Artificial Intelligence (AI) is no longer just the domain of sci-fi fans or tech nerds, as it’s becoming a key pillar of how we do business and live our future lives. While we still need time to see how far this prodigious technology will go, AI has always been a subject of interest to some of the brightest minds and well-known personalities. Here are some of their thoughts on AI.
Did you know that the average conversion rate on e-commerce sites globally is around 2.6 percent? That says a lot about the immense potential to grow online sales.
One common practice to achieve that growth is through remarketing. Now with advances in artificial intelligence (AI), you can take advantage of methods such as machine learning (ML) to ensure that your remarketing is more effective than ever.
Mike browses on your site and adds a pair of shoes to the shopping cart. He then abandons his cart without actually purchasing anything. As Mike continues his online activity, he is shown an ad promoting the very shoes that he was evaluating, pushing him to go back to your website and complete the purchase. The ad may offer a discount as an incentive. This is classic remarketing.
Remarketing aims to reach interested prospects who have not converted, and retarget them with relevant marketing messages that will entice them to purchase from you. It offers higher return on investment (ROI) because it engages people who have already shown an interest in your product and are hence more likely to convert.
Advertising is not the sole remarketing channel. Brands can re-engage with shoppers through eDMs and in-app notifications as well. The more targeted and relevant your messaging, the more effective your remarketing strategy.
And this is where AI can help.
Boost Retargeting Performance With AI-based Segmentation
Traditional segmentation marketing, on which remarketing is based, has its own drawbacks – it’s difficult to link the right person with the right product, especially after shoppers log out, and even more complex to sift through data on different interests and groups to discern true interest and needs. Campaigns rely heavily on experimentation to see what works, and this can be time-consuming and expensive.
In this context, AI can facilitate better user segmentation.
AI helps optimize remarketing strategies by using ML to interpret data around users’ purchase history and identify patterns that can help predict future purchase behavior.
For instance, an AI-powered audience buying platform like Appier’s CrossX Programmatic Platform uses deep learning algorithms to analyze dozens of user behaviors in real time and predict which users have a higher chance of converting. Marketers can then prioritize retargeting their most valuable users.
Trigger Conversions With Personalized Recommendations
ML can also combine user behavior analysis and product information to generate personalized recommendations, further improving chances of conversion.
Here are some benefits of optimizing remarketing strategy using AI:
- Retarget real shoppers for better ROI
ML can distinguish between shopping – actual conversion – and searching (window shopping) behavior, allowing you to retarget users with a higher likelihood of converting. Would you rather remarket to Elle who spends a lot of time browsing online but rarely buys or Patricia who visits an online site when she wants something and buys it?
- Capture fast-changing behaviors using deep learning models
ML can identify patterns and predict changes in shopping behavior to make accurate recommendations. For example, Daisy has been browsing coats for a few days, but a sudden change in weather means a warm weekend, and so she searches for a T-shirt on Friday night. AI will give you insights around the short-term change in Daisy’s shopping behavior and recommend T-shirts for her to buy, increasing the possibility of an immediate purchase.
- Retarget shoppers with the items they actually want to buy
AI can help you distinguish between serious intent to purchase from a user’s longtime browsing pattern versus something that they just checked out online but are not really interested in.
Offer Value Across the Customer’s Entire Shopping Journey
Remarketing offers value through the customer’s entire purchase journey online. Take a user who books a ticket to Tokyo. Traditional remarketing would target them with flight promotions to different destinations. This offers low value at best and is inaccurate targeting at worst. Once the user has booked their trip to Tokyo, they will next shop around for hotels, tour bookings, etc. And this what remarketing should address.
AI is enabling smarter marketing by making relevant recommendations based on previous purchasing behavior and data across ecommerce sites, expanding the number of products the consumer is exposed to. Marketers can use valuable insights on users’ intentions to retarget them with products that are similar in function, design, etc.
When AI is used to remarket cross screens, this proposition is further strengthened. Cross-screen remarketing allows you to reach your user on every screen they own and shorten the time between initial visit and purchase. And AI-powered platforms like CrossX help reduce the complexity of cross-screen remarketing by giving you insights on who to reach, on what screen, with what message and when.
As digital channels such as social media continue to be a vital tool for customer engagement and product promotion, it’s easy to overlook “old school” tools like email. However, recent improvements in email marketing – underpinned by artificial intelligence (AI) – are turning email marketing into a viable marketing tool.
Compared to the possible instant response on social channels, email marketing tends to be less effective due to its limitations, including difficulty in finding and retaining subscribers, and low open and click-through rates.
Conventional wisdom would have you change certain things about your email content to improve its performance, such as offering more discounts, crafting a better subject line, or sending messages with a different frequency. But these suggestions are based on the notion that, as a human, you can guess why readers are or aren’t connecting with your content. While true to a certain extent, this assumption requires a high degree of trial and error to arrive at the desired response from recipients.
Now, help from AI makes it possible for businesses to discover new lookalike customers, better understand and segment existing customers, predict topics of interest, and anticipate customer behaviors. These actions can help you solve some of the most vexing challenges with email marketing.
Increase Open Rates With AI-Powered Segmentation
There is a reason why your readers aren’t connecting to your content. By taking an AI-based approach, you can see a highly accurate analysis of the problem – as well as your audience’s needs – putting you considerably further along than if you only employ guesswork.
While only 21 percent of marketers in Asia Pacific delivered personalized email beyond just name in 2017, 76 percent of them indicated that they were keen to do better personalization in email marketing, according to a Econsultancy report.
The report also pointed out that using a data point in addition to the recipient’s name is twice as likely to trigger them to open the email. Imagining if AI could write a short novel that almost won a literary award, it can also analyze all the user data including the content consumed by users across screens, and then extract the most frequently used keywords to identify topics that your audience is most interested in, and create predictive segmentations.
Once you gain such actionable insights, you can then develop content or create offers that correspond closely with their preferences and needs. As AI is capable of identifying as many keywords as possible, you will have multiple touchpoints to engage with your audience.
You can even predict who will respond to your new campaign based on their responses to past campaigns, and customize mailing features that make it easier for them to do so.
For instance, a major online and print publisher in Taiwan used to send the same emails to all readers, resulting in low open and click-through rates. Content and headlines weren’t relevant or sufficiently attractive to trigger recipients’ interest.
By adopting an AI-based approach, the publisher used deep learning to link reader profiles with their online behaviors to establish segmented profiles based on key attributes, such as age and interests. This process allowed the publisher to tailor mailing lists to the right group with appropriate marketing content. As a result, its open rates increased by 42 percent, and click-through rates increased by as much as 107 percent.
Grow Your User Base With Lookalike Audiences
The right AI models can also analyze data gathered from users’ online activity to find those who “look like” your current customers, helping you develop targeted ads and other outreach efforts. This process starts with the breakdown of demographic data about your current customers with as much granularity as you choose. It can include data from your website, campaigns, apps, customer relationship management software, application programming interface integration, and more.
An AI-enabled platform then maps that information with additional sources to find close potential customer matches. Using this valuable data set, email outreach becomes less of a guessing game and more of a precise targeting tool.
Retain Subscribers with AI Prediction
Based on behavior patterns, the AI-enabled platform can help you identify subscribers who are likely to leave your service. Certain actions indicate their readiness to move on, but you can prevent this migration if you give them reasons to stay. Once you have identified this subset of your subscribers, you can plan and implement your re-engagement strategies, such as:
- Creating emails targeted solely to this group of “potential unsubscribers”, and segmenting further into interest groups.
- Offering surprises, deals or rewards specific to those groups.
- Using formatting and links to make it easy for readers to take action.
AI is the most promising tool that is driving personalization in email marketing like never before. The AI-based approach makes it possible to identify the behaviors and interests that should trigger customer engagement in email marketing, and determine how the content delivered should be customized to produce the desired outcome. The benefits mentioned above are testament to how AI can make this old marketing method thrive again.
By Yao-Nan Chen, Machine Learning Scientist, Appier
Unless you have been hibernating under a rock for a few years now, you already know that explosive growth in the volume of available data is disrupting business as we know it. This data can be a goldmine for businesses that know how to capture, analyze and use it to power artificial intelligence (AI) technology. And that’s where data science and my role come in.
IBM has predicted that demand for data scientists will increase by 28 percent by 2020. The Harvard Business Review, way back in 2012, said that being a data scientist is the sexiest job of the 21st century.
I have been working in data science since 2013 and I still come into work at Appier each day eager to solve new problems.
What Data Scientists Actually Do
Simply put, data science involves using data to generate solutions that solve practical, real-world problems. In the business world, examples revolve around AI-powered solutions, such as pushing recommendations for users based on their demographic or usage pattern, or analyzing why sales of a particular product is dropping.
Data scientists set out on solving such problems by first extracting and consolidating data, which we then analyze for patterns and trends. We use this to build predictive models, derive insights, and implement proof of concepts to test the proposed solution to the problem at hand. The problems that we work on are very specific and often have no one standard solution. Hence, data scientists are tasked with thinking out of the box to come up with a variety of possible solutions.
The impact of our solutions is known only when they are implemented; so often, if the solution fails to meet the desired outcome, we have to go back to the drawing board and start over. But this just adds to the challenge and the excitement of trying to pin down that elusive solution and make it work.
What Makes for a Good Data Scientist
Of course, every job has some less lovable bits and the burden of the data scientist is data cleaning! In most cases, the data we gather is ‘dirty’, with errors and discrepancies in it. For example, data showing that sales of a product have dropped dramatically may simply mean that malfunctioning machines have failed to capture the data accurately.
Most data scientists will agree that data cleaning is the most boring part of this job. Our inside joke is that data science is 80 percent cleaning of data and 20 percent complaining about it!
But jokes aside, data cleaning is painstaking but important work. If not done right, it can have a huge impact on the accuracy and reliability of insights.
Aside from this kind of assiduity and attention to detail, a good data scientist, no matter how good they are technically, must also have a thorough understanding of business domain and the organization’s business goals. Our solutions have to be creative, but also useful and practical.
Keeping Up with the Latest Research
In this context, keeping up with the latest research in the area of machine learning can help us stay on top of trends and monitor breakthroughs to specific problems. We don’t need to reinvent the wheel – if a particular problem has been solved before, we can always work off that.
I regularly read papers on advances in machine learning, as well as in the specific domains that I am interested in.
It’s equally important to engage in discussions with peers, keep track of their recent research and poll their opinions on machine learning trends. This will help you keep abreast of all that is happening in this area.
Growing Demand for AI Expertise
Unfortunately, there is a gap between the growing demand for data scientists and the supply of talent in the area. AI is a new track and there is a shortage of people with the required expertise. What widens the gap is that not every data scientist is a good business person. They may be stellar at solving problems in an academic or research-based environment, but often fall short when it comes to real-world business problems.
Data scientists today must constantly evolve in terms of skill set. As the adoption of AI and deep learning grows, we are automating lower-level tasks and moving onto more complex problems. We already have some mature tools that can be used to build simple models for many business cases, and these are becoming simpler to use.
In the near future, data scientists will be required to know how to leverage and use problem-specific information. As AI becomes more complex, data scientists will need to work on more abstract problems and leave simple processing and analyses to automation software.
About the author:
Yao-Nan Chen is Machine Learning Scientist at Appier. He has more than five years of experience in Machine Learning, Data Science and Data Engineering and three years of experience in practical E-commerce recommendation system. Prior to joining Appier, he worked at Yahoo Taiwan on E-commerce Recommendation System, App notification recommendation system, Model tuning for sales volume prediction, etc.
While data-driven artificial intelligence (AI) often dominates business and technology headlines these days, the coverage generally centers on the development of the technology. Yet for all the celebration around AI, the main concern for enterprises is still how they can get started with the technology, in order to generate actionable insights from their data.
Large technology-oriented organizations tend to build their own in-house data science team, which might not be the best approach for smaller companies with less resource or technical expertise. This is where AI-as-a-Service (AIaaS) could just be your alternative passage to AI-powered business.
In-House Data Science Team Is Not for Everyone
Although data science is one of the most in-demand professions worldwide, companies are facing a massive talent gap: There are more positions to fill than there are qualified data scientists available to fill them. Quality data scientists require a wide range of skills, which sometimes could vary depending on the nature of data to be analyzed and the scale and scope of work.
Apart from finding the right talent with the right expertise, recruiting them usually involves high salary, additional benefits, perks and a host of other incentives, as you might be competing with some of the best brands around the world.
Even if you invest in building a data science team, some of the roles may end up becoming obsolete. As AI continues to advance, data scientists are required to evolve as well. There will be an increasing trend towards both machine learning engineers and deep learning scientists, who can program AI to self-learn, which may set off another arm’s race for top talent.
Given the above challenges, companies can consider AIaaS as a fast lane to AI adoption for business.
AI off the Shelf
AI-as-a-Service is similar to the Software-as-a-Service (SaaS) from which its name derives. Businesses turn to SaaS providers for web-based software. In much the same way, enterprises can leverage AIaaS providers’ off-the-shelf AI tools and services for various needs, such as optimizing once-inefficient processes and saving on operating costs.
While adopting AI technology through AIaaS can help streamline your operation, it also allows you to focus on other core business functions, such as product development, marketing and sales, to make a significant impact on your business goals. For example, AI-powered customer segmentation and prediction enables you to understand customers more intimately, in order to offer a more engaging user experience, drive conversion and minimize customer churn.
“Although AI is being used to predict customer behavior to some extent, it will get a boost in the future. Businesses will use AI to detect if a customer is willing to purchase the product, seeking support or switching to another provider even before they actually approach,” said Liam Martin, Co-founder of Time Doctor, in The Next Web.
Estée Lauder, for example, wanted to raise brand awareness among young women online and drive mailing list sign-ups across all screens. It leveraged Appier’s CrossX AI technology to nurture and build a high value audience pool, effectively boosting cross-screen conversion by 300% to 1100%.
There are other benefits that companies turning to different AIaaS providers for their AI needs may experience, such as achieving insights and breakthroughs in data that humans may have overlooked, improving customer service through chatbots that are available around the clock, or helping protect their customer data through the use of smart contracts.
Finding the Right AIaaS Partner
Before you start a hunt for an AIaaS partner, you first need to identify the most valuable problem in your business that can be improved with AI, and what specific outcome you are trying to achieve.
With an increasing number of AIaaS providers available in the market, there are always basic questions to ask before you deciding on one, such as how they will harness your data or work with your own IT team. In addition, to ensure a smoother start, you also want to see whether the graphic user interface of the provider’s platform is easy to understand and navigate.
Look for evidence of the provider’s track record in the space. Successful AIaaS providers should be able to present you with case studies, research, reports, industry recognition and testimonials. When evaluating these materials, you will be able to see if they have successfully addressed the needs of enterprises with problems similar or even identical to your own.
Once you find your trusted AIaaS provider and go through their onboarding and integration process, you are set. You can begin optimizing, understanding, analyzing and predicting with precision and scale like never before.
There is more data available than ever before. The explosion in data volume could help marketers do their jobs better. However, in a lot of cases, it creates confusion, as marketers are swamped by the sheer volume, and find it hard to sort the useful data from the useless.
Thankfully, help is at hand. In this article, we will show you three simple steps to make sense of your data, in order to make it work for you and generate leads that can help expand your business. Think of it as a life raft to help you stay afloat in an ocean of data.
Step 1: Streamline the Data
This step involves bringing together all the data gathered with various methods from different sources all under one roof. This can include data from your official website, campaigns, apps, CRM, API integration and more.
With data from such disparate sources, it can be confusing to extract any meaningful insights. By integrating fragmented digital data and unifying different data sets with the right tool, you will be able to get a comprehensive view of a user, addressing one of marketers’ most common pain points.
This step will also allow you to focus on a specific business goal. Maybe the data shows your customer churn rate is too high and you want to bring it down. Or that you would like to expand your company’s reach into new customers and new markets. Providing the data is accurate, it should provide a realistic snapshot of your company’s health, and help you gain actionable insights to achieve your business goals.
Step 2: Data Segmentation Based on AI Prediction
Your data is all collated and streamlined, and you know which areas you would like to focus on, such as more registrations for membership or subscriptions to your newsletter. Now how do you go about achieving those goals? Thankfully, artificial intelligence (AI)-powered systems can create AI models to predict customer behavior, and all without hiring a team of in-house data scientists.
As the customer journey increasingly involves multiple screens, it becomes harder for a human brain to make sense of all the data from customers’ cross-screen conversion paths, and segment users by multiple dimensions. However, AI systems can work on the multiple dimensions to create AI prediction models, forecasting variables like conversions and churn rates based on the segmented data. For instance, by understanding previous campaigns’ impact on your business, you will be better placed to formulate a future strategy.
CommonWealth Magazine, Taiwan’s most influential economic news media, leveraged Appier’s Aixon platform to combine data from different sources to create a unified customer view across screens. Based on this unified user view, it was able to generate new audiences, improving subscriptions and increasing online sales.
In fact, for every dollar CommonWealth spent on reaching audiences identified by Aixon, it generated 12.2X worth of revenue. The campaigns exceeded its return on ad spend targets by 300 percent, while subscriptions and purchases increased by 404 percent.
Step 3: Lead-Generation Campaigns Based on AI Insights
Once you have these AI predictions at your disposal, you are ready to deploy your new campaigns in order to convert on your business goals.
More advanced systems can deliver adverts via multiple marketing channels at once. This will increase the chance of you reaching your users in meaningful ways, as it helps you ‘talk their language’. For instance, you can reach the target audience Andy on Facebook through a relevant banner ad with minimal text, and deliver a dynamic product ad when he visits an e-commerce site on a PC.
If you approach your customers in the wrong way, you will dissuade them from associating themselves with your brand – they will see it as something ‘not for them’, but for someone of a different age, outlook or with different interests.
As well as speaking to them in terms they understand, you can optimize each advert and campaign specific to the platform you are using in order to better engage your users.
For example, luxury automobile manufacturer Audi tapped AI technology to drive test drive leads across screens, delivering a cross-screen conversion rate between 23 and 48 percent higher than to a single screen.
Making the data work for you
Data isn’t the problem – chances are, you have the correct actionable data at your disposal right now, but only by using the correct system can you gain valuable insights that will help your business, and bring the useful data from arm’s length to being right at your fingertips.
Imagine that you are a marketer looking to run a targeted marketing campaign. What if you had a tool that could easily segment your market on the basis of factors like economic status, purchasing preferences, online shopping behavior, etc. so that you could customize your approach and messaging to each segment for maximum impact and conversion?
These are the kind of insights that deep learning (DL)* can offer.
DL refers to a family of advanced neural networks that mimic the way the brain processes information and extract goal-oriented models from scattered and abstract data. What differentiates it from traditional machine learning is the use of multiple layers of neurons to digest the information.
A DL program trains a computer to perform human-like tasks, such as speech recognition or predicting consumer behaviors. It is fed large amounts of data and taught what the desired output should be. The more data it’s fed, the better performance.
The program then applies calculations to achieve that output, modifying calculations and repeating the cycle until the desired outcome is achieved. The ‘deep’, hence refers to the number of processing layers that the data must pass through to achieve the outcome, and how the learning algorithms are stacked in a complex, hierarchical manner. The more levels or layers there are, the ‘deeper’ the learning.
DL can analyze huge volumes of data to detect patterns and predict trends and outcomes. This is especially interesting to marketers, finding application in predicting consumer behavior and campaign outcomes, marketing automation, sophisticated buyer segmentation and sales forecasting, to name a few use cases.
*Deep learning is not magic, but it is great at finding patterns.
Consumers engaging with brands, seeking customer services and making purchases through social media is fast becoming the norm. Businesses need to constantly adjust their approach to reach out and respond to consumers with relevant and personalized messages.
Take advantage of these four technologies that will help your online engagement with your customers reach a whole new level.
Chatbots: Manage the Volume of Online Messages, and Speed up Customer Service
Chatbots are seeing a surge in uptake and are helping marketers connect with their audience in new ways. According to Gartner, by 2020, 25 percent of customer service operations will use chatbots across engagement channels, and Facebook reports that its Messenger has more than 300,000 active bots.
Brands are increasingly using chatbots to manage the large volume of customer messages generated across social media, and respond to them in a human-like fashion. Powered by artificial intelligence (AI), bots become smarter over multiple interactions, and can be used to personalize marketers’ messages over time. They also help streamline and speed up customer service, allowing businesses to focus on bigger issues likes complaint resolution by taking care of routine customer queries.
AI-powered Prediction Engines: Access Granular Insights to Better Target Consumers and Boost Conversion
The use of AI in online engagement goes beyond just chatbots. Businesses are turning to AI to make sense of the huge volume of online data, access granular insights, predict audience behavior and boost user engagement. LinkedIn’s sophisticated machine learning algorithms, for instance, score candidates on the basis of location, work experience and other information, and use this to improve job-candidate matches.
On the marketing side of things, data-driven insights allow businesses to identify audiences more likely to convert and target them with relevant and customized messaging. For example, marketers can use Appier’s Aixon, a data intelligence platform, to discover new consumers, predict their behavior, and increase conversions by delivering targeted advertising.
Aixon’s recent integration with messaging app LINE extends these capabilities by allowing businesses to access powerful AI-driven insights into consumer behavior on LINE and push out highly targeted messages to them. For instance, a user visited an ecommerce site and added a dress to the shopping cart. A few seconds later, she would receive a push notification for an ad related this dress on her LINE app.
Augmented Reality: Offer Consumers Useful and Relevant Experiences that Increase Sales
In 2017, augmented reality (AR) moved beyond gaming and entertainment into business, with brands increasingly offering their customers useful AR experiences across social media channels. By 2020, 100 million consumers will shop in AR, and Apple’s launch of iPhone X, which offers users new AR capabilities and facial recognition, will only push more social platforms to integrate AR technology.
The use of AR can be a win-win for both customers and businesses. For example, when Ikea’s Place allows users to preview how an armchair will look in their homes before buying it, not only is the customer making a better-informed decision, but the brand is also demonstrating how it fits into the customer’s home and life, boosting conversion and sales.
L’Oréal, which has bought the ModiFace AR beauty app, believes that this will boost online sales. Using facial recognition and AR technology, users can try out make-up on the app to see how it looks on their faces before actually buying the products. Even simple face filters like those that Snapchat offers are fast becoming a hot new way to advertise.
Social Listening Tools: Listen to Consumer Conversations and Better Customize your Content
The popularity of social listening tools is growing as more businesses realize the importance of tapping into customer and peer-to-peer conversations online. Social listening tools will allow you to keep an ear to the ground and be involved in consumer conversations across different social channels. The zillions of gigabytes of data generated from these are a goldmine of insights into consumer needs and preferences.
Marketers can then leverage AI’s capabilities to identify keywords and phrases, and generate insights around demographics, trending topics of interest and sentiment, which will help them better understand customer intent throughout the journey. Marketers can use these insights to be relevant in consumers’ lives, by creating content that resonates with their consumers and offering them solutions that they need.
Technologies such as those mentioned above are helping businesses make informed decisions, reach consumers more effectively, and target them better. Adopt and embrace those technologies to stay visible and relevant in the age of customized marketing.
Q: What is GDPR?
The General Data Protection Regulation (“GDPR”) is a new legislation by the EU parliament that lays out requirements for data collection, storage, and usage practice.
This new law is meant to replace the 1995 EU Data Protection Directive (DPD) to significantly enhance the protection of the personal data of EU citizens and increase the obligations on organisations who collect or process personal data.
Q: When is the GDPR coming into effect?
The GDPR will become fully enforceable on May 25, 2018.
Q: Who does the GDPR affect?
Although the GDPR is an EU regulation, the territorial scope of GDPR is potentially far wider as it can also apply to non-EU businesses in certain cases. Businesses that market their products to or monitor the behavior of people in the EU are required to be GDPR compliant.
Q: What constitutes personal data under the GDPR?
Any information related to a natural person, defined as ‘Data Subject’ in GDPR, that can be used to directly or indirectly identify the person. It can be anything from a name, a photo, an email address, bank details, posts on social networking websites, medical information, or a computer IP address.
Q. How is Appier prepared for GDPR enforcement?
We respect the privacy of Data Subject and ensure our compliance with a range of privacy protection regulatory criteria. These include requirements imposed by EU data protection legislation such as General Data Protection Regulations (GDPR) starting in May 2018.
Q. Where can I find detailed information about the GDPR?
Please visit official GDPR resources available online for details.
By Junde Yu, Chief Business Officer, Appier
Josh Shozen, Senior Vice President of Enterprise Solution Sales for Japan and South Korea, Appier
I’m excited to welcome Satoshi (Josh) Shozen to our team as Senior Vice President of Enterprise Solution Sales for Japan and South Korea.
Based in Tokyo, Josh brings over 15 years’ experience in enterprise software sales management and digital marketing in Japan and the region where he has worked with global technology companies including Adobe, Proscape Technologies, and Microsoft.
Before joining Appier, Josh was the country manager for SundaySky Japan, the creator of SmartVideo platform, and helped develop the strategic partner ecosystem for selling into major FSI and Automotive clients.
In his new role, Josh will lead our teams in both countries to strengthen the business of Aixon, an AI-powered Data Intelligence Platform that Appier launched last July. More than 10 clients, including Japanese real estate portal LIFULL, have already successfully deployed Aixon and transformed their digital marketing campaigns.
While we continuously upgrade technology for Aixon’s functionality to help our customers’ business success, such as the recent integration with LINE Business Connect, we also see the strong potential of our business in both the marketing and data intelligence platforms.
With Josh on board, we are looking to accelerate our business in Japan and South Korea, especially the latter, a highly mobile-dominated market where 60-70 percent of e-commerce is made through mobile devices.
Graduated from the University of Washington, Josh started his career at Microsoft in Seattle as a solution sales specialist. He was transferred to Tokyo in 2003 to manage Microsoft’s Global Accounts segment for Japan as a Regional Business Manager. Josh later moved on to lead the Japan and APAC business as VP for Proscape Technologies before joining Adobe Japan as Director of Digital Marketing Solution Div. Enterprise Sales in 2012.
As an avid surfer, Josh enjoys a variety of watersports and outdoor activities with his family and friends in his spare time, either in Japan or at his other home on the Big Island of Hawaii.
About the author:
Junde Yu is the Chief Business Officer of Appier, a leading Artificial Intelligence (AI) company. He leads the company’s Enterprise business, which includes Aixon, an AI-based data intelligence platform. Junde joined Appier from App Annie, where he was Managing Director of Asia Pacific. He started at App Annie as its first sales rep in the region and grew the sales and marketing team in the region to achieve very extensive revenues across the Asia Pacific region. It was also here that he acquired an appreciation for how enterprises could derive tremendous value from data.