3 Steps to Make Sense of Your Data for Lead Generation

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.

AI 101: Deep Learning

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.

The Role of AI-driven Insights in Delivering Superior Customer Experience

The Role of AI-driven Insights in Delivering Superior Customer Experience

 

By Magic Tu, VP of Product Management, Appier

Customers are inundated with more choices than ever, while the convenience of digital makes it easy for them to switch to competing services through the web or mobile apps. As businesses come under pressure to gain new customers and retain existing ones in this fluid digital economy, many see improving customer experience as the key to coming out ahead. In order to do so, they need to understand their customers a lot better.

Understanding customers better

Defined as the sum of interaction between an organization and a customer, the customer experience isn’t solely determined by capabilities or pricing, but by consistently addressing what matters most to customers. For instance, a frequent guest disgruntled about a slow hotel check-in is likely to leave with a negative experience despite the hotel’s top-notch pool and gym. Yet a fellow traveller with family in tow may retain fond memories after being offered a complimentary bed for his young children to sleep in, even after that same slow check-in.

Over time, the sum of their experiences accrues to a tipping point that can result in one of them leaving for a competing hotel – or staying on as a highly loyal customer. Delivering exceptional customer experience therefore boils down to achieving a deep understanding of what individual customers want and delivering it.

But while the barista at the coffee shop around the corner can be expected to remember a regular’s preference and greet him or her by name, this isn’t scalable for most businesses. The only realistic way for brands to understand their customers better is to analyse customer records and transaction data for insights about individual preferences and general trends. They can leverage these insights to better tune existing services, develop new capabilities, or to create customised promotions that cater to individual customers.

Unfortunately, obtaining a unified view of customer behaviour is a challenge that most organizations struggle to overcome, and the multiple channels through which consumers communicate with brands today further complicate attempts to correlate them.

The power of AI

This is where artificial intelligence (AI) comes in. With the ability to tirelessly and accurately analyze huge volumes of data, AI can help businesses gain a competitive advantage by giving them insights they would not otherwise have. With AI, organizations can move away from a “gut-driven” marketing approach, where a marketer or product manager rely on their instincts, towards a data-driven strategy.

By cross-referencing customer data from a multitude of sources, AI can help build a highly accurate model of your customer base and determine the best ways to reach them.

AI can even take it a step further with predictive insights into the future behavior of your customers. One of the most exciting tools available today to marketers is predictive audience segmentation powered by AI. As part of the larger category of predictive analytics, predictive audience segmentation has the power to help companies identify a target audience with the highest potential for conversion to a sale or click or install, whatever your KPIs (key performance indicators) are.

The most advanced predictive audience segmentation tools look at behavioural patterns, collected from customer databases as well as from the internet, and combine them with demographic data to identify trends to single out the most promising leads. It goes even further in segmenting customers. It analyzes data to make recommendations that help you find and grow your target audience.

Commonwealth Magazine, a leading media group in Taiwan, used the predictive audience segmentation capabilities in Appier’s Aixon platform to discover new customers, increasing subscribers and purchases on their website by over 400 per cent.

A data-driven world

There is no question that AI is a vital technology for our data-driven economy. Many traditional marketing promotions are either conceived from past assumptions that are no longer be valid, or which cannot be scaled due to an over-dependence on experienced employees. On the other hand, the judicious use of AI can reveal pertinent insights that can be leveraged to craft more relevant promotions.

Of course, it is important to note that AI is not magic and cannot be expected to solve every business problem. There is also a structured process that that must be adhered to for successful AI application, starting from the gathering of the requisite data. Yet there is also no question that properly implemented, AI with its ability to deliver tireless analysis is a crucial building block to drive innovation and deliver tangible benefits to businesses.

About the author:

Magic Tu talks about the role of AI in driving superior customer experienceMagic Tu is VP of Product Management at Appier. He is responsible for streamlining the process of product development, from product ideation to product launch, collaborating with various teams including R&D, product management, marketing, and business development. Aixon, the AI audience prediction and analysis platform, is the first product that his team has launched.

Prior to joining Appier, Tu was the director of software product management at HTC, a smartphone and virtual reality device company, leading the overall software experience planning for the HTC Sense smartphone and accessories. He also led the program management team that took care of third party vendors and partners including Google, Yahoo, Microsoft, and Nokia for cross-company projects. Before HTC, Tu was the lead developer in Springsoft (now Synopsys), an electronic design automation company based in Taiwan. Tu has BS/MS degrees in computer science from National Taiwan University.

How Appier Fights Ad Fraud with Artificial Intelligence

Ad fraud is costing the industry billions of dollars. Joe Su, Appier’s Chief Technology Office, described in a blog post how Appier is using artificial intelligence to fight ad fraud. This infographic summarises how AI can help combat one of the top scourges of the advertising industry.

About Appier

Appier is a technology company which aims to provide artificial intelligence platforms to help enterprises solve their most challenging business problems. For more information please visit www.appier.com.

Fighting Ad Fraud with Artificial Intelligence

By Joe Su, Chief Technology Officer and co-founder, Appier

Advertising fraud, ad fraud for short, has become a major threat to the digital advertising industry. According to the Association of National Advertisers in the US, ad fraud will cost companies an estimated US$6.5 billion in 2017. A recent report by Juniper Research paints an even grimmer picture, estimating advertisers will lose US$19 billion to fraudulent activities next year. This figure, representing advertising on online and mobile devices, will continue to rise, reaching US$44 billion by 2022.

The industry has spent considerable resources looking for effective ways to mitigate the effects of ad fraud. I use mitigate deliberately because just as with cyber fraud or financial fraud, there is no way to totally eradicate the problem: you can only hope to stay one step ahead of the bad guys.

Most ad fraud countermeasures have centred on rule-based methods and these are effective ways to combat simple ad fraud activities. However, the ad fraud attempts are becoming more sophisticated and traditional countermeasures are inadequate today.

An AI-based approach

As ad fraud attempts become more sophisticated and difficult to detect, so must our fraud detection mechanisms evolve in tandem and the only way that this can be achieved is using artificial intelligence (AI).

An AI-based ad fraud detection system actually starts with a rule-based approach as the base but through self-learning, builds layers of defence that learn from each suspicious activity that it detects. An AI-based model also has the advantage of being able to view patterns on many more dimensions than a traditional system.Artificial Intelligence approach to fighting ad fraud

Traditional rule-based models typically analyzes activity on between one to three dimensions. An AI-based model analyzes over 80 dimensions at a time, enabling it to detect extremely sophisticated ad fraud patterns. With self-learning, AI-based models can evolve as ad fraud patterns evolve to evade traditional systems.

A Real World Study

To demonstrate the advantages of an AI-based approach, Appier examined data on its own network over four months from May to August this year involving over 4 billion campaign data points including ad clicks and app installs. What we found was that the AI-based fraud detection model was able to identify twice as many fraudulent transactions as the traditional rule-based model. The AI-based model also proved to be more cost-efficient for advertisers, yielding a 3.6 percent higher return on advertising spend (ROAS) than the traditional model.

The greatest advantage of AI though, was its ability to detect sophisticated ad fraud patterns not previously reported. On pattern that our AI system flagged is what we call “the chameleon”.  This is where dishonest publishers disguise themselves as legitimate publishers at first, only to generate fraudulent installs at a later date.

Another suspicious activity detected by our AI is what we have termed “inventory burst”. With this pattern, a fraudulent publisher will generate an abnormally high inventory count in the absence of an appropriate level of in-app registration activity.

Final Word

You can download the full report of Appier’s study here. Ad fraud is costing the industry billions of dollars and has become extremely difficult to detect. Traditional rule-based methods are limited in their ability to detect new and increasingly sophisticated ad fraud patterns. AN AI-based approach with its ability to analyze multidimensional data and with self-learning is a better approach to fighting ad fraud.

About the author:

Joe Su, CTO, AppierJoe Su is CTO and co-founder of Appier.  Su has been hacking and building systems since high school, where he won first prize in the 3rd Annual National Center for High-Powered Computing Programming Contest Taiwan. Since then he’s been involved in system design and development in a variety of areas, including social games, VoIP, distributed computing and online geographical information. Prior to founding Appier, Su co-founded and ran Plaxie, an independent game studio focused on developing intelligent mobile and social games. Previously, he joined Artdio Technology as a programmer and served as a researcher in Computer and Communications Research Laboratories (CCL) of Industrial Technology Research Institute (ITRI), a leading high-tech R&D institution in Taiwan.

Artificial Intelligence in 2018 will delight and amaze

By Dr Hsuan-Tien Lin, Chief Data Scientist, Appier

Artificial intelligence systems based on deep learning and machine learning techniques are now helping enterprises around the world, helping to influence sales, make operations more efficient, and generate new insights to boost productivity. The technology powers a wide range of hardware and software, and can be found in watches, phones and cars. AI is now so useful that IDC predicts global spending on cognitive and AI systems will reach US$12.5 billion in 2017, an increase of 59.3% over 2016.

Major industry analysts say the use of AI technologies reached an inflection point in 2017, and Appier agrees. In October 2017 Deepmind announced that the AlphaGo Zero AI learned how to play the game go without referencing any previous games, and yet was able to beat its AI predecessors at the game. We also saw in 2017 Imec demonstrate a self-learning chip that not only composes original music, but can learn to compose music in different genres after exposure to new types of music. Achieving desired outcomes without training on existing datasets and self-learning were thought to be out of reach before these discoveries.

Appier predicts that in 2018, the AI world will continue to deliver amazing capabilities straight out of science fiction:

  1. Artificial Intelligence becomes faster, more accurate and more versatile

We are on track for AI technology to become mainstream by 2020. Gartner has observed that AI will be found in apps and services and will lead to real benefits for digital initiatives through to 2025. According to IDC, the Asia-Pacific region will become the second-largest region for cognitive/AI spending by 2020.

Artificial Intelligence in 2018 will delight

More milestones will be reached in AI research, driving bigger and better hardware and software that can in turn achieve more accurate predictions and recommendations. Time to market for innovative new products and services will speed up too as AI helps to automate applications development and delivery. Mass manufacturing could disappear as brands can now target narrow market segments with small quantities of customized products, updating them as tastes change.

2. Artificial Intelligence will become validated as a business consultant

AI has already begun to make sense out of business data, providing insights and predictions to enhance marketing and improve business performance today. At Appier, the AI-based Aixon platform helps businesses predict consumer behavior, and the use cases are growing. Vendors will be able to showcase successes that reflect both quantitative and qualitative benefits for AI technology, while more businesses will be comfortable with adopting AI systems and relying on recommendations from AIs as they experience positive results for themselves.  

  1. Artificial Intelligence will become a core technology

Appier forecasts that AI will appear in more core technology. AI showed promise from 2011–2015, and will be increasingly commercialised from 2016–2020. From 2020, AI will becomes an essential part of our lives and technology that we can effectively use to solve problems.

We are mid-way through the 2016–2020 cycle. AI-powered systems will be put through their paces in various commercial trials in 2018, or deployed in limited environments in more cities. In response to market demand, more vendors will offer business-related software and services that provide data analytics powered by AI in 2018. As far as applications go, software that makes gaining and retaining customers easier – such as predicting which customers are likely to leave a brand or how to increase personalized interactions – will become popular.  

  1. Artificial Intelligence will gain trust as a user interface

Chatbots and voice-activated digital assistants such as Apple’s Siri and Amazon’s Alexa will become smarter and more versatile in 2018, encouraging more people to use them to get things done, and businesses to implement them for first-level customer service. These applications use AI to understand spoken or typed conversations and can interact more intelligently with humans than conventional software.

Beige Market Intelligence forecasts that the global chatbot market is likely to post a compound annual growth rate (CAGR) of more than 28% during the 2016–2022 period as awareness grows about their usefulness. In their October 2017 report entitled Predictions 2018: Digital Disruption is the New Normal for B2B Marketing, Forrester predicts more vendors will enter this market. Forrester also predicts that the technology will be powerful enough to identify potential customers, and follow up accordingly. While chatbots and virtual assistants typically handle short interactions, mining longer conversations could well be a possibility in the near future.

The role Artificial Intelligence will play in our lives

We will change the way we live and work due to AI, and for the better. While AI systems can do many tasks better than humans and will take over repetitive, time-consuming or physically-dangerous tasks from humans, they are unlikely to replace humans altogether. The new AI-based applications will raise our quality of life and allow us to have more time to do what we want.

Completely new kinds of jobs could be created for up to 80% of companies, a CapGemini study has found. Jobs that will be in demand will include data scientist and project manager. With AI potentially restructuring society and business, we may not be able to imagine what kinds of skills are needed for the future. The most useful career skill we can cultivate is the ability to adapt quickly to change.

Businesses which already have real-world customer data which can be used to train AI systems will likely be the ones in the lead. Gartner has found that 59% of organizations are working on AI strategies, while the rest have already tested AI solutions. In the Asia-Pacific region, businesses need to start thinking seriously about AI. Whether it is training an adaptable workforce, asking if the technology they want to purchase has AI components, and looking at building their own AI capabilities in-house, getting ready for an AI-capable world will stand them in good stead.

Stay tuned to the Appier News Center as we continue to track the exciting things taking place in Artificial Intelligence.

About the author:

Hsuan-Tien LinDr. Lin is a beloved figure in Asia’s artificial intelligence community. Prior to joining Appier as Chief Data Scientist, he was an Associate Professor of Department of Computer Science and Information Engineering at National Taiwan University (NTU). Dr. Lin’s research interests include theoretical foundations of machine learning, studies on new learning problems, and improvements on learning algorithms. In 2017, Dr. Lin received the Young Scholars’ Creativity Award from the Foundation for the Advancement of Outstanding Scholarship (FAOS). He also received the 2013 D.-Y. Wu Memorial Award from National Science Council of Taiwan and the 2012 K.-T. Li Young Researcher Award from the ACM Taipei Chapter.

Between 2010-2013, Dr. Lin co-led his university’s team to victory in machine learning competition KDDCup six times. He co-authored a machine learning textbook “Learning from Data”, which is a bestseller on Amazon. Online, his teachings on machine learning, hosted on Coursera, have been viewed millions of times. Dr. Lin also served as the Secretary General of Taiwanese Association for Artificial Intelligence between 2013 and 2014.

Dr. Lin received his PhD and MS in computer science from the California Institute of Technology, and served as a long time consultant before joining Appier formally.

 

 

From idea to business – how Appier pivoted 8 times!

Harvard is where our startup journey started. From idea to business, Appier experienced 8 pivots before finding success. The lesson we learnt was to fail fast but pivot faster! Follow that journey in this infographic.

From idea to business

About Appier

Appier is a technology company which aims to provide artificial intelligence (AI) platforms to help enterprises solve their most challenging business problems. Appier was established in 2012 by a passionate team of computer scientists and engineers with expertise in AI, data analysis and distributed systems. Appier serves around 1,000 global brands and agencies from offices in 14 markets across Asia, including Taipei, Singapore, Kuala Lumpur, Tokyo, Osaka, Sydney, Ho Chi Minh City, Manila, Hong Kong, Mumbai, New Delhi, Jakarta, Seoul, and Bangkok. For more information please visit www.appier.com.

Appier Celebrates 5 Years in AI

Appier celebrates our 5th Anniversary this year.  We share some milestones of the company’s progress in our journey towards Enterprise AI in this infographic.

Appier – then and now

Appier celebrates 5 years in AI

About Appier

Appier is a technology company which aims to provide artificial intelligence (AI) platforms to help enterprises solve their most challenging business problems. Appier was established in 2012 by a passionate team of computer scientists and engineers with expertise in AI, data analysis and distributed systems. Appier serves around 1,000 global brands and agencies from offices in 14 markets across Asia, including Taipei, Singapore, Kuala Lumpur, Tokyo, Osaka, Sydney, Ho Chi Minh City, Manila, Hong Kong, Mumbai, New Delhi, Jakarta, Seoul, and Bangkok. For more information please visit www.appier.com.

How Artificial Intelligence can Help Enterprises Gain Insights into Asian Consumers

By Jennie Johnson, Head of Marketing, Appier

Artificial intelligence (AI) is without doubt one of the most talked-about technologies today, and for good reason: advances in computation, processing power and storage, and the tremendous volumes of data generated, thanks to new mobile and cloud technologies have come together to drive a renaissance in AI.

On the other hand, this confluence of factors is also creating mountains of data – and a headache for any enterprise trying to make sense of all of it. Since Appier was established five years ago, we’ve accumulated a considerable database of billions of anonymized device profiles in Asia, which continues to learn as it grows. This data provides some very good insight into the cross-screen behaviors of people throughout the Asia-Pacific region.

Today, I wanted to share a few highlights from our newly-released 2H 2016 Cross-Screen User Behavior Report, generated from our analysis of over 1,800 billion Appier-run campaign data points.

Appier 2H 2016 Cross-Screen Report

For enterprises, one of the report’s key takeaways is just how important it is to understand the cross-device dynamics of today’s consumers. The complexity of these usage patterns make it difficult for marketers trying to reach them using conventional technology. Using AI, Appier is able to process these billions of data points quickly, detect patterns and even predict future behavior.

I encourage you to download the report to read at your leisure, but I wanted to share here a few key trends the report reveals.

1. A cross-device perspective is critical to understanding Asian consumers

51% of internet users across Asia own 2 or more devices. And among that group, more than half (26% of total users) regularly switch between four or more devices. Regionally, Taiwan users lead Asia in multi-device usage, with 40% using four or more devices, followed by Australia and Japan (29%), Singapore (28%) and Hong Kong (27%).

There’s a lot of talk about the importance of mobile in Asia, but the data from our report shows clearly that a single-device perspective provides an incomplete view of how users in Asia are interacting online. A cross-device perspective is essential for a complete understanding of the user journey.

Consider this: more users browse websites on their PCs during the work day than on mobile, even here in mobile-crazy Asia. But at night, this trend reverses. To be even more precise, our data shows that pageviews on PCs are highest between 2 and 3 pm while pageviews on mobiles peak between 9 and 10 pm.

Screen Shot 2017-09-04 at 3.50.20 PM

Another important point the report reveals is just how differently users in Asia reacted to online ads, depending on which device they were using. On average, 79% of users exhibited different behaviors across devices. 35% of users showed completely different behaviors. There are also important differences in this user behavior across the region: Korea logs the most varied behavior at 88%, while on the other end of the scale, Hong Kong stands at 51%.

2. Linking usage data across devices is essential for a comprehensive enterprise data strategy

As our report shows, only by viewing data across devices can you see a complete picture of your user’s journey, and only then will you be able to plan your marketing campaigns effectively. This level of detail is important for marketers, but it’s instructive for other parts of the enterprise as well. For example, the human resources department can use AI to determine where best to deploy the workforce to better serve the needs of the customer base or to identify key skills which HR requires.

3. Richer data sets lead to higher performing campaigns with greater ROI accuracy

Our data consistently shows that cross-screen campaigns perform better than single-screen campaigns. Click-through rates in Vietnam was 54% higher for cross-screen campaigns, while in Australia, the difference was 53%; in India, 48%; in Taiwan, 36%; in Hong Kong, 27% and in Japan and Korea, 19%.

Using Appier’s AI platform, we were also able to help our customers accurately identify the final conversion device on cross-screen campaigns, a critical piece of information for marketers. Across Asia, the smartphone accounted for 46% of final conversions. As usual, there were significant differences by country. The PC drives most final conversions In Australia (71%), India (41%), Malaysia (44%) and Vietnam (41%). The smartphone served as the final conversion device in all other countries – Hong Kong (48%), Indonesia (64%), Korea (62%), Singapore (53%) and Taiwan (48%).

The richer the data, the more successful the campaign. Our research shows three screen campaigns deliver as much as 160% more conversions than those on two screens.

4. Predicting actions is where AI truly shines

We have long believed that one of the biggest benefits of AI is helping predict future actions. Through a comprehensive analysis of the rich datasets that we have accumulated over the years, we have been able to help our customers with very precise and accurate predictions of what their target audiences will do.

One exciting area that we’re exploring is Aixon, a data intelligence platform that allows marketers at a variety of enterprises to discover new customers, enrich their understanding of their customer base, and make predictions using AI.

Some examples of ways that enterprises can use Aixon include:

  • A news publisher looking to increase their online subscriptions by identifying likely subscribers online.
  • A marketer analyzing data to discover what topics their users are interested in and integrating these insights into their CRM system to optimize their content strategy.
  • An e-commerce merchant driving more online sales or conversions by analyzing site data to predict which users are most likely to make online purchases.
  • A mobile app developer identifying users at risk of uninstalling their app so they can plan and implement re-engagement measures.
  • An online publisher increasing the value of their inventory by providing more granular analyses of site audiences to potential advertisers.

Final word

For more data, details, and insights to consumer behavior in Asia, go ahead and download the report. If you’d like to learn more about how AI can help you, don’t hesitate to get in touch with us for more information. Appier’s AI has been helping our customers navigate the complex, cross-device consumer space in Asia over the last five years but this is just the beginning. We believe AI has more to offer to enterprises.


About the author:

Jennie Johnson

Jennie Johnson is Head of Marketing at Appier. She oversees public relations, online and offline marketing, content and design. Prior to Appier, Jennie led a wide variety of consumer and business-facing product communications for Google across 14 markets in Asia and the US. Her last role at Google was leading a regional team devoted to building and sharing stories about how Google helps Asia’s businesses grow online. Jennie graduated from Harvard University with a Bachelors of Arts in East Asian Studies.

Editor’s note: This is the first post of our revamped Blog. Please stay tuned for more insights into enterprise artificial intelligence (AI) from Appier’s leadership team and other thought leaders from the industry. To re-print or re-post this blog, please write to [email protected]