Technical Insights: Basic C++ 11/14 for Python Programmers

Our new blog post features a short list of some common python programming patterns and their C++ equivalents. This can help programmers learn C++ in a more efficient way if he or she already knows Python.

Leave us a comment below and let us know what you’d like to see covered in our future posts!

█ Read More

Technical Insights: Introduction to GraphQL|goo.gl/d7PyXH

█ Join Us

Our current openings|goo.gl/rx1jce

 

Predictive Audience Segmentation: Take the Shortcut to Identify Your Target Audience

By Junde Yu, Chief Business Officer, Appier

The days of general advertising or marketing campaigns targeting the masses are numbered. Whether you are selling a bank loan or clothing online, you have to know your specific target audience.

The hard part? Identifying the right audience to help you maximize your marketing return on investment (ROI).

Analyzing and segmenting online traffic can be a painfully manual process. Efforts can range from educated guesses to applying simple analysis tools on data. While these will work when you are analyzing a handful of dimensions, the real challenge is when there is complex data or a combination of over 80 dimensions to analyze.

Powerful AI tools to target your audience

Today, one of the most exciting tools available to marketers is predictive audience segmentation powered by artificial intelligence (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.

Predictive Audience Segmentation

Commonwealth Magazine, one of the most influential magazines in Taiwan, experienced dramatic results when it used the powerful predictive audience segmentation capabilities in Appier’s Aixon platform. Not only did the publication discover valuable readers they hadn’t been able to reach in the past, the campaigns exceeded the Return on Ad Spend (ROAS) by 300%, and subscriptions and purchases increased by 404%, compared to KPIs.

The most advanced predictive audience segmentation tools look at behavioural patterns, combining it 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.

An AI-powered platform like Aixon can help you to:

  1. Get an accurate audience picture.

Getting a unified view of customer behaviour is hard, as organizations struggle to consolidate data from disparate sources like the corporate website or mobile apps, as people often consume content on multiple platforms. Also, the data may be piecemeal and disconnected.

Aixon is able to unify an organisation’s data from disparate sources and platform into a single user view. That view is layered with Appier’s own considerable mine of billions of anonymized device profiles in Asia, to provide a better contextual picture of the different customer segments. This precision in data allows for more accurate predictions and segmentations, distinguishing between customers who are likely to churn, refer other customers, or likely to make a purchasing decision. It may even unveil the top revenue generators.

  1. Drive more timely sales or conversions.

You can better reach customers when they are ready to make a purchase as they move through the buying cycle. Predictive audience segmentation can help to highlight customers when they are most receptive to drive more sales or conversions.

  1. Discover new markets.

Predictive audience segmentation technology can help to uncover new markets by analyzing data based on your objectives, and identify the top opportunities within them. The insights from the behavorial data together with the demographic data may identify new target segments. As new prospects are targeted, the algorithms will learn the company profiles and help to further refine your market segments.

An example is Japanese real estate information service provider LIFULL, which had a rich vein of CRM data that was untapped. The company has since worked with Appier to integrate and analyze its vast online and offline real estate databases. LIFULL is using Aixon to cut through the clutter of massive data to drive more effective online marketing programs and to develop new innovative businesses.

  1. Identify users who might leave your service and re-engage them online.

Aixon can use churn forecasting to identify the patterns and trends of customers who had churned or left in the past. Based on that data, it can forecast how likely existing customers may churn or leave your service. This insight can be invaluable to marketing and sales teams who can then follow up and re-engage with this segment of customers.

  1. Boost your revenue by finding the right audience for your advertisers.

By segmenting customers based on their behaviour and demographics, this allows you to find the right audience for your advertisers. This means that marketing, recommendations and promotions can be tailored for different customer segments.

Drop us a line…

Predictive audience segmentation tools can be easily used to boost marketing efforts, complementing existing technologies. Whether you are working on predictive marketing, personalization or just wanting to improve your marketing efforts in general, Aixon’s predictive audience segmentation technology can help you to seamlessly bridge the gap between identifying the target audience and marketing execution.

Aixon’s additional demographic and behavioural data, interests and keyword insights about the audience provides a richer level of detail that can help you to single out the most promising leads, and drive towards a more optimal conversion rate for a higher volume of sales.

Please don’t hesitate to reach out to me or anyone from my team to find out how Aixon can help you take the shortcut to targeting your audience.

About the author:

Junde YuJunde 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.

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.

Five years down, and many, many more to come!

By Chih-Han Yu, CEO and co-founder of Appier

When I started Appier five years ago, artificial intelligence (AI) was not a hot topic. It’s hard to imagine that the first few business ideas were actually brainstormed in our dorm room in Harvard, and we will always remember the joy of the very first time that our customers trusted us and gave us our first order.

In those first few years, we faced a lot of challenges, and we learned a lot, too. The first lessons we learned as entrepreneurs was to dream big and embrace failure. We keep this entrepreneurial spirit alive as our company grows, and we encourage our employees to develop their own entrepreneurial mindsets. When you’re not open to risks and failure, bold new ideas will never take off.

Over the past 5 years, we were always thinking about how to make a real impact to human society and industry with AI when our products go on the market. We experienced eight pivots, from AI-based social games to the marketing and data intelligence platforms for enterprises that led to our initial success. We will never give up and we will always have the faith of continuously innovating.

We have grown from a 4-person startup to an industry leader that has been recognized by Fortune Magazine as one of the most important companies driving the AI revolution. We continue to expand our business across countries in Asia and serve around 1,000 global brands and agencies, and we continue to hire for diversity in our teams. The best part of my life is to have the opportunity to work with such a bright and talented group of colleagues.

Over the next five years, we’ll expand our portfolio of AI solutions and build more functionality for enterprises who want to take advantage of recent advances in AI technology. We’ll also continue to hire the best talent and extend our engineering and AI research capabilities around the region.

Finally, I would like to give a big shout out and THANK YOU to everyone who has supported, encouraged and believed in Appier. Five years is just the beginning, and we’re ready to embark on our next AI adventure and look forward to the next five years and beyond!

In the meantime, please enjoy this video celebrating Appier’s 5th Anniversary.

 


About the author:

Chih-Han Yu is CEO and co-founder of Appier. Yu has authored dozens of research articles in the field of AI, robotics and machine learning, and has been awarded two US patents. In 2010 Yu obtained his doctoral degree in computer science from Harvard University, where he collaborated with the Wyss Institute for Biologically Inspired Engineering at Harvard Medical School to develop self-adapting robotics systems to help polio patients walk correctly. His doctoral thesis was nominated as the best thesis of the year in the field of multi-agent AI. Prior to Harvard, Yu earned his master’s degree from Stanford University, where he participated in the school’s champion DARPA Grand Challenge development team, whose winning prototype Stanley laid the basis for Google’s self driving car project. Most recently, Yu was named a Young Global Leader for 2016 by the World Economic Forum.

 

Technical Insights: Introduction to GraphQL

 

 

 

 

 

 

 

By Johnson Liang, Front-end Engineer, Appier

At Appier, we have been using GraphQL for around a year. GraphQL drives client-server communications for one of our main AI platforms, Aixon. We have benefitted a lot from GraphQL’s characteristics, such as the concept of “object fields” and its resolvers. Its declarative approach to whitelist all inputs and outputs makes it a great tool to build programming interfaces.

This presentation is designed to be an introduction to GraphQL and was originally delivered to other internal product teams in Appier. The talk is specifically designed for Node.JS or Python developers that have never tried GraphQL before. It provides succinct code examples in both programming languages to guide the audience through all the essential topics they should know in order to start building their own GraphQL schema and to run a GraphQL API server.

There are already quite a lot presentations on the internet explaining the high-level concepts of GraphQL. In this talk, I have put more emphasis on the actual source code required to get GraphQL running, providing a more pragmatic perspective to understanding GraphQL.

The talk covers the following topics:

  • Fundamental parts of a GraphQL server
  • Defining API shape – GraphQL schema
  • Resolving object fields
  • Mutative APIs
  • Making requests to a GraphQL server
  • Solving N+1 query problem: dataloader

Editor’s note: Speaker notes are available with Google Slides.