  financial information, economic indicators…

What can be done with Big Data?

It is mainly used for customer intimacy, discovering your customer profiles and target them on a one to one base. Finding their preferences and the hidden patterns to predict customer churn.

It can be used for optimisation, finding patterns of systematic problems hidden in your historical data. It can help for organising your maintenance, or to improve the supply-chain, finding better logistic solutions, optimise processes.

It is also used for innovation: It can help you create your new product. Looking at your competitors and finding the white-spaces or uncovering market trends.

And more generally, with all the available data you can create models forecasting future events and behaviors. Through what-if analysis to predict the outcomes of potential changes, you can direct your business strategy. It helps anticipating previously unforeseen opportunities, as well as avoiding costly situations, finding new revenue opportunities or identifying more effective business models.

As you see, there are great business opportunities!

How can we do all that?

There are many techniques like statistical analysis, data mining, text analysis, sentiment analysis, graph analysis, machine learning, predictive analysis, neural networks, conceptual clustering…

You may have heard already some of those words that sound promising but that also sound very complicated. And even so, the Big Data field is growing exponentially as men are running for it.  There are only 10% of women, don’t you want to be part of it? Companies that took this wave are thriving, well ahead of classical business. They are proposing you the right product at the right time, with the features you are looking for, for the price you are willing to pay. They are  increasing their profits while shaping our future with the products and business strategies they are creating.

I hear you saying: This is great but I don’t know a thing about this and it sounds so complicated. I’m here to tell you that not all of it is that difficult.

YOU could be in Big Data.

If you are in computing you have a leg up. And if you like mathematics you’ll enjoy being a data scientist. But you could be in Big Data even if you are not a techy person.  If you are in HR, in marketing, if you are a manager or a decision-maker with the right mindset open to data, you can exploit the Big Data wave.

Even if you see the potential, women tend to think ‘it’s not for me, I don’t have the competencies’.

Let me use some feminine stereotypes to illustrate we have the basic skills:

  • We have a tradition of getting together and talking too much.  And we have a tendency to be matchmakers.  We can put those skills of information gathering and making connections to good use finding relationships between data.
  • Who recognises herself in this? We are control freaks and plan everything, even the time of our loved ones.  Don’t you have a TODO list for your partner on Saturdays?  I do: Love, since you are driving Alex to the scouts, could you please pass by and drop the trousers at the dry cleaner?  What if you knew what your GPS knows already, that a road is blocked?  You could have asked him to bring some bread back as he’s going to pass near the bakery.  Don’t you feel satisfaction when doing things efficiently, optimising the Saturday time? So imagine tapping into all the available information and using it to improve the processes, it’s a rewarding job.
  • And if you have artistic skills, visualisation is your field. This is a new branch of data science, they are creating new techniques very interesting to show more than 3 dimensions of data, so you can see easily relationships graphically.
  • Generally speaking, I think we women have a natural talent to be data analysts: the ‘What if’ comes natural to us, we always investigate all possibilities before deciding for one, isn’t it?

Summarising, we saw there is business in here, and that we have the basic skills to be in the Data business.

Moreover, it is important that more women move into this field, not only because of the many business opportunities, but also because there are ethical issues involved in Big Data. We can mention data privacy and price gauging as some of these issues, but there are other business models that can be controversial.

The rules of what can be done with the data and what is off-limits, are being defined right now.  Let’s not miss the opportunity to give our view on this.

As an example, there is a great initiative from the Data2X program of the UN, who’s doing a research on women’s freedom of movements through satellite images and their phone geolocation.  Are they limited in their movements in some countries, do they have access to education, to health care? Great initiative, but what about the same at a private level: is following the movement of your partner with her/his phone geolocation ethical? What about tracking the movement of your children, as it’s done already in some countries?

It’s important to have our saying in the ethical uses of all those lakes of data and be represented in the decisions that will define our future society. We, women, have a natural tendency of looking after our loved ones, taking their needs in consideration. That’s what Big Data is needing, people that set the rules for using the incredible amounts of data, taking into account the different perspectives and with a long term view in mind. It’s the moment to use our feminine voice to shape a better society for all of us, participating also in the creation of the new business models.

In this workshop you will hear success stories to show you the opportunities to be included in this field. I hope you’ll join the Big Data movement.