Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

Advocating for Diversity and Inclusion, Day In and Day Out

Today, March 8, is International Women’s Day (IWD), when governments, employers and women themselves celebrate female success and the contributions that women have made to society. Advocating for women is critical, and as a growing technology company, we certainly shoulder some of the responsibilities alongside others in the industry.

However, we can’t advocate for women or any other groups on just one day of the year. Organizations including ours need to look at diversity and inclusion on a daily basis, and make sure we are considering it in every area of the business, from hiring to team structure and recognizing achievements.

It is proven that diverse and inclusive teams solve business problems faster, allowing things to get done more quickly, and also make for happier and more productive employees. So how can companies make sure they have a culture that welcomes everyone and gives them space to contribute and experiment?

It certainly starts with hiring. At Appier, we focus on skill-based hiring, making sure we have the best people to do the job rather than look at race, gender or any other identifying factor. Culture fit is also incredibly important. Appier is a startup, so collaboration across functions is key to our success, and we need people who bring the right attitude first and foremost. At a young company where processes are still being established, flexibility is key, so teams often have to pool resources to make things happen.

Inclusion doesn’t just mean getting a certain number of different sorts of people on the team, it also means ensuring that everyone feels they can contribute. Leaders need to look around daily to see if everyone is getting a voice in meetings; if everyone has the equipment and knowledge they need to be successful; if everyone feels comfortable advocating for herself; if everyone is being welcomed and respected by colleagues, and so on.

If any of these things aren’t happening, then leaders need to take a look at existing processes to make sure they are working, or at the very least carve out time for those who might need additional support.

Ultimately, we need to think about equity before equality. Equality means treating everyone equally, and while fairness is critical to functioning workforce, we need to look first at whether or not we are providing equity to everyone. Equity means giving each individual the specific tools they need to succeed. These will vary from person to person, and once everyone is equipped and on an even plane, we can go on to ensure equality.

Jennifer Huang, our director of strategy and business development, joined Suchitra Narayan of SGInnovate, K. Thanaletchimi, NTUC Central Committee Member, and Aziza Sheerin, regional director of General Assembly on a panel in Singapore today to discuss these issues in celebration of IWD. They agreed that we are on a journey and we have got some way to go, but if everyone in the mix – employers, teachers, government leaders, women themselves, their friends and families – continues to advocate for fair treatment, we will continue to see progress towards relationships, workplaces and societies that provide equally for all, leading to a brighter future for everyone.


Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.


What Is Supervised Learning?

Broadly speaking, there are two types of machine learning algorithms: supervised and unsupervised learning. Supervised learning is the more common of the two, and is typically easier to implement than unsupervised learning.   What Is Supervised Learning? Supervised learning algorithms are designed to learn by example. They are used when the human practitioner knows the answer to a problem, and wants to train the AI to be able to find it out. It is like learning with the assistance of a teacher, guiding the algorithm towards the ‘correct’ answer, as opposed to an unsupervised learning algorithm, which is like a child learning on their own by experimentation and trial and error. To train a supervised learning algorithm, you will need to pair a set of inputs with specific outputs. The algorithm will then search for patterns within the inputs to correlate with the outputs. Based on this training data, the supervised learning algorithm can then take in unseen inputs and determine which label to assign them. The aim? To predict the correct label for newly presented input data in order to categorize it and make sense of it. Supervised learning: · Is simpler and more common than unsupervised learning ·

How to Ride the Third Wave of AI

Author | Min Sun, Chief AI Scientist, Appier We are at a very exciting juncture in the development of artificial intelligence (AI), starting to see implementations of the third wave of the technology – this involves machines far surpassing human capabilities in various application domains, and that creates all kinds of opportunities for businesses. To leverage this to its full potential, companies need to rethink how they operate and put AI at the heart of everything they do.   Making Waves: How AI Is Changing the Way We Do Business The first AI wave started with statistics-based systems – the best-known use would probably be information retrieval algorithms used by big internet companies like Google in the early years of AI  (thinking of the PageRank search engine). The second wave was about many more machine learning techniques, like logistic regressions, supporting vector machines, and so on. This is used in all kinds of businesses like banking and digital marketing tools. The third wave is deep learning, of which the use is manifest in so-called perception AI – this relates to our human perception system including sight, hearing, touch and so on. Think of speech recognition and image recognition. It’s used

How AI Is Making Quality Not Quantity the Answer to Ad Effectiveness

How many times should a prospect see an ad for it to be effective? It’s a decades-old marketing dilemma – one that digital has made both easier and more difficult to manage. However, this question of effectiveness isn’t just about quantity. It’s also about quality aka finding and targeting the ideal audience – something artificial intelligence (AI) can help you do better.   Why Frequency Capping Isn’t Enough While frequency capping has certainly helped with controlling ad exposure by enabling limit setting on an individual’s contact with a campaign, the question of how many times is best continues to cause stress. Too few times and prospects may not notice it, or notice it enough; too many and there’s a chance it might move from being memorable to annoying, resulting in a poor response and, worse still, a damaged brand reputation. Plenty of research has been done over the years in an attempt to find an answer. While brands like Procter & Gamble have capped their digital ad exposure at three times a month, Facebook claims one to two impressions weekly over at least 10 weeks for a campaign would be ideal. However, searching for the right answer is kind of