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.

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Negotiating contracts, getting the deal.

This post is about THE basic stuff in business: how to get the contract, and how to make it be a good deal.

Talking about Money, by www.vco-global.com

Image from Talking about Money, www.vco-global.com

Lately, I’ve participated in 2 great talks for women entrepreneurs from PWI and PWN Munich. One was about about discussing money and remunerations and the other about sales.  The key basic principles both speakers mentioned are that you have to gain the trust of your customer before entering in the deal negotiation, and that you have to involve the customer in the construction of the deal.  Become his partner, not his servant.

Here are my gained insights on the process:

  • Open up the conversation simple but get to have your customer curious by what you can provide. This can be done with a case study, a blunt (but realistic 😉 statement like “my previous customer gained a 50%  ROI” or “solved his problem in x months”, something that will put him in an attentive mode.
    Once there, he will want to hear more.  So now, you have gained the right to ask questions.  He’ll accept to give information in order to go further and hear your solution.
  • That’s your opportunity to ask questions to learn about his problem and adapt the proposal to his needs. This part of asking questions is crucial, use it for contextual questionning: the more insight you have on the situation of the customer the better you’ll be to evaluate the work involved.In the first questions you will be learning about the customers’ situation, ask factual and context questions.  But remember that you are entitled to just a few questions before he gets bored: in this phase he’s not learning anything.  So at some point, you move to next phase, where you have to challenge his description of the situation using your previous experience, and give away some insight of the ‘solution’ you could provide, but don’t go into much detail.
  • It’s during this second ‘challenging’ questioning phase that you are gaining his trust.  Because you are proving that you understand his situation, that you had previous experiences with the same challenges.  You are rising questions that prove that you know what issues are in stake, making him think about them, giving him insight he may not have on the challenges ahead.Be ‘Columbus’ guiding the customer to see the solution. You are also gaining insight on the level of understanding of your customer on the problematic at hand.  And you’re setting the value you are bringing to the table with your proposal by the same way: the less he knows, the more you are bringing to the table.Here are some examples of great contextual questions:

    – about the stakeholders: “Who else is involved in the decision process? ”

    – about their previous experiences, good and bad: the work will be harder if there is a reluctant stakeholder in the game, or you may add value with your experience if they had a bad previous experience already.

    – about time constraints: “Why is it important to solve this issue NOW?” A question like “What would happen in a year if we don’t do this?” makes them realize the value of your proposal.

    – remind him of his PAIN: “What could happen if you don’t do ..? It’s better than you saying: “If you don’t do .. then …”

    By the end, you should have learned about the context of your work, the available or expected budget, you may have learned about your competitors (if any) and about the decision making process.
    Think of this process as Diagnose before you prescribe.

  • Co-create the solution with the customer. Don’t push the sale, make the customer wanting the purchase. Come up with him with different options, like 3 proposals with different levels of scope and price, so he can choose the budget (and content) he’s willing to sign.  The great advantage to co-create the proposal, is that you don’t have to convince him of the proposal, he did it with you.  You have his ‘buy in’ from the beginning.
    Let the customer be the HERO that comes to ask you for the solution. Better than saying “The benefits of my solution are…” is when the customer says “Your solution could help us with …”
  • Negotiate money issues at the end, when he’s convinced of the value added of the deal.
  • To end the conversation: “How would you like to proceed?”  opens the line to “I could send you a letter of understanding”: that’s the HAPPY ENDing you are looking for!
    Who will say that first sentence?  With a big SILENCE you could make him ask for it 😉

Great HAPPY ENDing to all your deals!!!

Many thanks to Jack Vincent and John Niland for their insights and entertaining presentations.

 

 

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Sexism spotted with Maths!

cc-restore2

I did a talk in May this year called ‘Restore the balance of data’ at the Data Innovation Summit.  It was about sexism and other biases that are implicit in our existing electronic traces (actual and historical data) and my concern because we are using that data as baseline information to create the new prediction algorithms.

I’ve discussed this many times at home when preparing the talk.  We had vivid discussions with my husband and lovely sons over our family Sunday lunches. That’s how it didn’t surprise me that my eldest son, Alex, thought of me when reading  this article of the MIT Technology Review about sexism in our language.

The article is about a dataset of texts that researchers are using to “better understand everything from machine translation to intelligent Web searching.”  They are transforming words in the text into vectors, and then applying mathematical properties to derive meaning:

It turned out that words with similar meanings occupied similar parts of this vector space. And the relationships between words could be captured by simple vector algebra. For example, “man is to king as woman is to queen” or, using the common notation, “man : king :: woman : queen.” Other relationships quickly emerged too such as  “sister : woman :: brother : man,” and so on. These relationships are known as word embeddings.

The article is about the problem that researchers have identified on this data set, they say “: it is blatantly sexist.”  Here are some examples they provide:

But ask the database “father : doctor :: mother : x” and it will say x = nurse. And the query “man : computer programmer :: woman : x” gives x = homemaker.

Thinking about it, isn’t it obvious that if we have biases on our behavior, the writings about our world would be biased too?  And anything derived from our biased writing traces will reflect our views with all our biases too.

So we learned to extrapolate from our old behavior to predict our future behaviour… just to discover that we don’t like what we are getting out of it!  Our old behavior, amplified by the algorithm, doesn’t seem so good isn’t it? It’s clearer than ever that we don’t want to continue behaving like that in the future… Well, that’s a positive point, it’s good that this uncovers our blind spots, isn’t it?

Now the good news: it can be fixed!

The Boston team has a solution. Since a vector space is a mathematical object, it can be manipulated with standard mathematical tools.

The solution is obvious. Sexism can be thought of as a kind of warping of this vector space. Indeed, the gender bias itself is a property that the team can search for in the vector space. So fixing it is just a question of applying the opposite warp in a way that preserves the overall structure of the space.

Oh, seems so easy…for mathematicians anyway 😉  But no, even for mathematicians it is difficult to find and to measure the distortions:

That’s the theory. In practice, the tricky part is measuring the nature of this warping. The team does this by searching the vector space for word pairs that produce a similar vector to “she: he.” This reveals a huge list of gender analogies. For example, she;he::midwife:doctor; sewing:carpentry; registered_nurse:physician; whore:coward; hairdresser:barber; nude:shirtless; boobs:ass; giggling:grinning; nanny:chauffeur, and so on.

Having compiled a comprehensive list of gender biased pairs, the team used this data to work out how it is reflected in the shape of the vector space and how the space can be transformed to remove this warping. They call this process  “hard de-biasing.”

Finally, they use the transformed vector space to produce a new list of gender analogies[…]

Read the full article if you are interested on their process to de-biased.  Their conclusion, with which I completely agree is:

“One perspective on bias in word embeddings is that it merely reflects bias in society, and therefore one should attempt to debias society rather than word embeddings,” say Bolukbasi and co. “However, by reducing the bias in today’s computer systems (or at least not amplifying the bias), which is increasingly reliant on word embeddings, in a small way debiased word embeddings can hopefully contribute to reducing gender bias in society.”

That seems a worthy goal. As the Boston team concludes: “At the very least, machine learning should not be used to inadvertently amplify these biases.”

 

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Learning how to learn

Learning how to learn

I’m an eternal learner.

There are so many interesting things that time is precious, so when I came accross this MOOC I couldn’t but enroll and check it out.  Anything that helps learning stuff while reducing the needed studying time really appeals to me!

I’m talking about the online course from Coursera called Learning how to learn, by Barbara Oakley and Terrence Sejnowski, created by the University of California.  I cannot but recommend it to everyone, there are plenty of good tips to make the process of learning easier. Here are my take-aways:

  • Create the habit of doing timeboxing work, using for example the “pomodoro technique”(*) where you set intervals of 25 minutes of working time, following by 5 minutes’ break (or by a longer break after 4 consecutives working slots).  Concentrating in the process (it’s time for my 25 minutes of work)  will make it easier to avoid procrastination.
    And don’t forget to gratify yourself after a focussed  interval of time spent working (a coffee, a piece of chocolate, or wandering on your garden to enjoy a sunny day as today 😉
  • Program the toughest things first, we have more energy to tackle our resistance during the morning.
  • Add time of relaxation and physical exercise to let the studied material ‘sink in’ and get connected in your brain, it’s part of the learning process!
  • The best way to fix the studied material is not to read it over and over, but to recall the information, and space the recalling over time. My son’s favorite method is using flash cards.
  • Test yourself, do exercises in different contexts, so that you make more connections to retrieve the chunks of material.
  • Prepare today your TO DO list for tomorrow, it will have time to be absorbed and tomorrow it will not occupy one slot of your working memory.

I’m sure there are many other tips I didn’t mention that may appeal to you, if you decide to follow the course drop me a line to let me know your peaks 🙂

*: The pomodoro technique has 5 fundamental stages : planning, tracking, recording, processing and visualizing. In the planning phase, tasks are prioritized by recording them in a “To Do Today” list. This enables us to estimate the effort that is required for the tasks. As pomodoros are completed, they are recorded, adding a sense of accomplishment and providing raw data for subsequent self-observation and improvements. At the end of the day, you get a concrete feedback on your estimates, if there are still tasks on the list… you are like me, too optimistic! 😉

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DIS2016 Restore the balance of data

Two weeks ago was the Data Innovation Summit 2016.  I was due to speak using the presentation format of ‘ignite’.  For the ones who don’t know this format, it’s a nightmare! Out of joke, it means that slides go automatically at regular intervals (15″ in my case).  You cannot stop it, you don’t control the flow… so to be synchronized, you really have to prepare your speech in advance, you must know exactly how much time it takes to explain each of your points, what examples you’ll be presenting (check it out, 15 seconds go very quickly when you’re looking for your words :-))).

So here it is, my 5′ presentation, if you only count the time on scene…

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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.

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Pre-Crime unit for tracking Terrorists?

minority-report-11-3Due to last events in Belgium, the terrorist bomb attacks in Zaventem and Brussels, I couldn’t but remember the article from Bloomberg Businessweek talking about pre-crime: ‘China Tries Its Hand at Pre-Crime’.  They refer us to the film Minority Report, with Tom Cruise, that takes place in a future society where three mutants foresee all crime before it occurs. Plugged into a great machine, these “precogs” are at the base of a police unit (Pre-Crime unit) that arrests murderers before they commit their crimes.

China Electronics Technology company won recently the contract for constructing the ‘United information environment’ as they call it, an ‘antiterrorism’ platform as declared by the Chinese government:

The Communist Party has directed [them] to develop software to collate data on jobs, hobbies, consumption habits, and other behavior of ordinary citizens to predict terrorist acts before they occur.

This may seem a little too much to ask, if you think about it you may need every daily detail to be able to predict terrorist behaviour, but in a country like China where the state has control over their citizens since many decades, where they have no privacy limits to respect and a good network of informants…

A draft cybersecurity law unveiled in July grants the government almost unbridled access to user data in the name of national security. “If neither legal restrictions nor unfettered political debate about Big Brother surveillance is a factor for a regime, then there are many different sorts of data that could be collated and cross-referenced to help identify possible terrorists or subversives,” says Paul Pillar, a nonresident fellow at the Brookings Institution.

See how now there is also a new target: subversives.  the article continues:

China was a surveillance state long before Edward Snowden clued Americans in to the extent of domestic spying. Since the Mao era, the government has kept a secret file, called a dang’an, on almost everyone. Dang’an contain school reports, health records, work permits, personality assessments, and other information that might be considered confidential and private in other countries. The contents of the dang’an can determine whether a citizen is eligible for a promotion or can secure a coveted urban residency permit. The government revealed last year that it was also building a nationwide database that would score citizens on their trustworthiness.

Wait a second, who’s defining what is ‘trustworthiness’, and what if you’re not?

New antiterror laws that went into effect on Jan. 1 allow authorities to gain access to bank accounts, telecommunications, and a national network of surveillance cameras called Skynet. Companies including Baidu, China’s leading search engine; Tencent, operator of the popular social messaging app WeChat; and Sina, which controls the Weibo microblogging site, already cooperate with official requests for information, according to a report from the U.S. Congressional Research Service. A Baidu spokesman says the company wasn’t involved in the new antiterror initiative.

So Skynet is here now (remember Terminator Genisys?). Even if right after a horrendous crime you can be tempted to be happy that this ‘pre-crime’ initiative is being constructed, there are way too many negative aspects still to consider before having such a tool. Like in which hands will it be, who’s defining what is a crime, what about your free will of changing your mind, to mention some.  Let’s begin thinking how to tackle them.

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Great visualisation tips

I would like to share with you this article on the Harvard Business Review.  They give excellent advice to ‘make extreme numbers resonate’.  They give 3 examples to illustrate their tips:

  1. Challenge: Green Mountain sold 18 billion coffee pods in two years. How can you give people a concrete sense of just how many objects that is?
    HBR-Visual Huge numbers- R1601Z_VS_CUPS_B-1024x774

 

  1. Challenge: Only three in 10,000 high school basketball players ever make it to the NBA. How can you give someone a deep understanding of the rarity of that feat?
    HBR -Visual small numbers-R1601Z_VS_BASKETBALL-1024x584

 

  1. Challenge: Every year tens of thousands of people leave one U.S. city for another. How can you show changes on this scale when it’s so hard to keep track of complex movement? […]

 

HBR -Visual complexity -R1601Z_VS_MOVEMENT-1024x568

In the first example, they give tips to visualise huge numbers, the second one is for small numbers, but the the third one is really interesting, as it shows an extremely clear way to picture complexity.

 

 

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Good resolution for 2016: let’s improve our communications skills

Dr Travis Bradberry wrote this post in Linkedin some days ago about “Why We Struggle to Communicate”.

Communication is the real work of leadership; you simply can’t become a great leader until you are a great communicator.”

communication-importanceYes, communication is critical in leadership, inspiring people and taking into account every member of the team. For an entrepreneur, it allows you to transmit your thoughts and ideas better, improving the chance of convincing investors and make ‘it’ happen.  For intrapreneurs, it helps aligning people towards the same goal. But in the end, it is an essential skill for everyone because understanding each other is the basis for better collaboration with your professional and personal relations. 

So join me on this New Year’s resolution for 2016:  let’s improve our communications skills following the strategies to take action that the author states in his article:

Speak to groups as individuals.[…] You want to be emotionally genuine and exude the same feelings, energy, and attention you would one-on-one.[…]
Talk so people will listen. […] means you adjust your message on the fly to stay with your audience […].
Listen so people will talk. […] you must give people ample opportunity to speak their minds.[…]
Connect emotionally.[…] Show them what drives you, what you care about […].
Read body language. Your authority makes it hard for people to say what’s really on their minds.[…] Pay as much attention to what isn’t said as what is said […].
Prepare your intent.  Don’t prepare a speech; develop an understanding of what the focus of a conversation needs to be […].
Skip the jargon. […]

And the last advice:

Practice active listening. Active listening is a simple technique that ensures people feel heard, an essential component of good communication. To practice active listening:

  • Spend more time listening than you do talking.
  • Do not answer questions with questions.
  • Avoid finishing other people’s sentences.
  • Focus more on the other person than you do on yourself.
  • Focus on what people are saying right now, not on what their interests are.
  • Reframe what the other person has said to make sure you understand him or her correctly (“So you’re telling me that this budget needs further consideration, right?”)
  • Think about what you’re going to say after someone has finished speaking, not while he or she is speaking.
  • Ask plenty of questions.
  • Never interrupt.
  • Don’t take notes.

Happy 2016!

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