The issue of data protection has become a hot topic during the 2021 World Artificial Intelligence Conference (WAIC), with workshops, forums and speakers pledging to enhance data security and exhibiting related technologies, after the removal of the “Chinese Uber” Didi Chuxing app from domestic app stores raised concerns and discussions about how data should be treated in cross-border business. 

Several speakers told the Global Times on the sidelines of WAIC that the trend of using technologies and services for data privacy protection while still unlocking the value of big data is “on a rapidly rising curve,” as artificial intelligence companies face tightened regulation requirements on data protection. 

A number of forums are dedicated to the discussion of creating a balance between unlocking the value created by big data and keeping the date safe through technical means.

One such technology is called the Privacy-Preservation Computation (PPC), which is a set of cutting-edge technologies that allow companies to analyze data jointly without seeing the specific data. 

For example, the technology will allow companies to know what tea brands 20-30 girls like to drink based on a pool of data, but they wouldn’t be able to see the original data of each person involved.

“I think China is leading in Privacy-Preservation Computation. Especially this year, many new companies have emerged in the field of privacy calculation, including companies that used to engage in data, safety business or even blockchain,” a person at the Ant Group booth told the Global Times. 

Ant is also using PPC technologies by “scale application,” while promoting the technology to other companies commercially, the person said. 

Ant Group demonstrated its AntChain Morse MPC platform. The platform utilizes cutting-edge Secure Multi-Party Computation technology, cryptography and privacy protection technology to ensure that all data cooperative parties can share data while keeping their original data private, which has been applied in intelligent risk management and joint scientific research.

Wang Shuang, founder of NVXClouds Tech, also a speaker at the WAIC, said that the market of privacy calculation has surged this year. 

Data also shows a booming trend of Chinese companies’ usage and sales of data privacy services. According to a report published by the National Industrial Information Security Development Research Center, the number of China’s privacy calculation patents has risen rapidly in the past two years, with most patents applied by Ant Group, Alibaba and WeBank.

The report showed that patent applications started to rise abruptly in 2017, and there were nearly 5,000 privacy calculation patents by the end of April. 

Such a trend is very likely to accelerate after Didi’s sudden removal from domestic app stores struck alarm bells among domestic companies that Chinese regulators would toughen management over data privacy protection, experts said. 

Didi’s shares slumped for four consecutive days after the removal. On Thursday, the company’s shares slumped by 5.37 percent to $11.27 on the New York Stock Exchange.

“Didi’s rectification should serve as an example of how Chinese regulators would supervise big data platforms’ data treatment model. In the future, the direction of such supervision would become clearer, which would exert a positive influence on national security as well as privacy protection,” Kuang Yuqing, founder of Lens Company Research and a veteran financial commentator, told the Global Times. 

The problem of data privacy protection has become more sensitive for cross-border flow of data, like the Didi case, as China and the US’ relations deteriorate amid US sanctions against Chinese tech companies, according to some speakers.

“There is a strong demand for PPC in cross-border data flow, especially among the unmanned vehicle enterprises and cross-border financial institutes,” Bian Yang, vice president of Fudata Technology, told the Global Times.

Wang also agrees that PPC technology can help companies share analyzed data abroad under government authorization and data compliance, but keep the source of the data at home.

But experts still pointed out some challenges in the current data privacy sector. An Guangyong, an expert at the China Mergers and Acquisitions Association, told the Global Times that in terms of data privacy protection, China still lacks regulations as well as technologies.

“In Western countries, the updating speed of credit and related laws is most frequent (among all laws), and I think China should also formulate relevant laws to regulate data privacy, while updating it from time to time,” he told the Global Times. 

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The jury is still out on what makes Gen Z’ers tick, but while the research is still ongoing there is much evidence to suggest that a marketing strategy utilizing machine learning is exponentially more effective with the next generation.

One thing is abundantly clear to every marketer worth his salt; Gen Z customers are “ninja-level” efficient at swatting away regular ads and pop-ups. They are strongly immune to hard sales and obvious sales content.

Despite all the difficulties that marketers are facing in reaching a wide Gen Z audience, Jonathan Jadali, CEO and Founder at Ascend Agency has found great success in leading Gen Z-focused startups to victory in this marketing struggle.

So what makes the typical Gen Z customer tick and how can businesses and startups build a brand that is appealing to them, utilizing cutting edge technologies?

Jadali shares the ways in which he has used a data and machine-learning strategy in getting many of his clients from obscurity to domination of the Gen Z market.

Train Your AI To Get A Bit Messy Sometimes

Content, as they say, is king, but the wrong kind of content isn’t even fit to be a pawn in this game. To get startups headed in the right direction, Jonathan often helps direct his clients at Ascend Agency on creating the right type of content for the right type of client. 

Emotional EQuity: How Leaders Use Empathy To Inspire Successful Teams

While most brands are focused on putting out well-curated video and image content in a bid to drive engagement on their social media platforms, Jadali advises that this might not be the best way to go if Gen Z’ers are your target audience. 

The ideal Gen Z customer thrives on spontaneous and ‘messy’ content. As Jadali states, “Gen Z customers are all about being real…they connect well with unfiltered and unedited content because it tends to feel less salesy than others.”

For instance, a makeup brand is better off posting a video of a makeup session, in front of a cluttered vanity table, than a photoshoot with a perfectly made-up face.

This is important to keep in mind when implementing any machine learning into your marketing strategy. Whether you are creating a chat bot, or building a data-driven marketing campaign – it’s important that your system learns to be imperfect. 

When AI or Machine Learning is used in marketing, sometimes it can come off as, well, robotic. Gen Z will be an important moment for machine learning marketing as it will help us get closer to contextual AI – machines that more accurately predict and reflect human behavior.

Gen Z wants to see the messiness of life and its process reflected in your content. Brands that do this,  are the brands that they are drawn to and often build loyalty for. 

Don’t Sell Them Products

How does it look? How effective is it? How satisfying is your service? All these are valid marketing questions and things that in the past had been asked by your millennial customer base.  

According to Jadali, these questions do not matter nearly as much to a Gen Z audience. 

“Clearly, customers want products that work and businesses that deliver, but with a Gen Z audience, that doesn’t seem to be the right way to lead in marketing to them.”

Having worked with both Fortune 500 companies and smaller startups alike in the last 3 years since Ascend Agency launched, Jadali is fairly certain that Gen Z customers are way more attracted to how your business makes them feel. 

This is where machine learning can really come in handy. Understanding your customers’ moods and habits can help you tap into what makes them feel great about themselves and the products  in their lives. 

Gen Z customers are tired of hearing about how amazing your product is, businesses have been hyping up their products for as long as businesses have existed and Gen Z’ers aren’t having any more of it. In Jonathan’s words, “Sell experiences, not products, and your products will head out of your door as well.”

“What dominant feeling do you want to evoke with your content?” A question that is popularly asked at the Ascend Agency office, is one that has helped brands build consistency in their content style and delivery and that has brought the Gen Z customers in their droves.

This question can be answered through aggregated customer data that helps you better understand the emotions from brands that they also engage with.   

Red Bull is a great example of a brand that utilizes data and machine learning in this manner. Their video content covers high-risk sports, like Skydiving, Bungee jumping, etc. From customer data processed by predictive analytics and machine learning systems, the dominant feeling Red Bull chose to evoke is one of courage and strength. 

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