Options for targeting digital advertising in a way that doesn't rely on cookies are increasing, thanks to advances in predictive analytics and AI that will ultimately lessen the current dominance of Google, Facebook, and other large-scale content aggregators. Google announced earlier this month that it will no longer allow third-party cookies to collect data via its Chrome browser. Many companies have historically relied on those cookies to better target their digital advertising, as the cookies enable digital ad networks and social media sites to create a profile of an end user without knowing specifically who that individual is. While that approach doesn't necessarily breach anyone's privacy, it does give many users the feeling that some entity is tracking the sites they visit in a way that makes them uncomfortable. Providers of other browsers, such as Safari from Apple and the open source Firefox browser, have already abandoned third-party cookies.

Traditional marketing tools lack the flexibility, scalability, and comprehensiveness to address many of the challenges faced by modern companies. With growing digitization and an always-online audience, more marketing teams now require artificial intelligence (AI) to stay competitive. Before rushing to hire a data science team, you need to evaluate the third-party AI solutions already available on the market. Many vendors use "AI" in their sales pitches, but lack credible research and engineering teams that can productize and operationalize cutting-edge AI research. In this article, we feature companies with proven AI and ML expertise that are transforming marketing activities with state-of-the-art AI-driven solutions.

The marketing world is evolving at warp speed thanks to advances in technologies such as machine learning. Peter Day looks at key trends that have brought us to where we are and those that will dictate the future. If the last five years have demonstrated anything, it's the awesome power and opportunity that can be unleashed by data when it's properly understood – and the problems that can arise when it's not. Those divergent possible outcomes are increasingly a reality for every organisation as they grapple with the once incomprehensible mountains of information that they possess. With the emergence and increasing availability of technologies such as machine learning, those mountains of data are looking ever more conquerable and more easily navigated.

Quantcast, an AI technology company focused on the marketing and publishing industries, announced that it will expand its advertising solutions into seven markets across Asia, connecting brands with an online audience of up to 300 million consumers. The company already operates in Australia and New Zealand. Marketers across Hong Kong, Indonesia, Malaysia, Philippines, Singapore, Taiwan, and Thailand will now be able to tap into Quantcast's live data insights drawn from more than 100 million online destinations to drive more effective brand awareness and performance campaigns. Andrew Double, Quantcast's Australia and New Zealand Managing Director, will taking on an expanded role to lead the company's growth in the Asia-Pacific region. Konrad Feldman, Quantcast's Chief Executive Officer and Founder commented, "Marketing is at a tipping point with AI set to transform every customer experience, every company and every industry. Marketers in APAC are looking for better ways to engage digital audiences that are both efficient and effective. We're excited to help brands and their agency partners leverage the power of Q, our audience behavior platform."

Artificial intelligence (AI) has increasingly dominated the news agenda in 2017. This was highlighted in November when the UK Government's Autumn budget included a £20 million investment pledge for businesses working in AI. With research from Deloitte showing that 85 percent of UK businesses plan to invest in AI by 2020, it's clear that firms across the board are waking up to its benefits and are starting to take decisive action. More and more brands are making headway with AI in digital advertising, from better audience insights and targeting to campaign measurement. The results are evident in more relevant, targeted and measurable campaigns.

Artificial intelligence (AI) has increasingly dominated the news agenda in 2017. This was highlighted in November when the UK Government's Autumn budget included a £20 million investment pledge for businesses working in AI. With research from Deloitte showing that 85 percent of UK businesses plan to invest in AI by 2020, it's clear that firms across the board are waking up to its benefits and are starting to take decisive action. More and more brands are making headway with AI in digital advertising, from better audience insights and targeting to campaign measurement. The results are evident in more relevant, targeted and measurable campaigns. However, there are still questions over how the technology will impact creative roles and skills in the long term.

We all look forward to the day we can step into our cars and get to our destinations without touching the steering wheel. While there is no question that automation is the way of the future, there are plenty of obstacles to overcome before getting fully automated vehicles onto the road. A major obstacle is trust: how do you convince users to trust their lives with automation you've built? Although not quite as perilous as highway driving, Quantcast has high stakes in the billion dollar programmatic advertising industry. Our clients trust us to make decisions on their behalf, with the expectation that their brand will be protected and their budget will be put to effective use.

Understanding the demographics of users of online social networks has important applications for health, marketing, and public messaging. In this paper, we predict the demographics of Twitter users based on whom they follow. Whereas most prior approaches rely on a supervised learning approach, in which individual users are labeled with demographics, we instead create a distantly labeled dataset by collecting audience measurement data for 1,500 websites (e.g., 50% of visitors to gizmodo.com are estimated to have a bachelor's degree). We then fit a regression model to predict these demographics using information about the followers of each website on Twitter. The resulting average held-out correlation is .77 across six different variables (gender, age, ethnicity, education, income, and child status). We additionally validate the model on a smaller set of Twitter users labeled individually for ethnicity and gender, finding performance that is surprisingly competitive with a fully supervised approach.