14Aug

What is Machine Learning?

 

Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives “computers the ability to learn without being explicitly programmed.” Wikipedia.

Let’s decode what this definition means.

Although not specifically mentioned in the definition, the first most important concept to grasp is the feature of self-learning. This refers to the application of statistical models to detect patterns and improve performance based on data and empirical information; all without direct programming commands. This is what Arthur Samuel describes as “the ability to learn without being explicitly programmed.”

Samuel doesn’t propose that machines formulate decisions without any programming. On the contrary, machine learning is heavily dependent on computer programming. Samuel’s observation is that machines don’t need a direct input command in order to perform a set task, but rather, input data.

An example of an input command would be typing ‘2+2’ into a programming language such as Python and hitting ‘Enter.’

>>> 2+2

4

>>>

This represents a direct command with a direct answer.

Input data, however, is different. Data is fed to the machine, an algorithm is selected, hyperparameters (settings) are adjusted, and the machine is instructed to conduct its own analysis. The machine proceeds to decipher patterns found in the data through the process of trial and error. The machine’s hypothesis, formed from analyzing the data, can then be utilized to predict future values.

Although there exists a strong relationship between the programmer and the machine, they stand a layer apart compared to traditional programming. This is because the machine is formulating decisions based on its own experience and the data at hand, mimicking the process of human-based decision making.

As an example, let’s say the machine identifies a relationship between data scientists who like watching cat videos or a pattern amongst the physical traits of baseball players and their likelihood of winning the season’s Most Valuable Player (MVP) award.

In both cases, the machine is programmed to conduct the task of examining YouTube browsing habits of data scientists and assessing the physical features of previous baseball MVPs respectively. However, in neither case, was the machine programmed to predict a direct outcome. The programmer fed the input data, configured the computing power and nominated the algorithms, but the final decision is determined by the machine’s self-learning process.

The difference between machine learning and traditional programming may seem trivial at first but it will make more sense as you run through examples and come to appreciate the special power of self-learning.

The second most important thing to take away from this post is how machine learning fits into the broader landscape of data and computer science. This means understanding how machine learning interrelates with parent fields and sister disciplines. This is important, as these are the terms you will see time and again when searching for relevant study materials and hear mentioned ad nauseam in machine learning courses. Relevant disciplines can also be difficult and confusing to tell apart at first glance, such as ‘machine learning’ and ‘data mining.’

The lineage of machine learning can be understood by first examining its forefathers. Machine learning derives first from computer science. This particular relationship was introduced by Arthur Samuel at the start of this post. Computer science encompasses everything interrelated to the design and use of computers.

Within the all-encompassing space of computer science is the next broad field of data science. Narrower than computer science, data science comprises methods and systems to extract knowledge and insights from data through the use of computers.

Popping out from computer science and data science as the third babushka doll is artificial intelligence. Artificial intelligence, or AI, encompasses the ability of machines to perform intellectual and cognitive tasks. Comparable to the way the Industrial Revolution gave birth to machines that could simulate physical activities, AI is now driving the development of machines capable of simulating cognitive tasks.

While still broad but dramatically more honed than computer science and data science, AI contains numerous subfields that are popular today. These subfields include search and planning, reasoning and knowledge representation, perception, natural language processing (NLP), and of course, machine learning. Machine learning bleeds into other fields of AI, including NLP and perception through the shared use of self-learning algorithms.

For students with an interest in AI, machine learning provides an excellent starting point. Given the conceptual ambiguity of AI, machine learning offers a narrow and practical domain of study. Algorithms taught in machine learning can be applied across other disciplines, including perception and natural language processing. In addition, a Master’s degree is adequate to develop a certain level of expertise in machine learning, but you would need a PhD to make any significant progress in AI.

As mentioned earlier, machine learning also overlaps with data mining. Data mining is a sister discipline that focuses on discovering and unearthing patterns in large datasets.

Popular self-learning algorithms such as k-means clustering, association analysis, and regression analysis are used in both data mining and machine learning to analyze data. But where machine learning focuses on the incremental process of self-learning, data mining narrows its efforts on cleaning up large datasets to uncover valuable new insight.

The difference between data mining and machine learning can be explained through an analogy to two teams of archaeologists.

The first team of archaeologists focus on removing debris that lies in the way of valuable items hidden from sight. The team’s primary goal is to excavate the area, find new valuable discoveries, and then pack up their equipment and leave. A day later, they will fly to another exotic destination and start a new project with no correlation to the site they excavated the day before.

The second team is also in the business of excavating historical sites but their approach is different. They hold off from excavating the main pit for many weeks. In that time, they visit other relevant archaeological sites in the region and examine how each site was excavated. After returning to the site of their own project, they apply this knowledge to excavate smaller pits surrounding the main pit.

The archaeologists then analyze the results. After reflecting on their experience excavating one pit, they improve their efforts at tackling the next. This includes predicting the amount of time it takes to excavate the pit, understanding variance and patterns found in the local terrain and developing new strategies to improve the accuracy of their work. From this experience, they are able to optimize their approach to form a strategic model that they will implement to excavate the main pit.

If it is not already clear, the first team subscribes to data mining and the second team to machine learning.

At a micro-level, both data mining and machine learning appear similar and use many of the same tools. Both teams make a living excavating historical sites to discover valuable items. But in practice, their methodology is different. The machine learning team focus on self-learning and improving future predictions based on previous experience. Meanwhile, the data mining team concentrate on excavating the target area as effectively as possible before moving on to the next clean-up job.

29Jul

Old & New Marketing Tactics That Do Actually Work in China

Change in China tends to be a long time coming. Little transpires for years – if not decades – and then something new comes along and pole vaults past everything.

Take the restaurant industry as an example. It saw little change for years and then hipster and fusion-inspired eateries sprang up right next door to family-run restaurants still serving bowls and cups encased in plastic.

Then China went from cash to online payments almost overnight and bypassed the era of mass credit card ownership we still have in the West.

And new apartment blocks continue springing up opposite relics of Soviet design from the 1980’s. From restaurants to apartment buildings, China is a patchwork of ultra new and evidence of a bygone era.

 

 

But change happens so fast that new and old can co-exist in harmony for extended periods too. In Hangzhou, public bikes share the road with their shiny competitors. No one knows how long the public bike model will last. But public bikes remain popular in Hangzhou despite the availability of their lazy-to-park rivals.

Marketing practices in China are no different. Marketing tactics have evolved so fast that old forms have yet to retire. They live on – unrecognizable from their successors – but effective until millions of late-adopters finally convert.

In this post, I turn my attention to old and new marketing tactics that coexist in China today.

The Old Way #1: Door-to-door Sales

One of the normalcies in my previous job was the constant stream of salespeople entering the office. Unsolicited sales calls were a daily occurrence and with each visit came a new flyer. Our office was a magnet for flyers. Mind you, this wasn’t helped by the fact we didn’t have a receptionist to play gatekeeper! Unchallenged, they fluttered in and out, clutching flyers with everything from lunchtime food options to commercial real estate.

Then there were the salespeople that could enter completely undetected. At times you’d get up to make yourself a coffee and come back to find a new flyer on your desk for the gym downstairs or a local English academy.

While they can be great masters of stealth, China’s door-to-door sales army are less well-trained in the art of a persuasive sales pitch. The standard protocol is to directly walk in and ask: “(insert product/service) xuyao ma?” and scan for a flat surface or a pair of hands to offload a flyer. At the sign of any resistance, the typical follow-up response is to leave and try next door.

While my current workplace has physical barriers to entry, I’m told this marketing tactic is alive and well in many office buildings in China.

The New Way #1: Online-to-offline

In the past, traditional brick and mortar businesses were constrained by location. The only way to scale the size of their customer base was to invest in flyers. Online-to-offline (O2O) has changed all that.

O2O is empowering offline businesses of all sizes to reach new customers through online technology and platforms such as Dianping and Meituan. This is enabling businesses to streamline operations, including booking systems and online payments, and tap into a new source of customers.

 

The Old Way #2: Stencil Advertising

While office buildings have long provided a steady target for door-to-door salespeople, apartment blocks have not. Bolted metal security doors command respect in China. Nor do apartment dwellers in Beijing or Shanghai have the habit of inviting the salesman in for a cuppa.

But small business operators have found an alternative route around this problem. Rather than wrestle the security door and a hostile reception, they have turned their attention to the unprotected hallways. The blank concrete corridors of residential buildings provide an endless canvas for free stencil advertising.

Common culprits include local plumbers, electricians, and locksmiths. These makeshift billboards are an eyesore for residents – until that darned day when you leave your keys inside or the shower pipe bursts!

 

The New Way #2: Live-streaming

Rather than interrupt people at home with door-to-door sales, live-streaming delivers advertising as a novel form of entertainment. Akin to the late night infomercials we’ve become accustomed to in the West, product demonstrations take place in China via online streaming.

Online personalities demand a captive audience as they push products in real-time to thousands of concurrent users. Live-streaming is transforming other industries as well. Real estate agents, for example, use video streaming to connect Chinese buyers with properties on the other side of the globe.

 

The Old Way #3: The Wagon & Megaphone Approach

In most Chinese suburbs, there’s usually at least one pesky sole-trader with a wagon and a megaphone. Wagons vary from portable fruit stands to vehicles hauling discarded household appliances.

Mornings and weekends are the best time to run marketing activities, as residents are most likely to be home or asleep. This is where the screechy megaphone kicks in. The trick is to pre-record a short marketing message, set it on repeat, and blast it over the megaphone. Elderly residents are then usually the first to rush downstairs. While the megaphone-wagon combination is a sure-fire way to grab attention, this is one old marketing tactic I hope and urge to dissolve first.

 

The New Way #3: Online Platforms

The new way to reach Chinese consumers when they’re at home is online. Whether you’re listing products on an e-commerce site or your own micro store, online platforms are a cost-effective and scalable channel to reach new customers. Establishing an online presence also builds trust with Chinese consumers, who use the Internet as a go-to resource before purchasing.

 

The Old Way #4: Mass Advertising

A number of factors have driven China’s adoption of instant messaging apps. Stickers, online payments, and free voice messaging have each played their role. Another driving force has been the refuge from the alternative – an SMS inbox overloaded with notifications and spammy advertising. Pesky messages are a daily and sometimes hourly occurrence in China, but obviously, it must work.

 

The New Way #4: Data-driven Advertising

Rather than spam an audience with mass advertising, companies can now tailor promotions to end-users. Using Big Data capabilities powered by the cloud, advertisers can issue recommendations to users based on their location, search history, and past purchasing behavior. Businesses can also analyze visitor behavior through analytical tools to drive website optimization.

 

Much has changed in China since I arrived in 2010, and much has not changed at all. Old and new seem light-years apart, and yet they co-exist in an extended period of twilight. Old marketing tricks still work in China. But new tactics provide the fastest path to cut through the noise, scale a business, and leap past the competition.

 

Check out the China Laowai 1.0 VS Laowai 2.0 infographic at www.contentin.asia >>

8May

Infographic: China Laowai 1.0 VS Laowai 2.0

Full infographic below ⇓

The golden days of expat packages (1995-2008) have long passed and the old guard of expats in China are making way for a new class of Laowai (foreigner).

Jobs at the high-end of town – namely consulting, finance, insurance, auto industry, banking and mining – have been largely localized, and foreign faces at large international companies are hard to come by. I can’t find it on the Interwebs but I know from an event I attended last year that the Danish Chamber of Commerce in China did a survey on foreign employees working at their member companies. The survey found that on average each member company had 0.75 foreigners (that’s less than one).

And of the major Australian companies based in Beijing, including the major banks, mining companies, Telstra, Cochlear and Macquarie, there is a grand total of one foreigner (born overseas) on the books, and only at the C-suite level (Sino Gas & Energy).

Laowai 2.0

These days foreign professionals gravitate to China’s blossoming startup scene, local tech companies, English teaching (still) and smaller international organizations -all workplaces where professional business attire is not the only dress code. This fits in well with the wardrobe/suitcase of young people coming straight into the workforce from life as a language student.

The new breed of foreign worker – or Laowai 2.0 – unfortunately don’t make the same super bucks with perks on top like paid accommodation, international schooling for the kids, language tuition, car and driver and regular paid trips back to the home country. But they do make up for it with senior-sounding job titles, serendipitous career paths, and an energetic lifestyle fuelled by craft beer, technology, romance, and youthful aspiration.

So let’s take a look at what it means to be a young up-and-coming Laowai today and how we compare to the trail-blazing generation who came before us.

Also, it should be said that Laowai emanate from all over the world and naturally come from diverse backgrounds. It was just difficult to squeeze all the diversity into the one infographic and still keep it simple.

If you ‘dig’ this infographic and want to see more like it, leave a comment below and let me know what you think.

19Feb

Google’s Machine Learning SEO is Obviously Smarter than Yahoo

The world of search engine optimization is changing and machine learning is firmly behind the new face of SEO.

As virtually everyone (outside of Mainland China and North Korea) with access to the Internet can use Google to search online, Google’s new machine learning SEO technology is an easy to digest example of machine learning.

Machine learning concentrates on a prediction based on already known properties learned from the data. For example, when you type into Google “machine learning”, it pops up with a list of search results.

But over time certain results on page one will receive fewer clicks than others. For example, perhaps result three receives fewer clicks than result four. Google’s machine learning based algorithm will recognize that users are ignoring result three and that entry will thereby begin drop in ranking.

Prior to the integration of machine learning into search engine algorithms, Google focused their search efforts around strings of letters.

Google indexed millions of web pages each day to track their content for strings of letters. This included strings of letters in the web page title, website menu, body text, meta tags, image descriptions and so forth.

With all these strings of letters and combinations on record, Google could match results based on the string of letters you entered into the search bar. If you typed in: “Donald Trump,” the search engine would then go away and look for strings of letters in the following order:

D-O-N-A-L-D T-R-U-M-P

While there are various factors that influence SEO rankings, including backlinks and page speed, string letter matching has always been a major part of Google’s SEO efforts. Web pages that contained the exact string of letters entered by the user would thereby feature prominently in the search results.

However, if you were to jumble up the letter sequence in any significant way, such as R-O-N-A-L-D D-R-U-M, the results would differ dramatically.

But Google’s new algorithm – backed by machine learning – looks at “Donald Trump” not as a string of letters but as an actual person. A person who has a defined age, a defined job profile, a list of relatives and so forth.

Google can thereby decipher information without only relying on matching strings of letters.

For instance, say you search: “Who is Donald Trump’s first wife?”

Prior to machine learning, Google would search its online repository for web pages containing those six keywords. However, the accuracy of search results could be variable.

The search engine, for example, may find an overwhelming number of web pages with keywords mentioning “Donald Trump’s wife” “Melania Trump” as the “First Lady” of the U.S. Google could thereby be tricked into featuring an article regarding Melania Trump within the first page of search results.

machine learning SEO

Yahoo is obviously confused…

Google though is much smarter thanks to the invisible hand of machine learning. Google is able to decipher words not strictly as strings of letters but things. Google knows Donald Trump is a person, and Google knows who his first wife is. It can then processes this information in rapid time to display information regarding Donald Trump’s first marriage to Ivana Trump.

machine learning SEO

Google is on the money!

But what’s even more exciting is Google’s new ability to understand interconnected search queries. For example, say you follow up the next Google search with the question: “Who was his second wife?”

Again, prior to machine learning, Google would search its online repository for web pages containing those exact keywords. But Google would not be able to connect your first search query with the second search query.

Machine learning though changes the way we search. Now, given that Google already knows our first search query was regarding, “Donald Trump”, it can thereby decipher the second search query with less specific information provided.

For example, you could follow up by asking: “Who is his wife?”

And BANG, Google will come back with results regarding Donald Trump’s wife – based on self-learning algorithms.

Google’s new line of learning and thinking is very similar to human behavior and which is why Google’s new technology falls within the field of machine learning.

 

4Nov
hangzhou city review to live

What’s it like to live and work in Hangzhou?

Three months ago I packed up my bags – and my life – and moved 1,200 kilometers south to the city of Hangzhou. This is my sixth city in China in almost as many years. After two years in Beijing – the longest I’ve stayed anywhere since high school – I’m now living in Hangzhou.

Hangzhou as a city has always piqued my interest because as much as I love living in Beijing, the city does have its drawbacks. Those being the obvious one’s like pollution, the cost of rent and five-month winters. To that end, I’d always wondered if there was a more viable option and a city in China that was warmer, cleaner and cheaper to live.

While most cities south of Beijing fit that criteria, I still wanted a large city that offered a decent lifestyle, good public transportation (as I have no longing to own a car in China!), clean air, expats sports, lots of urban greenery (not just a concrete jungle), a vibrant CDB, career opportunities and fast growth in the services industry (where I want to work in). That then eliminated probably most of the cities south of Beijing!

Hangzhou, Nanjing, Chongqing and Chengdu were the four choices on my wish list. Hangzhou and Nanjing both ticked the box for their proximity to Shanghai (an hour or so away on the bullet train), as well as being well-developed cities, with a strong expat sports scene and close access to beautiful lakes and mountains.

Chongqing and Chengdu, on the other hand, are geographically isolated from Beijing and Shanghai but even cheaper in terms of cost of living and rent. The trump card for these two cities is the Sichuan food and friendless of the locals (which I’ll try justify another day in a separate blog piece).

So when a job suddenly came up in Hangzhou, I wasn’t exactly fist bumping to leave the Jing because I still had to leave behind my friends and girlfriend, but I knew I was going to a top-notch city.

So now four months into my new environment, how does the city stack up and what’s it like to live in Hangzhou?

This is a question my Beijing friends often ask me and something I’ve thought about a lot. For me, it’s like the same feeling you get when you come out of one relationship and jump straight into another. At first, you can’t help but compare your current partner to your ex, and you might even find yourself wondering whether it’s possible to go back to your old relationship! Then as time goes on you stop thinking about the past and you fall in love with what you have now.

This is actually how I’ve come to find Hangzhou. Hard to compare with Beijing but amazing in its own right.

mazi

 

Food

In the three months I’ve been here I haven’t had a chance to try all the local food but I do love being back in xiaolongbao territory and trying out mazi! Although not strictly Hangzhou food, mazi is a popular sweet dish from the greater region. Longjing tea also hails from the mountains near Hangzhou. Overall I’ve been impressed with the food here. There are Western food options around and a few mediocre Japanese and Korean restaurants too. However, having been hanging out more with Chinese than foreign friends, I’ve eaten a lot more Sichuan, Dongbei and Zhejiang cuisine while here. As I get free meals at work I also eat there a lot too!

 

Work

Hangzhou has a few major companies including Alibaba, Netease, Nokia, and Bosch. However, Alibaba is the only major company to hire foreigners in larger numbers. Even then, there’d be less than 100 foreigners working for Alibaba in Hangzhou. But that number has doubled over the last 12 months as Ali ramps up its global expansion. The Alibaba Global Leadership Program has been a big driving force behind this trend too. However, unless you are working for Alibaba, decent job opportunities in Hangzhou do seem to be pretty limited. I don’t get the impression that Hangzhou is a city you can rock up, network like a door-knocker from the Church of Latter Day Saints and expect to find what you’re looking for.

That’s something I love about Shanghai and Beijing. You can arrive without knowing anyone and within a week hone in on people from your home country or industry with precision courtesy of chamber events, meetups or co-working space events. Hangzhou, unfortunately, doesn’t have the same depth of international companies or the platforms to find opportunities if they were there.

 

Pollution and air quality

Arriving before G20 skews my grading on air quality and something my Scottish colleague warned me upon arrival. And he wasn’t wrong! The perfect blue skies quickly turned grey after the summit and blue sky days are far more sporadic. It looks like Hangzhou on average suffers from modest pollution levels but certainly not to the same extreme as Beijing.

In terms of weather, Hangzhou has a wet climate and seems to rain a lot most months of the year. It is humid in summer – which I don’t mind at all – but also wet in winter, which I’m not a fan of. Indoors too can be cold as there’s no central heating like there is in the north of China. However, having just jumped off a plane last night from Beijing, there’s definitely a big gap in temperatures. As of late October Beijing is already a fresh 4 degrees Celsius, compared to Hangzhou which is about 15 degrees. Hangzhou will get colder but it looks like you do get a slightly longer Spring and Autumn as a result of living at a lower latitude. Winter though might sting later without the central heating!

 

Scenery

I already knew this before I came here, as I travelled to Hangzhou for a couple of days in 2010, but Hangzhou really is a spectacular city for scenery! Hangzhou is a mecca for Chinese tourists and tourism is a major revenue earner for the city. The West Lake is iconic and the picture below saves me trying to explain it to you in words. Besides the lake, there is the Hangzhou Wetlands (杭州西溪), and Lingyin Temple which has great hiking options nearby. Hangzhou is definitely the cleanest city I’ve seen too in China and well manicured which must be a by-product of hosting the G20 this year.

Hangzhou's famous West Lake!

 

Professional Networking

Networking in Hangzhou is a hard slog, because while there’s a decent amount of students and English teachers around, there’s not a huge number of expats working for companies and no chambers of commerce either. Shanghai has plenty of chamber and business networking events but given the distance from work to the Hangzhou East Train Station I haven’t bothered.

For my job, I would like to meet more contacts in the cloud industry, as well as startups, developers, and digital marketing professionals but for now, my options are limited to just my colleagues. Hangzhou also naturally doesn’t have the same international co-working scene like the bigger cities in Asia such as Seoul, Singapore, and Shanghai.

New ride in Hangzhou - made my life 10x more convenient!

 

Transportation

This so far has been my biggest disappointment with Hangzhou. No airport train express line and only 2.5 subway lines. The two and a bit subway lines currently open offer limited access to places I want to go and the airport and taxis are not exactly cheap  (starting at 11 RMB) for a second tier city. The drivers too tend to be conniving and always looking for a way to earn more money by pulling over to pick up additional passengers or avoiding to the meter. Though is not unique to Hangzhou.

Peak hour traffic is not as bad as Beijing but still slow and congested. I’ve also found it much harder accessing available Uber drivers here but Didi Dache has worked relatively well. The good news though is that there are new subway lines are on the way and I’m sure this will make a big difference to help get around the city. I’ve also bought an E-bike which is one of the best investments you can make in China for slashing reasonable distances into a matter of minutes.

hangzhou subway map

I wish Hangzhou’s subway map looked like this! Currently there are 2.5 lines open.

 

Housing

While any apartment near West Lake in Hangzhou is going to be super expensive, most of Hangzhou is affordable and cheap compared to Beijing and Shanghai. You can find a single person studio/loft in the downtown area of Hangzhou for RMB 4,000-5,000 a month.

Finding a small room in a shared house in Beijing and Shanghai usually costs about RMB 3500-4500, so for a similar arrangement in Hangzhou you’d be looking at RMB 2000-2500 a month. I’m currently living in a 50 square meter loft apartment with 2 bathrooms and 2 bedrooms for RMB 3,000 a month. Back in Beijing, I paid more than that for a shoe box room in a shared house!

Finding single room accommodation though can be hard if you’re living outside of the Hangzhou CBD, as most new housing estates have at least two bedrooms. Finding accommodation in Hangzhou is not as easy as Beijing or Shanghai as there’s fewer foreigners coming in and out, and a lower frequency of advertisements on expat websites. Your best bet is walking into a local housing agent, such as WoAiWoJia and Lianjia, so you have more control in choosing the location you want rather than hoping someone online posts a place near where you work. As public transport in Hangzhou can be problematic I’d lean towards living close to work/Uni or buying an electric scooter for $2000-3000 brand new. You can find cheaper bikes on the expat website too.

Playing Football (Soccer) in Hangzhou

 

Social and stuff to do

Hangzhou has a thriving expat sports scene with various football competitions going on across town. Once you can find someone who can add you to a WeChat group you’ll be able to easily network yourself into several other groups. I originally found a futsal group from asking in a WeChat group with all the international guys at work. A Russian guy I’ve still never met added me to the futsal group.

As most sporting networks in Hangzhou don’t have a dedicated website and rely on WeChat to communicate, the key is to network when you’re on the ground in Hangzhou in order to join a team. The Hangzhou expat website is another platform you can use to find sporting groups and leave questions. Other than soccer, there is rugby, martial arts, basketball and various other groups you can join too.

Meetup.com which was a great resource to find different social groups and activities when I was in Seoul and Beijing has been a bit of a let down in Hangzhou. While there are a few groups listed on the platform, events are few and far between.

hangzhou-food

Cost of Living

In terms of cost of living, Hangzhou is 5-10% cheaper than Beijing and Shanghai for most things including eating out and taxis. Rent is definitely though where you see a difference and an opportunity to save money or pimp out on a new pad! I’ve heard of 4-5 room villas (which is the closest you can get to a house in China) outside of the city for RMB 14,000 a month which is what you would pay for a one bedroom Hutong in Beijing (though a Hutong would be more central in terms of location).

 

Other Opportunities

One of the things I’ve enjoyed most about Hangzhou is the slower pace of life and hanging out more with local Chinese people. Given that my office location is outside of the CBD and there’s a real lack of foreigners at my work campus (just two of us), I’ve had the opportunity to speak a lot more Chinese than I have over the last few years.

The locals all speak relatively standard Mandarin, with a slight mispronunciation around ‘zh’ and ‘sh’. If you are looking to study Chinese I definitely wouldn’t overlook Hangzhou as a great place to learn. You’ll certainly learn more Chinese here than Shanghai where there are more distractions and complacency around speaking English.

 

Final Verdict

If you’re moving to work for a company that’s offered you a job or to pursue studying Chinese, then Hangzhou makes a great place to call home. It’s a clean, green city with great access to nature and it’s totally up to you how much you want to stick to expat circles or integrate with the locals. Beijing is great but I found it too easy for people to gravitate to their own nationality because even smaller countries like New Zealand and Ireland have decent representation. Hangzhou though will force you to hang out with a wider breadth of expat, and locals as well if you speak Chinese.

However unless you already have a job or a study program lined up in Hangzhou, it is not going to be an easy to rock up and find a non-teaching job.

And for me? While it took me a couple of months to settle in, I’m now really happy in Hangzhou and partly because it’s a new place to explore. Would I want to stay in Hangzhou for 3+ years? Maybe not… I’d rather go back to Beijing or even Shanghai so I can have more access to interest groups and hang out with more Aussies. But for now, Hangzhou is a great place to work on my Chinese and exploring new places.

 

25Oct

Thought about a career in content marketing? Air China’s racist remarks might be your ticket

 

Last month I was forwarded the same news story by three friends within a few hours. China’s flagship air carrier Air China had just made the news for the wrong reasons with some pretty mind-boggling safety advice for tourists visiting London.

Everyone’s talking about the racist messaging that Air China has been projecting to passengers flying onboard their international routes. What really surprised me though was how this content piece actually got approval for publication! It wasn’t even published online where they could blame an intern for being overly click-happy. Instead, this piece was published in print, which you would imagine must go through a more thorough review process than online content — which can be retracted and edited at any time.

Interestingly, racial equality is not a topical issue that divides opinion in China, and so the fact that these comments would be made in a public setting by a Chinese company is unsettling but not extraordinary. Why? Because China is a largely homogeneous society (91% Han) without a history of immigration and racial diversity. Externally racial comments are simply not going to offend the general population. What’s more, as most people in China have never been to London this piece would probably be read as factual information. However, the fact that this content piece was translated and published in English by one of China’s largest companies is what really shocked me.

But having worked in China for several years, I know all too well how this could have happened.

Content marketing in China is not a mature field as it is in the West, and Chinese companies tend to question the direct relationship between content marketing and revenue. As a result, content creation and marketing departments are often hard pressed for resources. This can be more problematic for international-facing companies such as Air China. Hiring foreign writers is expensive and requires messy visa support. Integrating foreign employees into a Chinese work environment can also be a difficult balance to strike for hiring managers.

The easy answer is to outsource. Outsourcing to English-language content writers, however, is not cheap, and there are of course alternatives. The Philippines, India and potentially Hong Kong are all cost-effective solutions to source English-language content. However given the multitude of existing content that Chinese companies have access to in Chinese characters, an even easier and more cost-effective option is to translate and publish their Chinese-language content into English.

The standard workflow is as follows:

  1. Content is sent to a third party translation company to be translated from Chinese to English.*
  2. Then sent to another outsourced company (usually from an English speaking country in Asia) to proof-read before publication. The final level of quality can differ from agency to agency and writer to writer.
  3. Content is published without the content owners ever reading the content.

* The alternative is to skip the first two steps and run the Chinese text through an online translation tool. ‘Chinglish’ is everywhere in China and this is part of the reason.

However, what non-native English speakers are less pre-disposed to do is filter content. This includes filtering content for cultural and moral considerations. Or potentially even challenge the content’s messaging to avoid a later crisis. Had a content marketing agency from any western country reviewed Air China’s content, surely they would have voiced an opinion before it went to print! You certainly don’t have to be a British agency to notice Air China’s calamity.

While as a native English speaker you can’t help by shudder at Air China’s stupendous blunder, there could be opportunities out there for any English speaker who can string a sentence together, to ensure this sort of mistake doesn’t become a regular occurrence. While it might be wishful thinking, this incident could be a potential catalyst for companies in China and elsewhere in Asia to invest more resources in sound content writing systems.

As currently the bar for English content writing in Asia is set low, almost any native speaker can start now building a career in this industry. Content writing and international marketing is also a promising career choice in Asia because thankfully it’s one skill set that can’t be easily localised. While most jobs at international companies in Asia have already been localised, there could soon be an upside in demand from Chinese companies looking for foreign hires.

China’s e-commerce companies, mobile phone carriers, airline companies, and other tech companies are all rushing to open offices abroad to establish a global network. Content marketing is a crucial channel to build trust in overseas markets and create brand recognition.

Young people with previous exposure to China — whether that be living in China or studying Chinese — are well placed to put themselves forward for these roles based both in their home country or in China. But this doesn’t need to be a pre-requisite.

Whether Air China’s massive slip-up will be a ground hog day occurrence or a groundswell moment to hire native English content writers remains to be seen. But let’s hope this story leads to more jobs and recognition for the industry. After all, someone out there is going to have to put together a pretty elaborate PR campaign to restore Air China’s global image!

 

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