No one needs reminding that the life of the third-party cookie is increasingly finite, with only just over a year left before they are obsolete. At the same time, privacy regulation is tightening and consumers are getting more and more data-savvy, with 90% wanting more data privacy built into their devices. The loss of a lot of data that marketers have, to date, taken for granted – meaning that more decisions will have to be made with less data. Despite alternatives to the third-party cookie progressing hugely over the past two years, the way ahead is still daunting for many marketers. Targeting and measurement are the areas where the lack of third-party data will be felt most strongly.

How do brands use Big Data & Machine Learning throughout the customer experience to improve their performance? We outline 3 ways to get started using data. Analysing the data that your business generates is vital to ensuring that you stay ahead in an increasingly competitive landscape. Businesses who adopt data-driven marketing are six times more likely to be profitable year-over-year, and they are more likely to have an advantage over competition (ADS, 2018). Today, data-driven marketing is either embedded or strategic for 78% of marketers and 64% of marketing executives are in strong agreement that data-driven marketing is crucial to success in a hyper-competitive global economy (CMO, 2016).

The Introduction gives an overview of artificial intelligence and its use in marketing, explains key terms, and sets the scene for following chapters. Here, we will bring you up to speed on what you need to know moving forward, whether you're new to the topic or an experienced digital marketer. How Does Marketing Software Use AI? This chapter provides an overview of how currently available AI systems can be deployed by purchasing commercial solutions. We look at what types of products are available and what they can do for your business.

AI is a regular feature of news stories across nearly every industry. But, says Stephen Upstone, the headlines hide the fact there's a big human role in making AI work effectively. From healthcare, to driverless cars, to cyber security, AI is increasingly seen as the Holy Grail for innovation, productivity and profitability. At the other end of the spectrum, nay-sayers preach the coming obsolescence of humans with the rise of the job-killing robots. But in reality, the use of AI – in marketing and advertising, at least – is not immediately as clear cut as the sensationalising headlines suggest.

Marketers and media practitioners are feeling more confident about their internal skills for getting a return from data-driven marketing. And they're beginning to think more seriously about artificial intelligence and blockchain as tech for handling data. Those are among the findings of the third annual report on the use of tools and people for managing audience data from the Interactive Advertising Bureau's Data Center of Excellence. "The Outlook for Data 2018: A Snapshot into the Evolving Role of Audience Insight," conducted by management consultancy firm The Winterberry Group, surveyed 99 members of an IAB special-interest council on this topic in December and January. Although a small sample, it consisted entirely of professionals focused on this topic.

Machine learning software and pattern recognition have come a long way since their birth during the 1960s, especially during the past decade. The rise in automated and AI-powered marketing, where repetitive tasks are delegated to machines and bots, is allowing many businesses to shift their focus to data-driven marketing, and to better address marketing challenges. In the absence of machine learning technology, the processing of large amounts of data – something required to predict customer behaviour – is incredibly time-consuming. It also reduces the chances of successful business outcomes. However, when all this data mining is processed by computers, efficient and accurate predictions can be made to determine the "next best action" for each individual customer.

Sydney-based startup, Hyper Anna, has secured $16 million in funding to fuel a global rollout of'Anna', a virtual data scientist that taps into business intelligence and delivers real-time insights based on natural language requests. The Series A round of funding was led by Sequoia China, with significant investments from Airtree Ventures, Westpac Reinventure and IAG Firemark Ventures. Both Westpac and general insurer, IAG, are early adopters of Hyper Anna and use the platform internally to answer questions about business performance. Hyper Anna co-founder and CEO, Natalie Nguyen, told CMO customers have largely been SSI and in the financial and insurance sectors, but said the product could easily apply to other sectors such as media and technology. "The fact that some of our investors are also our customers gives us great confidence in the company, in ourselves, and the product," she said.

The Drum in partnership with intent marketing specialist, Iotec, has launched a report exploring the application of machine learning to solve commercial challenges. The report, Machine Learning: Empowering the Next Generation of Marketing, examines what implications machine learning has and will have on the marketing world, identifies common misconceptions and sheds light on applicable AI-driven marketing solutions. A growing body of research indicates that machine learning is moving to the top of the marketing agenda. A survey by Demandbase and Wakefield Research revealed that 80% of marketing executives believe that AI will revolutionise marketing over the next five years. But the same survey found that only 26% are confident in their understanding of AI technologies and its application to marketing.

Artificial intelligence (AI) is perfectly poised to become the norm for market segmentation. Bojana Deskov, marketing insights and data specialist at digital engagement software Squiz, explains how AI can supercharge your marketing efforts. A.I's ability to translate tasks normally requiring human intelligence, such as decision-making, visual detection and analysis, or speech recognition, makes it the logical next step to help marketers divide broad target audience subsets. Ten years ago, marketers used techniques such as targeting and segmentation to understand their customer base and launch campaigns. But in the last few years, the explosion of data-driven marketing has seen AI shed its associations with science fiction to play a growing role in the future of business.